Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery.

PubMed

Huo, Zhiguang; Tseng, George

2017-06-01

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K -means (is- K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is- K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.

Agrigenomics for microalgal biofuel production: an overview of various bioinformatics resources and recent studies to link OMICS to bioenergy and bioeconomy.

PubMed

Misra, Namrata; Panda, Prasanna Kumar; Parida, Bikram Kumar

2013-11-01

Microalgal biofuels offer great promise in contributing to the growing global demand for alternative sources of renewable energy. However, to make algae-based fuels cost competitive with petroleum, lipid production capabilities of microalgae need to improve substantially. Recent progress in algal genomics, in conjunction with other "omic" approaches, has accelerated the ability to identify metabolic pathways and genes that are potential targets in the development of genetically engineered microalgal strains with optimum lipid content. In this review, we summarize the current bioeconomic status of global biofuel feedstocks with particular reference to the role of "omics" in optimizing sustainable biofuel production. We also provide an overview of the various databases and bioinformatics resources available to gain a more complete understanding of lipid metabolism across algal species, along with the recent contributions of "omic" approaches in the metabolic pathway studies for microalgal biofuel production.

Integration, warehousing, and analysis strategies of Omics data.

PubMed

Gedela, Srinubabu

2011-01-01

"-Omics" is a current suffix for numerous types of large-scale biological data generation procedures, which naturally demand the development of novel algorithms for data storage and analysis. With next generation genome sequencing burgeoning, it is pivotal to decipher a coding site on the genome, a gene's function, and information on transcripts next to the pure availability of sequence information. To explore a genome and downstream molecular processes, we need umpteen results at the various levels of cellular organization by utilizing different experimental designs, data analysis strategies and methodologies. Here comes the need for controlled vocabularies and data integration to annotate, store, and update the flow of experimental data. This chapter explores key methodologies to merge Omics data by semantic data carriers, discusses controlled vocabularies as eXtensible Markup Languages (XML), and provides practical guidance, databases, and software links supporting the integration of Omics data.

Assessment of pleiotropic transcriptome perturbations in Arabidopsis engineered for indirect insect defence.

PubMed

Houshyani, Benyamin; van der Krol, Alexander R; Bino, Raoul J; Bouwmeester, Harro J

2014-06-19

Molecular characterization is an essential step of risk/safety assessment of genetically modified (GM) crops. Holistic approaches for molecular characterization using omics platforms can be used to confirm the intended impact of the genetic engineering, but can also reveal the unintended changes at the omics level as a first assessment of potential risks. The potential of omics platforms for risk assessment of GM crops has rarely been used for this purpose because of the lack of a consensus reference and statistical methods to judge the significance or importance of the pleiotropic changes in GM plants. Here we propose a meta data analysis approach to the analysis of GM plants, by measuring the transcriptome distance to untransformed wild-types. In the statistical analysis of the transcriptome distance between GM and wild-type plants, values are compared with naturally occurring transcriptome distances in non-GM counterparts obtained from a database. Using this approach we show that the pleiotropic effect of genes involved in indirect insect defence traits is substantially equivalent to the variation in gene expression occurring naturally in Arabidopsis. Transcriptome distance is a useful screening method to obtain insight in the pleiotropic effects of genetic modification.

The DNA Data Bank of Japan launches a new resource, the DDBJ Omics Archive of functional genomics experiments.

PubMed

Kodama, Yuichi; Mashima, Jun; Kaminuma, Eli; Gojobori, Takashi; Ogasawara, Osamu; Takagi, Toshihisa; Okubo, Kousaku; Nakamura, Yasukazu

2012-01-01

The DNA Data Bank of Japan (DDBJ; http://www.ddbj.nig.ac.jp) maintains and provides archival, retrieval and analytical resources for biological information. The central DDBJ resource consists of public, open-access nucleotide sequence databases including raw sequence reads, assembly information and functional annotation. Database content is exchanged with EBI and NCBI within the framework of the International Nucleotide Sequence Database Collaboration (INSDC). In 2011, DDBJ launched two new resources: the 'DDBJ Omics Archive' (DOR; http://trace.ddbj.nig.ac.jp/dor) and BioProject (http://trace.ddbj.nig.ac.jp/bioproject). DOR is an archival database of functional genomics data generated by microarray and highly parallel new generation sequencers. Data are exchanged between the ArrayExpress at EBI and DOR in the common MAGE-TAB format. BioProject provides an organizational framework to access metadata about research projects and the data from the projects that are deposited into different databases. In this article, we describe major changes and improvements introduced to the DDBJ services, and the launch of two new resources: DOR and BioProject.

MinOmics, an Integrative and Immersive Tool for Multi-Omics Analysis.

PubMed

Maes, Alexandre; Martinez, Xavier; Druart, Karen; Laurent, Benoist; Guégan, Sean; Marchand, Christophe H; Lemaire, Stéphane D; Baaden, Marc

2018-06-21

Proteomic and transcriptomic technologies resulted in massive biological datasets, their interpretation requiring sophisticated computational strategies. Efficient and intuitive real-time analysis remains challenging. We use proteomic data on 1417 proteins of the green microalga Chlamydomonas reinhardtii to investigate physicochemical parameters governing selectivity of three cysteine-based redox post translational modifications (PTM): glutathionylation (SSG), nitrosylation (SNO) and disulphide bonds (SS) reduced by thioredoxins. We aim to understand underlying molecular mechanisms and structural determinants through integration of redox proteome data from gene- to structural level. Our interactive visual analytics approach on an 8.3 m2 display wall of 25 MPixel resolution features stereoscopic three dimensions (3D) representation performed by UnityMol WebGL. Virtual reality headsets complement the range of usage configurations for fully immersive tasks. Our experiments confirm that fast access to a rich cross-linked database is necessary for immersive analysis of structural data. We emphasize the possibility to display complex data structures and relationships in 3D, intrinsic to molecular structure visualization, but less common for omics-network analysis. Our setup is powered by MinOmics, an integrated analysis pipeline and visualization framework dedicated to multi-omics analysis. MinOmics integrates data from various sources into a materialized physical repository. We evaluate its performance, a design criterion for the framework.

Omicseq: a web-based search engine for exploring omics datasets

PubMed Central

Sun, Xiaobo; Pittard, William S.; Xu, Tianlei; Chen, Li; Zwick, Michael E.; Jiang, Xiaoqian; Wang, Fusheng

2017-01-01

Abstract The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve ‘findability’ of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. PMID:28402462

Improved bacteriophage genome data is necessary for integrating viral and bacterial ecology.

PubMed

Bibby, Kyle

2014-02-01

The recent rise in "omics"-enabled approaches has lead to improved understanding in many areas of microbial ecology. However, despite the importance that viruses play in a broad microbial ecology context, viral ecology remains largely not integrated into high-throughput microbial ecology studies. A fundamental hindrance to the integration of viral ecology into omics-enabled microbial ecology studies is the lack of suitable reference bacteriophage genomes in reference databases-currently, only 0.001% of bacteriophage diversity is represented in genome sequence databases. This commentary serves to highlight this issue and to promote bacteriophage genome sequencing as a valuable scientific undertaking to both better understand bacteriophage diversity and move towards a more holistic view of microbial ecology.

Omicseq: a web-based search engine for exploring omics datasets.

PubMed

Sun, Xiaobo; Pittard, William S; Xu, Tianlei; Chen, Li; Zwick, Michael E; Jiang, Xiaoqian; Wang, Fusheng; Qin, Zhaohui S

2017-07-03

The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve 'findability' of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

A Syst-OMICS Approach to Ensuring Food Safety and Reducing the Economic Burden of Salmonellosis.

PubMed

Emond-Rheault, Jean-Guillaume; Jeukens, Julie; Freschi, Luca; Kukavica-Ibrulj, Irena; Boyle, Brian; Dupont, Marie-Josée; Colavecchio, Anna; Barrere, Virginie; Cadieux, Brigitte; Arya, Gitanjali; Bekal, Sadjia; Berry, Chrystal; Burnett, Elton; Cavestri, Camille; Chapin, Travis K; Crouse, Alanna; Daigle, France; Danyluk, Michelle D; Delaquis, Pascal; Dewar, Ken; Doualla-Bell, Florence; Fliss, Ismail; Fong, Karen; Fournier, Eric; Franz, Eelco; Garduno, Rafael; Gill, Alexander; Gruenheid, Samantha; Harris, Linda; Huang, Carol B; Huang, Hongsheng; Johnson, Roger; Joly, Yann; Kerhoas, Maud; Kong, Nguyet; Lapointe, Gisèle; Larivière, Line; Loignon, Stéphanie; Malo, Danielle; Moineau, Sylvain; Mottawea, Walid; Mukhopadhyay, Kakali; Nadon, Céline; Nash, John; Ngueng Feze, Ida; Ogunremi, Dele; Perets, Ann; Pilar, Ana V; Reimer, Aleisha R; Robertson, James; Rohde, John; Sanderson, Kenneth E; Song, Lingqiao; Stephan, Roger; Tamber, Sandeep; Thomassin, Paul; Tremblay, Denise; Usongo, Valentine; Vincent, Caroline; Wang, Siyun; Weadge, Joel T; Wiedmann, Martin; Wijnands, Lucas; Wilson, Emily D; Wittum, Thomas; Yoshida, Catherine; Youfsi, Khadija; Zhu, Lei; Weimer, Bart C; Goodridge, Lawrence; Levesque, Roger C

2017-01-01

The Salmonella Syst-OMICS consortium is sequencing 4,500 Salmonella genomes and building an analysis pipeline for the study of Salmonella genome evolution, antibiotic resistance and virulence genes. Metadata, including phenotypic as well as genomic data, for isolates of the collection are provided through the Salmonella Foodborne Syst-OMICS database (SalFoS), at https://salfos.ibis.ulaval.ca/. Here, we present our strategy and the analysis of the first 3,377 genomes. Our data will be used to draw potential links between strains found in fresh produce, humans, animals and the environment. The ultimate goals are to understand how Salmonella evolves over time, improve the accuracy of diagnostic methods, develop control methods in the field, and identify prognostic markers for evidence-based decisions in epidemiology and surveillance.

Genelab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA Technical Reports Server (NTRS)

Reinsch, S. S.; Galazka, J..; Berrios, D. C; Chakravarty, K.; Fogle, H.; Lai, S.; Bokyo, V.; Timucin, L. R.; Tran, P.; Skidmore, M.

2016-01-01

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASA's premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

NaviCom: a web application to create interactive molecular network portraits using multi-level omics data.

PubMed

Dorel, Mathurin; Viara, Eric; Barillot, Emmanuel; Zinovyev, Andrei; Kuperstein, Inna

2017-01-01

Human diseases such as cancer are routinely characterized by high-throughput molecular technologies, and multi-level omics data are accumulated in public databases at increasing rate. Retrieval and visualization of these data in the context of molecular network maps can provide insights into the pattern of regulation of molecular functions reflected by an omics profile. In order to make this task easy, we developed NaviCom, a Python package and web platform for visualization of multi-level omics data on top of biological network maps. NaviCom is bridging the gap between cBioPortal, the most used resource of large-scale cancer omics data and NaviCell, a data visualization web service that contains several molecular network map collections. NaviCom proposes several standardized modes of data display on top of molecular network maps, allowing addressing specific biological questions. We illustrate how users can easily create interactive network-based cancer molecular portraits via NaviCom web interface using the maps of Atlas of Cancer Signalling Network (ACSN) and other maps. Analysis of these molecular portraits can help in formulating a scientific hypothesis on the molecular mechanisms deregulated in the studied disease. NaviCom is available at https://navicom.curie.fr. © The Author(s) 2017. Published by Oxford University Press.

Incorporating zebrafish omics into chemical biology and toxicology.

PubMed

Sukardi, Hendrian; Ung, Choong Yong; Gong, Zhiyuan; Lam, Siew Hong

2010-03-01

In this communication, we describe the general aspects of omics approaches for analyses of transcriptome, proteome, and metabolome, and how they can be strategically incorporated into chemical screening and perturbation studies using the zebrafish system. Pharmacological efficacy and selectivity of chemicals can be evaluated based on chemical-induced phenotypic effects; however, phenotypic observation has limitations in identifying mechanistic action of chemicals. We suggest adapting gene-expression-based high-throughput screening as a complementary strategy to zebrafish-phenotype-based screening for mechanistic insights about the mode of action and toxicity of a chemical, large-scale predictive applications and comparative analysis of chemical-induced omics signatures, which are useful to identify conserved biological responses, signaling pathways, and biomarkers. The potential mechanistic, predictive, and comparative applications of omics approaches can be implemented in the zebrafish system. Examples of these using the omics approaches in zebrafish, including data of ours and others, are presented and discussed. Omics also facilitates the translatability of zebrafish studies across species through comparison of conserved chemical-induced responses. This review is intended to update interested readers with the current omics approaches that have been applied in chemical studies on zebrafish and their potential in enhancing discovery in chemical biology.

Information Commons for Rice (IC4R)

PubMed Central

2016-01-01

Rice is the most important staple food for a large part of the world's human population and also a key model organism for plant research. Here, we present Information Commons for Rice (IC4R; http://ic4r.org), a rice knowledgebase featuring adoption of an extensible and sustainable architecture that integrates multiple omics data through community-contributed modules. Each module is developed and maintained by different committed groups, deals with data collection, processing and visualization, and delivers data on-demand via web services. In the current version, IC4R incorporates a variety of rice data through multiple committed modules, including genome-wide expression profiles derived entirely from RNA-Seq data, resequencing-based genomic variations obtained from re-sequencing data of thousands of rice varieties, plant homologous genes covering multiple diverse plant species, post-translational modifications, rice-related literatures and gene annotations contributed by the rice research community. Unlike extant related databases, IC4R is designed for scalability and sustainability and thus also features collaborative integration of rice data and low costs for database update and maintenance. Future directions of IC4R include incorporation of other omics data and association of multiple omics data with agronomically important traits, dedicating to build IC4R into a valuable knowledgebase for both basic and translational researches in rice. PMID:26519466

Integrated Approaches to Drug Discovery for Oxidative Stress-Related Retinal Diseases.

PubMed

Nishimura, Yuhei; Hara, Hideaki

2016-01-01

Excessive oxidative stress induces dysregulation of functional networks in the retina, resulting in retinal diseases such as glaucoma, age-related macular degeneration, and diabetic retinopathy. Although various therapies have been developed to reduce oxidative stress in retinal diseases, most have failed to show efficacy in clinical trials. This may be due to oversimplification of target selection for such a complex network as oxidative stress. Recent advances in high-throughput technologies have facilitated the collection of multilevel omics data, which has driven growth in public databases and in the development of bioinformatics tools. Integration of the knowledge gained from omics databases can be used to generate disease-related biological networks and to identify potential therapeutic targets within the networks. Here, we provide an overview of integrative approaches in the drug discovery process and provide simple examples of how the approaches can be exploited to identify oxidative stress-related targets for retinal diseases.

Integrated Approaches to Drug Discovery for Oxidative Stress-Related Retinal Diseases

PubMed Central

Hara, Hideaki

2016-01-01

Excessive oxidative stress induces dysregulation of functional networks in the retina, resulting in retinal diseases such as glaucoma, age-related macular degeneration, and diabetic retinopathy. Although various therapies have been developed to reduce oxidative stress in retinal diseases, most have failed to show efficacy in clinical trials. This may be due to oversimplification of target selection for such a complex network as oxidative stress. Recent advances in high-throughput technologies have facilitated the collection of multilevel omics data, which has driven growth in public databases and in the development of bioinformatics tools. Integration of the knowledge gained from omics databases can be used to generate disease-related biological networks and to identify potential therapeutic targets within the networks. Here, we provide an overview of integrative approaches in the drug discovery process and provide simple examples of how the approaches can be exploited to identify oxidative stress-related targets for retinal diseases. PMID:28053689

A Guide to the PLAZA 3.0 Plant Comparative Genomic Database.

PubMed

Vandepoele, Klaas

2017-01-01

PLAZA 3.0 is an online resource for comparative genomics and offers a versatile platform to study gene functions and gene families or to analyze genome organization and evolution in the green plant lineage. Starting from genome sequence information for over 35 plant species, precomputed comparative genomic data sets cover homologous gene families, multiple sequence alignments, phylogenetic trees, and genomic colinearity information within and between species. Complementary functional data sets, a Workbench, and interactive visualization tools are available through a user-friendly web interface, making PLAZA an excellent starting point to translate sequence or omics data sets into biological knowledge. PLAZA is available at http://bioinformatics.psb.ugent.be/plaza/ .

Unlimited Thirst for Genome Sequencing, Data Interpretation, and Database Usage in Genomic Era: The Road towards Fast-Track Crop Plant Improvement

PubMed Central

Govindaraj, Mahalingam

2015-01-01

The number of sequenced crop genomes and associated genomic resources is growing rapidly with the advent of inexpensive next generation sequencing methods. Databases have become an integral part of all aspects of science research, including basic and applied plant and animal sciences. The importance of databases keeps increasing as the volume of datasets from direct and indirect genomics, as well as other omics approaches, keeps expanding in recent years. The databases and associated web portals provide at a minimum a uniform set of tools and automated analysis across a wide range of crop plant genomes. This paper reviews some basic terms and considerations in dealing with crop plant databases utilization in advancing genomic era. The utilization of databases for variation analysis with other comparative genomics tools, and data interpretation platforms are well described. The major focus of this review is to provide knowledge on platforms and databases for genome-based investigations of agriculturally important crop plants. The utilization of these databases in applied crop improvement program is still being achieved widely; otherwise, the end for sequencing is not far away. PMID:25874133

iMETHYL: an integrative database of human DNA methylation, gene expression, and genomic variation.

PubMed

Komaki, Shohei; Shiwa, Yuh; Furukawa, Ryohei; Hachiya, Tsuyoshi; Ohmomo, Hideki; Otomo, Ryo; Satoh, Mamoru; Hitomi, Jiro; Sobue, Kenji; Sasaki, Makoto; Shimizu, Atsushi

2018-01-01

We launched an integrative multi-omics database, iMETHYL (http://imethyl.iwate-megabank.org). iMETHYL provides whole-DNA methylation (~24 million autosomal CpG sites), whole-genome (~9 million single-nucleotide variants), and whole-transcriptome (>14 000 genes) data for CD4 + T-lymphocytes, monocytes, and neutrophils collected from approximately 100 subjects. These data were obtained from whole-genome bisulfite sequencing, whole-genome sequencing, and whole-transcriptome sequencing, making iMETHYL a comprehensive database.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schaumberg, Andrew

The Omics Tools package provides several small trivial tools for work in genomics. This single portable package, the “omics.jar” file, is a toolbox that works in any Java-based environment, including PCs, Macs, and supercomputers. The number of tools is expected to grow. One tool (called cmsearch.hadoop or cmsearch.local), calls the external cmsearch program to predict non-coding RNA in a genome. The cmsearch program is part of the third-party Infernal package. Omics Tools does not contain Infernal. Infernal may be installed separately. The cmsearch.hadoop subtool requires Apache Hadoop and runs on a supercomputer, though cmsearch.local does not and runs on amore » server. Omics Tools does not contain Hadoop. Hadoop mat be installed separartely The other tools (cmgbk, cmgff, fastats, pal, randgrp, randgrpr, randsub) do not interface with third-party tools. Omics Tools is written in Java and Scala programming languages. Invoking the “help” command shows currently available tools, as shown below: schaumbe@gpint06:~/proj/omics$ java -jar omics.jar help Known commands are: cmgbk : compare cmsearch and GenBank Infernal hits cmgff : compare hits among two GFF (version 3) files cmsearch.hadoop : find Infernal hits in a genome, on your supercomputer cmsearch.local : find Infernal hits in a genome, on your workstation fastats : FASTA stats, e.g. # bases, GC content pal : stem-loop motif detection by palindromic sequence search (code stub) randgrp : random subsample without replacement, of groups randgrpr : random subsample with replacement, of groups (fast) randsub : random subsample without replacement, of file lines For more help regarding a particular command, use: java -jar omics.jar command help Usage: java -jar omics.jar command args« less

omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data

PubMed Central

Karathanasis, Nestoras; Tsamardinos, Ioannis

2016-01-01

Background The advance of omics technologies has made possible to measure several data modalities on a system of interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing the association of different molecular quantities with an outcome of interest. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes. Results We implemented the NPC methodology within “omicsNPC”, an R function specifically tailored for the characteristics of omics data. We compare omicsNPC against a range of alternative methods on simulated as well as on real data. Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs equally or significantly better than the other methods included in the study; furthermore, the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is easily applicable in a number of different scenarios, and (c) its results have improved biological interpretability. Conclusions The omicsNPC function competitively behaves in all comparisons conducted in this study. Taking into account that the method (i) requires minimal assumptions, (ii) it can be used on different studies designs and (iii) it captures the dependences among heterogeneous data modalities, omicsNPC provides a flexible and statistically powerful solution for the integrative analysis of different omics data. PMID:27812137

CircadiOmics: circadian omic web portal.

PubMed

Ceglia, Nicholas; Liu, Yu; Chen, Siwei; Agostinelli, Forest; Eckel-Mahan, Kristin; Sassone-Corsi, Paolo; Baldi, Pierre

2018-06-15

Circadian rhythms play a fundamental role at all levels of biological organization. Understanding the mechanisms and implications of circadian oscillations continues to be the focus of intense research. However, there has been no comprehensive and integrated way for accessing and mining all circadian omic datasets. The latest release of CircadiOmics (http://circadiomics.ics.uci.edu) fills this gap for providing the most comprehensive web server for studying circadian data. The newly updated version contains high-throughput 227 omic datasets corresponding to over 74 million measurements sampled over 24 h cycles. Users can visualize and compare oscillatory trajectories across species, tissues and conditions. Periodicity statistics (e.g. period, amplitude, phase, P-value, q-value etc.) obtained from BIO_CYCLE and other methods are provided for all samples in the repository and can easily be downloaded in the form of publication-ready figures and tables. New features and substantial improvements in performance and data volume make CircadiOmics a powerful web portal for integrated analysis of circadian omic data.

GeneLab: A Systems Biology Platform for Spaceflight Omics Data

NASA Technical Reports Server (NTRS)

Reinsch, Sigrid S.; Lai, San-Huei; Chen, Rick; Thompson, Terri; Berrios, Daniel; Fogle, Homer; Marcu, Oana; Timucin, Linda; Chakravarty, Kaushik; Coughlan, Joseph

2015-01-01

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. Resources to support large numbers of spaceflight investigations are limited. NASA's GeneLab project is maximizing the science output from these experiments by: (1) developing a unique public bioinformatics database that includes space bioscience relevant "omics" data (genomics, transcriptomics, proteomics, and metabolomics) and experimental metadata; (2) partnering with NASA-funded flight experiments through bio-sample sharing or sample augmentation to expedite omics data input to the GeneLab database; and (3) developing community-driven reference flight experiments. The first database, GeneLab Data System Version 1.0, went online in April 2015. V1.0 contains numerous flight datasets and has search and download capabilities. Version 2.0 will be released in 2016 and will link to analytic tools. In 2015 Genelab partnered with two Biological Research in Canisters experiments (BBRIC-19 and BRIC-20) which examine responses of Arabidopsis thaliana to spaceflight. GeneLab also partnered with Rodent Research-1 (RR1), the maiden flight to test the newly developed rodent habitat. GeneLab developed protocols for maxiumum yield of RNA, DNA and protein from precious RR-1 tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected. GeneLab is establishing partnerships with at least three planned flights for 2016. Organism-specific nationwide Science Definition Teams (SDTs) will define future GeneLab dedicated missions and ensure the broader scientific impact of the GeneLab missions. GeneLab ensures prompt release and open access to all high-throughput omics data from spaceflight and ground-based simulations of microgravity and radiation. Overall, GeneLab will facilitate the generation and query of parallel multi-omics data, and deep curation of metadata for integrative analysis, allowing researchers to uncover cellular networks as observed in systems biology platforms. Consequently, the scientific community will have access to a more complete picture of functional and regulatory networks responsive to the spaceflight environment.. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and enable emerging terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space. As a result, open access to the data will foster new hypothesis-driven research for future spaceflight studies spanning basic science to translational science.

A retrospective likelihood approach for efficient integration of multiple omics factors in case-control association studies.

PubMed

Balliu, Brunilda; Tsonaka, Roula; Boehringer, Stefan; Houwing-Duistermaat, Jeanine

2015-03-01

Integrative omics, the joint analysis of outcome and multiple types of omics data, such as genomics, epigenomics, and transcriptomics data, constitute a promising approach for powerful and biologically relevant association studies. These studies often employ a case-control design, and often include nonomics covariates, such as age and gender, that may modify the underlying omics risk factors. An open question is how to best integrate multiple omics and nonomics information to maximize statistical power in case-control studies that ascertain individuals based on the phenotype. Recent work on integrative omics have used prospective approaches, modeling case-control status conditional on omics, and nonomics risk factors. Compared to univariate approaches, jointly analyzing multiple risk factors with a prospective approach increases power in nonascertained cohorts. However, these prospective approaches often lose power in case-control studies. In this article, we propose a novel statistical method for integrating multiple omics and nonomics factors in case-control association studies. Our method is based on a retrospective likelihood function that models the joint distribution of omics and nonomics factors conditional on case-control status. The new method provides accurate control of Type I error rate and has increased efficiency over prospective approaches in both simulated and real data. © 2015 Wiley Periodicals, Inc.

Second Era of OMICS in Caries Research: Moving Past the Phase of Disillusionment.

PubMed

Nascimento, M M; Zaura, E; Mira, A; Takahashi, N; Ten Cate, J M

2017-07-01

Novel approaches using OMICS techniques enable a collective assessment of multiple related biological units, including genes, gene expression, proteins, and metabolites. In the past decade, next-generation sequencing ( NGS) technologies were improved by longer sequence reads and the development of genome databases and user-friendly pipelines for data analysis, all accessible at lower cost. This has generated an outburst of high-throughput data. The application of OMICS has provided more depth to existing hypotheses as well as new insights in the etiology of dental caries. For example, the determination of complete bacterial microbiomes of oral samples rather than selected species, together with oral metatranscriptome and metabolome analyses, supports the viewpoint of dysbiosis of the supragingival biofilms. In addition, metabolome studies have been instrumental in disclosing the contributions of major pathways for central carbon and amino acid metabolisms to biofilm pH homeostasis. New, often noncultured, oral streptococci have been identified, and their phenotypic characterization has revealed candidates for probiotic therapy. Although findings from OMICS research have been greatly informative, problems related to study design, data quality, integration, and reproducibility still need to be addressed. Also, the emergence and continuous updates of these computationally demanding technologies require expertise in advanced bioinformatics for reliable interpretation of data. Despite the obstacles cited above, OMICS research is expected to encourage the discovery of novel caries biomarkers and the development of next-generation diagnostics and therapies for caries control. These observations apply equally to the study of other oral diseases.

Use of Free/Libre Open Source Software in Sepsis "-Omics" Research: A Bibliometric, Comparative Analysis Among the United States, EU-28 Member States, and China.

PubMed

Evangelatos, Nikolaos; Satyamourthy, Kapaettu; Levidou, Georgia; Brand, Helmut; Bauer, Pia; Kouskouti, Christina; Brand, Angela

2018-05-01

"-Omics" systems sciences are at the epicenter of personalized medicine and public health, and drivers of knowledge-based biotechnology innovation. Bioinformatics, a core component of omics research, is one of the disciplines that first employed Free/Libre Open Source Software (FLOSS), and thus provided a fertile ground for its further development. Understanding the use and characteristics of FLOSS deployed in the omics field is valuable for future innovation strategies, policy and funding priorities. We conducted a bibliometric, longitudinal study of the use of FLOSS in sepsis omics research from 2011 to 2015 in the United States, EU-28 and China. Because sepsis is an interdisciplinary field at the intersection of multiple omics technologies and medical specialties, it was chosen as a model innovation ecosystem for this empirical analysis, which used publicly available data. Despite development of and competition from proprietary commercial software, scholars in omics continue to employ FLOSS routinely, and independent of the type of omics technology they work with. The number of articles using FLOSS increased significantly over time in the EU-28, as opposed to the United States and China (R = 0.96, p = 0.004). Furthermore, in an era where sharing of knowledge is being strongly advocated and promoted by public agencies and social institutions, we discuss possible correlations between the use of FLOSS and various funding sources in omics research. These observations and analyses provide new insights into the use of FLOSS in sepsis omics research across three (supra)national regions. Further benchmarking studies are warranted for FLOSS trends in other omics fields and geographical settings. These could, in time, lead to the development of new composite innovation and technology use metrics in omics systems sciences and bioinformatics communities.

UniVIO: A Multiple Omics Database with Hormonome and Transcriptome Data from Rice

PubMed Central

Sakurai, Tetsuya; Sakakibara, Hitoshi

2013-01-01

Plant hormones play important roles as signaling molecules in the regulation of growth and development by controlling the expression of downstream genes. Since the hormone signaling system represents a complex network involving functional cross-talk through the mutual regulation of signaling and metabolism, a comprehensive and integrative analysis of plant hormone concentrations and gene expression is important for a deeper understanding of hormone actions. We have developed a database named Uniformed Viewer for Integrated Omics (UniVIO: http://univio.psc.riken.jp/), which displays hormone-metabolome (hormonome) and transcriptome data in a single formatted (uniformed) heat map. At the present time, hormonome and transcriptome data obtained from 14 organ parts of rice plants at the reproductive stage and seedling shoots of three gibberellin signaling mutants are included in the database. The hormone concentration and gene expression data can be searched by substance name, probe ID, gene locus ID or gene description. A correlation search function has been implemented to enable users to obtain information of correlated substance accumulation and gene expression. In the correlation search, calculation method, range of correlation coefficient and plant samples can be selected freely. PMID:23314752

CHOgenome.org 2.0: Genome resources and website updates.

PubMed

Kremkow, Benjamin G; Baik, Jong Youn; MacDonald, Madolyn L; Lee, Kelvin H

2015-07-01

Chinese hamster ovary (CHO) cells are a major host cell line for the production of therapeutic proteins, and CHO cell and Chinese hamster (CH) genomes have recently been sequenced using next-generation sequencing methods. CHOgenome.org was launched in 2011 (version 1.0) to serve as a database repository and to provide bioinformatics tools for the CHO community. CHOgenome.org (version 1.0) maintained GenBank CHO-K1 genome data, identified CHO-omics literature, and provided a CHO-specific BLAST service. Recent major updates to CHOgenome.org (version 2.0) include new sequence and annotation databases for both CHO and CH genomes, a more user-friendly website, and new research tools, including a proteome browser and a genome viewer. CHO cell-line specific sequences and annotations facilitate cell line development opportunities, several of which are discussed. Moving forward, CHOgenome.org will host the increasing amount of CHO-omics data and continue to make useful bioinformatics tools available to the CHO community. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Meeting report: Ocean 'omics science, technology and cyberinfrastructure: current challenges and future requirements (August 20-23, 2013).

PubMed

Gilbert, Jack A; Dick, Gregory J; Jenkins, Bethany; Heidelberg, John; Allen, Eric; Mackey, Katherine R M; DeLong, Edward F

2014-06-15

The National Science Foundation's EarthCube End User Workshop was held at USC Wrigley Marine Science Center on Catalina Island, California in August 2013. The workshop was designed to explore and characterize the needs and tools available to the community that is focusing on microbial and physical oceanography research with a particular emphasis on 'omic research. The assembled researchers outlined the existing concerns regarding the vast data resources that are being generated, and how we will deal with these resources as their volume and diversity increases. Particular attention was focused on the tools for handling and analyzing the existing data, on the need for the construction and curation of diverse federated databases, as well as development of shared, interoperable, "big-data capable" analytical tools. The key outputs from this workshop include (i) critical scientific challenges and cyber infrastructure constraints, (ii) the current and future ocean 'omics science grand challenges and questions, and (iii) data management, analytical and associated and cyber-infrastructure capabilities required to meet critical current and future scientific challenges. The main thrust of the meeting and the outcome of this report is a definition of the 'omics tools, technologies and infrastructures that facilitate continued advance in ocean science biology, marine biogeochemistry, and biological oceanography.

Meeting report: Ocean ‘omics science, technology and cyberinfrastructure: current challenges and future requirements (August 20-23, 2013)

PubMed Central

Gilbert, Jack A; Dick, Gregory J.; Jenkins, Bethany; Heidelberg, John; Allen, Eric; Mackey, Katherine R. M.

2014-01-01

The National Science Foundation’s EarthCube End User Workshop was held at USC Wrigley Marine Science Center on Catalina Island, California in August 2013. The workshop was designed to explore and characterize the needs and tools available to the community that is focusing on microbial and physical oceanography research with a particular emphasis on ‘omic research. The assembled researchers outlined the existing concerns regarding the vast data resources that are being generated, and how we will deal with these resources as their volume and diversity increases. Particular attention was focused on the tools for handling and analyzing the existing data, on the need for the construction and curation of diverse federated databases, as well as development of shared, interoperable, “big-data capable” analytical tools. The key outputs from this workshop include (i) critical scientific challenges and cyber infrastructure constraints, (ii) the current and future ocean ‘omics science grand challenges and questions, and (iii) data management, analytical and associated and cyber-infrastructure capabilities required to meet critical current and future scientific challenges. The main thrust of the meeting and the outcome of this report is a definition of the ‘omics tools, technologies and infrastructures that facilitate continued advance in ocean science biology, marine biogeochemistry, and biological oceanography. PMID:25197495

MetaComp: comprehensive analysis software for comparative meta-omics including comparative metagenomics.

PubMed

Zhai, Peng; Yang, Longshu; Guo, Xiao; Wang, Zhe; Guo, Jiangtao; Wang, Xiaoqi; Zhu, Huaiqiu

2017-10-02

During the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data. To reveal the diversity of microbial communities and interactions between living conditions and microbes, it is necessary to introduce comparative analysis based upon integration of all four types of data mentioned above. Comparative meta-omics, especially comparative metageomics, has been established as a routine process to highlight the significant differences in taxon composition and functional gene abundance among microbiota samples. Meanwhile, biologists are increasingly concerning about the correlations between meta-omics features and environmental factors, which may further decipher the adaptation strategy of a microbial community. We developed a graphical comprehensive analysis software named MetaComp comprising a series of statistical analysis approaches with visualized results for metagenomics and other meta-omics data comparison. This software is capable to read files generated by a variety of upstream programs. After data loading, analyses such as multivariate statistics, hypothesis testing of two-sample, multi-sample as well as two-group sample and a novel function-regression analysis of environmental factors are offered. Here, regression analysis regards meta-omic features as independent variable and environmental factors as dependent variables. Moreover, MetaComp is capable to automatically choose an appropriate two-group sample test based upon the traits of input abundance profiles. We further evaluate the performance of its choice, and exhibit applications for metagenomics, metaproteomics and metabolomics samples. MetaComp, an integrative software capable for applying to all meta-omics data, originally distills the influence of living environment on microbial community by regression analysis. Moreover, since the automatically chosen two-group sample test is verified to be outperformed, MetaComp is friendly to users without adequate statistical training. These improvements are aiming to overcome the new challenges under big data era for all meta-omics data. MetaComp is available at: http://cqb.pku.edu.cn/ZhuLab/MetaComp/ and https://github.com/pzhaipku/MetaComp/ .

Plant Reactome: a resource for plant pathways and comparative analysis

PubMed Central

Naithani, Sushma; Preece, Justin; D'Eustachio, Peter; Gupta, Parul; Amarasinghe, Vindhya; Dharmawardhana, Palitha D.; Wu, Guanming; Fabregat, Antonio; Elser, Justin L.; Weiser, Joel; Keays, Maria; Fuentes, Alfonso Munoz-Pomer; Petryszak, Robert; Stein, Lincoln D.; Ware, Doreen; Jaiswal, Pankaj

2017-01-01

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX. PMID:27799469

Omics methods for probing the mode of action of natural and synthetic phytotoxins.

PubMed

Duke, Stephen O; Bajsa, Joanna; Pan, Zhiqiang

2013-02-01

For a little over a decade, omics methods (transcriptomics, proteomics, metabolomics, and physionomics) have been used to discover and probe the mode of action of both synthetic and natural phytotoxins. For mode of action discovery, the strategy for each of these approaches is to generate an omics profile for phytotoxins with known molecular targets and to compare this library of responses to the responses of compounds with unknown modes of action. Using more than one omics approach enhances the probability of success. Generally, compounds with the same mode of action generate similar responses with a particular omics method. Stress and detoxification responses to phytotoxins can be much clearer than effects directly related to the target site. Clues to new modes of action must be validated with in vitro enzyme effects or genetic approaches. Thus far, the only new phytotoxin target site discovered with omics approaches (metabolomics and physionomics) is that of cinmethylin and structurally related 5-benzyloxymethyl-1,2-isoxazolines. These omics approaches pointed to tyrosine amino-transferase as the target, which was verified by enzyme assays and genetic methods. In addition to being a useful tool of mode of action discovery, omics methods provide detailed information on genetic and biochemical impacts of phytotoxins. Such information can be useful in understanding the full impact of natural phytotoxins in both agricultural and natural ecosystems.

Improving the discoverability, accessibility, and citability of omics datasets: a case report.

PubMed

Darlington, Yolanda F; Naumov, Alexey; McOwiti, Apollo; Kankanamge, Wasula H; Becnel, Lauren B; McKenna, Neil J

2017-03-01

Although omics datasets represent valuable assets for hypothesis generation, model testing, and data validation, the infrastructure supporting their reuse lacks organization and consistency. Using nuclear receptor signaling transcriptomic datasets as proof of principle, we developed a model to improve the discoverability, accessibility, and citability of published omics datasets. Primary datasets were retrieved from archives, processed to extract data points, then subjected to metadata enrichment and gap filling. The resulting secondary datasets were exposed on responsive web pages to support mining of gene lists, discovery of related datasets, and single-click citation integration with popular reference managers. Automated processes were established to embed digital object identifier-driven links to the secondary datasets in associated journal articles, small molecule and gene-centric databases, and a dataset search engine. Our model creates multiple points of access to reprocessed and reannotated derivative datasets across the digital biomedical research ecosystem, promoting their visibility and usability across disparate research communities. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Reconstruction of genome-scale human metabolic models using omics data.

PubMed

Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup

2015-08-01

The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.

Plant Omics Data Center: An Integrated Web Repository for Interspecies Gene Expression Networks with NLP-Based Curation

PubMed Central

Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro

2015-01-01

Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. PMID:25505034

NASA GeneLab Project: Bridging Space Radiation Omics with Ground Studies.

PubMed

Beheshti, Afshin; Miller, Jack; Kidane, Yared; Berrios, Daniel; Gebre, Samrawit G; Costes, Sylvain V

2018-06-01

Accurate assessment of risks of long-term space missions is critical for human space exploration. It is essential to have a detailed understanding of the biological effects on humans living and working in deep space. Ionizing radiation from galactic cosmic rays (GCR) is a major health risk factor for astronauts on extended missions outside the protective effects of the Earth's magnetic field. Currently, there are gaps in our knowledge of the health risks associated with chronic low-dose, low-dose-rate ionizing radiation, specifically ions associated with high (H) atomic number (Z) and energy (E). The NASA GeneLab project ( https://genelab.nasa.gov/ ) aims to provide a detailed library of omics datasets associated with biological samples exposed to HZE. The GeneLab Data System (GLDS) includes datasets from both spaceflight and ground-based studies, a majority of which involve exposure to ionizing radiation. In addition to detailed information on radiation exposure for ground-based studies, GeneLab is adding detailed, curated dosimetry information for spaceflight experiments. GeneLab is the first comprehensive omics database for space-related research from which an investigator can generate hypotheses to direct future experiments, utilizing both ground and space biological radiation data. The GLDS is continually expanding as omics-related data are generated by the space life sciences community. Here we provide a brief summary of the space radiation-related data available at GeneLab.

Functional Enzyme-Based Approach for Linking Microbial Community Functions with Biogeochemical Process Kinetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Minjing; Qian, Wei-jun; Gao, Yuqian

The kinetics of biogeochemical processes in natural and engineered environmental systems are typically described using Monod-type or modified Monod-type models. These models rely on biomass as surrogates for functional enzymes in microbial community that catalyze biogeochemical reactions. A major challenge to apply such models is the difficulty to quantitatively measure functional biomass for constraining and validating the models. On the other hand, omics-based approaches have been increasingly used to characterize microbial community structure, functions, and metabolites. Here we proposed an enzyme-based model that can incorporate omics-data to link microbial community functions with biogeochemical process kinetics. The model treats enzymes asmore » time-variable catalysts for biogeochemical reactions and applies biogeochemical reaction network to incorporate intermediate metabolites. The sequences of genes and proteins from metagenomes, as well as those from the UniProt database, were used for targeted enzyme quantification and to provide insights into the dynamic linkage among functional genes, enzymes, and metabolites that are necessary to be incorporated in the model. The application of the model was demonstrated using denitrification as an example by comparing model-simulated with measured functional enzymes, genes, denitrification substrates and intermediates« less

Poly-Omic Prediction of Complex Traits: OmicKriging

PubMed Central

Wheeler, Heather E.; Aquino-Michaels, Keston; Gamazon, Eric R.; Trubetskoy, Vassily V.; Dolan, M. Eileen; Huang, R. Stephanie; Cox, Nancy J.; Im, Hae Kyung

2014-01-01

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. PMID:24799323

Precision medicine for psychopharmacology: a general introduction.

PubMed

Shin, Cheolmin; Han, Changsu; Pae, Chi-Un; Patkar, Ashwin A

2016-07-01

Precision medicine is an emerging medical model that can provide accurate diagnoses and tailored therapeutic strategies for patients based on data pertaining to genes, microbiomes, environment, family history and lifestyle. Here, we provide basic information about precision medicine and newly introduced concepts, such as the precision medicine ecosystem and big data processing, and omics technologies including pharmacogenomics, pharamacometabolomics, pharmacoproteomics, pharmacoepigenomics, connectomics and exposomics. The authors review the current state of omics in psychiatry and the future direction of psychopharmacology as it moves towards precision medicine. Expert commentary: Advances in precision medicine have been facilitated by achievements in multiple fields, including large-scale biological databases, powerful methods for characterizing patients (such as genomics, proteomics, metabolomics, diverse cellular assays, and even social networks and mobile health technologies), and computer-based tools for analyzing large amounts of data.

Transcriptome Analysis of PA Gain and Loss of Function Mutants.

PubMed

Marco, Francisco; Carrasco, Pedro

2018-01-01

Functional genomics has become a forefront methodology for plant science thanks to the widespread development of microarray technology. While technical difficulties associated with the process of obtaining raw expression data have been diminishing, allowing the appearance of tremendous amounts of transcriptome data in different databases, a common problem using "omic" technologies remains: the interpretation of these data and the inference of its biological meaning. In order to assist to this complex task, a wide variety of software tools have been developed. In this chapter we describe our current workflow of the application of some of these analyses. We have used it to compare the transcriptome of plants with differences in their polyamine levels.

Omics Data Complementarity Underlines Functional Cross-Communication in Yeast.

PubMed

Malod-Dognin, Noël; Pržulj, Nataša

2017-06-10

Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker's yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein-protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data.

Plant Reactome: a resource for plant pathways and comparative analysis.

PubMed

Naithani, Sushma; Preece, Justin; D'Eustachio, Peter; Gupta, Parul; Amarasinghe, Vindhya; Dharmawardhana, Palitha D; Wu, Guanming; Fabregat, Antonio; Elser, Justin L; Weiser, Joel; Keays, Maria; Fuentes, Alfonso Munoz-Pomer; Petryszak, Robert; Stein, Lincoln D; Ware, Doreen; Jaiswal, Pankaj

2017-01-04

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

NASA GeneLab Project: Bridging Space Radiation Omics with Ground Studies

NASA Technical Reports Server (NTRS)

Beheshti, Afshin; Miller, Jack; Kidane, Yared H.; Berrios, Daniel; Gebre, Samrawit G.; Costes, Sylvain V.

2018-01-01

Accurate assessment of risk factors for long-term space missions is critical for human space exploration: therefore it is essential to have a detailed understanding of the biological effects on humans living and working in deep space. Ionizing radiation from Galactic Cosmic Rays (GCR) is one of the major risk factors factor that will impact health of astronauts on extended missions outside the protective effects of the Earth's magnetic field. Currently there are gaps in our knowledge of the health risks associated with chronic low dose, low dose rate ionizing radiation, specifically ions associated with high (H) atomic number (Z) and energy (E). The GeneLab project (genelab.nasa.gov) aims to provide a detailed library of Omics datasets associated with biological samples exposed to HZE. The GeneLab Data System (GLDS) currently includes datasets from both spaceflight and ground-based studies, a majority of which involve exposure to ionizing radiation. In addition to detailed information for ground-based studies, we are in the process of adding detailed, curated dosimetry information for spaceflight missions. GeneLab is the first comprehensive Omics database for space related research from which an investigator can generate hypotheses to direct future experiments utilizing both ground and space biological radiation data. In addition to previously acquired data, the GLDS is continually expanding as Omics related data are generated by the space life sciences community. Here we provide a brief summary of space radiation related data available at GeneLab.

Plant Omics Data Center: an integrated web repository for interspecies gene expression networks with NLP-based curation.

PubMed

Ohyanagi, Hajime; Takano, Tomoyuki; Terashima, Shin; Kobayashi, Masaaki; Kanno, Maasa; Morimoto, Kyoko; Kanegae, Hiromi; Sasaki, Yohei; Saito, Misa; Asano, Satomi; Ozaki, Soichi; Kudo, Toru; Yokoyama, Koji; Aya, Koichiro; Suwabe, Keita; Suzuki, Go; Aoki, Koh; Kubo, Yasutaka; Watanabe, Masao; Matsuoka, Makoto; Yano, Kentaro

2015-01-01

Comprehensive integration of large-scale omics resources such as genomes, transcriptomes and metabolomes will provide deeper insights into broader aspects of molecular biology. For better understanding of plant biology, we aim to construct a next-generation sequencing (NGS)-derived gene expression network (GEN) repository for a broad range of plant species. So far we have incorporated information about 745 high-quality mRNA sequencing (mRNA-Seq) samples from eight plant species (Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, Sorghum bicolor, Vitis vinifera, Solanum tuberosum, Medicago truncatula and Glycine max) from the public short read archive, digitally profiled the entire set of gene expression profiles, and drawn GENs by using correspondence analysis (CA) to take advantage of gene expression similarities. In order to understand the evolutionary significance of the GENs from multiple species, they were linked according to the orthology of each node (gene) among species. In addition to other gene expression information, functional annotation of the genes will facilitate biological comprehension. Currently we are improving the given gene annotations with natural language processing (NLP) techniques and manual curation. Here we introduce the current status of our analyses and the web database, PODC (Plant Omics Data Center; http://bioinf.mind.meiji.ac.jp/podc/), now open to the public, providing GENs, functional annotations and additional comprehensive omics resources. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

Raising orphans from a metadata morass: A researcher's guide to re-use of public 'omics data.

PubMed

Bhandary, Priyanka; Seetharam, Arun S; Arendsee, Zebulun W; Hur, Manhoi; Wurtele, Eve Syrkin

2018-02-01

More than 15 petabases of raw RNAseq data is now accessible through public repositories. Acquisition of other 'omics data types is expanding, though most lack a centralized archival repository. Data-reuse provides tremendous opportunity to extract new knowledge from existing experiments, and offers a unique opportunity for robust, multi-'omics analyses by merging metadata (information about experimental design, biological samples, protocols) and data from multiple experiments. We illustrate how predictive research can be accelerated by meta-analysis with a study of orphan (species-specific) genes. Computational predictions are critical to infer orphan function because their coding sequences provide very few clues. The metadata in public databases is often confusing; a test case with Zea mays mRNA seq data reveals a high proportion of missing, misleading or incomplete metadata. This metadata morass significantly diminishes the insight that can be extracted from these data. We provide tips for data submitters and users, including specific recommendations to improve metadata quality by more use of controlled vocabulary and by metadata reviews. Finally, we advocate for a unified, straightforward metadata submission and retrieval system. Copyright © 2017 Elsevier B.V. All rights reserved.

[Exposome: from an intuition to a mandatory research field in occupational and enviromental medicine.

PubMed

Paganelli, Matteo; De Palma, Giuseppe; Apostoli, Pietro

2017-11-01

As Genomics aims at the collective characterization and quantification of genes, exposomics refers to the totality of lifetime environmental exposures, consisting in a novel approach to studying the role of the environment in human disease. The aim is to assess all human environmental and occupational exposures in order to better understand their contribution to human diseases. The "omics" revolution infact mostly regards the underlying method: scientific knowledge is expected to come from the analysis of increasingly extensive databases. The primary focus is on air pollution and water contaminants, but all the determinants of human exposure are conceptually part of the idea of exposome, including physical and psychological factors. Using 'omic' techniques the collected exposure data can be linked to biochemical and molecular changes in our body. Since the first formulation of the idea itself of Exposome many efforts have been made to translate the concept into research, in particular two important studies have been started in Europe. We herein suggest that Occupational Medicine could be a precious contributor to the growth of exposure science also in its omic side thanks to the methods and to the knowledges part of our background. Copyright© by Aracne Editrice, Roma, Italy.

NABIC: A New Access Portal to Search, Visualize, and Share Agricultural Genomics Data.

PubMed

Seol, Young-Joo; Lee, Tae-Ho; Park, Dong-Suk; Kim, Chang-Kug

2016-01-01

The National Agricultural Biotechnology Information Center developed an access portal to search, visualize, and share agricultural genomics data with a focus on South Korean information and resources. The portal features an agricultural biotechnology database containing a wide range of omics data from public and proprietary sources. We collected 28.4 TB of data from 162 agricultural organisms, with 10 types of omics data comprising next-generation sequencing sequence read archive, genome, gene, nucleotide, DNA chip, expressed sequence tag, interactome, protein structure, molecular marker, and single-nucleotide polymorphism datasets. Our genomic resources contain information on five animals, seven plants, and one fungus, which is accessed through a genome browser. We also developed a data submission and analysis system as a web service, with easy-to-use functions and cutting-edge algorithms, including those for handling next-generation sequencing data.

Creation of a digital slide and tissue microarray resource from a multi-institutional predictive toxicology study in the rat: an initial report from the PredTox group.

PubMed

Mulrane, Laoighse; Rexhepaj, Elton; Smart, Valerie; Callanan, John J; Orhan, Diclehan; Eldem, Türkan; Mally, Angela; Schroeder, Susanne; Meyer, Kirstin; Wendt, Maria; O'Shea, Donal; Gallagher, William M

2008-08-01

The widespread use of digital slides has only recently come to the fore with the development of high-throughput scanners and high performance viewing software. This development, along with the optimisation of compression standards and image transfer techniques, has allowed the technology to be used in wide reaching applications including integration of images into hospital information systems and histopathological training, as well as the development of automated image analysis algorithms for prediction of histological aberrations and quantification of immunohistochemical stains. Here, the use of this technology in the creation of a comprehensive library of images of preclinical toxicological relevance is demonstrated. The images, acquired using the Aperio ScanScope CS and XT slide acquisition systems, form part of the ongoing EU FP6 Integrated Project, Innovative Medicines for Europe (InnoMed). In more detail, PredTox (abbreviation for Predictive Toxicology) is a subproject of InnoMed and comprises a consortium of 15 industrial (13 large pharma, 1 technology provider and 1 SME) and three academic partners. The primary aim of this consortium is to assess the value of combining data generated from 'omics technologies (proteomics, transcriptomics, metabolomics) with the results from more conventional toxicology methods, to facilitate further informed decision making in preclinical safety evaluation. A library of 1709 scanned images was created of full-face sections of liver and kidney tissue specimens from male Wistar rats treated with 16 proprietary and reference compounds of known toxicity; additional biological materials from these treated animals were separately used to create 'omics data, that will ultimately be used to populate an integrated toxicological database. In respect to assessment of the digital slides, a web-enabled digital slide management system, Digital SlideServer (DSS), was employed to enable integration of the digital slide content into the 'omics database and to facilitate remote viewing by pathologists connected with the project. DSS also facilitated manual annotation of digital slides by the pathologists, specifically in relation to marking particular lesions of interest. Tissue microarrays (TMAs) were constructed from the specimens for the purpose of creating a repository of tissue from animals used in the study with a view to later-stage biomarker assessment. As the PredTox consortium itself aims to identify new biomarkers of toxicity, these TMAs will be a valuable means of validation. In summary, a large repository of histological images was created enabling the subsequent pathological analysis of samples through remote viewing and, along with the utilisation of TMA technology, will allow the validation of biomarkers identified by the PredTox consortium. The population of the PredTox database with these digitised images represents the creation of the first toxicological database integrating 'omics and preclinical data with histological images.

FragariaCyc: A Metabolic Pathway Database for Woodland Strawberry Fragaria vesca

PubMed Central

Naithani, Sushma; Partipilo, Christina M.; Raja, Rajani; Elser, Justin L.; Jaiswal, Pankaj

2016-01-01

FragariaCyc is a strawberry-specific cellular metabolic network based on the annotated genome sequence of Fragaria vesca L. ssp. vesca, accession Hawaii 4. It was built on the Pathway-Tools platform using MetaCyc as the reference. The experimental evidences from published literature were used for supporting/editing existing entities and for the addition of new pathways, enzymes, reactions, compounds, and small molecules in the database. To date, FragariaCyc comprises 66 super-pathways, 488 unique pathways, 2348 metabolic reactions, 3507 enzymes, and 2134 compounds. In addition to searching and browsing FragariaCyc, researchers can compare pathways across various plant metabolic networks and analyze their data using Omics Viewer tool. We view FragariaCyc as a resource for the community of researchers working with strawberry and related fruit crops. It can help understanding the regulation of overall metabolism of strawberry plant during development and in response to diseases and abiotic stresses. FragariaCyc is available online at http://pathways.cgrb.oregonstate.edu. PMID:26973684

ECOMICS: A Web-Based Toolkit for Investigating the Biomolecular Web in Ecosystems Using a Trans-omics Approach

PubMed Central

Morioka, Yusuke; Everroad, R. Craig; Shino, Amiu; Matsushima, Akihiro; Haruna, Hideaki; Moriya, Shigeharu; Toyoda, Tetsuro; Kikuchi, Jun

2012-01-01

Ecosystems can be conceptually thought of as interconnected environmental and metabolic systems, in which small molecules to macro-molecules interact through diverse networks. State-of-the-art technologies in post-genomic science offer ways to inspect and analyze this biomolecular web using omics-based approaches. Exploring useful genes and enzymes, as well as biomass resources responsible for anabolism and catabolism within ecosystems will contribute to a better understanding of environmental functions and their application to biotechnology. Here we present ECOMICS, a suite of web-based tools for ECosystem trans-OMICS investigation that target metagenomic, metatranscriptomic, and meta-metabolomic systems, including biomacromolecular mixtures derived from biomass. ECOMICS is made of four integrated webtools. E-class allows for the sequence-based taxonomic classification of eukaryotic and prokaryotic ribosomal data and the functional classification of selected enzymes. FT2B allows for the digital processing of NMR spectra for downstream metabolic or chemical phenotyping. Bm-Char allows for statistical assignment of specific compounds found in lignocellulose-based biomass, and HetMap is a data matrix generator and correlation calculator that can be applied to trans-omics datasets as analyzed by these and other web tools. This web suite is unique in that it allows for the monitoring of biomass metabolism in a particular environment, i.e., from macromolecular complexes (FT2DB and Bm-Char) to microbial composition and degradation (E-class), and makes possible the understanding of relationships between molecular and microbial elements (HetMap). This website is available to the public domain at: https://database.riken.jp/ecomics/. PMID:22319563

Metaproteomics as a Complementary Approach to Gut Microbiota in Health and Disease

NASA Astrophysics Data System (ADS)

Petriz, Bernardo A.; Franco, Octávio L.

2017-01-01

Classic studies on phylotype profiling are limited to the identification of microbial constituents, where information is lacking about the molecular interaction of these bacterial communities with the host genome and the possible outcomes in host biology. A range of OMICs approaches have provided great progress linking the microbiota to health and disease. However, the investigation of this context through proteomic mass spectrometry-based tools is still being improved. Therefore, metaproteomics or community proteogenomics has emerged as a complementary approach to metagenomic data, as a field in proteomics aiming to perform large-scale characterization of proteins from environmental microbiota such as the human gut. The advances in molecular separation methods coupled with mass spectrometry (e.g. LC-MS/MS) and proteome bioinformatics have been fundamental in these novel large-scale metaproteomic studies, which have further been performed in a wide range of samples including soil, plant and human environments. Metaproteomic studies will make major progress if a comprehensive database covering the genes and expresses proteins from all gut microbial species is developed. To this end, we here present some of the main limitations of metaproteomic studies in complex microbiota environments such as the gut, also addressing the up-to-date pipelines in sample preparation prior to fractionation/separation and mass spectrometry analysis. In addition, a novel approach to the limitations of metagenomic databases is also discussed. Finally, prospects are addressed regarding the application of metaproteomic analysis using a unified host-microbiome gene database and other meta-OMICs platforms.

NABIC: A New Access Portal to Search, Visualize, and Share Agricultural Genomics Data

PubMed Central

Seol, Young-Joo; Lee, Tae-Ho; Park, Dong-Suk; Kim, Chang-Kug

2016-01-01

The National Agricultural Biotechnology Information Center developed an access portal to search, visualize, and share agricultural genomics data with a focus on South Korean information and resources. The portal features an agricultural biotechnology database containing a wide range of omics data from public and proprietary sources. We collected 28.4 TB of data from 162 agricultural organisms, with 10 types of omics data comprising next-generation sequencing sequence read archive, genome, gene, nucleotide, DNA chip, expressed sequence tag, interactome, protein structure, molecular marker, and single-nucleotide polymorphism datasets. Our genomic resources contain information on five animals, seven plants, and one fungus, which is accessed through a genome browser. We also developed a data submission and analysis system as a web service, with easy-to-use functions and cutting-edge algorithms, including those for handling next-generation sequencing data. PMID:26848255

Nutrigenomics: the cutting edge and Asian perspectives.

PubMed

Kato, Hisanori

2008-01-01

One of the two major goals of nutrigenomics is to make full use of genomic information to reveal how genetic variations affect nutrients and other food factors and thereby realize tailor-made nutrition (nutrigenetics). The other major goal of nutrigenomics is to comprehensively understand the response of the body to diets and food factors through various 'omics' technologies such as transcriptomics, proteomics, and metabolomics. The most successfully exploited technology to date is transcriptome analysis, due mainly to its efficiency and high-throughput feature. This technology has already provided a substantial amount of data on, for instance, the novel function of food factors, the unknown mechanism of the effect of nutrients, and even safety issues of foods. The nutrigenomics database that we have created now holds the publication data of several hundred of such 'omics' studies. Furthermore, the transcriptomics approach is being applied to food safety issues. For ex-ample, the data we have obtained thus far suggest that this new technology will facilitate the safety evaluation of newly developed foods and will help clarify the mechanism of toxic effects resulting from the excessive intake of a nutrient. The 'omics' data accumulated by our group and others strongly support the promise of the systems biology approach to food and nutrition science.

[Clinical value evaluation of Chinese herbal formula in context of multi-omics network].

PubMed

Li, Bing; Han, Fei; Wang, Zhong; Wang, Yong-Yan

2017-03-01

Clinical value evaluation is the key issue to solve the problems such as high repetition rate, fuzzy clinical positioning, broad indications and unclear clinical values in Chinese herbal formula(Chinese patent medicine). By analyzing the challenges and opportunities of Chinese herbal formula in clinical value evaluation, this paper introduced a strategy of multi-omic network analysis. Through comparative analysis of three stroke treatment formulas, we suggested their different characteristic advantages for variant symptoms or phenotypes of stroke, which may provide reference for rational clinical choice. Such multi-omic network analysis strategy may open a unique angle of view for clinical evaluation and comparison of Chinese herbal formula. Copyright© by the Chinese Pharmaceutical Association.

Pharmacometabolomics Informs Quantitative Radiomics for Glioblastoma Diagnostic Innovation.

PubMed

Katsila, Theodora; Matsoukas, Minos-Timotheos; Patrinos, George P; Kardamakis, Dimitrios

2017-08-01

Applications of omics systems biology technologies have enormous promise for radiology and diagnostics in surgical fields. In this context, the emerging fields of radiomics (a systems scale approach to radiology using a host of technologies, including omics) and pharmacometabolomics (use of metabolomics for patient and disease stratification and guiding precision medicine) offer much synergy for diagnostic innovation in surgery, particularly in neurosurgery. This synthesis of omics fields and applications is timely because diagnostic accuracy in central nervous system tumors still challenges decision-making. Considering the vast heterogeneity in brain tumors, disease phenotypes, and interindividual variability in surgical and chemotherapy outcomes, we believe that diagnostic accuracy can be markedly improved by quantitative radiomics coupled to pharmacometabolomics and related health information technologies while optimizing economic costs of traditional diagnostics. In this expert review, we present an innovation analysis on a systems-level multi-omics approach toward diagnostic accuracy in central nervous system tumors. For this, we suggest that glioblastomas serve as a useful application paradigm. We performed a literature search on PubMed for articles published in English between 2006 and 2016. We used the search terms "radiomics," "glioblastoma," "biomarkers," "pharmacogenomics," "pharmacometabolomics," "pharmacometabonomics/pharmacometabolomics," "collaborative informatics," and "precision medicine." A list of the top 4 insights we derived from this literature analysis is presented in this study. For example, we found that (i) tumor grading needs to be better refined, (ii) diagnostic precision should be improved, (iii) standardization in radiomics is lacking, and (iv) quantitative radiomics needs to prove clinical implementation. We conclude with an interdisciplinary call to the metabolomics, pharmacy/pharmacology, radiology, and surgery communities that pharmacometabolomics coupled to information technologies (chemoinformatics tools, databases, collaborative systems) can inform quantitative radiomics, thus translating Big Data and information growth to knowledge growth, rational drug development and diagnostics innovation for glioblastomas, and possibly in other brain tumors.

PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data.

PubMed

Hernández-de-Diego, Rafael; Tarazona, Sonia; Martínez-Mira, Carlos; Balzano-Nogueira, Leandro; Furió-Tarí, Pedro; Pappas, Georgios J; Conesa, Ana

2018-05-25

The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental in better understanding interconnections across molecular layers and in fully utilizing the multi-omic resources available to make biological discoveries. We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information. Unlike other visualization tools, PaintOmics 3 covers a comprehensive pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, and more. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at www.paintomics.org.

An inference method from multi-layered structure of biomedical data.

PubMed

Kim, Myungjun; Nam, Yonghyun; Shin, Hyunjung

2017-05-18

Biological system is a multi-layered structure of omics with genome, epigenome, transcriptome, metabolome, proteome, etc., and can be further stretched to clinical/medical layers such as diseasome, drugs, and symptoms. One advantage of omics is that we can figure out an unknown component or its trait by inferring from known omics components. The component can be inferred by the ones in the same level of omics or the ones in different levels. To implement the inference process, an algorithm that can be applied to the multi-layered complex system is required. In this study, we develop a semi-supervised learning algorithm that can be applied to the multi-layered complex system. In order to verify the validity of the inference, it was applied to the prediction problem of disease co-occurrence with a two-layered network composed of symptom-layer and disease-layer. The symptom-disease layered network obtained a fairly high value of AUC, 0.74, which is regarded as noticeable improvement when comparing 0.59 AUC of single-layered disease network. If further stretched to whole layered structure of omics, the proposed method is expected to produce more promising results. This research has novelty in that it is a new integrative algorithm that incorporates the vertical structure of omics data, on contrary to other existing methods that integrate the data in parallel fashion. The results can provide enhanced guideline for disease co-occurrence prediction, thereby serve as a valuable tool for inference process of multi-layered biological system.

Database resources for the tuberculosis community.

PubMed

Lew, Jocelyne M; Mao, Chunhong; Shukla, Maulik; Warren, Andrew; Will, Rebecca; Kuznetsov, Dmitry; Xenarios, Ioannis; Robertson, Brian D; Gordon, Stephen V; Schnappinger, Dirk; Cole, Stewart T; Sobral, Bruno

2013-01-01

Access to online repositories for genomic and associated "-omics" datasets is now an essential part of everyday research activity. It is important therefore that the Tuberculosis community is aware of the databases and tools available to them online, as well as for the database hosts to know what the needs of the research community are. One of the goals of the Tuberculosis Annotation Jamboree, held in Washington DC on March 7th-8th 2012, was therefore to provide an overview of the current status of three key Tuberculosis resources, TubercuList (tuberculist.epfl.ch), TB Database (www.tbdb.org), and Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org). Here we summarize some key updates and upcoming features in TubercuList, and provide an overview of the PATRIC site and its online tools for pathogen RNA-Seq analysis. Copyright © 2012 Elsevier Ltd. All rights reserved.

Novel promoters and coding first exons in DLG2 linked to developmental disorders and intellectual disability.

PubMed

Reggiani, Claudio; Coppens, Sandra; Sekhara, Tayeb; Dimov, Ivan; Pichon, Bruno; Lufin, Nicolas; Addor, Marie-Claude; Belligni, Elga Fabia; Digilio, Maria Cristina; Faletra, Flavio; Ferrero, Giovanni Battista; Gerard, Marion; Isidor, Bertrand; Joss, Shelagh; Niel-Bütschi, Florence; Perrone, Maria Dolores; Petit, Florence; Renieri, Alessandra; Romana, Serge; Topa, Alexandra; Vermeesch, Joris Robert; Lenaerts, Tom; Casimir, Georges; Abramowicz, Marc; Bontempi, Gianluca; Vilain, Catheline; Deconinck, Nicolas; Smits, Guillaume

2017-07-19

Tissue-specific integrative omics has the potential to reveal new genic elements important for developmental disorders. Two pediatric patients with global developmental delay and intellectual disability phenotype underwent array-CGH genetic testing, both showing a partial deletion of the DLG2 gene. From independent human and murine omics datasets, we combined copy number variations, histone modifications, developmental tissue-specific regulation, and protein data to explore the molecular mechanism at play. Integrating genomics, transcriptomics, and epigenomics data, we describe two novel DLG2 promoters and coding first exons expressed in human fetal brain. Their murine conservation and protein-level evidence allowed us to produce new DLG2 gene models for human and mouse. These new genic elements are deleted in 90% of 29 patients (public and in-house) showing partial deletion of the DLG2 gene. The patients' clinical characteristics expand the neurodevelopmental phenotypic spectrum linked to DLG2 gene disruption to cognitive and behavioral categories. While protein-coding genes are regarded as well known, our work shows that integration of multiple omics datasets can unveil novel coding elements. From a clinical perspective, our work demonstrates that two new DLG2 promoters and exons are crucial for the neurodevelopmental phenotypes associated with this gene. In addition, our work brings evidence for the lack of cross-annotation in human versus mouse reference genomes and nucleotide versus protein databases.

Time to "go large" on biofilm research: advantages of an omics approach.

PubMed

Azevedo, Nuno F; Lopes, Susana P; Keevil, Charles W; Pereira, Maria O; Vieira, Maria J

2009-04-01

In nature, the biofilm mode of life is of great importance in the cell cycle for many microorganisms. Perhaps because of biofilm complexity and variability, the characterization of a given microbial system, in terms of biofilm formation potential, structure and associated physiological activity, in a large-scale, standardized and systematic manner has been hindered by the absence of high-throughput methods. This outlook is now starting to change as new methods involving the utilization of microtiter-plates and automated spectrophotometry and microscopy systems are being developed to perform large-scale testing of microbial biofilms. Here, we evaluate if the time is ripe to start an integrated omics approach, i.e., the generation and interrogation of large datasets, to biofilms--"biofomics". This omics approach would bring much needed insight into how biofilm formation ability is affected by a number of environmental, physiological and mutational factors and how these factors interplay between themselves in a standardized manner. This could then lead to the creation of a database where biofilm signatures are identified and interrogated. Nevertheless, and before embarking on such an enterprise, the selection of a versatile, robust, high-throughput biofilm growing device and of appropriate methods for biofilm analysis will have to be performed. Whether such device and analytical methods are already available, particularly for complex heterotrophic biofilms is, however, very debatable.

Challenges of molecular nutrition research 6: the nutritional phenotype database to store, share and evaluate nutritional systems biology studies

PubMed Central

Bouwman, Jildau; Dragsted, Lars O.; Drevon, Christian A.; Elliott, Ruan; de Groot, Philip; Kaput, Jim; Mathers, John C.; Müller, Michael; Pepping, Fre; Saito, Jahn; Scalbert, Augustin; Radonjic, Marijana; Rocca-Serra, Philippe; Travis, Anthony; Wopereis, Suzan; Evelo, Chris T.

2010-01-01

The challenge of modern nutrition and health research is to identify food-based strategies promoting life-long optimal health and well-being. This research is complex because it exploits a multitude of bioactive compounds acting on an extensive network of interacting processes. Whereas nutrition research can profit enormously from the revolution in ‘omics’ technologies, it has discipline-specific requirements for analytical and bioinformatic procedures. In addition to measurements of the parameters of interest (measures of health), extensive description of the subjects of study and foods or diets consumed is central for describing the nutritional phenotype. We propose and pursue an infrastructural activity of constructing the “Nutritional Phenotype database” (dbNP). When fully developed, dbNP will be a research and collaboration tool and a publicly available data and knowledge repository. Creation and implementation of the dbNP will maximize benefits to the research community by enabling integration and interrogation of data from multiple studies, from different research groups, different countries and different—omics levels. The dbNP is designed to facilitate storage of biologically relevant, pre-processed—omics data, as well as study descriptive and study participant phenotype data. It is also important to enable the combination of this information at different levels (e.g. to facilitate linkage of data describing participant phenotype, genotype and food intake with information on study design and—omics measurements, and to combine all of this with existing knowledge). The biological information stored in the database (i.e. genetics, transcriptomics, proteomics, biomarkers, metabolomics, functional assays, food intake and food composition) is tailored to nutrition research and embedded in an environment of standard procedures and protocols, annotations, modular data-basing, networking and integrated bioinformatics. The dbNP is an evolving enterprise, which is only sustainable if it is accepted and adopted by the wider nutrition and health research community as an open source, pre-competitive and publicly available resource where many partners both can contribute and profit from its developments. We introduce the Nutrigenomics Organisation (NuGO, http://www.nugo.org) as a membership association responsible for establishing and curating the dbNP. Within NuGO, all efforts related to dbNP (i.e. usage, coordination, integration, facilitation and maintenance) will be directed towards a sustainable and federated infrastructure. PMID:21052526

Omics and multi-omics approaches to study the biosynthesis of secondary metabolites in microorganisms.

PubMed

Palazzotto, Emilia; Weber, Tilmann

2018-04-12

Natural products produced by microorganisms represent the main source of bioactive molecules. The development of high-throughput (omics) techniques have importantly contributed to the renaissance of new antibiotic discovery increasing our understanding of complex mechanisms controlling the expression of biosynthetic gene clusters (BGCs) encoding secondary metabolites. In this context this review highlights recent progress in the use and integration of 'omics' approaches with focuses on genomics, transcriptomics, proteomics metabolomics meta-omics and combined omics as powerful strategy to discover new antibiotics. Copyright © 2018 Elsevier Ltd. All rights reserved.

VitisCyc: a metabolic pathway knowledgebase for grapevine (Vitis vinifera)

PubMed Central

Naithani, Sushma; Raja, Rajani; Waddell, Elijah N.; Elser, Justin; Gouthu, Satyanarayana; Deluc, Laurent G.; Jaiswal, Pankaj

2014-01-01

We have developed VitisCyc, a grapevine-specific metabolic pathway database that allows researchers to (i) search and browse the database for its various components such as metabolic pathways, reactions, compounds, genes and proteins, (ii) compare grapevine metabolic networks with other publicly available plant metabolic networks, and (iii) upload, visualize and analyze high-throughput data such as transcriptomes, proteomes, metabolomes etc. using OMICs-Viewer tool. VitisCyc is based on the genome sequence of the nearly homozygous genotype PN40024 of Vitis vinifera “Pinot Noir” cultivar with 12X v1 annotations and was built on BioCyc platform using Pathway Tools software and MetaCyc reference database. Furthermore, VitisCyc was enriched for plant-specific pathways and grape-specific metabolites, reactions and pathways. Currently VitisCyc harbors 68 super pathways, 362 biosynthesis pathways, 118 catabolic pathways, 5 detoxification pathways, 36 energy related pathways and 6 transport pathways, 10,908 enzymes, 2912 enzymatic reactions, 31 transport reactions and 2024 compounds. VitisCyc, as a community resource, can aid in the discovery of candidate genes and pathways that are regulated during plant growth and development, and in response to biotic and abiotic stress signals generated from a plant's immediate environment. VitisCyc version 3.18 is available online at http://pathways.cgrb.oregonstate.edu. PMID:25538713

SilkPathDB: a comprehensive resource for the study of silkworm pathogens

PubMed Central

Pan, Guo-Qing; Vossbrinck, Charles R.; Xu, Jin-Shan; Li, Chun-Feng; Chen, Jie; Long, Meng-Xian; Yang, Ming; Xu, Xiao-Fei; Xu, Chen; Debrunner-Vossbrinck, Bettina A.

2017-01-01

Silkworm pathogens have been heavily impeding the development of sericultural industry and play important roles in lepidopteran ecology, and some of which are used as biological insecticides. Rapid advances in studies on the omics of silkworm pathogens have produced a large amount of data, which need to be brought together centrally in a coherent and systematic manner. This will facilitate the reuse of these data for further analysis. We have collected genomic data for 86 silkworm pathogens from 4 taxa (fungi, microsporidia, bacteria and viruses) and from 4 lepidopteran hosts, and developed the open-access Silkworm Pathogen Database (SilkPathDB) to make this information readily available. The implementation of SilkPathDB involves integrating Drupal and GBrowse as a graphic interface for a Chado relational database which houses all of the datasets involved. The genomes have been assembled and annotated for comparative purposes and allow the search and analysis of homologous sequences, transposable elements, protein subcellular locations, including secreted proteins, and gene ontology. We believe that the SilkPathDB will aid researchers in the identification of silkworm parasites, understanding the mechanisms of silkworm infections, and the developmental ecology of silkworm parasites (gene expression) and their hosts. Database URL: http://silkpathdb.swu.edu.cn PMID:28365723

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software.

PubMed

Fabregat-Traver, Diego; Sharapov, Sodbo Zh; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo

2014-01-01

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the 'omics' context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.

Improving -Omics-Based Research and Precision Health in Minority Populations: Recommendations for Nurse Scientists.

PubMed

Taylor, Jacquelyn Y; Barcelona de Mendoza, Veronica

2018-01-01

The purpose of this article is to provide an overview of the role of nurse scientists in -omics-based research and to promote discussion around the conduct of -omics-based nursing research in minority communities. Nurses are advocates, educators, practitioners, scientists, and researchers, and are crucial to the design and successful implementation of -omics studies, particularly including minority communities. The contribution of nursing in this area of research is crucial to reducing health disparities. In this article, challenges in the conduct of -omics-based research in minority communities are discussed, and recommendations for improving diversity among nurse scientists, study participants, and utilization of training and continuing education programs in -omics are provided. Many opportunities exist for nurses to increase their knowledge in -omics and to continue to build the ranks of nurse scientists as leaders in -omics-based research. In order to work successfully with communities of color, nurse scientists must advocate for participation in the Precision Medicine Initiative, improve representation of nurse faculty of color, and increase utilization of training programs in -omics and lead such initiatives. All nursing care has the potential to be affected by the era of -omics and precision health. By taking an inclusive approach to diversity in nursing and -omics research, nurses will be well placed to be leaders in reducing health disparities through research, practice, and education. © 2017 Sigma Theta Tau International.

Prediction of Acute Mountain Sickness using a Blood-Based Test

DTIC Science & Technology

2016-01-01

2015): In quarter 17 we focused on two major tasks: getting the RNA purified and ready for chip analysis and working on the bioinformatics ... bioinformatics organization of all the data we will examine for this study. To remind the reviewer, we have a primary dataset of ~120 subjects who were studied...companion study, AltitudeOmics, to the database of gene studies to be analyzed for AMS prediction • expansion of a bioinformatics team to include an

Arrowland v1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

BIRKEL, GARRETT; GARCIA MARTIN, HECTOR; MORRELL, WILLIAM

"Arrowland" is a web-based software application primarily for mapping, integrating and visualizing a variety of metabolism data of living organisms, including but not limited to metabolomics, proteomics, transcriptomics and fluxomics. This software application makes multi-omics data analysis intuitive and interactive. It improves data sharing and communication by enabling users to visualize their omics data using a web browser (on a PC or mobile device). It increases user's productivity by simplifying multi-omics data analysis using well developed maps as a guide. Users using this tool can gain insights into their data sets that would be difficult or even impossible to teasemore » out by looking at raw number, or using their currently existing toolchains to generate static single-use maps. Arrowland helps users save time by visualizing relative changes in different conditions or over time, and helps users to produce more significant insights faster. Preexisting maps decrease the learning curve for beginners in the omics field. Sets of multi-omics data are presented in the browser, as a two-dimensional flowchart resembling a map, with varying levels of detail information, based on the scaling of the map. Users can pan and zoom to explore different maps, compare maps, upload their own research data sets onto desired maps, alter map appearance in ways that facilitate interpretation, visualization and analysis of the given data, and export data, reports and actionable items to help the user initiative.« less

Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study.

PubMed

Saw, Woei-Yuh; Tantoso, Erwin; Begum, Husna; Zhou, Lihan; Zou, Ruiyang; He, Cheng; Chan, Sze Ling; Tan, Linda Wei-Lin; Wong, Lai-Ping; Xu, Wenting; Moong, Don Kyin Nwe; Lim, Yenly; Li, Bowen; Pillai, Nisha Esakimuthu; Peterson, Trevor A; Bielawny, Tomasz; Meikle, Peter J; Mundra, Piyushkumar A; Lim, Wei-Yen; Luo, Ma; Chia, Kee-Seng; Ong, Rick Twee-Hee; Brunham, Liam R; Khor, Chiea-Chuen; Too, Heng Phon; Soong, Richie; Wenk, Markus R; Little, Peter; Teo, Yik-Ying

2017-09-21

The Singapore Integrative Omics Study provides valuable insights on establishing population reference measurement in 364 Chinese, Malay, and Indian individuals. These measurements include > 2.5 millions genetic variants, 21,649 transcripts expression, 282 lipid species quantification, and 284 clinical, lifestyle, and dietary variables. This concept paper introduces the depth of the data resource, and investigates the extent of ethnic variation at these omics and non-omics biomarkers. It is evident that there are specific biomarkers in each of these platforms to differentiate between the ethnicities, and intra-population analyses suggest that Chinese and Indians are the most biologically homogeneous and heterogeneous, respectively, of the three groups. Consistent patterns of correlations between lipid species also suggest the possibility of lipid tagging to simplify future lipidomics assays. The Singapore Integrative Omics Study is expected to allow the characterization of intra-omic and inter-omic correlations within and across all three ethnic groups through a systems biology approach.The Singapore Genome Variation projects characterized the genetics of Singapore's Chinese, Malay, and Indian populations. The Singapore Integrative Omics Study introduced here goes further in providing multi-omic measurements in individuals from these populations, including genetic, transcriptome, lipidome, and lifestyle data, and will facilitate the study of common diseases in Asian communities.

SoyFN: a knowledge database of soybean functional networks.

PubMed

Xu, Yungang; Guo, Maozu; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang

2014-01-01

Many databases for soybean genomic analysis have been built and made publicly available, but few of them contain knowledge specifically targeting the omics-level gene-gene, gene-microRNA (miRNA) and miRNA-miRNA interactions. Here, we present SoyFN, a knowledge database of soybean functional gene networks and miRNA functional networks. SoyFN provides user-friendly interfaces to retrieve, visualize, analyze and download the functional networks of soybean genes and miRNAs. In addition, it incorporates much information about KEGG pathways, gene ontology annotations and 3'-UTR sequences as well as many useful tools including SoySearch, ID mapping, Genome Browser, eFP Browser and promoter motif scan. SoyFN is a schema-free database that can be accessed as a Web service from any modern programming language using a simple Hypertext Transfer Protocol call. The Web site is implemented in Java, JavaScript, PHP, HTML and Apache, with all major browsers supported. We anticipate that this database will be useful for members of research communities both in soybean experimental science and bioinformatics. Database URL: http://nclab.hit.edu.cn/SoyFN.

An Updated Taxonomy and a Graphical Summary Tool for Optimal Classification and Comprehension of Omics Research.

PubMed

Pirih, Nina; Kunej, Tanja

2018-05-01

The volume of publications and the type of research approaches used in omics system sciences are vast and continue to expand rapidly. This increased complexity and heterogeneity of omics data are challenging data extraction, sensemaking, analyses, knowledge translation, and interpretation. An extended and dynamic taxonomy for the classification and summary of omics studies are essential. We present an updated taxonomy for classification of omics research studies based on four criteria: (1) type and number of genomic loci in a research study, (2) number of species and biological samples, (3) the type of omics technology (e.g., genomics, transcriptomics, and proteomics) and omics technology application type (e.g., pharmacogenomics and nutrigenomics), and (4) phenotypes. In addition, we present a graphical summary approach that enables the researchers to define the main characteristics of their study in a single figure, and offers the readers to rapidly grasp the published study and omics data. We searched the PubMed and the Web of Science from 09/2002 to 02/2018, including research and review articles, and identified 90 scientific publications. We propose a call toward omics studies' standardization for reporting in scientific literature. We anticipate the proposed classification scheme will usefully contribute to improved classification of published reports in genomics and other omics fields, and help data extraction from publications for future multiomics data integration.

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine.

PubMed

Wu, Po-Yen; Cheng, Chih-Wen; Kaddi, Chanchala D; Venugopalan, Janani; Hoffman, Ryan; Wang, May D

2017-02-01

Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare. In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling. To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine. Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

[Progress in omics research of Aspergillus niger].

PubMed

Sui, Yufei; Ouyang, Liming; Lu, Hongzhong; Zhuang, Yingping; Zhang, Siliang

2016-08-25

Aspergillus niger, as an important industrial fermentation strain, is widely applied in the production of organic acids and industrial enzymes. With the development of diverse omics technologies, the data of genome, transcriptome, proteome and metabolome of A. niger are increasing continuously, which declared the coming era of big data for the research in fermentation process of A. niger. The data analysis from single omics and the comparison of multi-omics, to the integrations of multi-omics based on the genome-scale metabolic network model largely extends the intensive and systematic understanding of the efficient production mechanism of A. niger. It also provides possibilities for the reasonable global optimization of strain performance by genetic modification and process regulation. We reviewed and summarized progress in omics research of A. niger, and proposed the development direction of omics research on this cell factory.

Air pollution and the fetal origin of disease: A systematic review of the molecular signatures of air pollution exposure in human placenta.

PubMed

Luyten, Leen J; Saenen, Nelly D; Janssen, Bram G; Vrijens, Karen; Plusquin, Michelle; Roels, Harry A; Debacq-Chainiaux, Florence; Nawrot, Tim S

2018-06-13

Fetal development is a crucial window of susceptibility in which exposure-related alterations can be induced on the molecular level, leading to potential changes in metabolism and development. The placenta serves as a gatekeeper between mother and fetus, and is in contact with environmental stressors throughout pregnancy. This makes the placenta as a temporary organ an informative non-invasive matrix suitable to investigate omics-related aberrations in association with in utero exposures such as ambient air pollution. To summarize and discuss the current evidence and define the gaps of knowledge concerning human placental -omics markers in association with prenatal exposure to ambient air pollution. Two investigators independently searched the PubMed, ScienceDirect, and Scopus databases to identify all studies published until January 2017 with an emphasis on epidemiological research on prenatal exposure to ambient air pollution and the effect on placental -omics signatures. From the initial 386 articles, 25 were retained following an a priori set inclusion and exclusion criteria. We identified eleven studies on the genome, two on the transcriptome, five on the epigenome, five on the proteome category, one study with both genomic and proteomic topics, and one study with both genomic and transcriptomic topics. Six studies discussed the triple relationship between exposure to air pollution during pregnancy, the associated placental -omics marker(s), and the potential effect on disease development later in life. So far, no metabolomic or exposomic data discussing associations between the placenta and prenatal exposure to air pollution have been published. Integration of placental biomarkers in an environmental epidemiological context enables researchers to address fundamental questions essential in unraveling the fetal origin of disease and helps to better define the pregnancy exposome of air pollution. Copyright © 2018 Elsevier Inc. All rights reserved.

Omics strategies for revealing Yersinia pestis virulence

PubMed Central

Yang, Ruifu; Du, Zongmin; Han, Yanping; Zhou, Lei; Song, Yajun; Zhou, Dongsheng; Cui, Yujun

2012-01-01

Omics has remarkably changed the way we investigate and understand life. Omics differs from traditional hypothesis-driven research because it is a discovery-driven approach. Mass datasets produced from omics-based studies require experts from different fields to reveal the salient features behind these data. In this review, we summarize omics-driven studies to reveal the virulence features of Yersinia pestis through genomics, trascriptomics, proteomics, interactomics, etc. These studies serve as foundations for further hypothesis-driven research and help us gain insight into Y. pestis pathogenesis. PMID:23248778

-Omic and Electronic Health Records Big Data Analytics for Precision Medicine

PubMed Central

Wu, Po-Yen; Cheng, Chih-Wen; Kaddi, Chanchala D.; Venugopalan, Janani; Hoffman, Ryan; Wang, May D.

2017-01-01

Objective Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of health care. Methods In this article, we present -omic and EHR data characteristics, associated challenges, and data analytics including data pre-processing, mining, and modeling. Results To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. Conclusion Big data analytics is able to address –omic and EHR data challenges for paradigm shift towards precision medicine. Significance Big data analytics makes sense of –omic and EHR data to improve healthcare outcome. It has long lasting societal impact. PMID:27740470

In-silico studies in Chinese herbal medicines' research: evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date.

PubMed

Barlow, D J; Buriani, A; Ehrman, T; Bosisio, E; Eberini, I; Hylands, P J

2012-04-10

The available databases that catalogue information on traditional Chinese medicines are reviewed in terms of their content and utility for in-silico research on Chinese herbal medicines, as too are the various protein database resources, and the software available for use in such studies. The software available for bioinformatics and 'omics studies of Chinese herbal medicines are summarised, and a critical evaluation given of the various in-silico methods applied in screening Chinese herbal medicines, including classification trees, neural networks, support vector machines, docking and inverse docking algorithms. Recommendations are made regarding any future in-silico studies of Chinese herbal medicines. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Metabolonote: a wiki-based database for managing hierarchical metadata of metabolome analyses.

PubMed

Ara, Takeshi; Enomoto, Mitsuo; Arita, Masanori; Ikeda, Chiaki; Kera, Kota; Yamada, Manabu; Nishioka, Takaaki; Ikeda, Tasuku; Nihei, Yoshito; Shibata, Daisuke; Kanaya, Shigehiko; Sakurai, Nozomu

2015-01-01

Metabolomics - technology for comprehensive detection of small molecules in an organism - lags behind the other "omics" in terms of publication and dissemination of experimental data. Among the reasons for this are difficulty precisely recording information about complicated analytical experiments (metadata), existence of various databases with their own metadata descriptions, and low reusability of the published data, resulting in submitters (the researchers who generate the data) being insufficiently motivated. To tackle these issues, we developed Metabolonote, a Semantic MediaWiki-based database designed specifically for managing metabolomic metadata. We also defined a metadata and data description format, called "Togo Metabolome Data" (TogoMD), with an ID system that is required for unique access to each level of the tree-structured metadata such as study purpose, sample, analytical method, and data analysis. Separation of the management of metadata from that of data and permission to attach related information to the metadata provide advantages for submitters, readers, and database developers. The metadata are enriched with information such as links to comparable data, thereby functioning as a hub of related data resources. They also enhance not only readers' understanding and use of data but also submitters' motivation to publish the data. The metadata are computationally shared among other systems via APIs, which facilitate the construction of novel databases by database developers. A permission system that allows publication of immature metadata and feedback from readers also helps submitters to improve their metadata. Hence, this aspect of Metabolonote, as a metadata preparation tool, is complementary to high-quality and persistent data repositories such as MetaboLights. A total of 808 metadata for analyzed data obtained from 35 biological species are published currently. Metabolonote and related tools are available free of cost at http://metabolonote.kazusa.or.jp/.

Progress in oral personalized medicine: contribution of 'omics'.

PubMed

Glurich, Ingrid; Acharya, Amit; Brilliant, Murray H; Shukla, Sanjay K

2015-01-01

Precision medicine (PM), representing clinically applicable personalized medicine, proactively integrates and interprets multidimensional personal health data, including clinical, 'omics', and environmental profiles, into clinical practice. Realization of PM remains in progress. The focus of this review is to provide a descriptive narrative overview of: 1) the current status of oral personalized medicine; and 2) recent advances in genomics and related 'omic' and emerging research domains contributing to advancing oral-systemic PM, with special emphasis on current understanding of oral microbiomes. A scan of peer-reviewed literature describing oral PM or 'omic'-based research conducted on humans/data published in English within the last 5 years in journals indexed in the PubMed database was conducted using mesh search terms. An evidence-based approach was used to report on recent advances with potential to advance PM in the context of historical critical and systematic reviews to delineate current state-of-the-art technologies. Special focus was placed on oral microbiome research associated with health and disease states, emerging research domains, and technological advances, which are positioning realization of PM. This review summarizes: 1) evolving conceptualization of personalized medicine; 2) emerging insight into roles of oral infectious and inflammatory processes as contributors to both oral and systemic diseases; 3) community shifts in microbiota that may contribute to disease; 4) evidence pointing to new uncharacterized potential oral pathogens; 5) advances in technological approaches to 'omics' research that will accelerate PM; 6) emerging research domains that expand insights into host-microbe interaction including inter-kingdom communication, systems and network analysis, and salivaomics; and 7) advances in informatics and big data analysis capabilities to facilitate interpretation of host and microbiome-associated datasets. Furthermore, progress in clinically applicable screening assays and biomarker definition to inform clinical care are briefly explored. Advancement of oral PM currently remains in research and discovery phases. Although substantive progress has been made in advancing the understanding of the role of microbiome dynamics in health and disease and is being leveraged to advance early efforts at clinical translation, further research is required to discern interpretable constituency patterns in the complex interactions of these microbial communities in health and disease. Advances in biotechnology and bioinformatics facilitating novel approaches to rapid analysis and interpretation of large datasets are providing new insights into oral health and disease, potentiating clinical application and advancing realization of PM within the next decade.

PANDA-view: An easy-to-use tool for statistical analysis and visualization of quantitative proteomics data.

PubMed

Chang, Cheng; Xu, Kaikun; Guo, Chaoping; Wang, Jinxia; Yan, Qi; Zhang, Jian; He, Fuchu; Zhu, Yunping

2018-05-22

Compared with the numerous software tools developed for identification and quantification of -omics data, there remains a lack of suitable tools for both downstream analysis and data visualization. To help researchers better understand the biological meanings in their -omics data, we present an easy-to-use tool, named PANDA-view, for both statistical analysis and visualization of quantitative proteomics data and other -omics data. PANDA-view contains various kinds of analysis methods such as normalization, missing value imputation, statistical tests, clustering and principal component analysis, as well as the most commonly-used data visualization methods including an interactive volcano plot. Additionally, it provides user-friendly interfaces for protein-peptide-spectrum representation of the quantitative proteomics data. PANDA-view is freely available at https://sourceforge.net/projects/panda-view/. 1987ccpacer@163.com and zhuyunping@gmail.com. Supplementary data are available at Bioinformatics online.

Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated "Knowledge-Based" Platform.

PubMed

Dubovenko, Alexey; Nikolsky, Yuri; Rakhmatulin, Eugene; Nikolskaya, Tatiana

2017-01-01

Analysis of NGS and other sequencing data, gene variants, gene expression, proteomics, and other high-throughput (OMICs) data is challenging because of its biological complexity and high level of technical and biological noise. One way to deal with both problems is to perform analysis with a high fidelity annotated knowledgebase of protein interactions, pathways, and functional ontologies. This knowledgebase has to be structured in a computer-readable format and must include software tools for managing experimental data, analysis, and reporting. Here, we present MetaCore™ and Key Pathway Advisor (KPA), an integrated platform for functional data analysis. On the content side, MetaCore and KPA encompass a comprehensive database of molecular interactions of different types, pathways, network models, and ten functional ontologies covering human, mouse, and rat genes. The analytical toolkit includes tools for gene/protein list enrichment analysis, statistical "interactome" tool for the identification of over- and under-connected proteins in the dataset, and a biological network analysis module made up of network generation algorithms and filters. The suite also features Advanced Search, an application for combinatorial search of the database content, as well as a Java-based tool called Pathway Map Creator for drawing and editing custom pathway maps. Applications of MetaCore and KPA include molecular mode of action of disease research, identification of potential biomarkers and drug targets, pathway hypothesis generation, analysis of biological effects for novel small molecule compounds and clinical applications (analysis of large cohorts of patients, and translational and personalized medicine).

Genomic Sequence Variation Markup Language (GSVML).

PubMed

Nakaya, Jun; Kimura, Michio; Hiroi, Kaei; Ido, Keisuke; Yang, Woosung; Tanaka, Hiroshi

2010-02-01

With the aim of making good use of internationally accumulated genomic sequence variation data, which is increasing rapidly due to the explosive amount of genomic research at present, the development of an interoperable data exchange format and its international standardization are necessary. Genomic Sequence Variation Markup Language (GSVML) will focus on genomic sequence variation data and human health applications, such as gene based medicine or pharmacogenomics. We developed GSVML through eight steps, based on case analysis and domain investigations. By focusing on the design scope to human health applications and genomic sequence variation, we attempted to eliminate ambiguity and to ensure practicability. We intended to satisfy the requirements derived from the use case analysis of human-based clinical genomic applications. Based on database investigations, we attempted to minimize the redundancy of the data format, while maximizing the data covering range. We also attempted to ensure communication and interface ability with other Markup Languages, for exchange of omics data among various omics researchers or facilities. The interface ability with developing clinical standards, such as the Health Level Seven Genotype Information model, was analyzed. We developed the human health-oriented GSVML comprising variation data, direct annotation, and indirect annotation categories; the variation data category is required, while the direct and indirect annotation categories are optional. The annotation categories contain omics and clinical information, and have internal relationships. For designing, we examined 6 cases for three criteria as human health application and 15 data elements for three criteria as data formats for genomic sequence variation data exchange. The data format of five international SNP databases and six Markup Languages and the interface ability to the Health Level Seven Genotype Model in terms of 317 items were investigated. GSVML was developed as a potential data exchanging format for genomic sequence variation data exchange focusing on human health applications. The international standardization of GSVML is necessary, and is currently underway. GSVML can be applied to enhance the utilization of genomic sequence variation data worldwide by providing a communicable platform between clinical and research applications. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Machine Learning Approaches to Increasing Value of Spaceflight Omics Databases

NASA Technical Reports Server (NTRS)

Gentry, Diana

2017-01-01

The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may soon enable unique insight into which measured phenomena correlate to biological mechanisms that are truly affected by spaceflight conditions; which are most likely to be confounded by other variables; and which are insufficiently characterized, significantly increasing existing and future science return from ISS and spaceflight missions.

Omics AnalySIs System for PRecision Oncology (OASISPRO): A Web-based Omics Analysis Tool for Clinical Phenotype Prediction.

PubMed

Yu, Kun-Hsing; Fitzpatrick, Michael R; Pappas, Luke; Chan, Warren; Kung, Jessica; Snyder, Michael

2017-09-12

Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general-purpose data-mining tool exists for physicians, medical researchers, and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients' clinical profiles, executes machine-learning algorithms of choice on the omics data, and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients' survival outcomes in many cancer types, including mesothelioma and adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. This web-based tool is available at http://tinyurl.com/oasispro ;source codes are available at http://tinyurl.com/oasisproSourceCode . © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

GenoBase: comprehensive resource database of Escherichia coli K-12

PubMed Central

Otsuka, Yuta; Muto, Ai; Takeuchi, Rikiya; Okada, Chihiro; Ishikawa, Motokazu; Nakamura, Koichiro; Yamamoto, Natsuko; Dose, Hitomi; Nakahigashi, Kenji; Tanishima, Shigeki; Suharnan, Sivasundaram; Nomura, Wataru; Nakayashiki, Toru; Aref, Walid G.; Bochner, Barry R.; Conway, Tyrrell; Gribskov, Michael; Kihara, Daisuke; Rudd, Kenneth E.; Tohsato, Yukako; Wanner, Barry L.; Mori, Hirotada

2015-01-01

Comprehensive experimental resources, such as ORFeome clone libraries and deletion mutant collections, are fundamental tools for elucidation of gene function. Data sets by omics analysis using these resources provide key information for functional analysis, modeling and simulation both in individual and systematic approaches. With the long-term goal of complete understanding of a cell, we have over the past decade created a variety of clone and mutant sets for functional genomics studies of Escherichia coli K-12. We have made these experimental resources freely available to the academic community worldwide. Accordingly, these resources have now been used in numerous investigations of a multitude of cell processes. Quality control is extremely important for evaluating results generated by these resources. Because the annotation has been changed since 2005, which we originally used for the construction, we have updated these genomic resources accordingly. Here, we describe GenoBase (http://ecoli.naist.jp/GB/), which contains key information about comprehensive experimental resources of E. coli K-12, their quality control and several omics data sets generated using these resources. PMID:25399415

Omics analysis of acetic acid tolerance in Saccharomyces cerevisiae.

PubMed

Geng, Peng; Zhang, Liang; Shi, Gui Yang

2017-05-01

Acetic acid is an inhibitor in industrial processes such as wine making and bioethanol production from cellulosic hydrolysate. It causes energy depletion, inhibition of metabolic enzyme activity, growth arrest and ethanol productivity losses in Saccharomyces cerevisiae. Therefore, understanding the mechanisms of the yeast responses to acetic acid stress is essential for improving acetic acid tolerance and ethanol production. Although 329 genes associated with acetic acid tolerance have been identified in the Saccharomyces genome and included in the database ( http://www.yeastgenome.org/observable/resistance_to_acetic_acid/overview ), the cellular mechanistic responses to acetic acid remain unclear in this organism. Post-genomic approaches such as transcriptomics, proteomics, metabolomics and chemogenomics are being applied to yeast and are providing insight into the mechanisms and interactions of genes, proteins and other components that together determine complex quantitative phenotypic traits such as acetic acid tolerance. This review focuses on these omics approaches in the response to acetic acid in S. cerevisiae. Additionally, several novel strains with improved acetic acid tolerance have been engineered by modifying key genes, and the application of these strains and recently acquired knowledge to industrial processes is also discussed.

Advances in Omics and Bioinformatics Tools for Systems Analyses of Plant Functions

PubMed Central

Mochida, Keiichi; Shinozaki, Kazuo

2011-01-01

Omics and bioinformatics are essential to understanding the molecular systems that underlie various plant functions. Recent game-changing sequencing technologies have revitalized sequencing approaches in genomics and have produced opportunities for various emerging analytical applications. Driven by technological advances, several new omics layers such as the interactome, epigenome and hormonome have emerged. Furthermore, in several plant species, the development of omics resources has progressed to address particular biological properties of individual species. Integration of knowledge from omics-based research is an emerging issue as researchers seek to identify significance, gain biological insights and promote translational research. From these perspectives, we provide this review of the emerging aspects of plant systems research based on omics and bioinformatics analyses together with their associated resources and technological advances. PMID:22156726

BiofOmics: a Web platform for the systematic and standardized collection of high-throughput biofilm data.

PubMed

Lourenço, Anália; Ferreira, Andreia; Veiga, Nuno; Machado, Idalina; Pereira, Maria Olivia; Azevedo, Nuno F

2012-01-01

Consortia of microorganisms, commonly known as biofilms, are attracting much attention from the scientific community due to their impact in human activity. As biofilm research grows to be a data-intensive discipline, the need for suitable bioinformatics approaches becomes compelling to manage and validate individual experiments, and also execute inter-laboratory large-scale comparisons. However, biofilm data is widespread across ad hoc, non-standardized individual files and, thus, data interchange among researchers, or any attempt of cross-laboratory experimentation or analysis, is hardly possible or even attempted. This paper presents BiofOmics, the first publicly accessible Web platform specialized in the management and analysis of data derived from biofilm high-throughput studies. The aim is to promote data interchange across laboratories, implementing collaborative experiments, and enable the development of bioinformatics tools in support of the processing and analysis of the increasing volumes of experimental biofilm data that are being generated. BiofOmics' data deposition facility enforces data structuring and standardization, supported by controlled vocabulary. Researchers are responsible for the description of the experiments, their results and conclusions. BiofOmics' curators interact with submitters only to enforce data structuring and the use of controlled vocabulary. Then, BiofOmics' search facility makes publicly available the profile and data associated with a submitted study so that any researcher can profit from these standardization efforts to compare similar studies, generate new hypotheses to be tested or even extend the conditions experimented in the study. BiofOmics' novelty lies in its support to standardized data deposition, the availability of computerizable data files and the free-of-charge dissemination of biofilm studies across the community. Hopefully, this will open promising research possibilities, namely the comparison of results between different laboratories, the reproducibility of methods within and between laboratories, and the development of guidelines and standardized protocols for biofilm formation operating procedures and analytical methods.

Single Cell Multi-Omics Technology: Methodology and Application.

PubMed

Hu, Youjin; An, Qin; Sheu, Katherine; Trejo, Brandon; Fan, Shuxin; Guo, Ying

2018-01-01

In the era of precision medicine, multi-omics approaches enable the integration of data from diverse omics platforms, providing multi-faceted insight into the interrelation of these omics layers on disease processes. Single cell sequencing technology can dissect the genotypic and phenotypic heterogeneity of bulk tissue and promises to deepen our understanding of the underlying mechanisms governing both health and disease. Through modification and combination of single cell assays available for transcriptome, genome, epigenome, and proteome profiling, single cell multi-omics approaches have been developed to simultaneously and comprehensively study not only the unique genotypic and phenotypic characteristics of single cells, but also the combined regulatory mechanisms evident only at single cell resolution. In this review, we summarize the state-of-the-art single cell multi-omics methods and discuss their applications, challenges, and future directions.

Single Cell Multi-Omics Technology: Methodology and Application

PubMed Central

Hu, Youjin; An, Qin; Sheu, Katherine; Trejo, Brandon; Fan, Shuxin; Guo, Ying

2018-01-01

In the era of precision medicine, multi-omics approaches enable the integration of data from diverse omics platforms, providing multi-faceted insight into the interrelation of these omics layers on disease processes. Single cell sequencing technology can dissect the genotypic and phenotypic heterogeneity of bulk tissue and promises to deepen our understanding of the underlying mechanisms governing both health and disease. Through modification and combination of single cell assays available for transcriptome, genome, epigenome, and proteome profiling, single cell multi-omics approaches have been developed to simultaneously and comprehensively study not only the unique genotypic and phenotypic characteristics of single cells, but also the combined regulatory mechanisms evident only at single cell resolution. In this review, we summarize the state-of-the-art single cell multi-omics methods and discuss their applications, challenges, and future directions. PMID:29732369

Identifying novel glioma associated pathways based on systems biology level meta-analysis.

PubMed

Hu, Yangfan; Li, Jinquan; Yan, Wenying; Chen, Jiajia; Li, Yin; Hu, Guang; Shen, Bairong

2013-01-01

With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this "-omics" data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma.

Framework for the quality assurance of 'omics technologies considering GLP requirements.

PubMed

Kauffmann, Hans-Martin; Kamp, Hennicke; Fuchs, Regine; Chorley, Brian N; Deferme, Lize; Ebbels, Timothy; Hackermüller, Jörg; Perdichizzi, Stefania; Poole, Alan; Sauer, Ursula G; Tollefsen, Knut E; Tralau, Tewes; Yauk, Carole; van Ravenzwaay, Ben

2017-12-01

'Omics technologies are gaining importance to support regulatory toxicity studies. Prerequisites for performing 'omics studies considering GLP principles were discussed at the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Workshop Applying 'omics technologies in Chemical Risk Assessment. A GLP environment comprises a standard operating procedure system, proper pre-planning and documentation, and inspections of independent quality assurance staff. To prevent uncontrolled data changes, the raw data obtained in the respective 'omics data recording systems have to be specifically defined. Further requirements include transparent and reproducible data processing steps, and safe data storage and archiving procedures. The software for data recording and processing should be validated, and data changes should be traceable or disabled. GLP-compliant quality assurance of 'omics technologies appears feasible for many GLP requirements. However, challenges include (i) defining, storing, and archiving the raw data; (ii) transparent descriptions of data processing steps; (iii) software validation; and (iv) ensuring complete reproducibility of final results with respect to raw data. Nevertheless, 'omics studies can be supported by quality measures (e.g., GLP principles) to ensure quality control, reproducibility and traceability of experiments. This enables regulators to use 'omics data in a fit-for-purpose context, which enhances their applicability for risk assessment. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrated omics for the identification of key functionalities in biological wastewater treatment microbial communities.

PubMed

Narayanasamy, Shaman; Muller, Emilie E L; Sheik, Abdul R; Wilmes, Paul

2015-05-01

Biological wastewater treatment plants harbour diverse and complex microbial communities which prominently serve as models for microbial ecology and mixed culture biotechnological processes. Integrated omic analyses (combined metagenomics, metatranscriptomics, metaproteomics and metabolomics) are currently gaining momentum towards providing enhanced understanding of community structure, function and dynamics in situ as well as offering the potential to discover novel biological functionalities within the framework of Eco-Systems Biology. The integration of information from genome to metabolome allows the establishment of associations between genetic potential and final phenotype, a feature not realizable by only considering single 'omes'. Therefore, in our opinion, integrated omics will become the future standard for large-scale characterization of microbial consortia including those underpinning biological wastewater treatment processes. Systematically obtained time and space-resolved omic datasets will allow deconvolution of structure-function relationships by identifying key members and functions. Such knowledge will form the foundation for discovering novel genes on a much larger scale compared with previous efforts. In general, these insights will allow us to optimize microbial biotechnological processes either through better control of mixed culture processes or by use of more efficient enzymes in bioengineering applications. © 2015 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Proteomics data exchange and storage: the need for common standards and public repositories.

PubMed

Jiménez, Rafael C; Vizcaíno, Juan Antonio

2013-01-01

Both the existence of data standards and public databases or repositories have been key factors behind the development of the existing "omics" approaches. In this book chapter we first review the main existing mass spectrometry (MS)-based proteomics resources: PRIDE, PeptideAtlas, GPMDB, and Tranche. Second, we report on the current status of the different proteomics data standards developed by the Proteomics Standards Initiative (PSI): the formats mzML, mzIdentML, mzQuantML, TraML, and PSI-MI XML are then reviewed. Finally, we present an easy way to query and access MS proteomics data in the PRIDE database, as a representative of the existing repositories, using the workflow management system (WMS) tool Taverna. Two different publicly available workflows are explained and described.

VaProS: a database-integration approach for protein/genome information retrieval.

PubMed

Gojobori, Takashi; Ikeo, Kazuho; Katayama, Yukie; Kawabata, Takeshi; Kinjo, Akira R; Kinoshita, Kengo; Kwon, Yeondae; Migita, Ohsuke; Mizutani, Hisashi; Muraoka, Masafumi; Nagata, Koji; Omori, Satoshi; Sugawara, Hideaki; Yamada, Daichi; Yura, Kei

2016-12-01

Life science research now heavily relies on all sorts of databases for genome sequences, transcription, protein three-dimensional (3D) structures, protein-protein interactions, phenotypes and so forth. The knowledge accumulated by all the omics research is so vast that a computer-aided search of data is now a prerequisite for starting a new study. In addition, a combinatory search throughout these databases has a chance to extract new ideas and new hypotheses that can be examined by wet-lab experiments. By virtually integrating the related databases on the Internet, we have built a new web application that facilitates life science researchers for retrieving experts' knowledge stored in the databases and for building a new hypothesis of the research target. This web application, named VaProS, puts stress on the interconnection between the functional information of genome sequences and protein 3D structures, such as structural effect of the gene mutation. In this manuscript, we present the notion of VaProS, the databases and tools that can be accessed without any knowledge of database locations and data formats, and the power of search exemplified in quest of the molecular mechanisms of lysosomal storage disease. VaProS can be freely accessed at http://p4d-info.nig.ac.jp/vapros/ .

Omics studies of citrus, grape and rosaceae fruit trees

PubMed Central

Shiratake, Katsuhiro; Suzuki, Mami

2016-01-01

Recent advance of bioinformatics and analytical apparatuses such as next generation DNA sequencer (NGS) and mass spectrometer (MS) has brought a big wave of comprehensive study to biology. Comprehensive study targeting all genes, transcripts (RNAs), proteins, metabolites, hormones, ions or phenotypes is called genomics, transcriptomics, proteomics, metabolomics, hormonomics, ionomics or phenomics, respectively. These omics are powerful approaches to identify key genes for important traits, to clarify events of physiological mechanisms and to reveal unknown metabolic pathways in crops. Recently, the use of omics approach has increased dramatically in fruit tree research. Although the most reported omics studies on fruit trees are transcriptomics, proteomics and metabolomics, and a few is reported on hormonomics and ionomics. In this article, we reviewed recent omics studies of major fruit trees, i.e. citrus, grapevine and rosaceae fruit trees. The effectiveness and prospects of omics in fruit tree research will as well be highlighted. PMID:27069397

Omics studies of citrus, grape and rosaceae fruit trees.

PubMed

Shiratake, Katsuhiro; Suzuki, Mami

2016-01-01

Recent advance of bioinformatics and analytical apparatuses such as next generation DNA sequencer (NGS) and mass spectrometer (MS) has brought a big wave of comprehensive study to biology. Comprehensive study targeting all genes, transcripts (RNAs), proteins, metabolites, hormones, ions or phenotypes is called genomics, transcriptomics, proteomics, metabolomics, hormonomics, ionomics or phenomics, respectively. These omics are powerful approaches to identify key genes for important traits, to clarify events of physiological mechanisms and to reveal unknown metabolic pathways in crops. Recently, the use of omics approach has increased dramatically in fruit tree research. Although the most reported omics studies on fruit trees are transcriptomics, proteomics and metabolomics, and a few is reported on hormonomics and ionomics. In this article, we reviewed recent omics studies of major fruit trees, i.e. citrus, grapevine and rosaceae fruit trees. The effectiveness and prospects of omics in fruit tree research will as well be highlighted.

Clustering multilayer omics data using MuNCut.

PubMed

Teran Hidalgo, Sebastian J; Ma, Shuangge

2018-03-14

Omics profiling is now a routine component of biomedical studies. In the analysis of omics data, clustering is an essential step and serves multiple purposes including for example revealing the unknown functionalities of omics units, assisting dimension reduction in outcome model building, and others. In the most recent omics studies, a prominent trend is to conduct multilayer profiling, which collects multiple types of genetic, genomic, epigenetic and other measurements on the same subjects. In the literature, clustering methods tailored to multilayer omics data are still limited. Directly applying the existing clustering methods to multilayer omics data and clustering each layer first and then combing across layers are both "suboptimal" in that they do not accommodate the interconnections within layers and across layers in an informative way. In this study, we develop the MuNCut (Multilayer NCut) clustering approach. It is tailored to multilayer omics data and sufficiently accounts for both across- and within-layer connections. It is based on the novel NCut technique and also takes advantages of regularized sparse estimation. It has an intuitive formulation and is computationally very feasible. To facilitate implementation, we develop the function muncut in the R package NcutYX. Under a wide spectrum of simulation settings, it outperforms competitors. The analysis of TCGA (The Cancer Genome Atlas) data on breast cancer and cervical cancer shows that MuNCut generates biologically meaningful results which differ from those using the alternatives. We propose a more effective clustering analysis of multiple omics data. It provides a new venue for jointly analyzing genetic, genomic, epigenetic and other measurements.

Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine.

PubMed

Pecak, Matija; Korošec, Peter; Kunej, Tanja

2018-06-01

Asthma is a common complex disorder and has been subject to intensive omics research for disease susceptibility and therapeutic innovation. Candidate biomarkers of asthma and its precision treatment demand that they stand the test of multiomics data triangulation before they can be prioritized for clinical applications. We classified the biomarkers of asthma after a search of the literature and based on whether or not a given biomarker candidate is reported in multiple omics platforms and methodologies, using PubMed and Web of Science, we identified omics studies of asthma conducted on diverse platforms using keywords, such as asthma, genomics, metabolomics, and epigenomics. We extracted data about asthma candidate biomarkers from 73 articles and developed a catalog of 190 potential asthma biomarkers (167 human, 23 animal data), comprising DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs. The data were sorted according to 13 omics types: genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, ncRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Importantly, we found that 10 candidate biomarkers were apparent in at least two or more omics levels, thus promising potential for further biomarker research and development and precision medicine applications. This multiomics catalog reported herein for the first time contributes to future decision-making on prioritization of biomarkers and validation efforts for precision medicine in asthma. The findings may also facilitate meta-analyses and integrative omics studies in the future.

Environmental OMICS: Current Status and Future Directions.

EPA Science Inventory

Objectives: Applications of OMICS to high throughput studies of changes of genes, RNAs, proteins and metabolites, and their associated functions in cells or organisms exposed to environmental chemicals has led to the emergence of a very active research field: environmental OMICS....

Inferring gene ontologies from pairwise similarity data

PubMed Central

Kramer, Michael; Dutkowski, Janusz; Yu, Michael; Bafna, Vineet; Ideker, Trey

2014-01-01

Motivation: While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that: analyze a full matrix of gene–gene pairwise similarities from -omics data;infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact; andrespect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms. Methods addressing these requirements are just beginning to emerge—none has been evaluated for GO inference. Methods: We consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that uniquely satisfy these requirements, compared with methods including standard clustering. CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding of a similarity matrix. We evaluate each method’s ability to reconstruct the GO biological process ontology from a similarity matrix based on (a) semantic similarities for GO itself or (b) three -omics datasets for yeast. Results: For task (a) using semantic similarity, CliXO accurately reconstructs GO (>99% precision, recall) and outperforms other approaches (<20% precision, <20% recall). For task (b) using -omics data, CliXO outperforms other methods using two -omics datasets and achieves ∼30% precision and recall using YeastNet v3, similar to an earlier approach (Network Extracted Ontology) and better than LocalFitness or standard clustering (20–25% precision, recall). Conclusion: This study provides algorithmic foundation for building gene ontologies by capturing hierarchical and pleiotropic structure embedded in biomolecular data. Contact: tideker@ucsd.edu PMID:24932003

OMICS-strategies and methods in the fight against doping.

PubMed

Reichel, Christian

2011-12-10

During the past decade OMICS-methods not only continued to have their impact on research strategies in life sciences and in particular molecular biology, but also started to be used for anti-doping control purposes. Research activities were mainly reasoned by the fact that several substances and methods, which were prohibited by the World Anti-Doping Agency (WADA), were or still are difficult to detect by direct methods. Transcriptomics, proteomics, and metabolomics in theory offer ideal platforms for the discovery of biomarkers for the indirect detection of the abuse of these substances and methods. Traditionally, the main focus of transcriptomics and proteomics projects has been on the prolonged detection of the misuse of human growth hormone (hGH), recombinant erythropoietin (rhEpo), and autologous blood transfusion. An additional benefit of the indirect or marker approach would also be that similarly acting substances might then be detected by a single method, without being forced to develop new direct detection methods for new but comparable prohibited substances (as has been the case, e.g. for the various forms of Epo analogs and biosimilars). While several non-OMICS-derived parameters for the indirect detection of doping are currently in use, for example the blood parameters of the hematological module of the athlete's biological passport, the outcome of most non-targeted OMICS-projects led to no direct application in routine doping control so far. The main reason is the inherent complexity of human transcriptomes, proteomes, and metabolomes and their inter-individual variability. The article reviews previous and recent research projects and their results and discusses future strategies for a more efficient application of OMICS-methods in doping control. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer.

PubMed

Pineda, Silvia; Van Steen, Kristel; Malats, Núria

2017-09-01

Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating different regulatory levels (i.e., blood) with those from tumor may also reveal important knowledge about the human genetic architecture. To model this multilevel structure, an integrative-expression quantitative trait loci (eQTL) analysis applying two-stage regression (2SR) was proposed. This approach first regressed tumor gene expression levels with tumor markers and the adjusted residuals from the previous model were then regressed with the germline genotypes measured in blood. Previously, we demonstrated that penalized regression methods in combination with a permutation-based MaxT method (Global-LASSO) is a promising tool to fix some of the challenges that high-throughput omics data analysis imposes. Here, we assessed whether Global-LASSO can also be applied when tumor and blood omics data are integrated. We further compared our strategy with two 2SR-approaches, one using multiple linear regression (2SR-MLR) and other using LASSO (2SR-LASSO). We applied the three models to integrate genomic, epigenomic, and transcriptomic data from tumor tissue with blood germline genotypes from 181 individuals with bladder cancer included in the TCGA Consortium. Global-LASSO provided a larger list of eQTLs than the 2SR methods, identified a previously reported eQTLs in prostate stem cell antigen (PSCA), and provided further clues on the complexity of APBEC3B loci, with a minimal false-positive rate not achieved by 2SR-MLR. It also represents an important contribution for omics integrative analysis because it is easy to apply and adaptable to any type of data. © 2017 WILEY PERIODICALS, INC.

From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms

PubMed Central

Redenšek, Sara; Dolžan, Vita

2018-01-01

Abstract Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics–DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context. PMID:29356624

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications.

PubMed

Kolker, Eugene; Özdemir, Vural; Martens, Lennart; Hancock, William; Anderson, Gordon; Anderson, Nathaniel; Aynacioglu, Sukru; Baranova, Ancha; Campagna, Shawn R; Chen, Rui; Choiniere, John; Dearth, Stephen P; Feng, Wu-Chun; Ferguson, Lynnette; Fox, Geoffrey; Frishman, Dmitrij; Grossman, Robert; Heath, Allison; Higdon, Roger; Hutz, Mara H; Janko, Imre; Jiang, Lihua; Joshi, Sanjay; Kel, Alexander; Kemnitz, Joseph W; Kohane, Isaac S; Kolker, Natali; Lancet, Doron; Lee, Elaine; Li, Weizhong; Lisitsa, Andrey; Llerena, Adrian; Macnealy-Koch, Courtney; Marshall, Jean-Claude; Masuzzo, Paola; May, Amanda; Mias, George; Monroe, Matthew; Montague, Elizabeth; Mooney, Sean; Nesvizhskii, Alexey; Noronha, Santosh; Omenn, Gilbert; Rajasimha, Harsha; Ramamoorthy, Preveen; Sheehan, Jerry; Smarr, Larry; Smith, Charles V; Smith, Todd; Snyder, Michael; Rapole, Srikanth; Srivastava, Sanjeeva; Stanberry, Larissa; Stewart, Elizabeth; Toppo, Stefano; Uetz, Peter; Verheggen, Kenneth; Voy, Brynn H; Warnich, Louise; Wilhelm, Steven W; Yandl, Gregory

2014-01-01

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications.

PubMed

Kolker, Eugene; Özdemir, Vural; Martens, Lennart; Hancock, William; Anderson, Gordon; Anderson, Nathaniel; Aynacioglu, Sukru; Baranova, Ancha; Campagna, Shawn R; Chen, Rui; Choiniere, John; Dearth, Stephen P; Feng, Wu-Chun; Ferguson, Lynnette; Fox, Geoffrey; Frishman, Dmitrij; Grossman, Robert; Heath, Allison; Higdon, Roger; Hutz, Mara H; Janko, Imre; Jiang, Lihua; Joshi, Sanjay; Kel, Alexander; Kemnitz, Joseph W; Kohane, Isaac S; Kolker, Natali; Lancet, Doron; Lee, Elaine; Li, Weizhong; Lisitsa, Andrey; Llerena, Adrian; MacNealy-Koch, Courtney; Marshall, Jean-Claude; Masuzzo, Paola; May, Amanda; Mias, George; Monroe, Matthew; Montague, Elizabeth; Mooney, Sean; Nesvizhskii, Alexey; Noronha, Santosh; Omenn, Gilbert; Rajasimha, Harsha; Ramamoorthy, Preveen; Sheehan, Jerry; Smarr, Larry; Smith, Charles V; Smith, Todd; Snyder, Michael; Rapole, Srikanth; Srivastava, Sanjeeva; Stanberry, Larissa; Stewart, Elizabeth; Toppo, Stefano; Uetz, Peter; Verheggen, Kenneth; Voy, Brynn H; Warnich, Louise; Wilhelm, Steven W; Yandl, Gregory

2013-12-01

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

"Omics" of maize stress response for sustainable food production: opportunities and challenges.

PubMed

Gong, Fangping; Yang, Le; Tai, Fuju; Hu, Xiuli; Wang, Wei

2014-12-01

Maize originated in the highlands of Mexico approximately 8700 years ago and is one of the most commonly grown cereal crops worldwide, followed by wheat and rice. Abiotic stresses (primarily drought, salinity, and high and low temperatures), together with biotic stresses (primarily fungi, viruses, and pests), negatively affect maize growth, development, and eventually production. To understand the response of maize to abiotic and biotic stresses and its mechanism of stress tolerance, high-throughput omics approaches have been used in maize stress studies. Integrated omics approaches are crucial for dissecting the temporal and spatial system-level changes that occur in maize under various stresses. In this comprehensive analysis, we review the primary types of stresses that threaten sustainable maize production; underscore the recent advances in maize stress omics, especially proteomics; and discuss the opportunities, challenges, and future directions of maize stress omics, with a view to sustainable food production. The knowledge gained from studying maize stress omics is instrumental for improving maize to cope with various stresses and to meet the food demands of the exponentially growing global population. Omics systems science offers actionable potential solutions for sustainable food production, and we present maize as a notable case study.

Application of meta-omics techniques to understand greenhouse gas emissions originating from ruminal metabolism.

PubMed

Wallace, Robert J; Snelling, Timothy J; McCartney, Christine A; Tapio, Ilma; Strozzi, Francesco

2017-01-16

Methane emissions from ruminal fermentation contribute significantly to total anthropological greenhouse gas (GHG) emissions. New meta-omics technologies are beginning to revolutionise our understanding of the rumen microbial community structure, metabolic potential and metabolic activity. Here we explore these developments in relation to GHG emissions. Microbial rumen community analyses based on small subunit ribosomal RNA sequence analysis are not yet predictive of methane emissions from individual animals or treatments. Few metagenomics studies have been directly related to GHG emissions. In these studies, the main genes that differed in abundance between high and low methane emitters included archaeal genes involved in methanogenesis, with others that were not apparently related to methane metabolism. Unlike the taxonomic analysis up to now, the gene sets from metagenomes may have predictive value. Furthermore, metagenomic analysis predicts metabolic function better than only a taxonomic description, because different taxa share genes with the same function. Metatranscriptomics, the study of mRNA transcript abundance, should help to understand the dynamic of microbial activity rather than the gene abundance; to date, only one study has related the expression levels of methanogenic genes to methane emissions, where gene abundance failed to do so. Metaproteomics describes the proteins present in the ecosystem, and is therefore arguably a better indication of microbial metabolism. Both two-dimensional polyacrylamide gel electrophoresis and shotgun peptide sequencing methods have been used for ruminal analysis. In our unpublished studies, both methods showed an abundance of archaeal methanogenic enzymes, but neither was able to discriminate high and low emitters. Metabolomics can take several forms that appear to have predictive value for methane emissions; ruminal metabolites, milk fatty acid profiles, faecal long-chain alcohols and urinary metabolites have all shown promising results. Rumen microbial amino acid metabolism lies at the root of excessive nitrogen emissions from ruminants, yet only indirect inferences for nitrogen emissions can be drawn from meta-omics studies published so far. Annotation of meta-omics data depends on databases that are generally weak in rumen microbial entries. The Hungate 1000 project and Global Rumen Census initiatives are therefore essential to improve the interpretation of sequence/metabolic information.

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.

PubMed

Hassani-Pak, Keywan; Rawlings, Christopher

2017-06-13

Genetics and "omics" studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.

Multi-omics Frontiers in Algal Research: Techniques and Progress to Explore Biofuels in the Postgenomics World.

PubMed

Rai, Vineeta; Karthikaichamy, Anbarasu; Das, Debasish; Noronha, Santosh; Wangikar, Pramod P; Srivastava, Sanjeeva

2016-07-01

Current momentum of microalgal research rests extensively in tapping the potential of multi-omics methodologies in regard to sustainable biofuels. Microalgal biomass is fermented to bioethanol; while lipids, particularly triacylglycerides (TAGs), are transesterified to biodiesels. Biodiesel has emerged as an ideal biofuel candidate; hence, its commercialization and use are increasingly being emphasized. Abiotic stresses exaggerate TAG accumulation, but the precise mechanisms are yet to be known. More recently, comprehensive multi-omics studies in microalgae have emerged from the biofuel perspective. Genomics and transcriptomics of microalgae have provided crucial leads and basic understanding toward lipid biosynthesis. Proteomics and metabolomics are now complementing "algal omics" and offer precise functional insights into the attendant static and dynamic physiological contexts. Indeed, the field has progressed from shotgun to targeted approaches. Notably, targeted proteomics studies in microalga are not yet reported. Several multi-omics tools and technologies that may be used to dig deeper into the microalgal physiology are examined and highlighted in this review. The article therefore aims to both introduce various available high-throughput biotechnologies and applications of "omics" in microalgae, and enlists a compendium of the emerging cutting edge literature. We suggest that a strategic and thoughtful combination of data streams from different omics platforms can provide a system-wide overview. The algal omics warrants closer attention in the future, with a view to technical, economic, and societal impacts that are anticipated in the current postgenomics era.

Epigenetic and genetic dissections of UV-induced global gene dysregulation in skin cells through multi-omics analyses

PubMed Central

Shen, Yao; Stanislauskas, Milda; Li, Gen; Zheng, Deyou; Liu, Liang

2017-01-01

To elucidate the complex molecular mechanisms underlying the adverse effects UV radiation (UVR) on skin homeostasis, we performed multi-omics studies to characterize UV-induced genetic and epigenetic changes. Human keratinocytes from a single donor treated with or without UVR were analyzed by RNA-seq, exome-seq, and H3K27ac ChIP-seq at 4 h and 72 h following UVR. Compared to the relatively moderate mutagenic effects of UVR, acute UV exposure induced substantial epigenomic and transcriptomic alterations, illuminating a previously underappreciated role of epigenomic and transcriptomic instability in skin pathogenesis. Integration of the multi-omics data revealed that UVR-induced transcriptional dysregulation of a subset of genes was attributable to either genetic mutations or global redistribution of H3K27ac. H3K27ac redistribution further led to the formation of distinctive super enhancers in UV-irradiated cells. Our analysis also identified several new UV target genes, including CYP24A1, GJA5, SLAMF7 and ETV1, which were frequently dysregulated in human squamous cell carcinomas, highlighting their potential as new molecular targets for prevention or treatment of UVR-induced skin cancers. Taken together, our concurrent multi-omics analyses provide new mechanistic insights into the complex molecular networks underlying UV photobiological effects, which have important implications in understanding its impact on skin homeostasis and pathogenesis. PMID:28211524

Listeriomics: an Interactive Web Platform for Systems Biology of Listeria

PubMed Central

Koutero, Mikael; Tchitchek, Nicolas; Cerutti, Franck; Lechat, Pierre; Maillet, Nicolas; Hoede, Claire; Chiapello, Hélène; Gaspin, Christine

2017-01-01

ABSTRACT As for many model organisms, the amount of Listeria omics data produced has recently increased exponentially. There are now >80 published complete Listeria genomes, around 350 different transcriptomic data sets, and 25 proteomic data sets available. The analysis of these data sets through a systems biology approach and the generation of tools for biologists to browse these various data are a challenge for bioinformaticians. We have developed a web-based platform, named Listeriomics, that integrates different tools for omics data analyses, i.e., (i) an interactive genome viewer to display gene expression arrays, tiling arrays, and sequencing data sets along with proteomics and genomics data sets; (ii) an expression and protein atlas that connects every gene, small RNA, antisense RNA, or protein with the most relevant omics data; (iii) a specific tool for exploring protein conservation through the Listeria phylogenomic tree; and (iv) a coexpression network tool for the discovery of potential new regulations. Our platform integrates all the complete Listeria species genomes, transcriptomes, and proteomes published to date. This website allows navigation among all these data sets with enriched metadata in a user-friendly format and can be used as a central database for systems biology analysis. IMPORTANCE In the last decades, Listeria has become a key model organism for the study of host-pathogen interactions, noncoding RNA regulation, and bacterial adaptation to stress. To study these mechanisms, several genomics, transcriptomics, and proteomics data sets have been produced. We have developed Listeriomics, an interactive web platform to browse and correlate these heterogeneous sources of information. Our website will allow listeriologists and microbiologists to decipher key regulation mechanism by using a systems biology approach. PMID:28317029

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

PubMed

Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

High-throughput bioinformatics with the Cyrille2 pipeline system

PubMed Central

Fiers, Mark WEJ; van der Burgt, Ate; Datema, Erwin; de Groot, Joost CW; van Ham, Roeland CHJ

2008-01-01

Background Modern omics research involves the application of high-throughput technologies that generate vast volumes of data. These data need to be pre-processed, analyzed and integrated with existing knowledge through the use of diverse sets of software tools, models and databases. The analyses are often interdependent and chained together to form complex workflows or pipelines. Given the volume of the data used and the multitude of computational resources available, specialized pipeline software is required to make high-throughput analysis of large-scale omics datasets feasible. Results We have developed a generic pipeline system called Cyrille2. The system is modular in design and consists of three functionally distinct parts: 1) a web based, graphical user interface (GUI) that enables a pipeline operator to manage the system; 2) the Scheduler, which forms the functional core of the system and which tracks what data enters the system and determines what jobs must be scheduled for execution, and; 3) the Executor, which searches for scheduled jobs and executes these on a compute cluster. Conclusion The Cyrille2 system is an extensible, modular system, implementing the stated requirements. Cyrille2 enables easy creation and execution of high throughput, flexible bioinformatics pipelines. PMID:18269742

GenoBase: comprehensive resource database of Escherichia coli K-12.

PubMed

Otsuka, Yuta; Muto, Ai; Takeuchi, Rikiya; Okada, Chihiro; Ishikawa, Motokazu; Nakamura, Koichiro; Yamamoto, Natsuko; Dose, Hitomi; Nakahigashi, Kenji; Tanishima, Shigeki; Suharnan, Sivasundaram; Nomura, Wataru; Nakayashiki, Toru; Aref, Walid G; Bochner, Barry R; Conway, Tyrrell; Gribskov, Michael; Kihara, Daisuke; Rudd, Kenneth E; Tohsato, Yukako; Wanner, Barry L; Mori, Hirotada

2015-01-01

Comprehensive experimental resources, such as ORFeome clone libraries and deletion mutant collections, are fundamental tools for elucidation of gene function. Data sets by omics analysis using these resources provide key information for functional analysis, modeling and simulation both in individual and systematic approaches. With the long-term goal of complete understanding of a cell, we have over the past decade created a variety of clone and mutant sets for functional genomics studies of Escherichia coli K-12. We have made these experimental resources freely available to the academic community worldwide. Accordingly, these resources have now been used in numerous investigations of a multitude of cell processes. Quality control is extremely important for evaluating results generated by these resources. Because the annotation has been changed since 2005, which we originally used for the construction, we have updated these genomic resources accordingly. Here, we describe GenoBase (http://ecoli.naist.jp/GB/), which contains key information about comprehensive experimental resources of E. coli K-12, their quality control and several omics data sets generated using these resources. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Formularity: Software for Automated Formula Assignment of Natural and Other Organic Matter from Ultrahigh-Resolution Mass Spectra

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tolić, Nikola; Liu, Yina; Liyu, Andrey

Ultrahigh-resolution mass spectrometry, such as Fourier transform ion-cyclotron resonance mass spectrometry (FT-ICR MS), can resolve thousands of molecular ions in complex organic matrices. A Compound Identification Algorithm (CIA) was previously developed for automated elemental formula assignment for natural organic matter (NOM). In this work we describe a user friendly interface for CIA, titled Formularity, which includes an additional functionality to perform search of formulas based on an Isotopic Pattern Algorithm (IPA). While CIA assigns elemental formulas for compounds containing C, H, O, N, S, and P, IPA is capable of assigning formulas for compounds containing other elements. We used halogenatedmore » organic compounds (HOC), a chemical class that is ubiquitous in nature as well as anthropogenic systems, as an example to demonstrate the capability of Formularity with IPA. A HOC standard mix was used to evaluate the identification confidence of IPA. The HOC spike in NOM and tap water were used to assess HOC identification in natural and anthropogenic matrices. Strategies for reconciliation of CIA and IPA assignments are discussed. Software and sample databases with documentation are freely available from the PNNL OMICS software repository https://omics.pnl.gov/software/formularity.« less

Omics for aquatic ecotoxicology: Control of extraneous variability to enhance the analysis of environmental effects

EPA Science Inventory

There are multiple sources of biological and technical variation in a typical ecotoxicology study that may not be revealed by traditional endpoints but that become apparent in an omics dataset. As researchers increasingly apply omics technologies to environmental studies, it will...

Omics methods for probing the mode of action of natural phytotoxins

USDA-ARS?s Scientific Manuscript database

For a little over a decade, omics methods (transcriptomics, proteomics, metabolomics, and physionomics) have been used to discover and probe the mode of action of both synthetic and natural phytotoxins. For mode of action discovery, the strategy for each of these approaches is to generate an omics...

OMICS DATA IN THE QUALITATIVE AND QUANTITATIVE CHARACTERIZATION OF THE MODE OF ACTION IN SUPPORT OF IRIS ASSESSMENTS

EPA Science Inventory

Knowledge and information generated using new tools/methods collectively called "Omics" technologies could have a profound effect on qualitative and quantitative characterizations of human health risk assessments.

The suffix "Omics" is a descriptor used for a series of e...

Towards more transparent and reproducible omics studies through a common metadata checklist and data publications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kolker, Eugene; Ozdemir, Vural; Martens , Lennart

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies omics studies are becoming increasingly prevalent yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research,. These three essential steps require consistent generation, capture, and distribution of the metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologiesmore » and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. This omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.« less

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

PubMed Central

Tebani, Abdellah; Afonso, Carlos; Marret, Stéphane; Bekri, Soumeya

2016-01-01

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era. PMID:27649151

Toward More Transparent and Reproducible Omics Studies Through a Common Metadata Checklist and Data Publications

PubMed Central

Özdemir, Vural; Martens, Lennart; Hancock, William; Anderson, Gordon; Anderson, Nathaniel; Aynacioglu, Sukru; Baranova, Ancha; Campagna, Shawn R.; Chen, Rui; Choiniere, John; Dearth, Stephen P.; Feng, Wu-Chun; Ferguson, Lynnette; Fox, Geoffrey; Frishman, Dmitrij; Grossman, Robert; Heath, Allison; Higdon, Roger; Hutz, Mara H.; Janko, Imre; Jiang, Lihua; Joshi, Sanjay; Kel, Alexander; Kemnitz, Joseph W.; Kohane, Isaac S.; Kolker, Natali; Lancet, Doron; Lee, Elaine; Li, Weizhong; Lisitsa, Andrey; Llerena, Adrian; MacNealy-Koch, Courtney; Marshall, Jean-Claude; Masuzzo, Paola; May, Amanda; Mias, George; Monroe, Matthew; Montague, Elizabeth; Mooney, Sean; Nesvizhskii, Alexey; Noronha, Santosh; Omenn, Gilbert; Rajasimha, Harsha; Ramamoorthy, Preveen; Sheehan, Jerry; Smarr, Larry; Smith, Charles V.; Smith, Todd; Snyder, Michael; Rapole, Srikanth; Srivastava, Sanjeeva; Stanberry, Larissa; Stewart, Elizabeth; Toppo, Stefano; Uetz, Peter; Verheggen, Kenneth; Voy, Brynn H.; Warnich, Louise; Wilhelm, Steven W.; Yandl, Gregory

2014-01-01

Abstract Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement. PMID:24456465

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations.

PubMed

Tebani, Abdellah; Afonso, Carlos; Marret, Stéphane; Bekri, Soumeya

2016-09-14

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.

SilkPathDB: a comprehensive resource for the study of silkworm pathogens.

PubMed

Li, Tian; Pan, Guo-Qing; Vossbrinck, Charles R; Xu, Jin-Shan; Li, Chun-Feng; Chen, Jie; Long, Meng-Xian; Yang, Ming; Xu, Xiao-Fei; Xu, Chen; Debrunner-Vossbrinck, Bettina A; Zhou, Ze-Yang

2017-01-01

Silkworm pathogens have been heavily impeding the development of sericultural industry and play important roles in lepidopteran ecology, and some of which are used as biological insecticides. Rapid advances in studies on the omics of silkworm pathogens have produced a large amount of data, which need to be brought together centrally in a coherent and systematic manner. This will facilitate the reuse of these data for further analysis. We have collected genomic data for 86 silkworm pathogens from 4 taxa (fungi, microsporidia, bacteria and viruses) and from 4 lepidopteran hosts, and developed the open-access Silkworm Pathogen Database (SilkPathDB) to make this information readily available. The implementation of SilkPathDB involves integrating Drupal and GBrowse as a graphic interface for a Chado relational database which houses all of the datasets involved. The genomes have been assembled and annotated for comparative purposes and allow the search and analysis of homologous sequences, transposable elements, protein subcellular locations, including secreted proteins, and gene ontology. We believe that the SilkPathDB will aid researchers in the identification of silkworm parasites, understanding the mechanisms of silkworm infections, and the developmental ecology of silkworm parasites (gene expression) and their hosts. http://silkpathdb.swu.edu.cn. © The Author(s) 2017. Published by Oxford University Press.

Databases and Web Tools for Cancer Genomics Study

PubMed Central

Yang, Yadong; Dong, Xunong; Xie, Bingbing; Ding, Nan; Chen, Juan; Li, Yongjun; Zhang, Qian; Qu, Hongzhu; Fang, Xiangdong

2015-01-01

Publicly-accessible resources have promoted the advance of scientific discovery. The era of genomics and big data has brought the need for collaboration and data sharing in order to make effective use of this new knowledge. Here, we describe the web resources for cancer genomics research and rate them on the basis of the diversity of cancer types, sample size, omics data comprehensiveness, and user experience. The resources reviewed include data repository and analysis tools; and we hope such introduction will promote the awareness and facilitate the usage of these resources in the cancer research community. PMID:25707591

Genic insights from integrated human proteomics in GeneCards.

PubMed

Fishilevich, Simon; Zimmerman, Shahar; Kohn, Asher; Iny Stein, Tsippi; Olender, Tsviya; Kolker, Eugene; Safran, Marilyn; Lancet, Doron

2016-01-01

GeneCards is a one-stop shop for searchable human gene annotations (http://www.genecards.org/). Data are automatically mined from ∼120 sources and presented in an integrated web card for every human gene. We report the application of recent advances in proteomics to enhance gene annotation and classification in GeneCards. First, we constructed the Human Integrated Protein Expression Database (HIPED), a unified database of protein abundance in human tissues, based on the publically available mass spectrometry (MS)-based proteomics sources ProteomicsDB, Multi-Omics Profiling Expression Database, Protein Abundance Across Organisms and The MaxQuant DataBase. The integrated database, residing within GeneCards, compares favourably with its individual sources, covering nearly 90% of human protein-coding genes. For gene annotation and comparisons, we first defined a protein expression vector for each gene, based on normalized abundances in 69 normal human tissues. This vector is portrayed in the GeneCards expression section as a bar graph, allowing visual inspection and comparison. These data are juxtaposed with transcriptome bar graphs. Using the protein expression vectors, we further defined a pairwise metric that helps assess expression-based pairwise proximity. This new metric for finding functional partners complements eight others, including sharing of pathways, gene ontology (GO) terms and domains, implemented in the GeneCards Suite. In parallel, we calculated proteome-based differential expression, highlighting a subset of tissues that overexpress a gene and subserving gene classification. This textual annotation allows users of VarElect, the suite's next-generation phenotyper, to more effectively discover causative disease variants. Finally, we define the protein-RNA expression ratio and correlation as yet another attribute of every gene in each tissue, adding further annotative information. The results constitute a significant enhancement of several GeneCards sections and help promote and organize the genome-wide structural and functional knowledge of the human proteome. Database URL:http://www.genecards.org/. © The Author(s) 2016. Published by Oxford University Press.

Computational Tools for Parsimony Phylogenetic Analysis of Omics Data

PubMed Central

Salazar, Jose; Amri, Hakima; Noursi, David

2015-01-01

Abstract High-throughput assays from genomics, proteomics, metabolomics, and next generation sequencing produce massive omics datasets that are challenging to analyze in biological or clinical contexts. Thus far, there is no publicly available program for converting quantitative omics data into input formats to be used in off-the-shelf robust phylogenetic programs. To the best of our knowledge, this is the first report on creation of two Windows-based programs, OmicsTract and SynpExtractor, to address this gap. We note, as a way of introduction and development of these programs, that one particularly useful bioinformatics inferential modeling is the phylogenetic cladogram. Cladograms are multidimensional tools that show the relatedness between subgroups of healthy and diseased individuals and the latter's shared aberrations; they also reveal some characteristics of a disease that would not otherwise be apparent by other analytical methods. The OmicsTract and SynpExtractor were written for the respective tasks of (1) accommodating advanced phylogenetic parsimony analysis (through standard programs of MIX [from PHYLIP] and TNT), and (2) extracting shared aberrations at the cladogram nodes. OmicsTract converts comma-delimited data tables through assigning each data point into a binary value (“0” for normal states and “1” for abnormal states) then outputs the converted data tables into the proper input file formats for MIX or with embedded commands for TNT. SynapExtractor uses outfiles from MIX and TNT to extract the shared aberrations of each node of the cladogram, matching them with identifying labels from the dataset and exporting them into a comma-delimited file. Labels may be gene identifiers in gene-expression datasets or m/z values in mass spectrometry datasets. By automating these steps, OmicsTract and SynpExtractor offer a veritable opportunity for rapid and standardized phylogenetic analyses of omics data; their model can also be extended to next generation sequencing (NGS) data. We make OmicsTract and SynpExtractor publicly and freely available for non-commercial use in order to strengthen and build capacity for the phylogenetic paradigm of omics analysis. PMID:26230532

A dedicated database system for handling multi-level data in systems biology.

PubMed

Pornputtapong, Natapol; Wanichthanarak, Kwanjeera; Nilsson, Avlant; Nookaew, Intawat; Nielsen, Jens

2014-01-01

Advances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging. To overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase. In this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

Application of omics data in regulatory toxicology: report of an international BfR expert workshop.

PubMed

Marx-Stoelting, P; Braeuning, A; Buhrke, T; Lampen, A; Niemann, L; Oelgeschlaeger, M; Rieke, S; Schmidt, F; Heise, T; Pfeil, R; Solecki, R

2015-11-01

Advances in omics techniques and molecular toxicology are necessary to provide new perspectives for regulatory toxicology. By the application of modern molecular techniques, more mechanistic information should be gained to support standard toxicity studies and to contribute to a reduction and refinement of animal experiments required for certain regulatory purposes. The relevance and applicability of data obtained by omics methods to regulatory purposes such as grouping of chemicals, mode of action analysis or classification and labelling needs further improvement, defined validation and cautious expert judgment. Based on the results of an international expert workshop organized 2014 by the Federal Institute for Risk Assessment in Berlin, this paper is aimed to provide a critical overview of the regulatory relevance and reliability of omics methods, basic requirements on data quality and validation, as well as regulatory criteria to decide which effects observed by omics methods should be considered adverse or non-adverse. As a way forward, it was concluded that the inclusion of omics data can facilitate a more flexible approach for regulatory risk assessment and may help to reduce or refine animal testing.

Comparative Transcriptomes and EVO-DEVO Studies Depending on Next Generation Sequencing.

PubMed

Liu, Tiancheng; Yu, Lin; Liu, Lei; Li, Hong; Li, Yixue

2015-01-01

High throughput technology has prompted the progressive omics studies, including genomics and transcriptomics. We have reviewed the improvement of comparative omic studies, which are attributed to the high throughput measurement of next generation sequencing technology. Comparative genomics have been successfully applied to evolution analysis while comparative transcriptomics are adopted in comparison of expression profile from two subjects by differential expression or differential coexpression, which enables their application in evolutionary developmental biology (EVO-DEVO) studies. EVO-DEVO studies focus on the evolutionary pressure affecting the morphogenesis of development and previous works have been conducted to illustrate the most conserved stages during embryonic development. Old measurements of these studies are based on the morphological similarity from macro view and new technology enables the micro detection of similarity in molecular mechanism. Evolutionary model of embryo development, which includes the "funnel-like" model and the "hourglass" model, has been evaluated by combination of these new comparative transcriptomic methods with prior comparative genomic information. Although the technology has promoted the EVO-DEVO studies into a new era, technological and material limitation still exist and further investigations require more subtle study design and procedure.

Novel insights, challenges and practical implications of DOHaD-omics research.

PubMed

Hodyl, Nicolette A; Muhlhausler, Beverly

2016-02-15

Research investigating the developmental origins of health and disease (DOHaD) has never had the technology to investigate physiology in such a data-rich capacity and at such a microlevel as it does now. A symposium at the inaugural meeting of the DOHaD Society of Australia and New Zealand outlined the advantages and challenges of using "-omics" technologies in DOHaD research. DOHaD studies with -omics approaches to generate large, rich datasets were discussed. We discuss implications for policy and practice and make recommendations to facilitate successful translation of results of future DOHaD-omics studies.

Omics Profiling in Precision Oncology*

PubMed Central

Yu, Kun-Hsing; Snyder, Michael

2016-01-01

Cancer causes significant morbidity and mortality worldwide, and is the area most targeted in precision medicine. Recent development of high-throughput methods enables detailed omics analysis of the molecular mechanisms underpinning tumor biology. These studies have identified clinically actionable mutations, gene and protein expression patterns associated with prognosis, and provided further insights into the molecular mechanisms indicative of cancer biology and new therapeutics strategies such as immunotherapy. In this review, we summarize the techniques used for tumor omics analysis, recapitulate the key findings in cancer omics studies, and point to areas requiring further research on precision oncology. PMID:27099341

PDXliver: a database of liver cancer patient derived xenograft mouse models.

PubMed

He, Sheng; Hu, Bo; Li, Chao; Lin, Ping; Tang, Wei-Guo; Sun, Yun-Fan; Feng, Fang-You-Min; Guo, Wei; Li, Jia; Xu, Yang; Yao, Qian-Lan; Zhang, Xin; Qiu, Shuang-Jian; Zhou, Jian; Fan, Jia; Li, Yi-Xue; Li, Hong; Yang, Xin-Rong

2018-05-09

Liver cancer is the second leading cause of cancer-related deaths and characterized by heterogeneity and drug resistance. Patient-derived xenograft (PDX) models have been widely used in cancer research because they reproduce the characteristics of original tumors. However, the current studies of liver cancer PDX mice are scattered and the number of available PDX models are too small to represent the heterogeneity of liver cancer patients. To improve this situation and to complement available PDX models related resources, here we constructed a comprehensive database, PDXliver, to integrate and analyze liver cancer PDX models. Currently, PDXliver contains 116 PDX models from Chinese liver cancer patients, 51 of them were established by the in-house PDX platform and others were curated from the public literatures. These models are annotated with complete information, including clinical characteristics of patients, genome-wide expression profiles, germline variations, somatic mutations and copy number alterations. Analysis of expression subtypes and mutated genes show that PDXliver represents the diversity of human patients. Another feature of PDXliver is storing drug response data of PDX mice, which makes it possible to explore the association between molecular profiles and drug sensitivity. All data can be accessed via the Browse and Search pages. Additionally, two tools are provided to interactively visualize the omics data of selected PDXs or to compare two groups of PDXs. As far as we known, PDXliver is the first public database of liver cancer PDX models. We hope that this comprehensive resource will accelerate the utility of PDX models and facilitate liver cancer research. The PDXliver database is freely available online at: http://www.picb.ac.cn/PDXliver/.

Spotlight on environmental omics and toxicology: a long way in a short time.

PubMed

Martyniuk, Christopher J; Simmons, Denina B

2016-09-01

The applications for high throughput omics technologies in environmental science have increased dramatically in recent years. Transcriptomics, proteomics, and metabolomics have been used to study how chemicals in our environment affect both aquatic and terrestrial organisms, and the characterization of molecular initiating events is a significant goal in toxicology to better predict adverse responses to toxicants. This special journal edition demonstrates the scope of the science that leverages omics-based methods in both laboratory and wild populations within the context of environmental toxicology, ranging from fish to mammals. It is important to recognize that the environment comprises one axis of the One Health concept - the idea that human health is unequivocally intertwined to our environment and to the organisms that inhabit that environment. We have much to learn from a comparative approach, and studies that integrate the transcriptome, proteome, and the metabolome are expected to offer the most detailed mechanism-based adverse outcome pathways that are applicable for use in both environmental monitoring and risk assessment. Copyright © 2016 Elsevier Inc. All rights reserved.

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software

PubMed Central

Fabregat-Traver, Diego; Sharapov, Sodbo Zh.; Hayward, Caroline; Rudan, Igor; Campbell, Harry; Aulchenko, Yurii; Bientinesi, Paolo

2014-01-01

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the ’omics’ context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations, increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNU GPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL. PMID:25717363

Risk Assessment and Communication Tools for Genotype Associations with Multifactorial Phenotypes: The Concept of “Edge Effect” and Cultivating an Ethical Bridge between Omics Innovations and Society

PubMed Central

Suarez-Kurtz, Guilherme; Stenne, Raphaëlle; Somogyi, Andrew A.; Someya, Toshiyuki; Kayaalp, S. Oğuz; Kolker, Eugene

2009-01-01

Abstract Applications of omics technologies in the postgenomics era swiftly expanded from rare monogenic disorders to multifactorial common complex diseases, pharmacogenomics, and personalized medicine. Already, there are signposts indicative of further omics technology investment in nutritional sciences (nutrigenomics), environmental health/ecology (ecogenomics), and agriculture (agrigenomics). Genotype–phenotype association studies are a centerpiece of translational research in omics science. Yet scientific and ethical standards and ways to assess and communicate risk information obtained from association studies have been neglected to date. This is a significant gap because association studies decisively influence which genetic loci become genetic tests in the clinic or products in the genetic test marketplace. A growing challenge concerns the interpretation of large overlap typically observed in distribution of quantitative traits in a genetic association study with a polygenic/multifactorial phenotype. To remedy the shortage of risk assessment and communication tools for association studies, this paper presents the concept of edge effect. That is, the shift in population edges of a multifactorial quantitative phenotype is a more sensitive measure (than population averages) to gauge the population level impact and by extension, policy significance of an omics marker. Empirical application of the edge effect concept is illustrated using an original analysis of warfarin pharmacogenomics and the VKORC1 genetic variation in a Brazilian population sample. These edge effect analyses are examined in relation to regulatory guidance development for association studies. We explain that omics science transcends the conventional laboratory bench space and includes a highly heterogeneous cast of stakeholders in society who have a plurality of interests that are often in conflict. Hence, communication of risk information in diagnostic medicine also demands attention to processes involved in production of knowledge and human values embedded in scientific practice, for example, why, how, by whom, and to what ends association studies are conducted, and standards are developed (or not). To ensure sustainability of omics innovations and forecast their trajectory, we need interventions to bridge the gap between omics laboratory and society. Appreciation of scholarship in history of omics science is one remedy to responsibly learn from the past to ensure a sustainable future in omics fields, both emerging (nutrigenomics, ecogenomics), and those that are more established (pharmacogenomics). Another measure to build public trust and sustainability of omics fields could be legislative initiatives to create a multidisciplinary oversight body, at arm's length from conflict of interests, to carry out independent, impartial, and transparent innovation analyses and prospective technology assessment. PMID:19290811

Risk assessment and communication tools for genotype associations with multifactorial phenotypes: the concept of "edge effect" and cultivating an ethical bridge between omics innovations and society.

PubMed

Ozdemir, Vural; Suarez-Kurtz, Guilherme; Stenne, Raphaëlle; Somogyi, Andrew A; Someya, Toshiyuki; Kayaalp, S Oğuz; Kolker, Eugene

2009-02-01

Applications of omics technologies in the postgenomics era swiftly expanded from rare monogenic disorders to multifactorial common complex diseases, pharmacogenomics, and personalized medicine. Already, there are signposts indicative of further omics technology investment in nutritional sciences (nutrigenomics), environmental health/ecology (ecogenomics), and agriculture (agrigenomics). Genotype-phenotype association studies are a centerpiece of translational research in omics science. Yet scientific and ethical standards and ways to assess and communicate risk information obtained from association studies have been neglected to date. This is a significant gap because association studies decisively influence which genetic loci become genetic tests in the clinic or products in the genetic test marketplace. A growing challenge concerns the interpretation of large overlap typically observed in distribution of quantitative traits in a genetic association study with a polygenic/multifactorial phenotype. To remedy the shortage of risk assessment and communication tools for association studies, this paper presents the concept of edge effect. That is, the shift in population edges of a multifactorial quantitative phenotype is a more sensitive measure (than population averages) to gauge the population level impact and by extension, policy significance of an omics marker. Empirical application of the edge effect concept is illustrated using an original analysis of warfarin pharmacogenomics and the VKORC1 genetic variation in a Brazilian population sample. These edge effect analyses are examined in relation to regulatory guidance development for association studies. We explain that omics science transcends the conventional laboratory bench space and includes a highly heterogeneous cast of stakeholders in society who have a plurality of interests that are often in conflict. Hence, communication of risk information in diagnostic medicine also demands attention to processes involved in production of knowledge and human values embedded in scientific practice, for example, why, how, by whom, and to what ends association studies are conducted, and standards are developed (or not). To ensure sustainability of omics innovations and forecast their trajectory, we need interventions to bridge the gap between omics laboratory and society. Appreciation of scholarship in history of omics science is one remedy to responsibly learn from the past to ensure a sustainable future in omics fields, both emerging (nutrigenomics, ecogenomics), and those that are more established (pharmacogenomics). Another measure to build public trust and sustainability of omics fields could be legislative initiatives to create a multidisciplinary oversight body, at arm's length from conflict of interests, to carry out independent, impartial, and transparent innovation analyses and prospective technology assessment.

Imaging and the completion of the omics paradigm in breast cancer.

PubMed

Leithner, D; Horvat, J V; Ochoa-Albiztegui, R E; Thakur, S; Wengert, G; Morris, E A; Helbich, T H; Pinker, K

2018-06-08

Within the field of oncology, "omics" strategies-genomics, transcriptomics, proteomics, metabolomics-have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with "omics" data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology-pathology correlation from the anatomical-histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.

Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.

PubMed

Wu, Dingming; Wang, Dongfang; Zhang, Michael Q; Gu, Jin

2015-12-01

One major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data. In this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types. LRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/ .

MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakayasu, Ernesto S.; Nicora, Carrie D.; Sims, Amy C.

2016-05-03

ABSTRACT Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. Themetabolite,protein, andlipidextraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of thismore » protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental,in vitro, and clinical). IMPORTANCEIn systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample.« less

Initiative for standardization of reporting genetics of male infertility.

PubMed

Traven, Eva; Ogrinc, Ana; Kunej, Tanja

2017-02-01

The number of publications on research of male infertility is increasing. Technologies used in research of male infertility generate complex results and various types of data that need to be appropriately managed, arranged, and made available to other researchers for further use. In our previous study, we collected over 800 candidate loci for male fertility in seven mammalian species. However, the continuation of the work towards a comprehensive database of candidate genes associated with different types of idiopathic human male infertility is challenging due to fragmented information, obtained from a variety of technologies and various omics approaches. Results are published in different forms and usually need to be excavated from the text, which hinders the gathering of information. Standardized reporting of genetic anomalies as well as causative and risk factors of male infertility therefore presents an important issue. The aim of the study was to collect examples of diverse genomic loci published in association with human male infertility and to propose a standardized format for reporting genetic causes of male infertility. From the currently available data we have selected 75 studies reporting 186 representative genomic loci which have been proposed as genetic risk factors for male infertility. Based on collected and formatted data, we suggested a first step towards unification of reporting the genetics of male infertility in original and review studies. The proposed initiative consists of five relevant data types: 1) genetic locus, 2) race/ethnicity, number of participants (infertile/controls), 3) methodology, 4) phenotype (clinical data, disease ontology, and disease comorbidity), and 5) reference. The proposed form for standardized reporting presents a baseline for further optimization with additional genetic and clinical information. This data standardization initiative will enable faster multi-omics data integration, database development and sharing, establishing more targeted hypotheses, and facilitating biomarker discovery.

Applied choline-omics: lessons from human metabolic studies for the integration of genomics research into nutrition practice.

PubMed

West, Allyson A; Caudill, Marie A

2014-08-01

Nutritional genomics, defined as the study of reciprocal interactions among nutrients, metabolic intermediates, and the genome, along with other closely related nutritional -omic fields (eg, epigenomics, transcriptomics, and metabolomics) have become vital areas of nutrition study and knowledge. Utilizing results from human metabolic research on the essential nutrient choline, this article illustrates how nutrigenetic, nutrigenomic, and inter-related -omic research has provided new insights into choline metabolism and its effect on physiologic processes. Findings from highlighted choline research are also discussed in the context of translation to clinical and public health nutrition applications. Overall, this article underscores the utility of -omic research methods in elucidating nutrient metabolism as well as the potential for nutritional -omic concepts and discoveries to be broadly applied in nutritional practice. Copyright © 2014 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Integrated omics dissection of proteome dynamics during cardiac remodeling.

PubMed

Lau, Edward; Cao, Quan; Lam, Maggie P Y; Wang, Jie; Ng, Dominic C M; Bleakley, Brian J; Lee, Jessica M; Liem, David A; Wang, Ding; Hermjakob, Henning; Ping, Peipei

2018-01-09

Transcript abundance and protein abundance show modest correlation in many biological models, but how this impacts disease signature discovery in omics experiments is rarely explored. Here we report an integrated omics approach, incorporating measurements of transcript abundance, protein abundance, and protein turnover to map the landscape of proteome remodeling in a mouse model of pathological cardiac hypertrophy. Analyzing the hypertrophy signatures that are reproducibly discovered from each omics data type across six genetic strains of mice, we find that the integration of transcript abundance, protein abundance, and protein turnover data leads to 75% gain in discovered disease gene candidates. Moreover, the inclusion of protein turnover measurements allows discovery of post-transcriptional regulations across diverse pathways, and implicates distinct disease proteins not found in steady-state transcript and protein abundance data. Our results suggest that multi-omics investigations of proteome dynamics provide important insights into disease pathogenesis in vivo.

Making the Most of Omics for Symbiosis Research

PubMed Central

Chaston, J.; Douglas, A.E.

2012-01-01

Omics, including genomics, proteomics and metabolomics, enable us to explain symbioses in terms of the underlying molecules and their interactions. The central task is to transform molecular catalogs of genes, metabolites etc. into a dynamic understanding of symbiosis function. We review four exemplars of omics studies that achieve this goal, through defined biological questions relating to metabolic integration and regulation of animal-microbial symbioses, the genetic autonomy of bacterial symbionts, and symbiotic protection of animal hosts from pathogens. As omic datasets become increasingly complex, computationally-sophisticated downstream analyses are essential to reveal interactions not evident to visual inspection of the data. We discuss two approaches, phylogenomics and transcriptional clustering, that can divide the primary output of omics studies – long lists of factors – into manageable subsets, and we describe how they have been applied to analyze large datasets and generate testable hypotheses. PMID:22983030

IDPT: Insights into potential intrinsically disordered proteins through transcriptomic analysis of genes for prostate carcinoma epigenetic data.

PubMed

Mallik, Saurav; Sen, Sagnik; Maulik, Ujjwal

2016-07-15

Involvement of intrinsically disordered proteins (IDPs) with various dreadful diseases like cancer is an interesting research topic. In order to gain novel insights into the regulation of IDPs, in this article, we perform a transcriptomic analysis of mRNAs (genes) for transcripts encoding IDPs on a human multi-omics prostate carcinoma dataset having both gene expression and methylation data. In this regard, firstly the genes that consist of both the expression and methylation data, and that are corresponding to the cancer-related prostate-tissue-specific disordered proteins of MobiDb database, are selected. We apply standard t-test for determining differentially expressed genes as well as differentially methylated genes. A network having these genes and their targeter miRNAs from Diana Tarbase v7.0 database and corresponding Transcription Factors from TRANSFAC and ITFP databases, is then built. Thereafter, we perform literature search, and KEGG pathway and Gene Ontology analyses using DAVID database. Finally, we report several significant potential gene-markers (with the corresponding IDPs) that have inverse relationship between differential expression and methylation patterns, and that are hub genes of the TF-miRNA-gene network. Copyright © 2016 Elsevier B.V. All rights reserved.

Omics Workshop Videocast Available | Office of Cancer Clinical Proteomics Research

Cancer.gov

The Omics Integration in Biology and Medicine Workshop, held on June 19th and 20th is now available for viewing on NIH Videocast: Day 1 and Day 2.  The workshop focused on the emerging field of integrating disparate omic data from genomics, proteomics, glycomics, etc. in order to better understand key biological processes and also improve clinical practice.

From data to knowledge: The future of multi-omics data analysis for the rhizosphere

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allen White, Richard; Borkum, Mark I.; Rivas-Ubach, Albert

The rhizosphere is the interface between a plant's roots and its surrounding soil. The rhizosphere microbiome, a complex microbial ecosystem, nourishes the terrestrial biosphere. Integrated multi-omics is a modern approach to systems biology that analyzes and interprets the datasets of multiple -omes of both individual organisms and multi-organism communities and consortia. The successful usage and application of integrated multi-omics to rhizospheric science is predicated upon the availability of rhizosphere-specific data, metadata and software. This review analyzes the availability of multi-omics data, metadata and software for rhizospheric science, identifying potential issues, challenges and opportunities.

Alzheimer's disease in the omics era.

PubMed

Sancesario, Giulia M; Bernardini, Sergio

2018-06-18

Recent progresses in high-throughput technologies have led to a new scenario in investigating pathologies, named the "Omics era", which integrate the opportunity to collect large amounts of data and information at the molecular and protein levels together with the development of novel computational and statistical tools that are able to analyze and filter such data. Subsequently, advances in genotyping arrays, next generation sequencing, mass spectrometry technology, and bioinformatics allowed for the simultaneous large-scale study of thousands of genes (genomics), epigenetics factors (epigenomics), RNA (transcriptomics), metabolites (metabolomics) and proteins(proteomics), with the possibility of integrating multiple types of omics data ("multi -omics"). All of these technological innovations have modified the approach to the study of complex diseases, such as Alzheimer's Disease (AD), thus representing a promising tool to investigate the relationship between several molecular pathways in AD as well as other pathologies. This review focuses on the current knowledge on the pathology of AD, the recent findings from Omics sciences, and the challenge of the use of Big Data. We then focus on future perspectives for Omics sciences, such as the discovery of novel diagnostic biomarkers or drugs. Copyright © 2018. Published by Elsevier Inc.

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

PubMed

Argelaguet, Ricard; Velten, Britta; Arnol, Damien; Dietrich, Sascha; Zenz, Thorsten; Marioni, John C; Buettner, Florian; Huber, Wolfgang; Stegle, Oliver

2018-06-20

Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation. © 2018 The Authors. Published under the terms of the CC BY 4.0 license.

A generic Transcriptomics Reporting Framework (TRF) for 'omics data processing and analysis.

PubMed

Gant, Timothy W; Sauer, Ursula G; Zhang, Shu-Dong; Chorley, Brian N; Hackermüller, Jörg; Perdichizzi, Stefania; Tollefsen, Knut E; van Ravenzwaay, Ben; Yauk, Carole; Tong, Weida; Poole, Alan

2017-12-01

A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in 'omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other 'omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during 'omics data processing and analysis, the TRF will increase confidence processing of 'omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory 'omics studies, and it can be executed using different commonly available software tools. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

“Omics” of Maize Stress Response for Sustainable Food Production: Opportunities and Challenges

PubMed Central

Gong, Fangping; Yang, Le; Tai, Fuju; Hu, Xiuli

2014-01-01

Abstract Maize originated in the highlands of Mexico approximately 8700 years ago and is one of the most commonly grown cereal crops worldwide, followed by wheat and rice. Abiotic stresses (primarily drought, salinity, and high and low temperatures), together with biotic stresses (primarily fungi, viruses, and pests), negatively affect maize growth, development, and eventually production. To understand the response of maize to abiotic and biotic stresses and its mechanism of stress tolerance, high-throughput omics approaches have been used in maize stress studies. Integrated omics approaches are crucial for dissecting the temporal and spatial system-level changes that occur in maize under various stresses. In this comprehensive analysis, we review the primary types of stresses that threaten sustainable maize production; underscore the recent advances in maize stress omics, especially proteomics; and discuss the opportunities, challenges, and future directions of maize stress omics, with a view to sustainable food production. The knowledge gained from studying maize stress omics is instrumental for improving maize to cope with various stresses and to meet the food demands of the exponentially growing global population. Omics systems science offers actionable potential solutions for sustainable food production, and we present maize as a notable case study. PMID:25401749

Systems view of adipogenesis via novel omics-driven and tissue-specific activity scoring of network functional modules

NASA Astrophysics Data System (ADS)

Nassiri, Isar; Lombardo, Rosario; Lauria, Mario; Morine, Melissa J.; Moyseos, Petros; Varma, Vijayalakshmi; Nolen, Greg T.; Knox, Bridgett; Sloper, Daniel; Kaput, Jim; Priami, Corrado

2016-07-01

The investigation of the complex processes involved in cellular differentiation must be based on unbiased, high throughput data processing methods to identify relevant biological pathways. A number of bioinformatics tools are available that can generate lists of pathways ranked by statistical significance (i.e. by p-value), while ideally it would be desirable to functionally score the pathways relative to each other or to other interacting parts of the system or process. We describe a new computational method (Network Activity Score Finder - NASFinder) to identify tissue-specific, omics-determined sub-networks and the connections with their upstream regulator receptors to obtain a systems view of the differentiation of human adipocytes. Adipogenesis of human SBGS pre-adipocyte cells in vitro was monitored with a transcriptomic data set comprising six time points (0, 6, 48, 96, 192, 384 hours). To elucidate the mechanisms of adipogenesis, NASFinder was used to perform time-point analysis by comparing each time point against the control (0 h) and time-lapse analysis by comparing each time point with the previous one. NASFinder identified the coordinated activity of seemingly unrelated processes between each comparison, providing the first systems view of adipogenesis in culture. NASFinder has been implemented into a web-based, freely available resource associated with novel, easy to read visualization of omics data sets and network modules.

Next generation microbiological risk assessment-Potential of omics data for hazard characterisation.

PubMed

Haddad, Nabila; Johnson, Nick; Kathariou, Sophia; Métris, Aline; Phister, Trevor; Pielaat, Annemarie; Tassou, Chrysoula; Wells-Bennik, Marjon H J; Zwietering, Marcel H

2018-04-12

According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of hazard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus refine surveillance programs with the collaboration of all stakeholders of the food chain. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data.

PubMed

Jang, Yeongjun; Yu, Namhee; Seo, Jihae; Kim, Sun; Lee, Sanghyuk

2016-03-18

Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .

Comparative Omics-Driven Genome Annotation Refinement: Application across Yersiniae

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rutledge, Alexandra C.; Jones, Marcus B.; Chauhan, Sadhana

2012-03-27

Genome sequencing continues to be a rapidly evolving technology, yet most downstream aspects of genome annotation pipelines remain relatively stable or are even being abandoned. To date, the perceived value of manual curation for genome annotations is not offset by the real cost and time associated with the process. In order to balance the large number of sequences generated, the annotation process is now performed almost exclusively in an automated fashion for most genome sequencing projects. One possible way to reduce errors inherent to automated computational annotations is to apply data from 'omics' measurements (i.e. transcriptional and proteomic) to themore » un-annotated genome with a proteogenomic-based approach. This approach does require additional experimental and bioinformatics methods to include omics technologies; however, the approach is readily automatable and can benefit from rapid developments occurring in those research domains as well. The annotation process can be improved by experimental validation of transcription and translation and aid in the discovery of annotation errors. Here the concept of annotation refinement has been extended to include a comparative assessment of genomes across closely related species, as is becoming common in sequencing efforts. Transcriptomic and proteomic data derived from three highly similar pathogenic Yersiniae (Y. pestis CO92, Y. pestis pestoides F, and Y. pseudotuberculosis PB1/+) was used to demonstrate a comprehensive comparative omic-based annotation methodology. Peptide and oligo measurements experimentally validated the expression of nearly 40% of each strain's predicted proteome and revealed the identification of 28 novel and 68 previously incorrect protein-coding sequences (e.g., observed frameshifts, extended start sites, and translated pseudogenes) within the three current Yersinia genome annotations. Gene loss is presumed to play a major role in Y. pestis acquiring its niche as a virulent pathogen, thus the discovery of many translated pseudogenes underscores a need for functional analyses to investigate hypotheses related to divergence. Refinements included the discovery of a seemingly essential ribosomal protein, several virulence-associated factors, and a transcriptional regulator, among other proteins, most of which are annotated as hypothetical, that were missed during annotation.« less

Determining conserved metabolic biomarkers from a million database queries.

PubMed

Kurczy, Michael E; Ivanisevic, Julijana; Johnson, Caroline H; Uritboonthai, Winnie; Hoang, Linh; Fang, Mingliang; Hicks, Matthew; Aldebot, Anthony; Rinehart, Duane; Mellander, Lisa J; Tautenhahn, Ralf; Patti, Gary J; Spilker, Mary E; Benton, H Paul; Siuzdak, Gary

2015-12-01

Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. METLIN can be accessed by logging on to: https://metlin.scripps.edu siuzdak@scripps.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

MetaboLights: towards a new COSMOS of metabolomics data management.

PubMed

Steinbeck, Christoph; Conesa, Pablo; Haug, Kenneth; Mahendraker, Tejasvi; Williams, Mark; Maguire, Eamonn; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Salek, Reza M; Griffin, Julian L

2012-10-01

Exciting funding initiatives are emerging in Europe and the US for metabolomics data production, storage, dissemination and analysis. This is based on a rich ecosystem of resources around the world, which has been build during the past ten years, including but not limited to resources such as MassBank in Japan and the Human Metabolome Database in Canada. Now, the European Bioinformatics Institute has launched MetaboLights, a database for metabolomics experiments and the associated metadata (http://www.ebi.ac.uk/metabolights). It is the first comprehensive, cross-species, cross-platform metabolomics database maintained by one of the major open access data providers in molecular biology. In October, the European COSMOS consortium will start its work on Metabolomics data standardization, publication and dissemination workflows. The NIH in the US is establishing 6-8 metabolomics services cores as well as a national metabolomics repository. This communication reports about MetaboLights as a new resource for Metabolomics research, summarises the related developments and outlines how they may consolidate the knowledge management in this third large omics field next to proteomics and genomics.

Minimum information required for a DMET experiment reporting.

PubMed

Kumuthini, Judit; Mbiyavanga, Mamana; Chimusa, Emile R; Pathak, Jyotishman; Somervuo, Panu; Van Schaik, Ron Hn; Dolzan, Vita; Mizzi, Clint; Kalideen, Kusha; Ramesar, Raj S; Macek, Milan; Patrinos, George P; Squassina, Alessio

2016-09-01

To provide pharmacogenomics reporting guidelines, the information and tools required for reporting to public omic databases. For effective DMET data interpretation, sharing, interoperability, reproducibility and reporting, we propose the Minimum Information required for a DMET Experiment (MIDE) reporting. MIDE provides reporting guidelines and describes the information required for reporting, data storage and data sharing in the form of XML. The MIDE guidelines will benefit the scientific community with pharmacogenomics experiments, including reporting pharmacogenomics data from other technology platforms, with the tools that will ease and automate the generation of such reports using the standardized MIDE XML schema, facilitating the sharing, dissemination, reanalysis of datasets through accessible and transparent pharmacogenomics data reporting.

Pesticide Biomarker Project

EPA Pesticide Factsheets

Uploaded datasets are detailed exposure information (chemical concentrations and water quality parameters) for exposures conducted in a flow through diluter system with larval Pimephales promelas to four different pyrethroid pesticides. The GEO submission URL links to the NCBI GEO database and contains gene expression data from whole larvae exposed to different concentrations of the pyrethroids across multiple experiments.This dataset is associated with the following publication:Biales, A., M. Kostich, A. Batt, M. See, R. Flick, D. Gordon, J. Lazorchak, and D. Bencic. Initial Development of a Multigene Omics-Based Exposure Biomarker for Pyrethroid Pesticides. CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY. CRC Press LLC, Boca Raton, FL, USA, 179(0): 27-35, (2016).

Updated Rice Kinase Database RKD 2.0: enabling transcriptome and functional analysis of rice kinase genes.

PubMed

Chandran, Anil Kumar Nalini; Yoo, Yo-Han; Cao, Peijian; Sharma, Rita; Sharma, Manoj; Dardick, Christopher; Ronald, Pamela C; Jung, Ki-Hong

2016-12-01

Protein kinases catalyze the transfer of a phosphate moiety from a phosphate donor to the substrate molecule, thus playing critical roles in cell signaling and metabolism. Although plant genomes contain more than 1000 genes that encode kinases, knowledge is limited about the function of each of these kinases. A major obstacle that hinders progress towards kinase characterization is functional redundancy. To address this challenge, we previously developed the rice kinase database (RKD) that integrated omics-scale data within a phylogenetics context. An updated version of rice kinase database (RKD) that contains metadata derived from NCBI GEO expression datasets has been developed. RKD 2.0 facilitates in-depth transcriptomic analyses of kinase-encoding genes in diverse rice tissues and in response to biotic and abiotic stresses and hormone treatments. We identified 261 kinases specifically expressed in particular tissues, 130 that are significantly up- regulated in response to biotic stress, 296 in response to abiotic stress, and 260 in response to hormones. Based on this update and Pearson correlation coefficient (PCC) analysis, we estimated that 19 out of 26 genes characterized through loss-of-function studies confer dominant functions. These were selected because they either had paralogous members with PCC values of <0.5 or had no paralog. Compared with the previous version of RKD, RKD 2.0 enables more effective estimations of functional redundancy or dominance because it uses comprehensive expression profiles rather than individual profiles. The integrated analysis of RKD with PCC establishes a single platform for researchers to select rice kinases for functional analyses.

Data integration in the era of omics: current and future challenges

PubMed Central

2014-01-01

To integrate heterogeneous and large omics data constitutes not only a conceptual challenge but a practical hurdle in the daily analysis of omics data. With the rise of novel omics technologies and through large-scale consortia projects, biological systems are being further investigated at an unprecedented scale generating heterogeneous and often large data sets. These data-sets encourage researchers to develop novel data integration methodologies. In this introduction we review the definition and characterize current efforts on data integration in the life sciences. We have used a web-survey to assess current research projects on data-integration to tap into the views, needs and challenges as currently perceived by parts of the research community. PMID:25032990

'Omics' techniques for identifying flooding-response mechanisms in soybean.

PubMed

Komatsu, Setsuko; Shirasaka, Naoki; Sakata, Katsumi

2013-11-20

Plant growth and productivity are adversely influenced by various environmental stresses, which often lead to reduced seedling growth and decreased crop yields. Plants respond to stressful conditions through changes in 'omics' profiles, including transcriptomics, proteomics, and metabolomics. Linking plant phenotype to gene expression patterns, protein abundance, and metabolite accumulation is one of the main challenges for improving agricultural production. 'Omics' approaches may shed insight into the mechanisms that function in soybean in response to environmental stresses, particularly flooding by frequent rain, which occurs worldwide due to changes in global climate. Flooding causes significant reductions in the growth and yield of several crops, especially soybean. The application of 'omics' techniques may facilitate the development of flood-tolerant cultivars of soybean. In this review, the use of 'omics' techniques towards understanding the flooding-responsive mechanisms of soybeans is discussed, as the findings from these studies are expected to have applications in both breeding and agronomy. This article is part of a Special Issue entitled: Translational Plant Proteomics. Copyright © 2012 Elsevier B.V. All rights reserved.

MOPED enables discoveries through consistently processed proteomics data

PubMed Central

Higdon, Roger; Stewart, Elizabeth; Stanberry, Larissa; Haynes, Winston; Choiniere, John; Montague, Elizabeth; Anderson, Nathaniel; Yandl, Gregory; Janko, Imre; Broomall, William; Fishilevich, Simon; Lancet, Doron; Kolker, Natali; Kolker, Eugene

2014-01-01

The Model Organism Protein Expression Database (MOPED, http://moped.proteinspire.org), is an expanding proteomics resource to enable biological and biomedical discoveries. MOPED aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm and yeast). The latest version of MOPED adds new estimates of protein abundance and concentration, as well as relative (differential) expression data. MOPED provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. MOPED supports the Human Proteome Project’s efforts to generate chromosome and diseases specific proteomes by providing links from proteins to chromosome and disease information, as well as many complementary resources. MOPED supports a new omics metadata checklist in order to harmonize data integration, analysis and use. MOPED’s development is driven by the user community, which spans 90 countries guiding future development that will transform MOPED into a multi-omics resource. MOPED encourages users to submit data in a simple format. They can use the metadata a checklist generate a data publication for this submission. As a result, MOPED will provide even greater insights into complex biological processes and systems and enable deeper and more comprehensive biological and biomedical discoveries. PMID:24350770

A Deeper Examination of Thorellius atrox Scorpion Venom Components with Omic Techonologies.

PubMed

Romero-Gutierrez, Teresa; Peguero-Sanchez, Esteban; Cevallos, Miguel A; Batista, Cesar V F; Ortiz, Ernesto; Possani, Lourival D

2017-12-12

This communication reports a further examination of venom gland transcripts and venom composition of the Mexican scorpion Thorellius atrox using RNA-seq and tandem mass spectrometry. The RNA-seq, which was performed with the Illumina protocol, yielded more than 20,000 assembled transcripts. Following a database search and annotation strategy, 160 transcripts were identified, potentially coding for venom components. A novel sequence was identified that potentially codes for a peptide with similarity to spider ω-agatoxins, which act on voltage-gated calcium channels, not known before to exist in scorpion venoms. Analogous transcripts were found in other scorpion species. They could represent members of a new scorpion toxin family, here named omegascorpins. The mass fingerprint by LC-MS identified 135 individual venom components, five of which matched with the theoretical masses of putative peptides translated from the transcriptome. The LC-MS/MS de novo sequencing allowed to reconstruct and identify 42 proteins encoded by assembled transcripts, thus validating the transcriptome analysis. Earlier studies conducted with this scorpion venom permitted the identification of only twenty putative venom components. The present work performed with more powerful and modern omic technologies demonstrates the capacity of accomplishing a deeper characterization of scorpion venom components and the identification of novel molecules with potential applications in biomedicine and the study of ion channel physiology.

Web-based metabolic network visualization with a zooming user interface

PubMed Central

2011-01-01

Background Displaying complex metabolic-map diagrams, for Web browsers, and allowing users to interact with them for querying and overlaying expression data over them is challenging. Description We present a Web-based metabolic-map diagram, which can be interactively explored by the user, called the Cellular Overview. The main characteristic of this application is the zooming user interface enabling the user to focus on appropriate granularities of the network at will. Various searching commands are available to visually highlight sets of reactions, pathways, enzymes, metabolites, and so on. Expression data from single or multiple experiments can be overlaid on the diagram, which we call the Omics Viewer capability. The application provides Web services to highlight the diagram and to invoke the Omics Viewer. This application is entirely written in JavaScript for the client browsers and connect to a Pathway Tools Web server to retrieve data and diagrams. It uses the OpenLayers library to display tiled diagrams. Conclusions This new online tool is capable of displaying large and complex metabolic-map diagrams in a very interactive manner. This application is available as part of the Pathway Tools software that powers multiple metabolic databases including Biocyc.org: The Cellular Overview is accessible under the Tools menu. PMID:21595965

Helminth.net: expansions to Nematode.net and an introduction to Trematode.net

PubMed Central

Martin, John; Rosa, Bruce A.; Ozersky, Philip; Hallsworth-Pepin, Kymberlie; Zhang, Xu; Bhonagiri-Palsikar, Veena; Tyagi, Rahul; Wang, Qi; Choi, Young-Jun; Gao, Xin; McNulty, Samantha N.; Brindley, Paul J.; Mitreva, Makedonka

2015-01-01

Helminth.net (http://www.helminth.net) is the new moniker for a collection of databases: Nematode.net and Trematode.net. Within this collection we provide services and resources for parasitic roundworms (nematodes) and flatworms (trematodes), collectively known as helminths. For over a decade we have provided resources for studying nematodes via our veteran site Nematode.net (http://nematode.net). In this article, (i) we provide an update on the expansions of Nematode.net that hosts omics data from 84 species and provides advanced search tools to the broad scientific community so that data can be mined in a useful and user-friendly manner and (ii) we introduce Trematode.net, a site dedicated to the dissemination of data from flukes, flatworm parasites of the class Trematoda, phylum Platyhelminthes. Trematode.net is an independent component of Helminth.net and currently hosts data from 16 species, with information ranging from genomic, functional genomic data, enzymatic pathway utilization to microbiome changes associated with helminth infections. The databases’ interface, with a sophisticated query engine as a backbone, is intended to allow users to search for multi-factorial combinations of species’ omics properties. This report describes updates to Nematode.net since its last description in NAR, 2012, and also introduces and presents its new sibling site, Trematode.net. PMID:25392426

Omics Integration in Biology and Medicine Workshop | Office of Cancer Clinical Proteomics Research

Cancer.gov

The focus of this meeting will be on the emerging field of integrating disparate omic data from genomics, proteomics, glycomics, etc. in order to better understand key biological processes and also improve clinical practice. Discussants will focus on identifying the technical and biological barriers in omic integration, with solutions to build a consensus towards data integration in bioscience and to better define phenotypes.

Integration of multi-omics data for integrative gene regulatory network inference.

PubMed

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun; Kang, Mingon

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

Educating future nursing scientists: Recommendations for integrating omics content in PhD programs.

PubMed

Conley, Yvette P; Heitkemper, Margaret; McCarthy, Donna; Anderson, Cindy M; Corwin, Elizabeth J; Daack-Hirsch, Sandra; Dorsey, Susan G; Gregory, Katherine E; Groer, Maureen W; Henly, Susan J; Landers, Timothy; Lyon, Debra E; Taylor, Jacquelyn Y; Voss, Joachim

2015-01-01

Preparing the next generation of nursing scientists to conduct high-impact, competitive, sustainable, innovative, and interdisciplinary programs of research requires that the curricula for PhD programs keep pace with emerging areas of knowledge and health care/biomedical science. A field of inquiry that holds great potential to influence our understanding of the underlying biology and mechanisms of health and disease is omics. For the purpose of this article, omics refers to genomics, transcriptomics, proteomics, epigenomics, exposomics, microbiomics, and metabolomics. Traditionally, most PhD programs in schools of nursing do not incorporate this content into their core curricula. As part of the Council for the Advancement of Nursing Science's Idea Festival for Nursing Science Education, a work group charged with addressing omics preparation for the next generation of nursing scientists was convened. The purpose of this article is to describe key findings and recommendations from the work group that unanimously and enthusiastically support the incorporation of omics content into the curricula of PhD programs in nursing. The work group also calls to action faculty in schools of nursing to develop strategies to enable students needing immersion in omics science and methods to execute their research goals. Copyright © 2015 Elsevier Inc. All rights reserved.

[Proteomics and personalized medicine].

PubMed

Rocchiccioli, Silvia; Tedeschi, Lorena; Citti, Lorenzo; Cecchettini, Antonella

2013-05-01

With the disclosure of the human genome a new era for bio-medicine has arisen, characterized by the challenge to investigate pathogenic mechanisms, studying simultaneously metabolites, DNA, RNA, and proteins. As a result, the "omics" revolution boomed, giving birth to a new medicine named "omics-based medicine". Among the other "omics", proteomics has been widely used in medicine, since it can produce a more "holistic" overview of a disease and provide a "constellation" of possible specific markers, a molecular fingerprinting that defines the clinical condition of an individual. Endpoint of this comprehensive and detailed analysis is the "diagnostic-omics", i.e. the achievement of personalized diagnoses with obvious benefits for prevention and therapy and this goal can be reached only with a perfect integration between clinicians and proteomists. To impact on the possible key factors involved in the pathological processes, oligonucleotide-based knock-down strategies can be helpful. They exploit omics-derived molecular tools (antisense, siRNA, ribozymes, decoys, and aptamers) that can be used to inhibit, at transcriptional or post-transcriptional levels, the events leading to protein synthesis, thus decreasing its expression. The identification of the pivotal mechanisms involved in diseases using global, "scenic" approaches such as the "omics" ones, and the subsequent validation and detailed description of the processes by specific molecular tools, can result in a more preventive, predictive and personalized medicine.

Integration of multi-omics data for integrative gene regulatory network inference

PubMed Central

Zarayeneh, Neda; Ko, Euiseong; Oh, Jung Hun; Suh, Sang; Liu, Chunyu; Gao, Jean; Kim, Donghyun

2017-01-01

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed. PMID:29354189

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

PubMed Central

Ciucci, Sara; Ge, Yan; Durán, Claudio; Palladini, Alessandra; Jiménez-Jiménez, Víctor; Martínez-Sánchez, Luisa María; Wang, Yuting; Sales, Susanne; Shevchenko, Andrej; Poser, Steven W.; Herbig, Maik; Otto, Oliver; Androutsellis-Theotokis, Andreas; Guck, Jochen; Gerl, Mathias J.; Cannistraci, Carlo Vittorio

2017-01-01

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics. PMID:28287094

Assessment of berberine as a multi-target antimicrobial: a multi-omics study for drug discovery and repositioning.

PubMed

Karaosmanoglu, Kubra; Sayar, Nihat Alpagu; Kurnaz, Isil Aksan; Akbulut, Berna Sariyar

2014-01-01

Postgenomics drug development is undergoing major transformation in the age of multi-omics studies and drug repositioning. Rather than applications solely in personalized medicine, omics science thus additionally offers a better understanding of a broader range of drug targets and drug repositioning. Berberine is an isoquinoline alkaloid found in many medicinal plants. We report here a whole genome microarray study in tandem with proteomics techniques for mining the plethora of targets that are putatively involved in the antimicrobial activity of berberine against Escherichia coli. We found DNA replication/repair and transcription to be triggered by berberine, indicating that nucleic acids, in general, are among its targets. Our combined transcriptomics and proteomics multi-omics findings underscore that, in the presence of berberine, cell wall or cell membrane transport and motility-related functions are also specifically regulated. We further report a general decline in metabolism, as seen by repression of genes in carbohydrate and amino acid metabolism, energy production, and conversion. An involvement of multidrug efflux pumps, as well as reduced membrane permeability for developing resistance against berberine in E. coli was noted. Collectively, these findings offer original and significant leads for omics-guided drug discovery and future repositioning approaches in the postgenomics era, using berberine as a multi-omics case study.

Renal injury in neonates: use of "omics" for developing precision medicine in neonatology.

PubMed

Joshi, Mandar S; Montgomery, Kelsey A; Giannone, Peter J; Bauer, John A; Hanna, Mina H

2017-01-01

Preterm birth is associated with increased risks of morbidity and mortality along with increased healthcare costs. Advances in medicine have enhanced survival for preterm infants but the overall incidence of major morbidities has changed very little. Abnormal renal development is an important consequence of premature birth. Acute kidney injury (AKI) in the neonatal period is multifactorial and may increase lifetime risk of chronic kidney disease.Traditional biomarkers in newborns suffer from considerable confounders, limiting their use for early identification of AKI. There is a need to develop novel biomarkers that can identify, in real time, the evolution of renal dysfunction in an early diagnostic, monitoring and prognostic fashion. Use of "omics", particularly metabolomics, may provide valuable information regarding functional pathways underlying AKI and prediction of clinical outcomes.The emerging knowledge generated by the application of "omics" (genomics, proteomics, metabolomics) in neonatology provides new insights that can help to identify markers of early diagnosis, disease progression, and identify new therapeutic targets. Additionally, omics will have major implications in the field of personalized healthcare in the future. Here, we will review the current knowledge of different omics technologies in neonatal-perinatal medicine including biomarker discovery, defining as yet unrecognized biologic therapeutic targets, and linking of omics to relevant standard indices and long-term outcomes.

Omics Research on the International Space Station

NASA Technical Reports Server (NTRS)

Love, John

2015-01-01

The International Space Station (ISS) is an orbiting laboratory whose goals include advancing science and technology research. Completion of ISS assembly ushered a new era focused on utilization, encompassing multiple disciplines such as Biology and Biotechnology, Physical Sciences, Technology Development and Demonstration, Human Research, Earth and Space Sciences, and Educational Activities. The research complement planned for upcoming ISS Expeditions 45&46 includes several investigations in the new field of omics, which aims to collectively characterize sets of biomolecules (e.g., genomic, epigenomic, transcriptomic, proteomic, and metabolomic products) that translate into organismic structure and function. For example, Multi-Omics is a JAXA investigation that analyzes human microbial metabolic cross-talk in the space ecosystem by evaluating data from immune dysregulation biomarkers, metabolic profiles, and microbiota composition. The NASA OsteoOmics investigation studies gravitational regulation of osteoblast genomics and metabolism. Tissue Regeneration uses pan-omics approaches with cells cultured in bioreactors to characterize factors involved in mammalian bone tissue regeneration in microgravity. Rodent Research-3 includes an experiment that implements pan-omics to evaluate therapeutically significant molecular circuits, markers, and biomaterials associated with microgravity wound healing and tissue regeneration in bone defective rodents. The JAXA Mouse Epigenetics investigation examines molecular alterations in organ specific gene expression patterns and epigenetic modifications, and analyzes murine germ cell development during long term spaceflight. Lastly, Twins Study ("Differential effects of homozygous twin astronauts associated with differences in exposure to spaceflight factors"), NASA's first foray into human omics research, applies integrated analyses to assess biomolecular responses to physical, physiological, and environmental stressors associated with spaceflight.

Multi-omics analysis provides insight to the Ignicoccus hospitalis-Nanoarchaeum equitans association.

PubMed

Rawle, Rachel A; Hamerly, Timothy; Tripet, Brian P; Giannone, Richard J; Wurch, Louie; Hettich, Robert L; Podar, Mircea; Copié, Valerie; Bothner, Brian

2017-09-01

Studies of interspecies interactions are inherently difficult due to the complex mechanisms which enable these relationships. A model system for studying interspecies interactions is the marine hyperthermophiles Ignicoccus hospitalis and Nanoarchaeum equitans. Recent independently-conducted 'omics' analyses have generated insights into the molecular factors modulating this association. However, significant questions remain about the nature of the interactions between these archaea. We jointly analyzed multiple levels of omics datasets obtained from published, independent transcriptomics, proteomics, and metabolomics analyses. DAVID identified functionally-related groups enriched when I. hospitalis is grown alone or in co-culture with N. equitans. Enriched molecular pathways were subsequently visualized using interaction maps generated using STRING. Key findings of our multi-level omics analysis indicated that I. hospitalis provides precursors to N. equitans for energy metabolism. Analysis indicated an overall reduction in diversity of metabolic precursors in the I. hospitalis-N. equitans co-culture, which has been connected to the differential use of ribosomal subunits and was previously unnoticed. We also identified differences in precursors linked to amino acid metabolism, NADH metabolism, and carbon fixation, providing new insights into the metabolic adaptions of I. hospitalis enabling the growth of N. equitans. This multi-omics analysis builds upon previously identified cellular patterns while offering new insights into mechanisms that enable the I. hospitalis-N. equitans association. Our study applies statistical and visualization techniques to a mixed-source omics dataset to yield a more global insight into a complex system, that was not readily discernable from separate omics studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

PubMed

Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

2017-07-15

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier Inc.

MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses

PubMed Central

Nakayasu, Ernesto S.; Nicora, Carrie D.; Sims, Amy C.; Burnum-Johnson, Kristin E.; Kim, Young-Mo; Kyle, Jennifer E.; Matzke, Melissa M.; Shukla, Anil K.; Chu, Rosalie K.; Schepmoes, Athena A.; Jacobs, Jon M.; Baric, Ralph S.; Webb-Robertson, Bobbie-Jo; Smith, Richard D.

2016-01-01

ABSTRACT Integrative multi-omics analyses can empower more effective investigation and complete understanding of complex biological systems. Despite recent advances in a range of omics analyses, multi-omic measurements of the same sample are still challenging and current methods have not been well evaluated in terms of reproducibility and broad applicability. Here we adapted a solvent-based method, widely applied for extracting lipids and metabolites, to add proteomics to mass spectrometry-based multi-omics measurements. The metabolite, protein, and lipid extraction (MPLEx) protocol proved to be robust and applicable to a diverse set of sample types, including cell cultures, microbial communities, and tissues. To illustrate the utility of this protocol, an integrative multi-omics analysis was performed using a lung epithelial cell line infected with Middle East respiratory syndrome coronavirus, which showed the impact of this virus on the host glycolytic pathway and also suggested a role for lipids during infection. The MPLEx method is a simple, fast, and robust protocol that can be applied for integrative multi-omic measurements from diverse sample types (e.g., environmental, in vitro, and clinical). IMPORTANCE In systems biology studies, the integration of multiple omics measurements (i.e., genomics, transcriptomics, proteomics, metabolomics, and lipidomics) has been shown to provide a more complete and informative view of biological pathways. Thus, the prospect of extracting different types of molecules (e.g., DNAs, RNAs, proteins, and metabolites) and performing multiple omics measurements on single samples is very attractive, but such studies are challenging due to the fact that the extraction conditions differ according to the molecule type. Here, we adapted an organic solvent-based extraction method that demonstrated broad applicability and robustness, which enabled comprehensive proteomics, metabolomics, and lipidomics analyses from the same sample. Author Video: An author video summary of this article is available. PMID:27822525

sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures.

PubMed

Zhang, Shu-Dong; Gant, Timothy W

2009-07-31

Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures. This paper introduces sscMap (statistically significant connections' map), a Java application designed to undertake connectivity mapping tasks using the recently published method. The software is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies. The utility of sscMap is two fold. First, it serves to make statistically significant connections between a user-supplied gene signature and the 6100 core reference profiles based on the Broad Institute expanded dataset. Second, it allows users to apply the same improved method to custom-built reference profiles which can be added to the database for future referencing. The software can be freely downloaded from http://purl.oclc.org/NET/sscMap.

GeneLab

NASA Technical Reports Server (NTRS)

Berrios, Daniel C.; Thompson, Terri G.

2015-01-01

NASA GeneLab is expected to capture and distribute omics data and experimental and process conditions most relevant to research community in their statistical and theoretical analysis of NASAs omics data.

Sarcoidosis Related Novel Candidate Genes Identified by Multi-Omics Integrative Analyses.

PubMed

Hočevar, Keli; Maver, Aleš; Kunej, Tanja; Peterlin, Borut

2018-05-01

Sarcoidosis is a multifactorial systemic disease characterized by granulomatous inflammation and greatly impacting on global public health. The etiology and mechanisms of sarcoidosis are not fully understood. Recent high-throughput biological research has generated vast amounts of multi-omics big data on sarcoidosis, but their significance remains to be determined. We sought to identify novel candidate regions, and genes consistently altered in heterogeneous omics studies so as to reveal the underlying molecular mechanisms. We conducted a comprehensive integrative literature analysis on global data on sarcoidosis, including genomic, transcriptomic, proteomic, and phenomic studies. We performed positional integration analysis of 38 eligible datasets originating from 17 different biological layers. Using the integration interval length of 50 kb, we identified 54 regions reaching significance value p ≤ 0.0001 and 15 regions with significance value p ≤ 0.00001, when applying more stringent criteria. Secondary literature analysis of the top 20 regions, with the most significant accumulation of signals, revealed several novel candidate genes for which associations with sarcoidosis have not yet been established, but have considerable support for their involvement based on omic data. These new plausible candidate genes include NELFE, CFB, EGFL7, AGPAT2, FKBPL, NRC3, and NEU1. Furthermore, annotated data were prepared to enable custom visualization and browsing of these sarcoidosis related omics evidence in the University of California Santa Cruz (UCSC) Genome Browser. Further multi-omics approaches are called for sarcoidosis biomarkers and diagnostic and therapeutic innovation. Our approach for harnessing multi-omics data and the findings presented herein reflect important steps toward understanding the etiology and underlying pathological mechanisms of sarcoidosis.

GeneLab Analysis Working Group Kick-Off Meeting

NASA Technical Reports Server (NTRS)

Costes, Sylvain V.

2018-01-01

Goals to achieve for GeneLab AWG - GL vision - Review of GeneLab AWG charter Timeline and milestones for 2018 Logistics - Monthly Meeting - Workshop - Internship - ASGSR Introduction of team leads and goals of each group Introduction of all members Q/A Three-tier Client Strategy to Democratize Data Physiological changes, pathway enrichment, differential expression, normalization, processing metadata, reproducibility, Data federation/integration with heterogeneous bioinformatics external databases The GLDS currently serves over 100 omics investigations to the biomedical community via open access. In order to expand the scope of metadata record searches via the GLDS, we designed a metadata warehouse that collects and updates metadata records from external systems housing similar data. To demonstrate the capabilities of federated search and retrieval of these data, we imported metadata records from three open-access data systems into the GLDS metadata warehouse: NCBI's Gene Expression Omnibus (GEO), EBI's PRoteomics IDEntifications (PRIDE) repository, and the Metagenomics Analysis server (MG-RAST). Each of these systems defines metadata for omics data sets differently. One solution to bridge such differences is to employ a common object model (COM) to which each systems' representation of metadata can be mapped. Warehoused metadata records are then transformed at ETL to this single, common representation. Queries generated via the GLDS are then executed against the warehouse, and matching records are shown in the COM representation (Fig. 1). While this approach is relatively straightforward to implement, the volume of the data in the omics domain presents challenges in dealing with latency and currency of records. Furthermore, the lack of a coordinated has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.

The MPLEx Protocol for Multi-omic Analyses of Soil Samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nicora, Carrie D.; Burnum-Johnson, Kristin E.; Nakayasu, Ernesto S.

Mass spectrometry (MS)-based integrated metaproteomic, metabolomic and lipidomic (multi-omic) studies are transforming our ability to understand and characterize microbial communities in environmental and biological systems. These measurements are even enabling enhanced analyses of complex soil microbial communities, which are the most complex microbial systems known to date. Multi-omic analyses, however, do have sample preparation challenges since separate extractions are typically needed for each omic study, thereby greatly amplifying the preparation time and amount of sample required. To address this limitation, a 3-in-1 method for simultaneous metabolite, protein, and lipid extraction (MPLEx) from the exact same soil sample was created bymore » adapting a solvent-based approach. This MPLEx protocol has proven to be simple yet robust for many sample types and even when utilized for limited quantities of complex soil samples. The MPLEx method also greatly enabled the rapid multi-omic measurements needed to gain a better understanding of the members of each microbial community, while evaluating the changes taking place upon biological and environmental perturbations.« less

Reflection on Molecular Approaches Influencing State-of-the-Art Bioremediation Design: Culturing to Microbial Community Fingerprinting to Omics

PubMed Central

Czaplicki, Lauren M.; Gunsch, Claudia K.

2017-01-01

Bioremediation is generally viewed as a cost effective and sustainable technology because it relies on microbes to transform pollutants into benign compounds. Advances in molecular biological analyses allow unprecedented microbial detection and are increasingly incorporated into bioremediation. Throughout history, state-of-the-art techniques have informed bioremediation strategies. However, the insights those techniques provided were not as in depth as those provided by recently developed omics tools. Advances in next generation sequencing (NGS) have now placed metagenomics and metatranscriptomics within reach of environmental engineers. As NGS costs decrease, metagenomics and metatranscriptomics have become increasingly feasible options to rapidly scan sites for specific degradative functions and identify microorganisms important in pollutant degradation. These omic techniques are capable of revolutionizing biological treatment in environmental engineering by allowing highly sensitive characterization of previously uncultured microorganisms. Omics enables the discovery of novel microorganisms for use in bioaugmentation and supports systematic optimization of biostimulation strategies. This review describes the omics journey from roots in biology and medicine to its current status in environmental engineering including potential future directions in commercial application. PMID:28348455

Omics Approaches for the Study of Adaptive Immunity to Staphylococcus aureus and the Selection of Vaccine Candidates

PubMed Central

Holtfreter, Silva; Kolata, Julia; Stentzel, Sebastian; Bauerfeind, Stephanie; Schmidt, Frank; Sundaramoorthy, Nandakumar; Bröker, Barbara M.

2016-01-01

Staphylococcus aureus is a dangerous pathogen both in hospitals and in the community. Due to the crisis of antibiotic resistance, there is an urgent need for new strategies to combat S. aureus infections, such as vaccination. Increasing our knowledge about the mechanisms of protection will be key for the successful prevention or treatment of S. aureus invasion. Omics technologies generate a comprehensive picture of the physiological and pathophysiological processes within cells, tissues, organs, organisms and even populations. This review provides an overview of the contribution of genomics, transcriptomics, proteomics, metabolomics and immunoproteomics to the current understanding of S. aureus‑host interaction, with a focus on the adaptive immune response to the microorganism. While antibody responses during colonization and infection have been analyzed in detail using immunoproteomics, the full potential of omics technologies has not been tapped yet in terms of T-cells. Omics technologies promise to speed up vaccine development by enabling reverse vaccinology approaches. In consequence, omics technologies are powerful tools for deepening our understanding of the “superbug” S. aureus and for improving its control. PMID:28248221

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database.

PubMed

Ikeda, Shun; Abe, Takashi; Nakamura, Yukiko; Kibinge, Nelson; Hirai Morita, Aki; Nakatani, Atsushi; Ono, Naoaki; Ikemura, Toshimichi; Nakamura, Kensuke; Altaf-Ul-Amin, Md; Kanaya, Shigehiko

2013-05-01

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.

Ginseng Genome Database: an open-access platform for genomics of Panax ginseng.

PubMed

Jayakodi, Murukarthick; Choi, Beom-Soon; Lee, Sang-Choon; Kim, Nam-Hoon; Park, Jee Young; Jang, Woojong; Lakshmanan, Meiyappan; Mohan, Shobhana V G; Lee, Dong-Yup; Yang, Tae-Jin

2018-04-12

The ginseng (Panax ginseng C.A. Meyer) is a perennial herbaceous plant that has been used in traditional oriental medicine for thousands of years. Ginsenosides, which have significant pharmacological effects on human health, are the foremost bioactive constituents in this plant. Having realized the importance of this plant to humans, an integrated omics resource becomes indispensable to facilitate genomic research, molecular breeding and pharmacological study of this herb. The first draft genome sequences of P. ginseng cultivar "Chunpoong" were reported recently. Here, using the draft genome, transcriptome, and functional annotation datasets of P. ginseng, we have constructed the Ginseng Genome Database http://ginsengdb.snu.ac.kr /, the first open-access platform to provide comprehensive genomic resources of P. ginseng. The current version of this database provides the most up-to-date draft genome sequence (of approximately 3000 Mbp of scaffold sequences) along with the structural and functional annotations for 59,352 genes and digital expression of genes based on transcriptome data from different tissues, growth stages and treatments. In addition, tools for visualization and the genomic data from various analyses are provided. All data in the database were manually curated and integrated within a user-friendly query page. This database provides valuable resources for a range of research fields related to P. ginseng and other species belonging to the Apiales order as well as for plant research communities in general. Ginseng genome database can be accessed at http://ginsengdb.snu.ac.kr /.

[Applications of meta-analysis in multi-omics].

PubMed

Han, Mingfei; Zhu, Yunping

2014-07-01

As a statistical method integrating multi-features and multi-data, meta-analysis was introduced to the field of life science in the 1990s. With the rapid advances in high-throughput technologies, life omics, the core of which are genomics, transcriptomics and proteomics, is becoming the new hot spot of life science. Although the fast output of massive data has promoted the development of omics study, it results in excessive data that are difficult to integrate systematically. In this case, meta-analysis is frequently applied to analyze different types of data and is improved continuously. Here, we first summarize the representative meta-analysis methods systematically, and then study the current applications of meta-analysis in various omics fields, finally we discuss the still-existing problems and the future development of meta-analysis.

Leveraging algal omics to reveal potential targets for augmenting TAG accumulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arora, Neha; Pienkos, Philip T.; Pruthi, Vikas

Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. Here, this review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and informmore » future metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy.« less

Leveraging Algal Omics to Reveal Potential Targets for Augmenting TAG Accumulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guarnieri, Michael T; Pienkos, Philip T; Arora, Neha

2018-04-18

Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. This review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and inform futuremore » metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy.« less

Resource recovery from wastewater: application of meta-omics to phosphorus and carbon management.

PubMed

Sales, Christopher M; Lee, Patrick K H

2015-06-01

A growing trend at wastewater treatment plants is the recovery of resources and energy from wastewater. Enhanced biological phosphorus removal and anaerobic digestion are two established biotechnology approaches for the recovery of phosphorus and carbon, respectively. Meta-omics approaches (meta-genomics, transcriptomics, proteomics, and metabolomics) are providing novel biological insights into these complex biological systems. In particular, genome-centric metagenomics analyses are revealing the function and physiology of individual community members. Querying transcripts, proteins and metabolites are emerging techniques that can inform the cellular responses under different conditions. Overall, meta-omics approaches are shedding light into complex microbial communities once regarded as 'blackboxes', but challenges remain to integrate information from meta-omics into engineering design and operation guidelines. Copyright © 2015 Elsevier Ltd. All rights reserved.

Leveraging algal omics to reveal potential targets for augmenting TAG accumulation

DOE PAGES

Arora, Neha; Pienkos, Philip T.; Pruthi, Vikas; ...

2018-04-18

Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. Here, this review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and informmore » future metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy.« less

Leveraging algal omics to reveal potential targets for augmenting TAG accumulation.

PubMed

Arora, Neha; Pienkos, Philip T; Pruthi, Vikas; Poluri, Krishna Mohan; Guarnieri, Michael T

2018-04-18

Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. This review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and inform future metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy. Copyright © 2018. Published by Elsevier Inc.

Validation of a novel biomarker panel for the detection of ovarian cancer

PubMed Central

Leung, Felix; Bernardini, Marcus Q.; Brown, Marshall D.; Zheng, Yingye; Molina, Rafael; Bast, Robert C.; Davis, Gerard; Serra, Stefano; Diamandis, Eleftherios P.; Kulasingam, Vathany

2016-01-01

Background Ovarian cancer (OvCa) is the most lethal gynecological malignancy. Our integrated -omics approach to OvCa biomarker discovery has identified kallikrein 6 (KLK6) and folate-receptor 1 (FOLR1) as promising candidates but these markers require further validation. Methods KLK6, FOLR1 CA125 and HE4 were investigated in three independent serum cohorts with a total of 20 healthy controls, 150 benign controls and 216 OvCa patients. The serum biomarker levels were determined by ELISA or automated immunoassay. Results All biomarkers demonstrated elevations in the sera of OvCa patients compared to controls (p<0.01). Overall, CA125 and HE4 displayed the strongest ability (AUC 0.80 and 0.82, respectively) to identify OvCa patients and the addition of HE4 to CA125 improved the sensitivity from 36% to 67% at a set specificity of 95%. As well, the combination of HE4 and FOLR1 was a strong predictor of OvCa diagnosis, displaying comparable sensitivity (65%) to the best performing CA125-based models (67%) at a set specificity of 95%. Conclusions The markers identified through our integrated –omics approach performed similarly to the clinically-approved markers CA125 and HE4. Furthermore, HE4 represents a powerful diagnostic marker for OvCa and should be used more routinely in a clinical setting. Impact The implications of our study are two-fold: (1) we have demonstrated the strengths of HE4 alone and in combination with CA125, lending credence to increasing its usage in the clinic; and (2) we have demonstrated the clinical utility of our integrated –omics approach to identifying novel serum markers with comparable performance to clinical markers. PMID:27448593

GeneLab: Open Science For Exploration

NASA Technical Reports Server (NTRS)

Galazka, Jonathan

2018-01-01

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. The GeneLab project houses spaceflight and spaceflight-relevant multi-omics data in a publicly accessible data commons, and collaborates with NASA-funded principal investigators to maximize the omics data from spaceflight and spaceflight-relevant experiments. I will discuss the current status of GeneLab and give specific examples of how the GeneLab data system has been used to gain insight into how biology responds to spaceflight conditions.

CPTAC Develops LinkedOmics – Public Web Portal to Analyze Multi-Omics Data Within and Across Cancer Types | Office of Cancer Clinical Proteomics Research

Cancer.gov

Multi-omics analysis has grown in popularity among biomedical researchers given the comprehensive characterization of thousands of molecular attributes in addition to clinical attributes. Several data portals have been devised to make these datasets directly available to the cancer research community. However, none of the existing data portals allow systematic exploration and interpretation of the complex relationships between the vast amount of clinical and molecular attributes. CPTAC investigator Dr.

Extracellular Enzyme Composition and Functional Characteristics of Aspergillus niger An-76 Induced by Food Processing Byproducts and Based on Integrated Functional Omics.

PubMed

Liu, Lin; Gong, Weili; Sun, Xiaomeng; Chen, Guanjun; Wang, Lushan

2018-02-07

Byproducts of food processing can be utilized for the production of high-value-added enzyme cocktails. In this study, we utilized integrated functional omics technology to analyze composition and functional characteristics of extracellular enzymes produced by Aspergillus niger grown on food processing byproducts. The results showed that oligosaccharides constituted by arabinose, xylose, and glucose in wheat bran were able to efficiently induce the production of extracellular enzymes of A. niger. Compared with other substrates, wheat bran was more effective at inducing the secretion of β-glucosidases from GH1 and GH3 families, as well as >50% of proteases from A1-family aspartic proteases. Compared with proteins induced by single wheat bran or soybean dregs, the protein yield induced by their mixture was doubled, and the time required to reach peak enzyme activity was shortened by 25%. This study provided a technical platform for the complex formulation of various substrates and functional analysis of extracellular enzymes.

Omics analysis of human bone to identify genes and molecular networks regulating skeletal remodeling in health and disease.

PubMed

Reppe, Sjur; Datta, Harish K; Gautvik, Kaare M

2017-08-01

The skeleton is a metabolically active organ throughout life where specific bone cell activity and paracrine/endocrine factors regulate its morphogenesis and remodeling. In recent years, an increasing number of reports have used multi-omics technologies to characterize subsets of bone biological molecular networks. The skeleton is affected by primary and secondary disease, lifestyle and many drugs. Therefore, to obtain relevant and reliable data from well characterized patient and control cohorts are vital. Here we provide a brief overview of omics studies performed on human bone, of which our own studies performed on trans-iliacal bone biopsies from postmenopausal women with osteoporosis (OP) and healthy controls are among the first and largest. Most other studies have been performed on smaller groups of patients, undergoing hip replacement for osteoarthritis (OA) or fracture, and without healthy controls. The major findings emerging from the combined studies are: 1. Unstressed and stressed bone show profoundly different gene expression reflecting differences in bone turnover and remodeling and 2. Omics analyses comparing healthy/OP and control/OA cohorts reveal characteristic changes in transcriptomics, epigenomics (DNA methylation), proteomics and metabolomics. These studies, together with genome-wide association studies, in vitro observations and transgenic animal models have identified a number of genes and gene products that act via Wnt and other signaling systems and are highly associated to bone density and fracture. Future challenge is to understand the functional interactions between bone-related molecular networks and their significance in OP and OA pathogenesis, and also how the genomic architecture is affected in health and disease. Copyright © 2017 Elsevier Inc. All rights reserved.

The Encyclopedia of Systems Biology and OMICS (first presentation) and The ISA Infrastructure for Multi-omics Data (second presentation) (GSC8 Meeting)

ScienceCinema

Kolker, Eugene; Sansone, Susanna

2018-01-15

The Genomic Standards Consortium was formed in September 2005. It is an international, open-membership working body which promotes standardization in the description of genomes and the exchange and integration of genomic data. The 2009 meeting was an activity of a five-year funding "Research Coordination Network" from the National Science Foundation and was organized held at the DOE Joint Genome Institute with organizational support provided by the JGI and by the University of California - San Diego. Eugene Kolker from Seattle Children's Hospital briefly discusses "The Encyclopedia of Systems Biology and OMICS," followed by Susanna Sansone from the EBI on "The ISA Infrastructure for multi-omics data" at the Genomic Standards Consortium's 8th meeting at the DOE JGI in Walnut Creek, CA. on Sept. 11, 2009.

Clinical multi-omics strategies for the effective cancer management.

PubMed

Yoo, Byong Chul; Kim, Kyung-Hee; Woo, Sang Myung; Myung, Jae Kyung

2017-08-15

Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis. In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

Differential Expression and Functional Analysis of High-Throughput -Omics Data Using Open Source Tools.

PubMed

Kebschull, Moritz; Fittler, Melanie Julia; Demmer, Ryan T; Papapanou, Panos N

2017-01-01

Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ, or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseases, offering a large advantage over earlier "candidate" gene or pathway analyses. A primary goal in the analysis of these high-throughput assays is the detection of those features among several thousand that differ between different groups of samples. In the context of oral biology, our group has successfully utilized -omics technology to identify key molecules and pathways in different diagnostic entities of periodontal disease.A major issue when inferring biological information from high-throughput -omics studies is the fact that the sheer volume of high-dimensional data generated by contemporary technology is not appropriately analyzed using common statistical methods employed in the biomedical sciences.In this chapter, we outline a robust and well-accepted bioinformatics workflow for the initial analysis of -omics data generated using microarrays or next-generation sequencing technology using open-source tools. Starting with quality control measures and necessary preprocessing steps for data originating from different -omics technologies, we next outline a differential expression analysis pipeline that can be used for data from both microarray and sequencing experiments, and offers the possibility to account for random or fixed effects. Finally, we present an overview of the possibilities for a functional analysis of the obtained data.

Using "Omics" and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases.

PubMed

Rebollar, Eria A; Antwis, Rachael E; Becker, Matthew H; Belden, Lisa K; Bletz, Molly C; Brucker, Robert M; Harrison, Xavier A; Hughey, Myra C; Kueneman, Jordan G; Loudon, Andrew H; McKenzie, Valerie; Medina, Daniel; Minbiole, Kevin P C; Rollins-Smith, Louise A; Walke, Jenifer B; Weiss, Sophie; Woodhams, Douglas C; Harris, Reid N

2016-01-01

Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called "omics," are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov-Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.

From Sample to Multi-Omics Conclusions in under 48 Hours

PubMed Central

Navas-Molina, Jose A.; Hyde, Embriette R.; Vázquez-Baeza, Yoshiki; Humphrey, Greg; Gaffney, James; Minich, Jeremiah J.; Melnik, Alexey V.; Herschend, Jakob; DeReus, Jeff; Durant, Austin; Dutton, Rachel J.; Khosroheidari, Mahdieh; Green, Clifford; da Silva, Ricardo; Dorrestein, Pieter C.; Knight, Rob

2016-01-01

ABSTRACT Multi-omics methods have greatly advanced our understanding of the biological organism and its microbial associates. However, they are not routinely used in clinical or industrial applications, due to the length of time required to generate and analyze omics data. Here, we applied a novel integrated omics pipeline for the analysis of human and environmental samples in under 48 h. Human subjects that ferment their own foods provided swab samples from skin, feces, oral cavity, fermented foods, and household surfaces to assess the impact of home food fermentation on their microbial and chemical ecology. These samples were analyzed with 16S rRNA gene sequencing, inferred gene function profiles, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics through the Qiita, PICRUSt, and GNPS pipelines, respectively. The human sample microbiomes clustered with the corresponding sample types in the American Gut Project (http://www.americangut.org), and the fermented food samples produced a separate cluster. The microbial communities of the household surfaces were primarily sourced from the fermented foods, and their consumption was associated with increased gut microbial diversity. Untargeted metabolomics revealed that human skin and fermented food samples had separate chemical ecologies and that stool was more similar to fermented foods than to other sample types. Metabolites from the fermented foods, including plant products such as procyanidin and pheophytin, were present in the skin and stool samples of the individuals consuming the foods. Some food metabolites were modified during digestion, and others were detected in stool intact. This study represents a first-of-its-kind analysis of multi-omics data that achieved time intervals matching those of classic microbiological culturing. IMPORTANCE Polymicrobial infections are difficult to diagnose due to the challenge in comprehensively cultivating the microbes present. Omics methods, such as 16S rRNA sequencing, metagenomics, and metabolomics, can provide a more complete picture of a microbial community and its metabolite production, without the biases and selectivity of microbial culture. However, these advanced methods have not been applied to clinical or industrial microbiology or other areas where complex microbial dysbioses require immediate intervention. The reason for this is the length of time required to generate and analyze omics data. Here, we describe the development and application of a pipeline for multi-omics data analysis in time frames matching those of the culture-based approaches often used for these applications. This study applied multi-omics methods effectively in clinically relevant time frames and sets a precedent toward their implementation in clinical medicine and industrial microbiology. PMID:27822524

Comparative Metabolic Physiology in the 'omics' Era: A Call to Arms, Paws, Flippers, and Claws

USDA-ARS?s Scientific Manuscript database

In nutrition, medicine, and animal science, metabolism research is often focused on solving questions using a single organism. Outcomes are most often linked to translational outcomes--understanding or treating a disease, optimizing nutritional status, improving select qualities of production anima...

Data on master regulators and transcription factor binding sites found by upstream analysis of multi-omics data on methotrexate resistance of colon cancer.

PubMed

Kel, AlexanderE

2017-02-01

Computational analysis of master regulators through the search for transcription factor binding sites followed by analysis of signal transduction networks of a cell is a new approach of causal analysis of multi-omics data. This paper contains results on analysis of multi-omics data that include transcriptomics, proteomics and epigenomics data of methotrexate (MTX) resistant colon cancer cell line. The data were used for analysis of mechanisms of resistance and for prediction of potential drug targets and promising compounds for reverting the MTX resistance of these cancer cells. We present all results of the analysis including the lists of identified transcription factors and their binding sites in genome and the list of predicted master regulators - potential drug targets. This data was generated in the study recently published in the article "Multi-omics "Upstream Analysis" of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer" (Kel et al., 2016) [4]. These data are of interest for researchers from the field of multi-omics data analysis and for biologists who are interested in identification of novel drug targets against NTX resistance.

Clever generation of rich SPARQL queries from annotated relational schema: application to Semantic Web Service creation for biological databases.

PubMed

Wollbrett, Julien; Larmande, Pierre; de Lamotte, Frédéric; Ruiz, Manuel

2013-04-15

In recent years, a large amount of "-omics" data have been produced. However, these data are stored in many different species-specific databases that are managed by different institutes and laboratories. Biologists often need to find and assemble data from disparate sources to perform certain analyses. Searching for these data and assembling them is a time-consuming task. The Semantic Web helps to facilitate interoperability across databases. A common approach involves the development of wrapper systems that map a relational database schema onto existing domain ontologies. However, few attempts have been made to automate the creation of such wrappers. We developed a framework, named BioSemantic, for the creation of Semantic Web Services that are applicable to relational biological databases. This framework makes use of both Semantic Web and Web Services technologies and can be divided into two main parts: (i) the generation and semi-automatic annotation of an RDF view; and (ii) the automatic generation of SPARQL queries and their integration into Semantic Web Services backbones. We have used our framework to integrate genomic data from different plant databases. BioSemantic is a framework that was designed to speed integration of relational databases. We present how it can be used to speed the development of Semantic Web Services for existing relational biological databases. Currently, it creates and annotates RDF views that enable the automatic generation of SPARQL queries. Web Services are also created and deployed automatically, and the semantic annotations of our Web Services are added automatically using SAWSDL attributes. BioSemantic is downloadable at http://southgreen.cirad.fr/?q=content/Biosemantic.

IPF-LASSO: Integrative L 1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data

PubMed Central

Jiang, Xiaoyu; Fuchs, Mathias

2017-01-01

As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed “omics” data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package ipflasso, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility. PMID:28546826

A Deeper Examination of Thorellius atrox Scorpion Venom Components with Omic Techonologies

PubMed Central

Romero-Gutierrez, Teresa; Batista, Cesar V. F.

2017-01-01

This communication reports a further examination of venom gland transcripts and venom composition of the Mexican scorpion Thorellius atrox using RNA-seq and tandem mass spectrometry. The RNA-seq, which was performed with the Illumina protocol, yielded more than 20,000 assembled transcripts. Following a database search and annotation strategy, 160 transcripts were identified, potentially coding for venom components. A novel sequence was identified that potentially codes for a peptide with similarity to spider ω-agatoxins, which act on voltage-gated calcium channels, not known before to exist in scorpion venoms. Analogous transcripts were found in other scorpion species. They could represent members of a new scorpion toxin family, here named omegascorpins. The mass fingerprint by LC-MS identified 135 individual venom components, five of which matched with the theoretical masses of putative peptides translated from the transcriptome. The LC-MS/MS de novo sequencing allowed to reconstruct and identify 42 proteins encoded by assembled transcripts, thus validating the transcriptome analysis. Earlier studies conducted with this scorpion venom permitted the identification of only twenty putative venom components. The present work performed with more powerful and modern omic technologies demonstrates the capacity of accomplishing a deeper characterization of scorpion venom components and the identification of novel molecules with potential applications in biomedicine and the study of ion channel physiology. PMID:29231872

Recent progress in the use of ‘omics technologies in brassicaceous vegetables

PubMed Central

Witzel, Katja; Neugart, Susanne; Ruppel, Silke; Schreiner, Monika; Wiesner, Melanie; Baldermann, Susanne

2015-01-01

Continuing advances in ‘omics methodologies and instrumentation is enhancing the understanding of how plants cope with the dynamic nature of their growing environment. ‘Omics platforms have been only recently extended to cover horticultural crop species. Many of the most widely cultivated vegetable crops belong to the genus Brassica: these include plants grown for their root (turnip, rutabaga/swede), their swollen stem base (kohlrabi), their leaves (cabbage, kale, pak choi) and their inflorescence (cauliflower, broccoli). Characterization at the genome, transcript, protein and metabolite levels has illustrated the complexity of the cellular response to a whole series of environmental stresses, including nutrient deficiency, pathogen attack, heavy metal toxicity, cold acclimation, and excessive and sub-optimal irradiation. This review covers recent applications of ‘omics technologies to the brassicaceous vegetables, and discusses future scenarios in achieving improvements in crop end-use quality. PMID:25926843

The Encyclopedia of Systems Biology and OMICS (first presentation) and The ISA Infrastructure for Multi-omics Data (second presentation) (GSC8 Meeting)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kolker, Eugene; Sansone, Susanna

2011-09-11

The Genomic Standards Consortium was formed in September 2005. It is an international, open-membership working body which promotes standardization in the description of genomes and the exchange and integration of genomic data. The 2009 meeting was an activity of a five-year funding "Research Coordination Network" from the National Science Foundation and was organized held at the DOE Joint Genome Institute with organizational support provided by the JGI and by the University of California - San Diego. Eugene Kolker from Seattle Children's Hospital briefly discusses "The Encyclopedia of Systems Biology and OMICS," followed by Susanna Sansone from the EBI on "Themore » ISA Infrastructure for multi-omics data" at the Genomic Standards Consortium's 8th meeting at the DOE JGI in Walnut Creek, CA. on Sept. 11, 2009.« less

Modelling plankton ecosystems in the meta-omics era. Are we ready?

PubMed

Stec, Krzysztof Franciszek; Caputi, Luigi; Buttigieg, Pier Luigi; D'Alelio, Domenico; Ibarbalz, Federico Matias; Sullivan, Matthew B; Chaffron, Samuel; Bowler, Chris; Ribera d'Alcalà, Maurizio; Iudicone, Daniele

2017-04-01

Recent progress in applying meta-omics approaches to the study of marine ecosystems potentially allows scientists to study the genetic and functional diversity of plankton at an unprecedented depth and with enhanced precision. However, while a range of persistent technical issues still need to be resolved, a much greater obstacle currently preventing a complete and integrated view of the marine ecosystem is the absence of a clear conceptual framework. Herein, we discuss the knowledge that has thus far been derived from conceptual and statistical modelling of marine plankton ecosystems, and illustrate the potential power of integrated meta-omics approaches in the field. We then propose the use of a semantic framework is necessary to support integrative ecological modelling in the meta-omics era, particularly when having to face the increased interdisciplinarity needed to address global issues related to climate change. Copyright © 2017. Published by Elsevier B.V.

Validation of standard operating procedures in a multicenter retrospective study to identify -omics biomarkers for chronic low back pain.

PubMed

Dagostino, Concetta; De Gregori, Manuela; Gieger, Christian; Manz, Judith; Gudelj, Ivan; Lauc, Gordan; Divizia, Laura; Wang, Wei; Sim, Moira; Pemberton, Iain K; MacDougall, Jane; Williams, Frances; Van Zundert, Jan; Primorac, Dragan; Aulchenko, Yurii; Kapural, Leonardo; Allegri, Massimo

2017-01-01

Chronic low back pain (CLBP) is one of the most common medical conditions, ranking as the greatest contributor to global disability and accounting for huge societal costs based on the Global Burden of Disease 2010 study. Large genetic and -omics studies provide a promising avenue for the screening, development and validation of biomarkers useful for personalized diagnosis and treatment (precision medicine). Multicentre studies are needed for such an effort, and a standardized and homogeneous approach is vital for recruitment of large numbers of participants among different centres (clinical and laboratories) to obtain robust and reproducible results. To date, no validated standard operating procedures (SOPs) for genetic/-omics studies in chronic pain have been developed. In this study, we validated an SOP model that will be used in the multicentre (5 centres) retrospective "PainOmics" study, funded by the European Community in the 7th Framework Programme, which aims to develop new biomarkers for CLBP through three different -omics approaches: genomics, glycomics and activomics. The SOPs describe the specific procedures for (1) blood collection, (2) sample processing and storage, (3) shipping details and (4) cross-check testing and validation before assays that all the centres involved in the study have to follow. Multivariate analysis revealed the absolute specificity and homogeneity of the samples collected by the five centres for all genetics, glycomics and activomics analyses. The SOPs used in our multicenter study have been validated. Hence, they could represent an innovative tool for the correct management and collection of reliable samples in other large-omics-based multicenter studies.

Multi-omics analysis provides insight to the Ignicoccus hospitalis - Nanoarchaeum equitans association

DOE PAGES

Rawle, Rachel A.; Hamerly, Timothy; Tripet, Brian P.; ...

2017-06-04

Studies of interspecies interactions are inherently difficult due to the complex mechanisms which enable these relationships. A model system for studying interspecies interactions is the marine hyperthermophiles Ignicoccus hospitalis and Nanoarchaeum equitans. Recent independently-conducted ‘omics’ analyses have generated insights into the molecular factors modulating this association. However, significant questions remain about the nature of the interactions between these archaea. We jointly analyzed multiple levels of omics datasets obtained from published, independent transcriptomics, proteomics, and metabolomics analyses. DAVID identified functionally-related groups enriched when I. hospitalis is grown alone or in co-culture with N. equitans. Enriched molecular pathways were subsequently visualized usingmore » interaction maps generated using STRING. Key findings of our multi-level omics analysis indicated that I. hospitalis provides precursors to N. equitans for energy metabolism. Analysis indicated an overall reduction in diversity of metabolic precursors in the I. hospitalis–N. equitans co-culture, which has been connected to the differential use of ribosomal subunits and was previously unnoticed. We also identified differences in precursors linked to amino acid metabolism, NADH metabolism, and carbon fixation, providing new insights into the metabolic adaptions of I. hospitalis enabling the growth of N. equitans. In conclusion, this multi-omics analysis builds upon previously identified cellular patterns while offering new insights into mechanisms that enable the I. hospitalis–N. equitans association. This study applies statistical and visualization techniques to a mixed-source omics dataset to yield a more global insight into a complex system, that was not readily discernable from separate omics studies.« less

Rebooting Bioresilience: A Multi-OMICS Approach to Tackle Global Catastrophic Biological Risks and Next-Generation Biothreats.

PubMed

Kambouris, Manousos E; Manoussopoulos, Yiannis; Kantzanou, Maria; Velegraki, Aristea; Gaitanis, Georgios; Arabatzis, Michalis; Patrinos, George P

2018-01-01

Global Catastrophic Biological Risks (GCBRs) refer to biological events-natural, deliberate, and accidental-of a global and lasting impact. This challenges the life scientists to raise their game on two hitherto neglected innovation frontiers: a veritable "futures" thinking to "think the unthinkable," and "systems thinking" so as to see both the trees and the forest when it comes to GCBRs. This innovation analysis article outlines the promise of Omics systems science biotechnologies, for example, to deploy rapid fire diagnostics for health security crises at GCBR level, possibly involving neopathogens and/or incurring epidemics (e.g., severe acute respiratory syndrome [SARS] and Ebola) that collectively threaten the lives of global society and interdependent biological ecosystems. Moreover, Omics encourages thinking beyond immediacy and in long-term strategies for biopreparedness and response innovation when the timelines are aggressive and compressed in response to crises such as GCBRs, but also to non-global but surging, multiple threats occurring as successive, overlapping, or distinct events, rather than as distinct entities-a prospect enforcing a reboot in Bioresilience. We define Next-Generation Bioresilience as "a systems approach against natural, accidental and perpetrated GCBRs using Omics technologies, and a shift in mentality, whereby the systems approach is expanded to include multiple plausible futures and expose unchecked assumptions attendant to risks, beyond technological determinism." In sum, it is time to think about the realistic potential of Omics biotechnologies beyond clinical practice and precision medicine so as to harness the opportunities and address the uncertainties associated not only with GCBRs but also with other emerging Omics applications in health and society.

Establishment of Kawasaki disease database based on metadata standard.

PubMed

Park, Yu Rang; Kim, Jae-Jung; Yoon, Young Jo; Yoon, Young-Kwang; Koo, Ha Yeong; Hong, Young Mi; Jang, Gi Young; Shin, Soo-Yong; Lee, Jong-Keuk

2016-07-01

Kawasaki disease (KD) is a rare disease that occurs predominantly in infants and young children. To identify KD susceptibility genes and to develop a diagnostic test, a specific therapy, or prevention method, collecting KD patients' clinical and genomic data is one of the major issues. For this purpose, Kawasaki Disease Database (KDD) was developed based on the efforts of Korean Kawasaki Disease Genetics Consortium (KKDGC). KDD is a collection of 1292 clinical data and genomic samples of 1283 patients from 13 KKDGC-participating hospitals. Each sample contains the relevant clinical data, genomic DNA and plasma samples isolated from patients' blood, omics data and KD-associated genotype data. Clinical data was collected and saved using the common data elements based on the ISO/IEC 11179 metadata standard. Two genome-wide association study data of total 482 samples and whole exome sequencing data of 12 samples were also collected. In addition, KDD includes the rare cases of KD (16 cases with family history, 46 cases with recurrence, 119 cases with intravenous immunoglobulin non-responsiveness, and 52 cases with coronary artery aneurysm). As the first public database for KD, KDD can significantly facilitate KD studies. All data in KDD can be searchable and downloadable. KDD was implemented in PHP, MySQL and Apache, with all major browsers supported.Database URL: http://www.kawasakidisease.kr. © The Author(s) 2016. Published by Oxford University Press.

Reproducibility and Transparency of Omics Research - Impacts on Human Health Risk Assessment

EPA Science Inventory

Omics technologies are becoming more widely used in toxicology, necessitating their consideration in human health hazard and risk assessment programs. Today, risk assessors in the United States Environmental Protection Agency’s Integrated Risk Information System (IRIS) Toxicologi...

Integrated omics analysis of specialized metabolism in medicinal plants.

PubMed

Rai, Amit; Saito, Kazuki; Yamazaki, Mami

2017-05-01

Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space.

PubMed

Zhou, Guangyan; Xia, Jianguo

2018-06-07

Biological networks play increasingly important roles in omics data integration and systems biology. Over the past decade, many excellent tools have been developed to support creation, analysis and visualization of biological networks. However, important limitations remain: most tools are standalone programs, the majority of them focus on protein-protein interaction (PPI) or metabolic networks, and visualizations often suffer from 'hairball' effects when networks become large. To help address these limitations, we developed OmicsNet - a novel web-based tool that allows users to easily create different types of molecular interaction networks and visually explore them in a three-dimensional (3D) space. Users can upload one or multiple lists of molecules of interest (genes/proteins, microRNAs, transcription factors or metabolites) to create and merge different types of biological networks. The 3D network visualization system was implemented using the powerful Web Graphics Library (WebGL) technology that works natively in most major browsers. OmicsNet supports force-directed layout, multi-layered perspective layout, as well as spherical layout to help visualize and navigate complex networks. A rich set of functions have been implemented to allow users to perform coloring, shading, topology analysis, and enrichment analysis. OmicsNet is freely available at http://www.omicsnet.ca.

Neuroblastoma treatment in the post-genomic era.

PubMed

Esposito, Maria Rosaria; Aveic, Sanja; Seydel, Anke; Tonini, Gian Paolo

2017-02-08

Neuroblastoma is an embryonic malignancy of early childhood originating from neural crest cells and showing heterogeneous biological, morphological, genetic and clinical characteristics. The correct stratification of neuroblastoma patients within risk groups (low, intermediate, high and ultra-high) is critical for the adequate treatment of the patients.High-throughput technologies in the Omics disciplines are leading to significant insights into the molecular pathogenesis of neuroblastoma. Nonetheless, further study of Omics data is necessary to better characterise neuroblastoma tumour biology. In the present review, we report an update of compounds that are used in preclinical tests and/or in Phase I-II trials for neuroblastoma. Furthermore, we recapitulate a number of compounds targeting proteins associated to neuroblastoma: MYCN (direct and indirect inhibitors) and downstream targets, Trk, ALK and its downstream signalling pathways. In particular, for the latter, given the frequency of ALK gene deregulation in neuroblastoma patients, we discuss on second-generation ALK inhibitors in preclinical or clinical phases developed for the treatment of neuroblastoma patients resistant to crizotinib.We summarise how Omics drive clinical trials for neuroblastoma treatment and how much the research of biological targets is useful for personalised medicine. Finally, we give an overview of the most recent druggable targets selected by Omics investigation and discuss how the Omics results can provide us additional advantages for overcoming tumour drug resistance.

Integrative FourD omics approach profiles the target network of the carbon storage regulatory system

PubMed Central

Sowa, Steven W.; Gelderman, Grant; Leistra, Abigail N.; Buvanendiran, Aishwarya; Lipp, Sarah; Pitaktong, Areen; Vakulskas, Christopher A.; Romeo, Tony; Baldea, Michael

2017-01-01

Abstract Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, using multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequencing crosslinking immunoprecipitation) to evaluate simultaneous changes in gene expression after imposing an environmental stress that accentuates the regulatory features of a network. Using this approach, we profiled the targets and potential regulatory mechanisms of a global regulatory system, the well-studied carbon storage regulatory (Csr) system of Escherichia coli, which is widespread among bacteria. Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct CsrA targets, including 50 novel ones, categorized their behaviors into distinct regulatory patterns, and performed in vivo fluorescence-based follow up experiments. The results of this work validate 17 novel mRNAs as authentic direct CsrA targets and demonstrate a generalizable strategy to integrate multiple lines of omics data to identify a core pool of regulator targets. PMID:28126921

'Omic' genetic technologies for herbal medicines in psychiatry.

PubMed

Sarris, Jerome; Ng, Chee Hong; Schweitzer, Isaac

2012-04-01

The field of genetics, which includes the use of 'omic' technologies, is an evolving area of science that has emerging application in phytotherapy. Omic studies include pharmacogenomics, proteomics and metabolomics. Herbal medicines, as monotherapies, or complex formulations such as traditional Chinese herbal prescriptions, may benefit from omic studies, and this new field may be termed 'herbomics'. Applying herbomics in the field of psychiatry may provide answers about which herbal interventions may be effective for individuals, which genetic processes are triggered, and the subsequent neurochemical pathways of activity. The use of proteomic technology can explore the differing epigenetic effects on neurochemical gene expression between individual herbs, isolated constituents and complex formulae. The possibilities of side effects or insufficient response to the herb can also be assessed via pharmacogenomic analysis of polymorphisms of cytochrome P450 liver enzymes or P-glycoprotein. While another novel application of omic technology is for the validation of the concept of synergy in individual herbal extracts and prescriptive formulations. Chronic administration of psychotropic herbal medicines may discover important effects on chromatin remodelling via modification of histone and DNA methylation. This paper focuses on the emerging field of herbomics, and is to our knowledge the first publication to explore this in the area of psychiatry. Copyright © 2011 John Wiley & Sons, Ltd.

SpirPro: A Spirulina proteome database and web-based tools for the analysis of protein-protein interactions at the metabolic level in Spirulina (Arthrospira) platensis C1.

PubMed

Senachak, Jittisak; Cheevadhanarak, Supapon; Hongsthong, Apiradee

2015-07-29

Spirulina (Arthrospira) platensis is the only cyanobacterium that in addition to being studied at the molecular level and subjected to gene manipulation, can also be mass cultivated in outdoor ponds for commercial use as a food supplement. Thus, encountering environmental changes, including temperature stresses, is common during the mass production of Spirulina. The use of cyanobacteria as an experimental platform, especially for photosynthetic gene manipulation in plants and bacteria, is becoming increasingly important. Understanding the mechanisms and protein-protein interaction networks that underlie low- and high-temperature responses is relevant to Spirulina mass production. To accomplish this goal, high-throughput techniques such as OMICs analyses are used. Thus, large datasets must be collected, managed and subjected to information extraction. Therefore, databases including (i) proteomic analysis and protein-protein interaction (PPI) data and (ii) domain/motif visualization tools are required for potential use in temperature response models for plant chloroplasts and photosynthetic bacteria. A web-based repository was developed including an embedded database, SpirPro, and tools for network visualization. Proteome data were analyzed integrated with protein-protein interactions and/or metabolic pathways from KEGG. The repository provides various information, ranging from raw data (2D-gel images) to associated results, such as data from interaction and/or pathway analyses. This integration allows in silico analyses of protein-protein interactions affected at the metabolic level and, particularly, analyses of interactions between and within the affected metabolic pathways under temperature stresses for comparative proteomic analysis. The developed tool, which is coded in HTML with CSS/JavaScript and depicted in Scalable Vector Graphics (SVG), is designed for interactive analysis and exploration of the constructed network. SpirPro is publicly available on the web at http://spirpro.sbi.kmutt.ac.th . SpirPro is an analysis platform containing an integrated proteome and PPI database that provides the most comprehensive data on this cyanobacterium at the systematic level. As an integrated database, SpirPro can be applied in various analyses, such as temperature stress response networking analysis in cyanobacterial models and interacting domain-domain analysis between proteins of interest.

Combined use of isotopic fingerprint and metabolomics analysis for the authentication of saw palmetto (Serenoa repens) extracts.

PubMed

Perini, Matteo; Paolini, Mauro; Camin, Federica; Appendino, Giovanni; Vitulo, Francesca; De Combarieu, Eric; Sardone, Nicola; Martinelli, Ernesto Marco; Pace, Roberto

2018-04-22

Saw palmetto (Serenoa repens, SP) is the most expensive oil source of the pharmaceutical and healthfood market, and its high cost and recurrent shortages have spurred the development of designer blends of fatty acids to mimic its phytochemical profile and fraudulently comply with the current authentication assays. To detect this adulteration, the combined use of isotopic fingerprint and omic analysis has been investigated, using Principal Component Analysis (PCA) to handle the complex databases generated by these techniques and to identify the possible source of the adulterants. Surprisingly, the presence of fatty acids of animal origin turned out to be widespread in commercial samples of saw palmetto oil. Copyright © 2018 Elsevier B.V. All rights reserved.

[Alternatives to animal testing].

PubMed

Fabre, Isabelle

2009-11-01

The use of alternative methods to animal testing are an integral part of the 3Rs concept (refine, reduce, replace) defined by Russel & Burch in 1959. These approaches include in silico methods (databases and computer models), in vitro physicochemical analysis, biological methods using bacteria or isolated cells, reconstructed enzyme systems, and reconstructed tissues. Emerging "omic" methods used in integrated approaches further help to reduce animal use, while stem cells offer promising approaches to toxicologic and pathophysiologic studies, along with organotypic cultures and bio-artificial organs. Only a few alternative methods can so far be used in stand-alone tests as substitutes for animal testing. The best way to use these methods is to integrate them in tiered testing strategies (ITS), in which animals are only used as a last resort.

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment.

PubMed

Brockmeier, Erica K; Hodges, Geoff; Hutchinson, Thomas H; Butler, Emma; Hecker, Markus; Tollefsen, Knut Erik; Garcia-Reyero, Natalia; Kille, Peter; Becker, Dörthe; Chipman, Kevin; Colbourne, John; Collette, Timothy W; Cossins, Andrew; Cronin, Mark; Graystock, Peter; Gutsell, Steve; Knapen, Dries; Katsiadaki, Ioanna; Lange, Anke; Marshall, Stuart; Owen, Stewart F; Perkins, Edward J; Plaistow, Stewart; Schroeder, Anthony; Taylor, Daisy; Viant, Mark; Ankley, Gerald; Falciani, Francesco

2017-08-01

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework. © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology.

Personalized medicine in human space flight: using Omics based analyses to develop individualized countermeasures that enhance astronaut safety and performance.

PubMed

Schmidt, Michael A; Goodwin, Thomas J

2013-01-01

Space flight is one of the most extreme conditions encountered by humans. Advances in Omics methodologies (genomics, transcriptomics, proteomics, and metabolomics) have revealed that unique differences exist between individuals. These differences can be amplified in extreme conditions, such as space flight. A better understanding of individual differences may allow us to develop personalized countermeasure packages that optimize the safety and performance of each astronaut. In this review, we explore the role of "Omics" in advancing our ability to: (1) more thoroughly describe the biological response of humans in space; (2) describe molecular attributes of individual astronauts that alter the risk profile prior to entering the space environment; (3) deploy Omics techniques in the development of personalized countermeasures; and (4) develop a comprehensive Omics-based assessment and countermeasure platform that will guide human space flight in the future. In this review, we advance the concept of personalized medicine in human space flight, with the goal of enhancing astronaut safety and performance. Because the field is vast, we explore selected examples where biochemical individuality might significantly impact countermeasure development. These include gene and small molecule variants associated with: (1) metabolism of therapeutic drugs used in space; (2) one carbon metabolism and DNA stability; (3) iron metabolism, oxidative stress and damage, and DNA stability; and (4) essential input (Mg and Zn) effects on DNA repair. From these examples, we advance the case that widespread Omics profiling should serve as the foundation for aerospace medicine and research, explore methodological considerations to advance the field, and suggest why personalized medicine may become the standard of care for humans in space.

The Role of Omics in the Application of Adverse Outcome Pathways for Chemical Risk Assessment

PubMed Central

Brockmeier, Erica K.; Hodges, Geoff; Hutchinson, Thomas H.; Butler, Emma; Hecker, Markus; Tollefsen, Knut Erik; Garcia-Reyero, Natalia; Kille, Peter; Becker, Dörthe; Chipman, Kevin; Colbourne, John; Collette, Timothy W.; Cossins, Andrew; Cronin, Mark; Graystock, Peter; Gutsell, Steve; Knapen, Dries; Katsiadaki, Ioanna; Lange, Anke; Marshall, Stuart; Owen, Stewart F.; Perkins, Edward J.; Plaistow, Stewart; Schroeder, Anthony; Taylor, Daisy; Viant, Mark; Ankley, Gerald; Falciani, Francesco

2017-01-01

Abstract In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (United Kingdom) in September 2014, a workshop was held to bring together experts in toxicology and regulatory science from academia, government and industry. The purpose of the workshop was to review the specific roles that high-content omics datasets (eg, transcriptomics, metabolomics, lipidomics, and proteomics) can hold within the adverse outcome pathway (AOP) framework for supporting ecological and human health risk assessments. In light of the growing number of examples of the application of omics data in the context of ecological risk assessment, we considered how omics datasets might continue to support the AOP framework. In particular, the role of omics in identifying potential AOP molecular initiating events and providing supportive evidence of key events at different levels of biological organization and across taxonomic groups was discussed. Areas with potential for short and medium-term breakthroughs were also discussed, such as providing mechanistic evidence to support chemical read-across, providing weight of evidence information for mode of action assignment, understanding biological networks, and developing robust extrapolations of species-sensitivity. Key challenges that need to be addressed were considered, including the need for a cohesive approach towards experimental design, the lack of a mutually agreed framework to quantitatively link genes and pathways to key events, and the need for better interpretation of chemically induced changes at the molecular level. This article was developed to provide an overview of ecological risk assessment process and a perspective on how high content molecular-level datasets can support the future of assessment procedures through the AOP framework. PMID:28525648

Omics-based biomarkers: current status and potential use in the clinic.

PubMed

Quezada, Héctor; Guzmán-Ortiz, Ana Laura; Díaz-Sánchez, Hugo; Valle-Rios, Ricardo; Aguirre-Hernández, Jesús

In recent years, the use of high-throughput omics technologies has led to the rapid discovery of many candidate biomarkers. However, few of them have made the transition to the clinic. In this review, the promise of omics technologies to contribute to the process of biomarker development is described. An overview of the current state in this area is presented with examples of genomics, proteomics, transcriptomics, metabolomics and microbiomics biomarkers in the field of oncology, along with some proposed strategies to accelerate their validation and translation to improve the care of patients with neoplasms. The inherent complexity underlying neoplasms combined with the requirement of developing well-designed biomarker discovery processes based on omics technologies present a challenge for the effective development of biomarkers that may be useful in guiding therapies, addressing disease risks, and predicting clinical outcomes. Copyright © 2017 Hospital Infantil de México Federico Gómez. Publicado por Masson Doyma México S.A. All rights reserved.

Twenty-five years of professional liability in pediatric ophthalmology and strabismus: the OMIC experience.

PubMed

Wiggins, Robert E; Gold, Robert S; Menke, Anne M

2015-12-01

To summarize the claims statistics of the Ophthalmic Mutual Insurance Company (OMIC) in the field of pediatric ophthalmology and strabismus (POS). Internal OMIC case summaries and defense counsel case evaluations of all claims in the field of POS closed between December 1, 1988, and February 19, 2013 were retrospectively analyzed. A total of 140 claims were closed over the 25-year study period, of which 44 were closed with an indemnity payment. Claims related to strabismus and retinopathy of prematurity (ROP) were most common, and claims related to ROP resulted in the highest indemnity and expense payments. Issues related to follow-up represented the most significant risk factor among system-related claims. Claims in pediatric ophthalmology and strabismus were infrequent but associated with three times higher average indemnity payments relative to all claims paid by OMIC during the course of the study. Copyright © 2015 American Association for Pediatric Ophthalmology and Strabismus. Published by Elsevier Inc. All rights reserved.

The state of rhizospheric science in the era of multi-omics: A practical guide to omics technologies

DOE PAGES

White, Richard Allen; Rivas-Ubach, Albert; Borkum, Mark I.; ...

2017-05-06

Over the past century, the significance of the rhizosphere has been increasingly recognized by the scientific community. Furthermore, this complex biological system is comprised of vast interconnected networks of microbial organisms that interact directly with their plant hosts, including archaea, bacteria, fungi, picoeukaryotes, and viruses. The rhizosphere provides a nutritional base to the terrestrial biosphere, and is integral to plant growth, crop production, and ecosystem health. There is little mechanistic understanding of the rhizosphere, but, and that constitutes a critical knowledge gap. It inhibits our ability to predict and control the terrestrial ecosystem to achieve desirable outcomes, such as bioenergymore » production, crop yield maximization, and soil-based carbon sequestration. Multi-omics have the potential to significantly advance our knowledge of rhizospheric science. Our review covers multi-omic techniques and technologies; methods and protocols for specific rhizospheric science questions; and the challenges to be addressed during this century of rhizospheric science.« less

Overview of 'Omics Technologies for Military Occupational Health Surveillance and Medicine.

PubMed

Bradburne, Christopher; Graham, David; Kingston, H M; Brenner, Ruth; Pamuku, Matt; Carruth, Lucy

2015-10-01

Systems biology ('omics) technologies are emerging as tools for the comprehensive analysis and monitoring of human health. In order for these tools to be used in military medicine, clinical sampling and biobanking will need to be optimized to be compatible with downstream processing and analysis for each class of molecule measured. This article provides an overview of 'omics technologies, including instrumentation, tools, and methods, and their potential application for warfighter exposure monitoring. We discuss the current state and the potential utility of personalized data from a variety of 'omics sources including genomics, epigenomics, transcriptomics, metabolomics, proteomics, lipidomics, and efforts to combine their use. Issues in the "sample-to-answer" workflow, including collection and biobanking are discussed, as well as national efforts for standardization and clinical interpretation. Establishment of these emerging capabilities, along with accurate xenobiotic monitoring, for the Department of Defense could provide new and effective tools for environmental health monitoring at all duty stations, including deployed locations. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

The state of rhizospheric science in the era of multi-omics: A practical guide to omics technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

White, Richard Allen; Rivas-Ubach, Albert; Borkum, Mark I.

Over the past century, the significance of the rhizosphere has been increasingly recognized by the scientific community. Furthermore, this complex biological system is comprised of vast interconnected networks of microbial organisms that interact directly with their plant hosts, including archaea, bacteria, fungi, picoeukaryotes, and viruses. The rhizosphere provides a nutritional base to the terrestrial biosphere, and is integral to plant growth, crop production, and ecosystem health. There is little mechanistic understanding of the rhizosphere, but, and that constitutes a critical knowledge gap. It inhibits our ability to predict and control the terrestrial ecosystem to achieve desirable outcomes, such as bioenergymore » production, crop yield maximization, and soil-based carbon sequestration. Multi-omics have the potential to significantly advance our knowledge of rhizospheric science. Our review covers multi-omic techniques and technologies; methods and protocols for specific rhizospheric science questions; and the challenges to be addressed during this century of rhizospheric science.« less

The state of rhizospheric science in the era of multi-omics: A practical guide to omics technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

White, Richard Allen; Rivas-Ubach, Albert; Borkum, Mark I.

Over the past century, the significance of the rhizosphere as a complex, biological system, comprised of vast, interconnected networks of microbial organisms that interact directly with their plant hosts (e.g., archæa, bacteria, fungi, eukaryotes, and viruses) has been increasingly recognized by the scientific community. Providing a nutritional base to the terrestrial biosphere, the rhizosphere is integral to plant growth, crop production and ecosystem health. Lack of mechanistic understanding of the rhizosphere constitutes a critical knowledge gap, inhibiting our ability to predict and control the terrestrial ecosystem in order to achieve desirable outcomes (e.g., bioenergy production, crop yield maximization, and soilbasedmore » carbon sequestration). Application of multi-omics has the potential to significantly advance our knowledge of rhizospheric science. This review covers: cutting- and bleeding-edge, multi-omic techniques and technologies; methods and protocols for specific rhizospheric science questions; and, challenges to be addressed during this century of rhizospheric science.« less

Applications of Deep Learning in Biomedicine.

PubMed

Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

2016-05-02

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

Are we closer to the vision? A proposed framework for incorporating omics into environmental assessments.

PubMed

Martyniuk, Christopher J

2018-04-01

Environmental science has benefited a great deal from omics-based technologies. High-throughput toxicology has defined adverse outcome pathways (AOPs), prioritized chemicals of concern, and identified novel actions of environmental chemicals. While many of these approaches are conducted under rigorous laboratory conditions, a significant challenge has been the interpretation of omics data in "real-world" exposure scenarios. Clarity in the interpretation of these data limits their use in environmental monitoring programs. In recent years, one overarching objective of many has been to address fundamental questions concerning experimental design and the robustness of data collected under the broad umbrella of environmental genomics. These questions include: (1) the likelihood that molecular profiles return to a predefined baseline level following remediation efforts, (2) how reference site selection in an urban environment influences interpretation of omics data and (3) what is the most appropriate species to monitor in the environment from an omics point of view. In addition, inter-genomics studies have been conducted to assess transcriptome reproducibility in toxicology studies. One lesson learned from inter-genomics studies is that there are core molecular networks that can be identified by multiple laboratories using the same platform. This supports the idea that "omics-networks" defined a priori may be a viable approach moving forward for evaluating environmental impacts over time. Both spatial and temporal variability in ecosystem structure is expected to influence molecular responses to environmental stressors, and it is important to recognize how these variables, as well as individual factor (i.e. sex, age, maturation), may confound interpretation of network responses to chemicals. This mini-review synthesizes the progress made towards adopting these tools into environmental monitoring and identifies future challenges to be addressed, as we move into the next era of high throughput sequencing. A conceptual framework for validating and incorporating molecular networks into environmental monitoring programs is proposed. As AOPs become more defined and their potential in environmental monitoring assessments becomes more recognized, the AOP framework may prove to be the conduit between omics and penultimate ecological responses for environmental risk assessments. Copyright © 2018 Elsevier B.V. All rights reserved.

Influenza-Omics and the Host Response: Recent Advances and Future Prospects

PubMed Central

Powell, Joshua D.; Waters, Katrina M.

2017-01-01

Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. The various-omics infection systems that include but are not limited to ferrets, mice, pigs, and even the controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infection outcomes. PMID:28604586

Influenza-Omics and the Host Response: Recent Advances and Future Prospects

DOE Office of Scientific and Technical Information (OSTI.GOV)

Powell, Joshua D.; Waters, Katrina M.

Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. Here, the various –omics infection systems that include but are not limited to ferrets, mice, pigs and even controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infectionmore » outcomes.« less

Mildew-Omics: How Global Analyses Aid the Understanding of Life and Evolution of Powdery Mildews.

PubMed

Bindschedler, Laurence V; Panstruga, Ralph; Spanu, Pietro D

2016-01-01

The common powdery mildew plant diseases are caused by ascomycete fungi of the order Erysiphales. Their characteristic life style as obligate biotrophs renders functional analyses in these species challenging, mainly because of experimental constraints to genetic manipulation. Global large-scale ("-omics") approaches are thus particularly valuable and insightful for the characterisation of the life and evolution of powdery mildews. Here we review the knowledge obtained so far from genomic, transcriptomic and proteomic studies in these fungi. We consider current limitations and challenges regarding these surveys and provide an outlook on desired future investigations on the basis of the various -omics technologies.

Influenza-Omics and the Host Response: Recent Advances and Future Prospects

DOE PAGES

Powell, Joshua D.; Waters, Katrina M.

2017-06-10

Influenza A viruses (IAV) continually evolve and have the capacity to cause global pandemics. Because IAV represents an ongoing threat, identifying novel therapies and host innate immune factors that contribute to IAV pathogenesis is of considerable interest. This review summarizes the relevant literature as it relates to global host responses to influenza infection at both the proteome and transcriptome level. Here, the various –omics infection systems that include but are not limited to ferrets, mice, pigs and even controlled infection of humans are reviewed. Discussion focuses on recent advances, remaining challenges, and knowledge gaps as it relates to influenza-omics infectionmore » outcomes.« less

The PerkinElmer Omics Laboratory (Seventh Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting 2012)

ScienceCinema

Smith, Todd

2017-12-22

Todd Smith of the PerkinElmer Omics Laboratory gives a talk about his lab and its work at the 7th Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting held in June, 2012 in Santa Fe, NM.

Integrating Omic Technologies into Aquatic Ecological Risk Assessment and Environmental Monitoring: Hurdles, Achievements and Future Outlook

EPA Science Inventory

In this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the UK Natural Environment Research Council (NERC) with an objective of moving omic technologies into chemical risk assessment and environmental monitoring. Objectiv...

Integrating Omic Technologies into Aquatic Ecological Risk Assessment and Environmental Monitoring: Hurdles, Achievements and Future Outlook

EPA Science Inventory

Background: In this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the UK Natural Environment Research Council (NERC) with a remit of moving omic technologies into chemical risk assessment and environmental monitoring. Obj...

The PerkinElmer Omics Laboratory (Seventh Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting 2012)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Todd

2012-06-01

Todd Smith of the PerkinElmer Omics Laboratory gives a talk about his lab and its work at the 7th Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting held in June, 2012 in Santa Fe, NM.

Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.

PubMed

Sehgal, Vasudha; Seviour, Elena G; Moss, Tyler J; Mills, Gordon B; Azencott, Robert; Ram, Prahlad T

2015-01-01

MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

PubMed

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Integrative FourD omics approach profiles the target network of the carbon storage regulatory system.

PubMed

Sowa, Steven W; Gelderman, Grant; Leistra, Abigail N; Buvanendiran, Aishwarya; Lipp, Sarah; Pitaktong, Areen; Vakulskas, Christopher A; Romeo, Tony; Baldea, Michael; Contreras, Lydia M

2017-02-28

Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, using multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequencing crosslinking immunoprecipitation) to evaluate simultaneous changes in gene expression after imposing an environmental stress that accentuates the regulatory features of a network. Using this approach, we profiled the targets and potential regulatory mechanisms of a global regulatory system, the well-studied carbon storage regulatory (Csr) system of Escherichia coli, which is widespread among bacteria. Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct CsrA targets, including 50 novel ones, categorized their behaviors into distinct regulatory patterns, and performed in vivo fluorescence-based follow up experiments. The results of this work validate 17 novel mRNAs as authentic direct CsrA targets and demonstrate a generalizable strategy to integrate multiple lines of omics data to identify a core pool of regulator targets. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Computational dynamic approaches for temporal omics data with applications to systems medicine.

PubMed

Liang, Yulan; Kelemen, Arpad

2017-01-01

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

PubMed

Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

2018-04-15

Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary data are available at Bioinformatics online.

The role of omics in the application of adverse outcome pathways for chemical risk assessment

EPA Science Inventory

In conjunction with the second International Environmental Omics Symposium (iEOS) conference, held at the University of Liverpool (UK) in September 2014, we held a workshop to bring together toxicology and regulatory science experts from academia, government and industry. The pur...

Systems approach for the selection of micro-RNAs as therapeutic biomarkers of anti-EGFR monoclonal antibody treatment in colorectal cancer

NASA Astrophysics Data System (ADS)

Deyati, Avisek; Bagewadi, Shweta; Senger, Philipp; Hofmann-Apitius, Martin; Novac, Natalia

2015-01-01

miRNA plays an important role in tumourgenesis by regulating expression of oncogenes and tumour suppressors. Thus affects cell proliferation and differentiation, apoptosis, invasion and angiogenesis. miRNAs are potential biomarkers for diagnosis, prognosis and therapies of different forms of cancer. However, relationship between response of cancer patients towards targeted therapy and the resulting modifications of the miRNA transcriptome in the context of pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have produced an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. Efficient translation of -omics data and accumulated knowledge to clinical decision-making are of paramount scientific and public health interest. Well-structured translational algorithms are needed to bridge the gap from databases to decisions. Herein, we present a novel SMARTmiR algorithm to prospectively predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer.

Legal Agreements and the Governance of Research Commons: Lessons from Materials Sharing in Mouse Genomics

PubMed Central

Mishra, Amrita

2014-01-01

Abstract Omics research infrastructure such as databases and bio-repositories requires effective governance to support pre-competitive research. Governance includes the use of legal agreements, such as Material Transfer Agreements (MTAs). We analyze the use of such agreements in the mouse research commons, including by two large-scale resource development projects: the International Knockout Mouse Consortium (IKMC) and International Mouse Phenotyping Consortium (IMPC). We combine an analysis of legal agreements and semi-structured interviews with 87 members of the mouse model research community to examine legal agreements in four contexts: (1) between researchers; (2) deposit into repositories; (3) distribution by repositories; and (4) exchanges between repositories, especially those that are consortium members of the IKMC and IMPC. We conclude that legal agreements for the deposit and distribution of research reagents should be kept as simple and standard as possible, especially when minimal enforcement capacity and resources exist. Simple and standardized legal agreements reduce transactional bottlenecks and facilitate the creation of a vibrant and sustainable research commons, supported by repositories and databases. PMID:24552652

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens

PubMed Central

Sosa, Ezequiel J; Burguener, Germán; Lanzarotti, Esteban; Radusky, Leandro; Pardo, Agustín M; Marti, Marcelo

2018-01-01

Abstract Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request. PMID:29106651

An overview on forensic analysis devoted to analytical chemists.

PubMed

Castillo-Peinado, L S; Luque de Castro, M D

2017-05-15

The present article has as main aim to show analytical chemists interested in forensic analysis the world they will face if decision in favor of being a forensic analytical chemist is adopted. With this purpose, the most outstanding aspects of forensic analysis in dealing with sampling (involving both bodily and no bodily samples), sample preparation, and analytical equipment used in detection, identification and quantitation of key sample components are critically discussed. The role of the great omics in forensic analysis, and the growing role of the youngest of the great omics -metabolomics- are also discussed. The foreseeable role of integrative omics is also outlined. Copyright © 2017 Elsevier B.V. All rights reserved.

Biomedical data integration in computational drug design and bioinformatics.

PubMed

Seoane, Jose A; Aguiar-Pulido, Vanessa; Munteanu, Cristian R; Rivero, Daniel; Rabunal, Juan R; Dorado, Julian; Pazos, Alejandro

2013-03-01

In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.

ChemiRs: a web application for microRNAs and chemicals.

PubMed

Su, Emily Chia-Yu; Chen, Yu-Sing; Tien, Yun-Cheng; Liu, Jeff; Ho, Bing-Ching; Yu, Sung-Liang; Singh, Sher

2016-04-18

MicroRNAs (miRNAs) are about 22 nucleotides, non-coding RNAs that affect various cellular functions, and play a regulatory role in different organisms including human. Until now, more than 2500 mature miRNAs in human have been discovered and registered, but still lack of information or algorithms to reveal the relations among miRNAs, environmental chemicals and human health. Chemicals in environment affect our health and daily life, and some of them can lead to diseases by inferring biological pathways. We develop a creditable online web server, ChemiRs, for predicting interactions and relations among miRNAs, chemicals and pathways. The database not only compares gene lists affected by chemicals and miRNAs, but also incorporates curated pathways to identify possible interactions. Here, we manually retrieved associations of miRNAs and chemicals from biomedical literature. We developed an online system, ChemiRs, which contains miRNAs, diseases, Medical Subject Heading (MeSH) terms, chemicals, genes, pathways and PubMed IDs. We connected each miRNA to miRBase, and every current gene symbol to HUGO Gene Nomenclature Committee (HGNC) for genome annotation. Human pathway information is also provided from KEGG and REACTOME databases. Information about Gene Ontology (GO) is queried from GO Online SQL Environment (GOOSE). With a user-friendly interface, the web application is easy to use. Multiple query results can be easily integrated and exported as report documents in PDF format. Association analysis of miRNAs and chemicals can help us understand the pathogenesis of chemical components. ChemiRs is freely available for public use at http://omics.biol.ntnu.edu.tw/ChemiRs .

Use of omics methods to determine the mode of action of natural phytotoxins

USDA-ARS?s Scientific Manuscript database

Technology has greatly increased the power of omics methods to profile transcription, protein, and metabolite responses to phytotoxins. These methods hold promise as a tool for providing clues to the modes of action of such compounds. However, to date, only two putative modes of action have been fou...

Perspectives of Physiology as a Discipline from Senior-Level Millennial-Generation Students

ERIC Educational Resources Information Center

Steury, Michael D.; Poteracki, James M.; Kelly, Kevin L.; Wehrwein, Erica A.

2015-01-01

In the last several decades, there has been a shift in the mindset of research structure from classical "systems or integrative biology" to more molecular focused "-omics" study. A recent topic of debate in physiological societies has been whether or not the "-omic" revolution has delivered in its promises in both…

Deciphering the complex: methodological overview of statistical models to derive OMICS-based biomarkers.

PubMed

Chadeau-Hyam, Marc; Campanella, Gianluca; Jombart, Thibaut; Bottolo, Leonardo; Portengen, Lutzen; Vineis, Paolo; Liquet, Benoit; Vermeulen, Roel C H

2013-08-01

Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets. Copyright © 2013 Wiley Periodicals, Inc.

A correlated meta-analysis strategy for data mining "OMIC" scans.

PubMed

Province, Michael A; Borecki, Ingrid B

2013-01-01

Meta-analysis is becoming an increasingly popular and powerful tool to integrate findings across studies and OMIC dimensions. But there is the danger that hidden dependencies between putatively "independent" studies can cause inflation of type I error, due to reinforcement of the evidence from false-positive findings. We present here a simple method for conducting meta-analyses that automatically estimates the degree of any such non-independence between OMIC scans and corrects the inference for it, retaining the proper type I error structure. The method does not require the original data from the source studies, but operates only on summary analysis results from these in OMIC scans. The method is applicable in a wide variety of situations including combining GWAS and or sequencing scan results across studies with dependencies due to overlapping subjects, as well as to scans of correlated traits, in a meta-analysis scan for pleiotropic genetic effects. The method correctly detects which scans are actually independent in which case it yields the traditional meta-analysis, so it may safely be used in all cases, when there is even a suspicion of correlation amongst scans.

From data to knowledge: The future of multi-omics data analysis for the rhizosphere

DOE PAGES

Allen White III, Richard; Borkum, Mark I.; Rivas-Ubach, Albert; ...

2017-05-04

The rhizosphere is the interface between the root system of a plant and its surrounding soil. The microbiome of the rhizosphere, which is the totality of all microbes present there, represents a complex microbial ecosystem that nourishes the terrestrial biosphere. In order to untangle the complexity of the rhizosphere, and of the rhizospheric microbiome in particular, an integrated multi-omics approach can be applied to reveal the composition of the rhizospheric microbiome (through 16S ribosomal amplicons and metagenomics), the functional properties of the microbiome (through metatranscriptomics and metaproteomics), and the signaling network within the rhizosphere (through metametabolomics). The successful application ofmore » integrated multi-omics to rhizospheric science depends on the availability of rhizosphere-specific data and on the appropriate software used to analyze omics data from the rhizosphere. Here, we analyze the availability of software suites that are normally applied to surrogate disciplines (e.g., soil and plants) but which can be used for rhizospheric science. We also identify potential issues, challenges, and opportunities for rhizosphere science.« less

Bivalve Omics: State of the Art and Potential Applications for the Biomonitoring of Harmful Marine Compounds

PubMed Central

Suárez-Ulloa, Victoria; Fernández-Tajes, Juan; Manfrin, Chiara; Gerdol, Marco; Venier, Paola; Eirín-López, José M.

2013-01-01

The extraordinary progress experienced by sequencing technologies and bioinformatics has made the development of omic studies virtually ubiquitous in all fields of life sciences nowadays. However, scientific attention has been quite unevenly distributed throughout the different branches of the tree of life, leaving molluscs, one of the most diverse animal groups, relatively unexplored and without representation within the narrow collection of well established model organisms. Within this Phylum, bivalve molluscs play a fundamental role in the functioning of the marine ecosystem, constitute very valuable commercial resources in aquaculture, and have been widely used as sentinel organisms in the biomonitoring of marine pollution. Yet, it has only been very recently that this complex group of organisms became a preferential subject for omic studies, posing new challenges for their integrative characterization. The present contribution aims to give a detailed insight into the state of the art of the omic studies and functional information analysis of bivalve molluscs, providing a timely perspective on the available data resources and on the current and prospective applications for the biomonitoring of harmful marine compounds. PMID:24189277

RepExplore: addressing technical replicate variance in proteomics and metabolomics data analysis.

PubMed

Glaab, Enrico; Schneider, Reinhard

2015-07-01

High-throughput omics datasets often contain technical replicates included to account for technical sources of noise in the measurement process. Although summarizing these replicate measurements by using robust averages may help to reduce the influence of noise on downstream data analysis, the information on the variance across the replicate measurements is lost in the averaging process and therefore typically disregarded in subsequent statistical analyses.We introduce RepExplore, a web-service dedicated to exploit the information captured in the technical replicate variance to provide more reliable and informative differential expression and abundance statistics for omics datasets. The software builds on previously published statistical methods, which have been applied successfully to biomedical omics data but are difficult to use without prior experience in programming or scripting. RepExplore facilitates the analysis by providing a fully automated data processing and interactive ranking tables, whisker plot, heat map and principal component analysis visualizations to interpret omics data and derived statistics. Freely available at http://www.repexplore.tk enrico.glaab@uni.lu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Deciphering functional diversification within the lichen microbiota by meta-omics.

PubMed

Cernava, Tomislav; Erlacher, Armin; Aschenbrenner, Ines Aline; Krug, Lisa; Lassek, Christian; Riedel, Katharina; Grube, Martin; Berg, Gabriele

2017-07-19

Recent evidence of specific bacterial communities extended the traditional concept of fungal-algal lichen symbioses by a further organismal kingdom. Although functional roles were already assigned to dominant members of the highly diversified microbiota, a substantial fraction of the ubiquitous colonizers remained unexplored. We employed a multi-omics approach to further characterize functional guilds in an unconventional model system. The general community structure of the lichen-associated microbiota was shown to be highly similar irrespective of the employed omics approach. Five highly abundant bacterial orders-Sphingomonadales, Rhodospirillales, Myxococcales, Chthoniobacterales, and Sphingobacteriales-harbor functions that are of substantial importance for the holobiome. Identified functions range from the provision of vitamins and cofactors to the degradation of phenolic compounds like phenylpropanoid, xylenols, and cresols. Functions that facilitate the persistence of Lobaria pulmonaria under unfavorable conditions were present in previously overlooked fractions of the microbiota. So far, unrecognized groups like Chthoniobacterales (Verrucomicrobia) emerged as functional protectors in the lichen microbiome. By combining multi-omics and imaging techniques, we highlight previously overlooked participants in the complex microenvironment of the lichens.

Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology.

PubMed

Fondi, Marco; Liò, Pietro

2015-02-01

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists. Copyright © 2015. Published by Elsevier GmbH.

Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes

NASA Astrophysics Data System (ADS)

Hultman, Jenni; Waldrop, Mark P.; Mackelprang, Rachel; David, Maude M.; McFarland, Jack; Blazewicz, Steven J.; Harden, Jennifer; Turetsky, Merritt R.; McGuire, A. David; Shah, Manesh B.; Verberkmoes, Nathan C.; Lee, Lang Ho; Mavrommatis, Kostas; Jansson, Janet K.

2015-05-01

Over 20% of Earth's terrestrial surface is underlain by permafrost with vast stores of carbon that, once thawed, may represent the largest future transfer of carbon from the biosphere to the atmosphere. This process is largely dependent on microbial responses, but we know little about microbial activity in intact, let alone in thawing, permafrost. Molecular approaches have recently revealed the identities and functional gene composition of microorganisms in some permafrost soils and a rapid shift in functional gene composition during short-term thaw experiments. However, the fate of permafrost carbon depends on climatic, hydrological and microbial responses to thaw at decadal scales. Here we use the combination of several molecular `omics' approaches to determine the phylogenetic composition of the microbial communities, including several draft genomes of novel species, their functional potential and activity in soils representing different states of thaw: intact permafrost, seasonally thawed active layer and thermokarst bog. The multi-omics strategy reveals a good correlation of process rates to omics data for dominant processes, such as methanogenesis in the bog, as well as novel survival strategies for potentially active microbes in permafrost.

Microarray platform for omics analysis

NASA Astrophysics Data System (ADS)

Mecklenburg, Michael; Xie, Bin

2001-09-01

Microarray technology has revolutionized genetic analysis. However, limitations in genome analysis has lead to renewed interest in establishing 'omic' strategies. As we enter the post-genomic era, new microarray technologies are needed to address these new classes of 'omic' targets, such as proteins, as well as lipids and carbohydrates. We have developed a microarray platform that combines self- assembling monolayers with the biotin-streptavidin system to provide a robust, versatile immobilization scheme. A hydrophobic film is patterned on the surface creating an array of tension wells that eliminates evaporation effects thereby reducing the shear stress to which biomolecules are exposed to during immobilization. The streptavidin linker layer makes it possible to adapt and/or develop microarray based assays using virtually any class of biomolecules including: carbohydrates, peptides, antibodies, receptors, as well as them ore traditional DNA based arrays. Our microarray technology is designed to furnish seamless compatibility across the various 'omic' platforms by providing a common blueprint for fabricating and analyzing arrays. The prototype microarray uses a microscope slide footprint patterned with 2 by 96 flat wells. Data on the microarray platform will be presented.

Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes.

PubMed

Hultman, Jenni; Waldrop, Mark P; Mackelprang, Rachel; David, Maude M; McFarland, Jack; Blazewicz, Steven J; Harden, Jennifer; Turetsky, Merritt R; McGuire, A David; Shah, Manesh B; VerBerkmoes, Nathan C; Lee, Lang Ho; Mavrommatis, Kostas; Jansson, Janet K

2015-05-14

Over 20% of Earth's terrestrial surface is underlain by permafrost with vast stores of carbon that, once thawed, may represent the largest future transfer of carbon from the biosphere to the atmosphere. This process is largely dependent on microbial responses, but we know little about microbial activity in intact, let alone in thawing, permafrost. Molecular approaches have recently revealed the identities and functional gene composition of microorganisms in some permafrost soils and a rapid shift in functional gene composition during short-term thaw experiments. However, the fate of permafrost carbon depends on climatic, hydrological and microbial responses to thaw at decadal scales. Here we use the combination of several molecular 'omics' approaches to determine the phylogenetic composition of the microbial communities, including several draft genomes of novel species, their functional potential and activity in soils representing different states of thaw: intact permafrost, seasonally thawed active layer and thermokarst bog. The multi-omics strategy reveals a good correlation of process rates to omics data for dominant processes, such as methanogenesis in the bog, as well as novel survival strategies for potentially active microbes in permafrost.

GEneSTATION 1.0: a synthetic resource of diverse evolutionary and functional genomic data for studying the evolution of pregnancy-associated tissues and phenotypes

PubMed Central

Kim, Mara; Cooper, Brian A.; Venkat, Rohit; Phillips, Julie B.; Eidem, Haley R.; Hirbo, Jibril; Nutakki, Sashank; Williams, Scott M.; Muglia, Louis J.; Capra, J. Anthony; Petren, Kenneth; Abbot, Patrick; Rokas, Antonis; McGary, Kriston L.

2016-01-01

Mammalian gestation and pregnancy are fast evolving processes that involve the interaction of the fetal, maternal and paternal genomes. Version 1.0 of the GEneSTATION database (http://genestation.org) integrates diverse types of omics data across mammals to advance understanding of the genetic basis of gestation and pregnancy-associated phenotypes and to accelerate the translation of discoveries from model organisms to humans. GEneSTATION is built using tools from the Generic Model Organism Database project, including the biology-aware database CHADO, new tools for rapid data integration, and algorithms that streamline synthesis and user access. GEneSTATION contains curated life history information on pregnancy and reproduction from 23 high-quality mammalian genomes. For every human gene, GEneSTATION contains diverse evolutionary (e.g. gene age, population genetic and molecular evolutionary statistics), organismal (e.g. tissue-specific gene and protein expression, differential gene expression, disease phenotype), and molecular data types (e.g. Gene Ontology Annotation, protein interactions), as well as links to many general (e.g. Entrez, PubMed) and pregnancy disease-specific (e.g. PTBgene, dbPTB) databases. By facilitating the synthesis of diverse functional and evolutionary data in pregnancy-associated tissues and phenotypes and enabling their quick, intuitive, accurate and customized meta-analysis, GEneSTATION provides a novel platform for comprehensive investigation of the function and evolution of mammalian pregnancy. PMID:26567549

Database Resources of the BIG Data Center in 2018

PubMed Central

Xu, Xingjian; Hao, Lili; Zhu, Junwei; Tang, Bixia; Zhou, Qing; Song, Fuhai; Chen, Tingting; Zhang, Sisi; Dong, Lili; Lan, Li; Wang, Yanqing; Sang, Jian; Hao, Lili; Liang, Fang; Cao, Jiabao; Liu, Fang; Liu, Lin; Wang, Fan; Ma, Yingke; Xu, Xingjian; Zhang, Lijuan; Chen, Meili; Tian, Dongmei; Li, Cuiping; Dong, Lili; Du, Zhenglin; Yuan, Na; Zeng, Jingyao; Zhang, Zhewen; Wang, Jinyue; Shi, Shuo; Zhang, Yadong; Pan, Mengyu; Tang, Bixia; Zou, Dong; Song, Shuhui; Sang, Jian; Xia, Lin; Wang, Zhennan; Li, Man; Cao, Jiabao; Niu, Guangyi; Zhang, Yang; Sheng, Xin; Lu, Mingming; Wang, Qi; Xiao, Jingfa; Zou, Dong; Wang, Fan; Hao, Lili; Liang, Fang; Li, Mengwei; Sun, Shixiang; Zou, Dong; Li, Rujiao; Yu, Chunlei; Wang, Guangyu; Sang, Jian; Liu, Lin; Li, Mengwei; Li, Man; Niu, Guangyi; Cao, Jiabao; Sun, Shixiang; Xia, Lin; Yin, Hongyan; Zou, Dong; Xu, Xingjian; Ma, Lina; Chen, Huanxin; Sun, Yubin; Yu, Lei; Zhai, Shuang; Sun, Mingyuan; Zhang, Zhang; Zhao, Wenming; Xiao, Jingfa; Bao, Yiming; Song, Shuhui; Hao, Lili; Li, Rujiao; Ma, Lina; Sang, Jian; Wang, Yanqing; Tang, Bixia; Zou, Dong; Wang, Fan

2018-01-01

Abstract The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn. PMID:29036542

The Promise of Multi-Omics and Clinical Data Integration to Identify and Target Personalized Healthcare Approaches in Autism Spectrum Disorders

PubMed Central

Higdon, Roger; Earl, Rachel K.; Stanberry, Larissa; Hudac, Caitlin M.; Montague, Elizabeth; Stewart, Elizabeth; Janko, Imre; Choiniere, John; Broomall, William; Kolker, Natali

2015-01-01

Abstract Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare. PMID:25831060

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rawle, Rachel A.; Hamerly, Timothy; Tripet, Brian P.

Studies of interspecies interactions are inherently difficult due to the complex mechanisms which enable these relationships. A model system for studying interspecies interactions is the marine hyperthermophiles Ignicoccus hospitalis and Nanoarchaeum equitans. Recent independently-conducted ‘omics’ analyses have generated insights into the molecular factors modulating this association. However, significant questions remain about the nature of the interactions between these archaea. We jointly analyzed multiple levels of omics datasets obtained from published, independent transcriptomics, proteomics, and metabolomics analyses. DAVID identified functionally-related groups enriched when I. hospitalis is grown alone or in co-culture with N. equitans. Enriched molecular pathways were subsequently visualized usingmore » interaction maps generated using STRING. Key findings of our multi-level omics analysis indicated that I. hospitalis provides precursors to N. equitans for energy metabolism. Analysis indicated an overall reduction in diversity of metabolic precursors in the I. hospitalis–N. equitans co-culture, which has been connected to the differential use of ribosomal subunits and was previously unnoticed. We also identified differences in precursors linked to amino acid metabolism, NADH metabolism, and carbon fixation, providing new insights into the metabolic adaptions of I. hospitalis enabling the growth of N. equitans. In conclusion, this multi-omics analysis builds upon previously identified cellular patterns while offering new insights into mechanisms that enable the I. hospitalis–N. equitans association. This study applies statistical and visualization techniques to a mixed-source omics dataset to yield a more global insight into a complex system, that was not readily discernable from separate omics studies.« less

Studying Cellular Signal Transduction with OMIC Technologies.

PubMed

Landry, Benjamin D; Clarke, David C; Lee, Michael J

2015-10-23

In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology. Copyright © 2015 Elsevier Ltd. All rights reserved.

Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes.

PubMed

Vitali, Francesca; Li, Qike; Schissler, A Grant; Berghout, Joanne; Kenost, Colleen; Lussier, Yves A

2017-12-18

The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments. © The Author 2017. Published by Oxford University Press.

Current dichotomy between traditional molecular biological and omic research in cancer biology and pharmacology.

PubMed

Reinhold, William C

2015-12-10

There is currently a split within the cancer research community between traditional molecular biological hypothesis-driven and the more recent "omic" forms or research. While the molecular biological approach employs the tried and true single alteration-single response formulations of experimentation, the omic employs broad-based assay or sample collection approaches that generate large volumes of data. How to integrate the benefits of these two approaches in an efficient and productive fashion remains an outstanding issue. Ideally, one would merge the understandability, exactness, simplicity, and testability of the molecular biological approach, with the larger amounts of data, simultaneous consideration of multiple alterations, consideration of genes both of known interest along with the novel, cross-sample comparisons among cell lines and patient samples, and consideration of directed questions while simultaneously gaining exposure to the novel provided by the omic approach. While at the current time integration of the two disciplines remains problematic, attempts to do so are ongoing, and will be necessary for the understanding of the large cell line screens including the Developmental Therapeutics Program's NCI-60, the Broad Institute's Cancer Cell Line Encyclopedia, and the Wellcome Trust Sanger Institute's Cancer Genome Project, as well as the the Cancer Genome Atlas clinical samples project. Going forward there is significant benefit to be had from the integration of the molecular biological and the omic forms or research, with the desired goal being improved translational understanding and application.

Rethinking psychiatry with OMICS science in the age of personalized P5 medicine: ready for psychiatome?

PubMed Central

2013-01-01

The Diagnostic and Statistical Manual of Mental Disorders (DSM) is universally acknowledged as the prominent reference textbook for the diagnosis and assessment of psychiatric diseases. However, since the publication of its first version in 1952, controversies have been raised concerning its reliability and validity and the need for other novel clinical tools has emerged. Currently the DSM is in its fourth edition and a new fifth edition is expected for release in 2013, in an intense intellectual debate and in a call for new proposals. Since 1952, psychiatry has undergone many changes and is emerging as unique field in the medical area in which a novel approach is being demanded for properly treating patients: not the classical “one-size-fits-all” approach, but a more targeted and tailored diagnosis and therapeutics, taking into account the complex interactions among genes and their products, environment, culture and the psychological apparatus of the subject. OMICS sciences, being based on high-throughput technologies, are systems biology related fields (like genomics, proteomics, transcriptomics and so on). In the frame of the P5 medicine (personalized, participatory, predictive, preventive, psycho-cognitive), they could establish links between psychiatric diseases, which are disorders with a final common symptomatology with vastly heterogeneous biological, environmental and sociological underpinnings, and by understanding the psychiatric diseases beyond their classic symptomatic or syndromal definitions using OMICS research, one can have a broader picture and unprecedented links and reclassification of psychiatric nosology. Importantly, by understanding the basis of heterogeneity in diseases through OMICS research, one could also personalize treatment of psychiatric illnesses. In this manuscript, we discuss a gap in the current psychiatric research, namely the missing logical link among OMICS, personalized medicine and reclassification of diseases. Moreover, we explore the importance of incorporating OMICS-based quantitative dimensional criteria, besides the classical qualitative and categorical approach. PMID:23849623

Five omic technologies are concordant in differentiating the biochemical characteristics of the berries of five grapevine (Vitis vinifera L.) cultivars.

PubMed

Ghan, Ryan; Van Sluyter, Steven C; Hochberg, Uri; Degu, Asfaw; Hopper, Daniel W; Tillet, Richard L; Schlauch, Karen A; Haynes, Paul A; Fait, Aaron; Cramer, Grant R

2015-11-16

Grape cultivars and wines are distinguishable by their color, flavor and aroma profiles. Omic analyses (transcripts, proteins and metabolites) are powerful tools for assessing biochemical differences in biological systems. Berry skins of red- (Cabernet Sauvignon, Merlot, Pinot Noir) and white-skinned (Chardonnay, Semillon) wine grapes were harvested near optimum maturity (°Brix-to-titratable acidity ratio) from the same experimental vineyard. The cultivars were exposed to a mild, seasonal water-deficit treatment from fruit set until harvest in 2011. Identical sample aliquots were analyzed for transcripts by grapevine whole-genome oligonucleotide microarray and RNAseq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies showed similar results across cultivars in all Omic datasets. Comparison of the processed data of genes mapped in RNAseq and microarray data revealed a strong Pearson's correlation (0.80). The exclusion of probesets associated with genes with potential for cross-hybridization on the microarray improved the correlation to 0.93. The overall concordance of protein with transcript data was low with a Pearson's correlation of 0.27 and 0.24 for the RNAseq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, which distinguished red from white grapes, yet provided detail of individual cultivar differences. The mild water deficit treatment did not significantly alter the abundance of proteins or metabolites measured in the five cultivars, but did have a small effect on gene expression. The five Omic technologies were consistent in distinguishing cultivar variation. There was high concordance between transcriptomic technologies, but generally protein abundance did not correlate well with transcript abundance. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species.

Unsupervised multiple kernel learning for heterogeneous data integration.

PubMed

Mariette, Jérôme; Villa-Vialaneix, Nathalie

2018-03-15

Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

Chapter 3: Omics and the Future of Sustainable Biomaterials

Treesearch

Juliet D. Tang; Susan V. Diehl

2014-01-01

With global focus on the conversion of biomass into products, fuels, and energy, there is a strong need for information that will lead to new sustainable products, applications, and biotechnological advances. The omics approach to biology is a discovery-driven method that may deliver solutions to these overarching problems. It gives scientists the ability to obtain a...

"Predatory" Online Journals Lure Scholars Who Are Eager to Publish

ERIC Educational Resources Information Center

Stratford, Michael

2012-01-01

OMICS Publishing Group is an open-access publisher operating under an author-pays model. Unlike traditional journal subscriptions in which readers or institutions pay to read content, OMICS relies on its contributors for financial support. Although the author-pays model is not a new phenomenon in the realm of open access, its recent popularity has…

Omics in the Arctic: Genome-enabled Contributions to Carbon Cycle Research in High-Latitude Ecosystems (JGI Seventh Annual User Meeting 2012: Genomics of Energy and Environment)

ScienceCinema

Wullschleger, Stan

2018-02-13

Stan Wullschleger of Oak Ridge National Laboratory on "Omics in the Arctic: Genome-enabled Contributions to Carbon Cycle Research in High-Latitude Ecosystems" on March 22, 2012 at the 7th Annual Genomics of Energy & Environment Meeting in Walnut Creek, California.

Omics and cachexia.

PubMed

Twelkmeyer, Brigitte; Tardif, Nicolas; Rooyackers, Olav

2017-05-01

The purpose of this review is to recapture recent advances in cachexia-related diseases, mainly cancer cachexia, and treatment using genomic, transcriptomics, proteomic, and metabolomics-related techniques. From recent studies in the cancer cachexia field it is clear that the tumor has a direct effect on distant organs via its secretome. The affected pathways on the other hand were largely known from earlier studies with changes in energy-related pathways (mainly lipid metabolism) and the protein degradation pathways. Treatment-oriented studies use mostly rodent models and in-vivo cultures and it is too early for human studies. Omics tools are powerful if used in the right way. Omics research has identified the tumor as an important player in cancer cachexia and some interesting novel treatments have been found in experimental models.

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome.

PubMed

McDonald, Daniel; Clemente, Jose C; Kuczynski, Justin; Rideout, Jai Ram; Stombaugh, Jesse; Wendel, Doug; Wilke, Andreas; Huse, Susan; Hufnagle, John; Meyer, Folker; Knight, Rob; Caporaso, J Gregory

2012-07-12

We present the Biological Observation Matrix (BIOM, pronounced "biome") format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the "ome-ome") grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses. The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages. The BIOM file format and the biom-format project are steps toward reducing the "bioinformatics bottleneck" that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.

Qupe--a Rich Internet Application to take a step forward in the analysis of mass spectrometry-based quantitative proteomics experiments.

PubMed

Albaum, Stefan P; Neuweger, Heiko; Fränzel, Benjamin; Lange, Sita; Mertens, Dominik; Trötschel, Christian; Wolters, Dirk; Kalinowski, Jörn; Nattkemper, Tim W; Goesmann, Alexander

2009-12-01

The goal of present -omics sciences is to understand biological systems as a whole in terms of interactions of the individual cellular components. One of the main building blocks in this field of study is proteomics where tandem mass spectrometry (LC-MS/MS) in combination with isotopic labelling techniques provides a common way to obtain a direct insight into regulation at the protein level. Methods to identify and quantify the peptides contained in a sample are well established, and their output usually results in lists of identified proteins and calculated relative abundance values. The next step is to move ahead from these abstract lists and apply statistical inference methods to compare measurements, to identify genes that are significantly up- or down-regulated, or to detect clusters of proteins with similar expression profiles. We introduce the Rich Internet Application (RIA) Qupe providing comprehensive data management and analysis functions for LC-MS/MS experiments. Starting with the import of mass spectra data the system guides the experimenter through the process of protein identification by database search, the calculation of protein abundance ratios, and in particular, the statistical evaluation of the quantification results including multivariate analysis methods such as analysis of variance or hierarchical cluster analysis. While a data model to store these results has been developed, a well-defined programming interface facilitates the integration of novel approaches. A compute cluster is utilized to distribute computationally intensive calculations, and a web service allows to interchange information with other -omics software applications. To demonstrate that Qupe represents a step forward in quantitative proteomics analysis an application study on Corynebacterium glutamicum has been carried out. Qupe is implemented in Java utilizing Hibernate, Echo2, R and the Spring framework. We encourage the usage of the RIA in the sense of the 'software as a service' concept, maintained on our servers and accessible at the following location: http://qupe.cebitec.uni-bielefeld.de. Supplementary data are available at Bioinformatics online.

Bio-crude transcriptomics: gene discovery and metabolic network reconstruction for the biosynthesis of the terpenome of the hydrocarbon oil-producing green alga, Botryococcus braunii race B (Showa).

PubMed

Molnár, István; Lopez, David; Wisecaver, Jennifer H; Devarenne, Timothy P; Weiss, Taylor L; Pellegrini, Matteo; Hackett, Jeremiah D

2012-10-30

Microalgae hold promise for yielding a biofuel feedstock that is sustainable, carbon-neutral, distributed, and only minimally disruptive for the production of food and feed by traditional agriculture. Amongst oleaginous eukaryotic algae, the B race of Botryococcus braunii is unique in that it produces large amounts of liquid hydrocarbons of terpenoid origin. These are comparable to fossil crude oil, and are sequestered outside the cells in a communal extracellular polymeric matrix material. Biosynthetic engineering of terpenoid bio-crude production requires identification of genes and reconstruction of metabolic pathways responsible for production of both hydrocarbons and other metabolites of the alga that compete for photosynthetic carbon and energy. A de novo assembly of 1,334,609 next-generation pyrosequencing reads form the Showa strain of the B race of B. braunii yielded a transcriptomic database of 46,422 contigs with an average length of 756 bp. Contigs were annotated with pathway, ontology, and protein domain identifiers. Manual curation allowed the reconstruction of pathways that produce terpenoid liquid hydrocarbons from primary metabolites, and pathways that divert photosynthetic carbon into tetraterpenoid carotenoids, diterpenoids, and the prenyl chains of meroterpenoid quinones and chlorophyll. Inventories of machine-assembled contigs are also presented for reconstructed pathways for the biosynthesis of competing storage compounds including triacylglycerol and starch. Regeneration of S-adenosylmethionine, and the extracellular localization of the hydrocarbon oils by active transport and possibly autophagy are also investigated. The construction of an annotated transcriptomic database, publicly available in a web-based data depository and annotation tool, provides a foundation for metabolic pathway and network reconstruction, and facilitates further omics studies in the absence of a genome sequence for the Showa strain of B. braunii, race B. Further, the transcriptome database empowers future biosynthetic engineering approaches for strain improvement and the transfer of desirable traits to heterologous hosts.

Bio-crude transcriptomics: Gene discovery and metabolic network reconstruction for the biosynthesis of the terpenome of the hydrocarbon oil-producing green alga, Botryococcus braunii race B (Showa)*

PubMed Central

2012-01-01

Background Microalgae hold promise for yielding a biofuel feedstock that is sustainable, carbon-neutral, distributed, and only minimally disruptive for the production of food and feed by traditional agriculture. Amongst oleaginous eukaryotic algae, the B race of Botryococcus braunii is unique in that it produces large amounts of liquid hydrocarbons of terpenoid origin. These are comparable to fossil crude oil, and are sequestered outside the cells in a communal extracellular polymeric matrix material. Biosynthetic engineering of terpenoid bio-crude production requires identification of genes and reconstruction of metabolic pathways responsible for production of both hydrocarbons and other metabolites of the alga that compete for photosynthetic carbon and energy. Results A de novo assembly of 1,334,609 next-generation pyrosequencing reads form the Showa strain of the B race of B. braunii yielded a transcriptomic database of 46,422 contigs with an average length of 756 bp. Contigs were annotated with pathway, ontology, and protein domain identifiers. Manual curation allowed the reconstruction of pathways that produce terpenoid liquid hydrocarbons from primary metabolites, and pathways that divert photosynthetic carbon into tetraterpenoid carotenoids, diterpenoids, and the prenyl chains of meroterpenoid quinones and chlorophyll. Inventories of machine-assembled contigs are also presented for reconstructed pathways for the biosynthesis of competing storage compounds including triacylglycerol and starch. Regeneration of S-adenosylmethionine, and the extracellular localization of the hydrocarbon oils by active transport and possibly autophagy are also investigated. Conclusions The construction of an annotated transcriptomic database, publicly available in a web-based data depository and annotation tool, provides a foundation for metabolic pathway and network reconstruction, and facilitates further omics studies in the absence of a genome sequence for the Showa strain of B. braunii, race B. Further, the transcriptome database empowers future biosynthetic engineering approaches for strain improvement and the transfer of desirable traits to heterologous hosts. PMID:23110428

Integration of gel-based and gel-free proteomic data for functional analysis of proteins through Soybean Proteome Database.

PubMed

Komatsu, Setsuko; Wang, Xin; Yin, Xiaojian; Nanjo, Yohei; Ohyanagi, Hajime; Sakata, Katsumi

2017-06-23

The Soybean Proteome Database (SPD) stores data on soybean proteins obtained with gel-based and gel-free proteomic techniques. The database was constructed to provide information on proteins for functional analyses. The majority of the data is focused on soybean (Glycine max 'Enrei'). The growth and yield of soybean are strongly affected by environmental stresses such as flooding. The database was originally constructed using data on soybean proteins separated by two-dimensional polyacrylamide gel electrophoresis, which is a gel-based proteomic technique. Since 2015, the database has been expanded to incorporate data obtained by label-free mass spectrometry-based quantitative proteomics, which is a gel-free proteomic technique. Here, the portions of the database consisting of gel-free proteomic data are described. The gel-free proteomic database contains 39,212 proteins identified in 63 sample sets, such as temporal and organ-specific samples of soybean plants grown under flooding stress or non-stressed conditions. In addition, data on organellar proteins identified in mitochondria, nuclei, and endoplasmic reticulum are stored. Furthermore, the database integrates multiple omics data such as genomics, transcriptomics, metabolomics, and proteomics. The SPD database is accessible at http://proteome.dc.affrc.go.jp/Soybean/. The Soybean Proteome Database stores data obtained from both gel-based and gel-free proteomic techniques. The gel-free proteomic database comprises 39,212 proteins identified in 63 sample sets, such as different organs of soybean plants grown under flooding stress or non-stressed conditions in a time-dependent manner. In addition, organellar proteins identified in mitochondria, nuclei, and endoplasmic reticulum are stored in the gel-free proteomics database. A total of 44,704 proteins, including 5490 proteins identified using a gel-based proteomic technique, are stored in the SPD. It accounts for approximately 80% of all predicted proteins from genome sequences, though there are over lapped proteins. Based on the demonstrated application of data stored in the database for functional analyses, it is suggested that these data will be useful for analyses of biological mechanisms in soybean. Furthermore, coupled with recent advances in information and communication technology, the usefulness of this database would increase in the analyses of biological mechanisms. Copyright © 2017 Elsevier B.V. All rights reserved.

EXPOsOMICS: final policy workshop and stakeholder consultation.

PubMed

Turner, Michelle C; Vineis, Paolo; Seleiro, Eduardo; Dijmarescu, Michaela; Balshaw, David; Bertollini, Roberto; Chadeau-Hyam, Marc; Gant, Timothy; Gulliver, John; Jeong, Ayoung; Kyrtopoulos, Soterios; Martuzzi, Marco; Miller, Gary W; Nawrot, Timothy; Nieuwenhuijsen, Mark; Phillips, David H; Probst-Hensch, Nicole; Samet, Jonathan; Vermeulen, Roel; Vlaanderen, Jelle; Vrijheid, Martine; Wild, Christopher; Kogevinas, Manolis

2018-02-15

The final meeting of the EXPOsOMICS project "Final Policy Workshop and Stakeholder Consultation" took place 28-29 March 2017 to present the main results of the project and discuss their implications both for future research and for regulatory and policy activities. This paper summarizes presentations and discussions at the meeting related with the main results and advances in exposome research achieved through the EXPOsOMICS project; on other parallel research initiatives on the study of the exposome in Europe and in the United States and their complementarity to EXPOsOMICS; lessons learned from these early studies on the exposome and how they may shape the future of research on environmental exposure assessment; and finally the broader implications of exposome research for risk assessment and policy development on environmental exposures. The main results of EXPOsOMICS in relation to studies of the external exposome and internal exposome in relation to both air pollution and water contaminants were presented as well as new technologies for environmental health research (adductomics) and advances in statistical methods. Although exposome research strengthens the scientific basis for policy development, there is a need in terms of showing added value for public health to: improve communication of research results to non-scientific audiences; target research to the broader landscape of societal challenges; and draw applicable conclusions. Priorities for future work include the development and standardization of methodologies and technologies for assessing the external and internal exposome, improved data sharing and integration, and the demonstration of the added value of exposome science over conventional approaches in answering priority policy questions.

Using Omics to Understand and Treat Pulmonary Vascular Disease.

PubMed

Hemnes, Anna R

2018-01-01

Pulmonary arterial hypertension (PAH) is a devastating disease for which there is no cure. Presently this condition is differentiated from other diseases of the pulmonary vasculature by a practitioner's history, physical examination, and clinical studies with clinical markers of disease severity primarily guiding therapeutic choices. New technologies such as next generation DNA sequencing, high throughput RNA sequencing, metabolomics and proteomics have greatly enhanced the amount of data that can be studied efficiently in patients with PAH and other rare diseases. There is emerging data on the use of these "Omics" for pulmonary vascular disease classification and diagnosis and also new work that suggests molecular markers, including Omics, may be used to more efficiently match patients to their own most effective therapies. This review focuses on the state of knowledge on molecular classification and treatment of PAH. Strengths and weaknesses of current Omic technologies are discussed and how these new technologies can be used in the future to improve diagnosis of pulmonary vascular disease, more effectively treat patients with existing and future drugs, and generate new understanding of disease pathogenesis and mechanisms underlying treatment success or failure. Bioinformatic methods to analyze the large volumes of data are developing rapidly, but still present major challenges to interpretation of potential Omic findings in pulmonary vascular disease, with low numbers of patients studied and a potentially high false discovery rate. With more experience, precise and established drug response definitions, this field with move forward and will likely be a major component of the clinical care of PH patients in the future.

Integrating multi-omic features exploiting Chromosome Conformation Capture data.

PubMed

Merelli, Ivan; Tordini, Fabio; Drocco, Maurizio; Aldinucci, Marco; Liò, Pietro; Milanesi, Luciano

2015-01-01

The representation, integration, and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture allows the analysis of the chromosome organization in the cell's natural state. While performed genome wide, this technique is usually called Hi-C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi-C data to describe the chromosomal neighborhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.

Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation

NASA Astrophysics Data System (ADS)

Koelmel, Jeremy P.; Kroeger, Nicholas M.; Gill, Emily L.; Ulmer, Candice Z.; Bowden, John A.; Patterson, Rainey E.; Yost, Richard A.; Garrett, Timothy J.

2017-05-01

Untargeted omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced; however, in the burgeoning fields of lipidomics, only a subset of lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the lipidome. Nevertheless, identifications from MS/MS are generally limited by the number of precursors that can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to lipidomic analyses in Red Cross plasma and substantia nigra tissue. Coverage of the lipidome was drastically improved using IE. When applying IE-Omics to Red Cross plasma and substantia nigra lipid extracts in positive ion mode, 69% and 40% more molecular identifications were obtained, respectively. In addition, applying IE-Omics to a lipidomics workflow increased the coverage of trace species, including odd-chained and short-chained diacylglycerides and oxidized lipid species. By increasing the coverage of the lipidome, applying IE to a lipidomics workflow increases the probability of finding biomarkers and provides additional information for determining etiology of disease.

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites

PubMed Central

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K.

2018-01-01

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly. PMID:29470400

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites.

PubMed

Zhang, Bofei; Hu, Senyang; Baskin, Elizabeth; Patt, Andrew; Siddiqui, Jalal K; Mathé, Ewy A

2018-02-22

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly.

Understanding and Designing the Strategies for the Microbe-Mediated Remediation of Environmental Contaminants Using Omics Approaches.

PubMed

Malla, Muneer A; Dubey, Anamika; Yadav, Shweta; Kumar, Ashwani; Hashem, Abeer; Abd Allah, Elsayed Fathi

2018-01-01

Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches.

Molecular signatures from omics data: from chaos to consensus.

PubMed

Sung, Jaeyun; Wang, Yuliang; Chandrasekaran, Sriram; Witten, Daniela M; Price, Nathan D

2012-08-01

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Integrated 'omics analysis reveals new drug-induced mitochondrial perturbations in human hepatocytes.

PubMed

Wolters, Jarno E J; van Breda, Simone G J; Grossmann, Jonas; Fortes, Claudia; Caiment, Florian; Kleinjans, Jos C S

2018-06-01

We performed a multiple 'omics study by integrating data on epigenomic, transcriptomic, and proteomic perturbations associated with mitochondrial dysfunction in primary human hepatocytes caused by the liver toxicant valproic acid (VPA), to deeper understand downstream events following epigenetic alterations in the mitochondrial genome. Furthermore, we investigated persistence of cross-omics changes after terminating drug treatment. Upon transient methylation changes of mitochondrial genes during VPA-treatment, increasing complexities of gene-interaction networks across time were demonstrated, which normalized during washout. Furthermore, co-expression between genes and their corresponding proteins increased across time. Additionally, in relation to persistently decreased ATP production, we observed decreased expression of mitochondrial complex I and III-V genes. Persistent transcripts and proteins were related to citric acid cycle and β-oxidation. In particular, we identified a potential novel mitochondrial-nuclear signaling axis, MT-CO2-FN1-MYC-CPT1. In summary, this cross-omics study revealed dynamic responses of the mitochondrial epigenome to an impulse toxicant challenge resulting in persistent mitochondrial dysfunctioning. Moreover, this approach allowed for discriminating between the toxic effect of VPA and adaptation. Copyright © 2018 Elsevier B.V. All rights reserved.

Omics of the marine medaka (Oryzias melastigma) and its relevance to marine environmental research.

PubMed

Kim, Bo-Mi; Kim, Jaebum; Choi, Ik-Young; Raisuddin, Sheikh; Au, Doris W T; Leung, Kenneth M Y; Wu, Rudolf S S; Rhee, Jae-Sung; Lee, Jae-Seong

2016-02-01

In recent years, the marine medaka (Oryzias melastigma), also known as the Indian medaka or brackish medaka, has been recognized as a model fish species for ecotoxicology and environmental research in the Asian region. O. melastigma has several promising features for research, which include a short generation period (3-4 months), daily spawning, small size (3-4 cm), transparent embryos, sexual dimorphism, and ease of mass culture in the laboratory. There have been extensive transcriptome and genome studies on the marine medaka in the past decade. Such omics data can be useful in understanding the signal transduction pathways of small teleosts in response to environmental stressors. An omics-integrated approach in the study of the marine medaka is important for strengthening its role as a small fish model for marine environmental studies. In this review, we present current omics information about the marine medaka and discuss its potential applications in the study of various molecular pathways that can be targets of marine environmental stressors, such as chemical pollutants. We believe that this review will encourage the use of this small fish as a model species in marine environmental research. Copyright © 2015 Elsevier Ltd. All rights reserved.

Unveiling network-based functional features through integration of gene expression into protein networks.

PubMed

Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali

2018-06-01

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.

Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

PubMed Central

Pathak, Ravi Ramesh; Davé, Vrushank

2014-01-01

Assimilation and integration of “omics” technologies, including genomics, epigenomics, proteomics, and metabolomics has readily altered the landscape of medical research in the last decade. The vast and complex nature of omics data can only be interpreted by linking molecular information at the organismic level, forming the foundation of systems biology. Research in pulmonary biology/medicine has necessitated integration of omics, network, systems and computational biology data to differentially diagnose, interpret, and prognosticate pulmonary diseases, facilitating improvement in therapy and treatment modalities. This review describes how to leverage this emerging technology in understanding pulmonary diseases at the systems level –called a “systomic” approach. Considering the operational wholeness of cellular and organ systems, diseased genome, proteome, and the metabolome needs to be conceptualized at the systems level to understand disease pathogenesis and progression. Currently available omics technology and resources require a certain degree of training and proficiency in addition to dedicated hardware and applications, making them relatively less user friendly for the pulmonary biologist and clinicians. Herein, we discuss the various strategies, computational tools and approaches required to study pulmonary diseases at the systems level for biomedical scientists and clinical researchers. PMID:24802001

The Twins Study: NASA's First Foray into 21st Century Omics Research

NASA Technical Reports Server (NTRS)

Kundrot, C. E.; Shelhamer, M.; Scott, G. B. I.

2015-01-01

The full array of 21st century omics-based research methods should be intelligently employed to reduce the health and performance risks that astronauts will be exposed to during exploration missions beyond low Earth Orbit. In March of 2015, US Astronaut Scott Kelly will launch to the International Space Station for a one year mission while his twin brother, Mark Kelly, a retired US Astronaut, remains on the ground. This situation presents an extremely rare flight opportunity to perform an integrated omics-based demonstration pilot study involving identical twin astronauts. A group of 10 principal investigators has been competitively selected, funded, and teamed together to form the Twins Study. A very broad range of biological function are being examined including the genome, epigenome, transcriptome, proteome, metabolome, gut microbiome, immunological response to vaccinations, indicators of atherosclerosis, physiological fluid shifts, and cognition. The plans for the Twins Study and an overview of initial results will be described as well as the technological and ethical issues raised for such spaceflight studies. An anticipated outcome of the Twins Study is that it will place NASA on a trajectory of using omics-based information to develop precision countermeasures for individual astronauts.

Perspective for Aquaponic Systems: "Omic" Technologies for Microbial Community Analysis.

PubMed

Munguia-Fragozo, Perla; Alatorre-Jacome, Oscar; Rico-Garcia, Enrique; Torres-Pacheco, Irineo; Cruz-Hernandez, Andres; Ocampo-Velazquez, Rosalia V; Garcia-Trejo, Juan F; Guevara-Gonzalez, Ramon G

2015-01-01

Aquaponics is the combined production of aquaculture and hydroponics, connected by a water recirculation system. In this productive system, the microbial community is responsible for carrying out the nutrient dynamics between the components. The nutrimental transformations mainly consist in the transformation of chemical species from toxic compounds into available nutrients. In this particular field, the microbial research, the "Omic" technologies will allow a broader scope of studies about a current microbial profile inside aquaponics community, even in those species that currently are unculturable. This approach can also be useful to understand complex interactions of living components in the system. Until now, the analog studies were made to set up the microbial characterization on recirculation aquaculture systems (RAS). However, microbial community composition of aquaponics is still unknown. "Omic" technologies like metagenomic can help to reveal taxonomic diversity. The perspectives are also to begin the first attempts to sketch the functional diversity inside aquaponic systems and its ecological relationships. The knowledge of the emergent properties inside the microbial community, as well as the understanding of the biosynthesis pathways, can derive in future biotechnological applications. Thus, the aim of this review is to show potential applications of current "Omic" tools to characterize the microbial community in aquaponic systems.

Understanding and Designing the Strategies for the Microbe-Mediated Remediation of Environmental Contaminants Using Omics Approaches

PubMed Central

Malla, Muneer A.; Dubey, Anamika; Yadav, Shweta; Kumar, Ashwani; Hashem, Abeer; Abd_Allah, Elsayed Fathi

2018-01-01

Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches. PMID:29915565

The emergence of molecular profiling and omics techniques in seagrass biology; furthering our understanding of seagrasses.

PubMed

Davey, Peter A; Pernice, Mathieu; Sablok, Gaurav; Larkum, Anthony; Lee, Huey Tyng; Golicz, Agnieszka; Edwards, David; Dolferus, Rudy; Ralph, Peter

2016-09-01

Seagrass meadows are disappearing at alarming rates as a result of increasing coastal development and climate change. The emergence of omics and molecular profiling techniques in seagrass research is timely, providing a new opportunity to address such global issues. Whilst these applications have transformed terrestrial plant research, they have only emerged in seagrass research within the past decade; In this time frame we have observed a significant increase in the number of publications in this nascent field, and as of this year the first genome of a seagrass species has been sequenced. In this review, we focus on the development of omics and molecular profiling and the utilization of molecular markers in the field of seagrass biology. We highlight the advances, merits and pitfalls associated with such technology, and importantly we identify and address the knowledge gaps, which to this day prevent us from understanding seagrasses in a holistic manner. By utilizing the powers of omics and molecular profiling technologies in integrated strategies, we will gain a better understanding of how these unique plants function at the molecular level and how they respond to on-going disturbance and climate change events.

The Emerging Oilseed Crop Sesamum indicum Enters the "Omics" Era.

PubMed

Dossa, Komivi; Diouf, Diaga; Wang, Linhai; Wei, Xin; Zhang, Yanxin; Niang, Mareme; Fonceka, Daniel; Yu, Jingyin; Mmadi, Marie A; Yehouessi, Louis W; Liao, Boshou; Zhang, Xiurong; Cisse, Ndiaga

2017-01-01

Sesame ( Sesamum indicum L.) is one of the oldest oilseed crops widely grown in Africa and Asia for its high-quality nutritional seeds. It is well adapted to harsh environments and constitutes an alternative cash crop for smallholders in developing countries. Despite its economic and nutritional importance, sesame is considered as an orphan crop because it has received very little attention from science. As a consequence, it lags behind the other major oil crops as far as genetic improvement is concerned. In recent years, the scenario has considerably changed with the decoding of the sesame nuclear genome leading to the development of various genomic resources including molecular markers, comprehensive genetic maps, high-quality transcriptome assemblies, web-based functional databases and diverse daft genome sequences. The availability of these tools in association with the discovery of candidate genes and quantitative trait locis for key agronomic traits including high oil content and quality, waterlogging and drought tolerance, disease resistance, cytoplasmic male sterility, high yield, pave the way to the development of some new strategies for sesame genetic improvement. As a result, sesame has graduated from an "orphan crop" to a "genomic resource-rich crop." With the limited research teams working on sesame worldwide, more synergic efforts are needed to integrate these resources in sesame breeding for productivity upsurge, ensuring food security and improved livelihood in developing countries. This review retraces the evolution of sesame research by highlighting the recent advances in the "Omics" area and also critically discusses the future prospects for a further genetic improvement and a better expansion of this crop.

Expanding Omics Resources for Improvement of Soybean Seed Composition Traits

PubMed Central

Chaudhary, Juhi; Patil, Gunvant B.; Sonah, Humira; Deshmukh, Rupesh K.; Vuong, Tri D.; Valliyodan, Babu; Nguyen, Henry T.

2015-01-01

Food resources of the modern world are strained due to the increasing population. There is an urgent need for innovative methods and approaches to augment food production. Legume seeds are major resources of human food and animal feed with their unique nutrient compositions including oil, protein, carbohydrates, and other beneficial nutrients. Recent advances in next-generation sequencing (NGS) together with “omics” technologies have considerably strengthened soybean research. The availability of well annotated soybean genome sequence along with hundreds of identified quantitative trait loci (QTL) associated with different seed traits can be used for gene discovery and molecular marker development for breeding applications. Despite the remarkable progress in these technologies, the analysis and mining of existing seed genomics data are still challenging due to the complexity of genetic inheritance, metabolic partitioning, and developmental regulations. Integration of “omics tools” is an effective strategy to discover key regulators of various seed traits. In this review, recent advances in “omics” approaches and their use in soybean seed trait investigations are presented along with the available databases and technological platforms and their applicability in the improvement of soybean. This article also highlights the use of modern breeding approaches, such as genome-wide association studies (GWAS), genomic selection (GS), and marker-assisted recurrent selection (MARS) for developing superior cultivars. A catalog of available important resources for major seed composition traits, such as seed oil, protein, carbohydrates, and yield traits are provided to improve the knowledge base and future utilization of this information in the soybean crop improvement programs. PMID:26635846

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology

PubMed Central

Yue, Zongliang; Zheng, Qi; Neylon, Michael T; Yoo, Minjae; Shin, Jimin; Zhao, Zhiying; Tan, Aik Choon

2018-01-01

Abstract Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug–gene, miRNA–gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/. PMID:29126216

Unraveling snake venom complexity with 'omics' approaches: challenges and perspectives.

PubMed

Zelanis, André; Tashima, Alexandre Keiji

2014-09-01

The study of snake venom proteomes (venomics) has been experiencing a burst of reports, however the comprehensive knowledge of the dynamic range of proteins present within a single venom, the set of post-translational modifications (PTMs) as well as the lack of a comprehensive database related to venom proteins are among the main challenges in venomics research. The phenotypic plasticity in snake venom proteomes together with their inherent toxin proteoform diversity, points out to the use of integrative analysis in order to better understand their actual complexity. In this regard, such a systems venomics task should encompass the integration of data from transcriptomic and proteomic studies (specially the venom gland proteome), the identification of biological PTMs, and the estimation of artifactual proteomes and peptidomes generated by sample handling procedures. Copyright © 2014 Elsevier Ltd. All rights reserved.

A comprehensive multi-omics approach uncovers adaptations for growth and survival of Pseudomonas aeruginosa on n-alkanes.

PubMed

Grady, Sarah L; Malfatti, Stephanie A; Gunasekera, Thusitha S; Dalley, Brian K; Lyman, Matt G; Striebich, Richard C; Mayhew, Michael B; Zhou, Carol L; Ruiz, Oscar N; Dugan, Larry C

2017-04-28

Examination of complex biological systems has long been achieved through methodical investigation of the system's individual components. While informative, this strategy often leads to inappropriate conclusions about the system as a whole. With the advent of high-throughput "omic" technologies, however, researchers can now simultaneously analyze an entire system at the level of molecule (DNA, RNA, protein, metabolite) and process (transcription, translation, enzyme catalysis). This strategy reduces the likelihood of improper conclusions, provides a framework for elucidation of genotype-phenotype relationships, and brings finer resolution to comparative genomic experiments. Here, we apply a multi-omic approach to analyze the gene expression profiles of two closely related Pseudomonas aeruginosa strains grown in n-alkanes or glycerol. The environmental P. aeruginosa isolate ATCC 33988 consumed medium-length (C 10 -C 16 ) n-alkanes more rapidly than the laboratory strain PAO1, despite high genome sequence identity (average nucleotide identity >99%). Our data shows that ATCC 33988 induces a characteristic set of genes at the transcriptional, translational and post-translational levels during growth on alkanes, many of which differ from those expressed by PAO1. Of particular interest was the lack of expression from the rhl operon of the quorum sensing (QS) system, resulting in no measurable rhamnolipid production by ATCC 33988. Further examination showed that ATCC 33988 lacked the entire lasI/lasR arm of the QS response. Instead of promoting expression of QS genes, ATCC 33988 up-regulates a small subset of its genome, including operons responsible for specific alkaline proteases and sphingosine metabolism. This work represents the first time results from RNA-seq, microarray, ribosome footprinting, proteomics, and small molecule LC-MS experiments have been integrated to compare gene expression in bacteria. Together, these data provide insights as to why strain ATCC 33988 is better adapted for growth and survival on n-alkanes.

FirebrowseR: an R client to the Broad Institute’s Firehose Pipeline

PubMed Central

Deng, Mario; Brägelmann, Johannes; Kryukov, Ivan; Saraiva-Agostinho, Nuno; Perner, Sven

2017-01-01

With its Firebrowse service (http://firebrowse.org/) the Broad Institute is making large-scale multi-platform omics data analysis results publicly available through a Representational State Transfer (REST) Application Programmable Interface (API). Querying this database through an API client from an arbitrary programming environment is an essential task, allowing other developers and researchers to focus on their analysis and avoid data wrangling. Hence, as a first result, we developed a workflow to automatically generate, test and deploy such clients for rapid response to API changes. Its underlying infrastructure, a combination of free and publicly available web services, facilitates the development of API clients. It decouples changes in server software from the client software by reacting to changes in the RESTful service and removing direct dependencies on a specific implementation of an API. As a second result, FirebrowseR, an R client to the Broad Institute’s RESTful Firehose Pipeline, is provided as a working example, which is built by the means of the presented workflow. The package’s features are demonstrated by an example analysis of cancer gene expression data. Database URL: https://github.com/mariodeng/ PMID:28062517

Clustering of 3D-Structure Similarity Based Network of Secondary Metabolites Reveals Their Relationships with Biological Activities.

PubMed

Ohtana, Yuki; Abdullah, Azian Azamimi; Altaf-Ul-Amin, Md; Huang, Ming; Ono, Naoaki; Sato, Tetsuo; Sugiura, Tadao; Horai, Hisayuki; Nakamura, Yukiko; Morita Hirai, Aki; Lange, Klaus W; Kibinge, Nelson K; Katsuragi, Tetsuo; Shirai, Tsuyoshi; Kanaya, Shigehiko

2014-12-01

Developing database systems connecting diverse species based on omics is the most important theme in big data biology. To attain this purpose, we have developed KNApSAcK Family Databases, which are utilized in a number of researches in metabolomics. In the present study, we have developed a network-based approach to analyze relationships between 3D structure and biological activity of metabolites consisting of four steps as follows: construction of a network of metabolites based on structural similarity (Step 1), classification of metabolites into structure groups (Step 2), assessment of statistically significant relations between structure groups and biological activities (Step 3), and 2-dimensional clustering of the constructed data matrix based on statistically significant relations between structure groups and biological activities (Step 4). Applying this method to a data set consisting of 2072 secondary metabolites and 140 biological activities reported in KNApSAcK Metabolite Activity DB, we obtained 983 statistically significant structure group-biological activity pairs. As a whole, we systematically analyzed the relationship between 3D-chemical structures of metabolites and biological activities. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens.

PubMed

Sosa, Ezequiel J; Burguener, Germán; Lanzarotti, Esteban; Defelipe, Lucas; Radusky, Leandro; Pardo, Agustín M; Marti, Marcelo; Turjanski, Adrián G; Fernández Do Porto, Darío

2018-01-04

Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

BRISK--research-oriented storage kit for biology-related data.

PubMed

Tan, Alan; Tripp, Ben; Daley, Denise

2011-09-01

In genetic science, large-scale international research collaborations represent a growing trend. These collaborations have demanding and challenging database, storage, retrieval and communication needs. These studies typically involve demographic and clinical data, in addition to the results from numerous genomic studies (omics studies) such as gene expression, eQTL, genome-wide association and methylation studies, which present numerous challenges, thus the need for data integration platforms that can handle these complex data structures. Inefficient methods of data transfer and access control still plague research collaboration. As science becomes more and more collaborative in nature, the need for a system that adequately manages data sharing becomes paramount. Biology-Related Information Storage Kit (BRISK) is a package of several web-based data management tools that provide a cohesive data integration and management platform. It was specifically designed to provide the architecture necessary to promote collaboration and expedite data sharing between scientists. The software, documentation, Java source code and demo are available at http://genapha.icapture.ubc.ca/brisk/index.jsp. BRISK was developed in Java, and tested on an Apache Tomcat 6 server with a MySQL database. denise.daley@hli.ubc.ca.

Pharmacodynamics and Systems Pharmacology Approaches to Repurposing Drugs in the Wake of Global Health Burden.

PubMed

Bai, Jane P F

2016-10-01

There are emergent needs for cost-effective treatment worldwide, for which repurposing to develop a drug with existing marketing approval of disease(s) for new disease(s) is a valid option. Although strategic mining of electronic health records has produced real-world evidences to inform drug repurposing, using omics data (drug and disease), knowledge base of protein interactions, and database of transcription factors have been explored. Structured integration of all the existing data under the framework of drug repurposing will facilitate decision making. The ability to foresee the need to integrate new data types produced by emergent technologies and to enable data connectivity in the context of human biology and targeted diseases, as well as to use the existing crucial quality data of all approved drugs will catapult the number of drugs being successfully repurposed. However, translational pharmacodynamics databases for modeling information across human biology in the context of host factors are lacking and are critically needed for drug repurposing to improve global public health, especially for the efforts to combat neglected tropic diseases as well as emergent infectious diseases such as Zika or Ebola virus. Published by Elsevier Inc.

FirebrowseR: an R client to the Broad Institute's Firehose Pipeline.

PubMed

Deng, Mario; Brägelmann, Johannes; Kryukov, Ivan; Saraiva-Agostinho, Nuno; Perner, Sven

2017-01-01

With its Firebrowse service (http://firebrowse.org/) the Broad Institute is making large-scale multi-platform omics data analysis results publicly available through a Representational State Transfer (REST) Application Programmable Interface (API). Querying this database through an API client from an arbitrary programming environment is an essential task, allowing other developers and researchers to focus on their analysis and avoid data wrangling. Hence, as a first result, we developed a workflow to automatically generate, test and deploy such clients for rapid response to API changes. Its underlying infrastructure, a combination of free and publicly available web services, facilitates the development of API clients. It decouples changes in server software from the client software by reacting to changes in the RESTful service and removing direct dependencies on a specific implementation of an API. As a second result, FirebrowseR, an R client to the Broad Institute's RESTful Firehose Pipeline, is provided as a working example, which is built by the means of the presented workflow. The package's features are demonstrated by an example analysis of cancer gene expression data.Database URL: https://github.com/mariodeng/. © The Author(s) 2017. Published by Oxford University Press.

Benchmarking Water Quality from Wastewater to Drinking Waters Using Reduced Transcriptome of Human Cells.

PubMed

Xia, Pu; Zhang, Xiaowei; Zhang, Hanxin; Wang, Pingping; Tian, Mingming; Yu, Hongxia

2017-08-15

One of the major challenges in environmental science is monitoring and assessing the risk of complex environmental mixtures. In vitro bioassays with limited key toxicological end points have been shown to be suitable to evaluate mixtures of organic pollutants in wastewater and recycled water. Omics approaches such as transcriptomics can monitor biological effects at the genome scale. However, few studies have applied omics approach in the assessment of mixtures of organic micropollutants. Here, an omics approach was developed for profiling bioactivity of 10 water samples ranging from wastewater to drinking water in human cells by a reduced human transcriptome (RHT) approach and dose-response modeling. Transcriptional expression of 1200 selected genes were measured by an Ampliseq technology in two cell lines, HepG2 and MCF7, that were exposed to eight serial dilutions of each sample. Concentration-effect models were used to identify differentially expressed genes (DEGs) and to calculate effect concentrations (ECs) of DEGs, which could be ranked to investigate low dose response. Furthermore, molecular pathways disrupted by different samples were evaluated by Gene Ontology (GO) enrichment analysis. The ability of RHT for representing bioactivity utilizing both HepG2 and MCF7 was shown to be comparable to the results of previous in vitro bioassays. Finally, the relative potencies of the mixtures indicated by RHT analysis were consistent with the chemical profiles of the samples. RHT analysis with human cells provides an efficient and cost-effective approach to benchmarking mixture of micropollutants and may offer novel insight into the assessment of mixture toxicity in water.

Comprehensive two-dimensional gas chromatography and food sensory properties: potential and challenges.

PubMed

Cordero, Chiara; Kiefl, Johannes; Schieberle, Peter; Reichenbach, Stephen E; Bicchi, Carlo

2015-01-01

Modern omics disciplines dealing with food flavor focus the analytical efforts on the elucidation of sensory-active compounds, including all possible stimuli of multimodal perception (aroma, taste, texture, etc.) by means of a comprehensive, integrated treatment of sample constituents, such as physicochemical properties, concentration in the matrix, and sensory properties (odor/taste quality, perception threshold). Such analyses require detailed profiling of known bioactive components as well as advanced fingerprinting techniques to catalog sample constituents comprehensively, quantitatively, and comparably across samples. Multidimensional analytical platforms support comprehensive investigations required for flavor analysis by combining information on analytes' identities, physicochemical behaviors (volatility, polarity, partition coefficient, and solubility), concentration, and odor quality. Unlike other omics, flavor metabolomics and sensomics include the final output of the biological phenomenon (i.e., sensory perceptions) as an additional analytical dimension, which is specifically and exclusively triggered by the chemicals analyzed. However, advanced omics platforms, which are multidimensional by definition, pose challenging issues not only in terms of coupling with detection systems and sample preparation, but also in terms of data elaboration and processing. The large number of variables collected during each analytical run provides a high level of information, but requires appropriate strategies to exploit fully this potential. This review focuses on advances in comprehensive two-dimensional gas chromatography and analytical platforms combining two-dimensional gas chromatography with olfactometry, chemometrics, and quantitative assays for food sensory analysis to assess the quality of a given product. We review instrumental advances and couplings, automation in sample preparation, data elaboration, and a selection of applications.

Database Resources of the BIG Data Center in 2018.

PubMed

2018-01-04

The BIG Data Center at Beijing Institute of Genomics (BIG) of the Chinese Academy of Sciences provides freely open access to a suite of database resources in support of worldwide research activities in both academia and industry. With the vast amounts of omics data generated at ever-greater scales and rates, the BIG Data Center is continually expanding, updating and enriching its core database resources through big-data integration and value-added curation, including BioCode (a repository archiving bioinformatics tool codes), BioProject (a biological project library), BioSample (a biological sample library), Genome Sequence Archive (GSA, a data repository for archiving raw sequence reads), Genome Warehouse (GWH, a centralized resource housing genome-scale data), Genome Variation Map (GVM, a public repository of genome variations), Gene Expression Nebulas (GEN, a database of gene expression profiles based on RNA-Seq data), Methylation Bank (MethBank, an integrated databank of DNA methylomes), and Science Wikis (a series of biological knowledge wikis for community annotations). In addition, three featured web services are provided, viz., BIG Search (search as a service; a scalable inter-domain text search engine), BIG SSO (single sign-on as a service; a user access control system to gain access to multiple independent systems with a single ID and password) and Gsub (submission as a service; a unified submission service for all relevant resources). All of these resources are publicly accessible through the home page of the BIG Data Center at http://bigd.big.ac.cn. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Lipid Identification by Untargeted Tandem Mass Spectrometry Coupled with Ultra-High-Pressure Liquid Chromatography.

PubMed

Gugiu, Gabriel B

2017-01-01

Lipidomics refers to the large-scale study of lipids in biological systems (Wenk, Nat Rev Drug Discov 4(7):594-610, 2005; Rolim et al., Gene 554(2):131-139, 2015). From a mass spectrometric point of view, by lipidomics we understand targeted or untargeted mass spectrometric analysis of lipids using either liquid chromatography (LC) (Castro-Perez et al., J Proteome Res 9(5):2377-2389, 2010) or shotgun (Han and Gross, Mass Spectrom Rev 24(3):367-412, 2005) approaches coupled with tandem mass spectrometry. This chapter describes the former methodology, which is becoming rapidly the preferred method for lipid identification owing to similarities with established omics workflows, such as proteomics (Washburn et al., Nat Biotechnol 19(3):242-247, 2001) or genomics (Yadav, J Biomol Tech: JBT 18(5):277, 2007). The workflow described consists in lipid extraction using a modified Bligh and Dyer method (Bligh and Dyer, Can J Biochem Physiol 37(8):911-917, 1959), ultra high pressure liquid chromatography fractionation of lipid samples on a reverse phase C18 column, followed by tandem mass spectrometric analysis and in silico database search for lipid identification based on MSMS spectrum matching (Kind et al., Nat Methods 10(8):755-758, 2013; Yamada et al., J Chromatogr A 1292:211-218, 2013; Taguchi and Ishikawa, J Chromatogr A 1217(25):4229-4239, 2010; Peake et al., Thermoscientifices 1-3, 2015) and accurate mass of parent ion (Sud et al., Nucleic Acids Res 35(database issue):D527-D532, 2007; Wishart et al., Nucleic Acids Res 35(database):D521-D526, 2007).

A semantic proteomics dashboard (SemPoD) for data management in translational research.

PubMed

Jayapandian, Catherine P; Zhao, Meng; Ewing, Rob M; Zhang, Guo-Qiang; Sahoo, Satya S

2012-01-01

One of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of 'omics data with clinical information to complete the cycle from the bench to the bedside. The role of contextual metadata, also called provenance information, is a key factor ineffective data integration, reproducibility of results, correct attribution of original source, and answering research queries involving "What", "Where", "When", "Which", "Who", "How", and "Why" (also known as the W7 model). But, at present there is limited or no effective approach to managing and leveraging provenance information for integrating data across studies or projects. Hence, there is an urgent need for a paradigm shift in creating a "provenance-aware" informatics platform to address this challenge. We introduce an ontology-driven, intuitive Semantic Proteomics Dashboard (SemPoD) that uses provenance together with domain information (semantic provenance) to enable researchers to query, compare, and correlate different types of data across multiple projects, and allow integration with legacy data to support their ongoing research. The SemPoD platform, currently in use at the Case Center for Proteomics and Bioinformatics (CPB), consists of three components: (a) Ontology-driven Visual Query Composer, (b) Result Explorer, and (c) Query Manager. Currently, SemPoD allows provenance-aware querying of 1153 mass-spectrometry experiments from 20 different projects. SemPod uses the systems molecular biology provenance ontology (SysPro) to support a dynamic query composition interface, which automatically updates the components of the query interface based on previous user selections and efficiently prunes the result set usinga "smart filtering" approach. The SysPro ontology re-uses terms from the PROV-ontology (PROV-O) being developed by the World Wide Web Consortium (W3C) provenance working group, the minimum information required for reporting a molecular interaction experiment (MIMIx), and the minimum information about a proteomics experiment (MIAPE) guidelines. The SemPoD was evaluated both in terms of user feedback and as scalability of the system. SemPoD is an intuitive and powerful provenance ontology-driven data access and query platform that uses the MIAPE and MIMIx metadata guideline to create an integrated view over large-scale systems molecular biology datasets. SemPoD leverages the SysPro ontology to create an intuitive dashboard for biologists to compose queries, explore the results, and use a query manager for storing queries for later use. SemPoD can be deployed over many existing database applications storing 'omics data, including, as illustrated here, the LabKey data-management system. The initial user feedback evaluating the usability and functionality of SemPoD has been very positive and it is being considered for wider deployment beyond the proteomics domain, and in other 'omics' centers.

The impact of post-genomics approaches in neurodegenerative demyelinating diseases: the case of Guillain-Barré syndrome.

PubMed

Villar, Margarita; Mateos-Hernandez, Lourdes; de la Fuente, Jose

2018-03-14

Why an autoimmune disease that is the main cause of the acute neuromuscular paralysis worldwide has not yet a well-characterized cause or an effective treatment? The existence of different clinical variants for the Guillain-Barré syndrome (GBS) coupled with the fact that a high number of pathogens can cause an infection that sometimes, but not always, precedes the development of the syndrome, confers a high degree of uncertainty for both prognosis and treatment. In the post-genomic era, the development of omics technologies for the high-throughput analysis of biological molecules is allowing the characterization of biological systems in a degree of depth unimaginable before. In this context, this work summarize the application of post-genomics technologies to the study of GBS. We performed a structured search of bibliographic databases for peer-reviewed research literature to outline the state of the art with regard the application of post-genomics technologies to the study of GBS. The quality of retrieved papers was assessed using standard tools and thirty-four were included in the review. To date, transcriptomics and proteomics have been the unique post-genomics approaches applied to GBS study. Most of these studies have been performed on cerebrospinal fluid samples and only few studies have been conducted with other samples such as serum, Schwann cells and human peripheral nerve. In the post-genomics era, transcriptomics and proteomics have shown the possibilities that omics technologies can offer for a better understanding of the immunological and pathological mechanisms involved in GBS and the identification of potential biomarkers, but these results have only shown the tip of the iceberg and there is still a long way to exploit the full potential that post-genomics approaches could offer to the study of the GBS. The integration of different omics datasets through a systems biology approach could allow network-based analyses to describe the complexity and functionality of the molecular mechanisms involved in the course of disease facilitating the discovery of novel biomarkers that could be used to improve the diagnosis, predict the disease progression, improve our understanding of the pathology, and serve as therapeutic targets for GBS. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction

PubMed Central

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Objective Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Methods Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Results Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Conclusions Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. PMID:25002459

Knowledge boosting: a graph-based integration approach with multi-omics data and genomic knowledge for cancer clinical outcome prediction.

PubMed

Kim, Dokyoon; Joung, Je-Gun; Sohn, Kyung-Ah; Shin, Hyunjung; Park, Yu Rang; Ritchie, Marylyn D; Kim, Ju Han

2015-01-01

Cancer can involve gene dysregulation via multiple mechanisms, so no single level of genomic data fully elucidates tumor behavior due to the presence of numerous genomic variations within or between levels in a biological system. We have previously proposed a graph-based integration approach that combines multi-omics data including copy number alteration, methylation, miRNA, and gene expression data for predicting clinical outcome in cancer. However, genomic features likely interact with other genomic features in complex signaling or regulatory networks, since cancer is caused by alterations in pathways or complete processes. Here we propose a new graph-based framework for integrating multi-omics data and genomic knowledge to improve power in predicting clinical outcomes and elucidate interplay between different levels. To highlight the validity of our proposed framework, we used an ovarian cancer dataset from The Cancer Genome Atlas for predicting stage, grade, and survival outcomes. Integrating multi-omics data with genomic knowledge to construct pre-defined features resulted in higher performance in clinical outcome prediction and higher stability. For the grade outcome, the model with gene expression data produced an area under the receiver operating characteristic curve (AUC) of 0.7866. However, models of the integration with pathway, Gene Ontology, chromosomal gene set, and motif gene set consistently outperformed the model with genomic data only, attaining AUCs of 0.7873, 0.8433, 0.8254, and 0.8179, respectively. Integrating multi-omics data and genomic knowledge to improve understanding of molecular pathogenesis and underlying biology in cancer should improve diagnostic and prognostic indicators and the effectiveness of therapies. © The Author 2014. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Past, Present, and Future of Informed Consent in Pain and Genomics Research: Challenges Facing Global Medical Community.

PubMed

Compagnone, Christian; Schatman, Michael E; Rauck, Richard L; Van Zundert, Jan; Kraus, Monika; Primorac, Dragan; Williams, Frances; Allegri, Massimo; Saccani Jordi, Gloria; Fanelli, Guido

2017-01-01

In recent decades, there has been a revision of the role of institutional review boards with the intention of protecting human subjects from harm and exploitation in research. Informed consent aims to protect the subject by explaining all of the benefits and risks associated with a specific research project. To date, there has not been a review published analyzing issues of informed consent in research in the field of genetic/Omics in subjects with chronic pain, and the current review aims to fill that gap in the ethical aspects of such investigation. Despite the extensive discussion on ethical challenges unique to the field of genetic/Omics, this is the first attempt at addressing ethical challenges regarding Informed Consent Forms for pain research as the primary focus. We see this contribution as an important one, for while ethical issues are too often ignored in pain research in general, the numerous arising ethical issues that are unique to pain genetic/Omics suggest that researchers in the field need to pay even greater attention to the rights of subjects/patients. This article presents the work of the Ethic Committee of the Pain-Omics Group (www.painomics.eu), a consortium of 11 centers that is running the Pain-Omics project funded by the European Community in the 7th Framework Program theme (HEALTH.2013.2.2.1-5-Understanding and controlling pain). The Ethic Committee is composed of 1 member of each group of the consortium as well as key opinion leaders in the field of ethics and pain more generally. © 2016 The Authors. Pain Practice published by Wiley Periodicals, Inc. on behalf of World Institute of Pain.

In Silico Computational Transcriptomics Reveals Novel Endocrine Disruptors in Largemouth Bass ( Micropterus salmoides).

PubMed

Basili, Danilo; Zhang, Ji-Liang; Herbert, John; Kroll, Kevin; Denslow, Nancy D; Martyniuk, Christopher J; Falciani, Francesco; Antczak, Philipp

2018-06-15

In recent years, decreases in fish populations have been attributed, in part, to the effect of environmental chemicals on ovarian development. To understand the underlying molecular events we developed a dynamic model of ovary development linking gene transcription to key physiological end points, such as gonadosomatic index (GSI), plasma levels of estradiol (E2) and vitellogenin (VTG), in largemouth bass ( Micropterus salmoides). We were able to identify specific clusters of genes, which are affected at different stages of ovarian development. A subnetwork was identified that closely linked gene expression and physiological end points and by interrogating the Comparative Toxicogenomic Database (CTD), quercetin and tretinoin (ATRA) were identified as two potential candidates that may perturb this system. Predictions were validated by investigation of reproductive associated transcripts using qPCR in ovary and in the liver of both male and female largemouth bass treated after a single injection of quercetin and tretinoin (10 and 100 μg/kg). Both compounds were found to significantly alter the expression of some of these genes. Our findings support the use of omics and online repositories for identification of novel, yet untested, compounds. This is the first study of a dynamic model that links gene expression patterns across stages of ovarian development.

Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.

PubMed

Miannay, Bertrand; Minvielle, Stéphane; Magrangeas, Florence; Guziolowski, Carito

2018-03-21

The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles.

The emerging CHO systems biology era: harnessing the 'omics revolution for biotechnology.

PubMed

Kildegaard, Helene Faustrup; Baycin-Hizal, Deniz; Lewis, Nathan E; Betenbaugh, Michael J

2013-12-01

Chinese hamster ovary (CHO) cells are the primary factories for biopharmaceuticals because of their capacity to correctly fold and post-translationally modify recombinant proteins compatible with humans. New opportunities are arising to enhance these cell factories, especially since the CHO-K1 cell line was recently sequenced. Now, the CHO systems biology era is underway. Critical 'omics data sets, including proteomics, transcriptomics, metabolomics, fluxomics, and glycomics, are emerging, allowing the elucidation of the molecular basis of CHO cell physiology. The incorporation of these data sets into mathematical models that describe CHO phenotypes will provide crucial biotechnology insights. As 'omics technologies and computational systems biology mature, genome-scale approaches will lead to major innovations in cell line development and metabolic engineering, thereby improving protein production and bioprocessing. Copyright © 2013 Elsevier Ltd. All rights reserved.

Tools for the functional interpretation of metabolomic experiments.

PubMed

Chagoyen, Monica; Pazos, Florencio

2013-11-01

The so-called 'omics' approaches used in modern biology aim at massively characterizing the molecular repertories of living systems at different levels. Metabolomics is one of the last additions to the 'omics' family and it deals with the characterization of the set of metabolites in a given biological system. As metabolomic techniques become more massive and allow characterizing larger sets of metabolites, automatic methods for analyzing these sets in order to obtain meaningful biological information are required. Only recently the first tools specifically designed for this task in metabolomics appeared. They are based on approaches previously used in transcriptomics and other 'omics', such as annotation enrichment analysis. These, together with generic tools for metabolic analysis and visualization not specifically designed for metabolomics will for sure be in the toolbox of the researches doing metabolomic experiments in the near future.

Omics-Based Identification of Biomarkers for Nasopharyngeal Carcinoma

PubMed Central

2015-01-01

Nasopharyngeal carcinoma (NPC) is a head and neck cancer that is highly found in distinct geographic areas, such as Southeast Asia. The management of NPC remains burdensome as the prognosis is poor due to the late presentation of the disease and the complex nature of NPC pathogenesis. Therefore, it is necessary to find effective molecular markers for early detection and therapeutic measure of NPC. In this paper, the discovery of molecular biomarker for NPC through the emerging omics technologies including genomics, miRNA-omics, transcriptomics, proteomics, and metabolomics will be extensively reviewed. These markers have been shown to play roles in various cellular pathways in NPC progression. The knowledge on their function will help us understand in more detail the complexity in tumor biology, leading to the better strategies for early detection, outcome prediction, detection of disease recurrence, and therapeutic approach. PMID:25999660

Computational Omics Pre-Awardees | Office of Cancer Clinical Proteomics Research

Cancer.gov

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) is pleased to announce the pre-awardees of the Computational Omics solicitation. Working with NVIDIA Foundation's Compute the Cure initiative and Leidos Biomedical Research Inc., the NCI, through this solicitation, seeks to leverage computational efforts to provide tools for the mining and interpretation of large-scale publicly available ‘omics’ datasets.

Data Standards for Omics Data: The Basis of Data Sharing and Reuse

PubMed Central

Chervitz, Stephen A.; Deutsch, Eric W.; Field, Dawn; Parkinson, Helen; Quackenbush, John; Rocca-Serra, Phillipe; Sansone, Susanna-Assunta; Stoeckert, Christian J.; Taylor, Chris F.; Taylor, Ronald; Ball, Catherine A.

2014-01-01

To facilitate sharing of Omics data, many groups of scientists have been working to establish the relevant data standards. The main components of data sharing standards are experiment description standards, data exchange standards, terminology standards, and experiment execution standards. Here we provide a survey of existing and emerging standards that are intended to assist the free and open exchange of large-format data. PMID:21370078

Advances in Integrating Traditional and Omic Biomarkers When Analyzing the Effects of the Mediterranean Diet Intervention in Cardiovascular Prevention

PubMed Central

Fitó, Montserrat; Melander, Olle; Martínez, José Alfredo; Toledo, Estefanía; Carpéné, Christian; Corella, Dolores

2016-01-01

Intervention with Mediterranean diet (MedDiet) has provided a high level of evidence in primary prevention of cardiovascular events. Besides enhancing protection from classical risk factors, an improvement has also been described in a number of non-classical ones. Benefits have been reported on biomarkers of oxidation, inflammation, cellular adhesion, adipokine production, and pro-thrombotic state. Although the benefits of the MedDiet have been attributed to its richness in antioxidants, the mechanisms by which it exercises its beneficial effects are not well known. It is thought that the integration of omics including genomics, transcriptomics, epigenomics, and metabolomics, into studies analyzing nutrition and cardiovascular diseases will provide new clues regarding these mechanisms. However, omics integration is still in its infancy. Currently, some single-omics analyses have provided valuable data, mostly in the field of genomics. Thus, several gene-diet interactions in determining both intermediate (plasma lipids, etc.) and final cardiovascular phenotypes (stroke, myocardial infarction, etc.) have been reported. However, few studies have analyzed changes in gene expression and, moreover very few have focused on epigenomic or metabolomic biomarkers related to the MedDiet. Nevertheless, these preliminary results can help to better understand the inter-individual differences in cardiovascular risk and dietary response for further applications in personalized nutrition. PMID:27598147

Systems Biology Approaches for Host–Fungal Interactions: An Expanding Multi-Omics Frontier

PubMed Central

Culibrk, Luka; Croft, Carys A.

2016-01-01

Abstract Opportunistic fungal infections are an increasing threat for global health, and for immunocompromised patients in particular. These infections are characterized by interaction between fungal pathogen and host cells. The exact mechanisms and the attendant variability in host and fungal pathogen interaction remain to be fully elucidated. The field of systems biology aims to characterize a biological system, and utilize this knowledge to predict the system's response to stimuli such as fungal exposures. A multi-omics approach, for example, combining data from genomics, proteomics, metabolomics, would allow a more comprehensive and pan-optic “two systems” biology of both the host and the fungal pathogen. In this review and literature analysis, we present highly specialized and nascent methods for analysis of multiple -omes of biological systems, in addition to emerging single-molecule visualization techniques that may assist in determining biological relevance of multi-omics data. We provide an overview of computational methods for modeling of gene regulatory networks, including some that have been applied towards the study of an interacting host and pathogen. In sum, comprehensive characterizations of host–fungal pathogen systems are now possible, and utilization of these cutting-edge multi-omics strategies may yield advances in better understanding of both host biology and fungal pathogens at a systems scale. PMID:26885725

Evaluation of diversity among common beans (Phaseolus vulgaris L.) from two centers of domestication using 'omics' technologies

PubMed Central

2010-01-01

Background Genetic diversity among wild accessions and cultivars of common bean (Phaseolus vulgaris L.) has been characterized using plant morphology, seed protein allozymes, random amplified polymorphic DNA, restriction fragment length polymorphisms, DNA sequence analysis, chloroplast DNA, and microsatellite markers. Yet, little is known about whether these traits, which distinguish among genetically distinct types of common bean, can be evaluated using omics technologies. Results Three 'omics' approaches: transcriptomics, proteomics, and metabolomics were used to qualitatively evaluate the diversity of common bean from two Centers of Domestication (COD). All three approaches were able to classify common bean according to their COD using unsupervised analyses; these findings are consistent with the hypothesis that differences exist in gene transcription, protein expression, and synthesis and metabolism of small molecules among common bean cultivars representative of different COD. Metabolomic analyses of multiple cultivars within two common bean gene pools revealed cultivar differences in small molecules that were of sufficient magnitude to allow identification of unique cultivar fingerprints. Conclusions Given the high-throughput and low cost of each of these 'omics' platforms, significant opportunities exist for their use in the rapid identification of traits of agronomic and nutritional importance as well as to characterize genetic diversity. PMID:21126341

Multi-omics approach to elucidate the gut microbiota activity: Metaproteomics and metagenomics connection.

PubMed

Guirro, Maria; Costa, Andrea; Gual-Grau, Andreu; Mayneris-Perxachs, Jordi; Torrell, Helena; Herrero, Pol; Canela, Núria; Arola, Lluís

2018-02-10

Over the last few years, the application of high-throughput meta-omics methods has provided great progress in improving the knowledge of the gut ecosystem and linking its biodiversity to host health conditions, offering complementary support to classical microbiology. Gut microbiota plays a crucial role in relevant diseases such as obesity or cardiovascular disease (CVD), and its regulation is closely influenced by several factors, such as dietary composition. In fact, polyphenol-rich diets are the most palatable treatment to prevent hypertension associated with CVD, although the polyphenol-microbiota interactions have not been completely elucidated. For this reason, the aim of this study was to evaluate microbiota effect in obese rats supplemented by hesperidin, after being fed with cafeteria or standard diet, using a multi meta-omics approaches combining strategy of metagenomics and metaproteomics analysis. We reported that cafeteria diet induces obesity, resulting in changes in the microbiota composition, which are related to functional alterations at proteome level. In addition, hesperidin supplementation alters microbiota diversity and also proteins involved in important metabolic pathways. Overall, going deeper into strategies to integrate omics sciences is necessary to understand the complex relationships between the host, gut microbiota, and diet. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap

PubMed Central

Xing, Eric P.; Curtis, Ross E.; Schoenherr, Georg; Lee, Seunghak; Yin, Junming; Puniyani, Kriti; Wu, Wei; Kinnaird, Peter

2014-01-01

With the continuous improvement in genotyping and molecular phenotyping technology and the decreasing typing cost, it is expected that in a few years, more and more clinical studies of complex diseases will recruit thousands of individuals for pan-omic genetic association analyses. Hence, there is a great need for algorithms and software tools that could scale up to the whole omic level, integrate different omic data, leverage rich structure information, and be easily accessible to non-technical users. We present GenAMap, an interactive analytics software platform that 1) automates the execution of principled machine learning methods that detect genome- and phenome-wide associations among genotypes, gene expression data, and clinical or other macroscopic traits, and 2) provides new visualization tools specifically designed to aid in the exploration of association mapping results. Algorithmically, GenAMap is based on a new paradigm for GWAS and PheWAS analysis, termed structured association mapping, which leverages various structures in the omic data. We demonstrate the function of GenAMap via a case study of the Brem and Kruglyak yeast dataset, and then apply it on a comprehensive eQTL analysis of the NIH heterogeneous stock mice dataset and report some interesting findings. GenAMap is available from http://sailing.cs.cmu.edu/genamap. PMID:24905018

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome

PubMed Central

2012-01-01

Background We present the Biological Observation Matrix (BIOM, pronounced “biome”) format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the “ome-ome”) grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses. Findings The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages. Conclusions The BIOM file format and the biom-format project are steps toward reducing the “bioinformatics bottleneck” that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium. PMID:23587224

Recent "omics" advances in Helicobacter pylori.

PubMed

Berthenet, Elvire; Sheppard, Sam; Vale, Filipa F

2016-09-01

The development of high-throughput whole genome sequencing (WGS) technologies is changing the face of microbiology, facilitating the comparison of large numbers of genomes from different lineages of a same organism. Our aim was to review the main advances on Helicobacter pylori "omics" and to understand how this is improving our knowledge of the biology, diversity and pathogenesis of H. pylori. Since the first H. pylori isolate was sequenced in 1997, 510 genomes have been deposited in the NCBI archive, providing a basis for improved understanding of the epidemiology and evolution of this important pathogen. This review focuses on works published between April 2015 and March 2016. Helicobacter "omics" is already making an impact and is a growing research field. Ultimately these advances will be translated into a routine clinical laboratory setting in order to improve public health. © 2016 John Wiley & Sons Ltd.

Metabolomics in Sepsis and Its Impact on Public Health.

PubMed

Evangelatos, Nikolaos; Bauer, Pia; Reumann, Matthias; Satyamoorthy, Kapaettu; Lehrach, Hans; Brand, Angela

2017-01-01

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software. © 2018 S. Karger AG, Basel.

Permafrost Meta-Omics and Climate Change

NASA Astrophysics Data System (ADS)

Mackelprang, Rachel; Saleska, Scott R.; Jacobsen, Carsten Suhr; Jansson, Janet K.; Taş, Neslihan

2016-06-01

Permanently frozen soil, or permafrost, covers a large portion of the Earth's terrestrial surface and represents a unique environment for cold-adapted microorganisms. As permafrost thaws, previously protected organic matter becomes available for microbial degradation. Microbes that decompose soil carbon produce carbon dioxide and other greenhouse gases, contributing substantially to climate change. Next-generation sequencing and other -omics technologies offer opportunities to discover the mechanisms by which microbial communities regulate the loss of carbon and the emission of greenhouse gases from thawing permafrost regions. Analysis of nucleic acids and proteins taken directly from permafrost-associated soils has provided new insights into microbial communities and their functions in Arctic environments that are increasingly impacted by climate change. In this article we review current information from various molecular -omics studies on permafrost microbial ecology and explore the relevance of these insights to our current understanding of the dynamics of permafrost loss due to climate change.

The Omics Revolution in Agricultural Research.

PubMed

Van Emon, Jeanette M

2016-01-13

The Agrochemicals Division cosponsored the 13th International Union of Pure and Applied Chemistry International Congress of Pesticide Chemistry held as part of the 248th National Meeting and Exposition of the American Chemical Society in San Francisco, CA, USA, August 10-14, 2014. The topic of the Congress was Crop, Environment, and Public Health Protection; Technologies for a Changing World. Over 1000 delegates participated in the Congress with interactive scientific programming in nine major topic areas including the challenges and opportunities of agricultural biotechnology. Plenary speakers addressed global issues related to the Congress theme prior to the daily technical sessions. The plenary lecture addressing the challenges and opportunities that omic technologies provide agricultural research is presented here. The plenary lecture provided the diverse audience with information on a complex subject to stimulate research ideas and provide a glimpse of the impact of omics on agricultural research.

Omics on bioleaching: current and future impacts.

PubMed

Martinez, Patricio; Vera, Mario; Bobadilla-Fazzini, Roberto A

2015-10-01

Bioleaching corresponds to the microbial-catalyzed process of conversion of insoluble metals into soluble forms. As an applied biotechnology globally used, it represents an extremely interesting field of research where omics techniques can be applied in terms of knowledge development, but moreover in terms of process design, control, and optimization. In this mini-review, the current state of genomics, proteomics, and metabolomics of bioleaching and the major impacts of these analytical methods at industrial scale are highlighted. In summary, genomics has been essential in the determination of the biodiversity of leaching processes and for development of conceptual and functional metabolic models. Proteomic impacts are mostly related to microbe-mineral interaction analysis, including copper resistance and biofilm formation. Early steps of metabolomics in the field of bioleaching have shown a significant potential for the use of metabolites as industrial biomarkers. Development directions are given in order to enhance the future impacts of the omics in biohydrometallurgy.

Deep-Sea Microbes: Linking Biogeochemical Rates to -Omics Approaches

NASA Astrophysics Data System (ADS)

Herndl, G. J.; Sintes, E.; Bayer, B.; Bergauer, K.; Amano, C.; Hansman, R.; Garcia, J.; Reinthaler, T.

2016-02-01

Over the past decade substantial progress has been made in determining deep ocean microbial activity and resolving some of the enigmas in understanding the deep ocean carbon flux. Also, metagenomics approaches have shed light onto the dark ocean's microbes but linking -omics approaches to biogeochemical rate measurements are generally rare in microbial oceanography and even more so for the deep ocean. In this presentation, we will show by combining metagenomics, -proteomics and biogeochemical rate measurements on the bulk and single-cell level that deep-sea microbes exhibit characteristics of generalists with a large genome repertoire, versatile in utilizing substrate as revealed by metaproteomics. This is in striking contrast with the apparently rather uniform dissolved organic matter pool in the deep ocean. Combining the different -omics approaches with metabolic rate measurements, we will highlight some major inconsistencies and enigmas in our understanding of the carbon cycling and microbial food web structure in the dark ocean.

Fluxomics - connecting 'omics analysis and phenotypes.

PubMed

Winter, Gal; Krömer, Jens O

2013-07-01

In our modern 'omics era, metabolic flux analysis (fluxomics) represents the physiological counterpart of its siblings transcriptomics, proteomics and metabolomics. Fluxomics integrates in vivo measurements of metabolic fluxes with stoichiometric network models to allow the determination of absolute flux through large networks of the central carbon metabolism. There are many approaches to implement fluxomics including flux balance analysis (FBA), (13) C fluxomics and (13) C-constrained FBA as well as many experimental settings for flux measurement including dynamic, stationary and semi-stationary. Here we outline the principles of the different approaches and their relative advantages. We demonstrate the unique contribution of flux analysis for phenotype elucidation using a thoroughly studied metabolic reaction as a case study, the microbial aerobic/anaerobic shift, highlighting the importance of flux analysis as a single layer of data as well as interlaced in multi-omics studies. © 2012 John Wiley & Sons Ltd and Society for Applied Microbiology.

shinyGISPA: A web application for characterizing phenotype by gene sets using multiple omics data combinations.

PubMed

Dwivedi, Bhakti; Kowalski, Jeanne

2018-01-01

While many methods exist for integrating multi-omics data or defining gene sets, there is no one single tool that defines gene sets based on merging of multiple omics data sets. We present shinyGISPA, an open-source application with a user-friendly web-based interface to define genes according to their similarity in several molecular changes that are driving a disease phenotype. This tool was developed to help facilitate the usability of a previously published method, Gene Integrated Set Profile Analysis (GISPA), among researchers with limited computer-programming skills. The GISPA method allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype. The tool provides an automated workflow that is highly scalable and adaptable to applications that go beyond genomic data merging analysis. It is available at http://shinygispa.winship.emory.edu/shinyGISPA/.

shinyGISPA: A web application for characterizing phenotype by gene sets using multiple omics data combinations

PubMed Central

Dwivedi, Bhakti

2018-01-01

While many methods exist for integrating multi-omics data or defining gene sets, there is no one single tool that defines gene sets based on merging of multiple omics data sets. We present shinyGISPA, an open-source application with a user-friendly web-based interface to define genes according to their similarity in several molecular changes that are driving a disease phenotype. This tool was developed to help facilitate the usability of a previously published method, Gene Integrated Set Profile Analysis (GISPA), among researchers with limited computer-programming skills. The GISPA method allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype. The tool provides an automated workflow that is highly scalable and adaptable to applications that go beyond genomic data merging analysis. It is available at http://shinygispa.winship.emory.edu/shinyGISPA/. PMID:29415010

Dimension reduction techniques for the integrative analysis of multi-omics data

PubMed Central

Zeleznik, Oana A.; Thallinger, Gerhard G.; Kuster, Bernhard; Gholami, Amin M.

2016-01-01

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput ‘omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease. PMID:26969681

The application of omics technologies in the functional evaluation of inulin and inulin-containing prebiotics dietary supplementation

PubMed Central

Tsurumaki, M; Kotake, M; Iwasaki, M; Saito, M; Tanaka, K; Aw, W; Fukuda, S; Tomita, M

2015-01-01

Inulin, a natural renewable polysaccharide resource produced by various plants in nature, has been reported to possess a significant number of diverse pharmaceutical and food applications. Recently, there has been rapid progress in high-throughput technologies and platforms to assay global mRNA, proteins, metabolites and gut microbiota. In this review, we will describe the current status of utilizing omics technologies of elucidating the impact of inulin and inulin-containing prebiotics at the transcriptome, proteome, metabolome and gut microbiome levels. Although many studies in this review have addressed the impact of inulin comprehensively, these omics technologies only enable us to understand physiological information at each different stage of mRNA, protein, metabolite and gut microbe. We believe that a synergistic approach is vital in order to fully illustrate the intricate beauty behind the relatively modest influence of food factors like inulin on host health. PMID:26619369

Growing trend of CE at the omics level: the frontier of systems biology--an update.

PubMed

Ban, Eunmi; Park, Soo Hyun; Kang, Min-Jung; Lee, Hyun-Jung; Song, Eun Joo; Yoo, Young Sook

2012-01-01

Omics is the study of proteins, peptides, genes, and metabolites in living organisms. Systems biology aims to understand the system through the study of the relationship between elements such as genes and proteins in biological system. Recently, systems biology emerged as the result of the advanced development of high-throughput analysis technologies such as DNA sequencers, DNA arrays, and mass spectrometry for omics studies. Among a number of analytical tools and technologies, CE and CE coupled to MS are promising and relatively rapidly developing tools with the potential to provide qualitative and quantitative analyses of biological molecules. With an emphasis on CE for systems biology, this review summarizes the method developments and applications of CE for the genomic, transcriptomic, proteomic, and metabolomic studies focusing on the drug discovery and disease diagnosis and therapies since 2009. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Omics Revolution in Agricultural Research

PubMed Central

2015-01-01

The Agrochemicals Division cosponsored the 13th International Union of Pure and Applied Chemistry International Congress of Pesticide Chemistry held as part of the 248th National Meeting and Exposition of the American Chemical Society in San Francisco, CA, USA, August 10–14, 2014. The topic of the Congress was Crop, Environment, and Public Health Protection; Technologies for a Changing World. Over 1000 delegates participated in the Congress with interactive scientific programming in nine major topic areas including the challenges and opportunities of agricultural biotechnology. Plenary speakers addressed global issues related to the Congress theme prior to the daily technical sessions. The plenary lecture addressing the challenges and opportunities that omic technologies provide agricultural research is presented here. The plenary lecture provided the diverse audience with information on a complex subject to stimulate research ideas and provide a glimpse of the impact of omics on agricultural research. PMID:26468989

Integrating data from biological experiments into metabolic networks with the DBE information system.

PubMed

Borisjuk, Ljudmilla; Hajirezaei, Mohammad-Reza; Klukas, Christian; Rolletschek, Hardy; Schreiber, Falk

2005-01-01

Modern 'omics'-technologies result in huge amounts of data about life processes. For analysis and data mining purposes this data has to be considered in the context of the underlying biological networks. This work presents an approach for integrating data from biological experiments into metabolic networks by mapping the data onto network elements and visualising the data enriched networks automatically. This methodology is implemented in DBE, an information system that supports the analysis and visualisation of experimental data in the context of metabolic networks. It consists of five parts: (1) the DBE-Database for consistent data storage, (2) the Excel-Importer application for the data import, (3) the DBE-Website as the interface for the system, (4) the DBE-Pictures application for the up- and download of binary (e. g. image) files, and (5) DBE-Gravisto, a network analysis and graph visualisation system. The usability of this approach is demonstrated in two examples.

ArrayExpress update--trends in database growth and links to data analysis tools.

PubMed

Rustici, Gabriella; Kolesnikov, Nikolay; Brandizi, Marco; Burdett, Tony; Dylag, Miroslaw; Emam, Ibrahim; Farne, Anna; Hastings, Emma; Ison, Jon; Keays, Maria; Kurbatova, Natalja; Malone, James; Mani, Roby; Mupo, Annalisa; Pedro Pereira, Rui; Pilicheva, Ekaterina; Rung, Johan; Sharma, Anjan; Tang, Y Amy; Ternent, Tobias; Tikhonov, Andrew; Welter, Danielle; Williams, Eleanor; Brazma, Alvis; Parkinson, Helen; Sarkans, Ugis

2013-01-01

The ArrayExpress Archive of Functional Genomics Data (http://www.ebi.ac.uk/arrayexpress) is one of three international functional genomics public data repositories, alongside the Gene Expression Omnibus at NCBI and the DDBJ Omics Archive, supporting peer-reviewed publications. It accepts data generated by sequencing or array-based technologies and currently contains data from almost a million assays, from over 30 000 experiments. The proportion of sequencing-based submissions has grown significantly over the last 2 years and has reached, in 2012, 15% of all new data. All data are available from ArrayExpress in MAGE-TAB format, which allows robust linking to data analysis and visualization tools, including Bioconductor and GenomeSpace. Additionally, R objects, for microarray data, and binary alignment format files, for sequencing data, have been generated for a significant proportion of ArrayExpress data.

A systems biology-led insight into the role of the proteome in neurodegenerative diseases.

PubMed

Fasano, Mauro; Monti, Chiara; Alberio, Tiziana

2016-09-01

Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases. Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer's disease, Parkinson's disease and other neurodegenerative disorders, as well as of affected human tissues. Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology

PubMed Central

Afendi, Farit M.; Ono, Naoaki; Nakamura, Yukiko; Nakamura, Kensuke; Darusman, Latifah K.; Kibinge, Nelson; Morita, Aki Hirai; Tanaka, Ken; Horai, Hisayuki; Altaf-Ul-Amin, Md.; Kanaya, Shigehiko

2013-01-01

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data. The present study reviews the usage of KNApSAcK Family DB in metabolomics and related area, discusses several statistical methods for handling multivariate data and shows their application on Indonesian blended herbal medicines (Jamu) as a case study. Exploration using Biplot reveals many plants are rarely utilized while some plants are highly utilized toward specific efficacy. Furthermore, the ingredients of Jamu formulas are modeled using Partial Least Squares Discriminant Analysis (PLS-DA) in order to predict their efficacy. The plants used in each Jamu medicine served as the predictors, whereas the efficacy of each Jamu provided the responses. This model produces 71.6% correct classification in predicting efficacy. Permutation test then is used to determine plants that serve as main ingredients in Jamu formula by evaluating the significance of the PLS-DA coefficients. Next, in order to explain the role of plants that serve as main ingredients in Jamu medicines, information of pharmacological activity of the plants is added to the predictor block. Then N-PLS-DA model, multiway version of PLS-DA, is utilized to handle the three-dimensional array of the predictor block. The resulting N-PLS-DA model reveals that the effects of some pharmacological activities are specific for certain efficacy and the other activities are diverse toward many efficacies. Mathematical modeling introduced in the present study can be utilized in global analysis of big data targeting to reveal the underlying biology. PMID:24688691

"Watching the Detectives" report of the general assembly of the EU project DETECTIVE Brussels, 24-25 November 2015.

PubMed

Fernando, Ruani N; Chaudhari, Umesh; Escher, Sylvia E; Hengstler, Jan G; Hescheler, Jürgen; Jennings, Paul; Keun, Hector C; Kleinjans, Jos C S; Kolde, Raivo; Kollipara, Laxmikanth; Kopp-Schneider, Annette; Limonciel, Alice; Nemade, Harshal; Nguemo, Filomain; Peterson, Hedi; Prieto, Pilar; Rodrigues, Robim M; Sachinidis, Agapios; Schäfer, Christoph; Sickmann, Albert; Spitkovsky, Dimitry; Stöber, Regina; van Breda, Simone G J; van de Water, Bob; Vivier, Manon; Zahedi, René P; Vinken, Mathieu; Rogiers, Vera

2016-06-01

SEURAT-1 is a joint research initiative between the European Commission and Cosmetics Europe aiming to develop in vitro- and in silico-based methods to replace the in vivo repeated dose systemic toxicity test used for the assessment of human safety. As one of the building blocks of SEURAT-1, the DETECTIVE project focused on a key element on which in vitro toxicity testing relies: the development of robust and reliable, sensitive and specific in vitro biomarkers and surrogate endpoints that can be used for safety assessments of chronically acting toxicants, relevant for humans. The work conducted by the DETECTIVE consortium partners has established a screening pipeline of functional and "-omics" technologies, including high-content and high-throughput screening platforms, to develop and investigate human biomarkers for repeated dose toxicity in cellular in vitro models. Identification and statistical selection of highly predictive biomarkers in a pathway- and evidence-based approach constitute a major step in an integrated approach towards the replacement of animal testing in human safety assessment. To discuss the final outcomes and achievements of the consortium, a meeting was organized in Brussels. This meeting brought together data-producing and supporting consortium partners. The presentations focused on the current state of ongoing and concluding projects and the strategies employed to identify new relevant biomarkers of toxicity. The outcomes and deliverables, including the dissemination of results in data-rich "-omics" databases, were discussed as were the future perspectives of the work completed under the DETECTIVE project. Although some projects were still in progress and required continued data analysis, this report summarizes the presentations, discussions and the outcomes of the project.

Establishment and maintenance of a standardized glioma tissue bank: Huashan experience.

PubMed

Aibaidula, Abudumijiti; Lu, Jun-feng; Wu, Jin-song; Zou, He-jian; Chen, Hong; Wang, Yu-qian; Qin, Zhi-yong; Yao, Yu; Gong, Ye; Che, Xiao-ming; Zhong, Ping; Li, Shi-qi; Bao, Wei-min; Mao, Ying; Zhou, Liang-fu

2015-06-01

Cerebral glioma is the most common brain tumor as well as one of the top ten malignant tumors in human beings. In spite of the great progress on chemotherapy and radiotherapy as well as the surgery strategies during the past decades, the mortality and morbidity are still high. One of the major challenges is to explore the pathogenesis and invasion of glioma at various "omics" levels (such as proteomics or genomics) and the clinical implications of biomarkers for diagnosis, prognosis or treatment of glioma patients. Establishment of a standardized tissue bank with high quality biospecimens annotated with clinical information is pivotal to the solution of these questions as well as the drug development process and translational research on glioma. Therefore, based on previous experience of tissue banks, standardized protocols for sample collection and storage were developed. We also developed two systems for glioma patient and sample management, a local database for medical records and a local image database for medical images. For future set-up of a regional biobank network in Shanghai, we also founded a centralized database for medical records. Hence we established a standardized glioma tissue bank with sufficient clinical data and medical images in Huashan Hospital. By September, 2013, tissues samples from 1,326 cases were collected. Histological diagnosis revealed that 73 % were astrocytic tumors, 17 % were oligodendroglial tumors, 2 % were oligoastrocytic tumors, 4 % were ependymal tumors and 4 % were other central nervous system neoplasms.

LipidHome: a database of theoretical lipids optimized for high throughput mass spectrometry lipidomics.

PubMed

Foster, Joseph M; Moreno, Pablo; Fabregat, Antonio; Hermjakob, Henning; Steinbeck, Christoph; Apweiler, Rolf; Wakelam, Michael J O; Vizcaíno, Juan Antonio

2013-01-01

Protein sequence databases are the pillar upon which modern proteomics is supported, representing a stable reference space of predicted and validated proteins. One example of such resources is UniProt, enriched with both expertly curated and automatic annotations. Taken largely for granted, similar mature resources such as UniProt are not available yet in some other "omics" fields, lipidomics being one of them. While having a seasoned community of wet lab scientists, lipidomics lies significantly behind proteomics in the adoption of data standards and other core bioinformatics concepts. This work aims to reduce the gap by developing an equivalent resource to UniProt called 'LipidHome', providing theoretically generated lipid molecules and useful metadata. Using the 'FASTLipid' Java library, a database was populated with theoretical lipids, generated from a set of community agreed upon chemical bounds. In parallel, a web application was developed to present the information and provide computational access via a web service. Designed specifically to accommodate high throughput mass spectrometry based approaches, lipids are organised into a hierarchy that reflects the variety in the structural resolution of lipid identifications. Additionally, cross-references to other lipid related resources and papers that cite specific lipids were used to annotate lipid records. The web application encompasses a browser for viewing lipid records and a 'tools' section where an MS1 search engine is currently implemented. LipidHome can be accessed at http://www.ebi.ac.uk/apweiler-srv/lipidhome.

A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data.

PubMed

Li, Hongkai; Yuan, Zhongshang; Ji, Jiadong; Xu, Jing; Zhang, Tao; Zhang, Xiaoshuai; Xue, Fuzhong

2016-03-09

We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ(2) test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ(2) test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones.

Integrated ‘omics analysis for studying the microbial community response to a pH perturbation of a cellulose-degrading bioreactor culture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boaro, Amy A.; Kim, Young-Mo; Konopka, Allan

2014-12-01

Integrated ‘omics have been used on pure cultures and co-cultures, yet they have not been applied to complex microbial communities to examine questions of perturbation response. In this study, we used integrated ‘omics to measure the perturbation response of a cellulose-degrading bioreactor community fed with microcrystalline cellulose (Avicel). We predicted that a pH decrease by addition of a pulse of acid would reduce microbial community diversity and temporarily reduce reactor function such as cellulose degradation. However, 16S rDNA pyrosequencing results revealed increased alpha diversity in the microbial community after the perturbation, and a persistence of the dominant community members overmore » the duration of the experiment. Proteomics results showed a decrease in activity of proteins associated with Fibrobacter succinogenes two days after the perturbation followed by increased protein abundances six days after the perturbation. The decrease in cellulolytic activity suggested by the proteomics was confirmed by the accumulation of Avicel in the reactor. Metabolomics showed a pattern similar to that of the proteome, with amino acid production decreasing two days after the perturbation and increasing after six days. This study demonstrated that community ‘omics data provides valuable information about the interactions and function of anaerobic cellulolytic community members after a perturbation.« less

Framework for the quantitative weight-of-evidence analysis of 'omics data for regulatory purposes.

PubMed

Bridges, Jim; Sauer, Ursula G; Buesen, Roland; Deferme, Lize; Tollefsen, Knut E; Tralau, Tewes; van Ravenzwaay, Ben; Poole, Alan; Pemberton, Mark

2017-12-01

A framework for the quantitative weight-of-evidence (QWoE) analysis of 'omics data for regulatory purposes is presented. The QWoE framework encompasses seven steps to evaluate 'omics data (also together with non-'omics data): (1) Hypothesis formulation, identification and weighting of lines of evidence (LoEs). LoEs conjoin different (types of) studies that are used to critically test the hypothesis. As an essential component of the QWoE framework, step 1 includes the development of templates for scoring sheets that predefine scoring criteria with scores of 0-4 to enable a quantitative determination of study quality and data relevance; (2) literature searches and categorisation of studies into the pre-defined LoEs; (3) and (4) quantitative assessment of study quality and data relevance using the respective pre-defined scoring sheets for each study; (5) evaluation of LoE-specific strength of evidence based upon the study quality and study relevance scores of the studies conjoined in the respective LoE; (6) integration of the strength of evidence from the individual LoEs to determine the overall strength of evidence; (7) characterisation of uncertainties and conclusion on the QWoE. To put the QWoE framework in practice, case studies are recommended to confirm the relevance of its different steps, or to adapt them as necessary. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Comparative multi-omics systems analysis of Escherichia coli strains B and K-12.

PubMed

Yoon, Sung Ho; Han, Mee-Jung; Jeong, Haeyoung; Lee, Choong Hoon; Xia, Xiao-Xia; Lee, Dae-Hee; Shim, Ji Hoon; Lee, Sang Yup; Oh, Tae Kwang; Kim, Jihyun F

2012-05-25

Elucidation of a genotype-phenotype relationship is critical to understand an organism at the whole-system level. Here, we demonstrate that comparative analyses of multi-omics data combined with a computational modeling approach provide a framework for elucidating the phenotypic characteristics of organisms whose genomes are sequenced. We present a comprehensive analysis of genome-wide measurements incorporating multifaceted holistic data - genome, transcriptome, proteome, and phenome - to determine the differences between Escherichia coli B and K-12 strains. A genome-scale metabolic network of E. coli B was reconstructed and used to identify genetic bases of the phenotypes unique to B compared with K-12 through in silico complementation testing. This systems analysis revealed that E. coli B is well-suited for production of recombinant proteins due to a greater capacity for amino acid biosynthesis, fewer proteases, and lack of flagella. Furthermore, E. coli B has an additional type II secretion system and a different cell wall and outer membrane composition predicted to be more favorable for protein secretion. In contrast, E. coli K-12 showed a higher expression of heat shock genes and was less susceptible to certain stress conditions. This integrative systems approach provides a high-resolution system-wide view and insights into why two closely related strains of E. coli, B and K-12, manifest distinct phenotypes. Therefore, systematic understanding of cellular physiology and metabolism of the strains is essential not only to determine culture conditions but also to design recombinant hosts.

Comparative multi-omics systems analysis of Escherichia coli strains B and K-12

PubMed Central

2012-01-01

Background Elucidation of a genotype-phenotype relationship is critical to understand an organism at the whole-system level. Here, we demonstrate that comparative analyses of multi-omics data combined with a computational modeling approach provide a framework for elucidating the phenotypic characteristics of organisms whose genomes are sequenced. Results We present a comprehensive analysis of genome-wide measurements incorporating multifaceted holistic data - genome, transcriptome, proteome, and phenome - to determine the differences between Escherichia coli B and K-12 strains. A genome-scale metabolic network of E. coli B was reconstructed and used to identify genetic bases of the phenotypes unique to B compared with K-12 through in silico complementation testing. This systems analysis revealed that E. coli B is well-suited for production of recombinant proteins due to a greater capacity for amino acid biosynthesis, fewer proteases, and lack of flagella. Furthermore, E. coli B has an additional type II secretion system and a different cell wall and outer membrane composition predicted to be more favorable for protein secretion. In contrast, E. coli K-12 showed a higher expression of heat shock genes and was less susceptible to certain stress conditions. Conclusions This integrative systems approach provides a high-resolution system-wide view and insights into why two closely related strains of E. coli, B and K-12, manifest distinct phenotypes. Therefore, systematic understanding of cellular physiology and metabolism of the strains is essential not only to determine culture conditions but also to design recombinant hosts. PMID:22632713

GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data.

PubMed

Ben-Ari Fuchs, Shani; Lieder, Iris; Stelzer, Gil; Mazor, Yaron; Buzhor, Ella; Kaplan, Sergey; Bogoch, Yoel; Plaschkes, Inbar; Shitrit, Alina; Rappaport, Noa; Kohn, Asher; Edgar, Ron; Shenhav, Liraz; Safran, Marilyn; Lancet, Doron; Guan-Golan, Yaron; Warshawsky, David; Shtrichman, Ronit

2016-03-01

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

Omic personality: implications of stable transcript and methylation profiles for personalized medicine.

PubMed

Tabassum, Rubina; Sivadas, Ambily; Agrawal, Vartika; Tian, Haozheng; Arafat, Dalia; Gibson, Greg

2015-08-13

Personalized medicine is predicated on the notion that individual biochemical and genomic profiles are relatively constant in times of good health and to some extent predictive of disease or therapeutic response. We report a pilot study quantifying gene expression and methylation profile consistency over time, addressing the reasons for individual uniqueness, and its relation to N = 1 phenotypes. Whole blood samples from four African American women, four Caucasian women, and four Caucasian men drawn from the Atlanta Center for Health Discovery and Well Being study at three successive 6-month intervals were profiled by RNA-Seq, miRNA-Seq, and Illumina Methylation 450 K arrays. Standard regression approaches were used to evaluate the proportion of variance for each type of omic measure among individuals, and to quantify correlations among measures and with clinical attributes related to wellness. Longitudinal omic profiles were in general highly consistent over time, with an average of 67 % variance in transcript abundance, 42 % in CpG methylation level (but 88 % for the most differentiated CpG per gene), and 50 % in miRNA abundance among individuals, which are all comparable to 74 % variance among individuals for 74 clinical traits. One third of the variance could be attributed to differential blood cell type abundance, which was also fairly stable over time, and a lesser amount to expression quantitative trait loci (eQTL) effects. Seven conserved axes of covariance that capture diverse aspects of immune function explained over half of the variance. These axes also explained a considerable proportion of individually extreme transcript abundance, namely approximately 100 genes that were significantly up-regulated or down-regulated in each person and were in some cases enriched for relevant gene activities that plausibly associate with clinical attributes. A similar fraction of genes had individually divergent methylation levels, but these did not overlap with the transcripts, and fewer than 20 % of genes had significantly correlated methylation and gene expression. People express an "omic personality" consisting of peripheral blood transcriptional and epigenetic profiles that are constant over the course of a year and reflect various types of immune activity. Baseline genomic profiles can provide a window into the molecular basis of traits that might be useful for explaining medical conditions or guiding personalized health decisions.

The pathway not taken: understanding 'omics data in the perinatal context.

PubMed

Edlow, Andrea G; Slonim, Donna K; Wick, Heather C; Hui, Lisa; Bianchi, Diana W

2015-07-01

'Omics analysis of large datasets has an increasingly important role in perinatal research, but understanding gene expression analyses in the fetal context remains a challenge. We compared the interpretation provided by a widely used systems biology resource (ingenuity pathway analysis [IPA]) with that from gene set enrichment analysis (GSEA) with functional annotation curated specifically for the fetus (Developmental FunctionaL Annotation at Tufts [DFLAT]). Using amniotic fluid supernatant transcriptome datasets previously produced by our group, we analyzed 3 different developmental perturbations: aneuploidy (Trisomy 21 [T21]), hemodynamic (twin-twin transfusion syndrome [TTTS]), and metabolic (maternal obesity) vs sex- and gestational age-matched control subjects. Differentially expressed probe sets were identified with the use of paired t-tests with the Benjamini-Hochberg correction for multiple testing (P < .05). Functional analyses were performed with IPA and GSEA/DFLAT. Outputs were compared for biologic relevance to the fetus. Compared with control subjects, there were 414 significantly dysregulated probe sets in T21 fetuses, 2226 in TTTS recipient twins, and 470 in fetuses of obese women. Each analytic output was unique but complementary. For T21, both IPA and GSEA/DFLAT identified dysregulation of brain, cardiovascular, and integumentary system development. For TTTS, both analytic tools identified dysregulation of cell growth/proliferation, immune and inflammatory signaling, brain, and cardiovascular development. For maternal obesity, both tools identified dysregulation of immune and inflammatory signaling, brain and musculoskeletal development, and cell death. GSEA/DFLAT identified substantially more dysregulated biologic functions in fetuses of obese women (1203 vs 151). For all 3 datasets, GSEA/DFLAT provided more comprehensive information about brain development. IPA consistently provided more detailed annotation about cell death. IPA produced many dysregulated terms that pertained to cancer (14 in T21, 109 in TTTS, 26 in maternal obesity); GSEA/DFLAT did not. Interpretation of the fetal amniotic fluid supernatant transcriptome depends on the analytic program, which suggests that >1 resource should be used. Within IPA, physiologic cellular proliferation in the fetus produced many "false positive" annotations that pertained to cancer, which reflects its bias toward adult diseases. This study supports the use of gene annotation resources with a developmental focus, such as DFLAT, for 'omics studies in perinatal medicine. Copyright © 2015 Elsevier Inc. All rights reserved.

Why proteomics is not the new genomics and the future of mass spectrometry in cell biology.

PubMed

Sidoli, Simone; Kulej, Katarzyna; Garcia, Benjamin A

2017-01-02

Mass spectrometry (MS) is an essential part of the cell biologist's proteomics toolkit, allowing analyses at molecular and system-wide scales. However, proteomics still lag behind genomics in popularity and ease of use. We discuss key differences between MS-based -omics and other booming -omics technologies and highlight what we view as the future of MS and its role in our increasingly deep understanding of cell biology. © 2017 Sidoli et al.

iss051e034105

NASA Image and Video Library

2017-05-02

iss051e034105 (May 2, 2017) --- Commander Peggy Whitson is working on the OsteoOmics bone cell study that utilizes the Microgravity Science Glovebox inside the U.S. Destiny laboratory. OsteoOmics investigates the molecular mechanisms that dictate bone loss in microgravity by examining osteoblasts, which form bone, and osteoclasts, which dissolves bone. This leads to better preventative care or therapeutic treatments for people suffering bone loss as a result of bone diseases like osteopenia and osteoporosis, or for patients on prolonged bed rest.

Contributions of polyunsaturated fatty acids (PUFA) on cerebral neurobiology: an integrated omics approach with epigenomic focus

DTIC Science & Technology

2016-12-01

acids (PUFA) on cerebral neurobiology: an integrated omics approach with epigenomic focus Nabarun Chakrabortya,b, Seid Muhiea,b, Raina Kumara,c, Aarti...C57BL/6j mice fed on any of these three diets from their neonatal age to midlife. Integrating the multiomics data, we focused on the genes encoding both...been evaluated in the context of a wide variety of health issues [23]. The escalated risks of pathological and psychological disease have been

The -omics Era- Toward a Systems-Level Understanding of Streptomyces

PubMed Central

Zhou, Zhan; Gu, Jianying; Du, Yi-Ling; Li, Yong-Quan; Wang, Yufeng

2011-01-01

Streptomyces is a group of soil bacteria of medicinal, economic, ecological, and industrial importance. It is renowned for its complex biology in gene regulation, antibiotic production, morphological differentiation, and stress response. In this review, we provide an overview of the recent advances in Streptomyces biology inspired by -omics based high throughput technologies. In this post-genomic era, vast amounts of data have been integrated to provide significant new insights into the fundamental mechanisms of system control and regulation dynamics of Streptomyces. PMID:22379394

Automatic Tool for Local Assembly Structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whole community shotgun sequencing of total DNA (i.e. metagenomics) and total RNA (i.e. metatranscriptomics) has provided a wealth of information in the microbial community structure, predicted functions, metabolic networks, and is even able to reconstruct complete genomes directly. Here we present ATLAS (Automatic Tool for Local Assembly Structures) a comprehensive pipeline for assembly, annotation, genomic binning of metagenomic and metatranscriptomic data with an integrated framework for Multi-Omics. This will provide an open source tool for the Multi-Omic community at large.

Would Virchow be a systems biologist? A discourse on the philosophy of science with implications for pathological research.

PubMed

Stenzinger, Albrecht; Klauschen, Frederick; Wittschieber, Daniel; Weichert, Wilko; Denkert, Carsten; Dietel, Manfred; Roller, Claudio

2010-06-01

Research in pathology spans from merely descriptive work to functional studies, "-omics" approaches and, more recently, systems biology. The work presented here aims at placing pathological research into an epistemological context. Aided by Rudolf Virchow, we give an overview on the philosophy of science including the Wiener Kreis, Popper, Kuhn, Fleck and Rheinberger and demonstrate their implications for routine diagnostics and science in pathology. A focus is on the fields of "-omics" and systems pathology.

Evaluating biological variation in non-transgenic crops: executive summary from the ILSI Health and Environmental Sciences Institute workshop, November 16-17, 2009, Paris, France.

PubMed

Doerrer, Nancy; Ladics, Gregory; McClain, Scott; Herouet-Guicheney, Corinne; Poulsen, Lars K; Privalle, Laura; Stagg, Nicola

2010-12-01

The International Life Sciences Institute Health and Environmental Sciences Institute Protein Allergenicity Technical Committee hosted an international workshop November 16-17, 2009, in Paris, France, with over 60 participants from academia, government, and industry to review and discuss the potential utility of "-omics" technologies for assessing the variability in plant gene, protein, and metabolite expression. The goal of the workshop was to illustrate how a plant's constituent makeup and phenotypic processes can be surveyed analytically. Presentations on the "-omics" techniques (i.e., genomics, proteomics, and metabolomics) highlighted the workshop, and summaries of these presentations are published separately in this supplemental issue. This paper summarizes key messages, as well as the consensus points reached, in a roundtable discussion on eight specific questions posed during the final session of the workshop. The workshop established some common, though not unique, challenges for all "-omics" techniques, and include (a) standardization of separation/extraction and analytical techniques; (b) difficulty in associating environmental impacts (e.g., planting, soil texture, location, climate, stress) with potential alterations in plants at genomic, proteomic, and metabolomic levels; (c) many independent analytical measurements, but few replicates/subjects--poorly defined accuracy and precision; and (d) bias--a lack of hypothesis-driven science. Information on natural plant variation is critical in establishing the utility of new technologies due to the variability in specific analytes that may result from genetic differences (crop genotype), different crop management practices (conventional high input, low input, organic), interaction between genotype and environment, and the use of different breeding methods. For example, variations of several classes of proteins were reported among different soybean, rice, or wheat varieties or varieties grown at different locations. Data on the variability of allergenic proteins are important in defining the risk of potential allergenicity. Once established as a standardized assay, survey approaches such as the "-omics" techniques can be considered in a hypothesis-driven analysis of plants, such as determining unintended effects in genetically modified (GM) crops. However, the analysis should include both the GM and control varieties that have the same breeding history and exposure to the same environmental conditions. Importantly, the biological relevance and safety significance of changes in "-omic" data are still unknown. Furthermore, the current compositional assessment for evaluating the substantial equivalence of GM crops is robust, comprehensive, and a good tool for food safety assessments. The overall consensus of the workshop participants was that many "-omics" techniques are extremely useful in the discovery and research phases of biotechnology, and are valuable for hypothesis generation. However, there are many methodological shortcomings identified with "-omics" approaches, a paucity of reference materials, and a lack of focused strategy for their use that currently make them not conducive for the safety assessment of GM crops. Copyright © 2010 Elsevier Inc. All rights reserved.

Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework.

PubMed

Voillet, Valentin; Besse, Philippe; Liaubet, Laurence; San Cristobal, Magali; González, Ignacio

2016-10-03

In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical methods cannot be directly applied to incomplete datasets. To overcome this issue, we propose a multiple imputation (MI) approach in a multivariate framework. In this study, we focus on multiple factor analysis (MFA) as a tool to compare and integrate multiple layers of information. MI involves filling the missing rows with plausible values, resulting in M completed datasets. MFA is then applied to each completed dataset to produce M different configurations (the matrices of coordinates of individuals). Finally, the M configurations are combined to yield a single consensus solution. We assessed the performance of our method, named MI-MFA, on two real omics datasets. Incomplete artificial datasets with different patterns of missingness were created from these data. The MI-MFA results were compared with two other approaches i.e., regularized iterative MFA (RI-MFA) and mean variable imputation (MVI-MFA). For each configuration resulting from these three strategies, the suitability of the solution was determined against the true MFA configuration obtained from the original data and a comprehensive graphical comparison showing how the MI-, RI- or MVI-MFA configurations diverge from the true configuration was produced. Two approaches i.e., confidence ellipses and convex hulls, to visualize and assess the uncertainty due to missing values were also described. We showed how the areas of ellipses and convex hulls increased with the number of missing individuals. A free and easy-to-use code was proposed to implement the MI-MFA method in the R statistical environment. We believe that MI-MFA provides a useful and attractive method for estimating the coordinates of individuals on the first MFA components despite missing rows. MI-MFA configurations were close to the true configuration even when many individuals were missing in several data tables. This method takes into account the uncertainty of MI-MFA configurations induced by the missing rows, thereby allowing the reliability of the results to be evaluated.

A tutorial of diverse genome analysis tools found in the CoGe web-platform using Plasmodium spp. as a model

PubMed Central

Castillo, Andreina I; Nelson, Andrew D L; Haug-Baltzell, Asher K; Lyons, Eric

2018-01-01

Abstract Integrated platforms for storage, management, analysis and sharing of large quantities of omics data have become fundamental to comparative genomics. CoGe (https://genomevolution.org/coge/) is an online platform designed to manage and study genomic data, enabling both data- and hypothesis-driven comparative genomics. CoGe’s tools and resources can be used to organize and analyse both publicly available and private genomic data from any species. Here, we demonstrate the capabilities of CoGe through three example workflows using 17 Plasmodium genomes as a model. Plasmodium genomes present unique challenges for comparative genomics due to their rapidly evolving and highly variable genomic AT/GC content. These example workflows are intended to serve as templates to help guide researchers who would like to use CoGe to examine diverse aspects of genome evolution. In the first workflow, trends in genome composition and amino acid usage are explored. In the second, changes in genome structure and the distribution of synonymous (Ks) and non-synonymous (Kn) substitution values are evaluated across species with different levels of evolutionary relatedness. In the third workflow, microsyntenic analyses of multigene families’ genomic organization are conducted using two Plasmodium-specific gene families—serine repeat antigen, and cytoadherence-linked asexual gene—as models. In general, these example workflows show how to achieve quick, reproducible and shareable results using the CoGe platform. We were able to replicate previously published results, as well as leverage CoGe’s tools and resources to gain additional insight into various aspects of Plasmodium genome evolution. Our results highlight the usefulness of the CoGe platform, particularly in understanding complex features of genome evolution. Database URL: https://genomevolution.org/coge/

The Perseus computational platform for comprehensive analysis of (prote)omics data.

PubMed

Tyanova, Stefka; Temu, Tikira; Sinitcyn, Pavel; Carlson, Arthur; Hein, Marco Y; Geiger, Tamar; Mann, Matthias; Cox, Jürgen

2016-09-01

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

A compendium of multi-omic sequence information from the Saanich Inlet water column

DOE PAGES

Hawley, Alyse K.; Torres-Beltran, Monica; Zaikova, Elena; ...

2017-10-31

Microbial communities play vital roles in earth’s geochemical cycles. Within marine oxygen minimum zones (OMZs) gradients of oxygen, nitrate and sulfide create redox gradients that drive biogeochemical cycling of carbon, nitrogen and sulphur. Climate-change induced expansion and intensification of OMZs and associated biogeochemical activities has significant implications for green house gas production i.e. nitrous oxide and methane. Next generation sequencing technologies have enabled observations of changes in microbial community structure and expression of RNA and protein along these redox gradients within OMZs. Here, we present a multi-omic time series dataset from Saanich Inlet spanning six years, including high spatial resolutionmore » small subunit ribosomal RNA tags, metagenomes, metatranscriptomes, and metaproteomes. As a result, this compendium provides paired multi-omic datasets over multiple time points providing a basis for exploring shifts in microbial community interactions and regulation of metabolic activities both along redox gradients and over time with implications for global climate models.« less

A compendium of multi-omic sequence information from the Saanich Inlet water column

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hawley, Alyse K.; Torres-Beltran, Monica; Zaikova, Elena

Microbial communities play vital roles in earth’s geochemical cycles. Within marine oxygen minimum zones (OMZs) gradients of oxygen, nitrate and sulfide create redox gradients that drive biogeochemical cycling of carbon, nitrogen and sulphur. Climate-change induced expansion and intensification of OMZs and associated biogeochemical activities has significant implications for green house gas production i.e. nitrous oxide and methane. Next generation sequencing technologies have enabled observations of changes in microbial community structure and expression of RNA and protein along these redox gradients within OMZs. Here, we present a multi-omic time series dataset from Saanich Inlet spanning six years, including high spatial resolutionmore » small subunit ribosomal RNA tags, metagenomes, metatranscriptomes, and metaproteomes. As a result, this compendium provides paired multi-omic datasets over multiple time points providing a basis for exploring shifts in microbial community interactions and regulation of metabolic activities both along redox gradients and over time with implications for global climate models.« less

Making sense of OMICS data in population-based environmental health studies.

PubMed

Kyrtopoulos, Soterios A

2013-08-01

Although experience from the application of OMICS technologies in population-based environmental health studies is still relatively limited, the accumulated evidence shows that it can allow the identification of features (genes, proteins, and metabolites), or sets of such features, which are targeted by particular exposures or correlate with disease risk. Such features or profiles can therefore serve as biomarkers of exposure or disease risk. Blood-based OMIC profiles appear to reflect to some extent events occurring in target tissues and are associated with toxicity or disease and therefore have the potential to facilitate the elucidation of exposure-disease relationships. Further progress in this direction requires better understanding of the significance of exposure-induced network perturbations for disease initiation and progression and the development of a framework that combines agnostic searches with the utilization of prior knowledge, taking account of particular elements which characterize the structure and evolution of complex systems and brings in principles of systems biology. Copyright © 2013 Wiley Periodicals, Inc.

New strategy for drug discovery by large-scale association analysis of molecular networks of different species.

PubMed

Zhang, Bo; Fu, Yingxue; Huang, Chao; Zheng, Chunli; Wu, Ziyin; Zhang, Wenjuan; Yang, Xiaoyan; Gong, Fukai; Li, Yuerong; Chen, Xiaoyu; Gao, Shuo; Chen, Xuetong; Li, Yan; Lu, Aiping; Wang, Yonghua

2016-02-25

The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.

Omics Approach to Identify Factors Involved in Brassica Disease Resistance.

PubMed

Francisco, Marta; Soengas, Pilar; Velasco, Pablo; Bhadauria, Vijai; Cartea, Maria E; Rodríguez, Victor M

2016-01-01

Understanding plant's defense mechanisms and their response to biotic stresses is of fundamental meaning for the development of resistant crop varieties and more productive agriculture. The Brassica genus involves a large variety of economically important species and cultivars used as vegetable source, oilseeds, forage and ornamental. Damage caused by pathogens attack affects negatively various aspects of plant growth, development, and crop productivity. Over the last few decades, advances in plant physiology, genetics, and molecular biology have greatly improved our understanding of plant responses to biotic stress conditions. In this regard, various 'omics' technologies enable qualitative and quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. In this review, we have described advances in 'omic' tools (genomics, transcriptomics, proteomics and metabolomics) in the view of conventional and modern approaches being used to elucidate the molecular mechanisms that underlie Brassica disease resistance.

Analysis Commons, A Team Approach to Discovery in a Big-Data Environment for Genetic Epidemiology

PubMed Central

Brody, Jennifer A.; Morrison, Alanna C.; Bis, Joshua C.; O'Connell, Jeffrey R.; Brown, Michael R.; Huffman, Jennifer E.; Ames, Darren C.; Carroll, Andrew; Conomos, Matthew P.; Gabriel, Stacey; Gibbs, Richard A.; Gogarten, Stephanie M.; Gupta, Namrata; Jaquish, Cashell E.; Johnson, Andrew D.; Lewis, Joshua P.; Liu, Xiaoming; Manning, Alisa K.; Papanicolaou, George J.; Pitsillides, Achilleas N.; Rice, Kenneth M.; Salerno, William; Sitlani, Colleen M.; Smith, Nicholas L.; Heckbert, Susan R.; Laurie, Cathy C.; Mitchell, Braxton D.; Vasan, Ramachandran S.; Rich, Stephen S.; Rotter, Jerome I.; Wilson, James G.; Boerwinkle, Eric; Psaty, Bruce M.; Cupples, L. Adrienne

2017-01-01

Summary paragraph The exploding volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created a cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multi-center WGS analyses, including data sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation, and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for transforming WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations. PMID:29074945

Using combined bio-omics methods to evaluate the complicated toxic effects of mixed chemical wastewater and its treated effluent.

PubMed

Zhang, Yan; Deng, Yongfeng; Zhao, Yanping; Ren, Hongqiang

2014-05-15

Mixed chemical wastewaters (MCWW) from industrial park contain complex mixtures of trace contaminants, which cannot be effectively removed by wastewater treatment plants (WWTP) and have become an unignored threat to ambient environment. However, limited information is available to evaluate the complicated toxic effects of MCWW and its effluent from wastewater treatment plant (WTPE) from the perspective of bio-omics. In this study, mice were exposed to the MCWW and WTPE for 90 days and distinct differences in the hepatic transcriptome and serum metabolome were analyzed by digital gene expression (DGE) and proton nuclear magnetic resonance ((1)H-NMR) spectra, respectively. Our results indicated that disruption of lipid metabolism in liver and hepatotoxicity were induced by both MCWW and WTPE exposure. WTPE is still a health risk to the environment, which is in need of more attention. Furthermore, we demonstrated the potential ability of bio-omics approaches for evaluating toxic effects of MCWW and WTPE. Copyright © 2014 Elsevier B.V. All rights reserved.

Metagenomic insights into the ecology and physiology of microbes in bioelectrochemical systems.

PubMed

Kouzuma, Atsushi; Ishii, Shun'ichi; Watanabe, Kazuya

2018-05-01

In bioelectrochemical systems (BESs), electrons are transferred between electrochemically active microbes (EAMs) and conductive materials, such as electrodes, via extracellular electron transfer (EET) pathways, and electrons thus transferred stimulate intracellular catabolic reactions. Catabolic and EET pathways have extensively been studied for several model EAMs, such as Shewanella oneidensis MR-1 and Geobacter sulfurreducens PCA, whereas it is also important to understand the ecophysiology of EAMs in naturally occurring microbiomes, such as those in anode biofilms in microbial fuel cells treating wastewater. Recent studies have exploited metagenomics and metatranscriptomics (meta-omics) approaches to characterize EAMs in BES-associated microbiomes. Here we review recent BES studies that used meta-omics approaches and show that these studies have discovered unexpected features of EAMs and deepened our understanding of functions and behaviors of microbes in BESs. It is desired that more studies will employ meta-omics approaches for advancing our knowledge on microbes in BESs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Manipulating environmental stresses and stress tolerance of microalgae for enhanced production of lipids and value-added products-A review.

PubMed

Chen, Bailing; Wan, Chun; Mehmood, Muhammad Aamer; Chang, Jo-Shu; Bai, Fengwu; Zhao, Xinqing

2017-11-01

Microalgae have promising potential to produce lipids and a variety of high-value chemicals. Suitable stress conditions such as nitrogen starvation and high salinity could stimulate synthesis and accumulation of lipids and high-value products by microalgae, therefore, various stress-modification strategies were developed to manipulate and optimize cultivation processes to enhance bioproduction efficiency. On the other hand, advancements in omics-based technologies have boosted the research to globally understand microalgal gene regulation under stress conditions, which enable further improvement of production efficiency via genetic engineering. Moreover, integration of multi-omics data, synthetic biology design, and genetic engineering manipulations exhibits a tremendous potential in the betterment of microalgal biorefinery. This review discusses the process manipulation strategies and omics studies on understanding the regulation of metabolite biosynthesis under various stressful conditions, and proposes genetic engineering of microalgae to improve bioproduction via manipulating stress tolerance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Network Analysis of Rodent Transcriptomes in Spaceflight

NASA Technical Reports Server (NTRS)

Ramachandran, Maya; Fogle, Homer; Costes, Sylvain

2017-01-01

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

Environmental "Omics" of International Space Station: Insights, Significance, and Consequences

NASA Astrophysics Data System (ADS)

Venkateswaran, Kasthuri

2016-07-01

The NASA Space Biology program funded two multi-year studies to catalogue International Space Station (ISS) environmental microbiome. The first Microbial Observatory (MO) experiment will generate a microbial census of the ISS surfaces and atmosphere using advanced molecular microbial community analysis "omics" techniques, supported by traditional culture-based methods and state-of-the art molecular techniques. The second MO experiment will measure presence of viral and select bacterial and fungal pathogens on ISS surfaces and correlate their presence on crew. The "omics" methodologies of the MO experiments will serve as the foundation for an extensive microbial census, offering significant insight into spaceflight-induced changes in the populations of beneficial and potentially harmful microbes. The safety of crewmembers and the maintenance of hardware are the primary goals for monitoring microorganisms in this closed habitat. The statistical analysis of the ISS microbiomes showed that three bacterial phyla dominated both in ISS and Earth cleanrooms, but varied in their abundances. While members of Actinobacteria were predominant on ISS, Proteobacteria dominated the Earth cleanrooms. Alpha diversity estimators indicated a significant drop in viable microbial diversity. To better characterize the shared community composition among samples, beta-diversity metrics analysis were conducted. At the bacterial species level characterization, the microbial community composition is strongly associated with sampling site. Results of the study indicate significant differences between ISS and Earth cleanroom microbiomes in terms of community structure and composition. Bacterial strains isolated from ISS surfaces were also tested for their resistance to nine antibiotics using conventional disc method and Vitek 2 system. Most of the Staphylococcus aureus strains were resistant to penicillin. Five strains were specifically resistant to erythromycin and the ermA gene was also detected. The nine-erythromycin sensitive S. aureus strains exhibited spontaneous mutation when rifampin was tested. Some of the S. aureus strains tolerated gentamycin and tobramycin but cefazolin, cefoxitin, ciprofloxacin and oxacillin inhibited the growth of the S. aureus. Whole genome sequencing (WGS) of 21 ISS strains, exhibiting resistance to various antibiotics, was carried out. The antibiotic resistant genes deduced from the WGS were compared with the resistomes generated directly from the gene pool of the environmental samples. Using a targeted amplification panel consisting of over 500 antimicrobial resistance genes, we were able to confirm the results of the phenotypic assays. Specifically, the presence of multiple β-lactamase genes was observed. The class A β-lactamase genes, tem-1 (ampicillin-resistance) and ctx-M-14 (cefotaxime conferring gene), were found in multiple sites of ISS. In addition, presence of mecA gene (penicillin clusters) was confirmed in several sampling locations from both ISS flights. Finally, the existence of the ermA gene (erythromycin) was established. These results suggest widespread and consistent distribution of multiple antibiotic resistance genes throughout the ISS. The resistome data generated via molecular methods will be extremely important in determining the microbial significance to the crew health and the ISS maintenance. These data sets will be placed in the NASA GeneLab bioinformatics environment - consisting of a database, computational tools, and improved methods - that would subsequently be made open to the scientific research community to encourage innovation.

Ecosystems Biology Approaches To Determine Key Fitness Traits of Soil Microorganisms

NASA Astrophysics Data System (ADS)

Brodie, E.; Zhalnina, K.; Karaoz, U.; Cho, H.; Nuccio, E. E.; Shi, S.; Lipton, M. S.; Zhou, J.; Pett-Ridge, J.; Northen, T.; Firestone, M.

2014-12-01

The application of theoretical approaches such as trait-based modeling represent powerful tools to explain and perhaps predict complex patterns in microbial distribution and function across environmental gradients in space and time. These models are mostly deterministic and where available are built upon a detailed understanding of microbial physiology and response to environmental factors. However as most soil microorganisms have not been cultivated, for the majority our understanding is limited to insights from environmental 'omic information. Information gleaned from 'omic studies of complex systems should be regarded as providing hypotheses, and these hypotheses should be tested under controlled laboratory conditions if they are to be propagated into deterministic models. In a semi-arid Mediterranean grassland system we are attempting to dissect microbial communities into functional guilds with defined physiological traits and are using a range of 'omics approaches to characterize their metabolic potential and niche preference. Initially, two physiologically relevant time points (peak plant activity and prior to wet-up) were sampled and metagenomes sequenced deeply (600-900 Gbp). Following assembly, differential coverage and nucleotide frequency binning were carried out to yield draft genomes. In addition, using a range of cultivation media we have isolated a broad range of bacteria representing abundant bacterial genotypes and with genome sequences of almost 40 isolates are testing genomic predictions regarding growth rate, temperature and substrate utilization in vitro. This presentation will discuss the opportunities and challenges in parameterizing microbial functional guilds from environmental 'omic information for use in trait-based models.

Research from the NASA Twins Study and Omics in Support of Mars Missions

NASA Technical Reports Server (NTRS)

Kundrot, C.; Shelhamer, M.; Scott, G.

2015-01-01

The NASA Twins Study, NASA's first foray into integrated omic studies in humans, illustrates how an integrated omics approach can be brought to bear on the challenges to human health and performance on a Mars mission. The NASA Twins Study involves US Astronaut Scott Kelly and his identical twin brother, Mark Kelly, a retired US Astronaut. No other opportunity to study a twin pair for a prolonged period with one subject in space and one on the ground is available for the foreseeable future. A team of 10 principal investigators are conducting the Twins Study, examining a very broad range of biological functions including the genome, epigenome, transcriptome, proteome, metabolome, gut microbiome, immunological response to vaccinations, indicators of atherosclerosis, physiological fluid shifts, and cognition. A novel aspect of the study is the integrated study of molecular, physiological, cognitive, and microbiological properties. Major sample and data collection from both subjects for this study began approximately six months before Scott Kelly's one year mission on the ISS, continue while Scott Kelly is in flight and will conclude approximately six months after his return to Earth. Mark Kelly will remain on Earth during this study, in a lifestyle unconstrained by this study, thereby providing a measure of normal variation in the properties being studied. An overview of initial results and the future plans will be described as well as the technological and ethical issues raised for spaceflight studies involving omics.

Omics Advances in Ecotoxicology.

PubMed

Zhang, Xiaowei; Xia, Pu; Wang, Pingping; Yang, Jianghu; Baird, Donald J

2018-04-03

Toxic substances in the environment generate adverse effects at all levels of biological organization from the molecular level to community and ecosystem. Given this complexity, it is not surprising that ecotoxicologists have struggled to address the full consequences of toxic substance release at ecosystem level, due to the limits of observational and experimental tools to reveal the changes in deep structure at different levels of organization. -Omics technologies, consisting of genomics and ecogenomics, have the power to reveal, in unprecedented detail, the cellular processes of an individual or biodiversity of a community in response to environmental change with high sample/observation throughput. This represents a historic opportunity to transform the way we study toxic substances in ecosystems, through direct linkage of ecological effects with the systems biology of organisms. Three recent examples of -omics advance in the assessment of toxic substances are explored here: (1) the use of functional genomics in the discovery of novel molecular mechanisms of toxicity of chemicals in the environment; (2) the development of laboratory pipelines of dose-dependent, reduced transcriptomics to support high-throughput chemical testing at the biological pathway level; and (3) the use of eDNA metabarcoding approaches for assessing chemical effects on biological communities in mesocosm experiments and through direct observation in field monitoring. -Omics advances in ecotoxicological studies not only generate new knowledge regarding mechanisms of toxicity and environmental effect, improving the relevance and immediacy of laboratory toxicological assessment, but can provide a wholly new paradigm for ecotoxicology by linking ecological models to mechanism-based, systems biology approaches.

Incorporation of omics analyses into artificial gravity research for space exploration countermeasure development.

PubMed

Schmidt, Michael A; Goodwin, Thomas J; Pelligra, Ralph

The next major steps in human spaceflight include flyby, orbital, and landing missions to the Moon, Mars, and near earth asteroids. The first crewed deep space mission is expected to launch in 2022, which affords less than 7 years to address the complex question of whether and how to apply artificial gravity to counter the effects of prolonged weightlessness. Various phenotypic changes are demonstrated during artificial gravity experiments. However, the molecular dynamics (genotype and molecular phenotypes) that underlie these morphological, physiological, and behavioral phenotypes are far more complex than previously understood. Thus, targeted molecular assessment of subjects under various G conditions can be expected to miss important patterns of molecular variance that inform the more general phenotypes typically being measured. Use of omics methods can help detect changes across broad molecular networks, as various G-loading paradigms are applied. This will be useful in detecting off-target, or unanticipated effects of the different gravity paradigms applied to humans or animals. Insights gained from these approaches may eventually be used to inform countermeasure development or refine the deployment of existing countermeasures. This convergence of the omics and artificial gravity research communities may be critical if we are to develop the proper artificial gravity solutions under the severely compressed timelines currently established. Thus, the omics community may offer a unique ability to accelerate discovery, provide new insights, and benefit deep space missions in ways that have not been previously considered.

Annotation-based inference of transporter function.

PubMed

Lee, Thomas J; Paulsen, Ian; Karp, Peter

2008-07-01

We present a method for inferring and constructing transport reactions for transporter proteins based primarily on the analysis of the names of individual proteins in the genome annotation of an organism. Transport reactions are declarative descriptions of transporter activities, and thus can be manipulated computationally, unlike free-text protein names. Once transporter activities are encoded as transport reactions, a number of computational analyses are possible including database queries by transporter activity; inclusion of transporters into an automatically generated metabolic-map diagram that can be painted with omics data to aid in their interpretation; detection of anomalies in the metabolic and transport networks, such as substrates that are transported into the cell but are not inputs to any metabolic reaction or pathway; and comparative analyses of the transport capabilities of different organisms. On randomly selected organisms, the method achieves precision and recall rates of 0.93 and 0.90, respectively in identifying transporter proteins by name within the complete genome. The method obtains 67.5% accuracy in predicting complete transport reactions; if allowance is made for predictions that are overly general yet not incorrect, reaction prediction accuracy is 82.5%. The method is implemented as part of PathoLogic, the inference component of the Pathway Tools software. Pathway Tools is freely available to researchers at non-commercial institutions, including source code; a fee applies to commercial institutions. Supplementary data are available at Bioinformatics online.

Study of cnidarian-algal symbiosis in the "omics" age.

PubMed

Meyer, Eli; Weis, Virginia M

2012-08-01

The symbiotic associations between cnidarians and dinoflagellate algae (Symbiodinium) support productive and diverse ecosystems in coral reefs. Many aspects of this association, including the mechanistic basis of host-symbiont recognition and metabolic interaction, remain poorly understood. The first completed genome sequence for a symbiotic anthozoan is now available (the coral Acropora digitifera), and extensive expressed sequence tag resources are available for a variety of other symbiotic corals and anemones. These resources make it possible to profile gene expression, protein abundance, and protein localization associated with the symbiotic state. Here we review the history of "omics" studies of cnidarian-algal symbiosis and the current availability of sequence resources for corals and anemones, identifying genes putatively involved in symbiosis across 10 anthozoan species. The public availability of candidate symbiosis-associated genes leaves the field of cnidarian-algal symbiosis poised for in-depth comparative studies of sequence diversity and gene expression and for targeted functional studies of genes associated with symbiosis. Reviewing the progress to date suggests directions for future investigations of cnidarian-algal symbiosis that include (i) sequencing of Symbiodinium, (ii) proteomic analysis of the symbiosome membrane complex, (iii) glycomic analysis of Symbiodinium cell surfaces, and (iv) expression profiling of the gastrodermal cells hosting Symbiodinium.

Cross-omics comparison of stress responses in mesothelial cells exposed to heat- versus filter-sterilized peritoneal dialysis fluids.

PubMed

Kratochwill, Klaus; Bender, Thorsten O; Lichtenauer, Anton M; Herzog, Rebecca; Tarantino, Silvia; Bialas, Katarzyna; Jörres, Achim; Aufricht, Christoph

2015-01-01

Recent research suggests that cytoprotective responses, such as expression of heat-shock proteins, might be inadequately induced in mesothelial cells by heat-sterilized peritoneal dialysis (PD) fluids. This study compares transcriptome data and multiple protein expression profiles for providing new insight into regulatory mechanisms. Two-dimensional difference gel electrophoresis (2D-DIGE) based proteomics and topic defined gene expression microarray-based transcriptomics techniques were used to evaluate stress responses in human omental peritoneal mesothelial cells in response to heat- or filter-sterilized PD fluids. Data from selected heat-shock proteins were validated by 2D western-blot analysis. Comparison of proteomics and transcriptomics data discriminated differentially regulated protein abundance into groups depending on correlating or noncorrelating transcripts. Inadequate abundance of several heat-shock proteins following exposure to heat-sterilized PD fluids is not reflected on the mRNA level indicating interference beyond transcriptional regulation. For the first time, this study describes evidence for posttranscriptional inadequacy of heat-shock protein expression by heat-sterilized PD fluids as a novel cytotoxic property. Cross-omics technologies introduce a novel way of understanding PDF bioincompatibility and searching for new interventions to reestablish adequate cytoprotective responses.

An OMIC approach to elaborate the antibacterial mechanisms of different alkaloids.

PubMed

Avci, Fatma Gizem; Sayar, Nihat Alpagu; Sariyar Akbulut, Berna

2018-05-01

Plant-derived substances have regained interest in the fight against antibiotic resistance owing to their distinct antimicrobial mechanisms and multi-target properties. With the recent advances in instrumentation and analysis techniques, OMIC approaches are extensively used for target identification and elucidation of the mechanism of phytochemicals in drug discovery. In the current study, RNA sequencing based transcriptional profiling together with global differential protein expression analysis was used to comparatively elaborate the activities and the effects of the plant alkaloids boldine, bulbocapnine, and roemerine along with the well-known antimicrobial alkaloid berberine in Bacillus subtilis cells. The transcriptomic findings were validated by qPCR. Images from scanning electron microscope were obtained to visualize the effects on the whole-cells. The results showed that among the three selected alkaloids, only roemerine possessed antibacterial activity. Unlike berberine, which is susceptible to efflux through multidrug resistance pumps, roemerine accumulated in the cells. This in turn resulted in oxidative stress and building up of reactive oxygen species, which eventually deregulated various pathways such as iron uptake. Treatment with boldine or bulbocapnine slightly affected various metabolic pathways but has not changed the growth patterns at all. Copyright © 2018 Elsevier Ltd. All rights reserved.

BioVLAB-mCpG-SNP-EXPRESS: A system for multi-level and multi-perspective analysis and exploration of DNA methylation, sequence variation (SNPs), and gene expression from multi-omics data.

PubMed

Chae, Heejoon; Lee, Sangseon; Seo, Seokjun; Jung, Daekyoung; Chang, Hyeonsook; Nephew, Kenneth P; Kim, Sun

2016-12-01

Measuring gene expression, DNA sequence variation, and DNA methylation status is routinely done using high throughput sequencing technologies. To analyze such multi-omics data and explore relationships, reliable bioinformatics systems are much needed. Existing systems are either for exploring curated data or for processing omics data in the form of a library such as R. Thus scientists have much difficulty in investigating relationships among gene expression, DNA sequence variation, and DNA methylation using multi-omics data. In this study, we report a system called BioVLAB-mCpG-SNP-EXPRESS for the integrated analysis of DNA methylation, sequence variation (SNPs), and gene expression for distinguishing cellular phenotypes at the pairwise and multiple phenotype levels. The system can be deployed on either the Amazon cloud or a publicly available high-performance computing node, and the data analysis and exploration of the analysis result can be conveniently done using a web-based interface. In order to alleviate analysis complexity, all the process are fully automated, and graphical workflow system is integrated to represent real-time analysis progression. The BioVLAB-mCpG-SNP-EXPRESS system works in three stages. First, it processes and analyzes multi-omics data as input in the form of the raw data, i.e., FastQ files. Second, various integrated analyses such as methylation vs. gene expression and mutation vs. methylation are performed. Finally, the analysis result can be explored in a number of ways through a web interface for the multi-level, multi-perspective exploration. Multi-level interpretation can be done by either gene, gene set, pathway or network level and multi-perspective exploration can be explored from either gene expression, DNA methylation, sequence variation, or their relationship perspective. The utility of the system is demonstrated by performing analysis of phenotypically distinct 30 breast cancer cell line data set. BioVLAB-mCpG-SNP-EXPRESS is available at http://biohealth.snu.ac.kr/software/biovlab_mcpg_snp_express/. Copyright © 2016 Elsevier Inc. All rights reserved.

'Omics' biomarkers associated with chronic low back pain: protocol of a retrospective longitudinal study.

PubMed

Allegri, Massimo; De Gregori, Manuela; Minella, Cristina E; Klersy, Catherine; Wang, Wei; Sim, Moira; Gieger, Christian; Manz, Judith; Pemberton, Iain K; MacDougall, Jane; Williams, Frances Mk; Van Zundert, Jan; Buyse, Klaas; Lauc, Gordan; Gudelj, Ivan; Primorac, Dragan; Skelin, Andrea; Aulchenko, Yurii S; Karssen, Lennart C; Kapural, Leonardo; Rauck, Richard; Fanelli, Guido

2016-10-19

Chronic low back pain (CLBP) produces considerable direct costs as well as indirect burdens for society, industry and health systems. CLBP is characterised by heterogeneity, inclusion of several pain syndromes, different underlying molecular pathologies and interaction with psychosocial factors that leads to a range of clinical manifestations. There is still much to understand in the underlying pathological processes and the non-psychosocial factors which account for differences in outcomes. Biomarkers that may be objectively used for diagnosis and personalised, targeted and cost-effective treatment are still lacking. Therefore, any data that may be obtained at the '-omics' level (glycomics, Activomics and genome-wide association studies-GWAS) may be helpful to use as dynamic biomarkers for elucidating CLBP pathogenesis and may ultimately provide prognostic information too. By means of a retrospective, observational, case-cohort, multicentre study, we aim to investigate new promising biomarkers potentially able to solve some of the issues related to CLBP. The study follows a two-phase, 1:2 case-control model. A total of 12 000 individuals (4000 cases and 8000 controls) will be enrolled; clinical data will be registered, with particular attention to pain characteristics and outcomes of pain treatments. Blood samples will be collected to perform -omics studies. The primary objective is to recognise genetic variants associated with CLBP; secondary objectives are to study glycomics and Activomics profiles associated with CLBP. The study is part of the PainOMICS project funded by European Community in the Seventh Framework Programme. The study has been approved from competent ethical bodies and copies of approvals were provided to the European Commission before starting the study. Results of the study will be reviewed by the Scientific Board and Ethical Committee of the PainOMICS Consortium. The scientific results will be disseminated through peer-reviewed journals. NCT02037789; Pre-results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Systems metabolic engineering: the creation of microbial cell factories by rational metabolic design and evolution.

PubMed

Furusawa, Chikara; Horinouchi, Takaaki; Hirasawa, Takashi; Shimizu, Hiroshi

2013-01-01

It is widely acknowledged that in order to establish sustainable societies, production processes should shift from petrochemical-based processes to bioprocesses. Because bioconversion technologies, in which biomass resources are converted to valuable materials, are preferable to processes dependent on fossil resources, the former should be further developed. The following two approaches can be adopted to improve cellular properties and obtain high productivity and production yield of target products: (1) optimization of cellular metabolic pathways involved in various bioprocesses and (2) creation of stress-tolerant cells that can be active even under severe stress conditions in the bioprocesses. Recent progress in omics analyses has facilitated the analysis of microorganisms based on bioinformatics data for molecular breeding and bioprocess development. Systems metabolic engineering is a new area of study, and it has been defined as a methodology in which metabolic engineering and systems biology are integrated to upgrade the designability of industrially useful microorganisms. This chapter discusses multi-omics analyses and rational design methods for molecular breeding. The first is an example of the rational design of metabolic networks for target production by flux balance analysis using genome-scale metabolic models. Recent progress in the development of genome-scale metabolic models and the application of these models to the design of desirable metabolic networks is also described in this example. The second is an example of evolution engineering with omics analyses for the creation of stress-tolerant microorganisms. Long-term culture experiments to obtain the desired phenotypes and omics analyses to identify the phenotypic changes are described here.

Cross-Platform Toxicogenomics for the Prediction of Non-Genotoxic Hepatocarcinogenesis in Rat

PubMed Central

Metzger, Ute; Templin, Markus F.; Plummer, Simon; Ellinger-Ziegelbauer, Heidrun; Zell, Andreas

2014-01-01

In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens. PMID:24830643

CAS-viewer: web-based tool for splicing-guided integrative analysis of multi-omics cancer data.

PubMed

Han, Seonggyun; Kim, Dongwook; Kim, Youngjun; Choi, Kanghoon; Miller, Jason E; Kim, Dokyoon; Lee, Younghee

2018-04-20

The Cancer Genome Atlas (TCGA) project is a public resource that provides transcriptomic, DNA sequence, methylation, and clinical data for 33 cancer types. Transforming the large size and high complexity of TCGA cancer genome data into integrated knowledge can be useful to promote cancer research. Alternative splicing (AS) is a key regulatory mechanism of genes in human cancer development and in the interaction with epigenetic factors. Therefore, AS-guided integration of existing TCGA data sets will make it easier to gain insight into the genetic architecture of cancer risk and related outcomes. There are already existing tools analyzing and visualizing alternative mRNA splicing patterns for large-scale RNA-seq experiments. However, these existing web-based tools are limited to the analysis of individual TCGA data sets at a time, such as only transcriptomic information. We implemented CAS-viewer (integrative analysis of Cancer genome data based on Alternative Splicing), a web-based tool leveraging multi-cancer omics data from TCGA. It illustrates alternative mRNA splicing patterns along with methylation, miRNAs, and SNPs, and then provides an analysis tool to link differential transcript expression ratio to methylation, miRNA, and splicing regulatory elements for 33 cancer types. Moreover, one can analyze AS patterns with clinical data to identify potential transcripts associated with different survival outcome for each cancer. CAS-viewer is a web-based application for transcript isoform-driven integration of multi-omics data in multiple cancer types and will aid in the visualization and possible discovery of biomarkers for cancer by integrating multi-omics data from TCGA.

Serial-omics characterization of equine urine

PubMed Central

Yuan, Min; Breitkopf, Susanne B.

2017-01-01

Horse urine is easily collected and contains molecules readily measurable using mass spectrometry that can be used as biomarkers representative of health, disease or drug tampering. This study aimed at analyzing microliter levels of horse urine to purify, identify and quantify proteins, polar metabolites and non-polar lipids. Urine from a healthy 12 year old quarter horse mare on a diet of grass hay and vitamin/mineral supplements with limited pasture access was collected for serial-omics characterization. The urine was treated with methyl tert-butyl ether (MTBE) and methanol to partition into three distinct layers for protein, non-polar lipid and polar metabolite content from a single liquid-liquid extraction and was repeated two times. Each layer was analyzed by high performance liquid chromatography—high resolution tandem mass spectrometry (LC-MS/MS) to obtain protein sequence and relative protein levels as well as identify and quantify small polar metabolites and lipids. The results show 46 urine proteins, many related to normal kidney function, structural and circulatory proteins as well as 474 small polar metabolites but only 10 lipid molecules. Metabolites were mostly related to urea cycle and ammonia recycling as well as amino acid related pathways, plant diet specific molecules, etc. The few lipids represented triglycerides and phospholipids. These data show a complete mass spectrometry based—omics characterization of equine urine from a single 333 μL mid-stream urine aliquot. These omics data help serve as a baseline for healthy mare urine composition and the analyses can be used to monitor disease progression, health status, monitor drug use, etc. PMID:29028822

Integrative Analysis of Omics Big Data.

PubMed

Yu, Xiang-Tian; Zeng, Tao

2018-01-01

The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is "bottom-up integration" mode with follow-up manual integration, and the other one is "top-down integration" mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration.

PubMed

Cambiaghi, Alice; Ferrario, Manuela; Masseroli, Marco

2017-05-01

Metabolomics is a rapidly growing field consisting of the analysis of a large number of metabolites at a system scale. The two major goals of metabolomics are the identification of the metabolites characterizing each organism state and the measurement of their dynamics under different situations (e.g. pathological conditions, environmental factors). Knowledge about metabolites is crucial for the understanding of most cellular phenomena, but this information alone is not sufficient to gain a comprehensive view of all the biological processes involved. Integrated approaches combining metabolomics with transcriptomics and proteomics are thus required to obtain much deeper insights than any of these techniques alone. Although this information is available, multilevel integration of different 'omics' data is still a challenge. The handling, processing, analysis and integration of these data require specialized mathematical, statistical and bioinformatics tools, and several technical problems hampering a rapid progress in the field exist. Here, we review four main tools for number of users or provided features (MetaCoreTM, MetaboAnalyst, InCroMAP and 3Omics) out of the several available for metabolomic data analysis and integration with other 'omics' data, highlighting their strong and weak aspects; a number of related issues affecting data analysis and integration are also identified and discussed. Overall, we provide an objective description of how some of the main currently available software packages work, which may help the experimental practitioner in the choice of a robust pipeline for metabolomic data analysis and integration. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

EU Framework 6 Project: Predictive Toxicology (PredTox)-overview and outcome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suter, Laura, E-mail: Laura.suter-dick@roche.com; Schroeder, Susanne; Meyer, Kirstin

2011-04-15

In this publication, we report the outcome of the integrated EU Framework 6 Project: Predictive Toxicology (PredTox), including methodological aspects and overall conclusions. Specific details including data analysis and interpretation are reported in separate articles in this issue. The project, partly funded by the EU, was carried out by a consortium of 15 pharmaceutical companies, 2 SMEs, and 3 universities. The effects of 16 test compounds were characterized using conventional toxicological parameters and 'omics' technologies. The three major observed toxicities, liver hypertrophy, bile duct necrosis and/or cholestasis, and kidney proximal tubular damage were analyzed in detail. The combined approach ofmore » 'omics' and conventional toxicology proved a useful tool for mechanistic investigations and the identification of putative biomarkers. In our hands and in combination with histopathological assessment, target organ transcriptomics was the most prolific approach for the generation of mechanistic hypotheses. Proteomics approaches were relatively time-consuming and required careful standardization. NMR-based metabolomics detected metabolite changes accompanying histopathological findings, providing limited additional mechanistic information. Conversely, targeted metabolite profiling with LC/GC-MS was very useful for the investigation of bile duct necrosis/cholestasis. In general, both proteomics and metabolomics were supportive of other findings. Thus, the outcome of this program indicates that 'omics' technologies can help toxicologists to make better informed decisions during exploratory toxicological studies. The data support that hypothesis on mode of action and discovery of putative biomarkers are tangible outcomes of integrated 'omics' analysis. Qualification of biomarkers remains challenging, in particular in terms of identification, mechanistic anchoring, appropriate specificity, and sensitivity.« less

Omics Approaches for the Engineering of Pathogen Resistant Plants.

PubMed

Gomez-Casati, Diego F; Pagani, María A; Busi, María V; Bhadauria, Vijai

2016-01-01

The attack of different pathogens, such as bacteria, fungi and viruses has a negative impact on crop production. In counter such attacks, plants have developed different strategies involving the modification of gene expression, activation of several metabolic pathways and post-translational modification of proteins, which culminate into the accumulation of primary and secondary metabolites implicated in plant defense responses. The recent advancement in omics techniques allows the increase coverage of plants transcriptomes, proteomes and metabolomes during pathogen attack, and the modulation of the response after the infection. Omics techniques also allow us to learn more about the biological cycle of the pathogens in addition to the identification of novel virulence factors in pathogens and their host targets. Both approaches become important to decipher the mechanism underlying pathogen attacks and to develop strategies for improving disease-resistant plants. In this review, we summarize some of the contribution of genomics, transcriptomics, proteomics, metabolomics and metallomics in devising the strategies to obtain plants with increased resistance to pathogens. These approaches constitute important research tools in the development of new technologies for the protection against diseases and increase plant production.

Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules

PubMed Central

Bersanelli, Matteo; Mosca, Ettore; Remondini, Daniel; Castellani, Gastone; Milanesi, Luciano

2016-01-01

A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD. PMID:27731320

The first decade of MALDI protein profiling: a lesson in translational biomarker research.

PubMed

Albrethsen, Jakob

2011-05-16

MALDI protein profiling has identified several important challenges in omics-based biomarker research. First, research into the analytical performance of a novel omics-platform of potential diagnostic impact must be carried out in a critical manner and according to common guidelines. Evaluation studies should be performed at an early time and preferably before massive advancement into explorative biomarker research. In particular, MALDI profiling underscores the need for an adequate understanding of the causal relationship between molecular abundance and the quantitative measure in multivariate biomarker research. Secondly, MALDI profiling has raised awareness of the significant risk of false-discovery in biomarker research due to several confounding factors, including sample processing and unspecific host-response to disease. Here, the experience from MALDI profiling supports that a central challenge in unbiased molecular profiling is to pinpoint the aberrations of clinical interest among potentially massive unspecific changes that can accompany disease. The lessons from the first decade of MALDI protein profiling are relevant for future efforts in advancing omics-based biomarker research beyond the laboratory setting and into clinical verification. Copyright © 2011 Elsevier B.V. All rights reserved.

Omics-based approaches reveal phospholipids remodeling of Rhizopus oryzae responding to furfural stress for fumaric acid-production from xylose.

PubMed

Pan, Xinrong; Liu, Huanhuan; Liu, Jiao; Wang, Cheng; Wen, Jianping

2016-12-01

In order to relieve the toxicity of furfural on Rhizopus oryzae fermentation, the molecular mechanism of R. oryzae responding to furfural stress for fumaric acid-production was investigated by omics-based approaches. In metabolomics analysis, 29 metabolites including amino acid, sugars, polyols and fatty acids showed significant changes for maintaining the basic cell metabolism at the cost of lowering fumaric acid production. To further uncover the survival mechanism, lipidomics was carried out, revealing that phosphatidylcholine, phosphatidylglycerol, phosphatidylinositol and polyunsaturated acyl chains might be closely correlated with R. oryzae's adapting to furfural stress. Based on the above omics analysis, lecithin, inositol and soybean oil were exogenously supplemented separately with an optimized concentration in the presence of furfural, which increased fumaric acid titer from 5.78g/L to 10.03g/L, 10.05g/L and 12.13g/L (increased by 73.5%, 73.8% and 110%, respectively). These findings provide a methodological guidance for hemicellulose-fumaric acid development. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Systematic Approach to Time-series Metabolite Profiling and RNA-seq Analysis of Chinese Hamster Ovary Cell Culture.

PubMed

Hsu, Han-Hsiu; Araki, Michihiro; Mochizuki, Masao; Hori, Yoshimi; Murata, Masahiro; Kahar, Prihardi; Yoshida, Takanobu; Hasunuma, Tomohisa; Kondo, Akihiko

2017-03-02

Chinese hamster ovary (CHO) cells are the primary host used for biopharmaceutical protein production. The engineering of CHO cells to produce higher amounts of biopharmaceuticals has been highly dependent on empirical approaches, but recent high-throughput "omics" methods are changing the situation in a rational manner. Omics data analyses using gene expression or metabolite profiling make it possible to identify key genes and metabolites in antibody production. Systematic omics approaches using different types of time-series data are expected to further enhance understanding of cellular behaviours and molecular networks for rational design of CHO cells. This study developed a systematic method for obtaining and analysing time-dependent intracellular and extracellular metabolite profiles, RNA-seq data (enzymatic mRNA levels) and cell counts from CHO cell cultures to capture an overall view of the CHO central metabolic pathway (CMP). We then calculated correlation coefficients among all the profiles and visualised the whole CMP by heatmap analysis and metabolic pathway mapping, to classify genes and metabolites together. This approach provides an efficient platform to identify key genes and metabolites in CHO cell culture.

Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas

PubMed Central

Hou, Yu; Guo, Huahu; Cao, Chen; Li, Xianlong; Hu, Boqiang; Zhu, Ping; Wu, Xinglong; Wen, Lu; Tang, Fuchou; Huang, Yanyi; Peng, Jirun

2016-01-01

Single-cell genome, DNA methylome, and transcriptome sequencing methods have been separately developed. However, to accurately analyze the mechanism by which transcriptome, genome and DNA methylome regulate each other, these omic methods need to be performed in the same single cell. Here we demonstrate a single-cell triple omics sequencing technique, scTrio-seq, that can be used to simultaneously analyze the genomic copy-number variations (CNVs), DNA methylome, and transcriptome of an individual mammalian cell. We show that large-scale CNVs cause proportional changes in RNA expression of genes within the gained or lost genomic regions, whereas these CNVs generally do not affect DNA methylation in these regions. Furthermore, we applied scTrio-seq to 25 single cancer cells derived from a human hepatocellular carcinoma tissue sample. We identified two subpopulations within these cells based on CNVs, DNA methylome, or transcriptome of individual cells. Our work offers a new avenue of dissecting the complex contribution of genomic and epigenomic heterogeneities to the transcriptomic heterogeneity within a population of cells. PMID:26902283

Probing the evolution, ecology and physiology of marine protists using transcriptomics.

PubMed

Caron, David A; Alexander, Harriet; Allen, Andrew E; Archibald, John M; Armbrust, E Virginia; Bachy, Charles; Bell, Callum J; Bharti, Arvind; Dyhrman, Sonya T; Guida, Stephanie M; Heidelberg, Karla B; Kaye, Jonathan Z; Metzner, Julia; Smith, Sarah R; Worden, Alexandra Z

2017-01-01

Protists, which are single-celled eukaryotes, critically influence the ecology and chemistry of marine ecosystems, but genome-based studies of these organisms have lagged behind those of other microorganisms. However, recent transcriptomic studies of cultured species, complemented by meta-omics analyses of natural communities, have increased the amount of genetic information available for poorly represented branches on the tree of eukaryotic life. This information is providing insights into the adaptations and interactions between protists and other microorganisms and macroorganisms, but many of the genes sequenced show no similarity to sequences currently available in public databases. A better understanding of these newly discovered genes will lead to a deeper appreciation of the functional diversity and metabolic processes in the ocean. In this Review, we summarize recent developments in our understanding of the ecology, physiology and evolution of protists, derived from transcriptomic studies of cultured strains and natural communities, and discuss how these novel large-scale genetic datasets will be used in the future.

Lipidomics informatics for life-science.

PubMed

Schwudke, D; Shevchenko, A; Hoffmann, N; Ahrends, R

2017-11-10

Lipidomics encompasses analytical approaches that aim to identify and quantify the complete set of lipids, defined as lipidome in a given cell, tissue or organism as well as their interactions with other molecules. The majority of lipidomics workflows is based on mass spectrometry and has been proven as a powerful tool in system biology in concert with other Omics disciplines. Unfortunately, bioinformatics infrastructures for this relatively young discipline are limited only to some specialists. Search engines, quantification algorithms, visualization tools and databases developed by the 'Lipidomics Informatics for Life-Science' (LIFS) partners will be restructured and standardized to provide broad access to these specialized bioinformatics pipelines. There are many medical challenges related to lipid metabolic alterations that will be fostered by capacity building suggested by LIFS. LIFS as member of the 'German Network for Bioinformatics' (de.NBI) node for 'Bioinformatics for Proteomics' (BioInfra.Prot) and will provide access to the described software as well as to tutorials and consulting services via a unified web-portal. Copyright © 2017 Elsevier B.V. All rights reserved.

Agrigenomics for Microalgal Biofuel Production: An Overview of Various Bioinformatics Resources and Recent Studies to Link OMICS to Bioenergy and Bioeconomy

PubMed Central

Misra, Namrata; Parida, Bikram Kumar

2013-01-01

Abstract Microalgal biofuels offer great promise in contributing to the growing global demand for alternative sources of renewable energy. However, to make algae-based fuels cost competitive with petroleum, lipid production capabilities of microalgae need to improve substantially. Recent progress in algal genomics, in conjunction with other “omic” approaches, has accelerated the ability to identify metabolic pathways and genes that are potential targets in the development of genetically engineered microalgal strains with optimum lipid content. In this review, we summarize the current bioeconomic status of global biofuel feedstocks with particular reference to the role of “omics” in optimizing sustainable biofuel production. We also provide an overview of the various databases and bioinformatics resources available to gain a more complete understanding of lipid metabolism across algal species, along with the recent contributions of “omic” approaches in the metabolic pathway studies for microalgal biofuel production. PMID:24044362

Establishing a Twin Register: An Invaluable Resource for (Behavior) Genetic, Epidemiological, Biomarker, and 'Omics' Studies.

PubMed

Odintsova, Veronika V; Willemsen, Gonneke; Dolan, Conor V; Hottenga, Jouke-Jan; Martin, Nicholas G; Slagboom, P Eline; Ordoñana, Juan R; Boomsma, Dorret I

2018-06-01

Twin registers are wonderful research resources for research applications in medical and behavioral genetics, epidemiology, psychology, molecular genetics, and other areas of research. New registers continue to be launched all over the world as researchers from different disciplines recognize the potential to boost and widen their research agenda. In this article, we discuss multiple aspects that need to be taken into account when initiating a register, from its preliminary sketch to its actual development. This encompasses aspects related to the strategic planning and key elements of research designs, promotion and management of a twin register, including recruitment and retaining of twins and family members of twins, phenotyping, database organization, and collaborations between registers. We also present information on questions unique to twin registers and twin-biobanks, such as the assessment of zygosity by SNP arrays, the design of (biomarker) studies involving related participants, and the analyses of clustered data. Altogether, we provide a number of basic guidelines and recommendations for reflection when planning a twin register.

The clinical implications of immunogenomics in colorectal cancer: A path for precision medicine.

PubMed

Riley, Jenny M; Cross, Ashley W; Paulos, Chrystal M; Rubinstein, Mark P; Wrangle, John; Camp, E Ramsay

2018-04-15

Colorectal cancer (CRC) remains the third most common malignancy and the second-leading cause of cancer-related deaths in the United States. Large multi-omic databases, such as The Cancer Genome Atlas and the International Colorectal Cancer Subtyping Consortium, have identified distinct molecular subtypes related to anatomy. The identification of genomic alterations in CRC is now critical because of the recent success and US Food and Drug Administration approval of pembrolizumab and nivolumab for microsatellite-instable tumors. In parallel, landmark studies have established the prognostic significance of the CRC tumor-infiltrating lymphocyte and the clinical impact of the tumor immune microenvironment. As a result, there is a growing appreciation for immunogenomics, the interconnected relation between tumor genomics and the immune microenvironment. The clinical implications of CRC immunogenomics continue to expand, and it will likely serve as a guide for next-generation immunotherapy strategies for improving outcomes for this disease. Cancer 2018;124:1650-9. © 2018 American Cancer Society. © 2018 American Cancer Society.

Digital pathology in nephrology clinical trials, research, and pathology practice.

PubMed

Barisoni, Laura; Hodgin, Jeffrey B

2017-11-01

In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine. Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morpho-omic taxonomies of renal diseases. The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.

Applying computation biology and "big data" to develop multiplex diagnostics for complex chronic diseases such as osteoarthritis.

PubMed

Ren, Guomin; Krawetz, Roman

2015-01-01

The data explosion in the last decade is revolutionizing diagnostics research and the healthcare industry, offering both opportunities and challenges. These high-throughput "omics" techniques have generated more scientific data in the last few years than in the entire history of mankind. Here we present a brief summary of how "big data" have influenced early diagnosis of complex diseases. We will also review some of the most commonly used "omics" techniques and their applications in diagnostics. Finally, we will discuss the issues brought by these new techniques when translating laboratory discoveries to clinical practice.

Biomarkers intersect with the exposome

PubMed Central

Rappaport, Stephen M.

2016-01-01

The exposome concept promotes use of omic tools for discovering biomarkers of exposure and biomarkers of disease in studies of diseased and healthy populations. A two-stage scheme is presented for profiling omic features in serum to discover molecular biomarkers and then for applying these biomarkers in follow-up studies. The initial component, referred to as an exposome-wide-association study (EWAS), employs metabolomics and proteomics to interrogate the serum exposome and, ultimately, to identify, validate and differentiate biomarkers of exposure and biomarkers of disease. Follow-up studies employ knowledge-driven designs to explore disease causality, prevention, diagnosis, prognosis and treatment. PMID:22672124

cual-id: Globally Unique, Correctable, and Human-Friendly Sample Identifiers for Comparative Omics Studies.

PubMed

Chase, John H; Bolyen, Evan; Rideout, Jai Ram; Caporaso, J Gregory

2016-01-01

The number of samples in high-throughput comparative "omics" studies is increasing rapidly due to declining experimental costs. To keep sample data and metadata manageable and to ensure the integrity of scientific results as the scale of these projects continues to increase, it is essential that we transition to better-designed sample identifiers. Ideally, sample identifiers should be globally unique across projects, project teams, and institutions; short (to facilitate manual transcription); correctable with respect to common types of transcription errors; opaque, meaning that they do not contain information about the samples; and compatible with existing standards. We present cual-id, a lightweight command line tool that creates, or mints, sample identifiers that meet these criteria without reliance on centralized infrastructure. cual-id allows users to assign universally unique identifiers, or UUIDs, that are globally unique to their samples. UUIDs are too long to be conveniently written on sampling materials, such as swabs or microcentrifuge tubes, however, so cual-id additionally generates human-friendly 4- to 12-character identifiers that map to their UUIDs and are unique within a project. By convention, we use "cual-id" to refer to the software, "CualID" to refer to the short, human-friendly identifiers, and "UUID" to refer to the globally unique identifiers. CualIDs are used by humans when they manually write or enter identifiers, while the longer UUIDs are used by computers to unambiguously reference a sample. Finally, cual-id optionally generates printable label sticker sheets containing Code 128 bar codes and CualIDs for labeling of sample collection and processing materials. IMPORTANCE The adoption of identifiers that are globally unique, correctable, and easily handwritten or manually entered into a computer will be a major step forward for sample tracking in comparative omics studies. As the fields transition to more-centralized sample management, for example, across labs within an institution, across projects funded under a common program, or in systems designed to facilitate meta- and/or integrated analysis, sample identifiers generated with cual-id will not need to change; thus, costly and error-prone updating of data and metadata identifiers will be avoided. Further, using cual-id will ensure that transcription errors in sample identifiers do not require the discarding of otherwise-useful samples that may have been expensive to obtain. Finally, cual-id is simple to install and use and is free for all use. No centralized infrastructure is required to ensure global uniqueness, so it is feasible for any lab to get started using these identifiers within their existing infrastructure.

Detection of Patient Subgroups with Differential Expression in Omics Data: A Comprehensive Comparison of Univariate Measures

PubMed Central

Ahrens, Maike; Turewicz, Michael; Casjens, Swaantje; May, Caroline; Pesch, Beate; Stephan, Christian; Woitalla, Dirk; Gold, Ralf; Brüning, Thomas; Meyer, Helmut E.

2013-01-01

Detection of yet unknown subgroups showing differential gene or protein expression is a frequent goal in the analysis of modern molecular data. Applications range from cancer biology over developmental biology to toxicology. Often a control and an experimental group are compared, and subgroups can be characterized by differential expression for only a subgroup-specific set of genes or proteins. Finding such genes and corresponding patient subgroups can help in understanding pathological pathways, diagnosis and defining drug targets. The size of the subgroup and the type of differential expression determine the optimal strategy for subgroup identification. To date, commonly used software packages hardly provide statistical tests and methods for the detection of such subgroups. Different univariate methods for subgroup detection are characterized and compared, both on simulated and on real data. We present an advanced design for simulation studies: Data is simulated under different distributional assumptions for the expression of the subgroup, and performance results are compared against theoretical upper bounds. For each distribution, different degrees of deviation from the majority of observations are considered for the subgroup. We evaluate classical approaches as well as various new suggestions in the context of omics data, including outlier sum, PADGE, and kurtosis. We also propose the new FisherSum score. ROC curve analysis and AUC values are used to quantify the ability of the methods to distinguish between genes or proteins with and without certain subgroup patterns. In general, FisherSum for small subgroups and -test for large subgroups achieve best results. We apply each method to a case-control study on Parkinson's disease and underline the biological benefit of the new method. PMID:24278130

Genome, transcriptome and methylome sequencing of a primitively eusocial wasp reveal a greatly reduced DNA methylation system in a social insect.

PubMed

Standage, Daniel S; Berens, Ali J; Glastad, Karl M; Severin, Andrew J; Brendel, Volker P; Toth, Amy L

2016-04-01

Comparative genomics of social insects has been intensely pursued in recent years with the goal of providing insights into the evolution of social behaviour and its underlying genomic and epigenomic basis. However, the comparative approach has been hampered by a paucity of data on some of the most informative social forms (e.g. incipiently and primitively social) and taxa (especially members of the wasp family Vespidae) for studying social evolution. Here, we provide a draft genome of the primitively eusocial model insect Polistes dominula, accompanied by analysis of caste-related transcriptome and methylome sequence data for adult queens and workers. Polistes dominula possesses a fairly typical hymenopteran genome, but shows very low genomewide GC content and some evidence of reduced genome size. We found numerous caste-related differences in gene expression, with evidence that both conserved and novel genes are related to caste differences. Most strikingly, these -omics data reveal a major reduction in one of the major epigenetic mechanisms that has been previously suggested to be important for caste differences in social insects: DNA methylation. Along with a conspicuous loss of a key gene associated with environmentally responsive DNA methylation (the de novo DNA methyltransferase Dnmt3), these wasps have greatly reduced genomewide methylation to almost zero. In addition to providing a valuable resource for comparative analysis of social insect evolution, our integrative -omics data for this important behavioural and evolutionary model system call into question the general importance of DNA methylation in caste differences and evolution in social insects. © 2016 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

PubMed Central

2015-01-01

Background Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. Results and discussion Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology. Conclusions Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery. PMID:25923811

A practical data processing workflow for multi-OMICS projects.

PubMed

Kohl, Michael; Megger, Dominik A; Trippler, Martin; Meckel, Hagen; Ahrens, Maike; Bracht, Thilo; Weber, Frank; Hoffmann, Andreas-Claudius; Baba, Hideo A; Sitek, Barbara; Schlaak, Jörg F; Meyer, Helmut E; Stephan, Christian; Eisenacher, Martin

2014-01-01

Multi-OMICS approaches aim on the integration of quantitative data obtained for different biological molecules in order to understand their interrelation and the functioning of larger systems. This paper deals with several data integration and data processing issues that frequently occur within this context. To this end, the data processing workflow within the PROFILE project is presented, a multi-OMICS project that aims on identification of novel biomarkers and the development of new therapeutic targets for seven important liver diseases. Furthermore, a software called CrossPlatformCommander is sketched, which facilitates several steps of the proposed workflow in a semi-automatic manner. Application of the software is presented for the detection of novel biomarkers, their ranking and annotation with existing knowledge using the example of corresponding Transcriptomics and Proteomics data sets obtained from patients suffering from hepatocellular carcinoma. Additionally, a linear regression analysis of Transcriptomics vs. Proteomics data is presented and its performance assessed. It was shown, that for capturing profound relations between Transcriptomics and Proteomics data, a simple linear regression analysis is not sufficient and implementation and evaluation of alternative statistical approaches are needed. Additionally, the integration of multivariate variable selection and classification approaches is intended for further development of the software. Although this paper focuses only on the combination of data obtained from quantitative Proteomics and Transcriptomics experiments, several approaches and data integration steps are also applicable for other OMICS technologies. Keeping specific restrictions in mind the suggested workflow (or at least parts of it) may be used as a template for similar projects that make use of different high throughput techniques. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan. Copyright © 2013 Elsevier B.V. All rights reserved.

Morphomics: An integral part of systems biology of the human placenta.

PubMed

Mayhew, T M

2015-04-01

The placenta is a transient organ the functioning of which has health consequences far beyond the embryo/fetus. Understanding the biology of any system (organ, organism, single cell, etc) requires a comprehensive and inclusive approach which embraces all the biomedical disciplines and 'omic' technologies and then integrates information obtained from all of them. Among the latest 'omics' is morphomics. The terms morphome and morphomics have been applied incoherently in biology and biomedicine but, recently, they have been given clear and widescale definitions. Morphomics is placed in the context of other 'omics' and its pertinent technologies and tools for sampling and quantitation are reviewed. Emphasis is accorded to the importance of random sampling principles in systems biology and the value of combining 3D quantification with alternative imaging techniques to advance knowledge and understanding of the human placental morphome. By analogy to other 'omes', the morphome is the totality of morphological features within a system and morphomics is the systematic study of those structures. Information about structure is required at multiple levels of resolution in order to understand better the processes by which a given system alters with time, experimental treatment or environmental insult. Therefore, morphomics research includes all imaging techniques at all levels of achievable resolution from gross anatomy and medical imaging, via optical and electron microscopy, to molecular characterisation. Quantification is an important element of all 'omics' studies and, because biological systems exist and operate in 3-dimensional (3D) space, precise descriptions of form, content and spatial relationships require the quantification of structure in 3D. These considerations are relevant to future study contributions to the Human Placenta Project. Copyright © 2015 Elsevier Ltd. All rights reserved.

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases.

PubMed

Moulos, Panagiotis; Klein, Julie; Jupp, Simon; Stevens, Robert; Bascands, Jean-Loup; Schanstra, Joost P

2013-07-24

Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.

MPLEx: a method for simultaneous pathogen inactivation and extraction of samples for multi-omics profiling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burnum-Johnson, Kristin E.; Kyle, Jennifer E.; Eisfeld, Amie J.

The continued emergence and spread of infectious agents is of increasing concern due to increased population growth and the associated increased livestock production to meet food demands, increased urbanization and land-use changes, and greater travel. A systems biology approach to infectious disease research can significantly advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can only take place subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivationmore » and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. Partial inactivation was observed for pathogens without exposed lipid membranes including 99.99% inactivation of community-associated methicillin-resistant Staphylococcus aureus, 99.6% and >99% inactivation of Clostridium difficile spores and vegetative cells, respectively, and 50% inactivation of adenovirus type 5. To demonstrate that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses, we highlight select proteomics, metabolomics and lipidomics data from human epithelial lung cells infected with wild-type and mutant forms of influenza H7N9. We believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen.« less

A multi-omics based ecological analysis of coastal marine sediments from Gladstone, in Australia's Central Queensland, and Heron Island, a nearby fringing platform reef.

PubMed

Beale, D J; Crosswell, J; Karpe, A V; Ahmed, W; Williams, M; Morrison, P D; Metcalfe, S; Staley, C; Sadowsky, M J; Palombo, E A; Steven, A D L

2017-12-31

The impact of anthropogenic factors arising from point and non-point pollution sources at a multi commodity marine port and its surrounding ecosystems were studied using sediment samples collected from a number of onshore (Gladstone Harbour and Facing Island) and offshore (Heron Island and Fitzroy Reefs) sites in Australia's Central Queensland. Sediment samples were analyzed for trace metals, organic carbon, polycyclic aromatic hydrocarbons (PAH), emerging chemicals of concern (ECC) and sterols. Similarly, the biological and biochemical interaction between the reef and its environment was analyzed by the multi-omic tools of next-generation sequencing characterization of the bacterial community and microbial community metabolic profiling. Overall, the trace elements were observed at the lower end of the Australian environmental guideline values at the offshore sites, while higher values were observed for the onshore locations Nickel and copper were observed above the high trigger value threshold at the onshore sites. The levels of PAH were below limits of detection across all sites. However, some of the ECC and sterols were observed at higher concentrations at both onshore and offshore locations, notably, the cholesterol family sterols and 17α-ethynylestradiol. Multi-omic analyses also indicated possible thermal and photo irradiation stressors on the bacterial communities at all the tested sites. The observed populations of γ-proteobacteria were found in combination with an increased pool of fatty acids that indicate fatty acid synthesis and utilisation of the intermediates of the shikimate pathways. This study demonstrates the value of applying a multi-omics approach for ecological assessments, in which a more detailed assessment of physical and chemical contaminants and their impact on the community bacterial biome is obtained. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Integrative Exploratory Analysis of Two or More Genomic Datasets.

PubMed

Meng, Chen; Culhane, Aedin

2016-01-01

Exploratory analysis is an essential step in the analysis of high throughput data. Multivariate approaches such as correspondence analysis (CA), principal component analysis, and multidimensional scaling are widely used in the exploratory analysis of single dataset. Modern biological studies often assay multiple types of biological molecules (e.g., mRNA, protein, phosphoproteins) on a same set of biological samples, thereby creating multiple different types of omics data or multiassay data. Integrative exploratory analysis of these multiple omics data is required to leverage the potential of multiple omics studies. In this chapter, we describe the application of co-inertia analysis (CIA; for analyzing two datasets) and multiple co-inertia analysis (MCIA; for three or more datasets) to address this problem. These methods are powerful yet simple multivariate approaches that represent samples using a lower number of variables, allowing a more easily identification of the correlated structure in and between multiple high dimensional datasets. Graphical representations can be employed to this purpose. In addition, the methods simultaneously project samples and variables (genes, proteins) onto the same lower dimensional space, so the most variant variables from each dataset can be selected and associated with samples, which can be further used to facilitate biological interpretation and pathway analysis. We applied CIA to explore the concordance between mRNA and protein expression in a panel of 60 tumor cell lines from the National Cancer Institute. In the same 60 cell lines, we used MCIA to perform a cross-platform comparison of mRNA gene expression profiles obtained on four different microarray platforms. Last, as an example of integrative analysis of multiassay or multi-omics data we analyzed transcriptomic, proteomic, and phosphoproteomic data from pluripotent (iPS) and embryonic stem (ES) cell lines.

Meta-analysis of pathway enrichment: combining independent and dependent omics data sets.

PubMed

Kaever, Alexander; Landesfeind, Manuel; Feussner, Kirstin; Morgenstern, Burkhard; Feussner, Ivo; Meinicke, Peter

2014-01-01

A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.

Gut Microbiota Dysbiosis as Risk and Premorbid Factors of IBD and IBS Along the Childhood-Adulthood Transition.

PubMed

Putignani, Lorenza; Del Chierico, Federica; Vernocchi, Pamela; Cicala, Michele; Cucchiara, Salvatore; Dallapiccola, Bruno

2016-02-01

Gastrointestinal disorders, although clinically heterogeneous, share pathogenic mechanisms, including genetic susceptibility, impaired gut barrier function, altered microbiota, and environmental triggers (infections, social and behavioral factors, epigenetic control, and diet). Gut microbiota has been studied for inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) in either children or adults, while modifiable gut microbiota features, acting as risk and premorbid factors along the childhood-adulthood transition, have not been thoroughly investigated so far. Indeed, the relationship between variations of the entire host/microbiota/environmental scenario and clinical phenotypes is still not fully understood. In this respect, tracking gut dysbiosis grading may help deciphering host phenotype-genotype associations and microbiota shifts in an integrated top-down omics-based approach within large-scale pediatric and adult case-control cohorts. Large-scale gut microbiota signatures and host inflammation patterns may be integrated with dietary habits, under genetic and epigenetic constraints, providing gut dysbiosis profiles acting as risk predictors of IBD or IBS in preclinical cases. Tracking dysbiosis supports new personalized/stratified IBD and IBS prevention programmes, generating Decision Support System tools. They include (1) high risk or flare-up recurrence -omics-based dysbiosis profiles; (2) microbial and molecular biomarkers of health and disease; (3) -omics-based pipelines for laboratory medicine diagnostics; (4) health apps for self-management of score-based dietary profiles, which can be shared with clinicians for nutritional habit and lifestyle amendment; (5) -omics profiling data warehousing and public repositories for IBD and IBS profile consultation. Dysbiosis-related indexes can represent novel laboratory and clinical medicine tools preventing or postponing the disease, finally interfering with its natural history.

The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

PubMed Central

2013-01-01

Background Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases. Results In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease. Conclusions The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner. PMID:23883183

Identifying Health Information Technology Needs of Oncologists to Facilitate the Adoption of Genomic Medicine: Recommendations From the 2016 American Society of Clinical Oncology Omics and Precision Oncology Workshop.

PubMed

Hughes, Kevin S; Ambinder, Edward P; Hess, Gregory P; Yu, Peter Paul; Bernstam, Elmer V; Routbort, Mark J; Clemenceau, Jean Rene; Hamm, John T; Febbo, Phillip G; Domchek, Susan M; Chen, James L; Warner, Jeremy L

2017-09-20

At the ASCO Data Standards and Interoperability Summit held in May 2016, it was unanimously decided that four areas of current oncology clinical practice have serious, unmet health information technology needs. The following areas of need were identified: 1) omics and precision oncology, 2) advancing interoperability, 3) patient engagement, and 4) value-based oncology. To begin to address these issues, ASCO convened two complementary workshops: the Omics and Precision Oncology Workshop in October 2016 and the Advancing Interoperability Workshop in December 2016. A common goal was to address the complexity, enormity, and rapidly changing nature of genomic information, which existing electronic health records are ill equipped to manage. The subject matter experts invited to the Omics and Precision Oncology Workgroup were tasked with the responsibility of determining a specific, limited need that could be addressed by a software application (app) in the short-term future, using currently available genomic knowledge bases. Hence, the scope of this workshop was to determine the basic functionality of one app that could serve as a test case for app development. The goal of the second workshop, described separately, was to identify the specifications for such an app. This approach was chosen both to facilitate the development of a useful app and to help ASCO and oncologists better understand the mechanics, difficulties, and gaps in genomic clinical decision support tool development. In this article, we discuss the key challenges and recommendations identified by the workshop participants. Our hope is to narrow the gap between the practicing oncologist and ongoing national efforts to provide precision oncology and value-based care to cancer patients.

Integration of multi-omics techniques and physiological phenotyping within a holistic phenomics approach to study senescence in model and crop plants.

PubMed

Großkinsky, Dominik K; Syaifullah, Syahnada Jaya; Roitsch, Thomas

2018-02-12

The study of senescence in plants is complicated by diverse levels of temporal and spatial dynamics as well as the impact of external biotic and abiotic factors and crop plant management. Whereas the molecular mechanisms involved in developmentally regulated leaf senescence are very well understood, in particular in the annual model plant species Arabidopsis, senescence of other organs such as the flower, fruit, and root is much less studied as well as senescence in perennials such as trees. This review addresses the need for the integration of multi-omics techniques and physiological phenotyping into holistic phenomics approaches to dissect the complex phenomenon of senescence. That became feasible through major advances in the establishment of various, complementary 'omics' technologies. Such an interdisciplinary approach will also need to consider knowledge from the animal field, in particular in relation to novel regulators such as small, non-coding RNAs, epigenetic control and telomere length. Such a characterization of phenotypes via the acquisition of high-dimensional datasets within a systems biology approach will allow us to systematically characterize the various programmes governing senescence beyond leaf senescence in Arabidopsis and to elucidate the underlying molecular processes. Such a multi-omics approach is expected to also spur the application of results from model plants to agriculture and their verification for sustainable and environmentally friendly improvement of crop plant stress resilience and productivity and contribute to improvements based on postharvest physiology for the food industry and the benefit of its customers. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Object-oriented regression for building predictive models with high dimensional omics data from translational studies.

PubMed

Zhao, Lue Ping; Bolouri, Hamid

2016-04-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient's similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient's HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015). Copyright © 2016 Elsevier Inc. All rights reserved.

Object-Oriented Regression for Building Predictive Models with High Dimensional Omics Data from Translational Studies

PubMed Central

Zhao, Lue Ping; Bolouri, Hamid

2016-01-01

Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and to make the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient’s similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient’s HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (p=0.015). PMID:26972839

MPLEx: a method for simultaneous pathogen inactivation and extraction of samples for multi-omics profiling.

PubMed

Burnum-Johnson, Kristin E; Kyle, Jennifer E; Eisfeld, Amie J; Casey, Cameron P; Stratton, Kelly G; Gonzalez, Juan F; Habyarimana, Fabien; Negretti, Nicholas M; Sims, Amy C; Chauhan, Sadhana; Thackray, Larissa B; Halfmann, Peter J; Walters, Kevin B; Kim, Young-Mo; Zink, Erika M; Nicora, Carrie D; Weitz, Karl K; Webb-Robertson, Bobbie-Jo M; Nakayasu, Ernesto S; Ahmer, Brian; Konkel, Michael E; Motin, Vladimir; Baric, Ralph S; Diamond, Michael S; Kawaoka, Yoshihiro; Waters, Katrina M; Smith, Richard D; Metz, Thomas O

2017-01-26

The continued emergence and spread of infectious agents is of great concern, and systems biology approaches to infectious disease research can advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can take place only subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivation and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This single-sample metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of clinically important bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, and West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. In addition, >99% inactivation, which increased with solvent exposure time, was also observed for pathogens without exposed lipid membranes including community-associated methicillin-resistant Staphylococcus aureus, Clostridium difficile spores and vegetative cells, and adenovirus type 5. The overall pipeline of inactivation and subsequent proteomic, metabolomic, and lipidomic analyses was evaluated using a human epithelial lung cell line infected with wild-type and mutant influenza H7N9 viruses, thereby demonstrating that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses. Based on these experimental results, we believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen with high success for pathogens with exposed lipid membranes.

MPLEx: A method for simultaneous pathogen inactivation and extraction of samples for multi-omics profiling

PubMed Central

Burnum-Johnson, Kristin E.; Kyle, Jennifer E.; Eisfeld, Amie J.; Casey, Cameron P.; Stratton, Kelly G.; Gonzalez, Juan F.; Habyarimana, Fabien; Negretti, Nicholas M.; Sims, Amy C.; Chauhan, Sadhana; Thackray, Larissa B.; Halfmann, Peter J.; Walters, Kevin B.; Kim, Young-Mo; Zink, Erika M.; Nicora, Carrie D.; Weitz, Karl K.; Webb-Robertson, Bobbie-Jo M.; Nakayasu, Ernesto S.; Ahmer, Brian; Konkel, Michael E.; Motin, Vladimir; Baric, Ralph S.; Diamond, Michael S.; Kawaoka, Yoshihiro; Waters, Katrina M.; Smith, Richard D.; Metz, Thomas O.

2017-01-01

The continued emergence and spread of infectious agents is of great concern, and systems biology approaches to infectious disease research can advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can take place only subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivation and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This single-sample metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of clinically important bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, and West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. In addition, >99% inactivation, which increased with solvent exposure time, was also observed for pathogens without exposed lipid membranes including community-associated methicillin-resistant Staphylococcus aureus, Clostridium difficile spores and vegetative cells, and adenovirus type 5. The overall pipeline of inactivation and subsequent proteomic, metabolomic, and lipidomic analyses was evaluated using a human epithelial lung cell line infected with wild-type and mutant influenza H7N9 viruses, thereby demonstrating that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses. Based on these experimental results, we believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen with high success for pathogens with exposed lipid membranes. PMID:28091625

Comparative Genomics and Systems Biology of Malaria Parasites Plasmodium

PubMed Central

Cai, Hong; Zhou, Zhan; Gu, Jianying; Wang, Yufeng

2013-01-01

Malaria is a serious infectious disease that causes over one million deaths yearly. It is caused by a group of protozoan parasites in the genus Plasmodium. No effective vaccine is currently available and the elevated levels of resistance to drugs in use underscore the pressing need for novel antimalarial targets. In this review, we survey omics centered developments in Plasmodium biology, which have set the stage for a quantum leap in our understanding of the fundamental processes of the parasite life cycle and mechanisms of drug resistance and immune evasion. PMID:24298232

A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline

PubMed Central

Rudnick, Paul A.; Markey, Sanford P.; Roth, Jeri; Mirokhin, Yuri; Yan, Xinjian; Tchekhovskoi, Dmitrii V.; Edwards, Nathan J.; Thangudu, Ratna R.; Ketchum, Karen A.; Kinsinger, Christopher R.; Mesri, Mehdi; Rodriguez, Henry; Stein, Stephen E.

2016-01-01

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced large proteomics datasets from the mass spectrometric interrogation of tumor samples previously analyzed by The Cancer Genome Atlas (TCGA) program. The availability of the genomic and proteomic data is enabling proteogenomic study for both reference (i.e., contained in major sequence databases) and non-reference markers of cancer. The CPTAC labs have focused on colon, breast, and ovarian tissues in the first round of analyses; spectra from these datasets were produced from 2D LC-MS/MS analyses and represent deep coverage. To reduce the variability introduced by disparate data analysis platforms (e.g., software packages, versions, parameters, sequence databases, etc.), the CPTAC Common Data Analysis Platform (CDAP) was created. The CDAP produces both peptide-spectrum-match (PSM) reports and gene-level reports. The pipeline processes raw mass spectrometry data according to the following: (1) Peak-picking and quantitative data extraction, (2) database searching, (3) gene-based protein parsimony, and (4) false discovery rate (FDR)-based filtering. The pipeline also produces localization scores for the phosphopeptide enrichment studies using the PhosphoRS program. Quantitative information for each of the datasets is specific to the sample processing, with PSM and protein reports containing the spectrum-level or gene-level (“rolled-up”) precursor peak areas and spectral counts for label-free or reporter ion log-ratios for 4plex iTRAQ™. The reports are available in simple tab-delimited formats and, for the PSM-reports, in mzIdentML. The goal of the CDAP is to provide standard, uniform reports for all of the CPTAC data, enabling comparisons between different samples and cancer types as well as across the major ‘omics fields. PMID:26860878

A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline.

PubMed

Rudnick, Paul A; Markey, Sanford P; Roth, Jeri; Mirokhin, Yuri; Yan, Xinjian; Tchekhovskoi, Dmitrii V; Edwards, Nathan J; Thangudu, Ratna R; Ketchum, Karen A; Kinsinger, Christopher R; Mesri, Mehdi; Rodriguez, Henry; Stein, Stephen E

2016-03-04

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced large proteomics data sets from the mass spectrometric interrogation of tumor samples previously analyzed by The Cancer Genome Atlas (TCGA) program. The availability of the genomic and proteomic data is enabling proteogenomic study for both reference (i.e., contained in major sequence databases) and nonreference markers of cancer. The CPTAC laboratories have focused on colon, breast, and ovarian tissues in the first round of analyses; spectra from these data sets were produced from 2D liquid chromatography-tandem mass spectrometry analyses and represent deep coverage. To reduce the variability introduced by disparate data analysis platforms (e.g., software packages, versions, parameters, sequence databases, etc.), the CPTAC Common Data Analysis Platform (CDAP) was created. The CDAP produces both peptide-spectrum-match (PSM) reports and gene-level reports. The pipeline processes raw mass spectrometry data according to the following: (1) peak-picking and quantitative data extraction, (2) database searching, (3) gene-based protein parsimony, and (4) false-discovery rate-based filtering. The pipeline also produces localization scores for the phosphopeptide enrichment studies using the PhosphoRS program. Quantitative information for each of the data sets is specific to the sample processing, with PSM and protein reports containing the spectrum-level or gene-level ("rolled-up") precursor peak areas and spectral counts for label-free or reporter ion log-ratios for 4plex iTRAQ. The reports are available in simple tab-delimited formats and, for the PSM-reports, in mzIdentML. The goal of the CDAP is to provide standard, uniform reports for all of the CPTAC data to enable comparisons between different samples and cancer types as well as across the major omics fields.

Metabolomics and Personalized Medicine.

PubMed

Koen, Nadia; Du Preez, Ilse; Loots, Du Toit

2016-01-01

Current clinical practice strongly relies on the prognosis, diagnosis, and treatment of diseases using methods determined and averaged for the specific diseased cohort/population. Although this approach complies positively with most patients, misdiagnosis, treatment failure, relapse, and adverse drug effects are common occurrences in many individuals, which subsequently hamper the control and eradication of a number of diseases. These incidences can be explained by individual variation in the genome, transcriptome, proteome, and metabolome of a patient. Various "omics" approaches have investigated the influence of these factors on a molecular level, with the intention of developing personalized approaches to disease diagnosis and treatment. Metabolomics, the newest addition to the "omics" domain and the closest to the observed phenotype, reflects changes occurring at all molecular levels, as well as influences resulting from other internal and external factors. By comparing the metabolite profiles of two or more disease phenotypes, metabolomics can be applied to identify biomarkers related to the perturbation being investigated. These biomarkers can, in turn, be used to develop personalized prognostic, diagnostic, and treatment approaches, and can also be applied to the monitoring of disease progression, treatment efficacy, predisposition to drug-related side effects, and potential relapse. In this review, we discuss the contributions that metabolomics has made, and can potentially still make, towards the field of personalized medicine. © 2016 Elsevier Inc. All rights reserved.

Urinary metallomics as a novel biomarker discovery platform: Breast cancer as a case study.

PubMed

Burton, Casey; Dan, Yongbo; Donovan, Ariel; Liu, Kun; Shi, Honglan; Ma, Yinfa; Bosnak, Cynthia P

2016-01-15

Urinary metallomics is presented here as a new "omics" approach that aims to facilitate personalized cancer screening and prevention by improving our understanding of urinary metals in disease. Twenty-two urinary metals were examined with inductively-coupled plasma-mass spectrometry in 138 women newly diagnosed with breast cancer and benign conditions. Urinary metals from spot urine samples were adjusted to renal dilution using urine specific gravity. Two urinary metals, copper (P-value=0.036) and lead (P-value=0.003), were significantly increased in the urine of breast cancer patients. A multivariate model that comprised copper, lead, and patient age afforded encouraging discriminatory power (AUC: 0.728, P-value<0.0005), while univariate models of copper (61.7% sensitivity, 50.0% specificity) and lead (76.6% sensitivity, 51.2% specificity) at optimized cutoff thresholds compared favorably with other breast cancer diagnostic modalities such as mammography. Correlations found among various metals suggested potential geographic and dietary influences on the urine metallome that warrant further investigation. This proof-of-concept work introduces urinary metallomics as a noninvasive, potentially transformative "omics" approach to early cancer detection. Urinary copper and lead have also been preliminarily identified as potential breast cancer biomarkers. Copyright © 2015 Elsevier B.V. All rights reserved.

Mass spectrometry based lipidomics: an overview of technological platforms.

PubMed

Köfeler, Harald C; Fauland, Alexander; Rechberger, Gerald N; Trötzmüller, Martin

2012-01-05

One decade after the genomic and the proteomic life science revolution, new 'omics' fields are emerging. The metabolome encompasses the entity of small molecules-Most often end products of a catalytic process regulated by genes and proteins-with the lipidome being its fat soluble subdivision. Within recent years, lipids are more and more regarded not only as energy storage compounds but also as interactive players in various cellular regulation cycles and thus attain rising interest in the bio-medical community. The field of lipidomics is, on one hand, fuelled by analytical technology advances, particularly mass spectrometry and chromatography, but on the other hand new biological questions also drive analytical technology developments. Compared to fairly standardized genomic or proteomic high-throughput protocols, the high degree of molecular heterogeneity adds a special analytical challenge to lipidomic analysis. In this review, we will take a closer look at various mass spectrometric platforms for lipidomic analysis. We will focus on the advantages and limitations of various experimental setups like 'shotgun lipidomics', liquid chromatography-Mass spectrometry (LC-MS) and matrix assisted laser desorption ionization-time of flight (MALDI-TOF) based approaches. We will also examine available software packages for data analysis, which nowadays is in fact the rate limiting step for most 'omics' workflows.

Integrated network analysis and effective tools in plant systems biology

PubMed Central

Fukushima, Atsushi; Kanaya, Shigehiko; Nishida, Kozo

2014-01-01

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms. PMID:25408696

Big Data Transforms Discovery-Utilization Therapeutics Continuum.

PubMed

Waldman, S A; Terzic, A

2016-03-01

Enabling omic technologies adopt a holistic view to produce unprecedented insights into the molecular underpinnings of health and disease, in part, by generating massive high-dimensional biological data. Leveraging these systems-level insights as an engine driving the healthcare evolution is maximized through integration with medical, demographic, and environmental datasets from individuals to populations. Big data analytics has accordingly emerged to add value to the technical aspects of storage, transfer, and analysis required for merging vast arrays of omic-, clinical-, and eco-datasets. In turn, this new field at the interface of biology, medicine, and information science is systematically transforming modern therapeutics across discovery, development, regulation, and utilization. © 2015 ASCPT.

Biological Networks for Cancer Candidate Biomarkers Discovery

PubMed Central

Yan, Wenying; Xue, Wenjin; Chen, Jiajia; Hu, Guang

2016-01-01

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field. PMID:27625573

An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function.

PubMed

Li, Hao; Wang, Xu; Rukina, Daria; Huang, Qingyao; Lin, Tao; Sorrentino, Vincenzo; Zhang, Hongbo; Bou Sleiman, Maroun; Arends, Danny; McDaid, Aaron; Luan, Peiling; Ziari, Naveed; Velázquez-Villegas, Laura A; Gariani, Karim; Kutalik, Zoltan; Schoonjans, Kristina; Radcliffe, Richard A; Prins, Pjotr; Morgenthaler, Stephan; Williams, Robert W; Auwerx, Johan

2018-01-24

Identifying genetic and environmental factors that impact complex traits and common diseases is a high biomedical priority. Here, we developed, validated, and implemented a series of multi-layered systems approaches, including (expression-based) phenome-wide association, transcriptome-/proteome-wide association, and (reverse-) mediation analysis, in an open-access web server (systems-genetics.org) to expedite the systems dissection of gene function. We applied these approaches to multi-omics datasets from the BXD mouse genetic reference population, and identified and validated associations between genes and clinical and molecular phenotypes, including previously unreported links between Rpl26 and body weight, and Cpt1a and lipid metabolism. Furthermore, through mediation and reverse-mediation analysis we established regulatory relations between genes, such as the co-regulation of BCKDHA and BCKDHB protein levels, and identified targets of transcription factors E2F6, ZFP277, and ZKSCAN1. Our multifaceted toolkit enabled the identification of gene-gene and gene-phenotype links that are robust and that translate well across populations and species, and can be universally applied to any populations with multi-omics datasets. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

The search for drug-targetable diagnostic, prognostic and predictive biomarkers in chronic graft-versus-host disease.

PubMed

Ren, Hong-Gang; Adom, Djamilatou; Paczesny, Sophie

2018-05-01

Chronic graft-versus-host disease (cGVHD) continues to be the leading cause of late morbidity and mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT), which is an increasingly applied curative method for both benign and malignant hematologic disorders. Biomarker identification is crucial for the development of noninvasive and cost-effective cGVHD diagnostic, prognostic, and predictive test for use in clinic. Furthermore, biomarkers may help to gain a better insight on ongoing pathophysiological processes. The recent widespread application of omics technologies including genomics, transcriptomics, proteomics and cytomics provided opportunities to discover novel biomarkers. Areas covered: This review focuses on biomarkers identified through omics that play a critical role in target identification for drug development, and that were verified in at least two independent cohorts. It also summarizes the current status on omics tools used to identify these useful cGVHD targets. We briefly list the biomarkers identified and verified so far. We further address challenges associated to their exploitation and application in the management of cGVHD patients. Finally, insights on biomarkers that are drug targetable and represent potential therapeutic targets are discussed. Expert commentary: We focus on biomarkers that play an essential role in target identification.

Standardization and omics science: technical and social dimensions are inseparable and demand symmetrical study.

PubMed

Holmes, Christina; McDonald, Fiona; Jones, Mavis; Ozdemir, Vural; Graham, Janice E

2010-06-01

Standardization is critical to scientists and regulators to ensure the quality and interoperability of research processes, as well as the safety and efficacy of the attendant research products. This is perhaps most evident in the case of "omics science," which is enabled by a host of diverse high-throughput technologies such as genomics, proteomics, and metabolomics. But standards are of interest to (and shaped by) others far beyond the immediate realm of individual scientists, laboratories, scientific consortia, or governments that develop, apply, and regulate them. Indeed, scientific standards have consequences for the social, ethical, and legal environment in which innovative technologies are regulated, and thereby command the attention of policy makers and citizens. This article argues that standardization of omics science is both technical and social. A critical synthesis of the social science literature indicates that: (1) standardization requires a degree of flexibility to be practical at the level of scientific practice in disparate sites; (2) the manner in which standards are created, and by whom, will impact their perceived legitimacy and therefore their potential to be used; and (3) the process of standardization itself is important to establishing the legitimacy of an area of scientific research.

Deciphering Biochemical Network: from particles to planes then to spaces

NASA Astrophysics Data System (ADS)

Ye, Xinhao; Zhang, Siliang; Engineer Research CenterBiotechnology, National

2004-03-01

Today when we are still infatuated with the booming systematic fashion in life science, we, especially as biologist, ironically have fallen down into a sub-systematic maze. That is, although rapid advances in "omics" sciences ceaselessly provided so-called global or large-scale maps to exhibit the corresponding subnet, seldom paid attention to connecting these distinct but close-knit functional modules. Fortunately, a group of physicists recently cast off this natural moat and integrated multi-scale biological network into a simple life's pyramid. However, if extended this pyramid to a 3D structure in view of XYZ axis constructed by the temporal, spatial and organized characteristics respectively, it should be noted that this from-universal-to-particular pyramid is only a transverse section while the achievements in diverse "omics" sciences consist of relative longitudinal ones. On that footing, if analogizing the development of systems biology in last decades as a huge leap from discrete particles (typically in "a paper = a gene" era) to several planes (that is relative to corresponding OMICS science), we might rationally predict a next "space" era is coming soon to untangle and map the multi-tiered biological network really in a whole.

Multi-omics Reveal Specific Targets of the RNA-Binding Protein Puf3p and Its Orchestration of Mitochondrial Biogenesis.

PubMed

Lapointe, Christopher P; Stefely, Jonathan A; Jochem, Adam; Hutchins, Paul D; Wilson, Gary M; Kwiecien, Nicholas W; Coon, Joshua J; Wickens, Marvin; Pagliarini, David J

2018-01-24

Coenzyme Q (CoQ) is a redox-active lipid required for mitochondrial oxidative phosphorylation (OxPhos). How CoQ biosynthesis is coordinated with the biogenesis of OxPhos protein complexes is unclear. Here, we show that the Saccharomyces cerevisiae RNA-binding protein (RBP) Puf3p regulates CoQ biosynthesis. To establish the mechanism for this regulation, we employed a multi-omic strategy to identify mRNAs that not only bind Puf3p but also are regulated by Puf3p in vivo. The CoQ biosynthesis enzyme Coq5p is a critical Puf3p target: Puf3p regulates the abundance of Coq5p and prevents its detrimental hyperaccumulation, thereby enabling efficient CoQ production. More broadly, Puf3p represses a specific set of proteins involved in mitochondrial protein import, translation, and OxPhos complex assembly (pathways essential to prime mitochondrial biogenesis). Our data reveal a mechanism for post-transcriptionally coordinating CoQ production with OxPhos biogenesis, and they demonstrate the power of multi-omics for defining genuine targets of RBPs. Copyright © 2017 Elsevier Inc. All rights reserved.

A Multi-Omic View of Host-Pathogen-Commensal Interplay in Salmonella-Mediated Intestinal Infection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaiser, Brooke LD; Li, Jie; Sanford, James A.

The potential for commensal microorganisms indigenous to a host (the ‘microbiome’ or ‘microbiota’) to alter infection outcome by influencing host-pathogen interplay is largely unknown. We used a multi-omics “systems” approach, incorporating proteomics, metabolomics, glycomics, and metagenomics, to explore the molecular interplay between the murine host, the pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium), and commensal gut microorganisms during intestinal infection with S. Typhimurium. We find proteomic evidence that S. Typhimurium thrives within the infected 129/SvJ mouse gut without antibiotic pre-treatment, inducing inflammation and disrupting the intestinal microbiome (e.g., suppressing Bacteroidetes and Firmicutes while promoting growth of Salmonella and Enterococcus). Alterationmore » of the host microbiome population structure was highly correlated with gut environmental changes, including the accumulation of metabolites normally consumed by commensal microbiota. Finally, the less characterized phase of S. Typhimurium’s lifecycle was investigated, and both proteomic and glycomic evidence suggests S. Typhimurium may take advantage of increased fucose moieties to metabolize fucose while growing in the gut. The application of multiple omics measurements to Salmonella-induced intestinal inflammation provides insights into complex molecular strategies employed during pathogenesis between host, pathogen, and the microbiome.« less

"Omic" investigations of protozoa and worms for a deeper understanding of the human gut "parasitome".

PubMed

Marzano, Valeria; Mancinelli, Livia; Bracaglia, Giorgia; Del Chierico, Federica; Vernocchi, Pamela; Di Girolamo, Francesco; Garrone, Stefano; Tchidjou Kuekou, Hyppolite; D'Argenio, Patrizia; Dallapiccola, Bruno; Urbani, Andrea; Putignani, Lorenza

2017-11-01

The human gut has been continuously exposed to a broad spectrum of intestinal organisms, including viruses, bacteria, fungi, and parasites (protozoa and worms), over millions of years of coevolution, and plays a central role in human health. The modern lifestyles of Western countries, such as the adoption of highly hygienic habits, the extensive use of antimicrobial drugs, and increasing globalisation, have dramatically altered the composition of the gut milieu, especially in terms of its eukaryotic "citizens." In the past few decades, numerous studies have highlighted the composition and role of human intestinal bacteria in physiological and pathological conditions, while few investigations exist on gut parasites and particularly on their coexistence and interaction with the intestinal microbiota. Studies of the gut "parasitome" through "omic" technologies, such as (meta)genomics, transcriptomics, proteomics, and metabolomics, are herein reviewed to better understand their role in the relationships between intestinal parasites, host, and resident prokaryotes, whether pathogens or commensals. Systems biology-based profiles of the gut "parasitome" under physiological and severe disease conditions can indeed contribute to the control of infectious diseases and offer a new perspective of omics-assisted tropical medicine.

Epigenetics and epidemiology: models of study and examples.

PubMed

van Veldhoven, Karin; Rahman, Shati; Vineis, Paolo

2014-01-01

Epidemiological studies have successfully identified several environmental causes of disease, but often these studies are limited by methodological problems (e.g. lack of sensitivity and specificity in exposure assessment; confounding). Proposed approaches to improve observational studies of environmental associations are Mendelian randomization and the meet-in-the-middle (MITM) approach. The latter uses signals from the growing field of -omics as putative intermediate biomarkers in the pathogenetic process that links exposure with disease. The first part of this approach consists in the association between exposure and disease. The next step consists in the study of the relationship between (biomarkers of) exposure and intermediate -omic biomarkers of early effect; thirdly, the relation between the disease outcome and intermediate -omic biomarkers is assessed. We propose that when an association is found in all three steps it is possible that there is a casual association. One of the associations that have been investigated extensively in the recent years but is not completely understood is that between environmental endocrine disruptors and breast cancer. Here we present an example of how the "meet-in-the-middle" approach can be used to address the role of endocrine disruptors, by reviewing the relevant literature.

Growing trend of CE at the omics level: the frontier of systems biology.

PubMed

Oh, Eulsik; Hasan, Md Nabiul; Jamshed, Muhammad; Park, Soo Hyun; Hong, Hye-Min; Song, Eun Joo; Yoo, Young Sook

2010-01-01

In a novel attempt to comprehend the complexity of life, systems biology has recently emerged as a state-of-the-art approach for biological research in contrast to the reductionist approaches that have been used in molecular cell biology since the 1950s. Because a massive amount of information is required in many systems biology studies of life processes, we have increasingly come to depend on techniques that provide high-throughput omics data. CE and CE coupled to MS have served as powerful analytical tools for providing qualitative and quantitative omics data. Recent systems biology studies have focused strongly on the diagnosis and treatment of diseases. The increasing number of clinical research papers on drug discovery and disease therapies reflects this growing interest among scientists. Since such clinical research reflects one of the ultimate purposes of bioscience, these trends will be sustained for a long time. Thus, this review mainly focuses on the application of CE and CE-MS in diagnosis as well as on the latest CE methods developed. Furthermore, we outline the new challenges that arose in 2008 and later in elucidating the system-level functions of the bioconstituents of living organisms.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Laurence; Yurkovich, James T.; Lloyd, Colton J.

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thusmore » represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. Furthermore, this flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.« less

Identifying pathological biomarkers: histochemistry still ranks high in the omics era

PubMed Central

Pellicciari, C.; Malatesta, M.

2011-01-01

In recent years, omic analyses have been proposed as possible approaches to diagnosis, in particular for tumours, as they should be able to provide quantitative tools to detect and measure abnormalities in gene and protein expression, through the evaluation of transcription and translation products in the abnormal vs normal tissues. Unfortunately, this approach proved to be much less powerful than expected, due to both intrinsic technical limits and the nature itself of the pathological tissues to be investigated, the heterogeneity deriving from polyclonality and tissue phenotype variability between patients being a major limiting factor in the search for unique omic biomarkers. Especially in the last few years, the application of refined techniques for investigating gene expression in situ has greatly increased the diagnostic/prognostic potential of histochemistry, while the progress in light microscopy technology and in the methods for imaging molecules in vivo have provided valuable tools for elucidating the molecular events and the basic mechanisms leading to a pathological condition. Histochemical techniques thus remain irreplaceable in pathologist's armamentarium, and it may be expected that even in the future histochemistry will keep a leading position among the methodological approaches for clinical pathology. PMID:22297448

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data.

PubMed

Marco-Ramell, Anna; Palau-Rodriguez, Magali; Alay, Ania; Tulipani, Sara; Urpi-Sarda, Mireia; Sanchez-Pla, Alex; Andres-Lacueva, Cristina

2018-01-02

Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome.

Biodiversity Meets Neuroscience: From the Sequencing Ship (Ship-Seq) to Deciphering Parallel Evolution of Neural Systems in Omic's Era.

PubMed

Moroz, Leonid L

2015-12-01

The origins of neural systems and centralized brains are one of the major transitions in evolution. These events might occur more than once over 570-600 million years. The convergent evolution of neural circuits is evident from a diversity of unique adaptive strategies implemented by ctenophores, cnidarians, acoels, molluscs, and basal deuterostomes. But, further integration of biodiversity research and neuroscience is required to decipher critical events leading to development of complex integrative and cognitive functions. Here, we outline reference species and interdisciplinary approaches in reconstructing the evolution of nervous systems. In the "omic" era, it is now possible to establish fully functional genomics laboratories aboard of oceanic ships and perform sequencing and real-time analyses of data at any oceanic location (named here as Ship-Seq). In doing so, fragile, rare, cryptic, and planktonic organisms, or even entire marine ecosystems, are becoming accessible directly to experimental and physiological analyses by modern analytical tools. Thus, we are now in a position to take full advantages from countless "experiments" Nature performed for us in the course of 3.5 billion years of biological evolution. Together with progress in computational and comparative genomics, evolutionary neuroscience, proteomic and developmental biology, a new surprising picture is emerging that reveals many ways of how nervous systems evolved. As a result, this symposium provides a unique opportunity to revisit old questions about the origins of biological complexity. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

The current status and problems confronted in delivering precision medicine in Japan and Europe.

PubMed

Bando, Hideaki

Precision medicine has been defined as "a predictive, preventive, personalized, and participatory health care service delivery model." Today, developments in next-generation sequencing and information technology have made precision medicine possible, with massive amounts of genetic, "omics," clinical, environmental, and lifestyle data now available. Unfortunately, differences in governmental support and health care regulations have resulted in heterogeneous progress among countries. In Japan, for example, precision cancer screening and treatments are increasingly being promoted, with collaboration among research, governmental, and pharmaceutical agencies taking place in the nationwide SCRUM-Japan cancer genome screening project. The missions of SCRUM-Japan are to deliver the most appropriate therapeutic agents to the most suitable patients, and to play key roles in the development of multiplex diagnostic products and new indications for targeted therapy. Starting in February 2015 and ending in March 2017, the aim is to enroll 4750 patients with cancer (2350 patients with lung cancer and 2400 patients with gastrointestinal tract cancer). Compared with other developed countries, investments in scientific innovation for biomedical and omics research are matched or even surpassed in Europe, but regulatory differences in each countries are a major hurdle to rapid implementation. Although market approval for pharmaceuticals is centralized through the European Medicines Agency, access to health care is heterogeneously regulated at national levels, which undermines the consistency, comparability, and quality of precision medicine for cancer patients in Europe. In this review, we focus on the current progress of precision medicine in Japan and Europe, and clarify the differences in progress and the hurdles faced moving forward. Copyright © 2017 Elsevier Inc. All rights reserved.

Mildew-Omics: How Global Analyses Aid the Understanding of Life and Evolution of Powdery Mildews

PubMed Central

Bindschedler, Laurence V.; Panstruga, Ralph; Spanu, Pietro D.

2016-01-01

The common powdery mildew plant diseases are caused by ascomycete fungi of the order Erysiphales. Their characteristic life style as obligate biotrophs renders functional analyses in these species challenging, mainly because of experimental constraints to genetic manipulation. Global large-scale (“-omics”) approaches are thus particularly valuable and insightful for the characterisation of the life and evolution of powdery mildews. Here we review the knowledge obtained so far from genomic, transcriptomic and proteomic studies in these fungi. We consider current limitations and challenges regarding these surveys and provide an outlook on desired future investigations on the basis of the various –omics technologies. PMID:26913042

Early Diagnosis and Prediction of Anticancer Drug-induced Cardiotoxicity: From Cardiac Imaging to "Omics" Technologies.

PubMed

Madonna, Rosalinda

2017-07-01

Heart failure due to antineoplastic therapy remains a major cause of morbidity and mortality in oncological patients. These patients often have no prior manifestation of disease. There is therefore a need for accurate identification of individuals at risk of such events before the appearance of clinical manifestations. The present article aims to provide an overview of cardiac imaging as well as new "-omics" technologies, especially with regard to genomics and proteomics as promising tools for the early detection and prediction of cardiotoxicity and individual responses to antineoplastic drugs. Copyright © 2017 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.

SPIKE – a database, visualization and analysis tool of cellular signaling pathways

PubMed Central

Elkon, Ran; Vesterman, Rita; Amit, Nira; Ulitsky, Igor; Zohar, Idan; Weisz, Mali; Mass, Gilad; Orlev, Nir; Sternberg, Giora; Blekhman, Ran; Assa, Jackie; Shiloh, Yosef; Shamir, Ron

2008-01-01

Background Biological signaling pathways that govern cellular physiology form an intricate web of tightly regulated interlocking processes. Data on these regulatory networks are accumulating at an unprecedented pace. The assimilation, visualization and interpretation of these data have become a major challenge in biological research, and once met, will greatly boost our ability to understand cell functioning on a systems level. Results To cope with this challenge, we are developing the SPIKE knowledge-base of signaling pathways. SPIKE contains three main software components: 1) A database (DB) of biological signaling pathways. Carefully curated information from the literature and data from large public sources constitute distinct tiers of the DB. 2) A visualization package that allows interactive graphic representations of regulatory interactions stored in the DB and superposition of functional genomic and proteomic data on the maps. 3) An algorithmic inference engine that analyzes the networks for novel functional interplays between network components. SPIKE is designed and implemented as a community tool and therefore provides a user-friendly interface that allows registered users to upload data to SPIKE DB. Our vision is that the DB will be populated by a distributed and highly collaborative effort undertaken by multiple groups in the research community, where each group contributes data in its field of expertise. Conclusion The integrated capabilities of SPIKE make it a powerful platform for the analysis of signaling networks and the integration of knowledge on such networks with omics data. PMID:18289391

Data integration in physiology using Bayes' rule and minimum Bayes' factors: deubiquitylating enzymes in the renal collecting duct.

PubMed

Xue, Zhe; Chen, Jia-Xu; Zhao, Yue; Medvar, Barbara; Knepper, Mark A

2017-03-01

A major challenge in physiology is to exploit the many large-scale data sets available from "-omic" studies to seek answers to key physiological questions. In previous studies, Bayes' theorem has been used for this purpose. This approach requires a means to map continuously distributed experimental data to probabilities (likelihood values) to derive posterior probabilities from the combination of prior probabilities and new data. Here, we introduce the use of minimum Bayes' factors for this purpose and illustrate the approach by addressing a physiological question, "Which deubiquitylating enzymes (DUBs) encoded by mammalian genomes are most likely to regulate plasma membrane transport processes in renal cortical collecting duct principal cells?" To do this, we have created a comprehensive online database of 110 DUBs present in the mammalian genome (https://hpcwebapps.cit.nih.gov/ESBL/Database/DUBs/). We used Bayes' theorem to integrate available information from large-scale data sets derived from proteomic and transcriptomic studies of renal collecting duct cells to rank the 110 known DUBs with regard to likelihood of interacting with and regulating transport processes. The top-ranked DUBs were OTUB1, USP14, PSMD7, PSMD14, USP7, USP9X, OTUD4, USP10, and UCHL5. Among these USP7, USP9X, OTUD4, and USP10 are known to be involved in endosomal trafficking and have potential roles in endosomal recycling of plasma membrane proteins in the mammalian cortical collecting duct. Copyright © 2017 the American Physiological Society.

RAMONA: a Web application for gene set analysis on multilevel omics data.

PubMed

Sass, Steffen; Buettner, Florian; Mueller, Nikola S; Theis, Fabian J

2015-01-01

Decreasing costs of modern high-throughput experiments allow for the simultaneous analysis of altered gene activity on various molecular levels. However, these multi-omics approaches lead to a large amount of data, which is hard to interpret for a non-bioinformatician. Here, we present the remotely accessible multilevel ontology analysis (RAMONA). It offers an easy-to-use interface for the simultaneous gene set analysis of combined omics datasets and is an extension of the previously introduced MONA approach. RAMONA is based on a Bayesian enrichment method for the inference of overrepresented biological processes among given gene sets. Overrepresentation is quantified by interpretable term probabilities. It is able to handle data from various molecular levels, while in parallel coping with redundancies arising from gene set overlaps and related multiple testing problems. The comprehensive output of RAMONA is easy to interpret and thus allows for functional insight into the affected biological processes. With RAMONA, we provide an efficient implementation of the Bayesian inference problem such that ontologies consisting of thousands of terms can be processed in the order of seconds. RAMONA is implemented as ASP.NET Web application and publicly available at http://icb.helmholtz-muenchen.de/ramona. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Cytoplasmic male sterility (CMS) in hybrid breeding in field crops.

PubMed

Bohra, Abhishek; Jha, Uday C; Adhimoolam, Premkumar; Bisht, Deepak; Singh, Narendra P

2016-05-01

A comprehensive understanding of CMS/Rf system enabled by modern omics tools and technologies considerably improves our ability to harness hybrid technology for enhancing the productivity of field crops. Harnessing hybrid vigor or heterosis is a promising approach to tackle the current challenge of sustaining enhanced yield gains of field crops. In the context, cytoplasmic male sterility (CMS) owing to its heritable nature to manifest non-functional male gametophyte remains a cost-effective system to promote efficient hybrid seed production. The phenomenon of CMS stems from a complex interplay between maternally-inherited (mitochondrion) and bi-parental (nucleus) genomic elements. In recent years, attempts aimed to comprehend the sterility-inducing factors (orfs) and corresponding fertility determinants (Rf) in plants have greatly increased our access to candidate genomic segments and the cloned genes. To this end, novel insights obtained by applying state-of-the-art omics platforms have substantially enriched our understanding of cytoplasmic-nuclear communication. Concomitantly, molecular tools including DNA markers have been implicated in crop hybrid breeding in order to greatly expedite the progress. Here, we review the status of diverse sterility-inducing cytoplasms and associated Rf factors reported across different field crops along with exploring opportunities for integrating modern omics tools with CMS-based hybrid breeding.

Principles of proteome allocation are revealed using proteomic data and genome-scale models

PubMed Central

Yang, Laurence; Yurkovich, James T.; Lloyd, Colton J.; Ebrahim, Ali; Saunders, Michael A.; Palsson, Bernhard O.

2016-01-01

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. This flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models. PMID:27857205

Standardization and Omics Science: Technical and Social Dimensions Are Inseparable and Demand Symmetrical Study

PubMed Central

McDonald, Fiona; Jones, Mavis; Ozdemir, Vural; Graham, Janice E.

2010-01-01

Abstract Standardization is critical to scientists and regulators to ensure the quality and interoperability of research processes, as well as the safety and efficacy of the attendant research products. This is perhaps most evident in the case of “omics science,” which is enabled by a host of diverse high-throughput technologies such as genomics, proteomics, and metabolomics. But standards are of interest to (and shaped by) others far beyond the immediate realm of individual scientists, laboratories, scientific consortia, or governments that develop, apply, and regulate them. Indeed, scientific standards have consequences for the social, ethical, and legal environment in which innovative technologies are regulated, and thereby command the attention of policy makers and citizens. This article argues that standardization of omics science is both technical and social. A critical synthesis of the social science literature indicates that: (1) standardization requires a degree of flexibility to be practical at the level of scientific practice in disparate sites; (2) the manner in which standards are created, and by whom, will impact their perceived legitimacy and therefore their potential to be used; and (3) the process of standardization itself is important to establishing the legitimacy of an area of scientific research. PMID:20455752

Advancing stroke genomic research in the age of Trans-Omics big data science: Emerging priorities and opportunities.

PubMed

Owolabi, Mayowa; Peprah, Emmanuel; Xu, Huichun; Akinyemi, Rufus; Tiwari, Hemant K; Irvin, Marguerite R; Wahab, Kolawole Wasiu; Arnett, Donna K; Ovbiagele, Bruce

2017-11-15

We systematically reviewed the genetic variants associated with stroke in genome-wide association studies (GWAS) and examined the emerging priorities and opportunities for rapidly advancing stroke research in the era of Trans-Omics science. Using the PRISMA guideline, we searched PubMed and NHGRI- EBI GWAS catalog for stroke studies from 2007 till May 2017. We included 31 studies. The major challenge is that the few validated variants could not account for the full genetic risk of stroke and have not been translated for clinical use. None of the studies included continental Africans. Genomic study of stroke among Africans presents a unique opportunity for the discovery, validation, functional annotation, Trans-Omics study and translation of genomic determinants of stroke with implications for global populations. This is because all humans originated from Africa, a continent with a unique genomic architecture and a distinctive epidemiology of stroke; as well as substantially higher heritability and resolution of fine mapping of stroke genes. Understanding the genomic determinants of stroke and the corresponding molecular mechanisms will revolutionize the development of a new set of precise biomarkers for stroke prediction, diagnosis and prognostic estimates as well as personalized interventions for reducing the global burden of stroke. Copyright © 2017 Elsevier B.V. All rights reserved.

Inflammaging and human longevity in the omics era.

PubMed

Monti, Daniela; Ostan, Rita; Borelli, Vincenzo; Castellani, Gastone; Franceschi, Claudio

2017-07-01

Inflammaging is a recent theory of aging originally proposed in 2000 where data and conceptualizations regarding the aging of the immune system (immunosenescence) and the evolution of immune responses from invertebrates to mammals converged. This theory has received an increasing number of citations and experimental confirmations. Here we present an updated version of inflammaging focused on omics data - particularly on glycomics - collected on centenarians, semi-supercentenarians and their offspring. Accordingly, we arrived to the following conclusions: i) inflammaging has a structure where specific combinations of pro- and anti-inflammatory mediators are involved; ii) inflammaging is systemic and more complex than we previously thought, as many organs, tissues and cell types participate in producing pro- and anti-inflammatory stimuli defined "molecular garbage"; iii) inflammaging is dynamic, can be propagated locally to neighboring cells and systemically from organ to organ by circulating products and microvesicles, and amplified by chronic age-related diseases constituting a "local fire", which in turn produces additional inflammatory stimuli and molecular garbage; iv) an integrated Systems Medicine approach is urgently needed to let emerge a robust and highly informative set/combination of omics markers able to better grasp the complex molecular core of inflammaging in elderly and centenarians. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A mutli-omic systems approach to elucidating Yersinia virulence mechanisms

PubMed Central

Ansong, Charles; Schrimpe-Rutledge, Alexandra C.; Mitchell, Hugh; Chauhan, Sadhana; Jones, Marcus B.; Kim, Young-Mo; McAteer, Kathleen; Deatherage Kaiser, Brooke L.; Dubois, Jennifer L.; Brewer, Heather M.; Frank, Bryan C.; McDermott, Jason E.; Metz, Thomas O.; Peterson, Scott N.; Smith, Richard D.; Motin, Vladimir L.; Adkins, Joshua N.

2012-01-01

The underlying mechanisms that lead to dramatic differences between closely related pathogens are not always readily apparent. For example, the genomes of Yersinia pestis (YP) the causative agent of plague with a high mortality rate and Yersinia pseudotuberculosis (YPT) an enteric pathogen with a modest mortality rate are highly similar with some species specific differences; however the molecular causes of their distinct clinical outcomes remain poorly understood. In this study, a temporal multi-omic analysis of YP and YPT at physiologically relevant temperatures was performed to gain insights into how an acute and highly lethal bacterial pathogen, YP, differs from its less virulent progenitor, YPT. This analysis revealed higher gene and protein expression levels of conserved major virulence factors in YP relative to YPT, including the Yop virulon and the pH6 antigen. This suggests that adaptation in the regulatory architecture, in addition to the presence of unique genetic material, may contribute to the increased pathogenenicity of YP relative to YPT. Additionally, global transcriptome and proteome responses of YP and YPT revealed conserved post-transcriptional control of metabolism and the translational machinery including the modulation of glutamate levels in Yersiniae. Finally, the omics data was coupled with a computational network analysis, allowing an efficient prediction of novel Yersinia virulence factors based on gene and protein expression patterns. PMID:23147219

Principles of proteome allocation are revealed using proteomic data and genome-scale models

DOE PAGES

Yang, Laurence; Yurkovich, James T.; Lloyd, Colton J.; ...

2016-11-18

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thusmore » represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. Furthermore, this flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.« less

A statistical framework for applying RNA profiling to chemical hazard detection.

PubMed

Kostich, Mitchell S

2017-12-01

Use of 'omics technologies in environmental science is expanding. However, application is mostly restricted to characterizing molecular steps leading from toxicant interaction with molecular receptors to apical endpoints in laboratory species. Use in environmental decision-making is limited, due to difficulty in elucidating mechanisms in sufficient detail to make quantitative outcome predictions in any single species or in extending predictions to aquatic communities. Here we introduce a mechanism-agnostic statistical approach, supplementing mechanistic investigation by allowing probabilistic outcome prediction even when understanding of molecular pathways is limited, and facilitating extrapolation from results in laboratory test species to predictions about aquatic communities. We use concepts familiar to environmental managers, supplemented with techniques employed for clinical interpretation of 'omics-based biomedical tests. We describe the framework in step-wise fashion, beginning with single test replicates of a single RNA variant, then extending to multi-gene RNA profiling, collections of test replicates, and integration of complementary data. In order to simplify the presentation, we focus on using RNA profiling for distinguishing presence versus absence of chemical hazards, but the principles discussed can be extended to other types of 'omics measurements, multi-class problems, and regression. We include a supplemental file demonstrating many of the concepts using the open source R statistical package. Published by Elsevier Ltd.

The Omics Dashboard for interactive exploration of gene-expression data.

PubMed

Paley, Suzanne; Parker, Karen; Spaulding, Aaron; Tomb, Jean-Francois; O'Maille, Paul; Karp, Peter D

2017-12-01

The Omics Dashboard is a software tool for interactive exploration and analysis of gene-expression datasets. The Omics Dashboard is organized as a hierarchy of cellular systems. At the highest level of the hierarchy the Dashboard contains graphical panels depicting systems such as biosynthesis, energy metabolism, regulation and central dogma. Each of those panels contains a series of X-Y plots depicting expression levels of subsystems of that panel, e.g. subsystems within the central dogma panel include transcription, translation and protein maturation and folding. The Dashboard presents a visual read-out of the expression status of cellular systems to facilitate a rapid top-down user survey of how all cellular systems are responding to a given stimulus, and to enable the user to quickly view the responses of genes within specific systems of interest. Although the Dashboard is complementary to traditional statistical methods for analysis of gene-expression data, we show how it can detect changes in gene expression that statistical techniques may overlook. We present the capabilities of the Dashboard using two case studies: the analysis of lipid production for the marine alga Thalassiosira pseudonana, and an investigation of a shift from anaerobic to aerobic growth for the bacterium Escherichia coli. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Omics Dashboard for interactive exploration of gene-expression data

PubMed Central

Paley, Suzanne; Parker, Karen; Spaulding, Aaron; Tomb, Jean-Francois; O’Maille, Paul

2017-01-01

Abstract The Omics Dashboard is a software tool for interactive exploration and analysis of gene-expression datasets. The Omics Dashboard is organized as a hierarchy of cellular systems. At the highest level of the hierarchy the Dashboard contains graphical panels depicting systems such as biosynthesis, energy metabolism, regulation and central dogma. Each of those panels contains a series of X–Y plots depicting expression levels of subsystems of that panel, e.g. subsystems within the central dogma panel include transcription, translation and protein maturation and folding. The Dashboard presents a visual read-out of the expression status of cellular systems to facilitate a rapid top-down user survey of how all cellular systems are responding to a given stimulus, and to enable the user to quickly view the responses of genes within specific systems of interest. Although the Dashboard is complementary to traditional statistical methods for analysis of gene-expression data, we show how it can detect changes in gene expression that statistical techniques may overlook. We present the capabilities of the Dashboard using two case studies: the analysis of lipid production for the marine alga Thalassiosira pseudonana, and an investigation of a shift from anaerobic to aerobic growth for the bacterium Escherichia coli. PMID:29040755

Gene Ontology-Based Analysis of Zebrafish Omics Data Using the Web Tool Comparative Gene Ontology.

PubMed

Ebrahimie, Esmaeil; Fruzangohar, Mario; Moussavi Nik, Seyyed Hani; Newman, Morgan

2017-10-01

Gene Ontology (GO) analysis is a powerful tool in systems biology, which uses a defined nomenclature to annotate genes/proteins within three categories: "Molecular Function," "Biological Process," and "Cellular Component." GO analysis can assist in revealing functional mechanisms underlying observed patterns in transcriptomic, genomic, and proteomic data. The already extensive and increasing use of zebrafish for modeling genetic and other diseases highlights the need to develop a GO analytical tool for this organism. The web tool Comparative GO was originally developed for GO analysis of bacterial data in 2013 ( www.comparativego.com ). We have now upgraded and elaborated this web tool for analysis of zebrafish genetic data using GOs and annotations from the Gene Ontology Consortium.

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

PubMed Central

Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

2013-01-01

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production. PMID:23555215

GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, miRNA, and mRNA data in GDC.

PubMed

Li, Ruidong; Qu, Han; Wang, Shibo; Wei, Julong; Zhang, Le; Ma, Renyuan; Lu, Jianming; Zhu, Jianguo; Zhong, Wei-De; Jia, Zhenyu

2018-03-02

The large-scale multidimensional omics data in the Genomic Data Commons (GDC) provides opportunities to investigate the crosstalk among different RNA species and their regulatory mechanisms in cancers. Easy-to-use bioinformatics pipelines are needed to facilitate such studies. We have developed a user-friendly R/Bioconductor package, named GDCRNATools, for downloading, organizing, and analyzing RNA data in GDC with an emphasis on deciphering the lncRNA-mRNA related competing endogenous RNAs (ceRNAs) regulatory network in cancers. Many widely used bioinformatics tools and databases are utilized in our package. Users can easily pack preferred downstream analysis pipelines or integrate their own pipelines into the workflow. Interactive shiny web apps built in GDCRNATools greatly improve visualization of results from the analysis. GDCRNATools is an R/Bioconductor package that is freely available at Bioconductor (http://bioconductor.org/packages/devel/bioc/html/GDCRNATools.html). Detailed instructions, manual and example code are also available in Github (https://github.com/Jialab-UCR/GDCRNATools). arthur.jia@ucr.edu or zhongwd2009@live.cn or doctorzhujianguo@163.com.

Nanoinformatics: developing new computing applications for nanomedicine

PubMed Central

Maojo, V.; Fritts, M.; Martin-Sanchez, F.; De la Iglesia, D.; Cachau, R.E.; Garcia-Remesal, M.; Crespo, J.; Mitchell, J.A.; Anguita, A.; Baker, N.; Barreiro, J.M.; Benitez, S. E.; De la Calle, G.; Facelli, J. C.; Ghazal, P.; Geissbuhler, A.; Gonzalez-Nilo, F.; Graf, N.; Grangeat, P.; Hermosilla, I.; Hussein, R.; Kern, J.; Koch, S.; Legre, Y.; Lopez-Alonso, V.; Lopez-Campos, G.; Milanesi, L.; Moustakis, V.; Munteanu, C.; Otero, P.; Pazos, A.; Perez-Rey, D.; Potamias, G.; Sanz, F.; Kulikowski, C.

2012-01-01

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended “nanotype” to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other –omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others. PMID:22942787

Searching for Clinically Relevant Biomarkers in Geriatric Oncology.

PubMed

Katsila, Theodora; Patrinos, George P; Kardamakis, Dimitrios

2018-01-01

Ageing, which is associated with a progressive decline and functional deterioration in multiple organ systems, is highly heterogeneous, both inter- and intraindividually. For this, tailored-made theranostics and optimum patient stratification become fundamental, when decision-making in elderly patients is considered. In particular, when cancer incidence and cancer-related mortality and morbidity are taken into account, elderly patient care is a public health concern. In this review, we focus on oncogeriatrics and highlight current opportunities and challenges with an emphasis on the unmet need of clinically relevant biomarkers in elderly cancer patients. We performed a literature search on PubMed and Scopus databases for articles published in English between 2000 and 2017 coupled to text mining and analysis. Considering the top insights, we derived from our literature analysis that information knowledge needs to turn into knowledge growth in oncogeriatrics towards clinically relevant biomarkers, cost-effective practices, updated educational schemes for health professionals (in particular, geriatricians and oncologists), and awareness of ethical issues. We conclude with an interdisciplinary call to omics, geriatricians, oncologists, informatics, and policy-makers communities that Big Data should be translated into decision-making in the clinic.

Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification.

PubMed

Fu, Shuyue; Liu, Xiang; Luo, Maochao; Xie, Ke; Nice, Edouard C; Zhang, Haiyuan; Huang, Canhua

2017-04-01

Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.

Medical malpractice predictors and risk factors for ophthalmologists performing LASIK and PRK surgery.

PubMed Central

Abbott, Richard L

2003-01-01

PURPOSE: To identify physician predictors in laser-assisted in-situ keratomileusis (LASIK) and photorefractive keratectomy (PRK) surgery that correlate with a higher risk for malpractice liability claims and lawsuits. METHODOLOGY: A retrospective, longitudinal, cohort study comparing physician characteristics of 100 consecutive Ophthalmic Mutual Insurance Company (OMIC) LASIK and PRK claims and suits to demographic and practice pattern data for all active refractive surgeons insured by OMIC between 1996 and 2002. Background information and data were obtained from OMIC underwriting applications, a physician practice pattern survey, and claims file records. Using an outcome of whether or not a physician had a prior history of a claim or suit, logistic regression analyses were used separately for each predictor as well as controlling for refractive surgery volume. RESULTS: Logistic regression analysis demonstrated that the most important predictor of filing a claim was surgical volume, with those performing more surgery having a greater risk of incurring a claim (odds ratio [OR], 31.4 for >1,000/year versus 0 to 20/year; 95% confidence interval [CI], 7.9 - 125; P = .0001). Having one or more prior claims was the only other predictor examined that remained statistically significant after controlling for patient volume (OR, 6.4; 95% CI 2.5 - 16.4; P = .0001). Physician gender, advertising, preoperative time spent with patient, and comanagement appeared to be strong predictors in multivariate analyses when surgical volume was greater than 100 cases per year. CONCLUSION: The chances of incurring a malpractice claim or suit for PRK or LASIK correlates significantly with higher surgical volume and a history of a prior claim or suit. Additional risk factors that increase in importance with higher surgical volume include gender, advertising, preoperative time spent with patient, and comanagement with optometrists. These findings may be used in the future to help improve the quality of care for patients undergoing refractive surgery and provide data for underwriting criteria and risk management protocols to proactively manage and reduce the risk of claims and lawsuits against refractive surgeons. PMID:14971582