Protein Bioinformatics Databases and Resources

PubMed Central

Chen, Chuming; Huang, Hongzhan; Wu, Cathy H.

2017-01-01

Many publicly available data repositories and resources have been developed to support protein related information management, data-driven hypothesis generation and biological knowledge discovery. To help researchers quickly find the appropriate protein related informatics resources, we present a comprehensive review (with categorization and description) of major protein bioinformatics databases in this chapter. We also discuss the challenges and opportunities for developing next-generation protein bioinformatics databases and resources to support data integration and data analytics in the Big Data era. PMID:28150231

Comprehensive Gene expression meta-analysis and integrated bioinformatic approaches reveal shared signatures between thrombosis and myeloproliferative disorders

PubMed Central

Jha, Prabhash Kumar; Vijay, Aatira; Sahu, Anita; Ashraf, Mohammad Zahid

2016-01-01

Thrombosis is a leading cause of morbidity and mortality in patients with myeloproliferative disorders (MPDs), particularly polycythemia vera (PV) and essential thrombocythemia (ET). Despite the attempts to establish a link between them, the shared biological mechanisms are yet to be characterized. An integrated gene expression meta-analysis of five independent publicly available microarray data of the three diseases was conducted to identify shared gene expression signatures and overlapping biological processes. Using INMEX bioinformatic tool, based on combined Effect Size (ES) approaches, we identified a total of 1,157 differentially expressed genes (DEGs) (697 overexpressed and 460 underexpressed genes) shared between the three diseases. EnrichR tool’s rich library was used for comprehensive functional enrichment and pathway analysis which revealed “mRNA Splicing” and “SUMO E3 ligases SUMOylate target proteins” among the most enriched terms. Network based meta-analysis identified MYC and FN1 to be the most highly ranked hub genes. Our results reveal that the alterations in biomarkers of the coagulation cascade like F2R, PROS1, SELPLG and ITGB2 were common between the three diseases. Interestingly, the study has generated a novel database of candidate genetic markers, pathways and transcription factors shared between thrombosis and MPDs, which might aid in the development of prognostic therapeutic biomarkers. PMID:27892526

A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples

PubMed Central

Naccache, Samia N.; Federman, Scot; Veeraraghavan, Narayanan; Zaharia, Matei; Lee, Deanna; Samayoa, Erik; Bouquet, Jerome; Greninger, Alexander L.; Luk, Ka-Cheung; Enge, Barryett; Wadford, Debra A.; Messenger, Sharon L.; Genrich, Gillian L.; Pellegrino, Kristen; Grard, Gilda; Leroy, Eric; Schneider, Bradley S.; Fair, Joseph N.; Martínez, Miguel A.; Isa, Pavel; Crump, John A.; DeRisi, Joseph L.; Sittler, Taylor; Hackett, John; Miller, Steve; Chiu, Charles Y.

2014-01-01

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (“sequence-based ultrarapid pathogen identification”), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7–500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times. PMID:24899342

16th IHIW: Global analysis of registry HLA haplotypes from 20 Million individuals: Report from the IHIW Registry Diversity Group

PubMed Central

Maiers, M; Gragert, L; Madbouly, A; Steiner, D; Marsh, S G E; Gourraud, P-A; Oudshoorn, M; Zanden, H; Schmidt, A H; Pingel, J; Hofmann, J; Müller, C; Eberhard, H-P

2013-01-01

This project has the goal to validate bioinformatics methods and tools for HLA haplotype frequency analysis specifically addressing unique issues of haematopoietic stem cell registry data sets. In addition to generating new methods and tools for the analysis of registry data sets, the intent is to produce a comprehensive analysis of HLA data from 20 million donors from the Bone Marrow Donors Worldwide (BMDW) database. This report summarizes the activity on this project as of the 16IHIW meeting in Liverpool. PMID:23280139

Prospects and limitations of full-text index structures in genome analysis

PubMed Central

Vyverman, Michaël; De Baets, Bernard; Fack, Veerle; Dawyndt, Peter

2012-01-01

The combination of incessant advances in sequencing technology producing large amounts of data and innovative bioinformatics approaches, designed to cope with this data flood, has led to new interesting results in the life sciences. Given the magnitude of sequence data to be processed, many bioinformatics tools rely on efficient solutions to a variety of complex string problems. These solutions include fast heuristic algorithms and advanced data structures, generally referred to as index structures. Although the importance of index structures is generally known to the bioinformatics community, the design and potency of these data structures, as well as their properties and limitations, are less understood. Moreover, the last decade has seen a boom in the number of variant index structures featuring complex and diverse memory-time trade-offs. This article brings a comprehensive state-of-the-art overview of the most popular index structures and their recently developed variants. Their features, interrelationships, the trade-offs they impose, but also their practical limitations, are explained and compared. PMID:22584621

A bioinformatics roadmap for the human vaccines project.

PubMed

Scheuermann, Richard H; Sinkovits, Robert S; Schenkelberg, Theodore; Koff, Wayne C

2017-06-01

Biomedical research has become a data intensive science in which high throughput experimentation is producing comprehensive data about biological systems at an ever-increasing pace. The Human Vaccines Project is a new public-private partnership, with the goal of accelerating development of improved vaccines and immunotherapies for global infectious diseases and cancers by decoding the human immune system. To achieve its mission, the Project is developing a Bioinformatics Hub as an open-source, multidisciplinary effort with the overarching goal of providing an enabling infrastructure to support the data processing, analysis and knowledge extraction procedures required to translate high throughput, high complexity human immunology research data into biomedical knowledge, to determine the core principles driving specific and durable protective immune responses.

A bioinformatics prediction approach towards analyzing the glycosylation, co-expression and interaction patterns of epithelial membrane antigen (EMA/MUC1)

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

Kalra, Rajkumar S., E-mail: renu-wadhwa@aist.go.jp; Wadhwa, Renu, E-mail: renu-wadhwa@aist.go.jp

2015-02-27

Epithelial membrane antigen (EMA or MUC1) is a heavily glycosylated, type I transmembrane glycoprotein commonly expressed by epithelial cells of duct organs. It has been shown to be aberrantly glycosylated in several diseases including cancer. Protein sequence based annotation and analysis of glycosylation profile of glycoproteins by robust computational and comprehensive algorithms provides possible insights to the mechanism(s) of anomalous glycosylation. In present report, by using a number of bioinformatics applications we studied EMA/MUC1 and explored its trans-membrane structural domain sequence that is widely subjected to glycosylation. Exploration of different extracellular motifs led to prediction of N and O-linked glycosylationmore » target sites. Based on the putative O-linked target sites, glycosylated moieties and pathways were envisaged. Furthermore, Protein network analysis demonstrated physical interaction of EMA with a number of proteins and confirmed its functional involvement in cell growth and proliferation pathways. Gene Ontology analysis suggested an involvement of EMA in a number of functions including signal transduction, protein binding, processing and transport along with glycosylation. Thus, present study explored potential of bioinformatics prediction approach in analyzing glycosylation, co-expression and interaction patterns of EMA/MUC1 glycoprotein.« less

Proteomic and N-glycoproteomic quantification reveal aberrant changes in the human saliva of oral ulcer patients.

PubMed

Zhang, Ying; Wang, Xi; Cui, Dan; Zhu, Jun

2016-12-01

Human whole saliva is a vital body fluid for studying the physiology and pathology of the oral cavity. As a powerful technique for biomarker discovery, MS-based proteomic strategies have been introduced for saliva analysis and identified hundreds of proteins and N-glycosylation sites. However, there is still a lack of quantitative analysis, which is necessary for biomarker screening and biological research. In this study, we establish an integrated workflow by the combination of stable isotope dimethyl labeling, HILIC enrichment, and high resolution MS for both quantification of the global proteome and N-glycoproteome of human saliva from oral ulcer patients. With the help of advanced bioinformatics, we comprehensively studied oral ulcers at both protein and glycoprotein scales. Bioinformatics analyses revealed that starch digestion and protein degradation activities are inhibited while the immune response is promoted in oral ulcer saliva. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Influenza Research Database: An integrated bioinformatics resource for influenza virus research.

PubMed

Zhang, Yun; Aevermann, Brian D; Anderson, Tavis K; Burke, David F; Dauphin, Gwenaelle; Gu, Zhiping; He, Sherry; Kumar, Sanjeev; Larsen, Christopher N; Lee, Alexandra J; Li, Xiaomei; Macken, Catherine; Mahaffey, Colin; Pickett, Brett E; Reardon, Brian; Smith, Thomas; Stewart, Lucy; Suloway, Christian; Sun, Guangyu; Tong, Lei; Vincent, Amy L; Walters, Bryan; Zaremba, Sam; Zhao, Hongtao; Zhou, Liwei; Zmasek, Christian; Klem, Edward B; Scheuermann, Richard H

2017-01-04

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Bioinformatics Education—Perspectives and Challenges out of Africa

PubMed Central

Adebiyi, Ezekiel F.; Alzohairy, Ahmed M.; Everett, Dean; Ghedira, Kais; Ghouila, Amel; Kumuthini, Judit; Mulder, Nicola J.; Panji, Sumir; Patterton, Hugh-G.

2015-01-01

The discipline of bioinformatics has developed rapidly since the complete sequencing of the first genomes in the 1990s. The development of many high-throughput techniques during the last decades has ensured that bioinformatics has grown into a discipline that overlaps with, and is required for, the modern practice of virtually every field in the life sciences. This has placed a scientific premium on the availability of skilled bioinformaticians, a qualification that is extremely scarce on the African continent. The reasons for this are numerous, although the absence of a skilled bioinformatician at academic institutions to initiate a training process and build sustained capacity seems to be a common African shortcoming. This dearth of bioinformatics expertise has had a knock-on effect on the establishment of many modern high-throughput projects at African institutes, including the comprehensive and systematic analysis of genomes from African populations, which are among the most genetically diverse anywhere on the planet. Recent funding initiatives from the National Institutes of Health and the Wellcome Trust are aimed at ameliorating this shortcoming. In this paper, we discuss the problems that have limited the establishment of the bioinformatics field in Africa, as well as propose specific actions that will help with the education and training of bioinformaticians on the continent. This is an absolute requirement in anticipation of a boom in high-throughput approaches to human health issues unique to data from African populations. PMID:24990350

Expanding roles in a library-based bioinformatics service program: a case study

PubMed Central

Li, Meng; Chen, Yi-Bu; Clintworth, William A

2013-01-01

Question: How can a library-based bioinformatics support program be implemented and expanded to continuously support the growing and changing needs of the research community? Setting: A program at a health sciences library serving a large academic medical center with a strong research focus is described. Methods: The bioinformatics service program was established at the Norris Medical Library in 2005. As part of program development, the library assessed users' bioinformatics needs, acquired additional funds, established and expanded service offerings, and explored additional roles in promoting on-campus collaboration. Results: Personnel and software have increased along with the number of registered software users and use of the provided services. Conclusion: With strategic efforts and persistent advocacy within the broader university environment, library-based bioinformatics service programs can become a key part of an institution's comprehensive solution to researchers' ever-increasing bioinformatics needs. PMID:24163602

A decade of Web Server updates at the Bioinformatics Links Directory: 2003-2012.

PubMed

Brazas, Michelle D; Yim, David; Yeung, Winston; Ouellette, B F Francis

2012-07-01

The 2012 Bioinformatics Links Directory update marks the 10th special Web Server issue from Nucleic Acids Research. Beginning with content from their 2003 publication, the Bioinformatics Links Directory in collaboration with Nucleic Acids Research has compiled and published a comprehensive list of freely accessible, online tools, databases and resource materials for the bioinformatics and life science research communities. The past decade has exhibited significant growth and change in the types of tools, databases and resources being put forth, reflecting both technology changes and the nature of research over that time. With the addition of 90 web server tools and 12 updates from the July 2012 Web Server issue of Nucleic Acids Research, the Bioinformatics Links Directory at http://bioinformatics.ca/links_directory/ now contains an impressive 134 resources, 455 databases and 1205 web server tools, mirroring the continued activity and efforts of our field.

NEIBank: Genomics and bioinformatics resources for vision research

PubMed Central

Peterson, Katherine; Gao, James; Buchoff, Patee; Jaworski, Cynthia; Bowes-Rickman, Catherine; Ebright, Jessica N.; Hauser, Michael A.; Hoover, David

2008-01-01

NEIBank is an integrated resource for genomics and bioinformatics in vision research. It includes expressed sequence tag (EST) data and sequence-verified cDNA clones for multiple eye tissues of several species, web-based access to human eye-specific SAGE data through EyeSAGE, and comprehensive, annotated databases of known human eye disease genes and candidate disease gene loci. All expression- and disease-related data are integrated in EyeBrowse, an eye-centric genome browser. NEIBank provides a comprehensive overview of current knowledge of the transcriptional repertoires of eye tissues and their relation to pathology. PMID:18648525

Drug target inference through pathway analysis of genomics data

PubMed Central

Ma, Haisu; Zhao, Hongyu

2013-01-01

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Orchestrating high-throughput genomic analysis with Bioconductor

PubMed Central

Huber, Wolfgang; Carey, Vincent J.; Gentleman, Robert; Anders, Simon; Carlson, Marc; Carvalho, Benilton S.; Bravo, Hector Corrada; Davis, Sean; Gatto, Laurent; Girke, Thomas; Gottardo, Raphael; Hahne, Florian; Hansen, Kasper D.; Irizarry, Rafael A.; Lawrence, Michael; Love, Michael I.; MacDonald, James; Obenchain, Valerie; Oleś, Andrzej K.; Pagès, Hervé; Reyes, Alejandro; Shannon, Paul; Smyth, Gordon K.; Tenenbaum, Dan; Waldron, Levi; Morgan, Martin

2015-01-01

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors. PMID:25633503

Comprehensive decision tree models in bioinformatics.

PubMed

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

A comprehensive bioinformatic analysis of hepatitis D virus full-length genomes.

PubMed

Delfino, C M; Cerrudo, C S; Biglione, M; Oubiña, J R; Ghiringhelli, P D; Mathet, V L

2018-02-06

In association with hepatitis B virus (HBV), hepatitis delta virus (HDV) is a subviral agent that may promote severe acute and chronic forms of liver disease. Based on the percentage of nucleotide identity of the genome, HDV was initially classified into three genotypes. However, since 2006, the original classification has been further expanded into eight clades/genotypes. The intergenotype divergence may be as high as 35%-40% over the entire RNA genome, whereas sequence heterogeneity among the isolates of a given genotype is <20%; furthermore, HDV recombinants have been clearly demonstrated. The genetic diversity of HDV is related to the geographic origin of the isolates. This study shows the first comprehensive bioinformatic analysis of the complete available set of HDV sequences, using both nucleotide and protein phylogenies (based on an evolutionary model selection, gamma distribution estimation, tree inference and phylogenetic distance estimation), protein composition analysis and comparison (based on the presence of invariant residues, molecular signatures, amino acid frequencies and mono- and di-amino acid compositional distances), as well as amino acid changes in sequence evolution. Taking into account the congruent and consistent results of both nucleotide and amino acid analyses of GenBank available sequences (recorded as of January, 2017), we propose that the eight hepatitis D virus genotypes may be grouped into three large genogroups fully supported by their shared characteristics. © 2018 John Wiley & Sons Ltd.

GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data.

PubMed

Rigbolt, Kristoffer T G; Vanselow, Jens T; Blagoev, Blagoy

2011-08-01

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.

GProX, a User-Friendly Platform for Bioinformatics Analysis and Visualization of Quantitative Proteomics Data*

PubMed Central

Rigbolt, Kristoffer T. G.; Vanselow, Jens T.; Blagoev, Blagoy

2011-01-01

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)1. The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net. PMID:21602510

A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples.

PubMed

Naccache, Samia N; Federman, Scot; Veeraraghavan, Narayanan; Zaharia, Matei; Lee, Deanna; Samayoa, Erik; Bouquet, Jerome; Greninger, Alexander L; Luk, Ka-Cheung; Enge, Barryett; Wadford, Debra A; Messenger, Sharon L; Genrich, Gillian L; Pellegrino, Kristen; Grard, Gilda; Leroy, Eric; Schneider, Bradley S; Fair, Joseph N; Martínez, Miguel A; Isa, Pavel; Crump, John A; DeRisi, Joseph L; Sittler, Taylor; Hackett, John; Miller, Steve; Chiu, Charles Y

2014-07-01

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI ("sequence-based ultrarapid pathogen identification"), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7-500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times. © 2014 Naccache et al.; Published by Cold Spring Harbor Laboratory Press.

Integrated web visualizations for protein-protein interaction databases.

PubMed

Jeanquartier, Fleur; Jean-Quartier, Claire; Holzinger, Andreas

2015-06-16

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. We selected M=10 out of N=53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.

Deep learning in bioinformatics.

PubMed

Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

2017-09-01

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond.

PubMed

Hiraoka, Satoshi; Yang, Ching-Chia; Iwasaki, Wataru

2016-09-29

Metagenomic approaches are now commonly used in microbial ecology to study microbial communities in more detail, including many strains that cannot be cultivated in the laboratory. Bioinformatic analyses make it possible to mine huge metagenomic datasets and discover general patterns that govern microbial ecosystems. However, the findings of typical metagenomic and bioinformatic analyses still do not completely describe the ecology and evolution of microbes in their environments. Most analyses still depend on straightforward sequence similarity searches against reference databases. We herein review the current state of metagenomics and bioinformatics in microbial ecology and discuss future directions for the field. New techniques will allow us to go beyond routine analyses and broaden our knowledge of microbial ecosystems. We need to enrich reference databases, promote platforms that enable meta- or comprehensive analyses of diverse metagenomic datasets, devise methods that utilize long-read sequence information, and develop more powerful bioinformatic methods to analyze data from diverse perspectives.

iMetaLab 1.0: A web platform for metaproteomics data analysis.

PubMed

Liao, Bo; Ning, Zhibin; Cheng, Kai; Zhang, Xu; Li, Leyuan; Mayne, Janice; Figeys, Daniel

2018-06-15

The human gut microbiota, a complex, dynamic and biodiverse community, has been increasingly shown to influence many aspects of health and disease. Metaproteomic analysis has proven to be a powerful approach to study the functionality of the microbiota. However, the processing and analyses of metaproteomic mass spectrometry (MS) data remains a daunting task in metaproteomics data analysis. We developed iMetaLab, a web based platform to provide a user-friendly and comprehensive data analysis pipeline with a focus on lowering the technical barrier for metaproteomics data analysis. iMetaLab is freely available through at http://imetalab.ca. Supplementary data are available at Bioinformatics online.

BioMaS: a modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS.

PubMed

Fosso, Bruno; Santamaria, Monica; Marzano, Marinella; Alonso-Alemany, Daniel; Valiente, Gabriel; Donvito, Giacinto; Monaco, Alfonso; Notarangelo, Pasquale; Pesole, Graziano

2015-07-01

Substantial advances in microbiology, molecular evolution and biodiversity have been carried out in recent years thanks to Metagenomics, which allows to unveil the composition and functions of mixed microbial communities in any environmental niche. If the investigation is aimed only at the microbiome taxonomic structure, a target-based metagenomic approach, here also referred as Meta-barcoding, is generally applied. This approach commonly involves the selective amplification of a species-specific genetic marker (DNA meta-barcode) in the whole taxonomic range of interest and the exploration of its taxon-related variants through High-Throughput Sequencing (HTS) technologies. The accessibility to proper computational systems for the large-scale bioinformatic analysis of HTS data represents, currently, one of the major challenges in advanced Meta-barcoding projects. BioMaS (Bioinformatic analysis of Metagenomic AmpliconS) is a new bioinformatic pipeline designed to support biomolecular researchers involved in taxonomic studies of environmental microbial communities by a completely automated workflow, comprehensive of all the fundamental steps, from raw sequence data upload and cleaning to final taxonomic identification, that are absolutely required in an appropriately designed Meta-barcoding HTS-based experiment. In its current version, BioMaS allows the analysis of both bacterial and fungal environments starting directly from the raw sequencing data from either Roche 454 or Illumina HTS platforms, following two alternative paths, respectively. BioMaS is implemented into a public web service available at https://recasgateway.ba.infn.it/ and is also available in Galaxy at http://galaxy.cloud.ba.infn.it:8080 (only for Illumina data). BioMaS is a friendly pipeline for Meta-barcoding HTS data analysis specifically designed for users without particular computing skills. A comparative benchmark, carried out by using a simulated dataset suitably designed to broadly represent the currently known bacterial and fungal world, showed that BioMaS outperforms QIIME and MOTHUR in terms of extent and accuracy of deep taxonomic sequence assignments.

Genetic Markers Analyses and Bioinformatic Approaches to Distinguish Between Olive Tree (Olea europaea L.) Cultivars.

PubMed

Ben Ayed, Rayda; Ben Hassen, Hanen; Ennouri, Karim; Rebai, Ahmed

2016-12-01

The genetic diversity of 22 olive tree cultivars (Olea europaea L.) sampled from different Mediterranean countries was assessed using 5 SNP markers (FAD2.1; FAD2.3; CALC; SOD and ANTHO3) located in four different genes. The genotyping analysis of the 22 cultivars with 5 SNP loci revealed 11 alleles (average 2.2 per allele). The dendrogram based on cultivar genotypes revealed three clusters consistent with the cultivars classification. Besides, the results obtained with the five SNPs were compared to those obtained with the SSR markers using bioinformatic analyses and by computing a cophenetic correlation coefficient, indicating the usefulness of the UPGMA method for clustering plant genotypes. Based on principal coordinate analysis using a similarity matrix, the first two coordinates, revealed 54.94 % of the total variance. This work provides a more comprehensive explanation of the diversity available in Tunisia olive cultivars, and an important contribution for olive breeding and olive oil authenticity.

MASS SPECTROMETRY-BASED METABOLOMICS

PubMed Central

Dettmer, Katja; Aronov, Pavel A.; Hammock, Bruce D.

2007-01-01

This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed. PMID:16921475

RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application.

PubMed

D'Antonio, Mattia; D'Onorio De Meo, Paolo; Pallocca, Matteo; Picardi, Ernesto; D'Erchia, Anna Maria; Calogero, Raffaele A; Castrignanò, Tiziana; Pesole, Graziano

2015-01-01

The study of RNA has been dramatically improved by the introduction of Next Generation Sequencing platforms allowing massive and cheap sequencing of selected RNA fractions, also providing information on strand orientation (RNA-Seq). The complexity of transcriptomes and of their regulative pathways make RNA-Seq one of most complex field of NGS applications, addressing several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.). In order to provide researchers with an effective and friendly resource for analyzing RNA-Seq data, we present here RAP (RNA-Seq Analysis Pipeline), a cloud computing web application implementing a complete but modular analysis workflow. This pipeline integrates both state-of-the-art bioinformatics tools for RNA-Seq analysis and in-house developed scripts to offer to the user a comprehensive strategy for data analysis. RAP is able to perform quality checks (adopting FastQC and NGS QC Toolkit), identify and quantify expressed genes and transcripts (with Tophat, Cufflinks and HTSeq), detect alternative splicing events (using SpliceTrap) and chimeric transcripts (with ChimeraScan). This pipeline is also able to identify splicing junctions and constitutive or alternative polyadenylation sites (implementing custom analysis modules) and call for statistically significant differences in genes and transcripts expression, splicing pattern and polyadenylation site usage (using Cuffdiff2 and DESeq). Through a user friendly web interface, the RAP workflow can be suitably customized by the user and it is automatically executed on our cloud computing environment. This strategy allows to access to bioinformatics tools and computational resources without specific bioinformatics and IT skills. RAP provides a set of tabular and graphical results that can be helpful to browse, filter and export analyzed data, according to the user needs.

FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies.

PubMed

Kim, Jiwoong; Kim, Min Soo; Koh, Andrew Y; Xie, Yang; Zhan, Xiaowei

2016-10-10

Given the lack of a complete and comprehensive library of microbial reference genomes, determining the functional profile of diverse microbial communities is challenging. The available functional analysis pipelines lack several key features: (i) an integrated alignment tool, (ii) operon-level analysis, and (iii) the ability to process large datasets. Here we introduce our open-sourced, stand-alone functional analysis pipeline for analyzing whole metagenomic and metatranscriptomic sequencing data, FMAP (Functional Mapping and Analysis Pipeline). FMAP performs alignment, gene family abundance calculations, and statistical analysis (three levels of analyses are provided: differentially-abundant genes, operons and pathways). The resulting output can be easily visualized with heatmaps and functional pathway diagrams. FMAP functional predictions are consistent with currently available functional analysis pipelines. FMAP is a comprehensive tool for providing functional analysis of metagenomic/metatranscriptomic sequencing data. With the added features of integrated alignment, operon-level analysis, and the ability to process large datasets, FMAP will be a valuable addition to the currently available functional analysis toolbox. We believe that this software will be of great value to the wider biology and bioinformatics communities.

Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine

PubMed Central

2016-01-01

Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs. PMID:27195526

PRAPI: post-transcriptional regulation analysis pipeline for Iso-Seq.

PubMed

Gao, Yubang; Wang, Huiyuan; Zhang, Hangxiao; Wang, Yongsheng; Chen, Jinfeng; Gu, Lianfeng

2018-05-01

The single-molecule real-time (SMRT) isoform sequencing (Iso-Seq) based on Pacific Bioscience (PacBio) platform has received increasing attention for its ability to explore full-length isoforms. Thus, comprehensive tools for Iso-Seq bioinformatics analysis are extremely useful. Here, we present a one-stop solution for Iso-Seq analysis, called PRAPI to analyze alternative transcription initiation (ATI), alternative splicing (AS), alternative cleavage and polyadenylation (APA), natural antisense transcripts (NAT), and circular RNAs (circRNAs) comprehensively. PRAPI is capable of combining Iso-Seq full-length isoforms with short read data, such as RNA-Seq or polyadenylation site sequencing (PAS-seq) for differential expression analysis of NAT, AS, APA and circRNAs. Furthermore, PRAPI can annotate new genes and correct mis-annotated genes when gene annotation is available. Finally, PRAPI generates high-quality vector graphics to visualize and highlight the Iso-Seq results. The Dockerfile of PRAPI is available at http://www.bioinfor.org/tool/PRAPI. lfgu@fafu.edu.cn.

Influenza Research Database: an integrated bioinformatics resource for influenza research and surveillance

PubMed Central

Squires, R. Burke; Noronha, Jyothi; Hunt, Victoria; García‐Sastre, Adolfo; Macken, Catherine; Baumgarth, Nicole; Suarez, David; Pickett, Brett E.; Zhang, Yun; Larsen, Christopher N.; Ramsey, Alvin; Zhou, Liwei; Zaremba, Sam; Kumar, Sanjeev; Deitrich, Jon; Klem, Edward; Scheuermann, Richard H.

2012-01-01

Please cite this paper as: Squires et al. (2012) Influenza research database: an integrated bioinformatics resource for influenza research and surveillance. Influenza and Other Respiratory Viruses 6(6), 404–416. Background  The recent emergence of the 2009 pandemic influenza A/H1N1 virus has highlighted the value of free and open access to influenza virus genome sequence data integrated with information about other important virus characteristics. Design  The Influenza Research Database (IRD, http://www.fludb.org) is a free, open, publicly‐accessible resource funded by the U.S. National Institute of Allergy and Infectious Diseases through the Bioinformatics Resource Centers program. IRD provides a comprehensive, integrated database and analysis resource for influenza sequence, surveillance, and research data, including user‐friendly interfaces for data retrieval, visualization and comparative genomics analysis, together with personal log in‐protected ‘workbench’ spaces for saving data sets and analysis results. IRD integrates genomic, proteomic, immune epitope, and surveillance data from a variety of sources, including public databases, computational algorithms, external research groups, and the scientific literature. Results  To demonstrate the utility of the data and analysis tools available in IRD, two scientific use cases are presented. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross‐protective immunity. Phylogenetic and geospatial analysis of sequences from wild bird surveillance samples revealed a possible evolutionary connection between influenza virus from Delaware Bay shorebirds and Alberta ducks. Conclusions  The IRD provides a wealth of integrated data and information about influenza virus to support research of the genetic determinants dictating virus pathogenicity, host range restriction and transmission, and to facilitate development of vaccines, diagnostics, and therapeutics. PMID:22260278

Nano-LC-ESI MS/MS analysis of proteins in dried sea dragon Solenognathus hardwickii and bioinformatic analysis of its protein expression profiling.

PubMed

Zhang, Dong-Mei; Feng, Li-Xing; Li, Lu; Liu, Miao; Jiang, Bao-Hong; Yang, Min; Li, Guo-Qiang; Wu, Wan-Ying; Guo, De-An; Liu, Xuan

2016-09-01

The sea dragon Solenognathus hardwickii has long been used as a traditional Chinese medicine for the treatment of various diseases, such as male impotency. To gain a comprehensive insight into the protein components of the sea dragon, shotgun proteomic analysis of its protein expression profiling was conducted in the present study. Proteins were extracted from dried sea dragon using a trichloroacetic acid/acetone precipitation method and then separated by SDS-PAGE. The protein bands were cut from the gel and digested by trypsin to generate peptide mixture. The peptide fragments were then analyzed using nano liquid chromatography tandem mass spectrometry (nano-LC-ESI MS/MS). 810 proteins and 1 577 peptides were identified in the dried sea dragon. The identified proteins exhibited molecular weight values ranging from 1 900 to 3 516 900 Da and pI values from 3.8 to 12.18. Bioinformatic analysis was conducted using the DAVID Bioinformatics Resources 6.7 Gene Ontology (GO) analysis tool to explore possible functions of the identified proteins. Ascribed functions of the proteins mainly included intracellular non-membrane-bound organelle, non-membrane-bounded organelle, cytoskeleton, structural molecule activity, calcium ion binding and etc. Furthermore, possible signal networks of the identified proteins were predicted using STRING (Search Tool for the Retrieval of Interacting Genes) database. Ribosomal protein synthesis was found to play an important role in the signal network. The results of this study, to best of our knowledge, were the first to provide a reference proteome profile for the sea dragon, and would aid in the understanding of the expression and functions of the identified proteins. Copyright © 2016 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

Does the microbiome and virome contribute to myalgic encephalomyelitis/chronic fatigue syndrome?

PubMed Central

Newberry, Fiona; Hsieh, Shen-Yuan; Wileman, Tom; Carding, Simon R.

2018-01-01

Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) (ME/CFS) is a disabling and debilitating disease of unknown aetiology. It is a heterogeneous disease characterized by various inflammatory, immune, viral, neurological and endocrine symptoms. Several microbiome studies have described alterations in the bacterial component of the microbiome (dysbiosis) consistent with a possible role in disease development. However, in focusing on the bacterial components of the microbiome, these studies have neglected the viral constituent known as the virome. Viruses, particularly those infecting bacteria (bacteriophages), have the potential to alter the function and structure of the microbiome via gene transfer and host lysis. Viral-induced microbiome changes can directly and indirectly influence host health and disease. The contribution of viruses towards disease pathogenesis is therefore an important area for research in ME/CFS. Recent advancements in sequencing technology and bioinformatics now allow more comprehensive and inclusive investigations of human microbiomes. However, as the number of microbiome studies increases, the need for greater consistency in study design and analysis also increases. Comparisons between different ME/CFS microbiome studies are difficult because of differences in patient selection and diagnosis criteria, sample processing, genome sequencing and downstream bioinformatics analysis. It is therefore important that microbiome studies adopt robust, reproducible and consistent study design to enable more reliable and valid comparisons and conclusions to be made between studies. This article provides a comprehensive review of the current evidence supporting microbiome alterations in ME/CFS patients. Additionally, the pitfalls and challenges associated with microbiome studies are discussed. PMID:29523751

Saliva Proteomics Analysis Offers Insights on Type 1 Diabetes Pathology in a Pediatric Population

PubMed Central

Pappa, Eftychia; Vastardis, Heleni; Mermelekas, George; Gerasimidi-Vazeou, Andriani; Zoidakis, Jerome; Vougas, Konstantinos

2018-01-01

The composition of the salivary proteome is affected by pathological conditions. We analyzed by high resolution mass spectrometry approaches saliva samples collected from children and adolescents with type 1 diabetes and healthy controls. The list of more than 2000 high confidence protein identifications constitutes a comprehensive characterization of the salivary proteome. Patients with good glycemic regulation and healthy individuals have comparable proteomic profiles. In contrast, a significant number of differentially expressed proteins were identified in the saliva of patients with poor glycemic regulation compared to patients with good glycemic control and healthy children. These proteins are involved in biological processes relevant to diabetic pathology such as endothelial damage and inflammation. Moreover, a putative preventive therapeutic approach was identified based on bioinformatic analysis of the deregulated salivary proteins. Thus, thorough characterization of saliva proteins in diabetic pediatric patients established a connection between molecular changes and disease pathology. This proteomic and bioinformatic approach highlights the potential of salivary diagnostics in diabetes pathology and opens the way for preventive treatment of the disease. PMID:29755368

First venom gland transcriptomic analysis of Iranian yellow scorpion "Odonthubuthus doriae" with some new findings.

PubMed

NaderiSoorki, Maryam; Galehdari, Hamid; Baradaran, Masomeh; Jalali, Amir

2016-09-15

Scorpion venom contains mixture of biologic molecules including selective toxins with medical capability. Odonthubuthus doriae (O. doriae) belonged to Buthidae family of scorpions and gained more interest among Iranian dangerous scorpion since 2005. We constructed the first cDNA library to explore the transcriptomic composition of this Iranian scorpiontelson. Then by used of bioinformatic software each expression sequence taq (EST) from the library analyzed and its quiddity was clear. Analysis showed that toxins (42%) had more venom transcript than other component such as antimicrobial peptides, venom peptides and cell proteins. Over 16% of transcripts didn't have any open reading frames (ORF), however their sequences showed similarity by other scorpion sequences. One EST didn't have any similarity by known scorpion peptides. For the first time; we report a comprehensive study of an Iranian scorpion with interesting and novel findings. We characterized a new putative sodium channel modifier in scorpions by some bioinformatics software, and then predicted its structure and function. Copyright © 2016. Published by Elsevier Ltd.

IDEOM: an Excel interface for analysis of LC-MS-based metabolomics data.

PubMed

Creek, Darren J; Jankevics, Andris; Burgess, Karl E V; Breitling, Rainer; Barrett, Michael P

2012-04-01

The application of emerging metabolomics technologies to the comprehensive investigation of cellular biochemistry has been limited by bottlenecks in data processing, particularly noise filtering and metabolite identification. IDEOM provides a user-friendly data processing application that automates filtering and identification of metabolite peaks, paying particular attention to common sources of noise and false identifications generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Building on advanced processing tools such as mzMatch and XCMS, it allows users to run a comprehensive pipeline for data analysis and visualization from a graphical user interface within Microsoft Excel, a familiar program for most biological scientists. IDEOM is provided free of charge at http://mzmatch.sourceforge.net/ideom.html, as a macro-enabled spreadsheet (.xlsb). Implementation requires Microsoft Excel (2007 or later). R is also required for full functionality. michael.barrett@glasgow.ac.uk Supplementary data are available at Bioinformatics online.

G-DOC Plus - an integrative bioinformatics platform for precision medicine.

PubMed

Bhuvaneshwar, Krithika; Belouali, Anas; Singh, Varun; Johnson, Robert M; Song, Lei; Alaoui, Adil; Harris, Michael A; Clarke, Robert; Weiner, Louis M; Gusev, Yuriy; Madhavan, Subha

2016-04-30

G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. G-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability. G-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu .

Analysis of functional redundancies within the Arabidopsis TCP transcription factor family.

PubMed

Danisman, Selahattin; van Dijk, Aalt D J; Bimbo, Andrea; van der Wal, Froukje; Hennig, Lars; de Folter, Stefan; Angenent, Gerco C; Immink, Richard G H

2013-12-01

Analyses of the functions of TEOSINTE-LIKE1, CYCLOIDEA, and PROLIFERATING CELL FACTOR1 (TCP) transcription factors have been hampered by functional redundancy between its individual members. In general, putative functionally redundant genes are predicted based on sequence similarity and confirmed by genetic analysis. In the TCP family, however, identification is impeded by relatively low overall sequence similarity. In a search for functionally redundant TCP pairs that control Arabidopsis leaf development, this work performed an integrative bioinformatics analysis, combining protein sequence similarities, gene expression data, and results of pair-wise protein-protein interaction studies for the 24 members of the Arabidopsis TCP transcription factor family. For this, the work completed any lacking gene expression and protein-protein interaction data experimentally and then performed a comprehensive prediction of potential functional redundant TCP pairs. Subsequently, redundant functions could be confirmed for selected predicted TCP pairs by genetic and molecular analyses. It is demonstrated that the previously uncharacterized class I TCP19 gene plays a role in the control of leaf senescence in a redundant fashion with TCP20. Altogether, this work shows the power of combining classical genetic and molecular approaches with bioinformatics predictions to unravel functional redundancies in the TCP transcription factor family.

Analysis of functional redundancies within the Arabidopsis TCP transcription factor family

PubMed Central

Danisman, Selahattin; de Folter, Stefan; Immink, Richard G. H.

2013-01-01

Analyses of the functions of TEOSINTE-LIKE1, CYCLOIDEA, and PROLIFERATING CELL FACTOR1 (TCP) transcription factors have been hampered by functional redundancy between its individual members. In general, putative functionally redundant genes are predicted based on sequence similarity and confirmed by genetic analysis. In the TCP family, however, identification is impeded by relatively low overall sequence similarity. In a search for functionally redundant TCP pairs that control Arabidopsis leaf development, this work performed an integrative bioinformatics analysis, combining protein sequence similarities, gene expression data, and results of pair-wise protein–protein interaction studies for the 24 members of the Arabidopsis TCP transcription factor family. For this, the work completed any lacking gene expression and protein–protein interaction data experimentally and then performed a comprehensive prediction of potential functional redundant TCP pairs. Subsequently, redundant functions could be confirmed for selected predicted TCP pairs by genetic and molecular analyses. It is demonstrated that the previously uncharacterized class I TCP19 gene plays a role in the control of leaf senescence in a redundant fashion with TCP20. Altogether, this work shows the power of combining classical genetic and molecular approaches with bioinformatics predictions to unravel functional redundancies in the TCP transcription factor family. PMID:24129704

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches.

PubMed

Oulas, Anastasis; Minadakis, George; Zachariou, Margarita; Sokratous, Kleitos; Bourdakou, Marilena M; Spyrou, George M

2017-11-27

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine. © The Author 2017. Published by Oxford University Press.

[Construction and application of bioinformatic analysis platform for aquatic pathogen based on the MilkyWay-2 supercomputer].

PubMed

Fang, Xiang; Li, Ning-qiu; Fu, Xiao-zhe; Li, Kai-bin; Lin, Qiang; Liu, Li-hui; Shi, Cun-bin; Wu, Shu-qin

2015-07-01

As a key component of life science, bioinformatics has been widely applied in genomics, transcriptomics, and proteomics. However, the requirement of high-performance computers rather than common personal computers for constructing a bioinformatics platform significantly limited the application of bioinformatics in aquatic science. In this study, we constructed a bioinformatic analysis platform for aquatic pathogen based on the MilkyWay-2 supercomputer. The platform consisted of three functional modules, including genomic and transcriptomic sequencing data analysis, protein structure prediction, and molecular dynamics simulations. To validate the practicability of the platform, we performed bioinformatic analysis on aquatic pathogenic organisms. For example, genes of Flavobacterium johnsoniae M168 were identified and annotated via Blast searches, GO and InterPro annotations. Protein structural models for five small segments of grass carp reovirus HZ-08 were constructed by homology modeling. Molecular dynamics simulations were performed on out membrane protein A of Aeromonas hydrophila, and the changes of system temperature, total energy, root mean square deviation and conformation of the loops during equilibration were also observed. These results showed that the bioinformatic analysis platform for aquatic pathogen has been successfully built on the MilkyWay-2 supercomputer. This study will provide insights into the construction of bioinformatic analysis platform for other subjects.

Bioinformatics Analysis of NBS-LRR Encoding Resistance Genes in Setaria italica.

PubMed

Zhao, Yan; Weng, Qiaoyun; Song, Jinhui; Ma, Hailian; Yuan, Jincheng; Dong, Zhiping; Liu, Yinghui

2016-06-01

In plants, resistance (R) genes are involved in pathogen recognition and subsequent activation of innate immune responses. The nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes family forms the largest R-gene family among plant genomes and play an important role in plant disease resistance. In this paper, comprehensive analysis of NBS-encoding genes is performed in the whole Setaria italica genome. A total of 96 NBS-LRR genes are identified, and comprehensive overview of the NBS-LRR genes is undertaken, including phylogenetic analysis, chromosome locations, conserved motifs of proteins, and gene expression. Based on the domain, these genes are divided into two groups and distributed in all Setaria italica chromosomes. Most NBS-LRR genes are located at the distal tip of the long arms of the chromosomes. Setaria italica NBS-LRR proteins share at least one nucleotide-biding domain and one leucine-rich repeat domain. Our results also show the duplication of NBS-LRR genes in Setaria italica is related to their gene structure.

A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data.

PubMed

Chen Peng; Ao Li

2017-01-01

The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .

The Cytotoxicity Mechanism of 6-Shogaol-Treated HeLa Human Cervical Cancer Cells Revealed by Label-Free Shotgun Proteomics and Bioinformatics Analysis.

PubMed

Liu, Qun; Peng, Yong-Bo; Qi, Lian-Wen; Cheng, Xiao-Lan; Xu, Xiao-Jun; Liu, Le-Le; Liu, E-Hu; Li, Ping

2012-01-01

Cervical cancer is one of the most common cancers among women in the world. 6-Shogaol is a natural compound isolated from the rhizome of ginger (Zingiber officinale). In this paper, we demonstrated that 6-shogaol induced apoptosis and G2/M phase arrest in human cervical cancer HeLa cells. Endoplasmic reticulum stress and mitochondrial pathway were involved in 6-shogaol-mediated apoptosis. Proteomic analysis based on label-free strategy by liquid chromatography chip quadrupole time-of-flight mass spectrometry was subsequently proposed to identify, in a non-target-biased manner, the molecular changes in cellular proteins in response to 6-shogaol treatment. A total of 287 proteins were differentially expressed in response to 24 h treatment with 15 μM 6-shogaol in HeLa cells. Significantly changed proteins were subjected to functional pathway analysis by multiple analyzing software. Ingenuity pathway analysis (IPA) suggested that 14-3-3 signaling is a predominant canonical pathway involved in networks which may be significantly associated with the process of apoptosis and G2/M cell cycle arrest induced by 6-shogaol. In conclusion, this work developed an unbiased protein analysis strategy by shotgun proteomics and bioinformatics analysis. Data observed provide a comprehensive analysis of the 6-shogaol-treated HeLa cell proteome and reveal protein alterations that are associated with its anticancer mechanism.

The Cytotoxicity Mechanism of 6-Shogaol-Treated HeLa Human Cervical Cancer Cells Revealed by Label-Free Shotgun Proteomics and Bioinformatics Analysis

PubMed Central

Liu, Qun; Peng, Yong-Bo; Qi, Lian-Wen; Cheng, Xiao-Lan; Xu, Xiao-Jun; Liu, Le-Le; Liu, E-Hu; Li, Ping

2012-01-01

Cervical cancer is one of the most common cancers among women in the world. 6-Shogaol is a natural compound isolated from the rhizome of ginger (Zingiber officinale). In this paper, we demonstrated that 6-shogaol induced apoptosis and G2/M phase arrest in human cervical cancer HeLa cells. Endoplasmic reticulum stress and mitochondrial pathway were involved in 6-shogaol-mediated apoptosis. Proteomic analysis based on label-free strategy by liquid chromatography chip quadrupole time-of-flight mass spectrometry was subsequently proposed to identify, in a non-target-biased manner, the molecular changes in cellular proteins in response to 6-shogaol treatment. A total of 287 proteins were differentially expressed in response to 24 h treatment with 15 μM 6-shogaol in HeLa cells. Significantly changed proteins were subjected to functional pathway analysis by multiple analyzing software. Ingenuity pathway analysis (IPA) suggested that 14-3-3 signaling is a predominant canonical pathway involved in networks which may be significantly associated with the process of apoptosis and G2/M cell cycle arrest induced by 6-shogaol. In conclusion, this work developed an unbiased protein analysis strategy by shotgun proteomics and bioinformatics analysis. Data observed provide a comprehensive analysis of the 6-shogaol-treated HeLa cell proteome and reveal protein alterations that are associated with its anticancer mechanism. PMID:23243437

eSBMTools 1.0: enhanced native structure-based modeling tools.

PubMed

Lutz, Benjamin; Sinner, Claude; Heuermann, Geertje; Verma, Abhinav; Schug, Alexander

2013-11-01

Molecular dynamics simulations provide detailed insights into the structure and function of biomolecular systems. Thus, they complement experimental measurements by giving access to experimentally inaccessible regimes. Among the different molecular dynamics techniques, native structure-based models (SBMs) are based on energy landscape theory and the principle of minimal frustration. Typically used in protein and RNA folding simulations, they coarse-grain the biomolecular system and/or simplify the Hamiltonian resulting in modest computational requirements while achieving high agreement with experimental data. eSBMTools streamlines running and evaluating SBM in a comprehensive package and offers high flexibility in adding experimental- or bioinformatics-derived restraints. We present a software package that allows setting up, modifying and evaluating SBM for both RNA and proteins. The implemented workflows include predicting protein complexes based on bioinformatics-derived inter-protein contact information, a standardized setup of protein folding simulations based on the common PDB format, calculating reaction coordinates and evaluating the simulation by free-energy calculations with weighted histogram analysis method or by phi-values. The modules interface with the molecular dynamics simulation program GROMACS. The package is open source and written in architecture-independent Python2. http://sourceforge.net/projects/esbmtools/. alexander.schug@kit.edu. Supplementary data are available at Bioinformatics online.

Workflows for microarray data processing in the Kepler environment.

PubMed

Stropp, Thomas; McPhillips, Timothy; Ludäscher, Bertram; Bieda, Mark

2012-05-17

Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis demands, including integration with new data sources. Bioinformatics pipelines are usually custom built for different applications, making them typically difficult to modify, extend and repurpose. Scientific workflow systems are intended to address these issues by providing general-purpose frameworks in which to develop and execute such pipelines. The Kepler workflow environment is a well-established system under continual development that is employed in several areas of scientific research. Kepler provides a flexible graphical interface, featuring clear display of parameter values, for design and modification of workflows. It has capabilities for developing novel computational components in the R, Python, and Java programming languages, all of which are widely used for bioinformatics algorithm development, along with capabilities for invoking external applications and using web services. We developed a series of fully functional bioinformatics pipelines addressing common tasks in microarray processing in the Kepler workflow environment. These pipelines consist of a set of tools for GFF file processing of NimbleGen chromatin immunoprecipitation on microarray (ChIP-chip) datasets and more comprehensive workflows for Affymetrix gene expression microarray bioinformatics and basic primer design for PCR experiments, which are often used to validate microarray results. Although functional in themselves, these workflows can be easily customized, extended, or repurposed to match the needs of specific projects and are designed to be a toolkit and starting point for specific applications. These workflows illustrate a workflow programming paradigm focusing on local resources (programs and data) and therefore are close to traditional shell scripting or R/BioConductor scripting approaches to pipeline design. Finally, we suggest that microarray data processing task workflows may provide a basis for future example-based comparison of different workflow systems. We provide a set of tools and complete workflows for microarray data analysis in the Kepler environment, which has the advantages of offering graphical, clear display of conceptual steps and parameters and the ability to easily integrate other resources such as remote data and web services.

Does the microbiome and virome contribute to myalgic encephalomyelitis/chronic fatigue syndrome?

PubMed

Newberry, Fiona; Hsieh, Shen-Yuan; Wileman, Tom; Carding, Simon R

2018-03-15

Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) (ME/CFS) is a disabling and debilitating disease of unknown aetiology. It is a heterogeneous disease characterized by various inflammatory, immune, viral, neurological and endocrine symptoms. Several microbiome studies have described alterations in the bacterial component of the microbiome (dysbiosis) consistent with a possible role in disease development. However, in focusing on the bacterial components of the microbiome, these studies have neglected the viral constituent known as the virome. Viruses, particularly those infecting bacteria (bacteriophages), have the potential to alter the function and structure of the microbiome via gene transfer and host lysis. Viral-induced microbiome changes can directly and indirectly influence host health and disease. The contribution of viruses towards disease pathogenesis is therefore an important area for research in ME/CFS. Recent advancements in sequencing technology and bioinformatics now allow more comprehensive and inclusive investigations of human microbiomes. However, as the number of microbiome studies increases, the need for greater consistency in study design and analysis also increases. Comparisons between different ME/CFS microbiome studies are difficult because of differences in patient selection and diagnosis criteria, sample processing, genome sequencing and downstream bioinformatics analysis. It is therefore important that microbiome studies adopt robust, reproducible and consistent study design to enable more reliable and valid comparisons and conclusions to be made between studies. This article provides a comprehensive review of the current evidence supporting microbiome alterations in ME/CFS patients. Additionally, the pitfalls and challenges associated with microbiome studies are discussed. © 2018 The Author(s).

Ecto-Fc MS identifies ligand-receptor interactions through extracellular domain Fc fusion protein baits and shotgun proteomic analysis

PubMed Central

Savas, Jeffrey N.; De Wit, Joris; Comoletti, Davide; Zemla, Roland; Ghosh, Anirvan

2015-01-01

Ligand-receptor interactions represent essential biological triggers which regulate many diverse and important cellular processes. We have developed a discovery-based proteomic biochemical protocol which couples affinity purification with multidimensional liquid chromatographic tandem mass spectrometry (LCLC-MS/MS) and bioinformatic analysis. Compared to previous approaches, our analysis increases sensitivity, shortens analysis duration, and boosts comprehensiveness. In this protocol, receptor extracellular domains are fused with the Fc region of IgG to generate fusion proteins that are purified from transfected HEK293T cells. These “ecto-Fcs” are coupled to protein A beads and serve as baits for binding assays with prey proteins extracted from rodent brain. After capture, the affinity purified proteins are digested into peptides and comprehensively analyzed by LCLC-MS/MS with ion trap mass spectrometers. In four working days, this protocol can generate shortlists of candidate ligand-receptor protein-protein interactions. Our “Ecto-Fc MS” approach outperforms antibody-based approaches and provides a reproducible and robust framework to identify extracellular ligand – receptor interactions. PMID:25101821

ZBIT Bioinformatics Toolbox: A Web-Platform for Systems Biology and Expression Data Analysis

PubMed Central

Römer, Michael; Eichner, Johannes; Dräger, Andreas; Wrzodek, Clemens; Wrzodek, Finja; Zell, Andreas

2016-01-01

Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/. PMID:26882475

RAP: RNA-Seq Analysis Pipeline, a new cloud-based NGS web application

PubMed Central

2015-01-01

Background The study of RNA has been dramatically improved by the introduction of Next Generation Sequencing platforms allowing massive and cheap sequencing of selected RNA fractions, also providing information on strand orientation (RNA-Seq). The complexity of transcriptomes and of their regulative pathways make RNA-Seq one of most complex field of NGS applications, addressing several aspects of the expression process (e.g. identification and quantification of expressed genes and transcripts, alternative splicing and polyadenylation, fusion genes and trans-splicing, post-transcriptional events, etc.). Moreover, the huge volume of data generated by NGS platforms introduces unprecedented computational and technological challenges to efficiently analyze and store sequence data and results. Methods In order to provide researchers with an effective and friendly resource for analyzing RNA-Seq data, we present here RAP (RNA-Seq Analysis Pipeline), a cloud computing web application implementing a complete but modular analysis workflow. This pipeline integrates both state-of-the-art bioinformatics tools for RNA-Seq analysis and in-house developed scripts to offer to the user a comprehensive strategy for data analysis. RAP is able to perform quality checks (adopting FastQC and NGS QC Toolkit), identify and quantify expressed genes and transcripts (with Tophat, Cufflinks and HTSeq), detect alternative splicing events (using SpliceTrap) and chimeric transcripts (with ChimeraScan). This pipeline is also able to identify splicing junctions and constitutive or alternative polyadenylation sites (implementing custom analysis modules) and call for statistically significant differences in genes and transcripts expression, splicing pattern and polyadenylation site usage (using Cuffdiff2 and DESeq). Results Through a user friendly web interface, the RAP workflow can be suitably customized by the user and it is automatically executed on our cloud computing environment. This strategy allows to access to bioinformatics tools and computational resources without specific bioinformatics and IT skills. RAP provides a set of tabular and graphical results that can be helpful to browse, filter and export analyzed data, according to the user needs. PMID:26046471

Mathematics and evolutionary biology make bioinformatics education comprehensible.

PubMed

Jungck, John R; Weisstein, Anton E

2013-09-01

The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes-the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software-the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a 'two-culture' problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses.

Mathematics and evolutionary biology make bioinformatics education comprehensible

PubMed Central

Weisstein, Anton E.

2013-01-01

The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes—the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software—the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a ‘two-culture’ problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses. PMID:23821621

miRToolsGallery: a tag-based and rankable microRNA bioinformatics resources database portal

PubMed Central

Chen, Liang; Heikkinen, Liisa; Wang, ChangLiang; Yang, Yang; Knott, K Emily

2018-01-01

Abstract Hundreds of bioinformatics tools have been developed for MicroRNA (miRNA) investigations including those used for identification, target prediction, structure and expression profile analysis. However, finding the correct tool for a specific application requires the tedious and laborious process of locating, downloading, testing and validating the appropriate tool from a group of nearly a thousand. In order to facilitate this process, we developed a novel database portal named miRToolsGallery. We constructed the portal by manually curating > 950 miRNA analysis tools and resources. In the portal, a query to locate the appropriate tool is expedited by being searchable, filterable and rankable. The ranking feature is vital to quickly identify and prioritize the more useful from the obscure tools. Tools are ranked via different criteria including the PageRank algorithm, date of publication, number of citations, average of votes and number of publications. miRToolsGallery provides links and data for the comprehensive collection of currently available miRNA tools with a ranking function which can be adjusted using different criteria according to specific requirements. Database URL: http://www.mirtoolsgallery.org PMID:29688355

Cloud-based interactive analytics for terabytes of genomic variants data.

PubMed

Pan, Cuiping; McInnes, Gregory; Deflaux, Nicole; Snyder, Michael; Bingham, Jonathan; Datta, Somalee; Tsao, Philip S

2017-12-01

Large scale genomic sequencing is now widely used to decipher questions in diverse realms such as biological function, human diseases, evolution, ecosystems, and agriculture. With the quantity and diversity these data harbor, a robust and scalable data handling and analysis solution is desired. We present interactive analytics using a cloud-based columnar database built on Dremel to perform information compression, comprehensive quality controls, and biological information retrieval in large volumes of genomic data. We demonstrate such Big Data computing paradigms can provide orders of magnitude faster turnaround for common genomic analyses, transforming long-running batch jobs submitted via a Linux shell into questions that can be asked from a web browser in seconds. Using this method, we assessed a study population of 475 deeply sequenced human genomes for genomic call rate, genotype and allele frequency distribution, variant density across the genome, and pharmacogenomic information. Our analysis framework is implemented in Google Cloud Platform and BigQuery. Codes are available at https://github.com/StanfordBioinformatics/mvp_aaa_codelabs. cuiping@stanford.edu or ptsao@stanford.edu. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2017. This work is written by US Government employees and are in the public domain in the US.

OY-TES-1 may regulate the malignant behavior of liver cancer via NANOG, CD9, CCND2 and CDCA3: a bioinformatic analysis combine with RNAi and oligonucleotide microarray.

PubMed

Hu, Qiping; Fu, Jun; Luo, Bin; Huang, Miao; Guo, Wenwen; Lin, Yongda; Xie, Xiaoxun; Xiao, Shaowen

2015-04-01

Given its tumor-specific expression, including liver cancer, OY-TES-1 is a potential molecular marker for the diagnosis and immunotherapy of liver cancers. However, investigations of the mechanisms and the role of OY-TES-1 in liver cancer are rare. In the present study, based on a comprehensive bioinformatic analysis combined with RNA interference (RNAi) and oligonucleotide microarray, we report for the first time that downregulation of OY-TES-1 resulted in significant changes in expression of NANOG, CD9, CCND2 and CDCA3 in the liver cancer cell line BEL-7404. NANOG, CD9, CCND2 and CDCA3 may be involved in cell proliferation, migration, invasion and apoptosis, yet also may be functionally related to each other and OY-TES-1. Among these molecules, we identified that NANOG, containing a Kazal-2 binding motif and homeobox, may be the most likely candidate protein interacting with OY-TES-1 in liver cancer. Thus, the present study may provide important information for further investigation of the roles of OY-TES-1 in liver cancer.

MicroRNAs as Potential Regulators of Glutathione Peroxidases Expression and Their Role in Obesity and Related Pathologies.

PubMed

Matoušková, Petra; Hanousková, Barbora; Skálová, Lenka

2018-04-14

Glutathione peroxidases (GPxs) belong to the eight-member family of phylogenetically related enzymes with different cellular localization, but distinct antioxidant function. Several GPxs are important selenoproteins. Dysregulated GPx expression is connected with severe pathologies, including obesity and diabetes. We performed a comprehensive bioinformatic analysis using the programs miRDB, miRanda, TargetScan, and Diana in the search for hypothetical microRNAs targeting 3'untranslated regions (3´UTR) of GPxs. We cross-referenced the literature for possible intersections between our results and available reports on identified microRNAs, with a special focus on the microRNAs related to oxidative stress, obesity, and related pathologies. We identified many microRNAs with an association with oxidative stress and obesity as putative regulators of GPxs. In particular, miR-185-5p was predicted by a larger number of programs to target six GPxs and thus could play the role as their master regulator. This microRNA was altered by selenium deficiency and can play a role as a feedback control of selenoproteins' expression. Through the bioinformatics analysis we revealed the potential connection of microRNAs, GPxs, obesity, and other redox imbalance related diseases.

PRGdb: a bioinformatics platform for plant resistance gene analysis

PubMed Central

Sanseverino, Walter; Roma, Guglielmo; De Simone, Marco; Faino, Luigi; Melito, Sara; Stupka, Elia; Frusciante, Luigi; Ercolano, Maria Raffaella

2010-01-01

PRGdb is a web accessible open-source (http://www.prgdb.org) database that represents the first bioinformatic resource providing a comprehensive overview of resistance genes (R-genes) in plants. PRGdb holds more than 16 000 known and putative R-genes belonging to 192 plant species challenged by 115 different pathogens and linked with useful biological information. The complete database includes a set of 73 manually curated reference R-genes, 6308 putative R-genes collected from NCBI and 10463 computationally predicted putative R-genes. Thanks to a user-friendly interface, data can be examined using different query tools. A home-made prediction pipeline called Disease Resistance Analysis and Gene Orthology (DRAGO), based on reference R-gene sequence data, was developed to search for plant resistance genes in public datasets such as Unigene and Genbank. New putative R-gene classes containing unknown domain combinations were discovered and characterized. The development of the PRG platform represents an important starting point to conduct various experimental tasks. The inferred cross-link between genomic and phenotypic information allows access to a large body of information to find answers to several biological questions. The database structure also permits easy integration with other data types and opens up prospects for future implementations. PMID:19906694

Cloud-based interactive analytics for terabytes of genomic variants data

PubMed Central

Pan, Cuiping; McInnes, Gregory; Deflaux, Nicole; Snyder, Michael; Bingham, Jonathan; Datta, Somalee; Tsao, Philip S

2017-01-01

Abstract Motivation Large scale genomic sequencing is now widely used to decipher questions in diverse realms such as biological function, human diseases, evolution, ecosystems, and agriculture. With the quantity and diversity these data harbor, a robust and scalable data handling and analysis solution is desired. Results We present interactive analytics using a cloud-based columnar database built on Dremel to perform information compression, comprehensive quality controls, and biological information retrieval in large volumes of genomic data. We demonstrate such Big Data computing paradigms can provide orders of magnitude faster turnaround for common genomic analyses, transforming long-running batch jobs submitted via a Linux shell into questions that can be asked from a web browser in seconds. Using this method, we assessed a study population of 475 deeply sequenced human genomes for genomic call rate, genotype and allele frequency distribution, variant density across the genome, and pharmacogenomic information. Availability and implementation Our analysis framework is implemented in Google Cloud Platform and BigQuery. Codes are available at https://github.com/StanfordBioinformatics/mvp_aaa_codelabs. Contact cuiping@stanford.edu or ptsao@stanford.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:28961771

Genome analysis of Excretory/Secretory proteins in Taenia solium reveals their Abundance of Antigenic Regions (AAR).

PubMed

Gomez, Sandra; Adalid-Peralta, Laura; Palafox-Fonseca, Hector; Cantu-Robles, Vito Adrian; Soberón, Xavier; Sciutto, Edda; Fragoso, Gladis; Bobes, Raúl J; Laclette, Juan P; Yauner, Luis del Pozo; Ochoa-Leyva, Adrián

2015-05-19

Excretory/Secretory (ES) proteins play an important role in the host-parasite interactions. Experimental identification of ES proteins is time-consuming and expensive. Alternative bioinformatics approaches are cost-effective and can be used to prioritize the experimental analysis of therapeutic targets for parasitic diseases. Here we predicted and functionally annotated the ES proteins in T. solium genome using an integration of bioinformatics tools. Additionally, we developed a novel measurement to evaluate the potential antigenicity of T. solium secretome using sequence length and number of antigenic regions of ES proteins. This measurement was formalized as the Abundance of Antigenic Regions (AAR) value. AAR value for secretome showed a similar value to that obtained for a set of experimentally determined antigenic proteins and was different to the calculated value for the non-ES proteins of T. solium genome. Furthermore, we calculated the AAR values for known helminth secretomes and they were similar to that obtained for T. solium. The results reveal the utility of AAR value as a novel genomic measurement to evaluate the potential antigenicity of secretomes. This comprehensive analysis of T. solium secretome provides functional information for future experimental studies, including the identification of novel ES proteins of therapeutic, diagnosis and immunological interest.

Mutational Signatures in Cancer (MuSiCa): a web application to implement mutational signatures analysis in cancer samples.

PubMed

Díaz-Gay, Marcos; Vila-Casadesús, Maria; Franch-Expósito, Sebastià; Hernández-Illán, Eva; Lozano, Juan José; Castellví-Bel, Sergi

2018-06-14

Mutational signatures have been proved as a valuable pattern in somatic genomics, mainly regarding cancer, with a potential application as a biomarker in clinical practice. Up to now, several bioinformatic packages to address this topic have been developed in different languages/platforms. MutationalPatterns has arisen as the most efficient tool for the comparison with the signatures currently reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) database. However, the analysis of mutational signatures is nowadays restricted to a small community of bioinformatic experts. In this work we present Mutational Signatures in Cancer (MuSiCa), a new web tool based on MutationalPatterns and built using the Shiny framework in R language. By means of a simple interface suited to non-specialized researchers, it provides a comprehensive analysis of the somatic mutational status of the supplied cancer samples. It permits characterizing the profile and burden of mutations, as well as quantifying COSMIC-reported mutational signatures. It also allows classifying samples according to the above signature contributions. MuSiCa is a helpful web application to characterize mutational signatures in cancer samples. It is accessible online at http://bioinfo.ciberehd.org/GPtoCRC/en/tools.html and source code is freely available at https://github.com/marcos-diazg/musica .

Tcof1-Related Molecular Networks in Treacher Collins Syndrome.

PubMed

Dai, Jiewen; Si, Jiawen; Wang, Minjiao; Huang, Li; Fang, Bing; Shi, Jun; Wang, Xudong; Shen, Guofang

2016-09-01

Treacher Collins syndrome (TCS) is a rare, autosomal-dominant disorder characterized by craniofacial deformities, and is primarily caused by mutations in the Tcof1 gene. This article was aimed to perform a comprehensive literature review and systematic bioinformatic analysis of Tcof1-related molecular networks in TCS. First, the up- and down-regulated genes in Tcof1 heterozygous haploinsufficient mutant mice embryos and Tcof1 knockdown and Tcof1 over-expressed neuroblastoma N1E-115 cells were obtained from the Gene Expression Omnibus database. The GeneDecks database was used to calculate the 500 genes most closely related to Tcof1. Then, the relationships between 4 gene sets (a predicted set and sets comparing the wildtype with the 3 Gene Expression Omnibus datasets) were analyzed using the DAVID, GeneMANIA and STRING databases. The analysis results showed that the Tcof1-related genes were enriched in various biological processes, including cell proliferation, apoptosis, cell cycle, differentiation, and migration. They were also enriched in several signaling pathways, such as the ribosome, p53, cell cycle, and WNT signaling pathways. Additionally, these genes clearly had direct or indirect interactions with Tcof1 and between each other. Literature review and bioinformatic analysis finds imply that special attention should be given to these pathways, as they may offer target points for TCS therapies.

Genome analysis of Excretory/Secretory proteins in Taenia solium reveals their Abundance of Antigenic Regions (AAR)

PubMed Central

Gomez, Sandra; Adalid-Peralta, Laura; Palafox-Fonseca, Hector; Cantu-Robles, Vito Adrian; Soberón, Xavier; Sciutto, Edda; Fragoso, Gladis; Bobes, Raúl J.; Laclette, Juan P.; Yauner, Luis del Pozo; Ochoa-Leyva, Adrián

2015-01-01

Excretory/Secretory (ES) proteins play an important role in the host-parasite interactions. Experimental identification of ES proteins is time-consuming and expensive. Alternative bioinformatics approaches are cost-effective and can be used to prioritize the experimental analysis of therapeutic targets for parasitic diseases. Here we predicted and functionally annotated the ES proteins in T. solium genome using an integration of bioinformatics tools. Additionally, we developed a novel measurement to evaluate the potential antigenicity of T. solium secretome using sequence length and number of antigenic regions of ES proteins. This measurement was formalized as the Abundance of Antigenic Regions (AAR) value. AAR value for secretome showed a similar value to that obtained for a set of experimentally determined antigenic proteins and was different to the calculated value for the non-ES proteins of T. solium genome. Furthermore, we calculated the AAR values for known helminth secretomes and they were similar to that obtained for T. solium. The results reveal the utility of AAR value as a novel genomic measurement to evaluate the potential antigenicity of secretomes. This comprehensive analysis of T. solium secretome provides functional information for future experimental studies, including the identification of novel ES proteins of therapeutic, diagnosis and immunological interest. PMID:25989346

Quantitative Analysis of the Trends Exhibited by the Three Interdisciplinary Biological Sciences: Biophysics, Bioinformatics, and Systems Biology.

PubMed

Kang, Jonghoon; Park, Seyeon; Venkat, Aarya; Gopinath, Adarsh

2015-12-01

New interdisciplinary biological sciences like bioinformatics, biophysics, and systems biology have become increasingly relevant in modern science. Many papers have suggested the importance of adding these subjects, particularly bioinformatics, to an undergraduate curriculum; however, most of their assertions have relied on qualitative arguments. In this paper, we will show our metadata analysis of a scientific literature database (PubMed) that quantitatively describes the importance of the subjects of bioinformatics, systems biology, and biophysics as compared with a well-established interdisciplinary subject, biochemistry. Specifically, we found that the development of each subject assessed by its publication volume was well described by a set of simple nonlinear equations, allowing us to characterize them quantitatively. Bioinformatics, which had the highest ratio of publications produced, was predicted to grow between 77% and 93% by 2025 according to the model. Due to the large number of publications produced in bioinformatics, which nearly matches the number published in biochemistry, it can be inferred that bioinformatics is almost equal in significance to biochemistry. Based on our analysis, we suggest that bioinformatics be added to the standard biology undergraduate curriculum. Adding this course to an undergraduate curriculum will better prepare students for future research in biology.

Silicon Era of Carbon-Based Life: Application of Genomics and Bioinformatics in Crop Stress Research

PubMed Central

Li, Man-Wah; Qi, Xinpeng; Ni, Meng; Lam, Hon-Ming

2013-01-01

Abiotic and biotic stresses lead to massive reprogramming of different life processes and are the major limiting factors hampering crop productivity. Omics-based research platforms allow for a holistic and comprehensive survey on crop stress responses and hence may bring forth better crop improvement strategies. Since high-throughput approaches generate considerable amounts of data, bioinformatics tools will play an essential role in storing, retrieving, sharing, processing, and analyzing them. Genomic and functional genomic studies in crops still lag far behind similar studies in humans and other animals. In this review, we summarize some useful genomics and bioinformatics resources available to crop scientists. In addition, we also discuss the major challenges and advancements in the “-omics” studies, with an emphasis on their possible impacts on crop stress research and crop improvement. PMID:23759993

In silico methods for evaluating human allergenicity to novel proteins: International Bioinformatics Workshop Meeting Report, 23-24 February 2005.

PubMed

Thomas, Karluss; Bannon, Gary; Hefle, Susan; Herouet, Corinne; Holsapple, Michael; Ladics, Gregory; MacIntosh, Sue; Privalle, Laura

2005-12-01

The ILSI Health and Environmental Sciences Institute (HESI) hosted an expert workshop 22-24 February 2005 in Mallorca, Spain, to review the state-of-the-science for conducting a sequence homology/bioinformatics evaluation in the context of a comprehensive allergenicity assessment for novel proteins, to obtain consensus on the value and role of bioinformatics in evaluating novel proteins, and to discuss the utility and methods of allergen-specific IgE testing in the diagnosis of food allergy. The workshop participants included over forty international experts from academia, industry, and government. The workshop was hosted by the HESI Protein Allergenicity Technical committee, which has established a long-term program whose mission is to advance the scientific understanding of the relevant parameters for characterizing the allergenic potential of novel proteins.

Bioinformatics programs are 31-fold over-represented among the highest impact scientific papers of the past two decades.

PubMed

Wren, Jonathan D

2016-09-01

To analyze the relative proportion of bioinformatics papers and their non-bioinformatics counterparts in the top 20 most cited papers annually for the past two decades. When defining bioinformatics papers as encompassing both those that provide software for data analysis or methods underlying data analysis software, we find that over the past two decades, more than a third (34%) of the most cited papers in science were bioinformatics papers, which is approximately a 31-fold enrichment relative to the total number of bioinformatics papers published. More than half of the most cited papers during this span were bioinformatics papers. Yet, the average 5-year JIF of top 20 bioinformatics papers was 7.7, whereas the average JIF for top 20 non-bioinformatics papers was 25.8, significantly higher (P < 4.5 × 10(-29)). The 20-year trend in the average JIF between the two groups suggests the gap does not appear to be significantly narrowing. For a sampling of the journals producing top papers, bioinformatics journals tended to have higher Gini coefficients, suggesting that development of novel bioinformatics resources may be somewhat 'hit or miss'. That is, relative to other fields, bioinformatics produces some programs that are extremely widely adopted and cited, yet there are fewer of intermediate success. jdwren@gmail.com Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments.

PubMed

Hackenberg, Michael; Sturm, Martin; Langenberger, David; Falcón-Pérez, Juan Manuel; Aransay, Ana M

2009-07-01

Next-generation sequencing allows now the sequencing of small RNA molecules and the estimation of their expression levels. Consequently, there will be a high demand of bioinformatics tools to cope with the several gigabytes of sequence data generated in each single deep-sequencing experiment. Given this scene, we developed miRanalyzer, a web server tool for the analysis of deep-sequencing experiments for small RNAs. The web server tool requires a simple input file containing a list of unique reads and its copy numbers (expression levels). Using these data, miRanalyzer (i) detects all known microRNA sequences annotated in miRBase, (ii) finds all perfect matches against other libraries of transcribed sequences and (iii) predicts new microRNAs. The prediction of new microRNAs is an especially important point as there are many species with very few known microRNAs. Therefore, we implemented a highly accurate machine learning algorithm for the prediction of new microRNAs that reaches AUC values of 97.9% and recall values of up to 75% on unseen data. The web tool summarizes all the described steps in a single output page, which provides a comprehensive overview of the analysis, adding links to more detailed output pages for each analysis module. miRanalyzer is available at http://web.bioinformatics.cicbiogune.es/microRNA/.

Bioinformatics approach for choosing the correct reference genes when studying gene expression in human keratinocytes.

PubMed

Beer, Lucian; Mlitz, Veronika; Gschwandtner, Maria; Berger, Tanja; Narzt, Marie-Sophie; Gruber, Florian; Brunner, Patrick M; Tschachler, Erwin; Mildner, Michael

2015-10-01

Reverse transcription polymerase chain reaction (qRT-PCR) has become a mainstay in many areas of skin research. To enable quantitative analysis, it is necessary to analyse expression of reference genes (RGs) for normalization of target gene expression. The selection of reliable RGs therefore has an important impact on the experimental outcome. In this study, we aimed to identify and validate the best suited RGs for qRT-PCR in human primary keratinocytes (KCs) over a broad range of experimental conditions using the novel bioinformatics tool 'RefGenes', which is based on a manually curated database of published microarray data. Expression of 6 RGs identified by RefGenes software and 12 commonly used RGs were validated by qRT-PCR. We assessed whether these 18 markers fulfilled the requirements for a valid RG by the comprehensive ranking of four bioinformatics tools and the coefficient of variation (CV). In an overall ranking, we found GUSB to be the most stably expressed RG, whereas the expression values of the commonly used RGs, GAPDH and B2M were significantly affected by varying experimental conditions. Our results identify RefGenes as a powerful tool for the identification of valid RGs and suggest GUSB as the most reliable RG for KCs. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Bioinformatics of prokaryotic RNAs

PubMed Central

Backofen, Rolf; Amman, Fabian; Costa, Fabrizio; Findeiß, Sven; Richter, Andreas S; Stadler, Peter F

2014-01-01

The genome of most prokaryotes gives rise to surprisingly complex transcriptomes, comprising not only protein-coding mRNAs, often organized as operons, but also harbors dozens or even hundreds of highly structured small regulatory RNAs and unexpectedly large levels of anti-sense transcripts. Comprehensive surveys of prokaryotic transcriptomes and the need to characterize also their non-coding components is heavily dependent on computational methods and workflows, many of which have been developed or at least adapted specifically for the use with bacterial and archaeal data. This review provides an overview on the state-of-the-art of RNA bioinformatics focusing on applications to prokaryotes. PMID:24755880

Bioinformatics/biostatistics: microarray analysis.

PubMed

Eichler, Gabriel S

2012-01-01

The quantity and complexity of the molecular-level data generated in both research and clinical settings require the use of sophisticated, powerful computational interpretation techniques. It is for this reason that bioinformatic analysis of complex molecular profiling data has become a fundamental technology in the development of personalized medicine. This chapter provides a high-level overview of the field of bioinformatics and outlines several, classic bioinformatic approaches. The highlighted approaches can be aptly applied to nearly any sort of high-dimensional genomic, proteomic, or metabolomic experiments. Reviewed technologies in this chapter include traditional clustering analysis, the Gene Expression Dynamics Inspector (GEDI), GoMiner (GoMiner), Gene Set Enrichment Analysis (GSEA), and the Learner of Functional Enrichment (LeFE).

EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats

PubMed Central

Ison, Jon; Kalaš, Matúš; Jonassen, Inge; Bolser, Dan; Uludag, Mahmut; McWilliam, Hamish; Malone, James; Lopez, Rodrigo; Pettifer, Steve; Rice, Peter

2013-01-01

Motivation: Advancing the search, publication and integration of bioinformatics tools and resources demands consistent machine-understandable descriptions. A comprehensive ontology allowing such descriptions is therefore required. Results: EDAM is an ontology of bioinformatics operations (tool or workflow functions), types of data and identifiers, application domains and data formats. EDAM supports semantic annotation of diverse entities such as Web services, databases, programmatic libraries, standalone tools, interactive applications, data schemas, datasets and publications within bioinformatics. EDAM applies to organizing and finding suitable tools and data and to automating their integration into complex applications or workflows. It includes over 2200 defined concepts and has successfully been used for annotations and implementations. Availability: The latest stable version of EDAM is available in OWL format from http://edamontology.org/EDAM.owl and in OBO format from http://edamontology.org/EDAM.obo. It can be viewed online at the NCBO BioPortal and the EBI Ontology Lookup Service. For documentation and license please refer to http://edamontology.org. This article describes version 1.2 available at http://edamontology.org/EDAM_1.2.owl. Contact: jison@ebi.ac.uk PMID:23479348

Rice Phospholipase A Superfamily: Organization, Phylogenetic and Expression Analysis during Abiotic Stresses and Development

PubMed Central

Singh, Amarjeet; Baranwal, Vinay; Shankar, Alka; Kanwar, Poonam; Ranjan, Rajeev; Yadav, Sandeep; Pandey, Amita; Kapoor, Sanjay; Pandey, Girdhar K.

2012-01-01

Background Phospholipase A (PLA) is an important group of enzymes responsible for phospholipid hydrolysis in lipid signaling. PLAs have been implicated in abiotic stress signaling and developmental events in various plants species. Genome-wide analysis of PLA superfamily has been carried out in dicot plant Arabidopsis. A comprehensive genome-wide analysis of PLAs has not been presented yet in crop plant rice. Methodology/Principal Findings A comprehensive bioinformatics analysis identified a total of 31 PLA encoding genes in the rice genome, which are divided into three classes; phospholipase A1 (PLA1), patatin like phospholipases (pPLA) and low molecular weight secretory phospholipase A2 (sPLA2) based on their sequences and phylogeny. A subset of 10 rice PLAs exhibited chromosomal duplication, emphasizing the role of duplication in the expansion of this gene family in rice. Microarray expression profiling revealed a number of PLA members expressing differentially and significantly under abiotic stresses and reproductive development. Comparative expression analysis with Arabidopsis PLAs revealed a high degree of functional conservation between the orthologs in two plant species, which also indicated the vital role of PLAs in stress signaling and plant development across different plant species. Moreover, sub-cellular localization of a few candidates suggests their differential localization and functional role in the lipid signaling. Conclusion/Significance The comprehensive analysis and expression profiling would provide a critical platform for the functional characterization of the candidate PLA genes in crop plants. PMID:22363522

Applying Instructional Design Theories to Bioinformatics Education in Microarray Analysis and Primer Design Workshops

ERIC Educational Resources Information Center

Shachak, Aviv; Ophir, Ron; Rubin, Eitan

2005-01-01

The need to support bioinformatics training has been widely recognized by scientists, industry, and government institutions. However, the discussion of instructional methods for teaching bioinformatics is only beginning. Here we report on a systematic attempt to design two bioinformatics workshops for graduate biology students on the basis of…

A toolbox for developing bioinformatics software

PubMed Central

Potrzebowski, Wojciech; Puton, Tomasz; Rother, Magdalena; Wywial, Ewa; Bujnicki, Janusz M.

2012-01-01

Creating useful software is a major activity of many scientists, including bioinformaticians. Nevertheless, software development in an academic setting is often unsystematic, which can lead to problems associated with maintenance and long-term availibility. Unfortunately, well-documented software development methodology is difficult to adopt, and technical measures that directly improve bioinformatic programming have not been described comprehensively. We have examined 22 software projects and have identified a set of practices for software development in an academic environment. We found them useful to plan a project, support the involvement of experts (e.g. experimentalists), and to promote higher quality and maintainability of the resulting programs. This article describes 12 techniques that facilitate a quick start into software engineering. We describe 3 of the 22 projects in detail and give many examples to illustrate the usage of particular techniques. We expect this toolbox to be useful for many bioinformatics programming projects and to the training of scientific programmers. PMID:21803787

Using Informatics-, Bioinformatics- and Genomics-Based Approaches for the Molecular Surveillance and Detection of Biothreat Agents

NASA Astrophysics Data System (ADS)

Seto, Donald

The convergence and wealth of informatics, bioinformatics and genomics methods and associated resources allow a comprehensive and rapid approach for the surveillance and detection of bacterial and viral organisms. Coupled with the continuing race for the fastest, most cost-efficient and highest-quality DNA sequencing technology, that is, "next generation sequencing", the detection of biological threat agents by `cheaper and faster' means is possible. With the application of improved bioinformatic tools for the understanding of these genomes and for parsing unique pathogen genome signatures, along with `state-of-the-art' informatics which include faster computational methods, equipment and databases, it is feasible to apply new algorithms to biothreat agent detection. Two such methods are high-throughput DNA sequencing-based and resequencing microarray-based identification. These are illustrated and validated by two examples involving human adenoviruses, both from real-world test beds.

Bioinformatics training: selecting an appropriate learning content management system--an example from the European Bioinformatics Institute.

PubMed

Wright, Victoria Ann; Vaughan, Brendan W; Laurent, Thomas; Lopez, Rodrigo; Brooksbank, Cath; Schneider, Maria Victoria

2010-11-01

Today's molecular life scientists are well educated in the emerging experimental tools of their trade, but when it comes to training on the myriad of resources and tools for dealing with biological data, a less ideal situation emerges. Often bioinformatics users receive no formal training on how to make the most of the bioinformatics resources and tools available in the public domain. The European Bioinformatics Institute, which is part of the European Molecular Biology Laboratory (EMBL-EBI), holds the world's most comprehensive collection of molecular data, and training the research community to exploit this information is embedded in the EBI's mission. We have evaluated eLearning, in parallel with face-to-face courses, as a means of training users of our data resources and tools. We anticipate that eLearning will become an increasingly important vehicle for delivering training to our growing user base, so we have undertaken an extensive review of Learning Content Management Systems (LCMSs). Here, we describe the process that we used, which considered the requirements of trainees, trainers and systems administrators, as well as taking into account our organizational values and needs. This review describes the literature survey, user discussions and scripted platform testing that we performed to narrow down our choice of platform from 36 to a single platform. We hope that it will serve as guidance for others who are seeking to incorporate eLearning into their bioinformatics training programmes.

A Web-based assessment of bioinformatics end-user support services at US universities.

PubMed

Messersmith, Donna J; Benson, Dennis A; Geer, Renata C

2006-07-01

This study was conducted to gauge the availability of bioinformatics end-user support services at US universities and to identify the providers of those services. The study primarily focused on the availability of short-term workshops that introduce users to molecular biology databases and analysis software. Websites of selected US universities were reviewed to determine if bioinformatics educational workshops were offered, and, if so, what organizational units in the universities provided them. Of 239 reviewed universities, 72 (30%) offered bioinformatics educational workshops. These workshops were located at libraries (N = 15), bioinformatics centers (N = 38), or other facilities (N = 35). No such training was noted on the sites of 167 universities (70%). Of the 115 bioinformatics centers identified, two-thirds did not offer workshops. This analysis of university Websites indicates that a gap may exist in the availability of workshops and related training to assist researchers in the use of bioinformatics resources, representing a potential opportunity for libraries and other facilities to provide training and assistance for this growing user group.

Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine

PubMed Central

Khoomrung, Sakda; Wanichthanarak, Kwanjeera; Nookaew, Intawat; Thamsermsang, Onusa; Seubnooch, Patcharamon; Laohapand, Tawee; Akarasereenont, Pravit

2017-01-01

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed. PMID:28769804

Application of machine learning methods in bioinformatics

NASA Astrophysics Data System (ADS)

Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

2018-05-01

Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

5th HUPO BPP Bioinformatics Meeting at the European Bioinformatics Institute in Hinxton, UK--Setting the analysis frame.

PubMed

Stephan, Christian; Hamacher, Michael; Blüggel, Martin; Körting, Gerhard; Chamrad, Daniel; Scheer, Christian; Marcus, Katrin; Reidegeld, Kai A; Lohaus, Christiane; Schäfer, Heike; Martens, Lennart; Jones, Philip; Müller, Michael; Auyeung, Kevin; Taylor, Chris; Binz, Pierre-Alain; Thiele, Herbert; Parkinson, David; Meyer, Helmut E; Apweiler, Rolf

2005-09-01

The Bioinformatics Committee of the HUPO Brain Proteome Project (HUPO BPP) meets regularly to execute the post-lab analyses of the data produced in the HUPO BPP pilot studies. On July 7, 2005 the members came together for the 5th time at the European Bioinformatics Institute (EBI) in Hinxton, UK, hosted by Rolf Apweiler. As a main result, the parameter set of the semi-automated data re-analysis of MS/MS spectra has been elaborated and the subsequent work steps have been defined.

Toll-Like Receptor Pathway as Mediator of Bisphosphonate Effects in Breast Cancer

DTIC Science & Technology

2005-07-01

Hematology-Oncology, Birmingham, AL 35294-3300, U.S.A 2University of Helsinki, Institute of Dentistry , Department of Oral and Maxillofacial Diseases...at Birmingham, Comprehensive Cancer Center, Biostatistics and Bioinformatics Unit 5Veterans Affairs Hospital, *Send all correspondence to Dr. Katri

Quantitative proteome-based systematic identification of SIRT7 substrates.

PubMed

Zhang, Chaohua; Zhai, Zichao; Tang, Ming; Cheng, Zhongyi; Li, Tingting; Wang, Haiying; Zhu, Wei-Guo

2017-07-01

SIRT7 is a class III histone deacetylase that is involved in numerous cellular processes. Only six substrates of SIRT7 have been reported thus far, so we aimed to systematically identify SIRT7 substrates using stable-isotope labeling with amino acids in cell culture (SILAC) coupled with quantitative mass spectrometry (MS). Using SIRT7 +/+ and SIRT7 -/- mouse embryonic fibroblasts as our model system, we identified and quantified 1493 acetylation sites in 789 proteins, of which 261 acetylation sites in 176 proteins showed ≥2-fold change in acetylation state between SIRT7 -/- and SIRT7 +/+ cells. These proteins were considered putative SIRT7 substrates and were carried forward for further analysis. We then validated the predictive efficiency of the SILAC-MS experiment by assessing substrate acetylation status in vitro in six predicted proteins. We also performed a bioinformatic analysis of the MS data, which indicated that many of the putative protein substrates were involved in metabolic processes. Finally, we expanded our list of candidate substrates by performing a bioinformatics-based prediction analysis of putative SIRT7 substrates, using our list of putative substrates as a positive training set, and again validated a subset of the proteins in vitro. In summary, we have generated a comprehensive list of SIRT7 candidate substrates. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Structural bioinformatics of the human spliceosomal proteome

PubMed Central

Korneta, Iga; Magnus, Marcin; Bujnicki, Janusz M.

2012-01-01

In this work, we describe the results of a comprehensive structural bioinformatics analysis of the spliceosomal proteome. We used fold recognition analysis to complement prior data on the ordered domains of 252 human splicing proteins. Examples of newly identified domains include a PWI domain in the U5 snRNP protein 200K (hBrr2, residues 258–338), while examples of previously known domains with a newly determined fold include the DUF1115 domain of the U4/U6 di-snRNP protein 90K (hPrp3, residues 540–683). We also established a non-redundant set of experimental models of spliceosomal proteins, as well as constructed in silico models for regions without an experimental structure. The combined set of structural models is available for download. Altogether, over 90% of the ordered regions of the spliceosomal proteome can be represented structurally with a high degree of confidence. We analyzed the reduced spliceosomal proteome of the intron-poor organism Giardia lamblia, and as a result, we proposed a candidate set of ordered structural regions necessary for a functional spliceosome. The results of this work will aid experimental and structural analyses of the spliceosomal proteins and complexes, and can serve as a starting point for multiscale modeling of the structure of the entire spliceosome. PMID:22573172

Human Disease Insight: An integrated knowledge-based platform for disease-gene-drug information.

PubMed

Tasleem, Munazzah; Ishrat, Romana; Islam, Asimul; Ahmad, Faizan; Hassan, Md Imtaiyaz

2016-01-01

The scope of the Human Disease Insight (HDI) database is not limited to researchers or physicians as it also provides basic information to non-professionals and creates disease awareness, thereby reducing the chances of patient suffering due to ignorance. HDI is a knowledge-based resource providing information on human diseases to both scientists and the general public. Here, our mission is to provide a comprehensive human disease database containing most of the available useful information, with extensive cross-referencing. HDI is a knowledge management system that acts as a central hub to access information about human diseases and associated drugs and genes. In addition, HDI contains well-classified bioinformatics tools with helpful descriptions. These integrated bioinformatics tools enable researchers to annotate disease-specific genes and perform protein analysis, search for biomarkers and identify potential vaccine candidates. Eventually, these tools will facilitate the analysis of disease-associated data. The HDI provides two types of search capabilities and includes provisions for downloading, uploading and searching disease/gene/drug-related information. The logistical design of the HDI allows for regular updating. The database is designed to work best with Mozilla Firefox and Google Chrome and is freely accessible at http://humandiseaseinsight.com. Copyright © 2015 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

The National Microbial Pathogen Database Resource (NMPDR): a genomics platform based on subsystem annotation.

PubMed

McNeil, Leslie Klis; Reich, Claudia; Aziz, Ramy K; Bartels, Daniela; Cohoon, Matthew; Disz, Terry; Edwards, Robert A; Gerdes, Svetlana; Hwang, Kaitlyn; Kubal, Michael; Margaryan, Gohar Rem; Meyer, Folker; Mihalo, William; Olsen, Gary J; Olson, Robert; Osterman, Andrei; Paarmann, Daniel; Paczian, Tobias; Parrello, Bruce; Pusch, Gordon D; Rodionov, Dmitry A; Shi, Xinghua; Vassieva, Olga; Vonstein, Veronika; Zagnitko, Olga; Xia, Fangfang; Zinner, Jenifer; Overbeek, Ross; Stevens, Rick

2007-01-01

The National Microbial Pathogen Data Resource (NMPDR) (http://www.nmpdr.org) is a National Institute of Allergy and Infections Disease (NIAID)-funded Bioinformatics Resource Center that supports research in selected Category B pathogens. NMPDR contains the complete genomes of approximately 50 strains of pathogenic bacteria that are the focus of our curators, as well as >400 other genomes that provide a broad context for comparative analysis across the three phylogenetic Domains. NMPDR integrates complete, public genomes with expertly curated biological subsystems to provide the most consistent genome annotations. Subsystems are sets of functional roles related by a biologically meaningful organizing principle, which are built over large collections of genomes; they provide researchers with consistent functional assignments in a biologically structured context. Investigators can browse subsystems and reactions to develop accurate reconstructions of the metabolic networks of any sequenced organism. NMPDR provides a comprehensive bioinformatics platform, with tools and viewers for genome analysis. Results of precomputed gene clustering analyses can be retrieved in tabular or graphic format with one-click tools. NMPDR tools include Signature Genes, which finds the set of genes in common or that differentiates two groups of organisms. Essentiality data collated from genome-wide studies have been curated. Drug target identification and high-throughput, in silico, compound screening are in development.

Indentification and Analysis of Occludin Phosphosites: A Combined Mass Spectroscoy and Bioinformatics Approach

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

Sundstrom, J.; Tash, B; Murakami, T

2009-01-01

The molecular function of occludin, an integral membrane component of tight junctions, remains unclear. VEGF-induced phosphorylation sites were mapped on occludin by combining MS data analysis with bioinformatics. In vivo phosphorylation of Ser490 was validated and protein interaction studies combined with crystal structure analysis suggest that Ser490 phosphorylation attenuates the interaction between occludin and ZO-1. This study demonstrates that combining MS data and bioinformatics can successfully identify novel phosphorylation sites from limiting samples.

A Web-based assessment of bioinformatics end-user support services at US universities

PubMed Central

Messersmith, Donna J.; Benson, Dennis A.; Geer, Renata C.

2006-01-01

Objectives: This study was conducted to gauge the availability of bioinformatics end-user support services at US universities and to identify the providers of those services. The study primarily focused on the availability of short-term workshops that introduce users to molecular biology databases and analysis software. Methods: Websites of selected US universities were reviewed to determine if bioinformatics educational workshops were offered, and, if so, what organizational units in the universities provided them. Results: Of 239 reviewed universities, 72 (30%) offered bioinformatics educational workshops. These workshops were located at libraries (N = 15), bioinformatics centers (N = 38), or other facilities (N = 35). No such training was noted on the sites of 167 universities (70%). Of the 115 bioinformatics centers identified, two-thirds did not offer workshops. Conclusions: This analysis of university Websites indicates that a gap may exist in the availability of workshops and related training to assist researchers in the use of bioinformatics resources, representing a potential opportunity for libraries and other facilities to provide training and assistance for this growing user group. PMID:16888663

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

PubMed

Zakrzewski, Martha; Proietti, Carla; Ellis, Jonathan J; Hasan, Shihab; Brion, Marie-Jo; Berger, Bernard; Krause, Lutz

2017-03-01

Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. The web-interface is accessible via http://cgenome.net/calypso/ . The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931 ). The software is freely available for non-commercial users. l.krause@uq.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging

PubMed Central

Ovesný, Martin; Křížek, Pavel; Borkovec, Josef; Švindrych, Zdeněk; Hagen, Guy M.

2014-01-01

Summary: ThunderSTORM is an open-source, interactive and modular plug-in for ImageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data. ThunderSTORM also offers a set of tools for creation of simulated data and quantitative performance evaluation of localization algorithms using Monte Carlo simulations. Availability and implementation: ThunderSTORM and the online documentation are both freely accessible at https://code.google.com/p/thunder-storm/ Contact: guy.hagen@lf1.cuni.cz Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24771516

Preliminary Study of Bioinformatics Patents and Their Classifications Registered in the KIPRIS Database.

PubMed

Park, Hyun-Seok

2012-12-01

Whereas a vast amount of new information on bioinformatics is made available to the public through patents, only a small set of patents are cited in academic papers. A detailed analysis of registered bioinformatics patents, using the existing patent search system, can provide valuable information links between science and technology. However, it is extremely difficult to select keywords to capture bioinformatics patents, reflecting the convergence of several underlying technologies. No single word or even several words are sufficient to identify such patents. The analysis of patent subclasses can provide valuable information. In this paper, I did a preliminary study of the current status of bioinformatics patents and their International Patent Classification (IPC) groups registered in the Korea Intellectual Property Rights Information Service (KIPRIS) database.

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Development of Bioinformatics Infrastructure for Genomics Research.

PubMed

Mulder, Nicola J; Adebiyi, Ezekiel; Adebiyi, Marion; Adeyemi, Seun; Ahmed, Azza; Ahmed, Rehab; Akanle, Bola; Alibi, Mohamed; Armstrong, Don L; Aron, Shaun; Ashano, Efejiro; Baichoo, Shakuntala; Benkahla, Alia; Brown, David K; Chimusa, Emile R; Fadlelmola, Faisal M; Falola, Dare; Fatumo, Segun; Ghedira, Kais; Ghouila, Amel; Hazelhurst, Scott; Isewon, Itunuoluwa; Jung, Segun; Kassim, Samar Kamal; Kayondo, Jonathan K; Mbiyavanga, Mamana; Meintjes, Ayton; Mohammed, Somia; Mosaku, Abayomi; Moussa, Ahmed; Muhammd, Mustafa; Mungloo-Dilmohamud, Zahra; Nashiru, Oyekanmi; Odia, Trust; Okafor, Adaobi; Oladipo, Olaleye; Osamor, Victor; Oyelade, Jellili; Sadki, Khalid; Salifu, Samson Pandam; Soyemi, Jumoke; Panji, Sumir; Radouani, Fouzia; Souiai, Oussama; Tastan Bishop, Özlem

2017-06-01

Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community. H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa. Copyright © 2017 World Heart Federation (Geneva). Published by Elsevier B.V. All rights reserved.

Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens.

PubMed

Doublet, Vincent; Poeschl, Yvonne; Gogol-Döring, Andreas; Alaux, Cédric; Annoscia, Desiderato; Aurori, Christian; Barribeau, Seth M; Bedoya-Reina, Oscar C; Brown, Mark J F; Bull, James C; Flenniken, Michelle L; Galbraith, David A; Genersch, Elke; Gisder, Sebastian; Grosse, Ivo; Holt, Holly L; Hultmark, Dan; Lattorff, H Michael G; Le Conte, Yves; Manfredini, Fabio; McMahon, Dino P; Moritz, Robin F A; Nazzi, Francesco; Niño, Elina L; Nowick, Katja; van Rij, Ronald P; Paxton, Robert J; Grozinger, Christina M

2017-03-02

Organisms typically face infection by diverse pathogens, and hosts are thought to have developed specific responses to each type of pathogen they encounter. The advent of transcriptomics now makes it possible to test this hypothesis and compare host gene expression responses to multiple pathogens at a genome-wide scale. Here, we performed a meta-analysis of multiple published and new transcriptomes using a newly developed bioinformatics approach that filters genes based on their expression profile across datasets. Thereby, we identified common and unique molecular responses of a model host species, the honey bee (Apis mellifera), to its major pathogens and parasites: the Microsporidia Nosema apis and Nosema ceranae, RNA viruses, and the ectoparasitic mite Varroa destructor, which transmits viruses. We identified a common suite of genes and conserved molecular pathways that respond to all investigated pathogens, a result that suggests a commonality in response mechanisms to diverse pathogens. We found that genes differentially expressed after infection exhibit a higher evolutionary rate than non-differentially expressed genes. Using our new bioinformatics approach, we unveiled additional pathogen-specific responses of honey bees; we found that apoptosis appeared to be an important response following microsporidian infection, while genes from the immune signalling pathways, Toll and Imd, were differentially expressed after Varroa/virus infection. Finally, we applied our bioinformatics approach and generated a gene co-expression network to identify highly connected (hub) genes that may represent important mediators and regulators of anti-pathogen responses. Our meta-analysis generated a comprehensive overview of the host metabolic and other biological processes that mediate interactions between insects and their pathogens. We identified key host genes and pathways that respond to phylogenetically diverse pathogens, representing an important source for future functional studies as well as offering new routes to identify or generate pathogen resilient honey bee stocks. The statistical and bioinformatics approaches that were developed for this study are broadly applicable to synthesize information across transcriptomic datasets. These approaches will likely have utility in addressing a variety of biological questions.

MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets.

PubMed

Xu, Xilin; Wu, Aiping; Zhang, Xinlei; Su, Mingming; Jiang, Taijiao; Yuan, Zhe-Ming

2016-01-01

High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).

Quantitative proteomic analysis of human lung tumor xenografts treated with the ectopic ATP synthase inhibitor citreoviridin.

PubMed

Wu, Yi-Hsuan; Hu, Chia-Wei; Chien, Chih-Wei; Chen, Yu-Ju; Huang, Hsuan-Cheng; Juan, Hsueh-Fen

2013-01-01

ATP synthase is present on the plasma membrane of several types of cancer cells. Citreoviridin, an ATP synthase inhibitor, selectively suppresses the proliferation and growth of lung cancer without affecting normal cells. However, the global effects of targeting ectopic ATP synthase in vivo have not been well defined. In this study, we performed quantitative proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) and provided a comprehensive insight into the complicated regulation by citreoviridin in a lung cancer xenograft model. With high reproducibility of the quantitation, we obtained quantitative proteomic profiling with 2,659 proteins identified. Bioinformatics analysis of the 141 differentially expressed proteins selected by their relative abundance revealed that citreoviridin induces alterations in the expression of glucose metabolism-related enzymes in lung cancer. The up-regulation of enzymes involved in gluconeogenesis and storage of glucose indicated that citreoviridin may reduce the glycolytic intermediates for macromolecule synthesis and inhibit cell proliferation. Using comprehensive proteomics, the results identify metabolic aspects that help explain the antitumorigenic effect of citreoviridin in lung cancer, which may lead to a better understanding of the links between metabolism and tumorigenesis in cancer therapy.

Quantitative Proteomic Analysis of Human Lung Tumor Xenografts Treated with the Ectopic ATP Synthase Inhibitor Citreoviridin

PubMed Central

Wu, Yi-Hsuan; Hu, Chia-Wei; Chien, Chih-Wei; Chen, Yu-Ju; Huang, Hsuan-Cheng; Juan, Hsueh-Fen

2013-01-01

ATP synthase is present on the plasma membrane of several types of cancer cells. Citreoviridin, an ATP synthase inhibitor, selectively suppresses the proliferation and growth of lung cancer without affecting normal cells. However, the global effects of targeting ectopic ATP synthase in vivo have not been well defined. In this study, we performed quantitative proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) and provided a comprehensive insight into the complicated regulation by citreoviridin in a lung cancer xenograft model. With high reproducibility of the quantitation, we obtained quantitative proteomic profiling with 2,659 proteins identified. Bioinformatics analysis of the 141 differentially expressed proteins selected by their relative abundance revealed that citreoviridin induces alterations in the expression of glucose metabolism-related enzymes in lung cancer. The up-regulation of enzymes involved in gluconeogenesis and storage of glucose indicated that citreoviridin may reduce the glycolytic intermediates for macromolecule synthesis and inhibit cell proliferation. Using comprehensive proteomics, the results identify metabolic aspects that help explain the antitumorigenic effect of citreoviridin in lung cancer, which may lead to a better understanding of the links between metabolism and tumorigenesis in cancer therapy. PMID:23990911

Taking Bioinformatics to Systems Medicine.

PubMed

van Kampen, Antoine H C; Moerland, Perry D

2016-01-01

Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.

Buying in to bioinformatics: an introduction to commercial sequence analysis software

PubMed Central

2015-01-01

Advancements in high-throughput nucleotide sequencing techniques have brought with them state-of-the-art bioinformatics programs and software packages. Given the importance of molecular sequence data in contemporary life science research, these software suites are becoming an essential component of many labs and classrooms, and as such are frequently designed for non-computer specialists and marketed as one-stop bioinformatics toolkits. Although beautifully designed and powerful, user-friendly bioinformatics packages can be expensive and, as more arrive on the market each year, it can be difficult for researchers, teachers and students to choose the right software for their needs, especially if they do not have a bioinformatics background. This review highlights some of the currently available and most popular commercial bioinformatics packages, discussing their prices, usability, features and suitability for teaching. Although several commercial bioinformatics programs are arguably overpriced and overhyped, many are well designed, sophisticated and, in my opinion, worth the investment. If you are just beginning your foray into molecular sequence analysis or an experienced genomicist, I encourage you to explore proprietary software bundles. They have the potential to streamline your research, increase your productivity, energize your classroom and, if anything, add a bit of zest to the often dry detached world of bioinformatics. PMID:25183247

Buying in to bioinformatics: an introduction to commercial sequence analysis software.

PubMed

Smith, David Roy

2015-07-01

Advancements in high-throughput nucleotide sequencing techniques have brought with them state-of-the-art bioinformatics programs and software packages. Given the importance of molecular sequence data in contemporary life science research, these software suites are becoming an essential component of many labs and classrooms, and as such are frequently designed for non-computer specialists and marketed as one-stop bioinformatics toolkits. Although beautifully designed and powerful, user-friendly bioinformatics packages can be expensive and, as more arrive on the market each year, it can be difficult for researchers, teachers and students to choose the right software for their needs, especially if they do not have a bioinformatics background. This review highlights some of the currently available and most popular commercial bioinformatics packages, discussing their prices, usability, features and suitability for teaching. Although several commercial bioinformatics programs are arguably overpriced and overhyped, many are well designed, sophisticated and, in my opinion, worth the investment. If you are just beginning your foray into molecular sequence analysis or an experienced genomicist, I encourage you to explore proprietary software bundles. They have the potential to streamline your research, increase your productivity, energize your classroom and, if anything, add a bit of zest to the often dry detached world of bioinformatics. © The Author 2014. Published by Oxford University Press.

Is there room for ethics within bioinformatics education?

PubMed

Taneri, Bahar

2011-07-01

When bioinformatics education is considered, several issues are addressed. At the undergraduate level, the main issue revolves around conveying information from two main and different fields: biology and computer science. At the graduate level, the main issue is bridging the gap between biology students and computer science students. However, there is an educational component that is rarely addressed within the context of bioinformatics education: the ethics component. Here, a different perspective is provided on bioinformatics education, and the current status of ethics is analyzed within the existing bioinformatics programs. Analysis of the existing undergraduate and graduate programs, in both Europe and the United States, reveals the minimal attention given to ethics within bioinformatics education. Given that bioinformaticians speedily and effectively shape the biomedical sciences and hence their implications for society, here redesigning of the bioinformatics curricula is suggested in order to integrate the necessary ethics education. Unique ethical problems awaiting bioinformaticians and bioinformatics ethics as a separate field of study are discussed. In addition, a template for an "Ethics in Bioinformatics" course is provided.

EVALLER: a web server for in silico assessment of potential protein allergenicity

PubMed Central

Barrio, Alvaro Martinez; Soeria-Atmadja, Daniel; Nistér, Anders; Gustafsson, Mats G.; Hammerling, Ulf; Bongcam-Rudloff, Erik

2007-01-01

Bioinformatics testing approaches for protein allergenicity, involving amino acid sequence comparisons, have evolved appreciably over the last several years to increased sophistication and performance. EVALLER, the web server presented in this article is based on our recently published ‘Detection based on Filtered Length-adjusted Allergen Peptides’ (DFLAP) algorithm, which affords in silico determination of potential protein allergenicity of high sensitivity and excellent specificity. To strengthen bioinformatics risk assessment in allergology EVALLER provides a comprehensive outline of its judgment on a query protein's potential allergenicity. Each such textual output incorporates a scoring figure, a confidence numeral of the assignment and information on high- or low-scoring matches to identified allergen-related motifs, including their respective location in accordingly derived allergens. The interface, built on a modified Perl Open Source package, enables dynamic and color-coded graphic representation of key parts of the output. Moreover, pertinent details can be examined in great detail through zoomed views. The server can be accessed at http://bioinformatics.bmc.uu.se/evaller.html. PMID:17537818

Introduction to bioinformatics.

PubMed

Can, Tolga

2014-01-01

Bioinformatics is an interdisciplinary field mainly involving molecular biology and genetics, computer science, mathematics, and statistics. Data intensive, large-scale biological problems are addressed from a computational point of view. The most common problems are modeling biological processes at the molecular level and making inferences from collected data. A bioinformatics solution usually involves the following steps: Collect statistics from biological data. Build a computational model. Solve a computational modeling problem. Test and evaluate a computational algorithm. This chapter gives a brief introduction to bioinformatics by first providing an introduction to biological terminology and then discussing some classical bioinformatics problems organized by the types of data sources. Sequence analysis is the analysis of DNA and protein sequences for clues regarding function and includes subproblems such as identification of homologs, multiple sequence alignment, searching sequence patterns, and evolutionary analyses. Protein structures are three-dimensional data and the associated problems are structure prediction (secondary and tertiary), analysis of protein structures for clues regarding function, and structural alignment. Gene expression data is usually represented as matrices and analysis of microarray data mostly involves statistics analysis, classification, and clustering approaches. Biological networks such as gene regulatory networks, metabolic pathways, and protein-protein interaction networks are usually modeled as graphs and graph theoretic approaches are used to solve associated problems such as construction and analysis of large-scale networks.

Edge Bioinformatics

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

Lo, Chien-Chi

2015-08-03

Edge Bioinformatics is a developmental bioinformatics and data management platform which seeks to supply laboratories with bioinformatics pipelines for analyzing data associated with common samples case goals. Edge Bioinformatics enables sequencing as a solution and forward-deployed situations where human-resources, space, bandwidth, and time are limited. The Edge bioinformatics pipeline was designed based on following USE CASES and specific to illumina sequencing reads. 1. Assay performance adjudication (PCR): Analysis of an existing PCR assay in a genomic context, and automated design of a new assay to resolve conflicting results; 2. Clinical presentation with extreme symptoms: Characterization of a known pathogen ormore » co-infection with a. Novel emerging disease outbreak or b. Environmental surveillance« less

AnaBench: a Web/CORBA-based workbench for biomolecular sequence analysis

PubMed Central

Badidi, Elarbi; De Sousa, Cristina; Lang, B Franz; Burger, Gertraud

2003-01-01

Background Sequence data analyses such as gene identification, structure modeling or phylogenetic tree inference involve a variety of bioinformatics software tools. Due to the heterogeneity of bioinformatics tools in usage and data requirements, scientists spend much effort on technical issues including data format, storage and management of input and output, and memorization of numerous parameters and multi-step analysis procedures. Results In this paper, we present the design and implementation of AnaBench, an interactive, Web-based bioinformatics Analysis workBench allowing streamlined data analysis. Our philosophy was to minimize the technical effort not only for the scientist who uses this environment to analyze data, but also for the administrator who manages and maintains the workbench. With new bioinformatics tools published daily, AnaBench permits easy incorporation of additional tools. This flexibility is achieved by employing a three-tier distributed architecture and recent technologies including CORBA middleware, Java, JDBC, and JSP. A CORBA server permits transparent access to a workbench management database, which stores information about the users, their data, as well as the description of all bioinformatics applications that can be launched from the workbench. Conclusion AnaBench is an efficient and intuitive interactive bioinformatics environment, which offers scientists application-driven, data-driven and protocol-driven analysis approaches. The prototype of AnaBench, managed by a team at the Université de Montréal, is accessible on-line at: . Please contact the authors for details about setting up a local-network AnaBench site elsewhere. PMID:14678565

Green Fluorescent Protein-Focused Bioinformatics Laboratory Experiment Suitable for Undergraduates in Biochemistry Courses

ERIC Educational Resources Information Center

Rowe, Laura

2017-01-01

An introductory bioinformatics laboratory experiment focused on protein analysis has been developed that is suitable for undergraduate students in introductory biochemistry courses. The laboratory experiment is designed to be potentially used as a "stand-alone" activity in which students are introduced to basic bioinformatics tools and…

Meta-Analysis of Placental Transcriptome Data Identifies a Novel Molecular Pathway Related to Preeclampsia.

PubMed

van Uitert, Miranda; Moerland, Perry D; Enquobahrie, Daniel A; Laivuori, Hannele; van der Post, Joris A M; Ris-Stalpers, Carrie; Afink, Gijs B

2015-01-01

Studies using the placental transcriptome to identify key molecules relevant for preeclampsia are hampered by a relatively small sample size. In addition, they use a variety of bioinformatics and statistical methods, making comparison of findings challenging. To generate a more robust preeclampsia gene expression signature, we performed a meta-analysis on the original data of 11 placenta RNA microarray experiments, representing 139 normotensive and 116 preeclamptic pregnancies. Microarray data were pre-processed and analyzed using standardized bioinformatics and statistical procedures and the effect sizes were combined using an inverse-variance random-effects model. Interactions between genes in the resulting gene expression signature were identified by pathway analysis (Ingenuity Pathway Analysis, Gene Set Enrichment Analysis, Graphite) and protein-protein associations (STRING). This approach has resulted in a comprehensive list of differentially expressed genes that led to a 388-gene meta-signature of preeclamptic placenta. Pathway analysis highlights the involvement of the previously identified hypoxia/HIF1A pathway in the establishment of the preeclamptic gene expression profile, while analysis of protein interaction networks indicates CREBBP/EP300 as a novel element central to the preeclamptic placental transcriptome. In addition, there is an apparent high incidence of preeclampsia in women carrying a child with a mutation in CREBBP/EP300 (Rubinstein-Taybi Syndrome). The 388-gene preeclampsia meta-signature offers a vital starting point for further studies into the relevance of these genes (in particular CREBBP/EP300) and their concomitant pathways as biomarkers or functional molecules in preeclampsia. This will result in a better understanding of the molecular basis of this disease and opens up the opportunity to develop rational therapies targeting the placental dysfunction causal to preeclampsia.

Viral pathogen discovery

PubMed Central

Chiu, Charles Y

2015-01-01

Viral pathogen discovery is of critical importance to clinical microbiology, infectious diseases, and public health. Genomic approaches for pathogen discovery, including consensus polymerase chain reaction (PCR), microarrays, and unbiased next-generation sequencing (NGS), have the capacity to comprehensively identify novel microbes present in clinical samples. Although numerous challenges remain to be addressed, including the bioinformatics analysis and interpretation of large datasets, these technologies have been successful in rapidly identifying emerging outbreak threats, screening vaccines and other biological products for microbial contamination, and discovering novel viruses associated with both acute and chronic illnesses. Downstream studies such as genome assembly, epidemiologic screening, and a culture system or animal model of infection are necessary to establish an association of a candidate pathogen with disease. PMID:23725672

sRNAdb: A small non-coding RNA database for gram-positive bacteria

PubMed Central

2012-01-01

Background The class of small non-coding RNA molecules (sRNA) regulates gene expression by different mechanisms and enables bacteria to mount a physiological response due to adaptation to the environment or infection. Over the last decades the number of sRNAs has been increasing rapidly. Several databases like Rfam or fRNAdb were extended to include sRNAs as a class of its own. Furthermore new specialized databases like sRNAMap (gram-negative bacteria only) and sRNATarBase (target prediction) were established. To the best of the authors’ knowledge no database focusing on sRNAs from gram-positive bacteria is publicly available so far. Description In order to understand sRNA’s functional and phylogenetic relationships we have developed sRNAdb and provide tools for data analysis and visualization. The data compiled in our database is assembled from experiments as well as from bioinformatics analyses. The software enables comparison and visualization of gene loci surrounding the sRNAs of interest. To accomplish this, we use a client–server based approach. Offline versions of the database including analyses and visualization tools can easily be installed locally on the user’s computer. This feature facilitates customized local addition of unpublished sRNA candidates and related information such as promoters or terminators using tab-delimited files. Conclusion sRNAdb allows a user-friendly and comprehensive comparative analysis of sRNAs from available sequenced gram-positive prokaryotic replicons. Offline versions including analysis and visualization tools facilitate complex user specific bioinformatics analyses. PMID:22883983

A comprehensive framework for functional diversity patterns of marine chromophytic phytoplankton using rbcL phylogeny

PubMed Central

Samanta, Brajogopal; Bhadury, Punyasloke

2016-01-01

Marine chromophytes are taxonomically diverse group of algae and contribute approximately half of the total oceanic primary production. To understand the global patterns of functional diversity of chromophytic phytoplankton, robust bioinformatics and statistical analyses including deep phylogeny based on 2476 form ID rbcL gene sequences representing seven ecologically significant oceanographic ecoregions were undertaken. In addition, 12 form ID rbcL clone libraries were generated and analyzed (148 sequences) from Sundarbans Biosphere Reserve representing the world’s largest mangrove ecosystem as part of this study. Global phylogenetic analyses recovered 11 major clades of chromophytic phytoplankton in varying proportions with several novel rbcL sequences in each of the seven targeted ecoregions. Majority of OTUs was found to be exclusive to each ecoregion, whereas some were shared by two or more ecoregions based on beta-diversity analysis. Present phylogenetic and bioinformatics analyses provide a strong statistical support for the hypothesis that different oceanographic regimes harbor distinct and coherent groups of chromophytic phytoplankton. It has been also shown as part of this study that varying natural selection pressure on form ID rbcL gene under different environmental conditions could lead to functional differences and overall fitness of chromophytic phytoplankton populations. PMID:26861415

Navigating through the Jungle of Allergens: Features and Applications of Allergen Databases.

PubMed

Radauer, Christian

2017-01-01

The increasing number of available data on allergenic proteins demanded the establishment of structured, freely accessible allergen databases. In this review article, features and applications of 6 of the most widely used allergen databases are discussed. The WHO/IUIS Allergen Nomenclature Database is the official resource of allergen designations. Allergome is the most comprehensive collection of data on allergens and allergen sources. AllergenOnline is aimed at providing a peer-reviewed database of allergen sequences for prediction of allergenicity of proteins, such as those planned to be inserted into genetically modified crops. The Structural Database of Allergenic Proteins (SDAP) provides a database of allergen sequences, structures, and epitopes linked to bioinformatics tools for sequence analysis and comparison. The Immune Epitope Database (IEDB) is the largest repository of T-cell, B-cell, and major histocompatibility complex protein epitopes including epitopes of allergens. AllFam classifies allergens into families of evolutionarily related proteins using definitions from the Pfam protein family database. These databases contain mostly overlapping data, but also show differences in terms of their targeted users, the criteria for including allergens, data shown for each allergen, and the availability of bioinformatics tools. © 2017 S. Karger AG, Basel.

Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides.

PubMed

Agyei, Dominic; Tsopmo, Apollinaire; Udenigwe, Chibuike C

2018-06-01

There are emerging advancements in the strategies used for the discovery and development of food-derived bioactive peptides because of their multiple food and health applications. Bioinformatics and peptidomics are two computational and analytical techniques that have the potential to speed up the development of bioactive peptides from bench to market. Structure-activity relationships observed in peptides form the basis for bioinformatics and in silico prediction of bioactive sequences encrypted in food proteins. Peptidomics, on the other hand, relies on "hyphenated" (liquid chromatography-mass spectrometry-based) techniques for the detection, profiling, and quantitation of peptides. Together, bioinformatics and peptidomics approaches provide a low-cost and effective means of predicting, profiling, and screening bioactive protein hydrolysates and peptides from food. This article discuses the basis, strengths, and limitations of bioinformatics and peptidomics approaches currently used for the discovery and analysis of food-derived bioactive peptides.

In silico analysis of protein toxin and bacteriocins from Lactobacillus paracasei SD1 genome and available online databases

PubMed Central

Surachat, Komwit; Sangket, Unitsa; Deachamag, Panchalika; Chotigeat, Wilaiwan

2017-01-01

Lactobacillus paracasei SD1 is a potential probiotic strain due to its ability to survive several conditions in human dental cavities. To ascertain its safety for human use, we therefore performed a comprehensive bioinformatics analysis and characterization of the bacterial protein toxins produced by this strain. We report the complete genome of Lactobacillus paracasei SD1 and its comparison to other Lactobacillus genomes. Additionally, we identify and analyze its protein toxins and antimicrobial proteins using reliable online database resources and establish its phylogenetic relationship with other bacterial genomes. Our investigation suggests that this strain is safe for human use and contains several bacteriocins that confer health benefits to the host. An in silico analysis of protein-protein interactions between the target bacteriocins and the microbial proteins gtfB and luxS of Streptococcus mutans was performed and is discussed here. PMID:28837656

Teaching bioinformatics and neuroinformatics by using free web-based tools.

PubMed

Grisham, William; Schottler, Natalie A; Valli-Marill, Joanne; Beck, Lisa; Beatty, Jackson

2010-01-01

This completely computer-based module's purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information's Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes-narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor's manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics.

Analysis of requirements for teaching materials based on the course bioinformatics for plant metabolism

NASA Astrophysics Data System (ADS)

Balqis, Widodo, Lukiati, Betty; Amin, Mohamad

2017-05-01

A way to improve the quality of learning in the course of Plant Metabolism in the Department of Biology, State University of Malang, is to develop teaching materials. This research evaluates the needs of bioinformatics-based teaching material in the course Plant Metabolism by the Analyze, Design, Develop, Implement, and Evaluate (ADDIE) development model. Data were collected through questionnaires distributed to the students in the Plant Metabolism course of the Department of Biology, University of Malang, and analysis of the plan of lectures semester (RPS). Learning gains of this course show that it is not yet integrated into the field of bioinformatics. All respondents stated that plant metabolism books do not include bioinformatics and fail to explain the metabolism of a chemical compound of a local plant in Indonesia. Respondents thought that bioinformatics can explain examples and metabolism of a secondary metabolite analysis techniques and discuss potential medicinal compounds from local plants. As many as 65% of the respondents said that the existing metabolism book could not be used to understand secondary metabolism in lectures of plant metabolism. Therefore, the development of teaching materials including plant metabolism-based bioinformatics is important to improve the understanding of the lecture material in plant metabolism.

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics

PubMed Central

2010-01-01

Background Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce. Description An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date. Conclusions Hadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms. PMID:21210976

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics.

PubMed

Taylor, Ronald C

2010-12-21

Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce. An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date. Hadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms.

GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor.

PubMed

Davis, Sean; Meltzer, Paul S

2007-07-15

Microarray technology has become a standard molecular biology tool. Experimental data have been generated on a huge number of organisms, tissue types, treatment conditions and disease states. The Gene Expression Omnibus (Barrett et al., 2005), developed by the National Center for Bioinformatics (NCBI) at the National Institutes of Health is a repository of nearly 140,000 gene expression experiments. The BioConductor project (Gentleman et al., 2004) is an open-source and open-development software project built in the R statistical programming environment (R Development core Team, 2005) for the analysis and comprehension of genomic data. The tools contained in the BioConductor project represent many state-of-the-art methods for the analysis of microarray and genomics data. We have developed a software tool that allows access to the wealth of information within GEO directly from BioConductor, eliminating many the formatting and parsing problems that have made such analyses labor-intensive in the past. The software, called GEOquery, effectively establishes a bridge between GEO and BioConductor. Easy access to GEO data from BioConductor will likely lead to new analyses of GEO data using novel and rigorous statistical and bioinformatic tools. Facilitating analyses and meta-analyses of microarray data will increase the efficiency with which biologically important conclusions can be drawn from published genomic data. GEOquery is available as part of the BioConductor project.

A Survey of Scholarly Literature Describing the Field of Bioinformatics Education and Bioinformatics Educational Research

ERIC Educational Resources Information Center

Magana, Alejandra J.; Taleyarkhan, Manaz; Alvarado, Daniela Rivera; Kane, Michael; Springer, John; Clase, Kari

2014-01-01

Bioinformatics education can be broadly defined as the teaching and learning of the use of computer and information technology, along with mathematical and statistical analysis for gathering, storing, analyzing, interpreting, and integrating data to solve biological problems. The recent surge of genomics, proteomics, and structural biology in the…

The most common technologies and tools for functional genome analysis.

PubMed

Gasperskaja, Evelina; Kučinskas, Vaidutis

2017-01-01

Since the sequence of the human genome is complete, the main issue is how to understand the information written in the DNA sequence. Despite numerous genome-wide studies that have already been performed, the challenge to determine the function of genes, gene products, and also their interaction is still open. As changes in the human genome are highly likely to cause pathological conditions, functional analysis is vitally important for human health. For many years there have been a variety of technologies and tools used in functional genome analysis. However, only in the past decade there has been rapid revolutionizing progress and improvement in high-throughput methods, which are ranging from traditional real-time polymerase chain reaction to more complex systems, such as next-generation sequencing or mass spectrometry. Furthermore, not only laboratory investigation, but also accurate bioinformatic analysis is required for reliable scientific results. These methods give an opportunity for accurate and comprehensive functional analysis that involves various fields of studies: genomics, epigenomics, proteomics, and interactomics. This is essential for filling the gaps in the knowledge about dynamic biological processes at both cellular and organismal level. However, each method has both advantages and limitations that should be taken into account before choosing the right method for particular research in order to ensure successful study. For this reason, the present review paper aims to describe the most frequent and widely-used methods for the comprehensive functional analysis.

Precision medicine needs pioneering clinical bioinformaticians.

PubMed

Gómez-López, Gonzalo; Dopazo, Joaquín; Cigudosa, Juan C; Valencia, Alfonso; Al-Shahrour, Fátima

2017-10-25

Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

METABOLOMICS IN MEDICAL SCIENCES--TRENDS, CHALLENGES AND PERSPECTIVES.

PubMed

Klupczyńska, Agnieszka; Dereziński, Paweł; Kokot, Zenon J

2015-01-01

Metabolomics is the latest of the "omic" technologies that involves comprehensive analysis of small molecule metabolites of an organism or a specific biological sample. Metabolomics provides an insight into the cell status and describes an actual health condition of organisms. Analysis of metabolome offers a unique opportunity to study the influence of genetic variation, disease, applied treatment or diet on endogenous metabolic state of organisms. There are many areas that might benefit from metabolomic research. In the article some applications of this novel "omic" technology in the field of medical sciences are presented. One of the most popular aims of metabolomic studies is biomarker discovery. Despite using the state-of-art analytical techniques along with advanced bioinformatic tools, metabolomic experiments encounter numerous difficulties and pitfalls. Challenges that researchers in the field of analysis of metabolome have to face include i.a., technical limitations, bioinformatic challenges and integration with other "omic" sciences. One of the grand challenges for studies in the field of metabolomics is to tackle the problem of data analysis, which is probably the most time consuming stage of metabolomic workflow and requires close collaboration between analysts, clinicians and experts in chemometric analysis. Implementation of metabolomics into clinical practice will be dependent on establishment of standardized protocols in analytical performance and data analysis and development of fit-for-purpose biomarker method validation. Metabolomics allows to achieve a sophisticated level of information about biological systems and opens up new perspectives in many fields of medicine, especially in oncology. Apart from its extensive cognitive significance, metabolomics manifests also a practical importance as it may lead to design of new non-invasive, sensitive and specific diagnostic techniques and development of new therapies.

Analyses of Brucella Pathogenesis, Host Immunity, and Vaccine Targets using Systems Biology and Bioinformatics

PubMed Central

He, Yongqun

2011-01-01

Brucella is a Gram-negative, facultative intracellular bacterium that causes zoonotic brucellosis in humans and various animals. Out of 10 classified Brucella species, B. melitensis, B. abortus, B. suis, and B. canis are pathogenic to humans. In the past decade, the mechanisms of Brucella pathogenesis and host immunity have been extensively investigated using the cutting edge systems biology and bioinformatics approaches. This article provides a comprehensive review of the applications of Omics (including genomics, transcriptomics, and proteomics) and bioinformatics technologies for the analysis of Brucella pathogenesis, host immune responses, and vaccine targets. Based on more than 30 sequenced Brucella genomes, comparative genomics is able to identify gene variations among Brucella strains that help to explain host specificity and virulence differences among Brucella species. Diverse transcriptomics and proteomics gene expression studies have been conducted to analyze gene expression profiles of wild type Brucella strains and mutants under different laboratory conditions. High throughput Omics analyses of host responses to infections with virulent or attenuated Brucella strains have been focused on responses by mouse and cattle macrophages, bovine trophoblastic cells, mouse and boar splenocytes, and ram buffy coat. Differential serum responses in humans and rams to Brucella infections have been analyzed using high throughput serum antibody screening technology. The Vaxign reverse vaccinology has been used to predict many Brucella vaccine targets. More than 180 Brucella virulence factors and their gene interaction networks have been identified using advanced literature mining methods. The recent development of community-based Vaccine Ontology and Brucellosis Ontology provides an efficient way for Brucella data integration, exchange, and computer-assisted automated reasoning. PMID:22919594

Analyses of Brucella pathogenesis, host immunity, and vaccine targets using systems biology and bioinformatics.

PubMed

He, Yongqun

2012-01-01

Brucella is a Gram-negative, facultative intracellular bacterium that causes zoonotic brucellosis in humans and various animals. Out of 10 classified Brucella species, B. melitensis, B. abortus, B. suis, and B. canis are pathogenic to humans. In the past decade, the mechanisms of Brucella pathogenesis and host immunity have been extensively investigated using the cutting edge systems biology and bioinformatics approaches. This article provides a comprehensive review of the applications of Omics (including genomics, transcriptomics, and proteomics) and bioinformatics technologies for the analysis of Brucella pathogenesis, host immune responses, and vaccine targets. Based on more than 30 sequenced Brucella genomes, comparative genomics is able to identify gene variations among Brucella strains that help to explain host specificity and virulence differences among Brucella species. Diverse transcriptomics and proteomics gene expression studies have been conducted to analyze gene expression profiles of wild type Brucella strains and mutants under different laboratory conditions. High throughput Omics analyses of host responses to infections with virulent or attenuated Brucella strains have been focused on responses by mouse and cattle macrophages, bovine trophoblastic cells, mouse and boar splenocytes, and ram buffy coat. Differential serum responses in humans and rams to Brucella infections have been analyzed using high throughput serum antibody screening technology. The Vaxign reverse vaccinology has been used to predict many Brucella vaccine targets. More than 180 Brucella virulence factors and their gene interaction networks have been identified using advanced literature mining methods. The recent development of community-based Vaccine Ontology and Brucellosis Ontology provides an efficient way for Brucella data integration, exchange, and computer-assisted automated reasoning.

A Survey of Scholarly Literature Describing the Field of Bioinformatics Education and Bioinformatics Educational Research

PubMed Central

Taleyarkhan, Manaz; Alvarado, Daniela Rivera; Kane, Michael; Springer, John; Clase, Kari

2014-01-01

Bioinformatics education can be broadly defined as the teaching and learning of the use of computer and information technology, along with mathematical and statistical analysis for gathering, storing, analyzing, interpreting, and integrating data to solve biological problems. The recent surge of genomics, proteomics, and structural biology in the potential advancement of research and development in complex biomedical systems has created a need for an educated workforce in bioinformatics. However, effectively integrating bioinformatics education through formal and informal educational settings has been a challenge due in part to its cross-disciplinary nature. In this article, we seek to provide an overview of the state of bioinformatics education. This article identifies: 1) current approaches of bioinformatics education at the undergraduate and graduate levels; 2) the most common concepts and skills being taught in bioinformatics education; 3) pedagogical approaches and methods of delivery for conveying bioinformatics concepts and skills; and 4) assessment results on the impact of these programs, approaches, and methods in students’ attitudes or learning. Based on these findings, it is our goal to describe the landscape of scholarly work in this area and, as a result, identify opportunities and challenges in bioinformatics education. PMID:25452484

Bioinformatics.

PubMed

Moore, Jason H

2007-11-01

Bioinformatics is an interdisciplinary field that blends computer science and biostatistics with biological and biomedical sciences such as biochemistry, cell biology, developmental biology, genetics, genomics, and physiology. An important goal of bioinformatics is to facilitate the management, analysis, and interpretation of data from biological experiments and observational studies. The goal of this review is to introduce some of the important concepts in bioinformatics that must be considered when planning and executing a modern biological research study. We review database resources as well as data mining software tools.

Bioinformatics and the allergy assessment of agricultural biotechnology products: industry practices and recommendations.

PubMed

Ladics, Gregory S; Cressman, Robert F; Herouet-Guicheney, Corinne; Herman, Rod A; Privalle, Laura; Song, Ping; Ward, Jason M; McClain, Scott

2011-06-01

Bioinformatic tools are being increasingly utilized to evaluate the degree of similarity between a novel protein and known allergens within the context of a larger allergy safety assessment process. Importantly, bioinformatics is not a predictive analysis that can determine if a novel protein will ''become" an allergen, but rather a tool to assess whether the protein is a known allergen or is potentially cross-reactive with an existing allergen. Bioinformatic tools are key components of the 2009 CodexAlimentarius Commission's weight-of-evidence approach, which encompasses a variety of experimental approaches for an overall assessment of the allergenic potential of a novel protein. Bioinformatic search comparisons between novel protein sequences, as well as potential novel fusion sequences derived from the genome and transgene, and known allergens are required by all regulatory agencies that assess the safety of genetically modified (GM) products. The objective of this paper is to identify opportunities for consensus in the methods of applying bioinformatics and to outline differences that impact a consistent and reliable allergy safety assessment. The bioinformatic comparison process has some critical features, which are outlined in this paper. One of them is a curated, publicly available and well-managed database with known allergenic sequences. In this paper, the best practices, scientific value, and food safety implications of bioinformatic analyses, as they are applied to GM food crops are discussed. Recommendations for conducting bioinformatic analysis on novel food proteins for potential cross-reactivity to known allergens are also put forth. Copyright © 2011 Elsevier Inc. All rights reserved.

Whole-genome sequencing for comparative genomics and de novo genome assembly.

PubMed

Benjak, Andrej; Sala, Claudia; Hartkoorn, Ruben C

2015-01-01

Next-generation sequencing technologies for whole-genome sequencing of mycobacteria are rapidly becoming an attractive alternative to more traditional sequencing methods. In particular this technology is proving useful for genome-wide identification of mutations in mycobacteria (comparative genomics) as well as for de novo assembly of whole genomes. Next-generation sequencing however generates a vast quantity of data that can only be transformed into a usable and comprehensible form using bioinformatics. Here we describe the methodology one would use to prepare libraries for whole-genome sequencing, and the basic bioinformatics to identify mutations in a genome following Illumina HiSeq or MiSeq sequencing, as well as de novo genome assembly following sequencing using Pacific Biosciences (PacBio).

The European Bioinformatics Institute's data resources 2014.

PubMed

Brooksbank, Catherine; Bergman, Mary Todd; Apweiler, Rolf; Birney, Ewan; Thornton, Janet

2014-01-01

Molecular Biology has been at the heart of the 'big data' revolution from its very beginning, and the need for access to biological data is a common thread running from the 1965 publication of Dayhoff's 'Atlas of Protein Sequence and Structure' through the Human Genome Project in the late 1990s and early 2000s to today's population-scale sequencing initiatives. The European Bioinformatics Institute (EMBL-EBI; http://www.ebi.ac.uk) is one of three organizations worldwide that provides free access to comprehensive, integrated molecular data sets. Here, we summarize the principles underpinning the development of these public resources and provide an overview of EMBL-EBI's database collection to complement the reviews of individual databases provided elsewhere in this issue.

The Ensembl genome database project.

PubMed

Hubbard, T; Barker, D; Birney, E; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Huminiecki, L; Kasprzyk, A; Lehvaslaiho, H; Lijnzaad, P; Melsopp, C; Mongin, E; Pettett, R; Pocock, M; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Clamp, M

2002-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.

Proteomics: a new approach to the study of disease.

PubMed

Chambers, G; Lawrie, L; Cash, P; Murray, G I

2000-11-01

The global analysis of cellular proteins has recently been termed proteomics and is a key area of research that is developing in the post-genome era. Proteomics uses a combination of sophisticated techniques including two-dimensional (2D) gel electrophoresis, image analysis, mass spectrometry, amino acid sequencing, and bio-informatics to resolve comprehensively, to quantify, and to characterize proteins. The application of proteomics provides major opportunities to elucidate disease mechanisms and to identify new diagnostic markers and therapeutic targets. This review aims to explain briefly the background to proteomics and then to outline proteomic techniques. Applications to the study of human disease conditions ranging from cancer to infectious diseases are reviewed. Finally, possible future advances are briefly considered, especially those which may lead to faster sample throughput and increased sensitivity for the detection of individual proteins. Copyright 2000 John Wiley & Sons, Ltd.

Bioinformatics clouds for big data manipulation.

PubMed

Dai, Lin; Gao, Xin; Guo, Yan; Xiao, Jingfa; Zhang, Zhang

2012-11-28

As advances in life sciences and information technology bring profound influences on bioinformatics due to its interdisciplinary nature, bioinformatics is experiencing a new leap-forward from in-house computing infrastructure into utility-supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues in the bioinformatics field. Here we review extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and present our perspectives on the adoption of cloud computing in bioinformatics. This article was reviewed by Frank Eisenhaber, Igor Zhulin, and Sandor Pongor.

An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer.

PubMed

Ryall, Karen A; Kim, Jihye; Klauck, Peter J; Shin, Jimin; Yoo, Minjae; Ionkina, Anastasia; Pitts, Todd M; Tentler, John J; Diamond, Jennifer R; Eckhardt, S Gail; Heasley, Lynn E; Kang, Jaewoo; Tan, Aik Choon

2015-01-01

Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis. We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. We validated our predictions using published and new experimental data. In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.

Toward a comprehensive and systematic methylome signature in colorectal cancers.

PubMed

Ashktorab, Hassan; Rahi, Hamed; Wansley, Daniel; Varma, Sudhir; Shokrani, Babak; Lee, Edward; Daremipouran, Mohammad; Laiyemo, Adeyinka; Goel, Ajay; Carethers, John M; Brim, Hassan

2013-08-01

CpG Island Methylator Phenotype (CIMP) is one of the underlying mechanisms in colorectal cancer (CRC). This study aimed to define a methylome signature in CRC through a methylation microarray analysis and a compilation of promising CIMP markers from the literature. Illumina HumanMethylation27 (IHM27) array data was generated and analyzed based on statistical differences in methylation data (1st approach) or based on overall differences in methylation percentages using lower 95% CI (2nd approach). Pyrosequencing was performed for the validation of nine genes. A meta-analysis was used to identify CIMP and non-CIMP markers that were hypermethylated in CRC but did not yet make it to the CIMP genes' list. Our 1st approach for array data analysis demonstrated the limitations in selecting genes for further validation, highlighting the need for the 2nd bioinformatics approach to adequately select genes with differential aberrant methylation. A more comprehensive list, which included non-CIMP genes, such as APC, EVL, CD109, PTEN, TWIST1, DCC, PTPRD, SFRP1, ICAM5, RASSF1A, EYA4, 30ST2, LAMA1, KCNQ5, ADHEF1, and TFPI2, was established. Array data are useful to categorize and cluster colonic lesions based on their global methylation profiles; however, its usefulness in identifying robust methylation markers is limited and rely on the data analysis method. We have identified 16 non-CIMP-panel genes for which we provide rationale for inclusion in a more comprehensive characterization of CIMP+ CRCs. The identification of a definitive list for methylome specific genes in CRC will contribute to better clinical management of CRC patients.

DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function.

PubMed

Cheng, Liang; Hu, Yang; Sun, Jie; Zhou, Meng; Jiang, Qinghua

2018-06-01

DincRNA aims to provide a comprehensive web-based bioinformatics toolkit to elucidate the entangled relationships among diseases and non-coding RNAs (ncRNAs) from the perspective of disease similarity. The quantitative way to illustrate relationships of pair-wise diseases always depends on their molecular mechanisms, and structures of the directed acyclic graph of Disease Ontology (DO). Corresponding methods for calculating similarity of pair-wise diseases involve Resnik's, Lin's, Wang's, PSB and SemFunSim methods. Recently, disease similarity was validated suitable for calculating functional similarities of ncRNAs and prioritizing ncRNA-disease pairs, and it has been widely applied for predicting the ncRNA function due to the limited biological knowledge from wet lab experiments of these RNAs. For this purpose, a large number of algorithms and priori knowledge need to be integrated. e.g. 'pair-wise best, pairs-average' (PBPA) and 'pair-wise all, pairs-maximum' (PAPM) methods for calculating functional similarities of ncRNAs, and random walk with restart (RWR) method for prioritizing ncRNA-disease pairs. To facilitate the exploration of disease associations and ncRNA function, DincRNA implemented all of the above eight algorithms based on DO and disease-related genes. Currently, it provides the function to query disease similarity scores, miRNA and lncRNA functional similarity scores, and the prioritization scores of lncRNA-disease and miRNA-disease pairs. http://bio-annotation.cn:18080/DincRNAClient/. biofomeng@hotmail.com or qhjiang@hit.edu.cn. Supplementary data are available at Bioinformatics online.

A longitudinal social network analysis of the editorial boards of medical informatics and bioinformatics journals.

PubMed

Malin, Bradley; Carley, Kathleen

2007-01-01

The goal of this research is to learn how the editorial staffs of bioinformatics and medical informatics journals provide support for cross-community exposure. Models such as co-citation and co-author analysis measure the relationships between researchers; but they do not capture how environments that support knowledge transfer across communities are organized. In this paper, we propose a social network analysis model to study how editorial boards integrate researchers from disparate communities. We evaluate our model by building relational networks based on the editorial boards of approximately 40 journals that serve as research outlets in medical informatics and bioinformatics. We track the evolution of editorial relationships through a longitudinal investigation over the years 2000 through 2005. Our findings suggest that there are research journals that support the collocation of editorial board members from the bioinformatics and medical informatics communities. Network centrality metrics indicate that editorial board members are located in the intersection of the communities and that the number of individuals in the intersection is growing with time. Social network analysis methods provide insight into the relationships between the medical informatics and bioinformatics communities. The number of editorial board members facilitating the publication intersection of the communities has grown, but the intersection remains dependent on a small group of individuals and fragile.

Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays

PubMed Central

Aryee, Martin J.; Jaffe, Andrew E.; Corrada-Bravo, Hector; Ladd-Acosta, Christine; Feinberg, Andrew P.; Hansen, Kasper D.; Irizarry, Rafael A.

2014-01-01

Motivation: The recently released Infinium HumanMethylation450 array (the ‘450k’ array) provides a high-throughput assay to quantify DNA methylation (DNAm) at ∼450 000 loci across a range of genomic features. Although less comprehensive than high-throughput sequencing-based techniques, this product is more cost-effective and promises to be the most widely used DNAm high-throughput measurement technology over the next several years. Results: Here we describe a suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. The software is structured to easily adapt to future versions of the technology. We include methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. We show how our software provides a powerful and flexible development platform for future methods. We also illustrate how our methods empower the technology to make discoveries previously thought to be possible only with sequencing-based methods. Availability and implementation: http://bioconductor.org/packages/release/bioc/html/minfi.html. Contact: khansen@jhsph.edu; rafa@jimmy.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24478339

No-boundary thinking in bioinformatics research

PubMed Central

2013-01-01

Currently there are definitions from many agencies and research societies defining “bioinformatics” as deriving knowledge from computational analysis of large volumes of biological and biomedical data. Should this be the bioinformatics research focus? We will discuss this issue in this review article. We would like to promote the idea of supporting human-infrastructure (HI) with no-boundary thinking (NT) in bioinformatics (HINT). PMID:24192339

Applying Instructional Design Theories to Bioinformatics Education in Microarray Analysis and Primer Design Workshops

PubMed Central

2005-01-01

The need to support bioinformatics training has been widely recognized by scientists, industry, and government institutions. However, the discussion of instructional methods for teaching bioinformatics is only beginning. Here we report on a systematic attempt to design two bioinformatics workshops for graduate biology students on the basis of Gagne's Conditions of Learning instructional design theory. This theory, although first published in the early 1970s, is still fundamental in instructional design and instructional technology. First, top-level as well as prerequisite learning objectives for a microarray analysis workshop and a primer design workshop were defined. Then a hierarchy of objectives for each workshop was created. Hands-on tutorials were designed to meet these objectives. Finally, events of learning proposed by Gagne's theory were incorporated into the hands-on tutorials. The resultant manuals were tested on a small number of trainees, revised, and applied in 1-day bioinformatics workshops. Based on this experience and on observations made during the workshops, we conclude that Gagne's Conditions of Learning instructional design theory provides a useful framework for developing bioinformatics training, but may not be optimal as a method for teaching it. PMID:16220141

Revealing biological information using data structuring and automated learning.

PubMed

Mohorianu, Irina; Moulton, Vincent

2010-11-01

The intermediary steps between a biological hypothesis, concretized in the input data, and meaningful results, validated using biological experiments, commonly employ bioinformatics tools. Starting with storage of the data and ending with a statistical analysis of the significance of the results, every step in a bioinformatics analysis has been intensively studied and the resulting methods and models patented. This review summarizes the bioinformatics patents that have been developed mainly for the study of genes, and points out the universal applicability of bioinformatics methods to other related studies such as RNA interference. More specifically, we overview the steps undertaken in the majority of bioinformatics analyses, highlighting, for each, various approaches that have been developed to reveal details from different perspectives. First we consider data warehousing, the first task that has to be performed efficiently, optimizing the structure of the database, in order to facilitate both the subsequent steps and the retrieval of information. Next, we review data mining, which occupies the central part of most bioinformatics analyses, presenting patents concerning differential expression, unsupervised and supervised learning. Last, we discuss how networks of interactions of genes or other players in the cell may be created, which help draw biological conclusions and have been described in several patents.

Assembly of a Comprehensive Regulatory Network for the Mammalian Circadian Clock: A Bioinformatics Approach

PubMed Central

Lehmann, Robert; Abreu, Monica; Fuhr, Luise; Herzel, Hanspeter; Leser, Ulf; Relógio, Angela

2015-01-01

By regulating the timing of cellular processes, the circadian clock provides a way to adapt physiology and behaviour to the geophysical time. In mammals, a light-entrainable master clock located in the suprachiasmatic nucleus (SCN) controls peripheral clocks that are present in virtually every body cell. Defective circadian timing is associated with several pathologies such as cancer and metabolic and sleep disorders. To better understand the circadian regulation of cellular processes, we developed a bioinformatics pipeline encompassing the analysis of high-throughput data sets and the exploitation of published knowledge by text-mining. We identified 118 novel potential clock-regulated genes and integrated them into an existing high-quality circadian network, generating the to-date most comprehensive network of circadian regulated genes (NCRG). To validate particular elements in our network, we assessed publicly available ChIP-seq data for BMAL1, REV-ERBα/β and RORα/γ proteins and found strong evidence for circadian regulation of Elavl1, Nme1, Dhx6, Med1 and Rbbp7 all of which are involved in the regulation of tumourigenesis. Furthermore, we identified Ncl and Ddx6, as targets of RORγ and REV-ERBα, β, respectively. Most interestingly, these genes were also reported to be involved in miRNA regulation; in particular, NCL regulates several miRNAs, all involved in cancer aggressiveness. Thus, NCL represents a novel potential link via which the circadian clock, and specifically RORγ, regulates the expression of miRNAs, with particular consequences in breast cancer progression. Our findings bring us one step forward towards a mechanistic understanding of mammalian circadian regulation, and provide further evidence of the influence of circadian deregulation in cancer. PMID:25945798

Web-based bioinformatics workflows for end-to-end RNA-seq data computation and analysis in agricultural animal species

USDA-ARS?s Scientific Manuscript database

Remarkable advances in next-generation sequencing (NGS) technologies, bioinformatics algorithms, and computational technologies have significantly accelerated genomic research. However, complicated NGS data analysis still remains as a major bottleneck. RNA-seq, as one of the major area in the NGS fi...

Assessing an effective undergraduate module teaching applied bioinformatics to biology students

PubMed Central

2018-01-01

Applied bioinformatics skills are becoming ever more indispensable for biologists, yet incorporation of these skills into the undergraduate biology curriculum is lagging behind, in part due to a lack of instructors willing and able to teach basic bioinformatics in classes that don’t specifically focus on quantitative skill development, such as statistics or computer sciences. To help undergraduate course instructors who themselves did not learn bioinformatics as part of their own education and are hesitant to plunge into teaching big data analysis, a module was developed that is written in plain-enough language, using publicly available computing tools and data, to allow novice instructors to teach next-generation sequence analysis to upper-level undergraduate students. To determine if the module allowed students to develop a better understanding of and appreciation for applied bioinformatics, various tools were developed and employed to assess the impact of the module. This article describes both the module and its assessment. Students found the activity valuable for their education and, in focus group discussions, emphasized that they saw a need for more and earlier instruction of big data analysis as part of the undergraduate biology curriculum. PMID:29324777

Bioinformatics clouds for big data manipulation

PubMed Central

2012-01-01

Abstract As advances in life sciences and information technology bring profound influences on bioinformatics due to its interdisciplinary nature, bioinformatics is experiencing a new leap-forward from in-house computing infrastructure into utility-supplied cloud computing delivered over the Internet, in order to handle the vast quantities of biological data generated by high-throughput experimental technologies. Albeit relatively new, cloud computing promises to address big data storage and analysis issues in the bioinformatics field. Here we review extant cloud-based services in bioinformatics, classify them into Data as a Service (DaaS), Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), and present our perspectives on the adoption of cloud computing in bioinformatics. Reviewers This article was reviewed by Frank Eisenhaber, Igor Zhulin, and Sandor Pongor. PMID:23190475

SYMBIOmatics: synergies in Medical Informatics and Bioinformatics--exploring current scientific literature for emerging topics.

PubMed

Rebholz-Schuhman, Dietrich; Cameron, Graham; Clark, Dominic; van Mulligen, Erik; Coatrieux, Jean-Louis; Del Hoyo Barbolla, Eva; Martin-Sanchez, Fernando; Milanesi, Luciano; Porro, Ivan; Beltrame, Francesco; Tollis, Ioannis; Van der Lei, Johan

2007-03-08

The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to http://www.symbiomatics.org). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000-2005 ("recent") and 1990-1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science.

SYMBIOmatics: Synergies in Medical Informatics and Bioinformatics – exploring current scientific literature for emerging topics

PubMed Central

Rebholz-Schuhman, Dietrich; Cameron, Graham; Clark, Dominic; van Mulligen, Erik; Coatrieux, Jean-Louis; Del Hoyo Barbolla, Eva; Martin-Sanchez, Fernando; Milanesi, Luciano; Porro, Ivan; Beltrame, Francesco; Tollis, Ioannis; Van der Lei, Johan

2007-01-01

Background The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to ). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. Results This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000–2005 ("recent") and 1990–1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. Conclusion We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science. PMID:17430562

Ensembl 2004.

PubMed

Birney, E; Andrews, D; Bevan, P; Caccamo, M; Cameron, G; Chen, Y; Clarke, L; Coates, G; Cox, T; Cuff, J; Curwen, V; Cutts, T; Down, T; Durbin, R; Eyras, E; Fernandez-Suarez, X M; Gane, P; Gibbins, B; Gilbert, J; Hammond, M; Hotz, H; Iyer, V; Kahari, A; Jekosch, K; Kasprzyk, A; Keefe, D; Keenan, S; Lehvaslaiho, H; McVicker, G; Melsopp, C; Meidl, P; Mongin, E; Pettett, R; Potter, S; Proctor, G; Rae, M; Searle, S; Slater, G; Smedley, D; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Storey, R; Ureta-Vidal, A; Woodwark, C; Clamp, M; Hubbard, T

2004-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organize biology around the sequences of large genomes. It is a comprehensive and integrated source of annotation of large genome sequences, available via interactive website, web services or flat files. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. The facilities of the system range from sequence analysis to data storage and visualization and installations exist around the world both in companies and at academic sites. With a total of nine genome sequences available from Ensembl and more genomes to follow, recent developments have focused mainly on closer integration between genomes and external data.

Analyzing the field of bioinformatics with the multi-faceted topic modeling technique.

PubMed

Heo, Go Eun; Kang, Keun Young; Song, Min; Lee, Jeong-Hoon

2017-05-31

Bioinformatics is an interdisciplinary field at the intersection of molecular biology and computing technology. To characterize the field as convergent domain, researchers have used bibliometrics, augmented with text-mining techniques for content analysis. In previous studies, Latent Dirichlet Allocation (LDA) was the most representative topic modeling technique for identifying topic structure of subject areas. However, as opposed to revealing the topic structure in relation to metadata such as authors, publication date, and journals, LDA only displays the simple topic structure. In this paper, we adopt the Tang et al.'s Author-Conference-Topic (ACT) model to study the field of bioinformatics from the perspective of keyphrases, authors, and journals. The ACT model is capable of incorporating the paper, author, and conference into the topic distribution simultaneously. To obtain more meaningful results, we use journals and keyphrases instead of conferences and bag-of-words.. For analysis, we use PubMed to collected forty-six bioinformatics journals from the MEDLINE database. We conducted time series topic analysis over four periods from 1996 to 2015 to further examine the interdisciplinary nature of bioinformatics. We analyze the ACT Model results in each period. Additionally, for further integrated analysis, we conduct a time series analysis among the top-ranked keyphrases, journals, and authors according to their frequency. We also examine the patterns in the top journals by simultaneously identifying the topical probability in each period, as well as the top authors and keyphrases. The results indicate that in recent years diversified topics have become more prevalent and convergent topics have become more clearly represented. The results of our analysis implies that overtime the field of bioinformatics becomes more interdisciplinary where there is a steady increase in peripheral fields such as conceptual, mathematical, and system biology. These results are confirmed by integrated analysis of topic distribution as well as top ranked keyphrases, authors, and journals.

Linking Advanced Visualization and MATLAB for the Analysis of 3D Gene Expression Data

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

Ruebel, Oliver; Keranen, Soile V.E.; Biggin, Mark

Three-dimensional gene expression PointCloud data generated by the Berkeley Drosophila Transcription Network Project (BDTNP) provides quantitative information about the spatial and temporal expression of genes in early Drosophila embryos at cellular resolution. The BDTNP team visualizes and analyzes Point-Cloud data using the software application PointCloudXplore (PCX). To maximize the impact of novel, complex data sets, such as PointClouds, the data needs to be accessible to biologists and comprehensible to developers of analysis functions. We address this challenge by linking PCX and Matlab via a dedicated interface, thereby providing biologists seamless access to advanced data analysis functions and giving bioinformatics researchersmore » the opportunity to integrate their analysis directly into the visualization application. To demonstrate the usefulness of this approach, we computationally model parts of the expression pattern of the gene even skipped using a genetic algorithm implemented in Matlab and integrated into PCX via our Matlab interface.« less

In Silico PCR Tools for a Fast Primer, Probe, and Advanced Searching.

PubMed

Kalendar, Ruslan; Muterko, Alexandr; Shamekova, Malika; Zhambakin, Kabyl

2017-01-01

The polymerase chain reaction (PCR) is fundamental to molecular biology and is the most important practical molecular technique for the research laboratory. The principle of this technique has been further used and applied in plenty of other simple or complex nucleic acid amplification technologies (NAAT). In parallel to laboratory "wet bench" experiments for nucleic acid amplification technologies, in silico or virtual (bioinformatics) approaches have been developed, among which in silico PCR analysis. In silico NAAT analysis is a useful and efficient complementary method to ensure the specificity of primers or probes for an extensive range of PCR applications from homology gene discovery, molecular diagnosis, DNA fingerprinting, and repeat searching. Predicting sensitivity and specificity of primers and probes requires a search to determine whether they match a database with an optimal number of mismatches, similarity, and stability. In the development of in silico bioinformatics tools for nucleic acid amplification technologies, the prospects for the development of new NAAT or similar approaches should be taken into account, including forward-looking and comprehensive analysis that is not limited to only one PCR technique variant. The software FastPCR and the online Java web tool are integrated tools for in silico PCR of linear and circular DNA, multiple primer or probe searches in large or small databases and for advanced search. These tools are suitable for processing of batch files that are essential for automation when working with large amounts of data. The FastPCR software is available for download at http://primerdigital.com/fastpcr.html and the online Java version at http://primerdigital.com/tools/pcr.html .

The European Bioinformatics Institute’s data resources 2014

PubMed Central

Brooksbank, Catherine; Bergman, Mary Todd; Apweiler, Rolf; Birney, Ewan; Thornton, Janet

2014-01-01

Molecular Biology has been at the heart of the ‘big data’ revolution from its very beginning, and the need for access to biological data is a common thread running from the 1965 publication of Dayhoff’s ‘Atlas of Protein Sequence and Structure’ through the Human Genome Project in the late 1990s and early 2000s to today’s population-scale sequencing initiatives. The European Bioinformatics Institute (EMBL-EBI; http://www.ebi.ac.uk) is one of three organizations worldwide that provides free access to comprehensive, integrated molecular data sets. Here, we summarize the principles underpinning the development of these public resources and provide an overview of EMBL-EBI’s database collection to complement the reviews of individual databases provided elsewhere in this issue. PMID:24271396

Data mining in newt-omics, the repository for omics data from the newt.

PubMed

Looso, Mario; Braun, Thomas

2015-01-01

Salamanders are an excellent model organism to study regenerative processes due to their unique ability to regenerate lost appendages or organs. Straightforward bioinformatics tools to analyze and take advantage of the growing number of "omics" studies performed in salamanders were lacking so far. To overcome this limitation, we have generated a comprehensive data repository for the red-spotted newt Notophthalmus viridescens, named newt-omics, merging omics style datasets on the transcriptome and proteome level including expression values and annotations. The resource is freely available via a user-friendly Web-based graphical user interface ( http://newt-omics.mpi-bn.mpg.de) that allows access and queries to the database without prior bioinformatical expertise. The repository is updated regularly, incorporating new published datasets from omics technologies.

Two interactive Bioinformatics courses at the Bielefeld University Bioinformatics Server.

PubMed

Sczyrba, Alexander; Konermann, Susanne; Giegerich, Robert

2008-05-01

Conferences in computational biology continue to provide tutorials on classical and new methods in the field. This can be taken as an indicator that education is still a bottleneck in our field's process of becoming an established scientific discipline. Bielefeld University has been one of the early providers of bioinformatics education, both locally and via the internet. The Bielefeld Bioinformatics Server (BiBiServ) offers a variety of older and new materials. Here, we report on two online courses made available recently, one introductory and one on the advanced level: (i) SADR: Sequence Analysis with Distributed Resources (http://bibiserv.techfak.uni-bielefeld.de/sadr/) and (ii) ADP: Algebraic Dynamic Programming in Bioinformatics (http://bibiserv.techfak.uni-bielefeld.de/dpcourse/).

An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics

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

Taylor, Ronald C.

Bioinformatics researchers are increasingly confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce. An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBasemore » project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date.« less

Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists.

PubMed

Zhu, Xun; Wolfgruber, Thomas K; Tasato, Austin; Arisdakessian, Cédric; Garmire, David G; Garmire, Lana X

2017-12-05

Single-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level. Computational methods to process scRNA-Seq data are not very accessible to bench scientists as they require a significant amount of bioinformatic skills. We have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface. Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene-expression normalization, imputation, gene filtering, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction. Granatum enables broad adoption of scRNA-Seq technology by empowering bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at http://garmiregroup.org/granatum/app.

Ensembl 2002: accommodating comparative genomics.

PubMed

Clamp, M; Andrews, D; Barker, D; Bevan, P; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Hubbard, T; Kasprzyk, A; Keefe, D; Lehvaslaiho, H; Iyer, V; Melsopp, C; Mongin, E; Pettett, R; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Birney, E

2003-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of human, mouse and other genome sequences, available as either an interactive web site or as flat files. Ensembl also integrates manually annotated gene structures from external sources where available. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. These range from sequence analysis to data storage and visualisation and installations exist around the world in both companies and at academic sites. With both human and mouse genome sequences available and more vertebrate sequences to follow, many of the recent developments in Ensembl have focusing on developing automatic comparative genome analysis and visualisation.

T7 lytic phage-displayed peptide libraries: construction and diversity characterization.

PubMed

Krumpe, Lauren R H; Mori, Toshiyuki

2014-01-01

In this chapter, we describe the construction of T7 bacteriophage (phage)-displayed peptide libraries and the diversity analyses of random amino acid sequences obtained from the libraries. We used commercially available reagents, Novagen's T7Select system, to construct the libraries. Using a combination of biotinylated extension primer and streptavidin-coupled magnetic beads, we were able to prepare library DNA without applying gel purification, resulting in extremely high ligation efficiencies. Further, we describe the use of bioinformatics tools to characterize library diversity. Amino acid frequency and positional amino acid diversity and hydropathy are estimated using the REceptor LIgand Contacts website http://relic.bio.anl.gov. Peptide net charge analysis and peptide hydropathy analysis are conducted using the Genetics Computer Group Wisconsin Package computational tools. A comprehensive collection of the estimated number of recombinants and titers of T7 phage-displayed peptide libraries constructed in our lab is included.

Profiling and bioinformatics analyses reveal differential circular RNA expression in radioresistant esophageal cancer cells.

PubMed

Su, Huafang; Lin, Fuqiang; Deng, Xia; Shen, Lanxiao; Fang, Ya; Fei, Zhenghua; Zhao, Lihao; Zhang, Xuebang; Pan, Huanle; Xie, Deyao; Jin, Xiance; Xie, Congying

2016-07-28

Acquired radioresistance during radiotherapy is considered as the most important reason for local tumor recurrence or treatment failure. Circular RNAs (circRNAs) have recently been identified as microRNA sponges and involve in various biological processes. The purpose of this study is to investigate the role of circRNAs in the radioresistance of esophageal cancer. Total RNA was isolated from human parental cell line KYSE-150 and self-established radioresistant esophageal cancer cell line KYSE-150R, and hybridized to Arraystar Human circRNA Array. Quantitative real-time PCR was used to confirm the circRNA expression profiles obtained from the microarray data. Bioinformatic tools including gene ontology (GO) analysis, KEGG pathway analysis and network analysis were done for further assessment. Among the detected candidate 3752 circRNA genes, significant upregulation of 57 circRNAs and downregulation of 17 circRNAs in human radioresistant esophageal cancer cell line KYSE-150R were observed compared with the parental cell line KYSE-150 (fold change ≥2.0 and P < 0.05). There were 9 out of these candidate circRNAs were validated by real-time PCR. GO analysis revealed that numerous target genes, including most microRNAs were involved in the biological processes. There were more than 400 target genes enrichment on Wnt signaling pathway. CircRNA_001059 and circRNA_000167 were the two largest nodes in circRNA/microRNA co-expression network. Our study revealed a comprehensive expression and functional profile of differentially expressed circRNAs in radioresistant esophageal cancer cells, indicating possible involvement of these dysregulated circRNAs in the development of radiation resistance.

Circular RNA In Invasive and Recurrent Clinical Nonfunctioning Pituitary Adenomas: Expression Profiles and Bioinformatic Analysis.

PubMed

Wang, Jianpeng; Wang, Dong; Wan, Dehong; Ma, Qingxia; Liu, Qian; Li, Jiye; Li, Zhaojian; Gao, Yang; Jiang, Guohui; Ma, Leina; Liu, Jia; Li, Chuzhong

2018-06-14

The invasion and recurrence of clinical nonfunctioning pituitary adenomas (NFA) often lead to surgical treatment failure. Circular RNAs (circRNAs) are a novel class of RNAs whose 3' and 5' ends are joined together and have been shown to play important roles in cancer development. Up to now, the roles of circRNAs remain unclear in invasive and recurrent NFA. We detected and summarized the circRNA expression pattern in 75 NFA tissues from 10 non-invasive cases and 65 invasive cases and 9 pairs NFA tumor tissues from 9 recurrent cases by circRNA microarrays. Accordingly, functional enrichment analysis and pathway analysis were performed and circRNA-microRNA(miRNA) network were generated by bioinformatic analysis tools. 5 new invasive NFA samples and 5 non-invasive NFA samples were collected to measure the microarray results. 570 dysregulated circRNAs (Invasive Tumor vs. Non-invasive Tumor) and 10 up-regulated circRNAs (Recurrent tumor Tissue vs. First surgery tumor Tissue) were identified based on the situation (FC>2, P<0.05). The parental genes of the dysregulated circRNAs in the comparison between invasion tumor and non-invasion tumor were found to be enriched in some cell adhesion signaling pathways such as Focal adhesion, Hippo signaling pathway, PI3K-Akt signaling pathway, and Adherens junction. The circRNA-miRNA network showed that the dysregulated circRNA may function as miRNA sponges. This is the first study to conduct and comprehensively analyze the circRNA expression profile in invasive and recurrent NFA. Our finding will provide evidence for the significance of circRNAs in NFA diagnosis, prognosis and clinical treatment. Copyright © 2018 Elsevier Inc. All rights reserved.

Phylogeny and evolution of plant cyclic nucleotide-gated ion channel (CNGC) gene family and functional analyses of tomato CNGCs

PubMed Central

Saand, Mumtaz Ali; Xu, You-Ping; Munyampundu, Jean-Pierre; Li, Wen; Zhang, Xuan-Rui; Cai, Xin-Zhong

2015-01-01

Cyclic nucleotide-gated ion channels (CNGCs) are calcium-permeable channels that are involved in various biological functions. Nevertheless, phylogeny and function of plant CNGCs are not well understood. In this study, 333 CNGC genes from 15 plant species were identified using comprehensive bioinformatics approaches. Extensive bioinformatics analyses demonstrated that CNGCs of Group IVa were distinct to those of other groups in gene structure and amino acid sequence of cyclic nucleotide-binding domain. A CNGC-specific motif that recognizes all identified plant CNGCs was generated. Phylogenetic analysis indicated that CNGC proteins of flowering plant species formed five groups. However, CNGCs of the non-vascular plant Physcomitrella patens clustered only in two groups (IVa and IVb), while those of the vascular non-flowering plant Selaginella moellendorffii gathered in four (IVa, IVb, I and II). These data suggest that Group IV CNGCs are most ancient and Group III CNGCs are most recently evolved in flowering plants. Furthermore, silencing analyses revealed that a set of CNGC genes might be involved in disease resistance and abiotic stress responses in tomato and function of SlCNGCs does not correlate with the group that they are belonging to. Our results indicate that Group IVa CNGCs are structurally but not functionally unique among plant CNGCs. PMID:26546226

Bioinformatics approach reveals systematic mechanism underlying lung adenocarcinoma.

PubMed

Wu, Xiya; Zhang, Wei; Hu, Yunhua; Yi, Xianghua

2015-01-01

The purpose of this work was to explore the systematic molecular mechanism of lung adenocarcinoma and gain a deeper insight into it. Comprehensive bioinformatics methods were applied. Initially, significant differentially expressed genes (DEGs) were analyzed from the Affymetrix microarray data (GSE27262) deposited in the Gene Expression Omnibus (GEO). Subsequently, gene ontology (GO) analysis was performed using online Database for Annotation, Visualization and Integration Discovery (DAVID) software. Finally, significant pathway crosstalk was investigated based on the information derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. According to our results, the N-terminal globular domain of the type X collagen (COL10A1) gene and transmembrane protein 100 (TMEM100) gene were identified to be the most significant DEGs in tumor tissue compared with the adjacent normal tissues. The main GO categories were biological process, cellular component and molecular function. In addition, the crosstalk was significantly different between non-small cell lung cancer pathways and inositol phosphate metabolism pathway, focal adhesion signal pathway, vascular smooth muscle contraction signal pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway and calcium signaling pathway in tumor. Dysfunctional genes and pathways may play key roles in the progression and development of lung adenocarcinoma. Our data provide a systematic perspective for understanding this mechanism and may be helpful in discovering an effective treatment for lung adenocarcinoma.

A Molecular Genetics Laboratory Course Applying Bioinformatics and Cell Biology in the Context of Original Research

PubMed Central

Pruitt, Wendy M.; Robinson, Lucy C.

2008-01-01

Research based laboratory courses have been shown to stimulate student interest in science and to improve scientific skills. We describe here a project developed for a semester-long research-based laboratory course that accompanies a genetics lecture course. The project was designed to allow students to become familiar with the use of bioinformatics tools and molecular biology and genetic approaches while carrying out original research. Students were required to present their hypotheses, experiments, and results in a comprehensive lab report. The lab project concerned the yeast casein kinase 1 (CK1) protein kinase Yck2. CK1 protein kinases are present in all organisms and are well conserved in primary structure. These enzymes display sequence features that differ from other protein kinase subfamilies. Students identified such sequences within the CK1 subfamily, chose a sequence to analyze, used available structural data to determine possible functions for their sequences, and designed mutations within the sequences. After generating the mutant alleles, these were expressed in yeast and tested for function by using two growth assays. The student response to the project was positive, both in terms of knowledge and skills increases and interest in research, and several students are continuing the analysis of mutant alleles as summer projects. PMID:19047427

Signature-Discovery Approach for Sample Matching of a Nerve-Agent Precursor using Liquid Chromatography–Mass Spectrometry, XCMS, and Chemometrics

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

Fraga, Carlos G.; Clowers, Brian H.; Moore, Ronald J.

2010-05-15

This report demonstrates the use of bioinformatic and chemometric tools on liquid chromatography mass spectrometry (LC-MS) data for the discovery of ultra-trace forensic signatures for sample matching of various stocks of the nerve-agent precursor known as methylphosphonic dichloride (dichlor). The use of the bioinformatic tool known as XCMS was used to comprehensively search and find candidate LC-MS peaks in a known set of dichlor samples. These candidate peaks were down selected to a group of 34 impurity peaks. Hierarchal cluster analysis and factor analysis demonstrated the potential of these 34 impurities peaks for matching samples based on their stock source.more » Only one pair of dichlor stocks was not differentiated from one another. An acceptable chemometric approach for sample matching was determined to be variance scaling and signal averaging of normalized duplicate impurity profiles prior to classification by k-nearest neighbors. Using this approach, a test set of dichlor samples were all correctly matched to their source stock. The sample preparation and LC-MS method permitted the detection of dichlor impurities presumably in the parts-per-trillion (w/w). The detection of a common impurity in all dichlor stocks that were synthesized over a 14-year period and by different manufacturers was an unexpected discovery. Our described signature-discovery approach should be useful in the development of a forensic capability to help in criminal investigations following chemical attacks.« less

The MIGenAS integrated bioinformatics toolkit for web-based sequence analysis

PubMed Central

Rampp, Markus; Soddemann, Thomas; Lederer, Hermann

2006-01-01

We describe a versatile and extensible integrated bioinformatics toolkit for the analysis of biological sequences over the Internet. The web portal offers convenient interactive access to a growing pool of chainable bioinformatics software tools and databases that are centrally installed and maintained by the RZG. Currently, supported tasks comprise sequence similarity searches in public or user-supplied databases, computation and validation of multiple sequence alignments, phylogenetic analysis and protein–structure prediction. Individual tools can be seamlessly chained into pipelines allowing the user to conveniently process complex workflows without the necessity to take care of any format conversions or tedious parsing of intermediate results. The toolkit is part of the Max-Planck Integrated Gene Analysis System (MIGenAS) of the Max Planck Society available at (click ‘Start Toolkit’). PMID:16844980

Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator.

PubMed

Garcia Castro, Alexander; Thoraval, Samuel; Garcia, Leyla J; Ragan, Mark A

2005-04-07

Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the proliferation of GUIs (Graphic User Interfaces) in bioinformatics, only some of them provide workflow capabilities; surprisingly, no meta-analysis of workflow operators and components in bioinformatics has been reported. We present a set of syntactic components and algebraic operators capable of representing analytical workflows in bioinformatics. Iteration, recursion, the use of conditional statements, and management of suspend/resume tasks have traditionally been implemented on an ad hoc basis and hard-coded; by having these operators properly defined it is possible to use and parameterize them as generic re-usable components. To illustrate how these operations can be orchestrated, we present GPIPE, a prototype graphic pipeline generator for PISE that allows the definition of a pipeline, parameterization of its component methods, and storage of metadata in XML formats. This implementation goes beyond the macro capacities currently in PISE. As the entire analysis protocol is defined in XML, a complete bioinformatic experiment (linked sets of methods, parameters and results) can be reproduced or shared among users. http://if-web1.imb.uq.edu.au/Pise/5.a/gpipe.html (interactive), ftp://ftp.pasteur.fr/pub/GenSoft/unix/misc/Pise/ (download). From our meta-analysis we have identified syntactic structures and algebraic operators common to many workflows in bioinformatics. The workflow components and algebraic operators can be assimilated into re-usable software components. GPIPE, a prototype implementation of this framework, provides a GUI builder to facilitate the generation of workflows and integration of heterogeneous analytical tools.

Microarray R-based analysis of complex lysate experiments with MIRACLE

PubMed Central

List, Markus; Block, Ines; Pedersen, Marlene Lemvig; Christiansen, Helle; Schmidt, Steffen; Thomassen, Mads; Tan, Qihua; Baumbach, Jan; Mollenhauer, Jan

2014-01-01

Motivation: Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected. Results: This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis. Availability: Project URL: http://www.nanocan.org/miracle/ Contact: mlist@health.sdu.dk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161257

Microarray R-based analysis of complex lysate experiments with MIRACLE.

PubMed

List, Markus; Block, Ines; Pedersen, Marlene Lemvig; Christiansen, Helle; Schmidt, Steffen; Thomassen, Mads; Tan, Qihua; Baumbach, Jan; Mollenhauer, Jan

2014-09-01

Reverse-phase protein arrays (RPPAs) allow sensitive quantification of relative protein abundance in thousands of samples in parallel. Typical challenges involved in this technology are antibody selection, sample preparation and optimization of staining conditions. The issue of combining effective sample management and data analysis, however, has been widely neglected. This motivated us to develop MIRACLE, a comprehensive and user-friendly web application bridging the gap between spotting and array analysis by conveniently keeping track of sample information. Data processing includes correction of staining bias, estimation of protein concentration from response curves, normalization for total protein amount per sample and statistical evaluation. Established analysis methods have been integrated with MIRACLE, offering experimental scientists an end-to-end solution for sample management and for carrying out data analysis. In addition, experienced users have the possibility to export data to R for more complex analyses. MIRACLE thus has the potential to further spread utilization of RPPAs as an emerging technology for high-throughput protein analysis. Project URL: http://www.nanocan.org/miracle/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

BIAS: Bioinformatics Integrated Application Software.

PubMed

Finak, G; Godin, N; Hallett, M; Pepin, F; Rajabi, Z; Srivastava, V; Tang, Z

2005-04-15

We introduce a development platform especially tailored to Bioinformatics research and software development. BIAS (Bioinformatics Integrated Application Software) provides the tools necessary for carrying out integrative Bioinformatics research requiring multiple datasets and analysis tools. It follows an object-relational strategy for providing persistent objects, allows third-party tools to be easily incorporated within the system and supports standards and data-exchange protocols common to Bioinformatics. BIAS is an OpenSource project and is freely available to all interested users at http://www.mcb.mcgill.ca/~bias/. This website also contains a paper containing a more detailed description of BIAS and a sample implementation of a Bayesian network approach for the simultaneous prediction of gene regulation events and of mRNA expression from combinations of gene regulation events. hallett@mcb.mcgill.ca.

Novel approaches for bioinformatic analysis of salivary RNA sequencing data for development.

PubMed

Kaczor-Urbanowicz, Karolina Elzbieta; Kim, Yong; Li, Feng; Galeev, Timur; Kitchen, Rob R; Gerstein, Mark; Koyano, Kikuye; Jeong, Sung-Hee; Wang, Xiaoyan; Elashoff, David; Kang, So Young; Kim, Su Mi; Kim, Kyoung; Kim, Sung; Chia, David; Xiao, Xinshu; Rozowsky, Joel; Wong, David T W

2018-01-01

Analysis of RNA sequencing (RNA-Seq) data in human saliva is challenging. Lack of standardization and unification of the bioinformatic procedures undermines saliva's diagnostic potential. Thus, it motivated us to perform this study. We applied principal pipelines for bioinformatic analysis of small RNA-Seq data of saliva of 98 healthy Korean volunteers including either direct or indirect mapping of the reads to the human genome using Bowtie1. Analysis of alignments to exogenous genomes by another pipeline revealed that almost all of the reads map to bacterial genomes. Thus, salivary exRNA has fundamental properties that warrant the design of unique additional steps while performing the bioinformatic analysis. Our pipelines can serve as potential guidelines for processing of RNA-Seq data of human saliva. Processing and analysis results of the experimental data generated by the exceRpt (v4.6.3) small RNA-seq pipeline (github.gersteinlab.org/exceRpt) are available from exRNA atlas (exrna-atlas.org). Alignment to exogenous genomes and their quantification results were used in this paper for the analyses of small RNAs of exogenous origin. dtww@ucla.edu. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

ENFIN--A European network for integrative systems biology.

PubMed

Kahlem, Pascal; Clegg, Andrew; Reisinger, Florian; Xenarios, Ioannis; Hermjakob, Henning; Orengo, Christine; Birney, Ewan

2009-11-01

Integration of biological data of various types and the development of adapted bioinformatics tools represent critical objectives to enable research at the systems level. The European Network of Excellence ENFIN is engaged in developing an adapted infrastructure to connect databases, and platforms to enable both the generation of new bioinformatics tools and the experimental validation of computational predictions. With the aim of bridging the gap existing between standard wet laboratories and bioinformatics, the ENFIN Network runs integrative research projects to bring the latest computational techniques to bear directly on questions dedicated to systems biology in the wet laboratory environment. The Network maintains internally close collaboration between experimental and computational research, enabling a permanent cycling of experimental validation and improvement of computational prediction methods. The computational work includes the development of a database infrastructure (EnCORE), bioinformatics analysis methods and a novel platform for protein function analysis FuncNet.

Technosciences in Academia: Rethinking a Conceptual Framework for Bioinformatics Undergraduate Curricula

NASA Astrophysics Data System (ADS)

Symeonidis, Iphigenia Sofia

This paper aims to elucidate guiding concepts for the design of powerful undergraduate bioinformatics degrees which will lead to a conceptual framework for the curriculum. "Powerful" here should be understood as having truly bioinformatics objectives rather than enrichment of existing computer science or life science degrees on which bioinformatics degrees are often based. As such, the conceptual framework will be one which aims to demonstrate intellectual honesty in regards to the field of bioinformatics. A synthesis/conceptual analysis approach was followed as elaborated by Hurd (1983). The approach takes into account the following: bioinfonnatics educational needs and goals as expressed by different authorities, five undergraduate bioinformatics degrees case-studies, educational implications of bioinformatics as a technoscience and approaches to curriculum design promoting interdisciplinarity and integration. Given these considerations, guiding concepts emerged and a conceptual framework was elaborated. The practice of bioinformatics was given a closer look, which led to defining tool-integration skills and tool-thinking capacity as crucial areas of the bioinformatics activities spectrum. It was argued, finally, that a process-based curriculum as a variation of a concept-based curriculum (where the concepts are processes) might be more conducive to the teaching of bioinformatics given a foundational first year of integrated science education as envisioned by Bialek and Botstein (2004). Furthermore, the curriculum design needs to define new avenues of communication and learning which bypass the traditional disciplinary barriers of academic settings as undertaken by Tador and Tidmor (2005) for graduate studies.

Bioinformatics core competencies for undergraduate life sciences education.

PubMed

Wilson Sayres, Melissa A; Hauser, Charles; Sierk, Michael; Robic, Srebrenka; Rosenwald, Anne G; Smith, Todd M; Triplett, Eric W; Williams, Jason J; Dinsdale, Elizabeth; Morgan, William R; Burnette, James M; Donovan, Samuel S; Drew, Jennifer C; Elgin, Sarah C R; Fowlks, Edison R; Galindo-Gonzalez, Sebastian; Goodman, Anya L; Grandgenett, Nealy F; Goller, Carlos C; Jungck, John R; Newman, Jeffrey D; Pearson, William; Ryder, Elizabeth F; Tosado-Acevedo, Rafael; Tapprich, William; Tobin, Tammy C; Toro-Martínez, Arlín; Welch, Lonnie R; Wright, Robin; Barone, Lindsay; Ebenbach, David; McWilliams, Mindy; Olney, Kimberly C; Pauley, Mark A

2018-01-01

Although bioinformatics is becoming increasingly central to research in the life sciences, bioinformatics skills and knowledge are not well integrated into undergraduate biology education. This curricular gap prevents biology students from harnessing the full potential of their education, limiting their career opportunities and slowing research innovation. To advance the integration of bioinformatics into life sciences education, a framework of core bioinformatics competencies is needed. To that end, we here report the results of a survey of biology faculty in the United States about teaching bioinformatics to undergraduate life scientists. Responses were received from 1,260 faculty representing institutions in all fifty states with a combined capacity to educate hundreds of thousands of students every year. Results indicate strong, widespread agreement that bioinformatics knowledge and skills are critical for undergraduate life scientists as well as considerable agreement about which skills are necessary. Perceptions of the importance of some skills varied with the respondent's degree of training, time since degree earned, and/or the Carnegie Classification of the respondent's institution. To assess which skills are currently being taught, we analyzed syllabi of courses with bioinformatics content submitted by survey respondents. Finally, we used the survey results, the analysis of the syllabi, and our collective research and teaching expertise to develop a set of bioinformatics core competencies for undergraduate biology students. These core competencies are intended to serve as a guide for institutions as they work to integrate bioinformatics into their life sciences curricula.

Bioinformatics core competencies for undergraduate life sciences education

PubMed Central

Wilson Sayres, Melissa A.; Hauser, Charles; Sierk, Michael; Robic, Srebrenka; Rosenwald, Anne G.; Smith, Todd M.; Triplett, Eric W.; Williams, Jason J.; Dinsdale, Elizabeth; Morgan, William R.; Burnette, James M.; Donovan, Samuel S.; Drew, Jennifer C.; Elgin, Sarah C. R.; Fowlks, Edison R.; Galindo-Gonzalez, Sebastian; Goodman, Anya L.; Grandgenett, Nealy F.; Goller, Carlos C.; Jungck, John R.; Newman, Jeffrey D.; Pearson, William; Ryder, Elizabeth F.; Tosado-Acevedo, Rafael; Tapprich, William; Tobin, Tammy C.; Toro-Martínez, Arlín; Welch, Lonnie R.; Wright, Robin; Ebenbach, David; McWilliams, Mindy; Olney, Kimberly C.

2018-01-01

Although bioinformatics is becoming increasingly central to research in the life sciences, bioinformatics skills and knowledge are not well integrated into undergraduate biology education. This curricular gap prevents biology students from harnessing the full potential of their education, limiting their career opportunities and slowing research innovation. To advance the integration of bioinformatics into life sciences education, a framework of core bioinformatics competencies is needed. To that end, we here report the results of a survey of biology faculty in the United States about teaching bioinformatics to undergraduate life scientists. Responses were received from 1,260 faculty representing institutions in all fifty states with a combined capacity to educate hundreds of thousands of students every year. Results indicate strong, widespread agreement that bioinformatics knowledge and skills are critical for undergraduate life scientists as well as considerable agreement about which skills are necessary. Perceptions of the importance of some skills varied with the respondent’s degree of training, time since degree earned, and/or the Carnegie Classification of the respondent’s institution. To assess which skills are currently being taught, we analyzed syllabi of courses with bioinformatics content submitted by survey respondents. Finally, we used the survey results, the analysis of the syllabi, and our collective research and teaching expertise to develop a set of bioinformatics core competencies for undergraduate biology students. These core competencies are intended to serve as a guide for institutions as they work to integrate bioinformatics into their life sciences curricula. PMID:29870542

Global Proteome Analysis Links Lysine Acetylation to Diverse Functions in Oryza Sativa.

PubMed

Xue, Chao; Liu, Shuai; Chen, Chen; Zhu, Jun; Yang, Xibin; Zhou, Yong; Guo, Rui; Liu, Xiaoyu; Gong, Zhiyun

2018-01-01

Lysine acetylation (Kac) is an important protein post-translational modification in both eukaryotes and prokaryotes. Herein, we report the results of a global proteome analysis of Kac and its diverse functions in rice (Oryza sativa). We identified 1353 Kac sites in 866 proteins in rice seedlings. A total of 11 Kac motifs are conserved, and 45% of the identified proteins are localized to the chloroplast. Among all acetylated proteins, 38 Kac sites are combined in core histones. Bioinformatics analysis revealed that Kac occurs on a diverse range of proteins involved in a wide variety of biological processes, especially photosynthesis. Protein-protein interaction networks of the identified proteins provided further evidence that Kac contributes to a wide range of regulatory functions. Furthermore, we demonstrated that the acetylation level of histone H3 (lysine 27 and 36) is increased in response to cold stress. In summary, our approach comprehensively profiles the regulatory roles of Kac in the growth and development of rice. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Navigating freely-available software tools for metabolomics analysis.

PubMed

Spicer, Rachel; Salek, Reza M; Moreno, Pablo; Cañueto, Daniel; Steinbeck, Christoph

2017-01-01

The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools. To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics. The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC-MS, GC-MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for. A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary. This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools' abilities to perform specific data analysis tasks e.g. peak picking.

[Tissue bank of the National Centre for Tumour Disease. An innovative platform for translational tumour].

PubMed

Herpel, E; Koleganova, N; Schirmacher, P

2008-11-01

The tissue bank of the National Centre for Tumour Diseases (NCT) in Heidelberg, Germany, was founded in 2005 by the University Hospital of Heidelberg and the German Cancer Research Centre as a section of the NCT. It is a nonprofit organization with a completely evaluated legal and ethical framework and supports the Comprehensive Cancer Centre concept. Its main aim is the acquisition and characterization of fresh-frozen and paraffin-embedded human tissues according to the standards of good scientific practice and the promotion of interdisciplinary tumour research of the comprehensive cancer centre and its cooperating partners. It also offers expert project assistance: a project leader can submit a short proposal, and the tissue collecting/preparing process will be performed in cooperation with a specialised pathologist and, if applicable, an experienced clinical researcher. The tissue bank is also a central platform for further developing of innovative technologies for tissue handling, e.g. multi-tissue-array and virtual microscopy, with links to digital image analysis and bioinformatics. Thus, the NCT tissue bank represents a model for innovative biobanking and for institutions with active interdisciplinary cancer research.

Comprehensive phylogeny of ray-finned fishes (Actinopterygii) based on transcriptomic and genomic data.

PubMed

Hughes, Lily C; Ortí, Guillermo; Huang, Yu; Sun, Ying; Baldwin, Carole C; Thompson, Andrew W; Arcila, Dahiana; Betancur-R, Ricardo; Li, Chenhong; Becker, Leandro; Bellora, Nicolás; Zhao, Xiaomeng; Li, Xiaofeng; Wang, Min; Fang, Chao; Xie, Bing; Zhou, Zhuocheng; Huang, Hai; Chen, Songlin; Venkatesh, Byrappa; Shi, Qiong

2018-05-14

Our understanding of phylogenetic relationships among bony fishes has been transformed by analysis of a small number of genes, but uncertainty remains around critical nodes. Genome-scale inferences so far have sampled a limited number of taxa and genes. Here we leveraged 144 genomes and 159 transcriptomes to investigate fish evolution with an unparalleled scale of data: >0.5 Mb from 1,105 orthologous exon sequences from 303 species, representing 66 out of 72 ray-finned fish orders. We apply phylogenetic tests designed to trace the effect of whole-genome duplication events on gene trees and find paralogy-free loci using a bioinformatics approach. Genome-wide data support the structure of the fish phylogeny, and hypothesis-testing procedures appropriate for phylogenomic datasets using explicit gene genealogy interrogation settle some long-standing uncertainties, such as the branching order at the base of the teleosts and among early euteleosts, and the sister lineage to the acanthomorph and percomorph radiations. Comprehensive fossil calibrations date the origin of all major fish lineages before the end of the Cretaceous.

The FaceBase Consortium: A comprehensive program to facilitate craniofacial research

PubMed Central

Hochheiser, Harry; Aronow, Bruce J.; Artinger, Kristin; Beaty, Terri H.; Brinkley, James F.; Chai, Yang; Clouthier, David; Cunningham, Michael L.; Dixon, Michael; Donahue, Leah Rae; Fraser, Scott E.; Hallgrimsson, Benedikt; Iwata, Junichi; Klein, Ophir; Marazita, Mary L.; Murray, Jeffrey C.; Murray, Stephen; de Villena, Fernando Pardo-Manuel; Postlethwait, John; Potter, Steven; Shapiro, Linda; Spritz, Richard; Visel, Axel; Weinberg, Seth M.; Trainor, Paul A.

2012-01-01

The FaceBase Consortium consists of ten interlinked research and technology projects whose goal is to generate craniofacial research data and technology for use by the research community through a central data management and integrated bioinformatics hub. Funded by the National Institute of Dental and Craniofacial Research (NIDCR) and currently focused on studying the development of the middle region of the face, the Consortium will produce comprehensive datasets of global gene expression patterns, regulatory elements and sequencing; will generate anatomical and molecular atlases; will provide human normative facial data and other phenotypes; conduct follow up studies of a completed genome-wide association study; generate independent data on the genetics of craniofacial development, build repositories of animal models and of human samples and data for community access and analysis; and will develop software tools and animal models for analyzing and functionally testing and integrating these data. The FaceBase website (http://www.facebase.org) will serve as a web home for these efforts, providing interactive tools for exploring these datasets, together with discussion forums and other services to support and foster collaboration within the craniofacial research community. PMID:21458441

Proteomics and bioinformatics analysis of altered protein expression in the placental villous tissue from early recurrent miscarriage patients.

PubMed

Pan, Hai-Tao; Ding, Hai-Gang; Fang, Min; Yu, Bin; Cheng, Yi; Tan, Ya-Jing; Fu, Qi-Qin; Lu, Bo; Cai, Hong-Guang; Jin, Xin; Xia, Xian-Qing; Zhang, Tao

2018-01-01

Recurrent miscarriage (RM) affects 5% of women, it has an adverse emotional impact on women. Because of the complexities of early development, the mechanism of recurrent miscarriage is still unclear. We hypothesized that abnormal placenta leads to early recurrent miscarriage (ERM). The aim of this study was to identify ERM associated factors in human placenta villous tissue using proteomics. Investigation of these differences in protein expression in parallel profiling is essential to understand the comprehensive pathophysiological mechanism underlying recurrent miscarriage (RM). To gain more insight into mechanisms of recurrent miscarriage (RM), a comparative proteome profile of the human placenta villous tissue in normal and RM pregnancies was analyzed using iTRAQ technology and bioinformatics analysis used by Ingenuity Pathway Analysis (IPA) software. In this study, we employed an iTRAQ based proteomics analysis of four placental villous tissues from patients with early recurrent miscarriage (ERM) and four from normal pregnant women. Finally, we identified 2805 proteins and 79,998 peptides between patients with RM and normal matched group. Further analysis identified 314 differentially expressed proteins in placental villous tissue (≥1.3-fold, Student's t-test, p < 0.05); 209 proteins showed the increased expression while 105 proteins showed decreased expression. These 314 proteins were analyzed by Ingenuity Pathway Analysis (IPA) and were found to play important roles in the growth of embryo. Furthermore, network analysis show that Angiotensinogen (AGT), MAPK14 and Prothrombin (F2) are core factors in early embryonic development. We used another 8 independent samples (4 cases and 4 controls) to cross validation of the proteomic data. This study has identified several proteins that are associated with early development, these results may supply new insight into mechanisms behind recurrent miscarriage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Online Tools for Bioinformatics Analyses in Nutrition Sciences12

PubMed Central

Malkaram, Sridhar A.; Hassan, Yousef I.; Zempleni, Janos

2012-01-01

Recent advances in “omics” research have resulted in the creation of large datasets that were generated by consortiums and centers, small datasets that were generated by individual investigators, and bioinformatics tools for mining these datasets. It is important for nutrition laboratories to take full advantage of the analysis tools to interrogate datasets for information relevant to genomics, epigenomics, transcriptomics, proteomics, and metabolomics. This review provides guidance regarding bioinformatics resources that are currently available in the public domain, with the intent to provide a starting point for investigators who want to take advantage of the opportunities provided by the bioinformatics field. PMID:22983844

India's Computational Biology Growth and Challenges.

PubMed

Chakraborty, Chiranjib; Bandyopadhyay, Sanghamitra; Agoramoorthy, Govindasamy

2016-09-01

India's computational science is growing swiftly due to the outburst of internet and information technology services. The bioinformatics sector of India has been transforming rapidly by creating a competitive position in global bioinformatics market. Bioinformatics is widely used across India to address a wide range of biological issues. Recently, computational researchers and biologists are collaborating in projects such as database development, sequence analysis, genomic prospects and algorithm generations. In this paper, we have presented the Indian computational biology scenario highlighting bioinformatics-related educational activities, manpower development, internet boom, service industry, research activities, conferences and trainings undertaken by the corporate and government sectors. Nonetheless, this new field of science faces lots of challenges.

Detecting and characterizing circular RNAs

PubMed Central

Jeck, William R.; Sharpless, Norman E.

2014-01-01

Circular RNA transcripts were first identified in the early 1990s but knowledge of these species has remained limited, as their study has been difficult through traditional methods of RNA analysis. Now, novel bioinformatic approaches coupled with biochemical enrichment strategies and deep sequencing have allowed comprehensive studies of circular RNA species. Recent studies have revealed thousands of endogenous circular RNAs (circRNAs) in mammalian cells, some of which are highly abundant and evolutionarily conserved. Evidence is emerging that some circRNAs might regulate microRNA (miRNA) function, and roles in transcriptional control have also been suggested. Therefore, study of this class of non-coding RNAs has potential implications for therapeutic and research applications. We believe the key future challenge to the field will be to understand the regulation and function of these unusual molecules. PMID:24811520

Human Microbiome Acquisition and Bioinformatic Challenges in Metagenomic Studies

PubMed Central

2018-01-01

The study of the human microbiome has become a very popular topic. Our microbial counterpart, in fact, appears to play an important role in human physiology and health maintenance. Accordingly, microbiome alterations have been reported in an increasing number of human diseases. Despite the huge amount of data produced to date, less is known on how a microbial dysbiosis effectively contributes to a specific pathology. To fill in this gap, other approaches for microbiome study, more comprehensive than 16S rRNA gene sequencing, i.e., shotgun metagenomics and metatranscriptomics, are becoming more widely used. Methods standardization and the development of specific pipelines for data analysis are required to contribute to and increase our understanding of the human microbiome relationship with health and disease status. PMID:29382070

Prediction of anticancer peptides against MCF-7 breast cancer cells from the peptidomes of Achatina fulica mucus fractions.

PubMed

E-Kobon, Teerasak; Thongararm, Pennapa; Roytrakul, Sittiruk; Meesuk, Ladda; Chumnanpuen, Pramote

2016-01-01

Several reports have shown antimicrobial and anticancer activities of mucous glycoproteins extracted from the giant African snail Achatina fulica. Anticancer properties of the snail mucous peptides remain incompletely revealed. The aim of this study was to predict anticancer peptides from A. fulica mucus. Two of HPLC-separated mucous fractions (F2 and F5) showed in vitro cytotoxicity against the breast cancer cell line (MCF-7) and normal epithelium cell line (Vero). According to the mass spectrometric analysis, 404 and 424 peptides from the F2 and F5 fractions were identified. Our comprehensive bioinformatics workflow predicted 16 putative cationic and amphipathic anticancer peptides with diverse structures from these two peptidome data. These peptides would be promising molecules for new anti-breast cancer drug development.

Development of a Web-Enabled Informatics Platform for Manipulation of Gene Expression Data

DTIC Science & Technology

2004-12-01

genomic platforms such as metabolomics and proteomics , and to federated databases for knowledge management. A successful SBIR Phase I completed...measurements that require sophisticated bioinformatic platforms for data archival, management, integration, and analysis if researchers are to derive...web-enabled bioinformatic platform consisting of a Laboratory Information Management System (LIMS), an Analysis Information Management System (AIMS

Evolving Strategies for the Incorporation of Bioinformatics Within the Undergraduate Cell Biology Curriculum

PubMed Central

Honts, Jerry E.

2003-01-01

Recent advances in genomics and structural biology have resulted in an unprecedented increase in biological data available from Internet-accessible databases. In order to help students effectively use this vast repository of information, undergraduate biology students at Drake University were introduced to bioinformatics software and databases in three courses, beginning with an introductory course in cell biology. The exercises and projects that were used to help students develop literacy in bioinformatics are described. In a recently offered course in bioinformatics, students developed their own simple sequence analysis tool using the Perl programming language. These experiences are described from the point of view of the instructor as well as the students. A preliminary assessment has been made of the degree to which students had developed a working knowledge of bioinformatics concepts and methods. Finally, some conclusions have been drawn from these courses that may be helpful to instructors wishing to introduce bioinformatics within the undergraduate biology curriculum. PMID:14673489

Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers

PubMed Central

Chen, Hao; Zhu, Zhitu; Zhu, Yichun; Wang, Jian; Mei, Yunqing; Cheng, Yunfeng

2015-01-01

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein–protein or gene–gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed. PMID:25560835

H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa

PubMed Central

Mulder, Nicola J.; Adebiyi, Ezekiel; Alami, Raouf; Benkahla, Alia; Brandful, James; Doumbia, Seydou; Everett, Dean; Fadlelmola, Faisal M.; Gaboun, Fatima; Gaseitsiwe, Simani; Ghazal, Hassan; Hazelhurst, Scott; Hide, Winston; Ibrahimi, Azeddine; Jaufeerally Fakim, Yasmina; Jongeneel, C. Victor; Joubert, Fourie; Kassim, Samar; Kayondo, Jonathan; Kumuthini, Judit; Lyantagaye, Sylvester; Makani, Julie; Mansour Alzohairy, Ahmed; Masiga, Daniel; Moussa, Ahmed; Nash, Oyekanmi; Ouwe Missi Oukem-Boyer, Odile; Owusu-Dabo, Ellis; Panji, Sumir; Patterton, Hugh; Radouani, Fouzia; Sadki, Khalid; Seghrouchni, Fouad; Tastan Bishop, Özlem; Tiffin, Nicki; Ulenga, Nzovu

2016-01-01

The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet. PMID:26627985

Transcriptome Analysis Based on RNA-Seq in Understanding Pathogenic Mechanisms of Diseases and the Immune System of Fish: A Comprehensive Review

PubMed Central

Sudhagar, Arun; El-Matbouli, Mansour

2018-01-01

In recent years, with the advent of next-generation sequencing along with the development of various bioinformatics tools, RNA sequencing (RNA-Seq)-based transcriptome analysis has become much more affordable in the field of biological research. This technique has even opened up avenues to explore the transcriptome of non-model organisms for which a reference genome is not available. This has made fish health researchers march towards this technology to understand pathogenic processes and immune reactions in fish during the event of infection. Recent studies using this technology have altered and updated the previous understanding of many diseases in fish. RNA-Seq has been employed in the understanding of fish pathogens like bacteria, virus, parasites, and oomycetes. Also, it has been helpful in unraveling the immune mechanisms in fish. Additionally, RNA-Seq technology has made its way for future works, such as genetic linkage mapping, quantitative trait analysis, disease-resistant strain or broodstock selection, and the development of effective vaccines and therapies. Until now, there are no reviews that comprehensively summarize the studies which made use of RNA-Seq to explore the mechanisms of infection of pathogens and the defense strategies of fish hosts. This review aims to summarize the contemporary understanding and findings with regard to infectious pathogens and the immune system of fish that have been achieved through RNA-Seq technology. PMID:29342931

Planning bioinformatics workflows using an expert system.

PubMed

Chen, Xiaoling; Chang, Jeffrey T

2017-04-15

Bioinformatic analyses are becoming formidably more complex due to the increasing number of steps required to process the data, as well as the proliferation of methods that can be used in each step. To alleviate this difficulty, pipelines are commonly employed. However, pipelines are typically implemented to automate a specific analysis, and thus are difficult to use for exploratory analyses requiring systematic changes to the software or parameters used. To automate the development of pipelines, we have investigated expert systems. We created the Bioinformatics ExperT SYstem (BETSY) that includes a knowledge base where the capabilities of bioinformatics software is explicitly and formally encoded. BETSY is a backwards-chaining rule-based expert system comprised of a data model that can capture the richness of biological data, and an inference engine that reasons on the knowledge base to produce workflows. Currently, the knowledge base is populated with rules to analyze microarray and next generation sequencing data. We evaluated BETSY and found that it could generate workflows that reproduce and go beyond previously published bioinformatics results. Finally, a meta-investigation of the workflows generated from the knowledge base produced a quantitative measure of the technical burden imposed by each step of bioinformatics analyses, revealing the large number of steps devoted to the pre-processing of data. In sum, an expert system approach can facilitate exploratory bioinformatic analysis by automating the development of workflows, a task that requires significant domain expertise. https://github.com/jefftc/changlab. jeffrey.t.chang@uth.tmc.edu. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Planning bioinformatics workflows using an expert system

PubMed Central

Chen, Xiaoling; Chang, Jeffrey T.

2017-01-01

Abstract Motivation: Bioinformatic analyses are becoming formidably more complex due to the increasing number of steps required to process the data, as well as the proliferation of methods that can be used in each step. To alleviate this difficulty, pipelines are commonly employed. However, pipelines are typically implemented to automate a specific analysis, and thus are difficult to use for exploratory analyses requiring systematic changes to the software or parameters used. Results: To automate the development of pipelines, we have investigated expert systems. We created the Bioinformatics ExperT SYstem (BETSY) that includes a knowledge base where the capabilities of bioinformatics software is explicitly and formally encoded. BETSY is a backwards-chaining rule-based expert system comprised of a data model that can capture the richness of biological data, and an inference engine that reasons on the knowledge base to produce workflows. Currently, the knowledge base is populated with rules to analyze microarray and next generation sequencing data. We evaluated BETSY and found that it could generate workflows that reproduce and go beyond previously published bioinformatics results. Finally, a meta-investigation of the workflows generated from the knowledge base produced a quantitative measure of the technical burden imposed by each step of bioinformatics analyses, revealing the large number of steps devoted to the pre-processing of data. In sum, an expert system approach can facilitate exploratory bioinformatic analysis by automating the development of workflows, a task that requires significant domain expertise. Availability and Implementation: https://github.com/jefftc/changlab Contact: jeffrey.t.chang@uth.tmc.edu PMID:28052928

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

NGS for the Masses: Empowering Biologists to Improve Bioinformatics Productivity ( 7th Annual SFAF Meeting, 2012)

ScienceCinema

Qaadri, Kashef [Biomatters Inc., San Francisco, CA (United States)

2018-05-21

Kashef Qaadri on "NGS for the Masses: Empowering biologists to improve bioinformatic productivity" at the 2012 Sequencing, Finishing, Analysis in the Future Meeting held June 5-7, 2012 in Santa Fe, New Mexico.

NGS for the Masses: Empowering Biologists to Improve Bioinformatics Productivity ( 7th Annual SFAF Meeting, 2012)

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

Qaadri, Kashef

2012-06-01

Kashef Qaadri on "NGS for the Masses: Empowering biologists to improve bioinformatic productivity" at the 2012 Sequencing, Finishing, Analysis in the Future Meeting held June 5-7, 2012 in Santa Fe, New Mexico.

In silico identification and comparative analysis of differentially expressed genes in human and mouse tissues

PubMed Central

Pao, Sheng-Ying; Lin, Win-Li; Hwang, Ming-Jing

2006-01-01

Background Screening for differentially expressed genes on the genomic scale and comparative analysis of the expression profiles of orthologous genes between species to study gene function and regulation are becoming increasingly feasible. Expressed sequence tags (ESTs) are an excellent source of data for such studies using bioinformatic approaches because of the rich libraries and tremendous amount of data now available in the public domain. However, any large-scale EST-based bioinformatics analysis must deal with the heterogeneous, and often ambiguous, tissue and organ terms used to describe EST libraries. Results To deal with the issue of tissue source, in this work, we carefully screened and organized more than 8 million human and mouse ESTs into 157 human and 108 mouse tissue/organ categories, to which we applied an established statistic test using different thresholds of the p value to identify genes differentially expressed in different tissues. Further analysis of the tissue distribution and level of expression of human and mouse orthologous genes showed that tissue-specific orthologs tended to have more similar expression patterns than those lacking significant tissue specificity. On the other hand, a number of orthologs were found to have significant disparity in their expression profiles, hinting at novel functions, divergent regulation, or new ortholog relationships. Conclusion Comprehensive statistics on the tissue-specific expression of human and mouse genes were obtained in this very large-scale, EST-based analysis. These statistical results have been organized into a database, freely accessible at our website , for easy searching of human and mouse tissue-specific genes and for investigating gene expression profiles in the context of comparative genomics. Comparative analysis showed that, although highly tissue-specific genes tend to exhibit similar expression profiles in human and mouse, there are significant exceptions, indicating that orthologous genes, while sharing basic genomic properties, could result in distinct phenotypes. PMID:16626500

Bioinformatics analysis reveals biophysical and evolutionary insights into the 3-nitrotyrosine post-translational modification in the human proteome

PubMed Central

Ng, John Y.; Boelen, Lies; Wong, Jason W. H.

2013-01-01

Protein 3-nitrotyrosine is a post-translational modification that commonly arises from the nitration of tyrosine residues. This modification has been detected under a wide range of pathological conditions and has been shown to alter protein function. Whether 3-nitrotyrosine is important in normal cellular processes or is likely to affect specific biological pathways remains unclear. Using GPS-YNO2, a recently described 3-nitrotyrosine prediction algorithm, a set of predictions for nitrated residues in the human proteome was generated. In total, 9.27 per cent of the proteome was predicted to be nitratable (27 922/301 091). By matching the predictions against a set of curated and experimentally validated 3-nitrotyrosine sites in human proteins, it was found that GPS-YNO2 is able to predict 73.1 per cent (404/553) of these sites. Furthermore, of these sites, 42 have been shown to be nitrated endogenously, with 85.7 per cent (36/42) of these predicted to be nitrated. This demonstrates the feasibility of using the predicted dataset for a whole proteome analysis. A comprehensive bioinformatics analysis was subsequently performed on predicted and all experimentally validated nitrated tyrosine. This found mild but specific biophysical constraints that affect the susceptibility of tyrosine to nitration, and these may play a role in increasing the likelihood of 3-nitrotyrosine to affect processes, including phosphorylation and DNA binding. Furthermore, examining the evolutionary conservation of predicted 3-nitrotyrosine showed that, relative to non-nitrated tyrosine residues, 3-nitrotyrosine residues are generally less conserved. This suggests that, at least in the majority of cases, 3-nitrotyrosine is likely to have a deleterious effect on protein function and less likely to be important in normal cellular function. PMID:23389939

Modern Computational Techniques for the HMMER Sequence Analysis

PubMed Central

2013-01-01

This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies. PMID:25937944

Collaborative Model for Acceleration of Individualized Therapy of Colon Cancer

DTIC Science & Technology

2012-10-01

will be analyzed by one of two methods. The human CRC explants will be assessed (in our CLIA-certified UCCC Pathology Core) using the DxS Scorpion ...also developing ways to select patients for those treatments. Unfortunately the lack of such strategies is what led to thousands of CRC patients with...individualized therapy for patients with KRAS mutant colorectal cancer (CRC) using a comprehensive bioinformatics approach and novel preclinical

A comprehensive iterative approach is highly effective in diagnosing individuals who are exome negative.

PubMed

Shashi, Vandana; Schoch, Kelly; Spillmann, Rebecca; Cope, Heidi; Tan, Queenie K-G; Walley, Nicole; Pena, Loren; McConkie-Rosell, Allyn; Jiang, Yong-Hui; Stong, Nicholas; Need, Anna C; Goldstein, David B

2018-06-15

Sixty to seventy-five percent of individuals with rare and undiagnosed phenotypes remain undiagnosed after exome sequencing (ES). With standard ES reanalysis resolving 10-15% of the ES negatives, further approaches are necessary to maximize diagnoses in these individuals. In 38 ES negative patients an individualized genomic-phenotypic approach was employed utilizing (1) phenotyping; (2) reanalyses of FASTQ files, with innovative bioinformatics; (3) targeted molecular testing; (4) genome sequencing (GS); and (5) conferring of clinical diagnoses when pathognomonic clinical findings occurred. Certain and highly likely diagnoses were made in 18/38 (47%) individuals, including identifying two new developmental disorders. The majority of diagnoses (>70%) were due to our bioinformatics, phenotyping, and targeted testing identifying variants that were undetected or not prioritized on prior ES. GS diagnosed 3/18 individuals with structural variants not amenable to ES. Additionally, tentative diagnoses were made in 3 (8%), and in 5 individuals (13%) candidate genes were identified. Overall, diagnoses/potential leads were identified in 26/38 (68%). Our comprehensive approach to ES negatives maximizes the ES and clinical data for both diagnoses and candidate gene identification, without GS in the majority. This iterative approach is cost-effective and is pertinent to the current conundrum of ES negatives.

Comprehensive and integrative analysis identifies microRNA-106 as a novel non-invasive biomarker for detection of gastric cancer.

PubMed

Peng, Qiliang; Shen, Yi; Lin, Kaisu; Zou, Li; Shen, Yuntian; Zhu, Yaqun

2018-05-15

Recently, accumulating evidences have revealed that microRNA-106 (miR-106) may serve as a non-invasive and cost-effective biomarker in gastric cancer (GC) detection. However, inconsistent results have prevented its application to clinical practice. As a result of this, a comprehensive meta-analysis was conducted to evaluate the diagnostic performance of miR-106 alone and miR-106-related combination markers for GC detection. Meanwhile, an integrative bioinformatics analysis was performed to explore the function of miR-106 at the systems biology level. The results in our work showed that sensitivity of 0.71 (95% CI 0.65-0.76) and specificity of 0.82 (0.72-0.88), with the under area AUC (area under the curve) value of 0.80 (0.76-0.83) for miR-106 alone. Prospectively, miR-106-related combination markers improved the combined sensitivity, specificity and AUC, describing the discriminatory ability of 0.78 (0.65-0.87), 0.83 (0.77-0.89) and 0.88 (0.85-0.90) in the present analysis. Furthermore, targets of miR-106 were obtained and enriched by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, revealing their associations with the occurrence and development of GC. Hub genes and significant modules were identified from the protein-protein interaction networks constructed by miR-106 targets and found closely associated with the initiation and progression of GC again. Our comprehensive and integrative analysis revealed that miR-106 may be suitable as a diagnostic biomarker for GC while microRNA combination biomarkers may provide a new alternative for clinical application. However, it is necessary to conduct large-scale population-based studies and biological experiments to further investigate the diagnostic value of miR-106.

Cake: a bioinformatics pipeline for the integrated analysis of somatic variants in cancer genomes

PubMed Central

Rashid, Mamunur; Robles-Espinoza, Carla Daniela; Rust, Alistair G.; Adams, David J.

2013-01-01

Summary: We have developed Cake, a bioinformatics software pipeline that integrates four publicly available somatic variant-calling algorithms to identify single nucleotide variants with higher sensitivity and accuracy than any one algorithm alone. Cake can be run on a high-performance computer cluster or used as a stand-alone application. Availabilty: Cake is open-source and is available from http://cakesomatic.sourceforge.net/ Contact: da1@sanger.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:23803469

BGDMdocker: a Docker workflow for data mining and visualization of bacterial pan-genomes and biosynthetic gene clusters.

PubMed

Cheng, Gong; Lu, Quan; Ma, Ling; Zhang, Guocai; Xu, Liang; Zhou, Zongshan

2017-01-01

Recently, Docker technology has received increasing attention throughout the bioinformatics community. However, its implementation has not yet been mastered by most biologists; accordingly, its application in biological research has been limited. In order to popularize this technology in the field of bioinformatics and to promote the use of publicly available bioinformatics tools, such as Dockerfiles and Images from communities, government sources, and private owners in the Docker Hub Registry and other Docker-based resources, we introduce here a complete and accurate bioinformatics workflow based on Docker. The present workflow enables analysis and visualization of pan-genomes and biosynthetic gene clusters of bacteria. This provides a new solution for bioinformatics mining of big data from various publicly available biological databases. The present step-by-step guide creates an integrative workflow through a Dockerfile to allow researchers to build their own Image and run Container easily.

BGDMdocker: a Docker workflow for data mining and visualization of bacterial pan-genomes and biosynthetic gene clusters

PubMed Central

Cheng, Gong; Zhang, Guocai; Xu, Liang

2017-01-01

Recently, Docker technology has received increasing attention throughout the bioinformatics community. However, its implementation has not yet been mastered by most biologists; accordingly, its application in biological research has been limited. In order to popularize this technology in the field of bioinformatics and to promote the use of publicly available bioinformatics tools, such as Dockerfiles and Images from communities, government sources, and private owners in the Docker Hub Registry and other Docker-based resources, we introduce here a complete and accurate bioinformatics workflow based on Docker. The present workflow enables analysis and visualization of pan-genomes and biosynthetic gene clusters of bacteria. This provides a new solution for bioinformatics mining of big data from various publicly available biological databases. The present step-by-step guide creates an integrative workflow through a Dockerfile to allow researchers to build their own Image and run Container easily. PMID:29204317

KNIME4NGS: a comprehensive toolbox for next generation sequencing analysis.

PubMed

Hastreiter, Maximilian; Jeske, Tim; Hoser, Jonathan; Kluge, Michael; Ahomaa, Kaarin; Friedl, Marie-Sophie; Kopetzky, Sebastian J; Quell, Jan-Dominik; Mewes, H Werner; Küffner, Robert

2017-05-15

Analysis of Next Generation Sequencing (NGS) data requires the processing of large datasets by chaining various tools with complex input and output formats. In order to automate data analysis, we propose to standardize NGS tasks into modular workflows. This simplifies reliable handling and processing of NGS data, and corresponding solutions become substantially more reproducible and easier to maintain. Here, we present a documented, linux-based, toolbox of 42 processing modules that are combined to construct workflows facilitating a variety of tasks such as DNAseq and RNAseq analysis. We also describe important technical extensions. The high throughput executor (HTE) helps to increase the reliability and to reduce manual interventions when processing complex datasets. We also provide a dedicated binary manager that assists users in obtaining the modules' executables and keeping them up to date. As basis for this actively developed toolbox we use the workflow management software KNIME. See http://ibisngs.github.io/knime4ngs for nodes and user manual (GPLv3 license). robert.kueffner@helmholtz-muenchen.de. Supplementary data are available at Bioinformatics online.

Bioinformatics and the Undergraduate Curriculum

ERIC Educational Resources Information Center

Maloney, Mark; Parker, Jeffrey; LeBlanc, Mark; Woodard, Craig T.; Glackin, Mary; Hanrahan, Michael

2010-01-01

Recent advances involving high-throughput techniques for data generation and analysis have made familiarity with basic bioinformatics concepts and programs a necessity in the biological sciences. Undergraduate students increasingly need training in methods related to finding and retrieving information stored in vast databases. The rapid rise of…

β-adrenergic-stimulated macrophages: Comprehensive localization in the M1–M2 spectrum

PubMed Central

Lamkin, Donald M.; Ho, Hsin-Yun; Ong, Tiffany H.; Kawanishi, Carly K.; Stoffers, Victoria L.; Ahlawat, Nivedita; Ma, Jeffrey C.Y.; Arevalo, Jesusa M. G.; Cole, Steve W.; Sloan, Erica K.

2016-01-01

β-adrenergic signaling can regulate macrophage involvement in several diseases and often produces anti-inflammatory properties in macrophages, which are similar to M2 properties in a dichotomous M1 vs. M2 macrophage taxonomy. However, it is not clear that β-adrenergic-stimulated macrophages may be classified strictly as M2. In this in vitro study, we utilized recently published criteria and transcriptome-wide bioinformatics methods to map the relative polarity of murine β-adrenergic-stimulated macrophages within a wider M1–M2 spectrum. Results show that β-adrenergic-stimulated macrophages did not fit entirely into any one predefined category of the M1–M2 spectrum but did express genes that are representative of some M2 side categories. Moreover, transcript origin analysis of genome-wide transcriptional profiles located β-adrenergic-stimulated macrophages firmly on the M2 side of the M1–M2 spectrum and found active suppression of M1 side gene transcripts. The signal transduction pathways involved were mapped through blocking experiments and bioinformatics analysis of transcription factor binding motifs. M2-promoting effects were mediated specifically through β2-adrenergic receptors and were associated with CREB, C/EBPβ, and ATF transcription factor pathways but not with established M1–M2 STAT pathways. Thus, β-adrenergic-signaling induces a macrophage transcriptome that locates on the M2 side of the M1–M2 spectrum but likely accomplishes this effect through a signaling pathway that is atypical for M2-spectrum macrophages. PMID:27485040

β-Adrenergic-stimulated macrophages: Comprehensive localization in the M1-M2 spectrum.

PubMed

Lamkin, Donald M; Ho, Hsin-Yun; Ong, Tiffany H; Kawanishi, Carly K; Stoffers, Victoria L; Ahlawat, Nivedita; Ma, Jeffrey C Y; Arevalo, Jesusa M G; Cole, Steve W; Sloan, Erica K

2016-10-01

β-Adrenergic signaling can regulate macrophage involvement in several diseases and often produces anti-inflammatory properties in macrophages, which are similar to M2 properties in a dichotomous M1 vs. M2 macrophage taxonomy. However, it is not clear that β-adrenergic-stimulated macrophages may be classified strictly as M2. In this in vitro study, we utilized recently published criteria and transcriptome-wide bioinformatics methods to map the relative polarity of murine β-adrenergic-stimulated macrophages within a wider M1-M2 spectrum. Results show that β-adrenergic-stimulated macrophages did not fit entirely into any one pre-defined category of the M1-M2 spectrum but did express genes that are representative of some M2 side categories. Moreover, transcript origin analysis of genome-wide transcriptional profiles located β-adrenergic-stimulated macrophages firmly on the M2 side of the M1-M2 spectrum and found active suppression of M1 side gene transcripts. The signal transduction pathways involved were mapped through blocking experiments and bioinformatics analysis of transcription factor binding motifs. M2-promoting effects were mediated specifically through β2-adrenergic receptors and were associated with CREB, C/EBPβ, and ATF transcription factor pathways but not with established M1-M2 STAT pathways. Thus, β-adrenergic-signaling induces a macrophage transcriptome that locates on the M2 side of the M1-M2 spectrum but likely accomplishes this effect through a signaling pathway that is atypical for M2-spectrum macrophages. Copyright © 2016 Elsevier Inc. All rights reserved.

DSSR-enhanced visualization of nucleic acid structures in Jmol

PubMed Central

Hanson, Robert M.

2017-01-01

Abstract Sophisticated and interactive visualizations are essential for making sense of the intricate 3D structures of macromolecules. For proteins, secondary structural components are routinely featured in molecular graphics visualizations. However, the field of RNA structural bioinformatics is still lagging behind; for example, current molecular graphics tools lack built-in support even for base pairs, double helices, or hairpin loops. DSSR (Dissecting the Spatial Structure of RNA) is an integrated and automated command-line tool for the analysis and annotation of RNA tertiary structures. It calculates a comprehensive and unique set of features for characterizing RNA, as well as DNA structures. Jmol is a widely used, open-source Java viewer for 3D structures, with a powerful scripting language. JSmol, its reincarnation based on native JavaScript, has a predominant position in the post Java-applet era for web-based visualization of molecular structures. The DSSR-Jmol integration presented here makes salient features of DSSR readily accessible, either via the Java-based Jmol application itself, or its HTML5-based equivalent, JSmol. The DSSR web service accepts 3D coordinate files (in mmCIF or PDB format) initiated from a Jmol or JSmol session and returns DSSR-derived structural features in JSON format. This seamless combination of DSSR and Jmol/JSmol brings the molecular graphics of 3D RNA structures to a similar level as that for proteins, and enables a much deeper analysis of structural characteristics. It fills a gap in RNA structural bioinformatics, and is freely accessible (via the Jmol application or the JSmol-based website http://jmol.x3dna.org). PMID:28472503

Using Kepler for Tool Integration in Microarray Analysis Workflows.

PubMed

Gan, Zhuohui; Stowe, Jennifer C; Altintas, Ilkay; McCulloch, Andrew D; Zambon, Alexander C

Increasing numbers of genomic technologies are leading to massive amounts of genomic data, all of which requires complex analysis. More and more bioinformatics analysis tools are being developed by scientist to simplify these analyses. However, different pipelines have been developed using different software environments. This makes integrations of these diverse bioinformatics tools difficult. Kepler provides an open source environment to integrate these disparate packages. Using Kepler, we integrated several external tools including Bioconductor packages, AltAnalyze, a python-based open source tool, and R-based comparison tool to build an automated workflow to meta-analyze both online and local microarray data. The automated workflow connects the integrated tools seamlessly, delivers data flow between the tools smoothly, and hence improves efficiency and accuracy of complex data analyses. Our workflow exemplifies the usage of Kepler as a scientific workflow platform for bioinformatics pipelines.

Scalability and Validation of Big Data Bioinformatics Software.

PubMed

Yang, Andrian; Troup, Michael; Ho, Joshua W K

2017-01-01

This review examines two important aspects that are central to modern big data bioinformatics analysis - software scalability and validity. We argue that not only are the issues of scalability and validation common to all big data bioinformatics analyses, they can be tackled by conceptually related methodological approaches, namely divide-and-conquer (scalability) and multiple executions (validation). Scalability is defined as the ability for a program to scale based on workload. It has always been an important consideration when developing bioinformatics algorithms and programs. Nonetheless the surge of volume and variety of biological and biomedical data has posed new challenges. We discuss how modern cloud computing and big data programming frameworks such as MapReduce and Spark are being used to effectively implement divide-and-conquer in a distributed computing environment. Validation of software is another important issue in big data bioinformatics that is often ignored. Software validation is the process of determining whether the program under test fulfils the task for which it was designed. Determining the correctness of the computational output of big data bioinformatics software is especially difficult due to the large input space and complex algorithms involved. We discuss how state-of-the-art software testing techniques that are based on the idea of multiple executions, such as metamorphic testing, can be used to implement an effective bioinformatics quality assurance strategy. We hope this review will raise awareness of these critical issues in bioinformatics.

FY02 CBNP Annual Report Input: Bioinformatics Support for CBNP Research and Deployments

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

Slezak, T; Wolinsky, M

2002-10-31

The events of FY01 dynamically reprogrammed the objectives of the CBNP bioinformatics support team, to meet rapidly-changing Homeland Defense needs and requests from other agencies for assistance: Use computational techniques to determine potential unique DNA signature candidates for microbial and viral pathogens of interest to CBNP researcher and to our collaborating partner agencies such as the Centers for Disease Control and Prevention (CDC), U.S. Department of Agriculture (USDA), Department of Defense (DOD), and Food and Drug Administration (FDA). Develop effective electronic screening measures for DNA signatures to reduce the cost and time of wet-bench screening. Build a comprehensive system formore » tracking the development and testing of DNA signatures. Build a chain-of-custody sample tracking system for field deployment of the DNA signatures as part of the BASIS project. Provide computational tools for use by CBNP Biological Foundations researchers.« less

Tools for visually exploring biological networks.

PubMed

Suderman, Matthew; Hallett, Michael

2007-10-15

Many tools exist for visually exploring biological networks including well-known examples such as Cytoscape, VisANT, Pathway Studio and Patika. These systems play a key role in the development of integrative biology, systems biology and integrative bioinformatics. The trend in the development of these tools is to go beyond 'static' representations of cellular state, towards a more dynamic model of cellular processes through the incorporation of gene expression data, subcellular localization information and time-dependent behavior. We provide a comprehensive review of the relative advantages and disadvantages of existing systems with two goals in mind: to aid researchers in efficiently identifying the appropriate existing tools for data visualization; to describe the necessary and realistic goals for the next generation of visualization tools. In view of the first goal, we provide in the Supplementary Material a systematic comparison of more than 35 existing tools in terms of over 25 different features. Supplementary data are available at Bioinformatics online.

Generalized Centroid Estimators in Bioinformatics

PubMed Central

Hamada, Michiaki; Kiryu, Hisanori; Iwasaki, Wataru; Asai, Kiyoshi

2011-01-01

In a number of estimation problems in bioinformatics, accuracy measures of the target problem are usually given, and it is important to design estimators that are suitable to those accuracy measures. However, there is often a discrepancy between an employed estimator and a given accuracy measure of the problem. In this study, we introduce a general class of efficient estimators for estimation problems on high-dimensional binary spaces, which represent many fundamental problems in bioinformatics. Theoretical analysis reveals that the proposed estimators generally fit with commonly-used accuracy measures (e.g. sensitivity, PPV, MCC and F-score) as well as it can be computed efficiently in many cases, and cover a wide range of problems in bioinformatics from the viewpoint of the principle of maximum expected accuracy (MEA). It is also shown that some important algorithms in bioinformatics can be interpreted in a unified manner. Not only the concept presented in this paper gives a useful framework to design MEA-based estimators but also it is highly extendable and sheds new light on many problems in bioinformatics. PMID:21365017

PIERO ontology for analysis of biochemical transformations: effective implementation of reaction information in the IUBMB enzyme list.

PubMed

Kotera, Masaaki; Nishimura, Yosuke; Nakagawa, Zen-ichi; Muto, Ai; Moriya, Yuki; Okamoto, Shinobu; Kawashima, Shuichi; Katayama, Toshiaki; Tokimatsu, Toshiaki; Kanehisa, Minoru; Goto, Susumu

2014-12-01

Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been verified, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been verified. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies. Instead, most of the bioinformatics studies simply use the identifiers of the enzymes, i.e. the Enzyme Commission (EC) numbers. We investigated the actual usage of terminology throughout the Enzyme List, and demonstrated that the partial characteristics of reactions cannot be retrieved by simply using EC numbers. Thus, we developed a novel ontology, named PIERO, for annotating biochemical transformations as follows. First, the terminology describing enzymatic reactions was retrieved from the Enzyme List, and was grouped into those related to overall reactions and biochemical transformations. Consequently, these terms were mapped onto the actual transformations taken from enzymatic reaction equations. This ontology was linked to Gene Ontology (GO) and EC numbers, allowing the extraction of common partial reaction characteristics from given sets of orthologous genes and the elucidation of possible enzymes from the given transformations. Further future development of the PIERO ontology should enhance the Enzyme List to promote the integration of genomics and metabolomics.

Why are they missing? : Bioinformatics characterization of missing human proteins.

PubMed

Elguoshy, Amr; Magdeldin, Sameh; Xu, Bo; Hirao, Yoshitoshi; Zhang, Ying; Kinoshita, Naohiko; Takisawa, Yusuke; Nameta, Masaaki; Yamamoto, Keiko; El-Refy, Ali; El-Fiky, Fawzy; Yamamoto, Tadashi

2016-10-21

NeXtProt is a web-based protein knowledge platform that supports research on human proteins. NeXtProt (release 2015-04-28) lists 20,060 proteins, among them, 3373 canonical proteins (16.8%) lack credible experimental evidence at protein level (PE2:PE5). Therefore, they are considered as "missing proteins". A comprehensive bioinformatic workflow has been proposed to analyze these "missing" proteins. The aims of current study were to analyze physicochemical properties, existence and distribution of the tryptic cleavage sites, and to pinpoint the signature peptides of the missing proteins. Our findings showed that 23.7% of missing proteins were hydrophobic proteins possessing transmembrane domains (TMD). Also, forty missing entries generate tryptic peptides were either out of mass detection range (>30aa) or mapped to different proteins (<9aa). Additionally, 21% of missing entries didn't generate any unique tryptic peptides. In silico endopeptidase combination strategy increased the possibility of missing proteins identification. Coherently, using both mature protein database and signal peptidome database could be a promising option to identify some missing proteins by targeting their unique N-terminal tryptic peptide from mature protein database and or C-terminus tryptic peptide from signal peptidome database. In conclusion, Identification of missing protein requires additional consideration during sample preparation, extraction, digestion and data analysis to increase its incidence of identification. Copyright © 2016. Published by Elsevier B.V.

Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

PubMed

Li, Fuyi; Li, Chen; Marquez-Lago, Tatiana T; Leier, André; Akutsu, Tatsuya; Purcell, Anthony W; Smith, A Ian; Lithgow, Trevor; Daly, Roger J; Song, Jiangning; Chou, Kuo-Chen

2018-06-27

Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/. Supplementary data are available at Bioinformatics online.

ReadXplorer—visualization and analysis of mapped sequences

PubMed Central

Hilker, Rolf; Stadermann, Kai Bernd; Doppmeier, Daniel; Kalinowski, Jörn; Stoye, Jens; Straube, Jasmin; Winnebald, Jörn; Goesmann, Alexander

2014-01-01

Motivation: Fast algorithms and well-arranged visualizations are required for the comprehensive analysis of the ever-growing size of genomic and transcriptomic next-generation sequencing data. Results: ReadXplorer is a software offering straightforward visualization and extensive analysis functions for genomic and transcriptomic DNA sequences mapped on a reference. A unique specialty of ReadXplorer is the quality classification of the read mappings. It is incorporated in all analysis functions and displayed in ReadXplorer's various synchronized data viewers for (i) the reference sequence, its base coverage as (ii) normalizable plot and (iii) histogram, (iv) read alignments and (v) read pairs. ReadXplorer's analysis capability covers RNA secondary structure prediction, single nucleotide polymorphism and deletion–insertion polymorphism detection, genomic feature and general coverage analysis. Especially for RNA-Seq data, it offers differential gene expression analysis, transcription start site and operon detection as well as RPKM value and read count calculations. Furthermore, ReadXplorer can combine or superimpose coverage of different datasets. Availability and implementation: ReadXplorer is available as open-source software at http://www.readxplorer.org along with a detailed manual. Contact: rhilker@mikrobio.med.uni-giessen.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24790157

Proteomic profiling of epileptogenesis in a rat model: Focus on cell stress, extracellular matrix and angiogenesis.

PubMed

Keck, Michael; van Dijk, Roelof Maarten; Deeg, Cornelia A; Kistler, Katharina; Walker, Andreas; von Rüden, Eva-Lotta; Russmann, Vera; Hauck, Stefanie M; Potschka, Heidrun

2018-04-01

Information about epileptogenesis-associated changes in protein expression patterns is of particular interest for future selection of target and biomarker candidates. Bioinformatic analysis of proteomic data sets can increase our knowledge about molecular alterations characterizing the different phases of epilepsy development following an initial epileptogenic insult. Here, we report findings from a focused analysis of proteomic data obtained for the hippocampus and parahippocampal cortex samples collected during the early post-insult phase, latency phase, and chronic phase of a rat model of epileptogenesis. The study focused on proteins functionally associated with cell stress, cell death, extracellular matrix (ECM) remodeling, cell-ECM interaction, cell-cell interaction, angiogenesis, and blood-brain barrier function. The analysis revealed prominent pathway enrichment providing information about the complex expression alterations of the respective protein groups. In the hippocampus, the number of differentially expressed proteins declined over time during the course of epileptogenesis. In contrast, a peak in the regulation of proteins linked with cell stress and death as well as ECM and cell-cell interaction became evident at later phases during epileptogenesis in the parahippocampal cortex. The data sets provide valuable information about the time course of protein expression patterns during epileptogenesis for a series of proteins. Moreover, the findings provide comprehensive novel information about expression alterations of proteins that have not been discussed yet in the context of epileptogenesis. These for instance include different members of the lamin protein family as well as the fermitin family member 2 (FERMT2). Induction of FERMT2 and other selected proteins, CD18 (ITGB2), CD44 and Nucleolin were confirmed by immunohistochemistry. Taken together, focused bioinformatic analysis of the proteomic data sets completes our knowledge about molecular alterations linked with cell death and cellular plasticity during epileptogenesis. The analysis provided can guide future selection of target and biomarker candidates. Copyright © 2018 Elsevier Inc. All rights reserved.

A Novel Homozygous Mutation in SPTBN2 Leads to Spinocerebellar Ataxia in a Consanguineous Family: Report of a New Infantile-Onset Case and Brief Review of the Literature.

PubMed

Al-Muhaizea, Mohammad A; AlMutairi, Faten; Almass, Rawan; AlHarthi, Safinaz; Aldosary, Mazhor S; Alsagob, Maysoon; AlOdaib, Ali; Colak, Dilek; Kaya, Namik

2018-06-01

The objective of this study was the identification of likely genes and mutations associated with an autosomal recessive (AR) rare spinocerebellar ataxia (SCA) phenotype in two patients with infantile onset, from a consanguineous family. Using genome-wide SNP screening, autozygosity mapping, targeted Sanger sequencing and nextgen sequencing, family segregation analysis, and comprehensive neuropanel, we discovered a novel mutation in SPTBN2. Next, we utilized multiple sequence alignment of amino acids from various species as well as crystal structures provided by protein data bank (PDB# 1WYQ and 1WJM) to model the mutation site and its effect on β-III-spectrin. Finally, we used various bioinformatic classifiers to determine pathogenicity of the missense variant. A comprehensive clinical and diagnostic workup including radiological exams were performed on the patients as part of routine patient care. The homozygous missense variant (c.1572C>T; p.R414C) detected in exon 2 was fully segregated in the family and absent in a large ethnic cohort as well as publicly available data sets. Our comprehensive targeted sequencing approaches did not reveal any other likely candidate variants or mutations in both patients. The two male siblings presented with delayed motor milestones and cognitive and learning disability. Brain MRI revealed isolated cerebellar atrophy more marked in midline inferior vermis at ages of 3 and 6.5 years. Sequence alignments of the amino acids for β-III-spectrin indicated that the arginine at 414 is highly conserved among various species and located towards the end of first spectrin repeat domain. Inclusive bioinformatic analysis predicted that the variant is to be damaging and disease causing. In addition to the novel mutation, a brief literature review of the previously reported mutations as well as clinical comparison of the cases were also presented. Our study reviews the previously reported SPTBN2 mutations and cases. Moreover, the novel mutation, p.R414C, adds up to the literature for the infantile-onset form of autosomal recessive ataxia associated with SPTBN2. Previously, few SPTBN2 recessive mutations have been reported in humans. Animal models especially the β-III -/- mouse model provided insights into early coordination and gait deficit suggestive of loss-of-function. It is expected to see more recessive SPTBN2 mutations appearing in the literature during the upcoming years.

Interoperability of GADU in using heterogeneous Grid resources for bioinformatics applications.

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

Sulakhe, D.; Rodriguez, A.; Wilde, M.

2008-03-01

Bioinformatics tools used for efficient and computationally intensive analysis of genetic sequences require large-scale computational resources to accommodate the growing data. Grid computational resources such as the Open Science Grid and TeraGrid have proved useful for scientific discovery. The genome analysis and database update system (GADU) is a high-throughput computational system developed to automate the steps involved in accessing the Grid resources for running bioinformatics applications. This paper describes the requirements for building an automated scalable system such as GADU that can run jobs on different Grids. The paper describes the resource-independent configuration of GADU using the Pegasus-based virtual datamore » system that makes high-throughput computational tools interoperable on heterogeneous Grid resources. The paper also highlights the features implemented to make GADU a gateway to computationally intensive bioinformatics applications on the Grid. The paper will not go into the details of problems involved or the lessons learned in using individual Grid resources as it has already been published in our paper on genome analysis research environment (GNARE) and will focus primarily on the architecture that makes GADU resource independent and interoperable across heterogeneous Grid resources.« less

PipeCraft: Flexible open-source toolkit for bioinformatics analysis of custom high-throughput amplicon sequencing data.

PubMed

Anslan, Sten; Bahram, Mohammad; Hiiesalu, Indrek; Tedersoo, Leho

2017-11-01

High-throughput sequencing methods have become a routine analysis tool in environmental sciences as well as in public and private sector. These methods provide vast amount of data, which need to be analysed in several steps. Although the bioinformatics may be applied using several public tools, many analytical pipelines allow too few options for the optimal analysis for more complicated or customized designs. Here, we introduce PipeCraft, a flexible and handy bioinformatics pipeline with a user-friendly graphical interface that links several public tools for analysing amplicon sequencing data. Users are able to customize the pipeline by selecting the most suitable tools and options to process raw sequences from Illumina, Pacific Biosciences, Ion Torrent and Roche 454 sequencing platforms. We described the design and options of PipeCraft and evaluated its performance by analysing the data sets from three different sequencing platforms. We demonstrated that PipeCraft is able to process large data sets within 24 hr. The graphical user interface and the automated links between various bioinformatics tools enable easy customization of the workflow. All analytical steps and options are recorded in log files and are easily traceable. © 2017 John Wiley & Sons Ltd.

Survey of MapReduce frame operation in bioinformatics.

PubMed

Zou, Quan; Li, Xu-Bin; Jiang, Wen-Rui; Lin, Zi-Yu; Li, Gui-Lin; Chen, Ke

2014-07-01

Bioinformatics is challenged by the fact that traditional analysis tools have difficulty in processing large-scale data from high-throughput sequencing. The open source Apache Hadoop project, which adopts the MapReduce framework and a distributed file system, has recently given bioinformatics researchers an opportunity to achieve scalable, efficient and reliable computing performance on Linux clusters and on cloud computing services. In this article, we present MapReduce frame-based applications that can be employed in the next-generation sequencing and other biological domains. In addition, we discuss the challenges faced by this field as well as the future works on parallel computing in bioinformatics. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Collaborative Model for Acceleration of Individualized Therapy of Colon Cancer

DTIC Science & Technology

2012-10-01

human CRC explants will be assessed (in our CLIA-certified UCCC Pathology Core) using the DxS Scorpion method (DxS, Manchester, UK) according to the...for those treatments. Unfortunately the lack of such strategies is what led to thousands of CRC patients with KRAS mutations being treated with...KRAS mutant colorectal cancer (CRC) using a comprehensive bioinformatics approach and novel preclinical models of human CRC. This proposal has the

A case study of tuning MapReduce for efficient Bioinformatics in the cloud

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

Shi, Lizhen; Wang, Zhong; Yu, Weikuan

The combination of the Hadoop MapReduce programming model and cloud computing allows biological scientists to analyze next-generation sequencing (NGS) data in a timely and cost-effective manner. Cloud computing platforms remove the burden of IT facility procurement and management from end users and provide ease of access to Hadoop clusters. However, biological scientists are still expected to choose appropriate Hadoop parameters for running their jobs. More importantly, the available Hadoop tuning guidelines are either obsolete or too general to capture the particular characteristics of bioinformatics applications. In this paper, we aim to minimize the cloud computing cost spent on bioinformatics datamore » analysis by optimizing the extracted significant Hadoop parameters. When using MapReduce-based bioinformatics tools in the cloud, the default settings often lead to resource underutilization and wasteful expenses. We choose k-mer counting, a representative application used in a large number of NGS data analysis tools, as our study case. Experimental results show that, with the fine-tuned parameters, we achieve a total of 4× speedup compared with the original performance (using the default settings). Finally, this paper presents an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud, and documents the key parameters that could lead to significant performance benefits.« less

Visualising "Junk" DNA through Bioinformatics

ERIC Educational Resources Information Center

Elwess, Nancy L.; Latourelle, Sandra M.; Cauthorn, Olivia

2005-01-01

One of the hottest areas of science today is the field in which biology, information technology,and computer science are merged into a single discipline called bioinformatics. This field enables the discovery and analysis of biological data, including nucleotide and amino acid sequences that are easily accessed through the use of computers. As…

Bioinformatics and Microarray Data Analysis on the Cloud.

PubMed

Calabrese, Barbara; Cannataro, Mario

2016-01-01

High-throughput platforms such as microarray, mass spectrometry, and next-generation sequencing are producing an increasing volume of omics data that needs large data storage and computing power. Cloud computing offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and thus, it may represent the key technology for facing those issues. In fact, in the recent years it has been adopted for the deployment of different bioinformatics solutions and services both in academia and in the industry. Although this, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This chapter reviews main academic and industrial cloud-based bioinformatics solutions; with a special focus on microarray data analysis solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.

eMZed: an open source framework in Python for rapid and interactive development of LC/MS data analysis workflows.

PubMed

Kiefer, Patrick; Schmitt, Uwe; Vorholt, Julia A

2013-04-01

The Python-based, open-source eMZed framework was developed for mass spectrometry (MS) users to create tailored workflows for liquid chromatography (LC)/MS data analysis. The goal was to establish a unique framework with comprehensive basic functionalities that are easy to apply and allow for the extension and modification of the framework in a straightforward manner. eMZed supports the iterative development and prototyping of individual evaluation strategies by providing a computing environment and tools for inspecting and modifying underlying LC/MS data. The framework specifically addresses non-expert programmers, as it requires only basic knowledge of Python and relies largely on existing successful open-source software, e.g. OpenMS. The framework eMZed and its documentation are freely available at http://emzed.biol.ethz.ch/. eMZed is published under the GPL 3.0 license, and an online discussion group is available at https://groups.google.com/group/emzed-users. Supplementary data are available at Bioinformatics online.

Genome-wide analysis of TCP family in tobacco.

PubMed

Chen, L; Chen, Y Q; Ding, A M; Chen, H; Xia, F; Wang, W F; Sun, Y H

2016-05-23

The TCP family is a transcription factor family, members of which are extensively involved in plant growth and development as well as in signal transduction in the response against many physiological and biochemical stimuli. In the present study, 61 TCP genes were identified in tobacco (Nicotiana tabacum) genome. Bioinformatic methods were employed for predicting and analyzing the gene structure, gene expression, phylogenetic analysis, and conserved domains of TCP proteins in tobacco. The 61 NtTCP genes were divided into three diverse groups, based on the division of TCP genes in tomato and Arabidopsis, and the results of the conserved domain and sequence analyses further confirmed the classification of the NtTCP genes. The expression pattern of NtTCP also demonstrated that majority of these genes play important roles in all the tissues, while some special genes exercise their functions only in specific tissues. In brief, the comprehensive and thorough study of the TCP family in other plants provides sufficient resources for studying the structure and functions of TCPs in tobacco.

Transcriptional profiling of Medicago truncatula meristematic root cells

PubMed Central

Holmes, Peta; Goffard, Nicolas; Weiller, Georg F; Rolfe, Barry G; Imin, Nijat

2008-01-01

Background The root apical meristem of crop and model legume Medicago truncatula is a significantly different stem cell system to that of the widely studied model plant species Arabidopsis thaliana. In this study we used the Affymetrix Medicago GeneChip® to compare the transcriptomes of meristem and non-meristematic root to identify root meristem specific candidate genes. Results Using mRNA from root meristem and non-meristem we were able to identify 324 and 363 transcripts differentially expressed from the two regions. With bioinformatics tools developed to functionally annotate the Medicago genome array we could identify significant changes in metabolism, signalling and the differentially expression of 55 transcription factors in meristematic and non-meristematic roots. Conclusion This is the first comprehensive analysis of M. truncatula root meristem cells using this genome array. This data will facilitate the mapping of regulatory and metabolic networks involved in the open root meristem of M. truncatula and provides candidates for functional analysis. PMID:18302802

PredictProtein—an open resource for online prediction of protein structural and functional features

PubMed Central

Yachdav, Guy; Kloppmann, Edda; Kajan, Laszlo; Hecht, Maximilian; Goldberg, Tatyana; Hamp, Tobias; Hönigschmid, Peter; Schafferhans, Andrea; Roos, Manfred; Bernhofer, Michael; Richter, Lothar; Ashkenazy, Haim; Punta, Marco; Schlessinger, Avner; Bromberg, Yana; Schneider, Reinhard; Vriend, Gerrit; Sander, Chris; Ben-Tal, Nir; Rost, Burkhard

2014-01-01

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org. PMID:24799431

A bioinformatics expert system linking functional data to anatomical outcomes in limb regeneration

PubMed Central

Lobo, Daniel; Feldman, Erica B.; Shah, Michelle; Malone, Taylor J.

2014-01-01

Abstract Amphibians and molting arthropods have the remarkable capacity to regenerate amputated limbs, as described by an extensive literature of experimental cuts, amputations, grafts, and molecular techniques. Despite a rich history of experimental effort, no comprehensive mechanistic model exists that can account for the pattern regulation observed in these experiments. While bioinformatics algorithms have revolutionized the study of signaling pathways, no such tools have heretofore been available to assist scientists in formulating testable models of large‐scale morphogenesis that match published data in the limb regeneration field. Major barriers to preventing an algorithmic approach are the lack of formal descriptions for experimental regenerative information and a repository to centralize storage and mining of functional data on limb regeneration. Establishing a new bioinformatics of shape would significantly accelerate the discovery of key insights into the mechanisms that implement complex regeneration. Here, we describe a novel mathematical ontology for limb regeneration to unambiguously encode phenotype, manipulation, and experiment data. Based on this formalism, we present the first centralized formal database of published limb regeneration experiments together with a user‐friendly expert system tool to facilitate its access and mining. These resources are freely available for the community and will assist both human biologists and artificial intelligence systems to discover testable, mechanistic models of limb regeneration. PMID:25729585

Tools and data services registry: a community effort to document bioinformatics resources

PubMed Central

Ison, Jon; Rapacki, Kristoffer; Ménager, Hervé; Kalaš, Matúš; Rydza, Emil; Chmura, Piotr; Anthon, Christian; Beard, Niall; Berka, Karel; Bolser, Dan; Booth, Tim; Bretaudeau, Anthony; Brezovsky, Jan; Casadio, Rita; Cesareni, Gianni; Coppens, Frederik; Cornell, Michael; Cuccuru, Gianmauro; Davidsen, Kristian; Vedova, Gianluca Della; Dogan, Tunca; Doppelt-Azeroual, Olivia; Emery, Laura; Gasteiger, Elisabeth; Gatter, Thomas; Goldberg, Tatyana; Grosjean, Marie; Grüning, Björn; Helmer-Citterich, Manuela; Ienasescu, Hans; Ioannidis, Vassilios; Jespersen, Martin Closter; Jimenez, Rafael; Juty, Nick; Juvan, Peter; Koch, Maximilian; Laibe, Camille; Li, Jing-Woei; Licata, Luana; Mareuil, Fabien; Mičetić, Ivan; Friborg, Rune Møllegaard; Moretti, Sebastien; Morris, Chris; Möller, Steffen; Nenadic, Aleksandra; Peterson, Hedi; Profiti, Giuseppe; Rice, Peter; Romano, Paolo; Roncaglia, Paola; Saidi, Rabie; Schafferhans, Andrea; Schwämmle, Veit; Smith, Callum; Sperotto, Maria Maddalena; Stockinger, Heinz; Vařeková, Radka Svobodová; Tosatto, Silvio C.E.; de la Torre, Victor; Uva, Paolo; Via, Allegra; Yachdav, Guy; Zambelli, Federico; Vriend, Gert; Rost, Burkhard; Parkinson, Helen; Løngreen, Peter; Brunak, Søren

2016-01-01

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools. PMID:26538599

Bioinformatics of cardiovascular miRNA biology.

PubMed

Kunz, Meik; Xiao, Ke; Liang, Chunguang; Viereck, Janika; Pachel, Christina; Frantz, Stefan; Thum, Thomas; Dandekar, Thomas

2015-12-01

MicroRNAs (miRNAs) are small ~22 nucleotide non-coding RNAs and are highly conserved among species. Moreover, miRNAs regulate gene expression of a large number of genes associated with important biological functions and signaling pathways. Recently, several miRNAs have been found to be associated with cardiovascular diseases. Thus, investigating the complex regulatory effect of miRNAs may lead to a better understanding of their functional role in the heart. To achieve this, bioinformatics approaches have to be coupled with validation and screening experiments to understand the complex interactions of miRNAs with the genome. This will boost the subsequent development of diagnostic markers and our understanding of the physiological and therapeutic role of miRNAs in cardiac remodeling. In this review, we focus on and explain different bioinformatics strategies and algorithms for the identification and analysis of miRNAs and their regulatory elements to better understand cardiac miRNA biology. Starting with the biogenesis of miRNAs, we present approaches such as LocARNA and miRBase for combining sequence and structure analysis including phylogenetic comparisons as well as detailed analysis of RNA folding patterns, functional target prediction, signaling pathway as well as functional analysis. We also show how far bioinformatics helps to tackle the unprecedented level of complexity and systemic effects by miRNA, underlining the strong therapeutic potential of miRNA and miRNA target structures in cardiovascular disease. In addition, we discuss drawbacks and limitations of bioinformatics algorithms and the necessity of experimental approaches for miRNA target identification. This article is part of a Special Issue entitled 'Non-coding RNAs'. Copyright © 2014 Elsevier Ltd. All rights reserved.

WGCNA: an R package for weighted correlation network analysis.

PubMed

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

WGCNA: an R package for weighted correlation network analysis

PubMed Central

Langfelder, Peter; Horvath, Steve

2008-01-01

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at . PMID:19114008

Relax with CouchDB - Into the non-relational DBMS era of Bioinformatics

PubMed Central

Manyam, Ganiraju; Payton, Michelle A.; Roth, Jack A.; Abruzzo, Lynne V.; Coombes, Kevin R.

2012-01-01

With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services. PMID:22609849

Agents in bioinformatics, computational and systems biology.

PubMed

Merelli, Emanuela; Armano, Giuliano; Cannata, Nicola; Corradini, Flavio; d'Inverno, Mark; Doms, Andreas; Lord, Phillip; Martin, Andrew; Milanesi, Luciano; Möller, Steffen; Schroeder, Michael; Luck, Michael

2007-01-01

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

A comprehensive molecular cytogenetic analysis of chromosome rearrangements in gibbons

PubMed Central

Capozzi, Oronzo; Carbone, Lucia; Stanyon, Roscoe R.; Marra, Annamaria; Yang, Fengtang; Whelan, Christopher W.; de Jong, Pieter J.; Rocchi, Mariano; Archidiacono, Nicoletta

2012-01-01

Chromosome rearrangements in small apes are up to 20 times more frequent than in most mammals. Because of their complexity, the full extent of chromosome evolution in these hominoids is not yet fully documented. However, previous work with array painting, BAC-FISH, and selective sequencing in two of the four karyomorphs has shown that high-resolution methods can precisely define chromosome breakpoints and map the complex flow of evolutionary chromosome rearrangements. Here we use these tools to precisely define the rearrangements that have occurred in the remaining two karyomorphs, genera Symphalangus (2n = 50) and Hoolock (2n = 38). This research provides the most comprehensive insight into the evolutionary origins of chromosome rearrangements involved in transforming small apes genome. Bioinformatics analyses of the human–gibbon synteny breakpoints revealed association with transposable elements and segmental duplications, providing some insight into the mechanisms that might have promoted rearrangements in small apes. In the near future, the comparison of gibbon genome sequences will provide novel insights to test hypotheses concerning the mechanisms of chromosome evolution. The precise definition of synteny block boundaries and orientation, chromosomal fusions, and centromere repositioning events presented here will facilitate genome sequence assembly for these close relatives of humans. PMID:22892276

CyanoEXpress: A web database for exploration and visualisation of the integrated transcriptome of cyanobacterium Synechocystis sp. PCC6803.

PubMed

Hernandez-Prieto, Miguel A; Futschik, Matthias E

2012-01-01

Synechocystis sp. PCC6803 is one of the best studied cyanobacteria and an important model organism for our understanding of photosynthesis. The early availability of its complete genome sequence initiated numerous transcriptome studies, which have generated a wealth of expression data. Analysis of the accumulated data can be a powerful tool to study transcription in a comprehensive manner and to reveal underlying regulatory mechanisms, as well as to annotate genes whose functions are yet unknown. However, use of divergent microarray platforms, as well as distributed data storage make meta-analyses of Synechocystis expression data highly challenging, especially for researchers with limited bioinformatic expertise and resources. To facilitate utilisation of the accumulated expression data for a wider research community, we have developed CyanoEXpress, a web database for interactive exploration and visualisation of transcriptional response patterns in Synechocystis. CyanoEXpress currently comprises expression data for 3073 genes and 178 environmental and genetic perturbations obtained in 31 independent studies. At present, CyanoEXpress constitutes the most comprehensive collection of expression data available for Synechocystis and can be freely accessed. The database is available for free at http://cyanoexpress.sysbiolab.eu.

Nutritional Lipidomics: Molecular Metabolism, Analytics, and Diagnostics

PubMed Central

Smilowitz, Jennifer T.; Zivkovic, Angela M.; Wan, Yu-Jui Yvonne; Watkins, Steve M.; Nording, Malin L.; Hammock, Bruce D.; German, J. Bruce

2013-01-01

The field of lipidomics is providing nutritional science a more comprehensive view of lipid intermediates. Lipidomics research takes advantage of the increase in accuracy and sensitivity of mass detection of mass spectrometry with new bioinformatics toolsets to characterize the structures and abundances of complex lipids. Yet, translating lipidomics to practice via nutritional interventions is still in its infancy. No single instrumentation platform is able to solve the varying analytical challenges of the different molecular lipid species. Biochemical pathways of lipid metabolism remain incomplete and the tools to map lipid compositional data to pathways are still being assembled. Biology itself is dauntingly complex and simply separating biological structures remains a key challenge to lipidomics. Nonetheless, the strategy of combining tandem analytical methods to perform the sensitive, high-throughput, quantitative and comprehensive analysis of lipid metabolites of very large numbers of molecules is poised to drive the field forward rapidly. Among the next steps for nutrition to understand the changes in structures, compositions and function of lipid biomolecules in response to diet is to describe their distribution within discrete functional compartments-lipoproteins. Additionally, lipidomics must tackle the task of assigning the functions of lipids as signaling molecules, nutrient sensors, and intermediates of metabolic pathways. PMID:23818328

The eBioKit, a stand-alone educational platform for bioinformatics.

PubMed

Hernández-de-Diego, Rafael; de Villiers, Etienne P; Klingström, Tomas; Gourlé, Hadrien; Conesa, Ana; Bongcam-Rudloff, Erik

2017-09-01

Bioinformatics skills have become essential for many research areas; however, the availability of qualified researchers is usually lower than the demand and training to increase the number of able bioinformaticians is an important task for the bioinformatics community. When conducting training or hands-on tutorials, the lack of control over the analysis tools and repositories often results in undesirable situations during training, as unavailable online tools or version conflicts may delay, complicate, or even prevent the successful completion of a training event. The eBioKit is a stand-alone educational platform that hosts numerous tools and databases for bioinformatics research and allows training to take place in a controlled environment. A key advantage of the eBioKit over other existing teaching solutions is that all the required software and databases are locally installed on the system, significantly reducing the dependence on the internet. Furthermore, the architecture of the eBioKit has demonstrated itself to be an excellent balance between portability and performance, not only making the eBioKit an exceptional educational tool but also providing small research groups with a platform to incorporate bioinformatics analysis in their research. As a result, the eBioKit has formed an integral part of training and research performed by a wide variety of universities and organizations such as the Pan African Bioinformatics Network (H3ABioNet) as part of the initiative Human Heredity and Health in Africa (H3Africa), the Southern Africa Network for Biosciences (SAnBio) initiative, the Biosciences eastern and central Africa (BecA) hub, and the International Glossina Genome Initiative.

The eBioKit, a stand-alone educational platform for bioinformatics

PubMed Central

Conesa, Ana; Bongcam-Rudloff, Erik

2017-01-01

Bioinformatics skills have become essential for many research areas; however, the availability of qualified researchers is usually lower than the demand and training to increase the number of able bioinformaticians is an important task for the bioinformatics community. When conducting training or hands-on tutorials, the lack of control over the analysis tools and repositories often results in undesirable situations during training, as unavailable online tools or version conflicts may delay, complicate, or even prevent the successful completion of a training event. The eBioKit is a stand-alone educational platform that hosts numerous tools and databases for bioinformatics research and allows training to take place in a controlled environment. A key advantage of the eBioKit over other existing teaching solutions is that all the required software and databases are locally installed on the system, significantly reducing the dependence on the internet. Furthermore, the architecture of the eBioKit has demonstrated itself to be an excellent balance between portability and performance, not only making the eBioKit an exceptional educational tool but also providing small research groups with a platform to incorporate bioinformatics analysis in their research. As a result, the eBioKit has formed an integral part of training and research performed by a wide variety of universities and organizations such as the Pan African Bioinformatics Network (H3ABioNet) as part of the initiative Human Heredity and Health in Africa (H3Africa), the Southern Africa Network for Biosciences (SAnBio) initiative, the Biosciences eastern and central Africa (BecA) hub, and the International Glossina Genome Initiative. PMID:28910280

Open discovery: An integrated live Linux platform of Bioinformatics tools.

PubMed

Vetrivel, Umashankar; Pilla, Kalabharath

2008-01-01

Historically, live linux distributions for Bioinformatics have paved way for portability of Bioinformatics workbench in a platform independent manner. Moreover, most of the existing live Linux distributions limit their usage to sequence analysis and basic molecular visualization programs and are devoid of data persistence. Hence, open discovery - a live linux distribution has been developed with the capability to perform complex tasks like molecular modeling, docking and molecular dynamics in a swift manner. Furthermore, it is also equipped with complete sequence analysis environment and is capable of running windows executable programs in Linux environment. Open discovery portrays the advanced customizable configuration of fedora, with data persistency accessible via USB drive or DVD. The Open Discovery is distributed free under Academic Free License (AFL) and can be downloaded from http://www.OpenDiscovery.org.in.

Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package

PubMed Central

2012-01-01

Background Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. Results In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Conclusions Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org. PMID:23281941

Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package.

PubMed

El-Kalioby, Mohamed; Abouelhoda, Mohamed; Krüger, Jan; Giegerich, Robert; Sczyrba, Alexander; Wall, Dennis P; Tonellato, Peter

2012-01-01

Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org.

Comparative analysis of sterol acquisition in the oomycetes Saprolegnia parasitica and Phytophthora infestans.

PubMed

Dahlin, Paul; Srivastava, Vaibhav; Ekengren, Sophia; McKee, Lauren S; Bulone, Vincent

2017-01-01

The oomycete class includes pathogens of animals and plants which are responsible for some of the most significant global losses in agriculture and aquaculture. There is a need to replace traditional chemical means of controlling oomycete growth with more targeted approaches, and the inhibition of sterol synthesis is one promising area. To better direct these efforts, we have studied sterol acquisition in two model organisms: the sterol-autotrophic Saprolegnia parasitica, and the sterol-heterotrophic Phytophthora infestans. We first present a comprehensive reconstruction of a likely sterol synthesis pathway for S. parasitica, causative agent of the disease saprolegniasis in fish. This pathway shows multiple potential routes of sterol synthesis, and draws on several avenues of new evidence: bioinformatic mining for genes with sterol-related functions, expression analysis of these genes, and analysis of the sterol profiles in mycelium grown in different media. Additionally, we explore the extent to which P. infestans, which causes the late blight in potato, can modify exogenously provided sterols. We consider whether the two very different approaches to sterol acquisition taken by these pathogens represent any specific survival advantages or potential drug targets.

Data Standards for Flow Cytometry

PubMed Central

SPIDLEN, JOSEF; GENTLEMAN, ROBERT C.; HAALAND, PERRY D.; LANGILLE, MORGAN; MEUR, NOLWENN LE; OCHS, MICHAEL F.; SCHMITT, CHARLES; SMITH, CLAYTON A.; TREISTER, ADAM S.; BRINKMAN, RYAN R.

2009-01-01

Flow cytometry (FCM) is an analytical tool widely used for cancer and HIV/AIDS research, and treatment, stem cell manipulation and detecting microorganisms in environmental samples. Current data standards do not capture the full scope of FCM experiments and there is a demand for software tools that can assist in the exploration and analysis of large FCM datasets. We are implementing a standardized approach to capturing, analyzing, and disseminating FCM data that will facilitate both more complex analyses and analysis of datasets that could not previously be efficiently studied. Initial work has focused on developing a community-based guideline for recording and reporting the details of FCM experiments. Open source software tools that implement this standard are being created, with an emphasis on facilitating reproducible and extensible data analyses. As well, tools for electronic collaboration will assist the integrated access and comprehension of experiments to empower users to collaborate on FCM analyses. This coordinated, joint development of bioinformatics standards and software tools for FCM data analysis has the potential to greatly facilitate both basic and clinical research—impacting a notably diverse range of medical and environmental research areas. PMID:16901228

A Critical Analysis of Assessment Quality in Genomics and Bioinformatics Education Research

ERIC Educational Resources Information Center

Campbell, Chad E.; Nehm, Ross H.

2013-01-01

The growing importance of genomics and bioinformatics methods and paradigms in biology has been accompanied by an explosion of new curricula and pedagogies. An important question to ask about these educational innovations is whether they are having a meaningful impact on students' knowledge, attitudes, or skills. Although assessments are…

Comprehensive genetic assessment of the ESR1 locus identifies a risk region for endometrial cancer.

PubMed

O'Mara, Tracy A; Glubb, Dylan M; Painter, Jodie N; Cheng, Timothy; Dennis, Joe; Attia, John; Holliday, Elizabeth G; McEvoy, Mark; Scott, Rodney J; Ashton, Katie; Proietto, Tony; Otton, Geoffrey; Shah, Mitul; Ahmed, Shahana; Healey, Catherine S; Gorman, Maggie; Martin, Lynn; Hodgson, Shirley; Fasching, Peter A; Hein, Alexander; Beckmann, Matthias W; Ekici, Arif B; Hall, Per; Czene, Kamila; Darabi, Hatef; Li, Jingmei; Dürst, Matthias; Runnebaum, Ingo; Hillemanns, Peter; Dörk, Thilo; Lambrechts, Diether; Depreeuw, Jeroen; Annibali, Daniela; Amant, Frederic; Zhao, Hui; Goode, Ellen L; Dowdy, Sean C; Fridley, Brooke L; Winham, Stacey J; Salvesen, Helga B; Njølstad, Tormund S; Trovik, Jone; Werner, Henrica M J; Tham, Emma; Liu, Tao; Mints, Miriam; Bolla, Manjeet K; Michailidou, Kyriaki; Tyrer, Jonathan P; Wang, Qin; Hopper, John L; Peto, Julian; Swerdlow, Anthony J; Burwinkel, Barbara; Brenner, Hermann; Meindl, Alfons; Brauch, Hiltrud; Lindblom, Annika; Chang-Claude, Jenny; Couch, Fergus J; Giles, Graham G; Kristensen, Vessela N; Cox, Angela; Pharoah, Paul D P; Dunning, Alison M; Tomlinson, Ian; Easton, Douglas F; Thompson, Deborah J; Spurdle, Amanda B

2015-10-01

Excessive exposure to estrogen is a well-established risk factor for endometrial cancer (EC), particularly for cancers of endometrioid histology. The physiological function of estrogen is primarily mediated by estrogen receptor alpha, encoded by ESR1. Consequently, several studies have investigated whether variation at the ESR1 locus is associated with risk of EC, with conflicting results. We performed comprehensive fine-mapping analyses of 3633 genotyped and imputed single nucleotide polymorphisms (SNPs) in 6607 EC cases and 37 925 controls. There was evidence of an EC risk signal located at a potential alternative promoter of the ESR1 gene (lead SNP rs79575945, P=1.86×10(-5)), which was stronger for cancers of endometrioid subtype (P=3.76×10(-6)). Bioinformatic analysis suggests that this risk signal is in a functionally important region targeting ESR1, and eQTL analysis found that rs79575945 was associated with expression of SYNE1, a neighbouring gene. In summary, we have identified a single EC risk signal located at ESR1, at study-wide significance. Given SNPs located at this locus have been associated with risk for breast cancer, also a hormonally driven cancer, this study adds weight to the rationale for performing informed candidate fine-scale genetic studies across cancer types. © 2015 Society for Endocrinology.

Identification of a Novel Rhabdovirus in Spodoptera frugiperda Cell Lines

PubMed Central

Ma, Hailun; Galvin, Teresa A.; Glasner, Dustin R.; Shaheduzzaman, Syed

2014-01-01

ABSTRACT The Sf9 cell line, derived from Spodoptera frugiperda, is used as a cell substrate for biological products, and no viruses have been reported in this cell line after extensive testing. We used degenerate PCR assays and massively parallel sequencing (MPS) to identify a novel RNA virus belonging to the order Mononegavirales in Sf9 cells. Sequence analysis of the assembled virus genome showed the presence of five open reading frames (ORFs) corresponding to the genes for the N, P, M, G, and L proteins in other rhabdoviruses and an unknown ORF of 111 amino acids located between the G- and L-protein genes. BLAST searches indicated that the S. frugiperda rhabdovirus (Sf-rhabdovirus) was related in a limited region of the L-protein gene to Taastrup virus, a newly discovered member of the Mononegavirales from a leafhopper (Hemiptera), and also to plant rhabdoviruses, particularly in the genus Cytorhabdovirus. Phylogenetic analysis of sequences in the L-protein gene indicated that Sf-rhabdovirus is a novel virus that branched with Taastrup virus. Rhabdovirus morphology was confirmed by transmission electron microscopy of filtered supernatant samples from Sf9 cells. Infectivity studies indicated potential transient infection by Sf-rhabdovirus in other insect cell lines, but there was no evidence of entry or virus replication in human cell lines. Sf-rhabdovirus sequences were also found in the Sf21 parental cell line of Sf9 cells but not in other insect cell lines, such as BT1-TN-5B1-4 (Tn5; High Five) cells and Schneider's Drosophila line 2 [D.Mel.(2); SL2] cells, indicating a species-specific infection. The results indicate that conventional methods may be complemented by state-of-the-art technologies with extensive bioinformatics analysis for identification of novel viruses. IMPORTANCE The Spodoptera frugiperda Sf9 cell line is used as a cell substrate for the development and manufacture of biological products. Extensive testing has not previously identified any viruses in this cell line. This paper reports on the identification and characterization of a novel rhabdovirus in Sf9 cells. This was accomplished through the use of next-generation sequencing platforms, de novo assembly tools, and extensive bioinformatics analysis. Rhabdovirus identification was further confirmed by transmission electron microscopy. Infectivity studies showed the lack of replication of Sf-rhabdovirus in human cell lines. The overall study highlights the use of a combinatorial testing approach including conventional methods and new technologies for evaluation of cell lines for unexpected viruses and use of comprehensive bioinformatics strategies for obtaining confident next-generation sequencing results. PMID:24672045

Molecular genetics of Leber congenital amaurosis in Chinese: New data from 66 probands and mutation overview of 159 probands.

PubMed

Xu, Yan; Xiao, Xueshan; Li, Shiqiang; Jia, Xiaoyun; Xin, Wei; Wang, Panfeng; Sun, Wenmin; Huang, Li; Guo, Xiangming; Zhang, Qingjiong

2016-08-01

Leber congenital amaurosis (LCA) is the most severe form of inherited retinal dystrophy. We have previously performed a mutational analysis of the known LCA-associated genes in probands with LCA by both Sanger and whole exome sequencing. In this study, whole exome sequencing was carried out on 66 new probabds with LCA. In conjunction with these data, the present study provides a comprehensive analysis of the spectrum and frequency of all known genes associated with retinal dystrophy in a total of 159 Chinese probands with LCA. The known genes responsible for all forms hereditary retinal dystrophy were included based on information from RetNet. The candidate variants were filtered by bioinformatics analysis and confirmed by Sanger sequencing. Potentially causative mutations were further validated in available family members. Overall, a total of 118 putative pathogenic mutations from 23 genes were identified in 56.6% (90/159) of probands. These mutations were harbored in 13 LCA-associated genes and in ten genes related to other forms of retinal dystrophy. The most frequently mutated gene in probands with LCA was GUCY2D (10.7%, 17/159). A series of mutational analyses suggests that all known genes associated with retinal dystrophy account for 56.6% of Chinese patients with LCA. A comprehensive molecular genetic analysis of Chinese patients with LCA provides an overview of the spectrum and frequency of ethno-specific mutations of all known genes, as well as indications about other unknown genes in the remaining probands who lacked identified mutations. Copyright © 2016 Elsevier Ltd. All rights reserved.

KDE Bioscience: platform for bioinformatics analysis workflows.

PubMed

Lu, Qiang; Hao, Pei; Curcin, Vasa; He, Weizhong; Li, Yuan-Yuan; Luo, Qing-Ming; Guo, Yi-Ke; Li, Yi-Xue

2006-08-01

Bioinformatics is a dynamic research area in which a large number of algorithms and programs have been developed rapidly and independently without much consideration so far of the need for standardization. The lack of such common standards combined with unfriendly interfaces make it difficult for biologists to learn how to use these tools and to translate the data formats from one to another. Consequently, the construction of an integrative bioinformatics platform to facilitate biologists' research is an urgent and challenging task. KDE Bioscience is a java-based software platform that collects a variety of bioinformatics tools and provides a workflow mechanism to integrate them. Nucleotide and protein sequences from local flat files, web sites, and relational databases can be entered, annotated, and aligned. Several home-made or 3rd-party viewers are built-in to provide visualization of annotations or alignments. KDE Bioscience can also be deployed in client-server mode where simultaneous execution of the same workflow is supported for multiple users. Moreover, workflows can be published as web pages that can be executed from a web browser. The power of KDE Bioscience comes from the integrated algorithms and data sources. With its generic workflow mechanism other novel calculations and simulations can be integrated to augment the current sequence analysis functions. Because of this flexible and extensible architecture, KDE Bioscience makes an ideal integrated informatics environment for future bioinformatics or systems biology research.

DSSR-enhanced visualization of nucleic acid structures in Jmol.

PubMed

Hanson, Robert M; Lu, Xiang-Jun

2017-07-03

Sophisticated and interactive visualizations are essential for making sense of the intricate 3D structures of macromolecules. For proteins, secondary structural components are routinely featured in molecular graphics visualizations. However, the field of RNA structural bioinformatics is still lagging behind; for example, current molecular graphics tools lack built-in support even for base pairs, double helices, or hairpin loops. DSSR (Dissecting the Spatial Structure of RNA) is an integrated and automated command-line tool for the analysis and annotation of RNA tertiary structures. It calculates a comprehensive and unique set of features for characterizing RNA, as well as DNA structures. Jmol is a widely used, open-source Java viewer for 3D structures, with a powerful scripting language. JSmol, its reincarnation based on native JavaScript, has a predominant position in the post Java-applet era for web-based visualization of molecular structures. The DSSR-Jmol integration presented here makes salient features of DSSR readily accessible, either via the Java-based Jmol application itself, or its HTML5-based equivalent, JSmol. The DSSR web service accepts 3D coordinate files (in mmCIF or PDB format) initiated from a Jmol or JSmol session and returns DSSR-derived structural features in JSON format. This seamless combination of DSSR and Jmol/JSmol brings the molecular graphics of 3D RNA structures to a similar level as that for proteins, and enables a much deeper analysis of structural characteristics. It fills a gap in RNA structural bioinformatics, and is freely accessible (via the Jmol application or the JSmol-based website http://jmol.x3dna.org). © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Diverse Array of New Viral Sequences Identified in Worldwide Populations of the Asian Citrus Psyllid (Diaphorina citri) Using Viral Metagenomics

PubMed Central

Nouri, Shahideh; Salem, Nidá; Nigg, Jared C.

2015-01-01

ABSTRACT The Asian citrus psyllid, Diaphorina citri, is the natural vector of the causal agent of Huanglongbing (HLB), or citrus greening disease. Together; HLB and D. citri represent a major threat to world citrus production. As there is no cure for HLB, insect vector management is considered one strategy to help control the disease, and D. citri viruses might be useful. In this study, we used a metagenomic approach to analyze viral sequences associated with the global population of D. citri. By sequencing small RNAs and the transcriptome coupled with bioinformatics analysis, we showed that the virus-like sequences of D. citri are diverse. We identified novel viral sequences belonging to the picornavirus superfamily, the Reoviridae, Parvoviridae, and Bunyaviridae families, and an unclassified positive-sense single-stranded RNA virus. Moreover, a Wolbachia prophage-related sequence was identified. This is the first comprehensive survey to assess the viral community from worldwide populations of an agricultural insect pest. Our results provide valuable information on new putative viruses, some of which may have the potential to be used as biocontrol agents. IMPORTANCE Insects have the most species of all animals, and are hosts to, and vectors of, a great variety of known and unknown viruses. Some of these most likely have the potential to be important fundamental and/or practical resources. In this study, we used high-throughput next-generation sequencing (NGS) technology and bioinformatics analysis to identify putative viruses associated with Diaphorina citri, the Asian citrus psyllid. D. citri is the vector of the bacterium causing Huanglongbing (HLB), currently the most serious threat to citrus worldwide. Here, we report several novel viral sequences associated with D. citri. PMID:26676774

Diverse Array of New Viral Sequences Identified in Worldwide Populations of the Asian Citrus Psyllid (Diaphorina citri) Using Viral Metagenomics.

PubMed

Nouri, Shahideh; Salem, Nidá; Nigg, Jared C; Falk, Bryce W

2015-12-16

The Asian citrus psyllid, Diaphorina citri, is the natural vector of the causal agent of Huanglongbing (HLB), or citrus greening disease. Together; HLB and D. citri represent a major threat to world citrus production. As there is no cure for HLB, insect vector management is considered one strategy to help control the disease, and D. citri viruses might be useful. In this study, we used a metagenomic approach to analyze viral sequences associated with the global population of D. citri. By sequencing small RNAs and the transcriptome coupled with bioinformatics analysis, we showed that the virus-like sequences of D. citri are diverse. We identified novel viral sequences belonging to the picornavirus superfamily, the Reoviridae, Parvoviridae, and Bunyaviridae families, and an unclassified positive-sense single-stranded RNA virus. Moreover, a Wolbachia prophage-related sequence was identified. This is the first comprehensive survey to assess the viral community from worldwide populations of an agricultural insect pest. Our results provide valuable information on new putative viruses, some of which may have the potential to be used as biocontrol agents. Insects have the most species of all animals, and are hosts to, and vectors of, a great variety of known and unknown viruses. Some of these most likely have the potential to be important fundamental and/or practical resources. In this study, we used high-throughput next-generation sequencing (NGS) technology and bioinformatics analysis to identify putative viruses associated with Diaphorina citri, the Asian citrus psyllid. D. citri is the vector of the bacterium causing Huanglongbing (HLB), currently the most serious threat to citrus worldwide. Here, we report several novel viral sequences associated with D. citri. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Thorough analysis of unorthodox ABO deletions called by the 1000 Genomes project.

PubMed

Möller, M; Hellberg, Å; Olsson, M L

2018-02-01

ABO remains the clinically most important blood group system, but despite earlier extensive research, significant findings are still being made. The vast majority of catalogued ABO null alleles are based on the c.261delG polymorphism. Apart from c.802G>A, other mechanisms for O alleles are rare. While analysing the data set from the 1000 Genomes (1000G) project, we encountered two previously uncharacterized deletions, which needed further exploration. The Erythrogene database, complemented with bioinformatics software, was used to analyse ABO in 2504 individuals from 1000G. DNA samples from selected 1000G donors and African blood donors were examined by allele-specific PCR and Sanger sequencing to characterize predicted deletions. A 5821-bp deletion encompassing exons 5-7 was called in twenty 1000G individuals, predominantly Africans. This allele was confirmed and its exact deletion point defined by bioinformatic analyses and in vitro experiments. A PCR assay was developed, and screening of African samples revealed three donors heterozygous for this deletion, which was thereby phenotypically established as an O allele. Analysis of upstream genetic markers indicated an ancestral origin from ABO*O.01.02. We estimate this deletion as the 3rd most common mechanism behind O alleles. A 24-bp deletion was called in nine individuals and showed greater diversity regarding ethnic distribution and allelic background. It could neither be confirmed by in silico nor in vitro experiments. A previously uncharacterized ABO deletion among Africans was comprehensively mapped and a genotyping strategy devised. The false prediction of another deletion emphasizes the need for cautious interpretation of NGS data and calls for strict validation routines. © 2017 International Society of Blood Transfusion.

The Landscape of MicroRNA, Piwi-Interacting RNA, and Circular RNA in Human Saliva

PubMed Central

Bahn, Jae Hoon; Zhang, Qing; Li, Feng; Chan, Tak-Ming; Lin, Xianzhi; Kim, Yong; Wong, David T.W.; Xiao, Xinshu

2015-01-01

BACKGROUND Extracellular RNAs (exRNAs) in human body fluids are emerging as effective biomarkers for detection of diseases. Saliva, as the most accessible and noninvasive body fluid, has been shown to harbor exRNA biomarkers for several human diseases. However, the entire spectrum of exRNA from saliva has not been fully characterized. METHODS Using high-throughput RNA sequencing (RNA-Seq), we conducted an in-depth bioinformatic analysis of noncoding RNAs (ncRNAs) in human cell-free saliva (CFS) from healthy individuals, with a focus on microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and circular RNAs (circRNAs). RESULTS Our data demonstrated robust reproducibility of miRNA and piRNA profiles across individuals. Furthermore, individual variability of these salivary RNA species was highly similar to those in other body fluids or cellular samples, despite the direct exposure of saliva to environmental impacts. By comparative analysis of >90 RNA-Seq data sets of different origins, we observed that piRNAs were surprisingly abundant in CFS compared with other body fluid or intracellular samples, with expression levels in CFS comparable to those found in embryonic stem cells and skin cells. Conversely, miRNA expression profiles in CFS were highly similar to those in serum and cerebrospinal fluid. Using a customized bioinformatics method, we identified >400 circRNAs in CFS. These data represent the first global characterization and experimental validation of circRNAs in any type of extracellular body fluid. CONCLUSIONS Our study provides a comprehensive landscape of ncRNA species in human saliva that will facilitate further biomarker discoveries and lay a foundation for future studies related to ncRNAs in human saliva. PMID:25376581

Intrageneric Primer Design: Bringing Bioinformatics Tools to the Class

ERIC Educational Resources Information Center

Lima, Andre O. S.; Garces, Sergio P. S.

2006-01-01

Bioinformatics is one of the fastest growing scientific areas over the last decade. It focuses on the use of informatics tools for the organization and analysis of biological data. An example of their importance is the availability nowadays of dozens of software programs for genomic and proteomic studies. Thus, there is a growing field (private…

Open discovery: An integrated live Linux platform of Bioinformatics tools

PubMed Central

Vetrivel, Umashankar; Pilla, Kalabharath

2008-01-01

Historically, live linux distributions for Bioinformatics have paved way for portability of Bioinformatics workbench in a platform independent manner. Moreover, most of the existing live Linux distributions limit their usage to sequence analysis and basic molecular visualization programs and are devoid of data persistence. Hence, open discovery ‐ a live linux distribution has been developed with the capability to perform complex tasks like molecular modeling, docking and molecular dynamics in a swift manner. Furthermore, it is also equipped with complete sequence analysis environment and is capable of running windows executable programs in Linux environment. Open discovery portrays the advanced customizable configuration of fedora, with data persistency accessible via USB drive or DVD. Availability The Open Discovery is distributed free under Academic Free License (AFL) and can be downloaded from http://www.OpenDiscovery.org.in PMID:19238235

Quantitative monitoring of Arabidopsis thaliana growth and development using high-throughput plant phenotyping

PubMed Central

Arend, Daniel; Lange, Matthias; Pape, Jean-Michel; Weigelt-Fischer, Kathleen; Arana-Ceballos, Fernando; Mücke, Ingo; Klukas, Christian; Altmann, Thomas; Scholz, Uwe; Junker, Astrid

2016-01-01

With the implementation of novel automated, high throughput methods and facilities in the last years, plant phenomics has developed into a highly interdisciplinary research domain integrating biology, engineering and bioinformatics. Here we present a dataset of a non-invasive high throughput plant phenotyping experiment, which uses image- and image analysis- based approaches to monitor the growth and development of 484 Arabidopsis thaliana plants (thale cress). The result is a comprehensive dataset of images and extracted phenotypical features. Such datasets require detailed documentation, standardized description of experimental metadata as well as sustainable data storage and publication in order to ensure the reproducibility of experiments, data reuse and comparability among the scientific community. Therefore the here presented dataset has been annotated using the standardized ISA-Tab format and considering the recently published recommendations for the semantical description of plant phenotyping experiments. PMID:27529152

The Potential for Emerging Microbiome-Mediated Therapeutics in Asthma.

PubMed

Ozturk, Ayse Bilge; Turturice, Benjamin Arthur; Perkins, David L; Finn, Patricia W

2017-08-10

In terms of immune regulating functions, analysis of the microbiome has led the development of therapeutic strategies that may be applicable to asthma management. This review summarizes the current literature on the gut and lung microbiota in asthma pathogenesis with a focus on the roles of innate molecules and new microbiome-mediated therapeutics. Recent clinical and basic studies to date have identified several possible therapeutics that can target innate immunity and the microbiota in asthma. Some of these drugs have shown beneficial effects in the treatment of certain asthma phenotypes and for protection against asthma during early life. Current clinical evidence does not support the use of these therapies for effective treatment of asthma. The integration of the data regarding microbiota with technologic advances, such as next generation sequencing and omics offers promise. Combining comprehensive bioinformatics, new molecules and approaches may shape future asthma treatment.

[Introduction of translational research in omics science to clinical anesthesia].

PubMed

Sugino, Shigekazu; Hayase, Tomo; Yamakage, Michiaki

2013-03-01

Much progress has been made in omics research following completion of the Human Genome Project. This comprehensive analysis produced a new discipline (i.e., bioinformatics), and its findings contributed to the clinical practice of anesthesiology. Genomes of patients show genetic variations and may predict the sensitivity to anesthetics and analgesics, incidence of adverse effects, and intensity of postsurgical pain. Changes in the transcriptomes of patients may also reflect anesthesia-related expression profiles of various types of neurons in the brain, and information on such changes may contribute to molecular targeted therapy in anesthetized patients. In addition, novel epigenome research may explain why environments change the phenotypes of clinical anesthesia. We currently hypothesize that female gender is associated with DNA methylation in pain-related and vomiting-related gene promoter regions at the genome-wide level and that epigenetic mechanisms are involved in gender differences in anesthesia practice.

Challenges and perspectives of metaproteomic data analysis.

PubMed

Heyer, Robert; Schallert, Kay; Zoun, Roman; Becher, Beatrice; Saake, Gunter; Benndorf, Dirk

2017-11-10

In nature microorganisms live in complex microbial communities. Comprehensive taxonomic and functional knowledge about microbial communities supports medical and technical application such as fecal diagnostics as well as operation of biogas plants or waste water treatment plants. Furthermore, microbial communities are crucial for the global carbon and nitrogen cycle in soil and in the ocean. Among the methods available for investigation of microbial communities, metaproteomics can approximate the activity of microorganisms by investigating the protein content of a sample. Although metaproteomics is a very powerful method, issues within the bioinformatic evaluation impede its success. In particular, construction of databases for protein identification, grouping of redundant proteins as well as taxonomic and functional annotation pose big challenges. Furthermore, growing amounts of data within a metaproteomics study require dedicated algorithms and software. This review summarizes recent metaproteomics software and addresses the introduced issues in detail. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Saccharomyces genome database informs human biology

PubMed Central

Skrzypek, Marek S; Nash, Robert S; Wong, Edith D; MacPherson, Kevin A; Karra, Kalpana; Binkley, Gail; Simison, Matt; Miyasato, Stuart R

2018-01-01

Abstract The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is an expertly curated database of literature-derived functional information for the model organism budding yeast, Saccharomyces cerevisiae. SGD constantly strives to synergize new types of experimental data and bioinformatics predictions with existing data, and to organize them into a comprehensive and up-to-date information resource. The primary mission of SGD is to facilitate research into the biology of yeast and to provide this wealth of information to advance, in many ways, research on other organisms, even those as evolutionarily distant as humans. To build such a bridge between biological kingdoms, SGD is curating data regarding yeast-human complementation, in which a human gene can successfully replace the function of a yeast gene, and/or vice versa. These data are manually curated from published literature, made available for download, and incorporated into a variety of analysis tools provided by SGD. PMID:29140510

Quantitative monitoring of Arabidopsis thaliana growth and development using high-throughput plant phenotyping.

PubMed

Arend, Daniel; Lange, Matthias; Pape, Jean-Michel; Weigelt-Fischer, Kathleen; Arana-Ceballos, Fernando; Mücke, Ingo; Klukas, Christian; Altmann, Thomas; Scholz, Uwe; Junker, Astrid

2016-08-16

With the implementation of novel automated, high throughput methods and facilities in the last years, plant phenomics has developed into a highly interdisciplinary research domain integrating biology, engineering and bioinformatics. Here we present a dataset of a non-invasive high throughput plant phenotyping experiment, which uses image- and image analysis- based approaches to monitor the growth and development of 484 Arabidopsis thaliana plants (thale cress). The result is a comprehensive dataset of images and extracted phenotypical features. Such datasets require detailed documentation, standardized description of experimental metadata as well as sustainable data storage and publication in order to ensure the reproducibility of experiments, data reuse and comparability among the scientific community. Therefore the here presented dataset has been annotated using the standardized ISA-Tab format and considering the recently published recommendations for the semantical description of plant phenotyping experiments.

Interpreter of maladies: redescription mining applied to biomedical data analysis.

PubMed

Waltman, Peter; Pearlman, Alex; Mishra, Bud

2006-04-01

Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed. The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease. As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention's Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathways in the hypothalamic-pituitary-adrenal axis affect CFS patients.

ontologyX: a suite of R packages for working with ontological data.

PubMed

Greene, Daniel; Richardson, Sylvia; Turro, Ernest

2017-04-01

Ontologies are widely used constructs for encoding and analyzing biomedical data, but the absence of simple and consistent tools has made exploratory and systematic analysis of such data unnecessarily difficult. Here we present three packages which aim to simplify such procedures. The ontologyIndex package enables arbitrary ontologies to be read into R, supports representation of ontological objects by native R types, and provides a parsimonius set of performant functions for querying ontologies. ontologySimilarity and ontologyPlot extend ontologyIndex with functionality for straightforward visualization and semantic similarity calculations, including statistical routines. ontologyIndex , ontologyPlot and ontologySimilarity are all available on the Comprehensive R Archive Network website under https://cran.r-project.org/web/packages/ . Daniel Greene dg333@cam.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

The forthcoming era of precision medicine.

PubMed

Gamulin, Stjepan

2016-11-01

The aim of this essay is to present the definition and principles of personalized or precision medicine, the perspective and barriers to its development and clinical application. The implementation of precision medicine in health care requires the coordinated efforts of all health care stakeholders (the biomedical community, government, regulatory bodies, patients' groups). Particularly, translational research with the integration of genomic and comprehensive data from all levels of the organism ("big data"), development of bioinformatics platforms enabling network analysis of disease etiopathogenesis, development of a legislative framework for handling personal data, and new paradigms of medical education are necessary for successful application of the concept of precision medicine in health care. In the present and future era of precision medicine, the collaboration of all participants in health care is necessary for its realization, resulting in improvement of diagnosis, prevention and therapy, based on a holistic, individually tailored approach. Copyright © 2016 by Academy of Sciences and Arts of Bosnia and Herzegovina.

Scalable web services for the PSIPRED Protein Analysis Workbench.

PubMed

Buchan, Daniel W A; Minneci, Federico; Nugent, Tim C O; Bryson, Kevin; Jones, David T

2013-07-01

Here, we present the new UCL Bioinformatics Group's PSIPRED Protein Analysis Workbench. The Workbench unites all of our previously available analysis methods into a single web-based framework. The new web portal provides a greatly streamlined user interface with a number of new features to allow users to better explore their results. We offer a number of additional services to enable computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web server access and new HADOOP packages. All software and services are available via the UCL Bioinformatics Group website at http://bioinf.cs.ucl.ac.uk/.

Comprehensive analysis of the N-glycan biosynthetic pathway using bioinformatics to generate UniCorn: A theoretical N-glycan structure database.

PubMed

Akune, Yukie; Lin, Chi-Hung; Abrahams, Jodie L; Zhang, Jingyu; Packer, Nicolle H; Aoki-Kinoshita, Kiyoko F; Campbell, Matthew P

2016-08-05

Glycan structures attached to proteins are comprised of diverse monosaccharide sequences and linkages that are produced from precursor nucleotide-sugars by a series of glycosyltransferases. Databases of these structures are an essential resource for the interpretation of analytical data and the development of bioinformatics tools. However, with no template to predict what structures are possible the human glycan structure databases are incomplete and rely heavily on the curation of published, experimentally determined, glycan structure data. In this work, a library of 45 human glycosyltransferases was used to generate a theoretical database of N-glycan structures comprised of 15 or less monosaccharide residues. Enzyme specificities were sourced from major online databases including Kyoto Encyclopedia of Genes and Genomes (KEGG) Glycan, Consortium for Functional Glycomics (CFG), Carbohydrate-Active enZymes (CAZy), GlycoGene DataBase (GGDB) and BRENDA. Based on the known activities, more than 1.1 million theoretical structures and 4.7 million synthetic reactions were generated and stored in our database called UniCorn. Furthermore, we analyzed the differences between the predicted glycan structures in UniCorn and those contained in UniCarbKB (www.unicarbkb.org), a database which stores experimentally described glycan structures reported in the literature, and demonstrate that UniCorn can be used to aid in the assignment of ambiguous structures whilst also serving as a discovery database. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biochemical characterization of the selenoproteome in Gallus gallus via bioinformatics analysis: structure-function relationships and interactions of binding molecules.

PubMed

Zhu, Shi-Yong; Li, Xue-Nan; Sun, Xiao-Chen; Lin, Jia; Li, Wei; Zhang, Cong; Li, Jin-Long

2017-02-22

Knowledge about mammalian selenoproteins is increasing. However, the selenoproteome of birds remains considerably less understood, especially concerning its biochemical characterization, structure-function relationships and the interactions of binding molecules. In this work, the SECIS elements, subcellular localization, protein domains and interactions of binding molecules of the selenoproteome in Gallus gallus were analyzed using bioinformatics tools. We carried out comprehensive analyses of the structure-function relationships and interactions of the binding molecules of selenoproteins, to provide biochemical characterization of the selenoproteome in Gallus gallus. Our data provided a wealth of information on the biochemical functions of bird selenoproteins. Members of the selenoproteome were found to be involved in various biological processes in chickens, such as in antioxidants, maintenance of the redox balance, Se transport, and interactions with metals. Six membrane-bound selenoproteins (SelI, SelK, SelS, SelT, DIO1 and DIO3) played important roles in maintaining the membrane integrity. Chicken selenoproteins were classified according to their ligand binding sites as zinc-containing matrix metalloselenoproteins (Sep15, MsrB1, SelW and SelM), POP-containing selenoproteins (GPx1-4), FAD-interacting selenoproteins (TrxR1-3), secretory transport selenoproteins (GPx3 and SelPa) and other selenoproteins. The results of our study provided new evidence for the unknown biological functions of the selenoproteome in birds. Future research is required to confirm the novel biochemical functions of bird selenoproteins.

Comprehensive Analysis of Interaction Networks of Telomerase Reverse Transcriptase with Multiple Bioinformatic Approaches: Deep Mining the Potential Functions of Telomere and Telomerase.

PubMed

Hou, Chunyu; Wang, Fei; Liu, Xuewen; Chang, Guangming; Wang, Feng; Geng, Xin

2017-08-01

Telomerase reverse transcriptase (TERT) is the protein component of telomerase complex. Evidence has accumulated showing that the nontelomeric functions of TERT are independent of telomere elongation. However, the mechanisms governing the interaction between TERT and its target genes are not clearly revealed. The biological functions of TERT are not fully elucidated and have thus far been underestimated. To further explore these functions, we investigated TERT interaction networks using multiple bioinformatic databases, including BioGRID, STRING, DAVID, GeneCards, GeneMANIA, PANTHER, miRWalk, mirTarBase, miRNet, miRDB, and TargetScan. In addition, network diagrams were built using Cytoscape software. As competing endogenous RNAs (ceRNAs) are endogenous transcripts that compete for the binding of microRNAs (miRNAs) by using shared miRNA recognition elements, they are involved in creating widespread regulatory networks. Therefore, the ceRNA regulatory networks of TERT were also investigated in this study. Interestingly, we found that the three genes PABPC1, SLC7A11, and TP53 were present in both TERT interaction networks and ceRNAs target genes. It was predicted that TERT might play nontelomeric roles in the generation or development of some rare diseases, such as Rift Valley fever and dyscalculia. Thus, our data will help to decipher the interaction networks of TERT and reveal the unknown functions of telomerase in cancer and aging-related diseases.

Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry.

PubMed

Freudenmann, Lena Katharina; Marcu, Ana; Stevanović, Stefan

2018-07-01

The entirety of human leukocyte antigen (HLA)-presented peptides is referred to as the HLA ligandome of a cell or tissue, in tumours often termed immunopeptidome. Mapping the tumour immunopeptidome by mass spectrometry (MS) comprehensively views the pathophysiologically relevant antigenic signature of human malignancies. MS is an unbiased approach stringently filtering the candidates to be tested as opposed to epitope prediction algorithms. In the setting of peptide-specific immunotherapies, MS-based strategies significantly diminish the risk of lacking clinical benefit, as they yield highly enriched amounts of truly presented peptides. Early immunopeptidomic efforts were severely limited by technical sensitivity and manual spectra interpretation. The technological progress with development of orbitrap mass analysers and enhanced chromatographic performance led to vast improvements in mass accuracy, sensitivity, resolution, and speed. Concomitantly, bioinformatic tools were developed to process MS data, integrate sequencing results, and deconvolute multi-allelic datasets. This enabled the immense advancement of tumour immunopeptidomics. Studying the HLA-presented peptide repertoire bears high potential for both answering basic scientific questions and translational application. Mapping the tumour HLA ligandome has started to significantly contribute to target identification for the design of peptide-specific cancer immunotherapies in clinical trials and compassionate need treatments. In contrast to prediction algorithms, rare HLA allotypes and HLA class II can be adequately addressed when choosing MS-guided target identification platforms. Herein, we review the identification of tumour HLA ligands focusing on sources, methods, bioinformatic data analysis, translational application, and provide an outlook on future developments. © 2018 John Wiley & Sons Ltd.

RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data.

PubMed

Shankar, Ravi D; Bhattacharya, Sanchita; Jujjavarapu, Chethan; Andorf, Sandra; Wiser, Jeffery A; Butte, Atul J

2017-04-01

: Open access to raw clinical and molecular data related to immunological studies has created a tremendous opportunity for data-driven science. We have developed RImmPort that prepares NIAID-funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data across different dimensions in one or more studies. Furthermore, RImmPort supports open formalisms, such as CDISC standards on the open source bioinformatics platform Bioconductor, to ensure that ImmPort curated study datasets are seamlessly accessible and ready for analysis, thus enabling innovative bioinformatics research in immunology. RImmPort is available as part of Bioconductor (bioconductor.org/packages/RImmPort). rshankar@stanford.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Mapping the Extracellular and Membrane Proteome Associated with the Vasculature and the Stroma in the Embryo*

PubMed Central

Soulet, Fabienne; Kilarski, Witold W.; Roux-Dalvai, Florence; Herbert, John M. J.; Sacewicz, Izabela; Mouton-Barbosa, Emmanuelle; Bicknell, Roy; Lalor, Patricia; Monsarrat, Bernard; Bikfalvi, Andreas

2013-01-01

In order to map the extracellular or membrane proteome associated with the vasculature and the stroma in an embryonic organism in vivo, we developed a biotinylation technique for chicken embryo and combined it with mass spectrometry and bioinformatic analysis. We also applied this procedure to implanted tumors growing on the chorioallantoic membrane or after the induction of granulation tissue. Membrane and extracellular matrix proteins were the most abundant components identified. Relative quantitative analysis revealed differential protein expression patterns in several tissues. Through a bioinformatic approach, we determined endothelial cell protein expression signatures, which allowed us to identify several proteins not yet reported to be associated with endothelial cells or the vasculature. This is the first study reported so far that applies in vivo biotinylation, in combination with robust label-free quantitative proteomics approaches and bioinformatic analysis, to an embryonic organism. It also provides the first description of the vascular and matrix proteome of the embryo that might constitute the starting point for further developments. PMID:23674615

Bioinformatics Identification of Modules of Transcription Factor Binding Sites in Alzheimer's Disease-Related Genes by In Silico Promoter Analysis and Microarrays

PubMed Central

Augustin, Regina; Lichtenthaler, Stefan F.; Greeff, Michael; Hansen, Jens; Wurst, Wolfgang; Trümbach, Dietrich

2011-01-01

The molecular mechanisms and genetic risk factors underlying Alzheimer's disease (AD) pathogenesis are only partly understood. To identify new factors, which may contribute to AD, different approaches are taken including proteomics, genetics, and functional genomics. Here, we used a bioinformatics approach and found that distinct AD-related genes share modules of transcription factor binding sites, suggesting a transcriptional coregulation. To detect additional coregulated genes, which may potentially contribute to AD, we established a new bioinformatics workflow with known multivariate methods like support vector machines, biclustering, and predicted transcription factor binding site modules by using in silico analysis and over 400 expression arrays from human and mouse. Two significant modules are composed of three transcription factor families: CTCF, SP1F, and EGRF/ZBPF, which are conserved between human and mouse APP promoter sequences. The specific combination of in silico promoter and multivariate analysis can identify regulation mechanisms of genes involved in multifactorial diseases. PMID:21559189

Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

PubMed

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2005-04-01

Tight turns have long been recognized as one of the three important features of proteins, together with alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns and most of the rest are gamma-turns. Analysis and prediction of beta-turns and gamma-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of beta-turns and gamma-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict beta-turns and gamma-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of beta-turns and gamma-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

SimExTargId: A comprehensive package for real-time LC-MS data acquisition and analysis.

PubMed

Edmands, William M B; Hayes, Josie; Rappaport, Stephen M

2018-05-22

Liquid chromatography mass spectrometry (LC-MS) is the favored method for untargeted metabolomic analysis of small molecules in biofluids. Here we present SimExTargId, an open-source R package for autonomous analysis of metabolomic data and real-time observation of experimental runs. This simultaneous, fully automated and multi-threaded (optional) package is a wrapper for vendor-independent format conversion (ProteoWizard), xcms- and CAMERA- based peak-picking, MetMSLine-based pre-processing and covariate-based statistical analysis. Users are notified of detrimental instrument drift or errors by email. Also included are two shiny applications, targetId for real-time MS2 target identification, and peakMonitor to monitor targeted metabolites. SimExTargId is publicly available under GNU LGPL v3.0 license at https://github.com/JosieLHayes/simExTargId, which includes a vignette with example data. SimExTargId should be installed on a dedicated data-processing workstation or server that is networked to the LC-MS platform to facilitate MS1 profiling of metabolomic data. josie.hayes@berkeley.edu. Supplementary data are available at Bioinformatics online.

Label-free quantitative proteomic analysis of human plasma-derived microvesicles to find protein signatures of abdominal aortic aneurysms.

PubMed

Martinez-Pinna, Roxana; Gonzalez de Peredo, Anne; Monsarrat, Bernard; Burlet-Schiltz, Odile; Martin-Ventura, Jose Luis

2014-08-01

To find potential biomarkers of abdominal aortic aneurysms (AAA), we performed a differential proteomic study based on human plasma-derived microvesicles. Exosomes and microparticles isolated from plasma of AAA patients and control subjects (n = 10 each group) were analyzed by a label-free quantitative MS-based strategy. Homemade and publicly available software packages have been used for MS data analysis. The application of two kinds of bioinformatic tools allowed us to find differential protein profiles from AAA patients. Some of these proteins found by the two analysis methods belong to main pathological mechanisms of AAA such as oxidative stress, immune-inflammation, and thrombosis. Data analysis from label-free MS-based experiments requires the use of sophisticated bioinformatic approaches to perform quantitative studies from complex protein mixtures. The application of two of these bioinformatic tools provided us a preliminary list of differential proteins found in plasma-derived microvesicles not previously associated to AAA, which could help us to understand the pathological mechanisms related to this disease. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Relax with CouchDB--into the non-relational DBMS era of bioinformatics.

PubMed

Manyam, Ganiraju; Payton, Michelle A; Roth, Jack A; Abruzzo, Lynne V; Coombes, Kevin R

2012-07-01

With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services. Copyright © 2012 Elsevier Inc. All rights reserved.

An overview of bioinformatics tools for epitope prediction: implications on vaccine development.

PubMed

Soria-Guerra, Ruth E; Nieto-Gomez, Ricardo; Govea-Alonso, Dania O; Rosales-Mendoza, Sergio

2015-02-01

Exploitation of recombinant DNA and sequencing technologies has led to a new concept in vaccination in which isolated epitopes, capable of stimulating a specific immune response, have been identified and used to achieve advanced vaccine formulations; replacing those constituted by whole pathogen-formulations. In this context, bioinformatics approaches play a critical role on analyzing multiple genomes to select the protective epitopes in silico. It is conceived that cocktails of defined epitopes or chimeric protein arrangements, including the target epitopes, may provide a rationale design capable to elicit convenient humoral or cellular immune responses. This review presents a comprehensive compilation of the most advantageous online immunological software and searchable, in order to facilitate the design and development of vaccines. An outlook on how these tools are supporting vaccine development is presented. HIV and influenza have been taken as examples of promising developments on vaccination against hypervariable viruses. Perspectives in this field are also envisioned. Copyright © 2014 Elsevier Inc. All rights reserved.

Bioinformatics analysis and detection of gelatinase encoded gene in Lysinibacillussphaericus

NASA Astrophysics Data System (ADS)

Repin, Rul Aisyah Mat; Mutalib, Sahilah Abdul; Shahimi, Safiyyah; Khalid, Rozida Mohd.; Ayob, Mohd. Khan; Bakar, Mohd. Faizal Abu; Isa, Mohd Noor Mat

2016-11-01

In this study, we performed bioinformatics analysis toward genome sequence of Lysinibacillussphaericus (L. sphaericus) to determine gene encoded for gelatinase. L. sphaericus was isolated from soil and gelatinase species-specific bacterium to porcine and bovine gelatin. This bacterium offers the possibility of enzymes production which is specific to both species of meat, respectively. The main focus of this research is to identify the gelatinase encoded gene within the bacteria of L. Sphaericus using bioinformatics analysis of partially sequence genome. From the research study, three candidate gene were identified which was, gelatinase candidate gene 1 (P1), NODE_71_length_93919_cov_158.931839_21 which containing 1563 base pair (bp) in size with 520 amino acids sequence; Secondly, gelatinase candidate gene 2 (P2), NODE_23_length_52851_cov_190.061386_17 which containing 1776 bp in size with 591 amino acids sequence; and Thirdly, gelatinase candidate gene 3 (P3), NODE_106_length_32943_cov_169.147919_8 containing 1701 bp in size with 566 amino acids sequence. Three pairs of oligonucleotide primers were designed and namely as, F1, R1, F2, R2, F3 and R3 were targeted short sequences of cDNA by PCR. The amplicons were reliably results in 1563 bp in size for candidate gene P1 and 1701 bp in size for candidate gene P3. Therefore, the results of bioinformatics analysis of L. Sphaericus resulting in gene encoded gelatinase were identified.

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

PubMed Central

Khomtchouk, Bohdan B; Weitz, Edmund; Karp, Peter D; Wahlestedt, Claes

2018-01-01

Abstract We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the ‘programmable programming language’. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology. PMID:28040748

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications.

PubMed

Khomtchouk, Bohdan B; Weitz, Edmund; Karp, Peter D; Wahlestedt, Claes

2018-05-01

We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the 'programmable programming language'. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology.

SeWeR: a customizable and integrated dynamic HTML interface to bioinformatics services.

PubMed

Basu, M K

2001-06-01

Sequence analysis using Web Resources (SeWeR) is an integrated, Dynamic HTML (DHTML) interface to commonly used bioinformatics services available on the World Wide Web. It is highly customizable, extendable, platform neutral, completely server-independent and can be hosted as a web page as well as being used as stand-alone software running within a web browser.

An "in silico" Bioinformatics Laboratory Manual for Bioscience Departments: "Prediction of Glycosylation Sites in Phosphoethanolamine Transferases"

ERIC Educational Resources Information Center

Alyuruk, Hakan; Cavas, Levent

2014-01-01

Genomics and proteomics projects have produced a huge amount of raw biological data including DNA and protein sequences. Although these data have been stored in data banks, their evaluation is strictly dependent on bioinformatics tools. These tools have been developed by multidisciplinary experts for fast and robust analysis of biological data.…

Supercomputing with toys: harnessing the power of NVIDIA 8800GTX and playstation 3 for bioinformatics problem.

PubMed

Wilson, Justin; Dai, Manhong; Jakupovic, Elvis; Watson, Stanley; Meng, Fan

2007-01-01

Modern video cards and game consoles typically have much better performance to price ratios than that of general purpose CPUs. The parallel processing capabilities of game hardware are well-suited for high throughput biomedical data analysis. Our initial results suggest that game hardware is a cost-effective platform for some computationally demanding bioinformatics problems.

VLSI Microsystem for Rapid Bioinformatic Pattern Recognition

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi; Lue, Jaw-Chyng

2009-01-01

A system comprising very-large-scale integrated (VLSI) circuits is being developed as a means of bioinformatics-oriented analysis and recognition of patterns of fluorescence generated in a microarray in an advanced, highly miniaturized, portable genetic-expression-assay instrument. Such an instrument implements an on-chip combination of polymerase chain reactions and electrochemical transduction for amplification and detection of deoxyribonucleic acid (DNA).

Bioinformatic pipelines in Python with Leaf

PubMed Central

2013-01-01

Background An incremental, loosely planned development approach is often used in bioinformatic studies when dealing with custom data analysis in a rapidly changing environment. Unfortunately, the lack of a rigorous software structuring can undermine the maintainability, communicability and replicability of the process. To ameliorate this problem we propose the Leaf system, the aim of which is to seamlessly introduce the pipeline formality on top of a dynamical development process with minimum overhead for the programmer, thus providing a simple layer of software structuring. Results Leaf includes a formal language for the definition of pipelines with code that can be transparently inserted into the user’s Python code. Its syntax is designed to visually highlight dependencies in the pipeline structure it defines. While encouraging the developer to think in terms of bioinformatic pipelines, Leaf supports a number of automated features including data and session persistence, consistency checks between steps of the analysis, processing optimization and publication of the analytic protocol in the form of a hypertext. Conclusions Leaf offers a powerful balance between plan-driven and change-driven development environments in the design, management and communication of bioinformatic pipelines. Its unique features make it a valuable alternative to other related tools. PMID:23786315

Shotgun proteomic analysis of Emiliania huxleyi, a marine phytoplankton species of major biogeochemical importance.

PubMed

Jones, Bethan M; Edwards, Richard J; Skipp, Paul J; O'Connor, C David; Iglesias-Rodriguez, M Debora

2011-06-01

Emiliania huxleyi is a unicellular marine phytoplankton species known to play a significant role in global biogeochemistry. Through the dual roles of photosynthesis and production of calcium carbonate (calcification), carbon is transferred from the atmosphere to ocean sediments. Almost nothing is known about the molecular mechanisms that control calcification, a process that is tightly regulated within the cell. To initiate proteomic studies on this important and phylogenetically remote organism, we have devised efficient protein extraction protocols and developed a bioinformatics pipeline that allows the statistically robust assignment of proteins from MS/MS data using preexisting EST sequences. The bioinformatics tool, termed BUDAPEST (Bioinformatics Utility for Data Analysis of Proteomics using ESTs), is fully automated and was used to search against data generated from three strains. BUDAPEST increased the number of identifications over standard protein database searches from 37 to 99 proteins when data were amalgamated. Proteins involved in diverse cellular processes were uncovered. For example, experimental evidence was obtained for a novel type I polyketide synthase and for various photosystem components. The proteomic and bioinformatic approaches developed in this study are of wider applicability, particularly to the oceanographic community where genomic sequence data for species of interest are currently scarce.

Towards big data science in the decade ahead from ten years of InCoB and the 1st ISCB-Asia Joint Conference

PubMed Central

2011-01-01

The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for Computational Biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB’s goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC Bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design. PMID:22372736

Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection.

PubMed

Li, Zhucui; Lu, Yan; Guo, Yufeng; Cao, Haijie; Wang, Qinhong; Shui, Wenqing

2018-10-31

Data analysis represents a key challenge for untargeted metabolomics studies and it commonly requires extensive processing of more than thousands of metabolite peaks included in raw high-resolution MS data. Although a number of software packages have been developed to facilitate untargeted data processing, they have not been comprehensively scrutinized in the capability of feature detection, quantification and marker selection using a well-defined benchmark sample set. In this study, we acquired a benchmark dataset from standard mixtures consisting of 1100 compounds with specified concentration ratios including 130 compounds with significant variation of concentrations. Five software evaluated here (MS-Dial, MZmine 2, XCMS, MarkerView, and Compound Discoverer) showed similar performance in detection of true features derived from compounds in the mixtures. However, significant differences between untargeted metabolomics software were observed in relative quantification of true features in the benchmark dataset. MZmine 2 outperformed the other software in terms of quantification accuracy and it reported the most true discriminating markers together with the fewest false markers. Furthermore, we assessed selection of discriminating markers by different software using both the benchmark dataset and a real-case metabolomics dataset to propose combined usage of two software for increasing confidence of biomarker identification. Our findings from comprehensive evaluation of untargeted metabolomics software would help guide future improvements of these widely used bioinformatics tools and enable users to properly interpret their metabolomics results. Copyright © 2018 Elsevier B.V. All rights reserved.

Crimean-Congo Hemorrhagic Fever Virus Gn Bioinformatic Analysis and Construction of a Recombinant Bacmid in Order to Express Gn by Baculovirus Expression System.

PubMed

Rahpeyma, Mehdi; Fotouhi, Fatemeh; Makvandi, Manouchehr; Ghadiri, Ata; Samarbaf-Zadeh, Alireza

2015-11-01

Crimean-Congo hemorrhagic fever virus (CCHFV) is a member of the nairovirus, a genus in the Bunyaviridae family, which causes a life threatening disease in human. Currently, there is no vaccine against CCHFV and detailed structural analysis of CCHFV proteins remains undefined. The CCHFV M RNA segment encodes two viral surface glycoproteins known as Gn and Gc. Viral glycoproteins can be considered as key targets for vaccine development. The current study aimed to investigate structural bioinformatics of CCHFV Gn protein and design a construct to make a recombinant bacmid to express by baculovirus system. To express the Gn protein in insect cells that can be used as antigen in animal model vaccine studies. Bioinformatic analysis of CCHFV Gn protein was performed and designed a construct and cloned into pFastBacHTb vector and a recombinant Gn-bacmid was generated by Bac to Bac system. Primary, secondary, and 3D structure of CCHFV Gn were obtained and PCR reaction with M13 forward and reverse primers confirmed the generation of recombinant bacmid DNA harboring Gn coding region under polyhedron promoter. Characterization of the detailed structure of CCHFV Gn by bioinformatics software provides the basis for development of new experiments and construction of a recombinant bacmid harboring CCHFV Gn, which is valuable for designing a recombinant vaccine against deadly pathogens like CCHFV.

SOBA: sequence ontology bioinformatics analysis.

PubMed

Moore, Barry; Fan, Guozhen; Eilbeck, Karen

2010-07-01

The advent of cheaper, faster sequencing technologies has pushed the task of sequence annotation from the exclusive domain of large-scale multi-national sequencing projects to that of research laboratories and small consortia. The bioinformatics burden placed on these laboratories, some with very little programming experience can be daunting. Fortunately, there exist software libraries and pipelines designed with these groups in mind, to ease the transition from an assembled genome to an annotated and accessible genome resource. We have developed the Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome. We envisage its use during annotation jamborees, genome comparison and for use by developers for rapid feedback during annotation software development and testing. SOBA also provides annotation consistency feedback to ensure correct use of terminology within annotations, and guides users to add new terms to the Sequence Ontology when required. SOBA is available at http://www.sequenceontology.org/cgi-bin/soba.cgi.

Freshwater Metaviromics and Bacteriophages: A Current Assessment of the State of the Art in Relation to Bioinformatic Challenges

PubMed Central

Bruder, Katherine; Malki, Kema; Cooper, Alexandria; Sible, Emily; Shapiro, Jason W.; Watkins, Siobhan C.; Putonti, Catherine

2016-01-01

Advances in bioinformatics and sequencing technologies have allowed for the analysis of complex microbial communities at an unprecedented rate. While much focus is often placed on the cellular members of these communities, viruses play a pivotal role, particularly bacteria-infecting viruses (bacteriophages); phages mediate global biogeochemical processes and drive microbial evolution through bacterial grazing and horizontal gene transfer. Despite their importance and ubiquity in nature, very little is known about the diversity and structure of viral communities. Though the need for culture-based methods for viral identification has been somewhat circumvented through metagenomic techniques, the analysis of metaviromic data is marred with many unique issues. In this review, we examine the current bioinformatic approaches for metavirome analyses and the inherent challenges facing the field as illustrated by the ongoing efforts in the exploration of freshwater phage populations. PMID:27375355

CHESS (CgHExpreSS): a comprehensive analysis tool for the analysis of genomic alterations and their effects on the expression profile of the genome.

PubMed

Lee, Mikyung; Kim, Yangseok

2009-12-16

Genomic alterations frequently occur in many cancer patients and play important mechanistic roles in the pathogenesis of cancer. Furthermore, they can modify the expression level of genes due to altered copy number in the corresponding region of the chromosome. An accumulating body of evidence supports the possibility that strong genome-wide correlation exists between DNA content and gene expression. Therefore, more comprehensive analysis is needed to quantify the relationship between genomic alteration and gene expression. A well-designed bioinformatics tool is essential to perform this kind of integrative analysis. A few programs have already been introduced for integrative analysis. However, there are many limitations in their performance of comprehensive integrated analysis using published software because of limitations in implemented algorithms and visualization modules. To address this issue, we have implemented the Java-based program CHESS to allow integrative analysis of two experimental data sets: genomic alteration and genome-wide expression profile. CHESS is composed of a genomic alteration analysis module and an integrative analysis module. The genomic alteration analysis module detects genomic alteration by applying a threshold based method or SW-ARRAY algorithm and investigates whether the detected alteration is phenotype specific or not. On the other hand, the integrative analysis module measures the genomic alteration's influence on gene expression. It is divided into two separate parts. The first part calculates overall correlation between comparative genomic hybridization ratio and gene expression level by applying following three statistical methods: simple linear regression, Spearman rank correlation and Pearson's correlation. In the second part, CHESS detects the genes that are differentially expressed according to the genomic alteration pattern with three alternative statistical approaches: Student's t-test, Fisher's exact test and Chi square test. By successive operations of two modules, users can clarify how gene expression levels are affected by the phenotype specific genomic alterations. As CHESS was developed in both Java application and web environments, it can be run on a web browser or a local machine. It also supports all experimental platforms if a properly formatted text file is provided to include the chromosomal position of probes and their gene identifiers. CHESS is a user-friendly tool for investigating disease specific genomic alterations and quantitative relationships between those genomic alterations and genome-wide gene expression profiling.

Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma.

PubMed

Pan, Yue; Lu, Lingyun; Chen, Junquan; Zhong, Yong; Dai, Zhehao

2018-01-01

This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established. Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes. Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma.

An integrated bioinformatics approach to improve two-color microarray quality-control: impact on biological conclusions.

PubMed

van Haaften, Rachel I M; Luceri, Cristina; van Erk, Arie; Evelo, Chris T A

2009-06-01

Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the need of an extensive bioinformatics analyses for array quality assessment before and after gene expression clustering and pathway analysis. A study focused on the effect of red wine polyphenols on rat colon mucosa was used to test the impact of quality control and normalisation steps on the biological conclusions. The integration of data visualization, pathway analysis and clustering revealed an artifact problem that was solved with an adapted normalisation. We propose a possible point to point standard analysis procedure, based on a combination of clustering and data visualization for the analysis of microarray data.

Rapid, comprehensive, and affordable mycobacterial diagnosis with whole-genome sequencing: a prospective study

PubMed Central

Pankhurst, Louise J; del Ojo Elias, Carlos; Votintseva, Antonina A; Walker, Timothy M; Cole, Kevin; Davies, Jim; Fermont, Jilles M; Gascoyne-Binzi, Deborah M; Kohl, Thomas A; Kong, Clare; Lemaitre, Nadine; Niemann, Stefan; Paul, John; Rogers, Thomas R; Roycroft, Emma; Smith, E Grace; Supply, Philip; Tang, Patrick; Wilcox, Mark H; Wordsworth, Sarah; Wyllie, David; Xu, Li; Crook, Derrick W

2016-01-01

Summary Background Slow and cumbersome laboratory diagnostics for Mycobacterium tuberculosis complex (MTBC) risk delayed treatment and poor patient outcomes. Whole-genome sequencing (WGS) could potentially provide a rapid and comprehensive diagnostic solution. In this prospective study, we compare real-time WGS with routine MTBC diagnostic workflows. Methods We compared sequencing mycobacteria from all newly positive liquid cultures with routine laboratory diagnostic workflows across eight laboratories in Europe and North America for diagnostic accuracy, processing times, and cost between Sept 6, 2013, and April 14, 2014. We sequenced specimens once using local Illumina MiSeq platforms and processed data centrally using a semi-automated bioinformatics pipeline. We identified species or complex using gene presence or absence, predicted drug susceptibilities from resistance-conferring mutations identified from reference-mapped MTBC genomes, and calculated genetic distance to previously sequenced UK MTBC isolates to detect outbreaks. WGS data processing and analysis was done by staff masked to routine reference laboratory and clinical results. We also did a microcosting analysis to assess the financial viability of WGS-based diagnostics. Findings Compared with routine results, WGS predicted species with 93% (95% CI 90–96; 322 of 345 specimens; 356 mycobacteria specimens submitted) accuracy and drug susceptibility also with 93% (91–95; 628 of 672 specimens; 168 MTBC specimens identified) accuracy, with one sequencing attempt. WGS linked 15 (16% [95% CI 10–26]) of 91 UK patients to an outbreak. WGS diagnosed a case of multidrug-resistant tuberculosis before routine diagnosis was completed and discovered a new multidrug-resistant tuberculosis cluster. Full WGS diagnostics could be generated in a median of 9 days (IQR 6–10), a median of 21 days (IQR 14–32) faster than final reference laboratory reports were produced (median of 31 days [IQR 21–44]), at a cost of £481 per culture-positive specimen, whereas routine diagnosis costs £518, equating to a WGS-based diagnosis cost that is 7% cheaper annually than are present diagnostic workflows. Interpretation We have shown that WGS has a scalable, rapid turnaround, and is a financially feasible method for full MTBC diagnostics. Continued improvements to mycobacterial processing, bioinformatics, and analysis will improve the accuracy, speed, and scope of WGS-based diagnosis. Funding National Institute for Health Research, Department of Health, Wellcome Trust, British Colombia Centre for Disease Control Foundation for Population and Public Health, Department of Clinical Microbiology, Trinity College Dublin. PMID:26669893

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

PubMed

Barros, Rodrigo C; Winck, Ana T; Machado, Karina S; Basgalupp, Márcio P; de Carvalho, André C P L F; Ruiz, Duncan D; de Souza, Osmar Norberto

2012-11-21

This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

PubMed Central

2012-01-01

Background This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor. PMID:23171000

BpWrapper: BioPerl-based sequence and tree utilities for rapid prototyping of bioinformatics pipelines.

PubMed

Hernández, Yözen; Bernstein, Rocky; Pagan, Pedro; Vargas, Levy; McCaig, William; Ramrattan, Girish; Akther, Saymon; Larracuente, Amanda; Di, Lia; Vieira, Filipe G; Qiu, Wei-Gang

2018-03-02

Automated bioinformatics workflows are more robust, easier to maintain, and results more reproducible when built with command-line utilities than with custom-coded scripts. Command-line utilities further benefit by relieving bioinformatics developers to learn the use of, or to interact directly with, biological software libraries. There is however a lack of command-line utilities that leverage popular Open Source biological software toolkits such as BioPerl ( http://bioperl.org ) to make many of the well-designed, robust, and routinely used biological classes available for a wider base of end users. Designed as standard utilities for UNIX-family operating systems, BpWrapper makes functionality of some of the most popular BioPerl modules readily accessible on the command line to novice as well as to experienced bioinformatics practitioners. The initial release of BpWrapper includes four utilities with concise command-line user interfaces, bioseq, bioaln, biotree, and biopop, specialized for manipulation of molecular sequences, sequence alignments, phylogenetic trees, and DNA polymorphisms, respectively. Over a hundred methods are currently available as command-line options and new methods are easily incorporated. Performance of BpWrapper utilities lags that of precompiled utilities while equivalent to that of other utilities based on BioPerl. BpWrapper has been tested on BioPerl Release 1.6, Perl versions 5.10.1 to 5.25.10, and operating systems including Apple macOS, Microsoft Windows, and GNU/Linux. Release code is available from the Comprehensive Perl Archive Network (CPAN) at https://metacpan.org/pod/Bio::BPWrapper . Source code is available on GitHub at https://github.com/bioperl/p5-bpwrapper . BpWrapper improves on existing sequence utilities by following the design principles of Unix text utilities such including a concise user interface, extensive command-line options, and standard input/output for serialized operations. Further, dozens of novel methods for manipulation of sequences, alignments, and phylogenetic trees, unavailable in existing utilities (e.g., EMBOSS, Newick Utilities, and FAST), are provided. Bioinformaticians should find BpWrapper useful for rapid prototyping of workflows on the command-line without creating custom scripts for comparative genomics and other bioinformatics applications.

Genomics for Everyone

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

Chain, Patrick

Genomics — the genetic mapping and DNA sequencing of sets of genes or the complete genomes of organisms, along with related genome analysis and database work — is emerging as one of the transformative sciences of the 21st century. But current bioinformatics tools are not accessible to most biological researchers. Now, a new computational and web-based tool called EDGE Bioinformatics is working to fulfill the promise of democratizing genomics.

Genome-wide analysis of regulatory proteases sequences identified through bioinformatics data mining in Taenia solium.

PubMed

Yan, Hong-Bin; Lou, Zhong-Zi; Li, Li; Brindley, Paul J; Zheng, Yadong; Luo, Xuenong; Hou, Junling; Guo, Aijiang; Jia, Wan-Zhong; Cai, Xuepeng

2014-06-04

Cysticercosis remains a major neglected tropical disease of humanity in many regions, especially in sub-Saharan Africa, Central America and elsewhere. Owing to the emerging drug resistance and the inability of current drugs to prevent re-infection, identification of novel vaccines and chemotherapeutic agents against Taenia solium and related helminth pathogens is a public health priority. The T. solium genome and the predicted proteome were reported recently, providing a wealth of information from which new interventional targets might be identified. In order to characterize and classify the entire repertoire of protease-encoding genes of T. solium, which act fundamental biological roles in all life processes, we analyzed the predicted proteins of this cestode through a combination of bioinformatics tools. Functional annotation was performed to yield insights into the signaling processes relevant to the complex developmental cycle of this tapeworm and to highlight a suite of the proteases as potential intervention targets. Within the genome of this helminth parasite, we identified 200 open reading frames encoding proteases from five clans, which correspond to 1.68% of the 11,902 protein-encoding genes predicted to be present in its genome. These proteases include calpains, cytosolic, mitochondrial signal peptidases, ubiquitylation related proteins, and others. Many not only show significant similarity to proteases in the Conserved Domain Database but have conserved active sites and catalytic domains. KEGG Automatic Annotation Server (KAAS) analysis indicated that ~60% of these proteases share strong sequence identities with proteins of the KEGG database, which are involved in human disease, metabolic pathways, genetic information processes, cellular processes, environmental information processes and organismal systems. Also, we identified signal peptides and transmembrane helices through comparative analysis with classes of important regulatory proteases. Phylogenetic analysis using Bayes approach provided support for inferring functional divergence among regulatory cysteine and serine proteases. Numerous putative proteases were identified for the first time in T. solium, and important regulatory proteases have been predicted. This comprehensive analysis not only complements the growing knowledge base of proteolytic enzymes, but also provides a platform from which to expand knowledge of cestode proteases and to explore their biochemistry and potential as intervention targets.

An architecture for genomics analysis in a clinical setting using Galaxy and Docker

PubMed Central

Digan, W; Countouris, H; Barritault, M; Baudoin, D; Laurent-Puig, P; Blons, H; Burgun, A

2017-01-01

Abstract Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e.g., in cancer) and to assist in the selection of the optimal treatment. Clinical bioinformatics handles the manipulation of the data generated by the sequencer, from the generation to the analysis and interpretation. Reproducibility and traceability are crucial issues in a clinical setting. We have designed an approach based on Docker container technology and Galaxy, the popular bioinformatics analysis support open-source software. Our solution simplifies the deployment of a small-size analytical platform and simplifies the process for the clinician. From the technical point of view, the tools embedded in the platform are isolated and versioned through Docker images. Along the Galaxy platform, we also introduce the AnalysisManager, a solution that allows single-click analysis for biologists and leverages standardized bioinformatics application programming interfaces. We added a Shiny/R interactive environment to ease the visualization of the outputs. The platform relies on containers and ensures the data traceability by recording analytical actions and by associating inputs and outputs of the tools to EDAM ontology through ReGaTe. The source code is freely available on Github at https://github.com/CARPEM/GalaxyDocker. PMID:29048555

An architecture for genomics analysis in a clinical setting using Galaxy and Docker.

PubMed

Digan, W; Countouris, H; Barritault, M; Baudoin, D; Laurent-Puig, P; Blons, H; Burgun, A; Rance, B

2017-11-01

Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e.g., in cancer) and to assist in the selection of the optimal treatment. Clinical bioinformatics handles the manipulation of the data generated by the sequencer, from the generation to the analysis and interpretation. Reproducibility and traceability are crucial issues in a clinical setting. We have designed an approach based on Docker container technology and Galaxy, the popular bioinformatics analysis support open-source software. Our solution simplifies the deployment of a small-size analytical platform and simplifies the process for the clinician. From the technical point of view, the tools embedded in the platform are isolated and versioned through Docker images. Along the Galaxy platform, we also introduce the AnalysisManager, a solution that allows single-click analysis for biologists and leverages standardized bioinformatics application programming interfaces. We added a Shiny/R interactive environment to ease the visualization of the outputs. The platform relies on containers and ensures the data traceability by recording analytical actions and by associating inputs and outputs of the tools to EDAM ontology through ReGaTe. The source code is freely available on Github at https://github.com/CARPEM/GalaxyDocker. © The Author 2017. Published by Oxford University Press.

An overview of topic modeling and its current applications in bioinformatics.

PubMed

Liu, Lin; Tang, Lin; Dong, Wen; Yao, Shaowen; Zhou, Wei

2016-01-01

With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers' ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research.

A generally applicable lightweight method for calculating a value structure for tools and services in bioinformatics infrastructure projects.

PubMed

Mayer, Gerhard; Quast, Christian; Felden, Janine; Lange, Matthias; Prinz, Manuel; Pühler, Alfred; Lawerenz, Chris; Scholz, Uwe; Glöckner, Frank Oliver; Müller, Wolfgang; Marcus, Katrin; Eisenacher, Martin

2017-10-30

Sustainable noncommercial bioinformatics infrastructures are a prerequisite to use and take advantage of the potential of big data analysis for research and economy. Consequently, funders, universities and institutes as well as users ask for a transparent value model for the tools and services offered. In this article, a generally applicable lightweight method is described by which bioinformatics infrastructure projects can estimate the value of tools and services offered without determining exactly the total costs of ownership. Five representative scenarios for value estimation from a rough estimation to a detailed breakdown of costs are presented. To account for the diversity in bioinformatics applications and services, the notion of service-specific 'service provision units' is introduced together with the factors influencing them and the main underlying assumptions for these 'value influencing factors'. Special attention is given on how to handle personnel costs and indirect costs such as electricity. Four examples are presented for the calculation of the value of tools and services provided by the German Network for Bioinformatics Infrastructure (de.NBI): one for tool usage, one for (Web-based) database analyses, one for consulting services and one for bioinformatics training events. Finally, from the discussed values, the costs of direct funding and the costs of payment of services by funded projects are calculated and compared. © The Author 2017. Published by Oxford University Press.

PsyGeNET: a knowledge platform on psychiatric disorders and their genes.

PubMed

Gutiérrez-Sacristán, Alba; Grosdidier, Solène; Valverde, Olga; Torrens, Marta; Bravo, Àlex; Piñero, Janet; Sanz, Ferran; Furlong, Laura I

2015-09-15

PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data search, visualization, filtering and sharing. PsyGeNET integrates information from DisGeNET and data extracted from the literature by text mining, which has been curated by domain experts. It currently contains 2642 associations between 1271 genes and 37 psychiatric disease concepts. In its first release, PsyGeNET is focused on three psychiatric disorders: major depression, alcohol and cocaine use disorders. PsyGeNET represents a comprehensive, open access resource for the analysis of the molecular mechanisms underpinning psychiatric disorders and their comorbidities. The PysGeNET platform is freely available at http://www.psygenet.org/. The PsyGeNET database is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). lfurlong@imim.es Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Generation of comprehensive thoracic oncology database--tool for translational research.

PubMed

Surati, Mosmi; Robinson, Matthew; Nandi, Suvobroto; Faoro, Leonardo; Demchuk, Carley; Kanteti, Rajani; Ferguson, Benjamin; Gangadhar, Tara; Hensing, Thomas; Hasina, Rifat; Husain, Aliya; Ferguson, Mark; Karrison, Theodore; Salgia, Ravi

2011-01-22

The Thoracic Oncology Program Database Project was created to serve as a comprehensive, verified, and accessible repository for well-annotated cancer specimens and clinical data to be available to researchers within the Thoracic Oncology Research Program. This database also captures a large volume of genomic and proteomic data obtained from various tumor tissue studies. A team of clinical and basic science researchers, a biostatistician, and a bioinformatics expert was convened to design the database. Variables of interest were clearly defined and their descriptions were written within a standard operating manual to ensure consistency of data annotation. Using a protocol for prospective tissue banking and another protocol for retrospective banking, tumor and normal tissue samples from patients consented to these protocols were collected. Clinical information such as demographics, cancer characterization, and treatment plans for these patients were abstracted and entered into an Access database. Proteomic and genomic data have been included in the database and have been linked to clinical information for patients described within the database. The data from each table were linked using the relationships function in Microsoft Access to allow the database manager to connect clinical and laboratory information during a query. The queried data can then be exported for statistical analysis and hypothesis generation.

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

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.

The Use of Behavior Models for Predicting Complex Operations

NASA Technical Reports Server (NTRS)

Gore, Brian F.

2010-01-01

Modeling and simulation (M&S) plays an important role when complex human-system notions are being proposed, developed and tested within the system design process. National Aeronautics and Space Administration (NASA) as an agency uses many different types of M&S approaches for predicting human-system interactions, especially when it is early in the development phase of a conceptual design. NASA Ames Research Center possesses a number of M&S capabilities ranging from airflow, flight path models, aircraft models, scheduling models, human performance models (HPMs), and bioinformatics models among a host of other kinds of M&S capabilities that are used for predicting whether the proposed designs will benefit the specific mission criteria. The Man-Machine Integration Design and Analysis System (MIDAS) is a NASA ARC HPM software tool that integrates many models of human behavior with environment models, equipment models, and procedural / task models. The challenge to model comprehensibility is heightened as the number of models that are integrated and the requisite fidelity of the procedural sets are increased. Model transparency is needed for some of the more complex HPMs to maintain comprehensibility of the integrated model performance. This will be exemplified in a recent MIDAS v5 application model and plans for future model refinements will be presented.

BIOINFORMATICS IN THE K-8 CLASSROOM: DESIGNING INNOVATIVE ACTIVITIES FOR TEACHER IMPLEMENTATION

PubMed Central

Shuster, Michele; Claussen, Kira; Locke, Melly; Glazewski, Krista

2016-01-01

At the intersection of biology and computer science is the growing field of bioinformatics—the analysis of complex datasets of biological relevance. Despite the increasing importance of bioinformatics and associated practical applications, these are not standard topics in elementary and middle school classrooms. We report on a pilot project and its evolution to support implementation of bioinformatics-based activities in elementary and middle school classrooms. Specifically, we ultimately designed a multi-day summer teacher professional development workshop, in which teachers design innovative classroom activities. By focusing on teachers, our design leverages enhanced teacher knowledge and confidence to integrate innovative instructional materials into K-8 classrooms and contributes to capacity building in STEM instruction. PMID:27429860

Bioinformatics Analysis Reveals Genes Involved in the Pathogenesis of Ameloblastoma and Keratocystic Odontogenic Tumor.

PubMed

Santos, Eliane Macedo Sobrinho; Santos, Hércules Otacílio; Dos Santos Dias, Ivoneth; Santos, Sérgio Henrique; Batista de Paula, Alfredo Maurício; Feltenberger, John David; Sena Guimarães, André Luiz; Farias, Lucyana Conceição

2016-01-01

Pathogenesis of odontogenic tumors is not well known. It is important to identify genetic deregulations and molecular alterations. This study aimed to investigate, through bioinformatic analysis, the possible genes involved in the pathogenesis of ameloblastoma (AM) and keratocystic odontogenic tumor (KCOT). Genes involved in the pathogenesis of AM and KCOT were identified in GeneCards. Gene list was expanded, and the gene interactions network was mapped using the STRING software. "Weighted number of links" (WNL) was calculated to identify "leader genes" (highest WNL). Genes were ranked by K-means method and Kruskal-Wallis test was used (P<0.001). Total interactions score (TIS) was also calculated using all interaction data generated by the STRING database, in order to achieve global connectivity for each gene. The topological and ontological analyses were performed using Cytoscape software and BinGO plugin. Literature review data was used to corroborate the bioinformatics data. CDK1 was identified as leader gene for AM. In KCOT group, results show PCNA and TP53 . Both tumors exhibit a power law behavior. Our topological analysis suggested leader genes possibly important in the pathogenesis of AM and KCOT, by clustering coefficient calculated for both odontogenic tumors (0.028 for AM, zero for KCOT). The results obtained in the scatter diagram suggest an important relationship of these genes with the molecular processes involved in AM and KCOT. Ontological analysis for both AM and KCOT demonstrated different mechanisms. Bioinformatics analyzes were confirmed through literature review. These results may suggest the involvement of promising genes for a better understanding of the pathogenesis of AM and KCOT.

Proteomic Analysis Reveals the Contribution of TGFβ/Smad4 Signaling Pathway to Cell Differentiation During Planarian Tail Regeneration.

PubMed

Chen, Xiaoguang; Xu, Cunshuan

2017-06-01

After planarian tail is cut off, posterior end of the remaining fragment will regenerate a new tail within about 1 week. However, many details of this process remain unclear up to date. For this reason, we performed the dynamic proteomic analysis of the regenerating tail fragments at 6, 12, 24, 72, 120, and 168 h post-amputation (hpa). Using two-dimensional electrophoresis (2-DE) in combination with MALDI-TOF-TOF/MS analysis, a total of 1088 peptides were identified as significantly changed between tail-cutting groups and 0-h group, 482 of which have identifiable protein names. Of these 482 proteins, there were 111 originating from the Turbellaria. Protein functional categorization showed that these 111 proteins are mainly related to differentiation and development, transcription and translation, cell signal transduction, and cell proliferation. The screening of key protein considered the transcription factor Smad4 as important protein for planarian tail regeneration. Cell signaling pathway analysis, combined with proteomic profiling of regenerating tail fragment, showed that TGFβ/Smad4 pathway was activated during planarian tail regeneration. Based on a comprehensive analysis of 2-DE MALDI-TOF-TOF/MS and bioinformatics analyses, it could be concluded that TGFβ/Smad4 pathway perhaps plays an important role in tail regeneration via promoting cell differentiation.

antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences.

PubMed

Medema, Marnix H; Blin, Kai; Cimermancic, Peter; de Jager, Victor; Zakrzewski, Piotr; Fischbach, Michael A; Weber, Tilmann; Takano, Eriko; Breitling, Rainer

2011-07-01

Bacterial and fungal secondary metabolism is a rich source of novel bioactive compounds with potential pharmaceutical applications as antibiotics, anti-tumor drugs or cholesterol-lowering drugs. To find new drug candidates, microbiologists are increasingly relying on sequencing genomes of a wide variety of microbes. However, rapidly and reliably pinpointing all the potential gene clusters for secondary metabolites in dozens of newly sequenced genomes has been extremely challenging, due to their biochemical heterogeneity, the presence of unknown enzymes and the dispersed nature of the necessary specialized bioinformatics tools and resources. Here, we present antiSMASH (antibiotics & Secondary Metabolite Analysis Shell), the first comprehensive pipeline capable of identifying biosynthetic loci covering the whole range of known secondary metabolite compound classes (polyketides, non-ribosomal peptides, terpenes, aminoglycosides, aminocoumarins, indolocarbazoles, lantibiotics, bacteriocins, nucleosides, beta-lactams, butyrolactones, siderophores, melanins and others). It aligns the identified regions at the gene cluster level to their nearest relatives from a database containing all other known gene clusters, and integrates or cross-links all previously available secondary-metabolite specific gene analysis methods in one interactive view. antiSMASH is available at http://antismash.secondarymetabolites.org.

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

DOE PAGES

Wang, Pin; Wang, Yunshan; Hang, Bo; ...

2016-07-11

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

Comparative analysis of sterol acquisition in the oomycetes Saprolegnia parasitica and Phytophthora infestans

PubMed Central

Dahlin, Paul; Srivastava, Vaibhav; Ekengren, Sophia; McKee, Lauren S.; Bulone, Vincent

2017-01-01

The oomycete class includes pathogens of animals and plants which are responsible for some of the most significant global losses in agriculture and aquaculture. There is a need to replace traditional chemical means of controlling oomycete growth with more targeted approaches, and the inhibition of sterol synthesis is one promising area. To better direct these efforts, we have studied sterol acquisition in two model organisms: the sterol-autotrophic Saprolegnia parasitica, and the sterol-heterotrophic Phytophthora infestans. We first present a comprehensive reconstruction of a likely sterol synthesis pathway for S. parasitica, causative agent of the disease saprolegniasis in fish. This pathway shows multiple potential routes of sterol synthesis, and draws on several avenues of new evidence: bioinformatic mining for genes with sterol-related functions, expression analysis of these genes, and analysis of the sterol profiles in mycelium grown in different media. Additionally, we explore the extent to which P. infestans, which causes the late blight in potato, can modify exogenously provided sterols. We consider whether the two very different approaches to sterol acquisition taken by these pathogens represent any specific survival advantages or potential drug targets. PMID:28152045

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

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

Wang, Pin; Wang, Yunshan; Hang, Bo

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

Genomics for Everyone

ScienceCinema

Chain, Patrick

2018-05-31

Genomics — the genetic mapping and DNA sequencing of sets of genes or the complete genomes of organisms, along with related genome analysis and database work — is emerging as one of the transformative sciences of the 21st century. But current bioinformatics tools are not accessible to most biological researchers. Now, a new computational and web-based tool called EDGE Bioinformatics is working to fulfill the promise of democratizing genomics.

Integrative workflows for metagenomic analysis

PubMed Central

Ladoukakis, Efthymios; Kolisis, Fragiskos N.; Chatziioannou, Aristotelis A.

2014-01-01

The rapid evolution of all sequencing technologies, described by the term Next Generation Sequencing (NGS), have revolutionized metagenomic analysis. They constitute a combination of high-throughput analytical protocols, coupled to delicate measuring techniques, in order to potentially discover, properly assemble and map allelic sequences to the correct genomes, achieving particularly high yields for only a fraction of the cost of traditional processes (i.e., Sanger). From a bioinformatic perspective, this boils down to many GB of data being generated from each single sequencing experiment, rendering the management or even the storage, critical bottlenecks with respect to the overall analytical endeavor. The enormous complexity is even more aggravated by the versatility of the processing steps available, represented by the numerous bioinformatic tools that are essential, for each analytical task, in order to fully unveil the genetic content of a metagenomic dataset. These disparate tasks range from simple, nonetheless non-trivial, quality control of raw data to exceptionally complex protein annotation procedures, requesting a high level of expertise for their proper application or the neat implementation of the whole workflow. Furthermore, a bioinformatic analysis of such scale, requires grand computational resources, imposing as the sole realistic solution, the utilization of cloud computing infrastructures. In this review article we discuss different, integrative, bioinformatic solutions available, which address the aforementioned issues, by performing a critical assessment of the available automated pipelines for data management, quality control, and annotation of metagenomic data, embracing various, major sequencing technologies and applications. PMID:25478562

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines.

PubMed

Kim, Baekdoo; Ali, Thahmina; Lijeron, Carlos; Afgan, Enis; Krampis, Konstantinos

2017-08-01

Processing of next-generation sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized postanalysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers toward seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform. We present an approach for abstracting the complex data operations of multistep, bioinformatics pipelines for NGS data analysis. As examples, we have deployed 2 pipelines for RNA sequencing and chromatin immunoprecipitation sequencing, preconfigured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines as simply as running a single bioinformatics tool. This is achieved using a "meta-script" that automatically starts the Bio-Docklets and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface. The pipeline output is postprocessed by integration with the Visual Omics Explorer framework, providing interactive data visualizations that users can access through a web browser. Our goal is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts on any computing environment, whether a laboratory workstation, university computer cluster, or a cloud service provider. Beyond end users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets. © The Authors 2017. Published by Oxford University Press.

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines

PubMed Central

Kim, Baekdoo; Ali, Thahmina; Lijeron, Carlos; Afgan, Enis

2017-01-01

Abstract Processing of next-generation sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized postanalysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers toward seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform. We present an approach for abstracting the complex data operations of multistep, bioinformatics pipelines for NGS data analysis. As examples, we have deployed 2 pipelines for RNA sequencing and chromatin immunoprecipitation sequencing, preconfigured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines as simply as running a single bioinformatics tool. This is achieved using a “meta-script” that automatically starts the Bio-Docklets and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface. The pipeline output is postprocessed by integration with the Visual Omics Explorer framework, providing interactive data visualizations that users can access through a web browser. Our goal is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts on any computing environment, whether a laboratory workstation, university computer cluster, or a cloud service provider. Beyond end users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets. PMID:28854616

Crimean-Congo Hemorrhagic Fever Virus Gn Bioinformatic Analysis and Construction of a Recombinant Bacmid in Order to Express Gn by Baculovirus Expression System

PubMed Central

Rahpeyma, Mehdi; Fotouhi, Fatemeh; Makvandi, Manouchehr; Ghadiri, Ata; Samarbaf-Zadeh, Alireza

2015-01-01

Background Crimean-Congo hemorrhagic fever virus (CCHFV) is a member of the nairovirus, a genus in the Bunyaviridae family, which causes a life threatening disease in human. Currently, there is no vaccine against CCHFV and detailed structural analysis of CCHFV proteins remains undefined. The CCHFV M RNA segment encodes two viral surface glycoproteins known as Gn and Gc. Viral glycoproteins can be considered as key targets for vaccine development. Objectives The current study aimed to investigate structural bioinformatics of CCHFV Gn protein and design a construct to make a recombinant bacmid to express by baculovirus system. Materials and Methods To express the Gn protein in insect cells that can be used as antigen in animal model vaccine studies. Bioinformatic analysis of CCHFV Gn protein was performed and designed a construct and cloned into pFastBacHTb vector and a recombinant Gn-bacmid was generated by Bac to Bac system. Results Primary, secondary, and 3D structure of CCHFV Gn were obtained and PCR reaction with M13 forward and reverse primers confirmed the generation of recombinant bacmid DNA harboring Gn coding region under polyhedron promoter. Conclusions Characterization of the detailed structure of CCHFV Gn by bioinformatics software provides the basis for development of new experiments and construction of a recombinant bacmid harboring CCHFV Gn, which is valuable for designing a recombinant vaccine against deadly pathogens like CCHFV. PMID:26862379

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community.

PubMed

Krampis, Konstantinos; Booth, Tim; Chapman, Brad; Tiwari, Bela; Bicak, Mesude; Field, Dawn; Nelson, Karen E

2012-03-19

A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them.

Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community

PubMed Central

2012-01-01

Background A steep drop in the cost of next-generation sequencing during recent years has made the technology affordable to the majority of researchers, but downstream bioinformatic analysis still poses a resource bottleneck for smaller laboratories and institutes that do not have access to substantial computational resources. Sequencing instruments are typically bundled with only the minimal processing and storage capacity required for data capture during sequencing runs. Given the scale of sequence datasets, scientific value cannot be obtained from acquiring a sequencer unless it is accompanied by an equal investment in informatics infrastructure. Results Cloud BioLinux is a publicly accessible Virtual Machine (VM) that enables scientists to quickly provision on-demand infrastructures for high-performance bioinformatics computing using cloud platforms. Users have instant access to a range of pre-configured command line and graphical software applications, including a full-featured desktop interface, documentation and over 135 bioinformatics packages for applications including sequence alignment, clustering, assembly, display, editing, and phylogeny. Each tool's functionality is fully described in the documentation directly accessible from the graphical interface of the VM. Besides the Amazon EC2 cloud, we have started instances of Cloud BioLinux on a private Eucalyptus cloud installed at the J. Craig Venter Institute, and demonstrated access to the bioinformatic tools interface through a remote connection to EC2 instances from a local desktop computer. Documentation for using Cloud BioLinux on EC2 is available from our project website, while a Eucalyptus cloud image and VirtualBox Appliance is also publicly available for download and use by researchers with access to private clouds. Conclusions Cloud BioLinux provides a platform for developing bioinformatics infrastructures on the cloud. An automated and configurable process builds Virtual Machines, allowing the development of highly customized versions from a shared code base. This shared community toolkit enables application specific analysis platforms on the cloud by minimizing the effort required to prepare and maintain them. PMID:22429538

ESAP plus: a web-based server for EST-SSR marker development.

PubMed

Ponyared, Piyarat; Ponsawat, Jiradej; Tongsima, Sissades; Seresangtakul, Pusadee; Akkasaeng, Chutipong; Tantisuwichwong, Nathpapat

2016-12-22

Simple sequence repeats (SSRs) have become widely used as molecular markers in plant genetic studies due to their abundance, high allelic variation at each locus and simplicity to analyze using conventional PCR amplification. To study plants with unknown genome sequence, SSR markers from Expressed Sequence Tags (ESTs), which can be obtained from the plant mRNA (converted to cDNA), must be utilized. With the advent of high-throughput sequencing technology, huge EST sequence data have been generated and are now accessible from many public databases. However, SSR marker identification from a large in-house or public EST collection requires a computational pipeline that makes use of several standard bioinformatic tools to design high quality EST-SSR primers. Some of these computational tools are not users friendly and must be tightly integrated with reference genomic databases. A web-based bioinformatic pipeline, called EST Analysis Pipeline Plus (ESAP Plus), was constructed for assisting researchers to develop SSR markers from a large EST collection. ESAP Plus incorporates several bioinformatic scripts and some useful standard software tools necessary for the four main procedures of EST-SSR marker development, namely 1) pre-processing, 2) clustering and assembly, 3) SSR mining and 4) SSR primer design. The proposed pipeline also provides two alternative steps for reducing EST redundancy and identifying SSR loci. Using public sugarcane ESTs, ESAP Plus automatically executed the aforementioned computational pipeline via a simple web user interface, which was implemented using standard PHP, HTML, CSS and Java scripts. With ESAP Plus, users can upload raw EST data and choose various filtering options and parameters to analyze each of the four main procedures through this web interface. All input EST data and their predicted SSR results will be stored in the ESAP Plus MySQL database. Users will be notified via e-mail when the automatic process is completed and they can download all the results through the web interface. ESAP Plus is a comprehensive and convenient web-based bioinformatic tool for SSR marker development. ESAP Plus offers all necessary EST-SSR development processes with various adjustable options that users can easily use to identify SSR markers from a large EST collection. With familiar web interface, users can upload the raw EST using the data submission page and visualize/download the corresponding EST-SSR information from within ESAP Plus. ESAP Plus can handle considerably large EST datasets. This EST-SSR discovery tool can be accessed directly from: http://gbp.kku.ac.th/esap_plus/ .

Importance of databases of nucleic acids for bioinformatic analysis focused to genomics

NASA Astrophysics Data System (ADS)

Jimenez-Gutierrez, L. R.; Barrios-Hernández, C. J.; Pedraza-Ferreira, G. R.; Vera-Cala, L.; Martinez-Perez, F.

2016-08-01

Recently, bioinformatics has become a new field of science, indispensable in the analysis of millions of nucleic acids sequences, which are currently deposited in international databases (public or private); these databases contain information of genes, RNA, ORF, proteins, intergenic regions, including entire genomes from some species. The analysis of this information requires computer programs; which were renewed in the use of new mathematical methods, and the introduction of the use of artificial intelligence. In addition to the constant creation of supercomputing units trained to withstand the heavy workload of sequence analysis. However, it is still necessary the innovation on platforms that allow genomic analyses, faster and more effectively, with a technological understanding of all biological processes.

Identification of a novel rhabdovirus in Spodoptera frugiperda cell lines.

PubMed

Ma, Hailun; Galvin, Teresa A; Glasner, Dustin R; Shaheduzzaman, Syed; Khan, Arifa S

2014-06-01

The Sf9 cell line, derived from Spodoptera frugiperda, is used as a cell substrate for biological products, and no viruses have been reported in this cell line after extensive testing. We used degenerate PCR assays and massively parallel sequencing (MPS) to identify a novel RNA virus belonging to the order Mononegavirales in Sf9 cells. Sequence analysis of the assembled virus genome showed the presence of five open reading frames (ORFs) corresponding to the genes for the N, P, M, G, and L proteins in other rhabdoviruses and an unknown ORF of 111 amino acids located between the G- and L-protein genes. BLAST searches indicated that the S. frugiperda rhabdovirus (Sf-rhabdovirus) was related in a limited region of the L-protein gene to Taastrup virus, a newly discovered member of the Mononegavirales from a leafhopper (Hemiptera), and also to plant rhabdoviruses, particularly in the genus Cytorhabdovirus. Phylogenetic analysis of sequences in the L-protein gene indicated that Sf-rhabdovirus is a novel virus that branched with Taastrup virus. Rhabdovirus morphology was confirmed by transmission electron microscopy of filtered supernatant samples from Sf9 cells. Infectivity studies indicated potential transient infection by Sf-rhabdovirus in other insect cell lines, but there was no evidence of entry or virus replication in human cell lines. Sf-rhabdovirus sequences were also found in the Sf21 parental cell line of Sf9 cells but not in other insect cell lines, such as BT1-TN-5B1-4 (Tn5; High Five) cells and Schneider's Drosophila line 2 [D.Mel.(2); SL2] cells, indicating a species-specific infection. The results indicate that conventional methods may be complemented by state-of-the-art technologies with extensive bioinformatics analysis for identification of novel viruses. The Spodoptera frugiperda Sf9 cell line is used as a cell substrate for the development and manufacture of biological products. Extensive testing has not previously identified any viruses in this cell line. This paper reports on the identification and characterization of a novel rhabdovirus in Sf9 cells. This was accomplished through the use of next-generation sequencing platforms, de novo assembly tools, and extensive bioinformatics analysis. Rhabdovirus identification was further confirmed by transmission electron microscopy. Infectivity studies showed the lack of replication of Sf-rhabdovirus in human cell lines. The overall study highlights the use of a combinatorial testing approach including conventional methods and new technologies for evaluation of cell lines for unexpected viruses and use of comprehensive bioinformatics strategies for obtaining confident next-generation sequencing results. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

PubMed

Angiuoli, Samuel V; White, James R; Matalka, Malcolm; White, Owen; Fricke, W Florian

2011-01-01

The widespread popularity of genomic applications is threatened by the "bioinformatics bottleneck" resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.

Resources and Costs for Microbial Sequence Analysis Evaluated Using Virtual Machines and Cloud Computing

PubMed Central

Angiuoli, Samuel V.; White, James R.; Matalka, Malcolm; White, Owen; Fricke, W. Florian

2011-01-01

Background The widespread popularity of genomic applications is threatened by the “bioinformatics bottleneck” resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. Results We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Conclusions Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers. PMID:22028928

Metabolomics and malaria biology

PubMed Central

Lakshmanan, Viswanathan; Rhee, Kyu Y.; Daily, Johanna P.

2010-01-01

Metabolomics has ushered in a novel and multi-disciplinary realm in biological research. It has provided researchers with a platform to combine powerful biochemical, statistical, computational, and bioinformatics techniques to delve into the mysteries of biology and disease. The application of metabolomics to study malaria parasites represents a major advance in our approach towards gaining a more comprehensive perspective on parasite biology and disease etiology. This review attempts to highlight some of the important aspects of the field of metabolomics, and its ongoing and potential future applications to malaria research. PMID:20970461

RNA-Rocket: an RNA-Seq analysis resource for infectious disease research

PubMed Central

Warren, Andrew S.; Aurrecoechea, Cristina; Brunk, Brian; Desai, Prerak; Emrich, Scott; Giraldo-Calderón, Gloria I.; Harb, Omar; Hix, Deborah; Lawson, Daniel; Machi, Dustin; Mao, Chunhong; McClelland, Michael; Nordberg, Eric; Shukla, Maulik; Vosshall, Leslie B.; Wattam, Alice R.; Will, Rebecca; Yoo, Hyun Seung; Sobral, Bruno

2015-01-01

Motivation: RNA-Seq is a method for profiling transcription using high-throughput sequencing and is an important component of many research projects that wish to study transcript isoforms, condition specific expression and transcriptional structure. The methods, tools and technologies used to perform RNA-Seq analysis continue to change, creating a bioinformatics challenge for researchers who wish to exploit these data. Resources that bring together genomic data, analysis tools, educational material and computational infrastructure can minimize the overhead required of life science researchers. Results: RNA-Rocket is a free service that provides access to RNA-Seq and ChIP-Seq analysis tools for studying infectious diseases. The site makes available thousands of pre-indexed genomes, their annotations and the ability to stream results to the bioinformatics resources VectorBase, EuPathDB and PATRIC. The site also provides a combination of experimental data and metadata, examples of pre-computed analysis, step-by-step guides and a user interface designed to enable both novice and experienced users of RNA-Seq data. Availability and implementation: RNA-Rocket is available at rnaseq.pathogenportal.org. Source code for this project can be found at github.com/cidvbi/PathogenPortal. Contact: anwarren@vt.edu Supplementary information: Supplementary materials are available at Bioinformatics online. PMID:25573919

RNA-Rocket: an RNA-Seq analysis resource for infectious disease research.

PubMed

Warren, Andrew S; Aurrecoechea, Cristina; Brunk, Brian; Desai, Prerak; Emrich, Scott; Giraldo-Calderón, Gloria I; Harb, Omar; Hix, Deborah; Lawson, Daniel; Machi, Dustin; Mao, Chunhong; McClelland, Michael; Nordberg, Eric; Shukla, Maulik; Vosshall, Leslie B; Wattam, Alice R; Will, Rebecca; Yoo, Hyun Seung; Sobral, Bruno

2015-05-01

RNA-Seq is a method for profiling transcription using high-throughput sequencing and is an important component of many research projects that wish to study transcript isoforms, condition specific expression and transcriptional structure. The methods, tools and technologies used to perform RNA-Seq analysis continue to change, creating a bioinformatics challenge for researchers who wish to exploit these data. Resources that bring together genomic data, analysis tools, educational material and computational infrastructure can minimize the overhead required of life science researchers. RNA-Rocket is a free service that provides access to RNA-Seq and ChIP-Seq analysis tools for studying infectious diseases. The site makes available thousands of pre-indexed genomes, their annotations and the ability to stream results to the bioinformatics resources VectorBase, EuPathDB and PATRIC. The site also provides a combination of experimental data and metadata, examples of pre-computed analysis, step-by-step guides and a user interface designed to enable both novice and experienced users of RNA-Seq data. RNA-Rocket is available at rnaseq.pathogenportal.org. Source code for this project can be found at github.com/cidvbi/PathogenPortal. anwarren@vt.edu Supplementary materials are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Combining medical informatics and bioinformatics toward tools for personalized medicine.

PubMed

Sarachan, B D; Simmons, M K; Subramanian, P; Temkin, J M

2003-01-01

Key bioinformatics and medical informatics research areas need to be identified to advance knowledge and understanding of disease risk factors and molecular disease pathology in the 21 st century toward new diagnoses, prognoses, and treatments. Three high-impact informatics areas are identified: predictive medicine (to identify significant correlations within clinical data using statistical and artificial intelligence methods), along with pathway informatics and cellular simulations (that combine biological knowledge with advanced informatics to elucidate molecular disease pathology). Initial predictive models have been developed for a pilot study in Huntington's disease. An initial bioinformatics platform has been developed for the reconstruction and analysis of pathways, and work has begun on pathway simulation. A bioinformatics research program has been established at GE Global Research Center as an important technology toward next generation medical diagnostics. We anticipate that 21 st century medical research will be a combination of informatics tools with traditional biology wet lab research, and that this will translate to increased use of informatics techniques in the clinic.

Application of bioinformatics in chronobiology research.

PubMed

Lopes, Robson da Silva; Resende, Nathalia Maria; Honorio-França, Adenilda Cristina; França, Eduardo Luzía

2013-01-01

Bioinformatics and other well-established sciences, such as molecular biology, genetics, and biochemistry, provide a scientific approach for the analysis of data generated through "omics" projects that may be used in studies of chronobiology. The results of studies that apply these techniques demonstrate how they significantly aided the understanding of chronobiology. However, bioinformatics tools alone cannot eliminate the need for an understanding of the field of research or the data to be considered, nor can such tools replace analysts and researchers. It is often necessary to conduct an evaluation of the results of a data mining effort to determine the degree of reliability. To this end, familiarity with the field of investigation is necessary. It is evident that the knowledge that has been accumulated through chronobiology and the use of tools derived from bioinformatics has contributed to the recognition and understanding of the patterns and biological rhythms found in living organisms. The current work aims to develop new and important applications in the near future through chronobiology research.

GOBLET: The Global Organisation for Bioinformatics Learning, Education and Training

PubMed Central

Atwood, Teresa K.; Bongcam-Rudloff, Erik; Brazas, Michelle E.; Corpas, Manuel; Gaudet, Pascale; Lewitter, Fran; Mulder, Nicola; Palagi, Patricia M.; Schneider, Maria Victoria; van Gelder, Celia W. G.

2015-01-01

In recent years, high-throughput technologies have brought big data to the life sciences. The march of progress has been rapid, leaving in its wake a demand for courses in data analysis, data stewardship, computing fundamentals, etc., a need that universities have not yet been able to satisfy—paradoxically, many are actually closing “niche” bioinformatics courses at a time of critical need. The impact of this is being felt across continents, as many students and early-stage researchers are being left without appropriate skills to manage, analyse, and interpret their data with confidence. This situation has galvanised a group of scientists to address the problems on an international scale. For the first time, bioinformatics educators and trainers across the globe have come together to address common needs, rising above institutional and international boundaries to cooperate in sharing bioinformatics training expertise, experience, and resources, aiming to put ad hoc training practices on a more professional footing for the benefit of all. PMID:25856076

Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets.

PubMed

Rideout, Jai Ram; Chase, John H; Bolyen, Evan; Ackermann, Gail; González, Antonio; Knight, Rob; Caporaso, J Gregory

2016-06-13

Bioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis. We present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others. Keemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes.

Evaluation of Quality Assessment Protocols for High Throughput Genome Resequencing Data

PubMed Central

Chiara, Matteo; Pavesi, Giulio

2017-01-01

Large-scale initiatives aiming to recover the complete sequence of thousands of human genomes are currently being undertaken worldwide, concurring to the generation of a comprehensive catalog of human genetic variation. The ultimate and most ambitious goal of human population scale genomics is the characterization of the so-called human “variome,” through the identification of causal mutations or haplotypes. Several research institutions worldwide currently use genotyping assays based on Next-Generation Sequencing (NGS) for diagnostics and clinical screenings, and the widespread application of such technologies promises major revolutions in medical science. Bioinformatic analysis of human resequencing data is one of the main factors limiting the effectiveness and general applicability of NGS for clinical studies. The requirement for multiple tools, to be combined in dedicated protocols in order to accommodate different types of data (gene panels, exomes, or whole genomes) and the high variability of the data makes difficult the establishment of a ultimate strategy of general use. While there already exist several studies comparing sensitivity and accuracy of bioinformatic pipelines for the identification of single nucleotide variants from resequencing data, little is known about the impact of quality assessment and reads pre-processing strategies. In this work we discuss major strengths and limitations of the various genome resequencing protocols are currently used in molecular diagnostics and for the discovery of novel disease-causing mutations. By taking advantage of publicly available data we devise and suggest a series of best practices for the pre-processing of the data that consistently improve the outcome of genotyping with minimal impacts on computational costs. PMID:28736571

Illuminate Knowledge Elements in Geoscience Literature

NASA Astrophysics Data System (ADS)

Ma, X.; Zheng, J. G.; Wang, H.; Fox, P. A.

2015-12-01

There are numerous dark data hidden in geoscience literature. Efficient retrieval and reuse of those data will greatly benefit geoscience researches of nowadays. Among the works of data rescue, a topic of interest is illuminating the knowledge framework, i.e. entities and relationships, embedded in documents. Entity recognition and linking have received extensive attention in news and social media analysis, as well as in bioinformatics. In the domain of geoscience, however, such works are limited. We will present our work on how to use knowledge bases on the Web, such as ontologies and vocabularies, to facilitate entity recognition and linking in geoscience literature. The work deploys an un-supervised collective inference approach [1] to link entity mentions in unstructured texts to a knowledge base, which leverages the meaningful information and structures in ontologies and vocabularies for similarity computation and entity ranking. Our work is still in the initial stage towards the detection of knowledge frameworks in literature, and we have been collecting geoscience ontologies and vocabularies in order to build a comprehensive geoscience knowledge base [2]. We hope the work will initiate new ideas and collaborations on dark data rescue, as well as on the synthesis of data and knowledge from geoscience literature. References: 1. Zheng, J., Howsmon, D., Zhang, B., Hahn, J., McGuinness, D.L., Hendler, J., and Ji, H. 2014. Entity linking for biomedical literature. In Proceedings of ACM 8th International Workshop on Data and Text Mining in Bioinformatics, Shanghai, China. 2. Ma, X. Zheng, J., 2015. Linking geoscience entity mentions to the Web of Data. ESIP 2015 Summer Meeting, Pacific Grove, CA.

Bioinformatics analyses of Shigella CRISPR structure and spacer classification.

PubMed

Wang, Pengfei; Zhang, Bing; Duan, Guangcai; Wang, Yingfang; Hong, Lijuan; Wang, Linlin; Guo, Xiangjiao; Xi, Yuanlin; Yang, Haiyan

2016-03-01

Clustered regularly interspaced short palindromic repeats (CRISPR) are inheritable genetic elements of a variety of archaea and bacteria and indicative of the bacterial ecological adaptation, conferring acquired immunity against invading foreign nucleic acids. Shigella is an important pathogen for anthroponosis. This study aimed to analyze the features of Shigella CRISPR structure and classify the spacers through bioinformatics approach. Among 107 Shigella, 434 CRISPR structure loci were identified with two to seven loci in different strains. CRISPR-Q1, CRISPR-Q4 and CRISPR-Q5 were widely distributed in Shigella strains. Comparison of the first and last repeats of CRISPR1, CRISPR2 and CRISPR3 revealed several base variants and different stem-loop structures. A total of 259 cas genes were found among these 107 Shigella strains. The cas gene deletions were discovered in 88 strains. However, there is one strain that does not contain cas gene. Intact clusters of cas genes were found in 19 strains. From comprehensive analysis of sequence signature and BLAST and CRISPRTarget score, the 708 spacers were classified into three subtypes: Type I, Type II and Type III. Of them, Type I spacer referred to those linked with one gene segment, Type II spacer linked with two or more different gene segments, and Type III spacer undefined. This study examined the diversity of CRISPR/cas system in Shigella strains, demonstrated the main features of CRISPR structure and spacer classification, which provided critical information for elucidation of the mechanisms of spacer formation and exploration of the role the spacers play in the function of the CRISPR/cas system.

Reactome diagram viewer: data structures and strategies to boost performance.

PubMed

Fabregat, Antonio; Sidiropoulos, Konstantinos; Viteri, Guilherme; Marin-Garcia, Pablo; Ping, Peipei; Stein, Lincoln; D'Eustachio, Peter; Hermjakob, Henning

2018-04-01

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources. Reactome is available online at: https://reactome.org. The diagram viewer is part of the Reactome pathway browser (https://reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://reactome.org/dev/diagram/. The source code is freely available at: https://github.com/reactome-pwp/diagram. fabregat@ebi.ac.uk or hhe@ebi.ac.uk. Supplementary data are available at Bioinformatics online.

Recommendations for Accurate Resolution of Gene and Isoform Allele-Specific Expression in RNA-Seq Data

PubMed Central

Wood, David L. A.; Nones, Katia; Steptoe, Anita; Christ, Angelika; Harliwong, Ivon; Newell, Felicity; Bruxner, Timothy J. C.; Miller, David; Cloonan, Nicole; Grimmond, Sean M.

2015-01-01

Genetic variation modulates gene expression transcriptionally or post-transcriptionally, and can profoundly alter an individual’s phenotype. Measuring allelic differential expression at heterozygous loci within an individual, a phenomenon called allele-specific expression (ASE), can assist in identifying such factors. Massively parallel DNA and RNA sequencing and advances in bioinformatic methodologies provide an outstanding opportunity to measure ASE genome-wide. In this study, matched DNA and RNA sequencing, genotyping arrays and computationally phased haplotypes were integrated to comprehensively and conservatively quantify ASE in a single human brain and liver tissue sample. We describe a methodological evaluation and assessment of common bioinformatic steps for ASE quantification, and recommend a robust approach to accurately measure SNP, gene and isoform ASE through the use of personalized haplotype genome alignment, strict alignment quality control and intragenic SNP aggregation. Our results indicate that accurate ASE quantification requires careful bioinformatic analyses and is adversely affected by sample specific alignment confounders and random sampling even at moderate sequence depths. We identified multiple known and several novel ASE genes in liver, including WDR72, DSP and UBD, as well as genes that contained ASE SNPs with imbalance direction discordant with haplotype phase, explainable by annotated transcript structure, suggesting isoform derived ASE. The methods evaluated in this study will be of use to researchers performing highly conservative quantification of ASE, and the genes and isoforms identified as ASE of interest to researchers studying those loci. PMID:25965996

BioPig: a Hadoop-based analytic toolkit for large-scale sequence data.

PubMed

Nordberg, Henrik; Bhatia, Karan; Wang, Kai; Wang, Zhong

2013-12-01

The recent revolution in sequencing technologies has led to an exponential growth of sequence data. As a result, most of the current bioinformatics tools become obsolete as they fail to scale with data. To tackle this 'data deluge', here we introduce the BioPig sequence analysis toolkit as one of the solutions that scale to data and computation. We built BioPig on the Apache's Hadoop MapReduce system and the Pig data flow language. Compared with traditional serial and MPI-based algorithms, BioPig has three major advantages: first, BioPig's programmability greatly reduces development time for parallel bioinformatics applications; second, testing BioPig with up to 500 Gb sequences demonstrates that it scales automatically with size of data; and finally, BioPig can be ported without modification on many Hadoop infrastructures, as tested with Magellan system at National Energy Research Scientific Computing Center and the Amazon Elastic Compute Cloud. In summary, BioPig represents a novel program framework with the potential to greatly accelerate data-intensive bioinformatics analysis.

A comparative proteomic strategy for subcellular proteome research: ICAT approach coupled with bioinformatics prediction to ascertain rat liver mitochondrial proteins and indication of mitochondrial localization for catalase.

PubMed

Jiang, Xiao-Sheng; Dai, Jie; Sheng, Quan-Hu; Zhang, Lei; Xia, Qi-Chang; Wu, Jia-Rui; Zeng, Rong

2005-01-01

Subcellular proteomics, as an important step to functional proteomics, has been a focus in proteomic research. However, the co-purification of "contaminating" proteins has been the major problem in all the subcellular proteomic research including all kinds of mitochondrial proteome research. It is often difficult to conclude whether these "contaminants" represent true endogenous partners or artificial associations induced by cell disruption or incomplete purification. To solve such a problem, we applied a high-throughput comparative proteome experimental strategy, ICAT approach performed with two-dimensional LC-MS/MS analysis, coupled with combinational usage of different bioinformatics tools, to study the proteome of rat liver mitochondria prepared with traditional centrifugation (CM) or further purified with a Nycodenz gradient (PM). A total of 169 proteins were identified and quantified convincingly in the ICAT analysis, in which 90 proteins have an ICAT ratio of PM:CM>1.0, while another 79 proteins have an ICAT ratio of PM:CM<1.0. Almost all the proteins annotated as mitochondrial according to Swiss-Prot annotation, bioinformatics prediction, and literature reports have a ratio of PM:CM>1.0, while proteins annotated as extracellular or secreted, cytoplasmic, endoplasmic reticulum, ribosomal, and so on have a ratio of PM:CM<1.0. Catalase and AP endonuclease 1, which have been known as peroxisomal and nuclear, respectively, have shown a ratio of PM:CM>1.0, confirming the reports about their mitochondrial location. Moreover, the 125 proteins with subcellular location annotation have been used as a testing dataset to evaluate the efficiency for ascertaining mitochondrial proteins by ICAT analysis and the bioinformatics tools such as PSORT, TargetP, SubLoc, MitoProt, and Predotar. The results indicated that ICAT analysis coupled with combinational usage of different bioinformatics tools could effectively ascertain mitochondrial proteins and distinguish contaminant proteins and even multilocation proteins. Using such a strategy, many novel proteins, known proteins without subcellular location annotation, and even known proteins that have been annotated as other locations have been strongly indicated for their mitochondrial location.

Bioinformatics: indispensable, yet hidden in plain sight?

PubMed

Bartlett, Andrew; Penders, Bart; Lewis, Jamie

2017-06-21

Bioinformatics has multitudinous identities, organisational alignments and disciplinary links. This variety allows bioinformaticians and bioinformatic work to contribute to much (if not most) of life science research in profound ways. The multitude of bioinformatic work also translates into a multitude of credit-distribution arrangements, apparently dismissing that work. We report on the epistemic and social arrangements that characterise the relationship between bioinformatics and life science. We describe, in sociological terms, the character, power and future of bioinformatic work. The character of bioinformatic work is such that its cultural, institutional and technical structures allow for it to be black-boxed easily. The result is that bioinformatic expertise and contributions travel easily and quickly, yet remain largely uncredited. The power of bioinformatic work is shaped by its dependency on life science work, which combined with the black-boxed character of bioinformatic expertise further contributes to situating bioinformatics on the periphery of the life sciences. Finally, the imagined futures of bioinformatic work suggest that bioinformatics will become ever more indispensable without necessarily becoming more visible, forcing bioinformaticians into difficult professional and career choices. Bioinformatic expertise and labour is epistemically central but often institutionally peripheral. In part, this is a result of the ways in which the character, power distribution and potential futures of bioinformatics are constituted. However, alternative paths can be imagined.

KISS for STRAP: user extensions for a protein alignment editor.

PubMed

Gille, Christoph; Lorenzen, Stephan; Michalsky, Elke; Frömmel, Cornelius

2003-12-12

The Structural Alignment Program STRAP is a comfortable comprehensive editor and analyzing tool for protein alignments. A wide range of functions related to protein sequences and protein structures are accessible with an intuitive graphical interface. Recent features include mapping of mutations and polymorphisms onto structures and production of high quality figures for publication. Here we address the general problem of multi-purpose program packages to keep up with the rapid development of bioinformatical methods and the demand for specific program functions. STRAP was remade implementing a novel design which aims at Keeping Interfaces in STRAP Simple (KISS). KISS renders STRAP extendable to bio-scientists as well as to bio-informaticians. Scientists with basic computer skills are capable of implementing statistical methods or embedding existing bioinformatical tools in STRAP themselves. For bio-informaticians STRAP may serve as an environment for rapid prototyping and testing of complex algorithms such as automatic alignment algorithms or phylogenetic methods. Further, STRAP can be applied as an interactive web applet to present data related to a particular protein family and as a teaching tool. JAVA-1.4 or higher. http://www.charite.de/bioinf/strap/

Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

PubMed

Liu, Zhi-Ping; Chen, Luonan

2016-01-01

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

Bellman’s GAP—a language and compiler for dynamic programming in sequence analysis

PubMed Central

Sauthoff, Georg; Möhl, Mathias; Janssen, Stefan; Giegerich, Robert

2013-01-01

Motivation: Dynamic programming is ubiquitous in bioinformatics. Developing and implementing non-trivial dynamic programming algorithms is often error prone and tedious. Bellman’s GAP is a new programming system, designed to ease the development of bioinformatics tools based on the dynamic programming technique. Results: In Bellman’s GAP, dynamic programming algorithms are described in a declarative style by tree grammars, evaluation algebras and products formed thereof. This bypasses the design of explicit dynamic programming recurrences and yields programs that are free of subscript errors, modular and easy to modify. The declarative modules are compiled into C++ code that is competitive to carefully hand-crafted implementations. This article introduces the Bellman’s GAP system and its language, GAP-L. It then demonstrates the ease of development and the degree of re-use by creating variants of two common bioinformatics algorithms. Finally, it evaluates Bellman’s GAP as an implementation platform of ‘real-world’ bioinformatics tools. Availability: Bellman’s GAP is available under GPL license from http://bibiserv.cebitec.uni-bielefeld.de/bellmansgap. This Web site includes a repository of re-usable modules for RNA folding based on thermodynamics. Contact: robert@techfak.uni-bielefeld.de Supplementary information: Supplementary data are available at Bioinformatics online PMID:23355290

Broad issues to consider for library involvement in bioinformatics*

PubMed Central

Geer, Renata C.

2006-01-01

Background: The information landscape in biological and medical research has grown far beyond literature to include a wide variety of databases generated by research fields such as molecular biology and genomics. The traditional role of libraries to collect, organize, and provide access to information can expand naturally to encompass these new data domains. Methods: This paper discusses the current and potential role of libraries in bioinformatics using empirical evidence and experience from eleven years of work in user services at the National Center for Biotechnology Information. Findings: Medical and science libraries over the last decade have begun to establish educational and support programs to address the challenges users face in the effective and efficient use of a plethora of molecular biology databases and retrieval and analysis tools. As more libraries begin to establish a role in this area, the issues they face include assessment of user needs and skills, identification of existing services, development of plans for new services, recruitment and training of specialized staff, and establishment of collaborations with bioinformatics centers at their institutions. Conclusions: Increasing library involvement in bioinformatics can help address information needs of a broad range of students, researchers, and clinicians and ultimately help realize the power of bioinformatics resources in making new biological discoveries. PMID:16888662

Open Reading Frame Phylogenetic Analysis on the Cloud

PubMed Central

2013-01-01

Phylogenetic analysis has become essential in researching the evolutionary relationships between viruses. These relationships are depicted on phylogenetic trees, in which viruses are grouped based on sequence similarity. Viral evolutionary relationships are identified from open reading frames rather than from complete sequences. Recently, cloud computing has become popular for developing internet-based bioinformatics tools. Biocloud is an efficient, scalable, and robust bioinformatics computing service. In this paper, we propose a cloud-based open reading frame phylogenetic analysis service. The proposed service integrates the Hadoop framework, virtualization technology, and phylogenetic analysis methods to provide a high-availability, large-scale bioservice. In a case study, we analyze the phylogenetic relationships among Norovirus. Evolutionary relationships are elucidated by aligning different open reading frame sequences. The proposed platform correctly identifies the evolutionary relationships between members of Norovirus. PMID:23671843

SoS Notebook: An Interactive Multi-Language Data Analysis Environment.

PubMed

Peng, Bo; Wang, Gao; Ma, Jun; Leong, Man Chong; Wakefield, Chris; Melott, James; Chiu, Yulun; Du, Di; Weinstein, John N

2018-05-22

Complex bioinformatic data analysis workflows involving multiple scripts in different languages can be difficult to consolidate, share, and reproduce. An environment that streamlines the entire processes of data collection, analysis, visualization and reporting of such multi-language analyses is currently lacking. We developed Script of Scripts (SoS) Notebook, a web-based notebook environment that allows the use of multiple scripting language in a single notebook, with data flowing freely within and across languages. SoS Notebook enables researchers to perform sophisticated bioinformatic analysis using the most suitable tools for different parts of the workflow, without the limitations of a particular language or complications of cross-language communications. SoS Notebook is hosted at http://vatlab.github.io/SoS/ and is distributed under a BSD license. bpeng@mdanderson.org.

Integrating Epigenomics into the Understanding of Biomedical Insight.

PubMed

Han, Yixing; He, Ximiao

2016-01-01

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

Integrating Epigenomics into the Understanding of Biomedical Insight

PubMed Central

Han, Yixing; He, Ximiao

2016-01-01

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics. PMID:27980397

Saccharomyces genome database informs human biology.

PubMed

Skrzypek, Marek S; Nash, Robert S; Wong, Edith D; MacPherson, Kevin A; Hellerstedt, Sage T; Engel, Stacia R; Karra, Kalpana; Weng, Shuai; Sheppard, Travis K; Binkley, Gail; Simison, Matt; Miyasato, Stuart R; Cherry, J Michael

2018-01-04

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is an expertly curated database of literature-derived functional information for the model organism budding yeast, Saccharomyces cerevisiae. SGD constantly strives to synergize new types of experimental data and bioinformatics predictions with existing data, and to organize them into a comprehensive and up-to-date information resource. The primary mission of SGD is to facilitate research into the biology of yeast and to provide this wealth of information to advance, in many ways, research on other organisms, even those as evolutionarily distant as humans. To build such a bridge between biological kingdoms, SGD is curating data regarding yeast-human complementation, in which a human gene can successfully replace the function of a yeast gene, and/or vice versa. These data are manually curated from published literature, made available for download, and incorporated into a variety of analysis tools provided by SGD. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Screening of matrix metalloproteinases available from the protein data bank: insights into biological functions, domain organization, and zinc binding groups.

PubMed

Nicolotti, Orazio; Miscioscia, Teresa Fabiola; Leonetti, Francesco; Muncipinto, Giovanni; Carotti, Angelo

2007-01-01

A total of 142 matrix metalloproteinase (MMP) X-ray crystallographic structures were retrieved from the Protein Data Bank (PDB) and analyzed by an automated and efficient routine, developed in-house, with a series of bioinformatic tools. Highly informative heat maps and hierarchical clusterograms provided a reliable and comprehensive representation of the relationships existing among MMPs, enlarging and complementing the current knowledge in the field. Multiple sequence and structural alignments permitted better location and display of key MMP motifs and quantification of the residue consensus at each amino acid position in the most critical binding subsites of MMPs. The MMP active site consensus sequences, the C-alpha root-mean-square deviation (RMSd) analysis of diverse enzymatic subsites, and the examination of the chemical nature, binding topologies, and zinc binding groups (ZBGs) of ligands extracted from crystallographic complexes provided useful insights on the structural arrangements of the most potent MMP inhibitors.

Phylogenetic-Derived Insights into the Evolution of Sialylation in Eukaryotes: Comprehensive Analysis of Vertebrate β-Galactoside α2,3/6-Sialyltransferases (ST3Gal and ST6Gal)

PubMed Central

Teppa, Roxana E.; Petit, Daniel; Plechakova, Olga; Cogez, Virginie; Harduin-Lepers, Anne

2016-01-01

Cell surface of eukaryotic cells is covered with a wide variety of sialylated molecules involved in diverse biological processes and taking part in cell–cell interactions. Although the physiological relevance of these sialylated glycoconjugates in vertebrates begins to be deciphered, the origin and evolution of the genetic machinery implicated in their biosynthetic pathway are poorly understood. Among the variety of actors involved in the sialylation machinery, sialyltransferases are key enzymes for the biosynthesis of sialylated molecules. This review focus on β-galactoside α2,3/6-sialyltransferases belonging to the ST3Gal and ST6Gal families. We propose here an outline of the evolutionary history of these two major ST families. Comparative genomics, molecular phylogeny and structural bioinformatics provided insights into the functional innovations in sialic acid metabolism and enabled to explore how ST-gene function evolved in vertebrates. PMID:27517905

Status and Future Perspectives of Utilizing Big Data in Neurosurgical and Stroke Research

PubMed Central

NISHIMURA, Ataru; NISHIMURA, Kunihiro; KADA, Akiko; IIHARA, Koji

2016-01-01

The management, analysis, and integration of Big Data have received increasing attention in healthcare research as well as in medical bioinformatics. The J-ASPECT study is the first nationwide survey in Japan on the real-world setting of stroke care using data obtained from the diagnosis procedure combination-based payment system. The J-ASPECT study demonstrated a significant association between comprehensive stroke care (CSC) capacity and the hospital volume of stroke interventions in Japan; further, it showed that CSC capabilities were associated with reduced in-hospital mortality rates. Our study aims to create new evidence and insight from ‘real world’ neurosurgical practice and stroke care in Japan using Big Data. The final aim of this study is to develop effective methods to bridge the evidence-practice gap in acute stroke healthcare. In this study, the authors describe the status and future perspectives of the development of a new method of stroke registry as a powerful tool for acute stroke care research. PMID:27680330

GenCLiP 2.0: a web server for functional clustering of genes and construction of molecular networks based on free terms.

PubMed

Wang, Jia-Hong; Zhao, Ling-Feng; Lin, Pei; Su, Xiao-Rong; Chen, Shi-Jun; Huang, Li-Qiang; Wang, Hua-Feng; Zhang, Hai; Hu, Zhen-Fu; Yao, Kai-Tai; Huang, Zhong-Xi

2014-09-01

Identifying biological functions and molecular networks in a gene list and how the genes may relate to various topics is of considerable value to biomedical researchers. Here, we present a web-based text-mining server, GenCLiP 2.0, which can analyze human genes with enriched keywords and molecular interactions. Compared with other similar tools, GenCLiP 2.0 offers two unique features: (i) analysis of gene functions with free terms (i.e. any terms in the literature) generated by literature mining or provided by the user and (ii) accurate identification and integration of comprehensive molecular interactions from Medline abstracts, to construct molecular networks and subnetworks related to the free terms. http://ci.smu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A new genome-mining tool redefines the lasso peptide biosynthetic landscape

PubMed Central

Tietz, Jonathan I.; Schwalen, Christopher J.; Patel, Parth S.; Maxson, Tucker; Blair, Patricia M.; Tai, Hua-Chia; Zakai, Uzma I.; Mitchell, Douglas A.

2016-01-01

Ribosomally synthesized and post-translationally modified peptide (RiPP) natural products are attractive for genome-driven discovery and re-engineering, but limitations in bioinformatic methods and exponentially increasing genomic data make large-scale mining difficult. We report RODEO (Rapid ORF Description and Evaluation Online), which combines hidden Markov model-based analysis, heuristic scoring, and machine learning to identify biosynthetic gene clusters and predict RiPP precursor peptides. We initially focused on lasso peptides, which display intriguing physiochemical properties and bioactivities, but their hypervariability renders them challenging prospects for automated mining. Our approach yielded the most comprehensive mapping of lasso peptide space, revealing >1,300 compounds. We characterized the structures and bioactivities of six lasso peptides, prioritized based on predicted structural novelty, including an unprecedented handcuff-like topology and another with a citrulline modification exceptionally rare among bacteria. These combined insights significantly expand the knowledge of lasso peptides, and more broadly, provide a framework for future genome-mining efforts. PMID:28244986

Bioinformatics identification and transcript profile analysis of the mitogen-activated protein kinase gene family in the diploid woodland strawberry Fragaria vesca

PubMed Central

Wei, Wei; Chai, Zhuangzhuang; Xie, Yinge; Gao, Kuan; Cui, Mengyuan; Jiang, Ying

2017-01-01

Mitogen-activated protein kinases (MAPKs) play essential roles in mediating biotic and abiotic stress responses in plants. However, the MAPK gene family in strawberry has not been systematically characterized. Here, we performed a genome-wide survey and identified 12 MAPK genes in the Fragaria vesca genome. Protein domain analysis indicated that all FvMAPKs have typical protein kinase domains. Sequence alignments and phylogenetic analysis classified the FvMAPK genes into four different groups. Conserved motif and exon-intron organization supported the evolutionary relationships inferred from the phylogenetic analysis. Analysis of the stress-related cis-regulatory element in the promoters and subcellular localization predictions of FvMAPKs were also performed. Gene transcript profile analysis showed that the majority of the FvMAPK genes were ubiquitously transcribed in strawberry leaves after Podosphaera aphanis inoculation and after treatment with cold, heat, drought, salt and the exogenous hormones abscisic acid, ethephon, methyl jasmonate, and salicylic acid. RT-qPCR showed that six selected FvMAPK genes comprehensively responded to various stimuli. Additionally, interaction networks revealed that the crucial signaling transduction controlled by FvMAPKs may be involved in the biotic and abiotic stress responses. Our results may provide useful information for future research on the function of the MAPK gene family and the genetic improvement of strawberry resistance to environmental stresses. PMID:28562633

Robust Bioinformatics Recognition with VLSI Biochip Microsystem

NASA Technical Reports Server (NTRS)

Lue, Jaw-Chyng L.; Fang, Wai-Chi

2006-01-01

A microsystem architecture for real-time, on-site, robust bioinformatic patterns recognition and analysis has been proposed. This system is compatible with on-chip DNA analysis means such as polymerase chain reaction (PCR)amplification. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function based on error backpropagation (EBP) algorithm is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.

Practical applications of the bioinformatics toolbox for narrowing quantitative trait loci.

PubMed

Burgess-Herbert, Sarah L; Cox, Allison; Tsaih, Shirng-Wern; Paigen, Beverly

2008-12-01

Dissecting the genes involved in complex traits can be confounded by multiple factors, including extensive epistatic interactions among genes, the involvement of epigenetic regulators, and the variable expressivity of traits. Although quantitative trait locus (QTL) analysis has been a powerful tool for localizing the chromosomal regions underlying complex traits, systematically identifying the causal genes remains challenging. Here, through its application to plasma levels of high-density lipoprotein cholesterol (HDL) in mice, we demonstrate a strategy for narrowing QTL that utilizes comparative genomics and bioinformatics techniques. We show how QTL detected in multiple crosses are subjected to both combined cross analysis and haplotype block analysis; how QTL from one species are mapped to the concordant regions in another species; and how genomewide scans associating haplotype groups with their phenotypes can be used to prioritize the narrowed regions. Then we illustrate how these individual methods for narrowing QTL can be systematically integrated for mouse chromosomes 12 and 15, resulting in a significantly reduced number of candidate genes, often from hundreds to <10. Finally, we give an example of how additional bioinformatics resources can be combined with experiments to determine the most likely quantitative trait genes.

Bioinformatics Pipelines for Targeted Resequencing and Whole-Exome Sequencing of Human and Mouse Genomes: A Virtual Appliance Approach for Instant Deployment

PubMed Central

Saeed, Isaam; Wong, Stephen Q.; Mar, Victoria; Goode, David L.; Caramia, Franco; Doig, Ken; Ryland, Georgina L.; Thompson, Ella R.; Hunter, Sally M.; Halgamuge, Saman K.; Ellul, Jason; Dobrovic, Alexander; Campbell, Ian G.; Papenfuss, Anthony T.; McArthur, Grant A.; Tothill, Richard W.

2014-01-01

Targeted resequencing by massively parallel sequencing has become an effective and affordable way to survey small to large portions of the genome for genetic variation. Despite the rapid development in open source software for analysis of such data, the practical implementation of these tools through construction of sequencing analysis pipelines still remains a challenging and laborious activity, and a major hurdle for many small research and clinical laboratories. We developed TREVA (Targeted REsequencing Virtual Appliance), making pre-built pipelines immediately available as a virtual appliance. Based on virtual machine technologies, TREVA is a solution for rapid and efficient deployment of complex bioinformatics pipelines to laboratories of all sizes, enabling reproducible results. The analyses that are supported in TREVA include: somatic and germline single-nucleotide and insertion/deletion variant calling, copy number analysis, and cohort-based analyses such as pathway and significantly mutated genes analyses. TREVA is flexible and easy to use, and can be customised by Linux-based extensions if required. TREVA can also be deployed on the cloud (cloud computing), enabling instant access without investment overheads for additional hardware. TREVA is available at http://bioinformatics.petermac.org/treva/. PMID:24752294

Ergatis: a web interface and scalable software system for bioinformatics workflows

PubMed Central

Orvis, Joshua; Crabtree, Jonathan; Galens, Kevin; Gussman, Aaron; Inman, Jason M.; Lee, Eduardo; Nampally, Sreenath; Riley, David; Sundaram, Jaideep P.; Felix, Victor; Whitty, Brett; Mahurkar, Anup; Wortman, Jennifer; White, Owen; Angiuoli, Samuel V.

2010-01-01

Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users. Results: We have developed a workflow management system named Ergatis that enables users to build, execute and monitor pipelines for computational analysis of genomics data. Ergatis contains preconfigured components and template pipelines for a number of common bioinformatics tasks such as prokaryotic genome annotation and genome comparisons. Outputs from many of these components can be loaded into a Chado relational database. Ergatis was designed to be accessible to a broad class of users and provides a user friendly, web-based interface. Ergatis supports high-throughput batch processing on distributed compute clusters and has been used for data management in a number of genome annotation and comparative genomics projects. Availability: Ergatis is an open-source project and is freely available at http://ergatis.sourceforge.net Contact: jorvis@users.sourceforge.net PMID:20413634

Technical advances in proteomics: new developments in data-independent acquisition.

PubMed

Hu, Alex; Noble, William S; Wolf-Yadlin, Alejandro

2016-01-01

The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.

Assessment of meat authenticity using bioinformatics, targeted peptide biomarkers and high-resolution mass spectrometry.

PubMed

Ruiz Orduna, Alberto; Husby, Erik; Yang, Charles T; Ghosh, Dipankar; Beaudry, Francis

2015-01-01

In recent years a significant increase of food fraud has been observed, ranging from false label claims to the use of additives and fillers to increase profitability. Recently in 2013 horse and pig DNAs were detected in beef products sold from several retailers. Mass spectrometry (MS) has become the workhorse in protein research, and the detection of marker proteins could serve for both animal species and tissue authentication. Meat species authenticity is performed in this paper using a well-defined proteogenomic annotation, carefully chosen surrogate tryptic peptides and analysis using a hybrid quadrupole-Orbitrap MS. Selected mammalian meat samples were homogenised and proteins were extracted and digested with trypsin. The samples were analysed using a high-resolution MS. Chromatography was achieved using a 30-min linear gradient along with a BioBasic C8 100 × 1 mm column at a flow rate of 75 µl min(-1). The MS was operated in full-scan high resolution and accurate mass. MS/MS spectra were collected for selected proteotypic peptides. Muscular proteins were methodically analysed in silico in order to generate tryptic peptide mass lists and theoretical MS/MS spectra. Following a comprehensive bottom-up proteomic analysis, we detected and identified a proteotypic myoglobin tryptic peptide (120-134) for each species with observed m/z below 1.3 ppm compared with theoretical values. Moreover, proteotypic peptides from myosin-1, myosin-2 and β-haemoglobin were also identified. This targeted method allowed comprehensive meat speciation down to 1% (w/w) of undesired product.

mORCA: sailing bioinformatics world with mobile devices.

PubMed

Díaz-Del-Pino, Sergio; Falgueras, Juan; Perez-Wohlfeil, Esteban; Trelles, Oswaldo

2018-03-01

Nearly 10 years have passed since the first mobile apps appeared. Given the fact that bioinformatics is a web-based world and that mobile devices are endowed with web-browsers, it seemed natural that bioinformatics would transit from personal computers to mobile devices but nothing could be further from the truth. The transition demands new paradigms, designs and novel implementations. Throughout an in-depth analysis of requirements of existing bioinformatics applications we designed and deployed an easy-to-use web-based lightweight mobile client. Such client is able to browse, select, compose automatically interface parameters, invoke services and monitor the execution of Web Services using the service's metadata stored in catalogs or repositories. mORCA is available at http://bitlab-es.com/morca/app as a web-app. It is also available in the App store by Apple and Play Store by Google. The software will be available for at least 2 years. ortrelles@uma.es. Source code, final web-app, training material and documentation is available at http://bitlab-es.com/morca. © The Author(s) 2017. Published by Oxford University Press.

A primer to frequent itemset mining for bioinformatics

PubMed Central

Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung Nghia; Vanden Berghe, Wim; Goethals, Bart

2015-01-01

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences. PMID:24162173

p3d--Python module for structural bioinformatics.

PubMed

Fufezan, Christian; Specht, Michael

2009-08-21

High-throughput bioinformatic analysis tools are needed to mine the large amount of structural data via knowledge based approaches. The development of such tools requires a robust interface to access the structural data in an easy way. For this the Python scripting language is the optimal choice since its philosophy is to write an understandable source code. p3d is an object oriented Python module that adds a simple yet powerful interface to the Python interpreter to process and analyse three dimensional protein structure files (PDB files). p3d's strength arises from the combination of a) very fast spatial access to the structural data due to the implementation of a binary space partitioning (BSP) tree, b) set theory and c) functions that allow to combine a and b and that use human readable language in the search queries rather than complex computer language. All these factors combined facilitate the rapid development of bioinformatic tools that can perform quick and complex analyses of protein structures. p3d is the perfect tool to quickly develop tools for structural bioinformatics using the Python scripting language.

Best practices in bioinformatics training for life scientists.

PubMed

Via, Allegra; Blicher, Thomas; Bongcam-Rudloff, Erik; Brazas, Michelle D; Brooksbank, Cath; Budd, Aidan; De Las Rivas, Javier; Dreyer, Jacqueline; Fernandes, Pedro L; van Gelder, Celia; Jacob, Joachim; Jimenez, Rafael C; Loveland, Jane; Moran, Federico; Mulder, Nicola; Nyrönen, Tommi; Rother, Kristian; Schneider, Maria Victoria; Attwood, Teresa K

2013-09-01

The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists.

Bellman's GAP--a language and compiler for dynamic programming in sequence analysis.

PubMed

Sauthoff, Georg; Möhl, Mathias; Janssen, Stefan; Giegerich, Robert

2013-03-01

Dynamic programming is ubiquitous in bioinformatics. Developing and implementing non-trivial dynamic programming algorithms is often error prone and tedious. Bellman's GAP is a new programming system, designed to ease the development of bioinformatics tools based on the dynamic programming technique. In Bellman's GAP, dynamic programming algorithms are described in a declarative style by tree grammars, evaluation algebras and products formed thereof. This bypasses the design of explicit dynamic programming recurrences and yields programs that are free of subscript errors, modular and easy to modify. The declarative modules are compiled into C++ code that is competitive to carefully hand-crafted implementations. This article introduces the Bellman's GAP system and its language, GAP-L. It then demonstrates the ease of development and the degree of re-use by creating variants of two common bioinformatics algorithms. Finally, it evaluates Bellman's GAP as an implementation platform of 'real-world' bioinformatics tools. Bellman's GAP is available under GPL license from http://bibiserv.cebitec.uni-bielefeld.de/bellmansgap. This Web site includes a repository of re-usable modules for RNA folding based on thermodynamics.

Best practices in bioinformatics training for life scientists

PubMed Central

Blicher, Thomas; Bongcam-Rudloff, Erik; Brazas, Michelle D.; Brooksbank, Cath; Budd, Aidan; De Las Rivas, Javier; Dreyer, Jacqueline; Fernandes, Pedro L.; van Gelder, Celia; Jacob, Joachim; Jimenez, Rafael C.; Loveland, Jane; Moran, Federico; Mulder, Nicola; Nyrönen, Tommi; Rother, Kristian; Schneider, Maria Victoria; Attwood, Teresa K.

2013-01-01

The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists. PMID:23803301

Crowdsourcing for bioinformatics

PubMed Central

Good, Benjamin M.; Su, Andrew I.

2013-01-01

Motivation: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base population and protein structure determination all benefit from human input. In some cases, people are needed in vast quantities, whereas in others, we need just a few with rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains. Results: Here, we provide a framework for understanding and applying several different types of crowdsourcing. The framework considers two broad classes: systems for solving large-volume ‘microtasks’ and systems for solving high-difficulty ‘megatasks’. Within these classes, we discuss system types, including volunteer labor, games with a purpose, microtask markets and open innovation contests. We illustrate each system type with successful examples in bioinformatics and conclude with a guide for matching problems to crowdsourcing solutions that highlights the positives and negatives of different approaches. Contact: bgood@scripps.edu PMID:23782614

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

The Comprehensive Antibiotic Resistance Database

PubMed Central

McArthur, Andrew G.; Waglechner, Nicholas; Nizam, Fazmin; Yan, Austin; Azad, Marisa A.; Baylay, Alison J.; Bhullar, Kirandeep; Canova, Marc J.; De Pascale, Gianfranco; Ejim, Linda; Kalan, Lindsay; King, Andrew M.; Koteva, Kalinka; Morar, Mariya; Mulvey, Michael R.; O'Brien, Jonathan S.; Pawlowski, Andrew C.; Piddock, Laura J. V.; Spanogiannopoulos, Peter; Sutherland, Arlene D.; Tang, Irene; Taylor, Patricia L.; Thaker, Maulik; Wang, Wenliang; Yan, Marie; Yu, Tennison

2013-01-01

The field of antibiotic drug discovery and the monitoring of new antibiotic resistance elements have yet to fully exploit the power of the genome revolution. Despite the fact that the first genomes sequenced of free living organisms were those of bacteria, there have been few specialized bioinformatic tools developed to mine the growing amount of genomic data associated with pathogens. In particular, there are few tools to study the genetics and genomics of antibiotic resistance and how it impacts bacterial populations, ecology, and the clinic. We have initiated development of such tools in the form of the Comprehensive Antibiotic Research Database (CARD; http://arpcard.mcmaster.ca). The CARD integrates disparate molecular and sequence data, provides a unique organizing principle in the form of the Antibiotic Resistance Ontology (ARO), and can quickly identify putative antibiotic resistance genes in new unannotated genome sequences. This unique platform provides an informatic tool that bridges antibiotic resistance concerns in health care, agriculture, and the environment. PMID:23650175

MULTI-DIMENSIONAL MASS SPECTROMETRY-BASED SHOTGUN LIPIDOMICS AND NOVEL STRATEGIES FOR LIPIDOMIC ANALYSES

PubMed Central

Han, Xianlin; Yang, Kui; Gross, Richard W.

2011-01-01

Since our last comprehensive review on multi-dimensional mass spectrometry-based shotgun lipidomics (Mass Spectrom. Rev. 24 (2005), 367), many new developments in the field of lipidomics have occurred. These developments include new strategies and refinements for shotgun lipidomic approaches that use direct infusion, including novel fragmentation strategies, identification of multiple new informative dimensions for mass spectrometric interrogation, and the development of new bioinformatic approaches for enhanced identification and quantitation of the individual molecular constituents that comprise each cell’s lipidome. Concurrently, advances in liquid chromatography-based platforms and novel strategies for quantitative matrix-assisted laser desorption/ionization mass spectrometry for lipidomic analyses have been developed. Through the synergistic use of this repertoire of new mass spectrometric approaches, the power and scope of lipidomics has been greatly expanded to accelerate progress toward the comprehensive understanding of the pleiotropic roles of lipids in biological systems. PMID:21755525

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.

Navigating the changing learning landscape: perspective from bioinformatics.ca

PubMed Central

Ouellette, B. F. Francis

2013-01-01

With the advent of YouTube channels in bioinformatics, open platforms for problem solving in bioinformatics, active web forums in computing analyses and online resources for learning to code or use a bioinformatics tool, the more traditional continuing education bioinformatics training programs have had to adapt. Bioinformatics training programs that solely rely on traditional didactic methods are being superseded by these newer resources. Yet such face-to-face instruction is still invaluable in the learning continuum. Bioinformatics.ca, which hosts the Canadian Bioinformatics Workshops, has blended more traditional learning styles with current online and social learning styles. Here we share our growing experiences over the past 12 years and look toward what the future holds for bioinformatics training programs. PMID:23515468

Serum proteome profiling in canine idiopathic dilated cardiomyopathy using TMT-based quantitative proteomics approach.

PubMed

Bilić, Petra; Guillemin, Nicolas; Kovačević, Alan; Beer Ljubić, Blanka; Jović, Ines; Galan, Asier; Eckersall, Peter David; Burchmore, Richard; Mrljak, Vladimir

2018-05-15

Idiopathic dilated cardiomyopathy (iDCM) is a primary myocardial disorder with an unknown aetiology, characterized by reduced contractility and ventricular dilation of the left or both ventricles. Naturally occurring canine iDCM was used herein to identify serum proteomic signature of the disease compared to the healthy state, providing an insight into underlying mechanisms and revealing proteins with biomarker potential. To achieve this, we used high-throughput label-based quantitative LC-MS/MS proteomics approach and bioinformatics analysis of the in silico inferred interactome protein network created from the initial list of differential proteins. To complement the proteomic analysis, serum biochemical parameters and levels of know biomarkers of cardiac function were measured. Several proteins with biomarker potential were identified, such as inter-alpha-trypsin inhibitor heavy chain H4, microfibril-associated glycoprotein 4 and apolipoprotein A-IV, which were validated using an independent method (Western blotting) and showed high specificity and sensitivity according to the receiver operating characteristic curve analysis. Bioinformatics analysis revealed involvement of different pathways in iDCM, such as complement cascade activation, lipoprotein particles dynamics, elastic fibre formation, GPCR signalling and respiratory electron transport chain. Idiopathic dilated cardiomyopathy is a severe primary myocardial disease of unknown cause, affecting both humans and dogs. This study is a contribution to the canine heart disease research by means of proteomic and bioinformatic state of the art analyses, following similar approach in human iDCM research. Importantly, we used serum as non-invasive and easily accessible biological source of information and contributed to the scarce data on biofluid proteome research on this topic. Bioinformatics analysis revealed biological pathways modulated in canine iDCM with potential of further targeted research. Also, several proteins with biomarker potential have been identified and successfully validated. Copyright © 2018 Elsevier B.V. All rights reserved.

CoryneBase: Corynebacterium Genomic Resources and Analysis Tools at Your Fingertips

PubMed Central

Tan, Mui Fern; Jakubovics, Nick S.; Wee, Wei Yee; Mutha, Naresh V. R.; Wong, Guat Jah; Ang, Mia Yang; Yazdi, Amir Hessam; Choo, Siew Woh

2014-01-01

Corynebacteria are used for a wide variety of industrial purposes but some species are associated with human diseases. With increasing number of corynebacterial genomes having been sequenced, comparative analysis of these strains may provide better understanding of their biology, phylogeny, virulence and taxonomy that may lead to the discoveries of beneficial industrial strains or contribute to better management of diseases. To facilitate the ongoing research of corynebacteria, a specialized central repository and analysis platform for the corynebacterial research community is needed to host the fast-growing amount of genomic data and facilitate the analysis of these data. Here we present CoryneBase, a genomic database for Corynebacterium with diverse functionality for the analysis of genomes aimed to provide: (1) annotated genome sequences of Corynebacterium where 165,918 coding sequences and 4,180 RNAs can be found in 27 species; (2) access to comprehensive Corynebacterium data through the use of advanced web technologies for interactive web interfaces; and (3) advanced bioinformatic analysis tools consisting of standard BLAST for homology search, VFDB BLAST for sequence homology search against the Virulence Factor Database (VFDB), Pairwise Genome Comparison (PGC) tool for comparative genomic analysis, and a newly designed Pathogenomics Profiling Tool (PathoProT) for comparative pathogenomic analysis. CoryneBase offers the access of a range of Corynebacterium genomic resources as well as analysis tools for comparative genomics and pathogenomics. It is publicly available at http://corynebacterium.um.edu.my/. PMID:24466021

Towards a career in bioinformatics

PubMed Central

2009-01-01

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation from 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 9-11, 2009 at Biopolis, Singapore. InCoB has actively engaged researchers from the area of life sciences, systems biology and clinicians, to facilitate greater synergy between these groups. To encourage bioinformatics students and new researchers, tutorials and student symposium, the Singapore Symposium on Computational Biology (SYMBIO) were organized, along with the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and the Clinical Bioinformatics (CBAS) Symposium. However, to many students and young researchers, pursuing a career in a multi-disciplinary area such as bioinformatics poses a Himalayan challenge. A collection to tips is presented here to provide signposts on the road to a career in bioinformatics. An overview of the application of bioinformatics to traditional and emerging areas, published in this supplement, is also presented to provide possible future avenues of bioinformatics investigation. A case study on the application of e-learning tools in undergraduate bioinformatics curriculum provides information on how to go impart targeted education, to sustain bioinformatics in the Asia-Pacific region. The next InCoB is scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010. PMID:19958508

Towards a career in bioinformatics.

PubMed

Ranganathan, Shoba

2009-12-03

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation from 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 9-11, 2009 at Biopolis, Singapore. InCoB has actively engaged researchers from the area of life sciences, systems biology and clinicians, to facilitate greater synergy between these groups. To encourage bioinformatics students and new researchers, tutorials and student symposium, the Singapore Symposium on Computational Biology (SYMBIO) were organized, along with the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and the Clinical Bioinformatics (CBAS) Symposium. However, to many students and young researchers, pursuing a career in a multi-disciplinary area such as bioinformatics poses a Himalayan challenge. A collection to tips is presented here to provide signposts on the road to a career in bioinformatics. An overview of the application of bioinformatics to traditional and emerging areas, published in this supplement, is also presented to provide possible future avenues of bioinformatics investigation. A case study on the application of e-learning tools in undergraduate bioinformatics curriculum provides information on how to go impart targeted education, to sustain bioinformatics in the Asia-Pacific region. The next InCoB is scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010.

Structural Feature Ions for Distinguishing N- and O-Linked Glycan Isomers by LC-ESI-IT MS/MS

NASA Astrophysics Data System (ADS)

Everest-Dass, Arun V.; Abrahams, Jodie L.; Kolarich, Daniel; Packer, Nicolle H.; Campbell, Matthew P.

2013-06-01

Glycomics is the comprehensive study of glycan expression in an organism, cell, or tissue that relies on effective analytical technologies to understand glycan structure-function relationships. Owing to the macro- and micro-heterogeneity of oligosaccharides, detailed structure characterization has required an orthogonal approach, such as a combination of specific exoglycosidase digestions, LC-MS/MS, and the development of bioinformatic resources to comprehensively profile a complex biological sample. Liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS/MS) has emerged as a key tool in the structural analysis of oligosaccharides because of its high sensitivity, resolution, and robustness. Here, we present a strategy that uses LC-ESI-MS/MS to characterize over 200 N- and O-glycans from human saliva glycoproteins, complemented by sequential exoglycosidase treatment, to further verify the annotated glycan structures. Fragment-specific substructure diagnostic ions were collated from an extensive screen of the literature available on the detailed structural characterization of oligosaccharides and, together with other specific glycan structure feature ions derived from cross-ring and glycosidic-linkage fragmentation, were used to characterize the glycans and differentiate isomers. The availability of such annotated mass spectrometric fragmentation spectral libraries of glycan structures, together with such substructure diagnostic ions, will be key inputs for the future development of the automated elucidation of oligosaccharide structures from MS/MS data.

Transcriptome Analysis of Human Immune Responses Following Live Vaccine Strain (LVS) Francisella Tularensis Vaccination

DTIC Science & Technology

2007-03-08

with CD3D 50848 PAR1/UBE3A Prader–Willi syndrome chromosome region 1, GMCSFRalpha precursor, IL3Ralpha precursor (CD123) Brain development...intervention programs justifiable? Emerg. Infect. Dis. 3, 83–94. iebel, U., Kindler , B., Pepperkok, R., 2004. ‘Harvester’: a fast meta search engine of human...protein resources. Bioinformatics 20, 1962–1963. iebel, U., Kindler , B., Pepperkok, R., 2005. Bioinformatic “Harvester”: a search engine for genome

Special Focus

PubMed Central

Nawrocki, Eric P.; Burge, Sarah W.

2013-01-01

The development of RNA bioinformatic tools began more than 30 y ago with the description of the Nussinov and Zuker dynamic programming algorithms for single sequence RNA secondary structure prediction. Since then, many tools have been developed for various RNA sequence analysis problems such as homology search, multiple sequence alignment, de novo RNA discovery, read-mapping, and many more. In this issue, we have collected a sampling of reviews and original research that demonstrate some of the many ways bioinformatics is integrated with current RNA biology research. PMID:23948768

Advantages and disadvantages in usage of bioinformatic programs in promoter region analysis

NASA Astrophysics Data System (ADS)

Pawełkowicz, Magdalena E.; Skarzyńska, Agnieszka; Posyniak, Kacper; ZiÄ bska, Karolina; PlÄ der, Wojciech; Przybecki, Zbigniew

2015-09-01

An important computational challenge is finding the regulatory elements across the promotor region. In this work we present the advantages and disadvantages from the application of different bioinformatics programs for localization of transcription factor binding sites in the upstream region of genes connected with sex determination in cucumber. We use PlantCARE, PlantPAN and SignalScan to find motifs in the promotor regions. The results have been compared and possible function of chosen motifs has been described.

Introducing W.A.T.E.R.S.: a workflow for the alignment, taxonomy, and ecology of ribosomal sequences.

PubMed

Hartman, Amber L; Riddle, Sean; McPhillips, Timothy; Ludäscher, Bertram; Eisen, Jonathan A

2010-06-12

For more than two decades microbiologists have used a highly conserved microbial gene as a phylogenetic marker for bacteria and archaea. The small-subunit ribosomal RNA gene, also known as 16 S rRNA, is encoded by ribosomal DNA, 16 S rDNA, and has provided a powerful comparative tool to microbial ecologists. Over time, the microbial ecology field has matured from small-scale studies in a select number of environments to massive collections of sequence data that are paired with dozens of corresponding collection variables. As the complexity of data and tool sets have grown, the need for flexible automation and maintenance of the core processes of 16 S rDNA sequence analysis has increased correspondingly. We present WATERS, an integrated approach for 16 S rDNA analysis that bundles a suite of publicly available 16 S rDNA analysis software tools into a single software package. The "toolkit" includes sequence alignment, chimera removal, OTU determination, taxonomy assignment, phylogentic tree construction as well as a host of ecological analysis and visualization tools. WATERS employs a flexible, collection-oriented 'workflow' approach using the open-source Kepler system as a platform. By packaging available software tools into a single automated workflow, WATERS simplifies 16 S rDNA analyses, especially for those without specialized bioinformatics, programming expertise. In addition, WATERS, like some of the newer comprehensive rRNA analysis tools, allows researchers to minimize the time dedicated to carrying out tedious informatics steps and to focus their attention instead on the biological interpretation of the results. One advantage of WATERS over other comprehensive tools is that the use of the Kepler workflow system facilitates result interpretation and reproducibility via a data provenance sub-system. Furthermore, new "actors" can be added to the workflow as desired and we see WATERS as an initial seed for a sizeable and growing repository of interoperable, easy-to-combine tools for asking increasingly complex microbial ecology questions.

Scalable computing for evolutionary genomics.

PubMed

Prins, Pjotr; Belhachemi, Dominique; Möller, Steffen; Smant, Geert

2012-01-01

Genomic data analysis in evolutionary biology is becoming so computationally intensive that analysis of multiple hypotheses and scenarios takes too long on a single desktop computer. In this chapter, we discuss techniques for scaling computations through parallelization of calculations, after giving a quick overview of advanced programming techniques. Unfortunately, parallel programming is difficult and requires special software design. The alternative, especially attractive for legacy software, is to introduce poor man's parallelization by running whole programs in parallel as separate processes, using job schedulers. Such pipelines are often deployed on bioinformatics computer clusters. Recent advances in PC virtualization have made it possible to run a full computer operating system, with all of its installed software, on top of another operating system, inside a "box," or virtual machine (VM). Such a VM can flexibly be deployed on multiple computers, in a local network, e.g., on existing desktop PCs, and even in the Cloud, to create a "virtual" computer cluster. Many bioinformatics applications in evolutionary biology can be run in parallel, running processes in one or more VMs. Here, we show how a ready-made bioinformatics VM image, named BioNode, effectively creates a computing cluster, and pipeline, in a few steps. This allows researchers to scale-up computations from their desktop, using available hardware, anytime it is required. BioNode is based on Debian Linux and can run on networked PCs and in the Cloud. Over 200 bioinformatics and statistical software packages, of interest to evolutionary biology, are included, such as PAML, Muscle, MAFFT, MrBayes, and BLAST. Most of these software packages are maintained through the Debian Med project. In addition, BioNode contains convenient configuration scripts for parallelizing bioinformatics software. Where Debian Med encourages packaging free and open source bioinformatics software through one central project, BioNode encourages creating free and open source VM images, for multiple targets, through one central project. BioNode can be deployed on Windows, OSX, Linux, and in the Cloud. Next to the downloadable BioNode images, we provide tutorials online, which empower bioinformaticians to install and run BioNode in different environments, as well as information for future initiatives, on creating and building such images.

Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis.

PubMed

Aniba, Mohamed Radhouene; Siguenza, Sophie; Friedrich, Anne; Plewniak, Frédéric; Poch, Olivier; Marchler-Bauer, Aron; Thompson, Julie Dawn

2009-01-01

The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented.

Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis

PubMed Central

Aniba, Mohamed Radhouene; Siguenza, Sophie; Friedrich, Anne; Plewniak, Frédéric; Poch, Olivier; Marchler-Bauer, Aron

2009-01-01

The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented. PMID:18971242

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform, Version 1.5 and 1.x.

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

Chain, Patrick; Lo, Chien-Chi; Li, Po-E

EDGE bioinformatics was developed to help biologists process Next Generation Sequencing data (in the form of raw FASTQ files), even if they have little to no bioinformatics expertise. EDGE is a highly integrated and interactive web-based platform that is capable of running many of the standard analyses that biologists require for viral, bacterial/archaeal, and metagenomic samples. EDGE provides the following analytical workflows: quality trimming and host removal, assembly and annotation, comparisons against known references, taxonomy classification of reads and contigs, whole genome SNP-based phylogenetic analysis, and PCR analysis. EDGE provides an intuitive web-based interface for user input, allows users tomore » visualize and interact with selected results (e.g. JBrowse genome browser), and generates a final detailed PDF report. Results in the form of tables, text files, graphic files, and PDFs can be downloaded. A user management system allows tracking of an individual’s EDGE runs, along with the ability to share, post publicly, delete, or archive their results.« less

The structural bioinformatics library: modeling in biomolecular science and beyond.

PubMed

Cazals, Frédéric; Dreyfus, Tom

2017-04-01

Software in structural bioinformatics has mainly been application driven. To favor practitioners seeking off-the-shelf applications, but also developers seeking advanced building blocks to develop novel applications, we undertook the design of the Structural Bioinformatics Library ( SBL , http://sbl.inria.fr ), a generic C ++/python cross-platform software library targeting complex problems in structural bioinformatics. Its tenet is based on a modular design offering a rich and versatile framework allowing the development of novel applications requiring well specified complex operations, without compromising robustness and performances. The SBL involves four software components (1-4 thereafter). For end-users, the SBL provides ready to use, state-of-the-art (1) applications to handle molecular models defined by unions of balls, to deal with molecular flexibility, to model macro-molecular assemblies. These applications can also be combined to tackle integrated analysis problems. For developers, the SBL provides a broad C ++ toolbox with modular design, involving core (2) algorithms , (3) biophysical models and (4) modules , the latter being especially suited to develop novel applications. The SBL comes with a thorough documentation consisting of user and reference manuals, and a bugzilla platform to handle community feedback. The SBL is available from http://sbl.inria.fr. Frederic.Cazals@inria.fr. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A HaemAtlas: characterizing gene expression in differentiated human blood cells.

PubMed

Watkins, Nicholas A; Gusnanto, Arief; de Bono, Bernard; De, Subhajyoti; Miranda-Saavedra, Diego; Hardie, Debbie L; Angenent, Will G J; Attwood, Antony P; Ellis, Peter D; Erber, Wendy; Foad, Nicola S; Garner, Stephen F; Isacke, Clare M; Jolley, Jennifer; Koch, Kerstin; Macaulay, Iain C; Morley, Sarah L; Rendon, Augusto; Rice, Kate M; Taylor, Niall; Thijssen-Timmer, Daphne C; Tijssen, Marloes R; van der Schoot, C Ellen; Wernisch, Lorenz; Winzer, Thilo; Dudbridge, Frank; Buckley, Christopher D; Langford, Cordelia F; Teichmann, Sarah; Göttgens, Berthold; Ouwehand, Willem H

2009-05-07

Hematopoiesis is a carefully controlled process that is regulated by complex networks of transcription factors that are, in part, controlled by signals resulting from ligand binding to cell-surface receptors. To further understand hematopoiesis, we have compared gene expression profiles of human erythroblasts, megakaryocytes, B cells, cytotoxic and helper T cells, natural killer cells, granulocytes, and monocytes using whole genome microarrays. A bioinformatics analysis of these data was performed focusing on transcription factors, immunoglobulin superfamily members, and lineage-specific transcripts. We observed that the numbers of lineage-specific genes varies by 2 orders of magnitude, ranging from 5 for cytotoxic T cells to 878 for granulocytes. In addition, we have identified novel coexpression patterns for key transcription factors involved in hematopoiesis (eg, GATA3-GFI1 and GATA2-KLF1). This study represents the most comprehensive analysis of gene expression in hematopoietic cells to date and has identified genes that play key roles in lineage commitment and cell function. The data, which are freely accessible, will be invaluable for future studies on hematopoiesis and the role of specific genes and will also aid the understanding of the recent genome-wide association studies.

A HaemAtlas: characterizing gene expression in differentiated human blood cells

PubMed Central

Gusnanto, Arief; de Bono, Bernard; De, Subhajyoti; Miranda-Saavedra, Diego; Hardie, Debbie L.; Angenent, Will G. J.; Attwood, Antony P.; Ellis, Peter D.; Erber, Wendy; Foad, Nicola S.; Garner, Stephen F.; Isacke, Clare M.; Jolley, Jennifer; Koch, Kerstin; Macaulay, Iain C.; Morley, Sarah L.; Rendon, Augusto; Rice, Kate M.; Taylor, Niall; Thijssen-Timmer, Daphne C.; Tijssen, Marloes R.; van der Schoot, C. Ellen; Wernisch, Lorenz; Winzer, Thilo; Dudbridge, Frank; Buckley, Christopher D.; Langford, Cordelia F.; Teichmann, Sarah; Göttgens, Berthold; Ouwehand, Willem H.

2009-01-01

Hematopoiesis is a carefully controlled process that is regulated by complex networks of transcription factors that are, in part, controlled by signals resulting from ligand binding to cell-surface receptors. To further understand hematopoiesis, we have compared gene expression profiles of human erythroblasts, megakaryocytes, B cells, cytotoxic and helper T cells, natural killer cells, granulocytes, and monocytes using whole genome microarrays. A bioinformatics analysis of these data was performed focusing on transcription factors, immunoglobulin superfamily members, and lineage-specific transcripts. We observed that the numbers of lineage-specific genes varies by 2 orders of magnitude, ranging from 5 for cytotoxic T cells to 878 for granulocytes. In addition, we have identified novel coexpression patterns for key transcription factors involved in hematopoiesis (eg, GATA3-GFI1 and GATA2-KLF1). This study represents the most comprehensive analysis of gene expression in hematopoietic cells to date and has identified genes that play key roles in lineage commitment and cell function. The data, which are freely accessible, will be invaluable for future studies on hematopoiesis and the role of specific genes and will also aid the understanding of the recent genome-wide association studies. PMID:19228925

polymapR - linkage analysis and genetic map construction from F1 populations of outcrossing polyploids.

PubMed

Bourke, Peter M; van Geest, Geert; Voorrips, Roeland E; Jansen, Johannes; Kranenburg, Twan; Shahin, Arwa; Visser, Richard G F; Arens, Paul; Smulders, Marinus J M; Maliepaard, Chris

2018-05-02

Polyploid species carry more than two copies of each chromosome, a condition found in many of the world's most important crops. Genetic mapping in polyploids is more complex than in diploid species, resulting in a lack of available software tools. These are needed if we are to realise all the opportunities offered by modern genotyping platforms for genetic research and breeding in polyploid crops. polymapR is an R package for genetic linkage analysis and integrated genetic map construction from bi-parental populations of outcrossing autopolyploids. It can currently analyse triploid, tetraploid and hexaploid marker datasets and is applicable to various crops including potato, leek, alfalfa, blueberry, chrysanthemum, sweet potato or kiwifruit. It can detect, estimate and correct for preferential chromosome pairing, and has been tested on high-density marker datasets from potato, rose and chrysanthemum, generating high-density integrated linkage maps in all of these crops. polymapR is freely available under the general public license from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polymapR. Chris Maliepaard chris.maliepaard@wur.nl or Roeland E. Voorrips roeland.voorrips@wur.nl. Supplementary data are available at Bioinformatics online.

Interplay between DMD Point Mutations and Splicing Signals in Dystrophinopathy Phenotypes

PubMed Central

Juan-Mateu, Jonàs; González-Quereda, Lidia; Rodríguez, Maria José; Verdura, Edgard; Lázaro, Kira; Jou, Cristina; Nascimento, Andrés; Jiménez-Mallebrera, Cecilia; Colomer, Jaume; Monges, Soledad; Lubieniecki, Fabiana; Foncuberta, Maria Eugenia; Pascual-Pascual, Samuel Ignacio; Molano, Jesús; Baiget, Montserrat; Gallano, Pia

2013-01-01

DMD nonsense and frameshift mutations lead to severe Duchenne muscular dystrophy while in-frame mutations lead to milder Becker muscular dystrophy. Exceptions are found in 10% of cases and the production of alternatively spliced transcripts is considered a key modifier of disease severity. Several exonic mutations have been shown to induce exon-skipping, while splice site mutations result in exon-skipping or activation of cryptic splice sites. However, factors determining the splicing pathway are still unclear. Point mutations provide valuable information regarding the regulation of pre-mRNA splicing and elements defining exon identity in the DMD gene. Here we provide a comprehensive analysis of 98 point mutations related to clinical phenotype and their effect on muscle mRNA and dystrophin expression. Aberrant splicing was found in 27 mutations due to alteration of splice sites or splicing regulatory elements. Bioinformatics analysis was performed to test the ability of the available algorithms to predict consequences on mRNA and to investigate the major factors that determine the splicing pathway in mutations affecting splicing signals. Our findings suggest that the splicing pathway is highly dependent on the interplay between splice site strength and density of regulatory elements. PMID:23536893

Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach

PubMed Central

Shi, Xingjie; Zhao, Qing; Huang, Jian; Xie, Yang; Ma, Shuangge

2015-01-01

Motivation: Both gene expression levels (GEs) and copy number alterations (CNAs) have important biological implications. GEs are partly regulated by CNAs, and much effort has been devoted to understanding their relations. The regulation analysis is challenging with one gene expression possibly regulated by multiple CNAs and one CNA potentially regulating the expressions of multiple genes. The correlations among GEs and among CNAs make the analysis even more complicated. The existing methods have limitations and cannot comprehensively describe the regulation. Results: A sparse double Laplacian shrinkage method is developed. It jointly models the effects of multiple CNAs on multiple GEs. Penalization is adopted to achieve sparsity and identify the regulation relationships. Network adjacency is computed to describe the interconnections among GEs and among CNAs. Two Laplacian shrinkage penalties are imposed to accommodate the network adjacency measures. Simulation shows that the proposed method outperforms the competing alternatives with more accurate marker identification. The Cancer Genome Atlas data are analysed to further demonstrate advantages of the proposed method. Availability and implementation: R code is available at http://works.bepress.com/shuangge/49/ Contact: shuangge.ma@yale.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26342102

Global analysis of lysine acetylation in strawberry leaves.

PubMed

Fang, Xianping; Chen, Wenyue; Zhao, Yun; Ruan, Songlin; Zhang, Hengmu; Yan, Chengqi; Jin, Liang; Cao, Lingling; Zhu, Jun; Ma, Huasheng; Cheng, Zhongyi

2015-01-01

Protein lysine acetylation is a reversible and dynamic post-translational modification. It plays an important role in regulating diverse cellular processes including chromatin dynamic, metabolic pathways, and transcription in both prokaryotes and eukaryotes. Although studies of lysine acetylome in plants have been reported, the throughput was not high enough, hindering the deep understanding of lysine acetylation in plant physiology and pathology. In this study, taking advantages of anti-acetyllysine-based enrichment and high-sensitive-mass spectrometer, we applied an integrated proteomic approach to comprehensively investigate lysine acetylome in strawberry. In total, we identified 1392 acetylation sites in 684 proteins, representing the largest dataset of acetylome in plants to date. To reveal the functional impacts of lysine acetylation in strawberry, intensive bioinformatic analysis was performed. The results significantly expanded our current understanding of plant acetylome and demonstrated that lysine acetylation is involved in multiple cellular metabolism and cellular processes. More interestingly, nearly 50% of all acetylated proteins identified in this work were localized in chloroplast and the vital role of lysine acetylation in photosynthesis was also revealed. Taken together, this study not only established the most extensive lysine acetylome in plants to date, but also systematically suggests the significant and unique roles of lysine acetylation in plants.

phylo-node: A molecular phylogenetic toolkit using Node.js.

PubMed

O'Halloran, Damien M

2017-01-01

Node.js is an open-source and cross-platform environment that provides a JavaScript codebase for back-end server-side applications. JavaScript has been used to develop very fast and user-friendly front-end tools for bioinformatic and phylogenetic analyses. However, no such toolkits are available using Node.js to conduct comprehensive molecular phylogenetic analysis. To address this problem, I have developed, phylo-node, which was developed using Node.js and provides a stable and scalable toolkit that allows the user to perform diverse molecular and phylogenetic tasks. phylo-node can execute the analysis and process the resulting outputs from a suite of software options that provides tools for read processing and genome alignment, sequence retrieval, multiple sequence alignment, primer design, evolutionary modeling, and phylogeny reconstruction. Furthermore, phylo-node enables the user to deploy server dependent applications, and also provides simple integration and interoperation with other Node modules and languages using Node inheritance patterns, and a customized piping module to support the production of diverse pipelines. phylo-node is open-source and freely available to all users without sign-up or login requirements. All source code and user guidelines are openly available at the GitHub repository: https://github.com/dohalloran/phylo-node.

Sybil--efficient constraint-based modelling in R.

PubMed

Gelius-Dietrich, Gabriel; Desouki, Abdelmoneim Amer; Fritzemeier, Claus Jonathan; Lercher, Martin J

2013-11-13

Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users. Here, we present sybil, an open source software library for constraint-based analyses in R; R is a free, platform-independent environment for statistical computing and graphics that is widely used in bioinformatics. Among other functions, sybil currently provides efficient methods for flux-balance analysis (FBA), MOMA, and ROOM that are about ten times faster than previous implementations when calculating the effect of whole-genome single gene deletions in silico on a complete E. coli metabolic model. Due to the object-oriented architecture of sybil, users can easily build analysis pipelines in R or even implement their own constraint-based algorithms. Based on its highly efficient communication with different mathematical optimisation programs, sybil facilitates the exploration of high-dimensional optimisation problems on small time scales. Sybil and all its dependencies are open source. Sybil and its documentation are available for download from the comprehensive R archive network (CRAN).

DNA methylation biomarkers for head and neck squamous cell carcinoma.

PubMed

Zhou, Chongchang; Ye, Meng; Ni, Shumin; Li, Qun; Ye, Dong; Li, Jinyun; Shen, Zhishen; Deng, Hongxia

2018-06-21

DNA methylation plays an important role in the etiology and pathogenesis of head and neck squamous cell carcinoma (HNSCC). The current study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) by a comprehensive bioinformatics analysis. In addition, we screened for DEGs affected by DNA methylation modification and further investigated their prognostic values for HNSCC. We included microarray data of DNA methylation (GSE25093 and GSE33202) and gene expression (GSE23036 and GSE58911) from Gene Expression Omnibus. Aberrantly methylated-DEGs were analyzed with R software. The Cancer Genome Atlas (TCGA) RNA sequencing and DNA methylation (Illumina HumanMethylation450) databases were utilized for validation. In total, 27 aberrantly methylated genes accompanied by altered expression were identified. After confirmation by The Cancer Genome Atlas (TCGA) database, 2 hypermethylated-low-expression genes (FAM135B and ZNF610) and 2 hypomethylated-high-expression genes (HOXA9 and DCC) were identified. A receiver operating characteristic (ROC) curve confirmed the diagnostic value of these four methylated genes for HNSCC. Multivariate Cox proportional hazards analysis showed that FAM135B methylation was a favorable independent prognostic biomarker for overall survival of HNSCC patients.

novPTMenzy: a database for enzymes involved in novel post-translational modifications

PubMed Central

Khater, Shradha; Mohanty, Debasisa

2015-01-01

With the recent discoveries of novel post-translational modifications (PTMs) which play important roles in signaling and biosynthetic pathways, identification of such PTM catalyzing enzymes by genome mining has been an area of major interest. Unlike well-known PTMs like phosphorylation, glycosylation, SUMOylation, no bioinformatics resources are available for enzymes associated with novel and unusual PTMs. Therefore, we have developed the novPTMenzy database which catalogs information on the sequence, structure, active site and genomic neighborhood of experimentally characterized enzymes involved in five novel PTMs, namely AMPylation, Eliminylation, Sulfation, Hydroxylation and Deamidation. Based on a comprehensive analysis of the sequence and structural features of these known PTM catalyzing enzymes, we have created Hidden Markov Model profiles for the identification of similar PTM catalyzing enzymatic domains in genomic sequences. We have also created predictive rules for grouping them into functional subfamilies and deciphering their mechanistic details by structure-based analysis of their active site pockets. These analytical modules have been made available as user friendly search interfaces of novPTMenzy database. It also has a specialized analysis interface for some PTMs like AMPylation and Eliminylation. The novPTMenzy database is a unique resource that can aid in discovery of unusual PTM catalyzing enzymes in newly sequenced genomes. Database URL: http://www.nii.ac.in/novptmenzy.html PMID:25931459

Precision phenotyping, panomics, and system-level bioinformatics to delineate complex biologies of atherosclerosis: rationale and design of the "Genetic Loci and the Burden of Atherosclerotic Lesions" study.

PubMed

Voros, Szilard; Maurovich-Horvat, Pal; Marvasty, Idean B; Bansal, Aruna T; Barnes, Michael R; Vazquez, Gustavo; Murray, Sarah S; Voros, Viktor; Merkely, Bela; Brown, Bradley O; Warnick, G Russell

2014-01-01

Complex biological networks of atherosclerosis are largely unknown. The main objective of the Genetic Loci and the Burden of Atherosclerotic Lesions study is to assemble comprehensive biological networks of atherosclerosis using advanced cardiovascular imaging for phenotyping, a panomic approach to identify underlying genomic, proteomic, metabolomic, and lipidomic underpinnings, analyzed by systems biology-driven bioinformatics. By design, this is a hypothesis-free unbiased discovery study collecting a large number of biologically related factors to examine biological associations between genomic, proteomic, metabolomic, lipidomic, and phenotypic factors of atherosclerosis. The Genetic Loci and the Burden of Atherosclerotic Lesions study (NCT01738828) is a prospective, multicenter, international observational study of atherosclerotic coronary artery disease. Approximately 7500 patients are enrolled and undergo non-contrast-enhanced coronary calcium scanning by CT for the detection and quantification of coronary artery calcium, as well as coronary artery CT angiography for the detection and quantification of plaque, stenosis, and overall coronary artery disease burden. In addition, patients undergo whole genome sequencing, DNA methylation, whole blood-based transcriptome sequencing, unbiased proteomics based on mass spectrometry, as well as metabolomics and lipidomics on a mass spectrometry platform. The study is analyzed in 3 subsequent phases, and each phase consists of a discovery cohort and an independent validation cohort. For the primary analysis, the primary phenotype will be the presence of any atherosclerotic plaque, as detected by cardiac CT. Additional phenotypic analyses will include per patient maximal luminal stenosis defined as 50% and 70% diameter stenosis. Single-omic and multi-omic associations will be examined for each phenotype; putative biomarkers will be assessed for association, calibration, discrimination, and reclassification. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Tiny giants of gene regulation: experimental strategies for microRNA functional studies

PubMed Central

Steinkraus, Bruno R.; Toegel, Markus

2016-01-01

The discovery over two decades ago of short regulatory microRNAs (miRNAs) has led to the inception of a vast biomedical research field dedicated to understanding these powerful orchestrators of gene expression. Here we aim to provide a comprehensive overview of the methods and techniques underpinning the experimental pipeline employed for exploratory miRNA studies in animals. Some of the greatest challenges in this field have been uncovering the identity of miRNA–target interactions and deciphering their significance with regard to particular physiological or pathological processes. These endeavors relied almost exclusively on the development of powerful research tools encompassing novel bioinformatics pipelines, high‐throughput target identification platforms, and functional target validation methodologies. Thus, in an unparalleled manner, the biomedical technology revolution unceasingly enhanced and refined our ability to dissect miRNA regulatory networks and understand their roles in vivo in the context of cells and organisms. Recurring motifs of target recognition have led to the creation of a large number of multifactorial bioinformatics analysis platforms, which have proved instrumental in guiding experimental miRNA studies. Subsequently, the need for discovery of miRNA–target binding events in vivo drove the emergence of a slew of high‐throughput multiplex strategies, which now provide a viable prospect for elucidating genome‐wide miRNA–target binding maps in a variety of cell types and tissues. Finally, deciphering the functional relevance of miRNA post‐transcriptional gene silencing under physiological conditions, prompted the evolution of a host of technologies enabling systemic manipulation of miRNA homeostasis as well as high‐precision interference with their direct, endogenous targets. WIREs Dev Biol 2016, 5:311–362. doi: 10.1002/wdev.223 For further resources related to this article, please visit the WIREs website. PMID:26950183

A time-and-motion approach to micro-costing of high-throughput genomic assays

PubMed Central

Costa, S.; Regier, D.A.; Meissner, B.; Cromwell, I.; Ben-Neriah, S.; Chavez, E.; Hung, S.; Steidl, C.; Scott, D.W.; Marra, M.A.; Peacock, S.J.; Connors, J.M.

2016-01-01

Background Genomic technologies are increasingly used to guide clinical decision-making in cancer control. Economic evidence about the cost-effectiveness of genomic technologies is limited, in part because of a lack of published comprehensive cost estimates. In the present micro-costing study, we used a time-and-motion approach to derive cost estimates for 3 genomic assays and processes—digital gene expression profiling (gep), fluorescence in situ hybridization (fish), and targeted capture sequencing, including bioinformatics analysis—in the context of lymphoma patient management. Methods The setting for the study was the Department of Lymphoid Cancer Research laboratory at the BC Cancer Agency in Vancouver, British Columbia. Mean per-case hands-on time and resource measurements were determined from a series of direct observations of each assay. Per-case cost estimates were calculated using a bottom-up costing approach, with labour, capital and equipment, supplies and reagents, and overhead costs included. Results The most labour-intensive assay was found to be fish at 258.2 minutes per case, followed by targeted capture sequencing (124.1 minutes per case) and digital gep (14.9 minutes per case). Based on a historical case throughput of 180 cases annually, the mean per-case cost (2014 Canadian dollars) was estimated to be $1,029.16 for targeted capture sequencing and bioinformatics analysis, $596.60 for fish, and $898.35 for digital gep with an 807-gene code set. Conclusions With the growing emphasis on personalized approaches to cancer management, the need for economic evaluations of high-throughput genomic assays is increasing. Through economic modelling and budget-impact analyses, the cost estimates presented here can be used to inform priority-setting decisions about the implementation of such assays in clinical practice. PMID:27803594

Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

PubMed Central

2010-01-01

Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions. Conclusions The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data. PMID:20937082

A Bioinformatics Facility for NASA

NASA Technical Reports Server (NTRS)

Schweighofer, Karl; Pohorille, Andrew

2006-01-01

Building on an existing prototype, we have fielded a facility with bioinformatics technologies that will help NASA meet its unique requirements for biological research. This facility consists of a cluster of computers capable of performing computationally intensive tasks, software tools, databases and knowledge management systems. Novel computational technologies for analyzing and integrating new biological data and already existing knowledge have been developed. With continued development and support, the facility will fulfill strategic NASA s bioinformatics needs in astrobiology and space exploration. . As a demonstration of these capabilities, we will present a detailed analysis of how spaceflight factors impact gene expression in the liver and kidney for mice flown aboard shuttle flight STS-108. We have found that many genes involved in signal transduction, cell cycle, and development respond to changes in microgravity, but that most metabolic pathways appear unchanged.

Data-driven advice for applying machine learning to bioinformatics problems

PubMed Central

Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

2017-01-01

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

Integrated Bio-Search: challenges and trends for the integration, search and comprehensive processing of biological information

PubMed Central

2014-01-01

Many efforts exist to design and implement approaches and tools for data capture, integration and analysis in the life sciences. Challenges are not only the heterogeneity, size and distribution of information sources, but also the danger of producing too many solutions for the same problem. Methodological, technological, infrastructural and social aspects appear to be essential for the development of a new generation of best practices and tools. In this paper, we analyse and discuss these aspects from different perspectives, by extending some of the ideas that arose during the NETTAB 2012 Workshop, making reference especially to the European context. First, relevance of using data and software models for the management and analysis of biological data is stressed. Second, some of the most relevant community achievements of the recent years, which should be taken as a starting point for future efforts in this research domain, are presented. Third, some of the main outstanding issues, challenges and trends are analysed. The challenges related to the tendency to fund and create large scale international research infrastructures and public-private partnerships in order to address the complex challenges of data intensive science are especially discussed. The needs and opportunities of Genomic Computing (the integration, search and display of genomic information at a very specific level, e.g. at the level of a single DNA region) are then considered. In the current data and network-driven era, social aspects can become crucial bottlenecks. How these may best be tackled to unleash the technical abilities for effective data integration and validation efforts is then discussed. Especially the apparent lack of incentives for already overwhelmed researchers appears to be a limitation for sharing information and knowledge with other scientists. We point out as well how the bioinformatics market is growing at an unprecedented speed due to the impact that new powerful in silico analysis promises to have on better diagnosis, prognosis, drug discovery and treatment, towards personalized medicine. An open business model for bioinformatics, which appears to be able to reduce undue duplication of efforts and support the increased reuse of valuable data sets, tools and platforms, is finally discussed. PMID:24564249

Biowep: a workflow enactment portal for bioinformatics applications.

PubMed

Romano, Paolo; Bartocci, Ezio; Bertolini, Guglielmo; De Paoli, Flavio; Marra, Domenico; Mauri, Giancarlo; Merelli, Emanuela; Milanesi, Luciano

2007-03-08

The huge amount of biological information, its distribution over the Internet and the heterogeneity of available software tools makes the adoption of new data integration and analysis network tools a necessity in bioinformatics. ICT standards and tools, like Web Services and Workflow Management Systems (WMS), can support the creation and deployment of such systems. Many Web Services are already available and some WMS have been proposed. They assume that researchers know which bioinformatics resources can be reached through a programmatic interface and that they are skilled in programming and building workflows. Therefore, they are not viable to the majority of unskilled researchers. A portal enabling these to take profit from new technologies is still missing. We designed biowep, a web based client application that allows for the selection and execution of a set of predefined workflows. The system is available on-line. Biowep architecture includes a Workflow Manager, a User Interface and a Workflow Executor. The task of the Workflow Manager is the creation and annotation of workflows. These can be created by using either the Taverna Workbench or BioWMS. Enactment of workflows is carried out by FreeFluo for Taverna workflows and by BioAgent/Hermes, a mobile agent-based middleware, for BioWMS ones. Main workflows' processing steps are annotated on the basis of their input and output, elaboration type and application domain by using a classification of bioinformatics data and tasks. The interface supports users authentication and profiling. Workflows can be selected on the basis of users' profiles and can be searched through their annotations. Results can be saved. We developed a web system that support the selection and execution of predefined workflows, thus simplifying access for all researchers. The implementation of Web Services allowing specialized software to interact with an exhaustive set of biomedical databases and analysis software and the creation of effective workflows can significantly improve automation of in-silico analysis. Biowep is available for interested researchers as a reference portal. They are invited to submit their workflows to the workflow repository. Biowep is further being developed in the sphere of the Laboratory of Interdisciplinary Technologies in Bioinformatics - LITBIO.

Biowep: a workflow enactment portal for bioinformatics applications

PubMed Central

Romano, Paolo; Bartocci, Ezio; Bertolini, Guglielmo; De Paoli, Flavio; Marra, Domenico; Mauri, Giancarlo; Merelli, Emanuela; Milanesi, Luciano

2007-01-01

Background The huge amount of biological information, its distribution over the Internet and the heterogeneity of available software tools makes the adoption of new data integration and analysis network tools a necessity in bioinformatics. ICT standards and tools, like Web Services and Workflow Management Systems (WMS), can support the creation and deployment of such systems. Many Web Services are already available and some WMS have been proposed. They assume that researchers know which bioinformatics resources can be reached through a programmatic interface and that they are skilled in programming and building workflows. Therefore, they are not viable to the majority of unskilled researchers. A portal enabling these to take profit from new technologies is still missing. Results We designed biowep, a web based client application that allows for the selection and execution of a set of predefined workflows. The system is available on-line. Biowep architecture includes a Workflow Manager, a User Interface and a Workflow Executor. The task of the Workflow Manager is the creation and annotation of workflows. These can be created by using either the Taverna Workbench or BioWMS. Enactment of workflows is carried out by FreeFluo for Taverna workflows and by BioAgent/Hermes, a mobile agent-based middleware, for BioWMS ones. Main workflows' processing steps are annotated on the basis of their input and output, elaboration type and application domain by using a classification of bioinformatics data and tasks. The interface supports users authentication and profiling. Workflows can be selected on the basis of users' profiles and can be searched through their annotations. Results can be saved. Conclusion We developed a web system that support the selection and execution of predefined workflows, thus simplifying access for all researchers. The implementation of Web Services allowing specialized software to interact with an exhaustive set of biomedical databases and analysis software and the creation of effective workflows can significantly improve automation of in-silico analysis. Biowep is available for interested researchers as a reference portal. They are invited to submit their workflows to the workflow repository. Biowep is further being developed in the sphere of the Laboratory of Interdisciplinary Technologies in Bioinformatics – LITBIO. PMID:17430563

How to use and integrate bioinformatics tools to compare proteomic data from distinct conditions? A tutorial using the pathological similarities between Aortic Valve Stenosis and Coronary Artery Disease as a case-study.

PubMed

Trindade, Fábio; Ferreira, Rita; Magalhães, Beatriz; Leite-Moreira, Adelino; Falcão-Pires, Inês; Vitorino, Rui

2018-01-16

Nowadays we are surrounded by a plethora of bioinformatics tools, powerful enough to deal with the large amounts of data arising from proteomic studies, but whose application is sometimes hard to find. Therefore, we used a specific clinical problem - to discriminate pathophysiology and potential biomarkers between two similar cardiovascular diseases, aortic valve stenosis (AVS) and coronary artery disease (CAD) - to make a step-by-step guide through four bioinformatics tools: STRING, DisGeNET, Cytoscape and ClueGO. Proteome data was collected from articles available on PubMed centered on proteomic studies enrolling subjects with AVS or CAD. Through the analysis of gene ontology provided by STRING and ClueGO we could find specific biological phenomena associated with AVS, such as down-regulation of elastic fiber assembly, and with CAD, such as up-regulation of plasminogen activation. Moreover, through Cytoscape and DisGeNET we could pinpoint surrogate markers either for AVS (e.g. popeye domain containing protein 2 and 28S ribosomal protein S36, mitochondrial) or for CAD (e.g. ankyrin repeat and SOCS box protein 7) which deserve future validation. Data recycling and integration as well as research orientation are among the main advantages of resorting to bioinformatics analysis, hence these tutorials can be of great convenience for proteomics investigators. As we saw for aortic valve stenosis and coronary artery disease, it can be of great relevance to perform preliminary bioinformatics analysis with already published proteomics data. It not only saves us time in the lab (avoiding work duplication) as it points out new hypothesis to explain the phenotypical presentation of the diseases as well as new surrogate markers with clinical relevance, deserving future scrutiny. These essential steps can be easily overcome if one follows the steps proposed in our tutorial for STRING, DisGeNET, Cytoscape and ClueGO utilization. Copyright © 2017 Elsevier B.V. All rights reserved.

Biophysics and bioinformatics of transcription regulation in bacteria and bacteriophages

NASA Astrophysics Data System (ADS)

Djordjevic, Marko

2005-11-01

Due to rapid accumulation of biological data, bioinformatics has become a very important branch of biological research. In this thesis, we develop novel bioinformatic approaches and aid design of biological experiments by using ideas and methods from statistical physics. Identification of transcription factor binding sites within the regulatory segments of genomic DNA is an important step towards understanding of the regulatory circuits that control expression of genes. We propose a novel, biophysics based algorithm, for the supervised detection of transcription factor (TF) binding sites. The method classifies potential binding sites by explicitly estimating the sequence-specific binding energy and the chemical potential of a given TF. In contrast with the widely used information theory based weight matrix method, our approach correctly incorporates saturation in the transcription factor/DNA binding probability. This results in a significant reduction in the number of expected false positives, and in the explicit appearance---and determination---of a binding threshold. The new method was used to identify likely genomic binding sites for the Escherichia coli TFs, and to examine the relationship between TF binding specificity and degree of pleiotropy (number of regulatory targets). We next address how parameters of protein-DNA interactions can be obtained from data on protein binding to random oligos under controlled conditions (SELEX experiment data). We show that 'robust' generation of an appropriate data set is achieved by a suitable modification of the standard SELEX procedure, and propose a novel bioinformatic algorithm for analysis of such data. Finally, we use quantitative data analysis, bioinformatic methods and kinetic modeling to analyze gene expression strategies of bacterial viruses. We study bacteriophage Xp10 that infects rice pathogen Xanthomonas oryzae. Xp10 is an unusual bacteriophage, which has morphology and genome organization that most closely resembles temperate phages, such as lambda. It, however, encodes its own T7-like RNA polymerase (characteristic of virulent phages), whose role in gene expression was unclear. Our analysis resulted in quantitative understanding of the role of both host and phage RNA polymerase, and in the identification of the previously unknown promoter sequence for Xp10 RNA polymerase. More generally, an increasing number of phage genomes are being sequenced every year, and we expect that methods of quantitative data analysis that we introduced will provide an efficient way to study gene expression strategies of novel bacterial viruses.

Scientists' sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support.

PubMed

Mirel, Barbara; Görg, Carsten

2014-04-26

A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists' analytical workflows and their implications for tool design.

Zebra: a web server for bioinformatic analysis of diverse protein families.

PubMed

Suplatov, Dmitry; Kirilin, Evgeny; Takhaveev, Vakil; Svedas, Vytas

2014-01-01

During evolution of proteins from a common ancestor, one functional property can be preserved while others can vary leading to functional diversity. A systematic study of the corresponding adaptive mutations provides a key to one of the most challenging problems of modern structural biology - understanding the impact of amino acid substitutions on protein function. The subfamily-specific positions (SSPs) are conserved within functional subfamilies but are different between them and, therefore, seem to be responsible for functional diversity in protein superfamilies. Consequently, a corresponding method to perform the bioinformatic analysis of sequence and structural data has to be implemented in the common laboratory practice to study the structure-function relationship in proteins and develop novel protein engineering strategies. This paper describes Zebra web server - a powerful remote platform that implements a novel bioinformatic analysis algorithm to study diverse protein families. It is the first application that provides specificity determinants at different levels of functional classification, therefore addressing complex functional diversity of large superfamilies. Statistical analysis is implemented to automatically select a set of highly significant SSPs to be used as hotspots for directed evolution or rational design experiments and analyzed studying the structure-function relationship. Zebra results are provided in two ways - (1) as a single all-in-one parsable text file and (2) as PyMol sessions with structural representation of SSPs. Zebra web server is available at http://biokinet.belozersky.msu.ru/zebra .

Scientists’ sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support

PubMed Central

2014-01-01

A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists’ analytical workflows and their implications for tool design. PMID:24766796

Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology⋆

PubMed Central

Fu, Wenjiang J.; Stromberg, Arnold J.; Viele, Kert; Carroll, Raymond J.; Wu, Guoyao

2009-01-01

Over the past two decades, there have been revolutionary developments in life science technologies characterized by high throughput, high efficiency, and rapid computation. Nutritionists now have the advanced methodologies for the analysis of DNA, RNA, protein, low-molecular-weight metabolites, as well as access to bioinformatics databases. Statistics, which can be defined as the process of making scientific inferences from data that contain variability, has historically played an integral role in advancing nutritional sciences. Currently, in the era of systems biology, statistics has become an increasingly important tool to quantitatively analyze information about biological macromolecules. This article describes general terms used in statistical analysis of large, complex experimental data. These terms include experimental design, power analysis, sample size calculation, and experimental errors (type I and II errors) for nutritional studies at population, tissue, cellular, and molecular levels. In addition, we highlighted various sources of experimental variations in studies involving microarray gene expression, real-time polymerase chain reaction, proteomics, and other bioinformatics technologies. Moreover, we provided guidelines for nutritionists and other biomedical scientists to plan and conduct studies and to analyze the complex data. Appropriate statistical analyses are expected to make an important contribution to solving major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine fetal retardation). PMID:20233650

Improving data workflow systems with cloud services and use of open data for bioinformatics research.

PubMed

Karim, Md Rezaul; Michel, Audrey; Zappa, Achille; Baranov, Pavel; Sahay, Ratnesh; Rebholz-Schuhmann, Dietrich

2017-04-16

Data workflow systems (DWFSs) enable bioinformatics researchers to combine components for data access and data analytics, and to share the final data analytics approach with their collaborators. Increasingly, such systems have to cope with large-scale data, such as full genomes (about 200 GB each), public fact repositories (about 100 TB of data) and 3D imaging data at even larger scales. As moving the data becomes cumbersome, the DWFS needs to embed its processes into a cloud infrastructure, where the data are already hosted. As the standardized public data play an increasingly important role, the DWFS needs to comply with Semantic Web technologies. This advancement to DWFS would reduce overhead costs and accelerate the progress in bioinformatics research based on large-scale data and public resources, as researchers would require less specialized IT knowledge for the implementation. Furthermore, the high data growth rates in bioinformatics research drive the demand for parallel and distributed computing, which then imposes a need for scalability and high-throughput capabilities onto the DWFS. As a result, requirements for data sharing and access to public knowledge bases suggest that compliance of the DWFS with Semantic Web standards is necessary. In this article, we will analyze the existing DWFS with regard to their capabilities toward public open data use as well as large-scale computational and human interface requirements. We untangle the parameters for selecting a preferable solution for bioinformatics research with particular consideration to using cloud services and Semantic Web technologies. Our analysis leads to research guidelines and recommendations toward the development of future DWFS for the bioinformatics research community. © The Author 2017. Published by Oxford University Press.

BioWarehouse: a bioinformatics database warehouse toolkit

PubMed Central

Lee, Thomas J; Pouliot, Yannick; Wagner, Valerie; Gupta, Priyanka; Stringer-Calvert, David WJ; Tenenbaum, Jessica D; Karp, Peter D

2006-01-01

Background This article addresses the problem of interoperation of heterogeneous bioinformatics databases. Results We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. Conclusion BioWarehouse embodies significant progress on the database integration problem for bioinformatics. PMID:16556315

BioWarehouse: a bioinformatics database warehouse toolkit.

PubMed

Lee, Thomas J; Pouliot, Yannick; Wagner, Valerie; Gupta, Priyanka; Stringer-Calvert, David W J; Tenenbaum, Jessica D; Karp, Peter D

2006-03-23

This article addresses the problem of interoperation of heterogeneous bioinformatics databases. We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. BioWarehouse embodies significant progress on the database integration problem for bioinformatics.

Agile parallel bioinformatics workflow management using Pwrake.

PubMed

Mishima, Hiroyuki; Sasaki, Kensaku; Tanaka, Masahiro; Tatebe, Osamu; Yoshiura, Koh-Ichiro

2011-09-08

In bioinformatics projects, scientific workflow systems are widely used to manage computational procedures. Full-featured workflow systems have been proposed to fulfil the demand for workflow management. However, such systems tend to be over-weighted for actual bioinformatics practices. We realize that quick deployment of cutting-edge software implementing advanced algorithms and data formats, and continuous adaptation to changes in computational resources and the environment are often prioritized in scientific workflow management. These features have a greater affinity with the agile software development method through iterative development phases after trial and error.Here, we show the application of a scientific workflow system Pwrake to bioinformatics workflows. Pwrake is a parallel workflow extension of Ruby's standard build tool Rake, the flexibility of which has been demonstrated in the astronomy domain. Therefore, we hypothesize that Pwrake also has advantages in actual bioinformatics workflows. We implemented the Pwrake workflows to process next generation sequencing data using the Genomic Analysis Toolkit (GATK) and Dindel. GATK and Dindel workflows are typical examples of sequential and parallel workflows, respectively. We found that in practice, actual scientific workflow development iterates over two phases, the workflow definition phase and the parameter adjustment phase. We introduced separate workflow definitions to help focus on each of the two developmental phases, as well as helper methods to simplify the descriptions. This approach increased iterative development efficiency. Moreover, we implemented combined workflows to demonstrate modularity of the GATK and Dindel workflows. Pwrake enables agile management of scientific workflows in the bioinformatics domain. The internal domain specific language design built on Ruby gives the flexibility of rakefiles for writing scientific workflows. Furthermore, readability and maintainability of rakefiles may facilitate sharing workflows among the scientific community. Workflows for GATK and Dindel are available at http://github.com/misshie/Workflows.

Agile parallel bioinformatics workflow management using Pwrake

PubMed Central

2011-01-01

Background In bioinformatics projects, scientific workflow systems are widely used to manage computational procedures. Full-featured workflow systems have been proposed to fulfil the demand for workflow management. However, such systems tend to be over-weighted for actual bioinformatics practices. We realize that quick deployment of cutting-edge software implementing advanced algorithms and data formats, and continuous adaptation to changes in computational resources and the environment are often prioritized in scientific workflow management. These features have a greater affinity with the agile software development method through iterative development phases after trial and error. Here, we show the application of a scientific workflow system Pwrake to bioinformatics workflows. Pwrake is a parallel workflow extension of Ruby's standard build tool Rake, the flexibility of which has been demonstrated in the astronomy domain. Therefore, we hypothesize that Pwrake also has advantages in actual bioinformatics workflows. Findings We implemented the Pwrake workflows to process next generation sequencing data using the Genomic Analysis Toolkit (GATK) and Dindel. GATK and Dindel workflows are typical examples of sequential and parallel workflows, respectively. We found that in practice, actual scientific workflow development iterates over two phases, the workflow definition phase and the parameter adjustment phase. We introduced separate workflow definitions to help focus on each of the two developmental phases, as well as helper methods to simplify the descriptions. This approach increased iterative development efficiency. Moreover, we implemented combined workflows to demonstrate modularity of the GATK and Dindel workflows. Conclusions Pwrake enables agile management of scientific workflows in the bioinformatics domain. The internal domain specific language design built on Ruby gives the flexibility of rakefiles for writing scientific workflows. Furthermore, readability and maintainability of rakefiles may facilitate sharing workflows among the scientific community. Workflows for GATK and Dindel are available at http://github.com/misshie/Workflows. PMID:21899774

Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice[S

PubMed Central

Leduc, Magalie S.; Hageman, Rachael S.; Verdugo, Ricardo A.; Tsaih, Shirng-Wern; Walsh, Kenneth; Churchill, Gary A.; Paigen, Beverly

2011-01-01

To identify genetic loci influencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identified one significant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to find cis-regulated expression QTL. We then narrowed the list of candidate genes under significant QTL using a “toolbox” of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis-regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, five genes (Polr3 h, Cyp2d22, Cyp2d26, Tspo, and Ttll12) as candidate genes for Chr 15 QTL. This study shows how bioinformatics can be used effectively to reduce candidate gene lists for QTL related to complex traits. PMID:21622629

Multi-loci diagnosis of acute lymphoblastic leukaemia with high-throughput sequencing and bioinformatics analysis.

PubMed

Ferret, Yann; Caillault, Aurélie; Sebda, Shéhérazade; Duez, Marc; Grardel, Nathalie; Duployez, Nicolas; Villenet, Céline; Figeac, Martin; Preudhomme, Claude; Salson, Mikaël; Giraud, Mathieu

2016-05-01

High-throughput sequencing (HTS) is considered a technical revolution that has improved our knowledge of lymphoid and autoimmune diseases, changing our approach to leukaemia both at diagnosis and during follow-up. As part of an immunoglobulin/T cell receptor-based minimal residual disease (MRD) assessment of acute lymphoblastic leukaemia patients, we assessed the performance and feasibility of the replacement of the first steps of the approach based on DNA isolation and Sanger sequencing, using a HTS protocol combined with bioinformatics analysis and visualization using the Vidjil software. We prospectively analysed the diagnostic and relapse samples of 34 paediatric patients, thus identifying 125 leukaemic clones with recombinations on multiple loci (TRG, TRD, IGH and IGK), including Dd2/Dd3 and Intron/KDE rearrangements. Sequencing failures were halved (14% vs. 34%, P = 0.0007), enabling more patients to be monitored. Furthermore, more markers per patient could be monitored, reducing the probability of false negative MRD results. The whole analysis, from sample receipt to clinical validation, was shorter than our current diagnostic protocol, with equal resources. V(D)J recombination was successfully assigned by the software, even for unusual recombinations. This study emphasizes the progress that HTS with adapted bioinformatics tools can bring to the diagnosis of leukaemia patients. © 2016 John Wiley & Sons Ltd.

Serial analysis of gene expression in a rat lung model of asthma.

PubMed

Yin, Lei-Miao; Jiang, Gong-Hao; Wang, Yu; Wang, Yan; Liu, Yan-Yan; Jin, Wei-Rong; Zhang, Zen; Xu, Yu-Dong; Yang, Yong-Qing

2008-11-01

The pathogenesis and molecular mechanism underlying asthma remain undetermined. The purpose of this study was to identify genes and pathways involved in the early airway response (EAR) phase of asthma by using serial analysis of gene expression (SAGE). Two SAGE tag libraries of lung tissues derived from a rat model of asthma and controls were generated. Bioinformatic analyses were carried out using the Database for Annotation, Visualization and IntegratedDiscovery Functional Annotation Tool, Gene Ontology (GO) TreeMachine and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A total of 26 552 SAGE tags of asthmatic rat lung were obtained, of which 12 221 were unique tags. Of the unique tags, 55.5% were matched with known genes. By comparison of the two libraries, 186 differentially expressed tags (P < 0.05) were identified, of which 103 were upregulated and 83 were downregulated. Using the bioinformatic tools these genes were classified into 23 functional groups, 15 KEGG pathways and 37 enriched GO categories. The bioinformatic analyses of gene distribution, enriched categories and the involvement of specific pathways in the SAGE libraries have provided information on regulatory networks of the EAR phase of asthma. Analyses of the regulated genes of interest may inform new hypotheses, increase our understanding of the disease and provide a foundation for future research.

Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

PubMed Central

2013-01-01

Background Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions. Methods This study describes the bioinformatic analysis of data generated from high throughput proteomics for identification of potential biomarkers of OA. The mass spectrometry data was generated using a canine explant model of articular cartilage treated with the pro-inflammatory cytokine interleukin 1 β (IL-1β). The bioinformatics analysis involved the application of machine learning and network analysis to the proteomic mass spectrometry data. A rule based machine learning technique, BioHEL, was used to create a model that classified the samples into their relevant treatment groups by identifying those proteins that separated samples into their respective groups. The proteins identified were considered to be potential biomarkers. Protein networks were also generated; from these networks, proteins pivotal to the classification were identified. Results BioHEL correctly classified eighteen out of twenty-three samples, giving a classification accuracy of 78.3% for the dataset. The dataset included the four classes of control, IL-1β, carprofen, and IL-1β and carprofen together. This exceeded the other machine learners that were used for a comparison, on the same dataset, with the exception of another rule-based method, JRip, which performed equally well. The proteins that were most frequently used in rules generated by BioHEL were found to include a number of relevant proteins including matrix metalloproteinase 3, interleukin 8 and matrix gla protein. Conclusions Using this protocol, combining an in vitro model of OA with bioinformatics analysis, a number of relevant extracellular matrix proteins were identified, thereby supporting the application of these bioinformatics tools for analysis of proteomic data from in vitro models of cartilage degradation. PMID:24330474

Advances in Sequencing Technologies for Understanding Hereditary Ataxias A Review

PubMed Central

Didonna, Alessandro; Opal, Puneet

2017-01-01

IMPORTANCE The hereditary progressive ataxias comprise genetic disorders that affect the cerebellum and its connections. Even though these diseases historically have been among the first familial disorders of the nervous system to have been recognized, progress in the field has been challenging because of the large number of ataxic genetic syndromes, many of which overlap in their clinical features. OBSERVATIONS We have taken a historical approach to demonstrate how our knowledge of the genetic basis of ataxic disorders has come about by novel techniques in gene sequencing and bioinformatics. Furthermore, we show that the genes implicated in ataxia, although seemingly unrelated, appear to encode for proteins that interact with each other in connected functional modules. CONCLUSIONS AND RELEVANCE It has taken approximately 150 years for neurologists to comprehensively unravel the genetic diversity of ataxias. There has been an explosion in our understanding of their molecular basis with the arrival of next-generation sequencing and computer-driven bioinformatics; this in turn has made hereditary ataxias an especially well-developed model group of diseases for gaining insights at a systems level into genes and cellular pathways that result in neurodegeneration. PMID:27749953

ELM server: a new resource for investigating short functional sites in modular eukaryotic proteins

PubMed Central

Puntervoll, Pål; Linding, Rune; Gemünd, Christine; Chabanis-Davidson, Sophie; Mattingsdal, Morten; Cameron, Scott; Martin, David M. A.; Ausiello, Gabriele; Brannetti, Barbara; Costantini, Anna; Ferrè, Fabrizio; Maselli, Vincenza; Via, Allegra; Cesareni, Gianni; Diella, Francesca; Superti-Furga, Giulio; Wyrwicz, Lucjan; Ramu, Chenna; McGuigan, Caroline; Gudavalli, Rambabu; Letunic, Ivica; Bork, Peer; Rychlewski, Leszek; Küster, Bernhard; Helmer-Citterich, Manuela; Hunter, William N.; Aasland, Rein; Gibson, Toby J.

2003-01-01

Multidomain proteins predominate in eukaryotic proteomes. Individual functions assigned to different sequence segments combine to create a complex function for the whole protein. While on-line resources are available for revealing globular domains in sequences, there has hitherto been no comprehensive collection of small functional sites/motifs comparable to the globular domain resources, yet these are as important for the function of multidomain proteins. Short linear peptide motifs are used for cell compartment targeting, protein–protein interaction, regulation by phosphorylation, acetylation, glycosylation and a host of other post-translational modifications. ELM, the Eukaryotic Linear Motif server at http://elm.eu.org/, is a new bioinformatics resource for investigating candidate short non-globular functional motifs in eukaryotic proteins, aiming to fill the void in bioinformatics tools. Sequence comparisons with short motifs are difficult to evaluate because the usual significance assessments are inappropriate. Therefore the server is implemented with several logical filters to eliminate false positives. Current filters are for cell compartment, globular domain clash and taxonomic range. In favourable cases, the filters can reduce the number of retained matches by an order of magnitude or more. PMID:12824381

Bioinformatic and Comparative Localization of Rab Proteins Reveals Functional Insights into the Uncharacterized GTPases Ypt10p and Ypt11p†

PubMed Central

Buvelot Frei, Stéphanie; Rahl, Peter B.; Nussbaum, Maria; Briggs, Benjamin J.; Calero, Monica; Janeczko, Stephanie; Regan, Andrew D.; Chen, Catherine Z.; Barral, Yves; Whittaker, Gary R.; Collins, Ruth N.

2006-01-01

A striking characteristic of a Rab protein is its steady-state localization to the cytosolic surface of a particular subcellular membrane. In this study, we have undertaken a combined bioinformatic and experimental approach to examine the evolutionary conservation of Rab protein localization. A comprehensive primary sequence classification shows that 10 out of the 11 Rab proteins identified in the yeast (Saccharomyces cerevisiae) genome can be grouped within a major subclass, each comprising multiple Rab orthologs from diverse species. We compared the locations of individual yeast Rab proteins with their localizations following ectopic expression in mammalian cells. Our results suggest that green fluorescent protein-tagged Rab proteins maintain localizations across large evolutionary distances and that the major known player in the Rab localization pathway, mammalian Rab-GDI, is able to function in yeast. These findings enable us to provide insight into novel gene functions and classify the uncharacterized Rab proteins Ypt10p (YBR264C) as being involved in endocytic function and Ypt11p (YNL304W) as being localized to the endoplasmic reticulum, where we demonstrate it is required for organelle inheritance. PMID:16980630

WordSeeker: concurrent bioinformatics software for discovering genome-wide patterns and word-based genomic signatures

PubMed Central

2010-01-01

Background An important focus of genomic science is the discovery and characterization of all functional elements within genomes. In silico methods are used in genome studies to discover putative regulatory genomic elements (called words or motifs). Although a number of methods have been developed for motif discovery, most of them lack the scalability needed to analyze large genomic data sets. Methods This manuscript presents WordSeeker, an enumerative motif discovery toolkit that utilizes multi-core and distributed computational platforms to enable scalable analysis of genomic data. A controller task coordinates activities of worker nodes, each of which (1) enumerates a subset of the DNA word space and (2) scores words with a distributed Markov chain model. Results A comprehensive suite of performance tests was conducted to demonstrate the performance, speedup and efficiency of WordSeeker. The scalability of the toolkit enabled the analysis of the entire genome of Arabidopsis thaliana; the results of the analysis were integrated into The Arabidopsis Gene Regulatory Information Server (AGRIS). A public version of WordSeeker was deployed on the Glenn cluster at the Ohio Supercomputer Center. Conclusion WordSeeker effectively utilizes concurrent computing platforms to enable the identification of putative functional elements in genomic data sets. This capability facilitates the analysis of the large quantity of sequenced genomic data. PMID:21210985

A review of bioinformatics training applied to research in molecular medicine, agriculture and biodiversity in Costa Rica and Central America.

PubMed

Orozco, Allan; Morera, Jessica; Jiménez, Sergio; Boza, Ricardo

2013-09-01

Today, Bioinformatics has become a scientific discipline with great relevance for the Molecular Biosciences and for the Omics sciences in general. Although developed countries have progressed with large strides in Bioinformatics education and research, in other regions, such as Central America, the advances have occurred in a gradual way and with little support from the Academia, either at the undergraduate or graduate level. To address this problem, the University of Costa Rica's Medical School, a regional leader in Bioinformatics in Central America, has been conducting a series of Bioinformatics workshops, seminars and courses, leading to the creation of the region's first Bioinformatics Master's Degree. The recent creation of the Central American Bioinformatics Network (BioCANET), associated to the deployment of a supporting computational infrastructure (HPC Cluster) devoted to provide computing support for Molecular Biology in the region, is providing a foundational stone for the development of Bioinformatics in the area. Central American bioinformaticians have participated in the creation of as well as co-founded the Iberoamerican Bioinformatics Society (SOIBIO). In this article, we review the most recent activities in education and research in Bioinformatics from several regional institutions. These activities have resulted in further advances for Molecular Medicine, Agriculture and Biodiversity research in Costa Rica and the rest of the Central American countries. Finally, we provide summary information on the first Central America Bioinformatics International Congress, as well as the creation of the first Bioinformatics company (Indromics Bioinformatics), spin-off the Academy in Central America and the Caribbean.

Bioinformatics and systems biology research update from the 15th International Conference on Bioinformatics (InCoB2016).

PubMed

Schönbach, Christian; Verma, Chandra; Bond, Peter J; Ranganathan, Shoba

2016-12-22

The International Conference on Bioinformatics (InCoB) has been publishing peer-reviewed conference papers in BMC Bioinformatics since 2006. Of the 44 articles accepted for publication in supplement issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics and BMC Systems Biology, 24 articles with a bioinformatics or systems biology focus are reviewed in this editorial. InCoB2017 is scheduled to be held in Shenzen, China, September 20-22, 2017.

Expanding the horizons of microRNA bioinformatics.

PubMed

Huntley, Rachael P; Kramarz, Barbara; Sawford, Tony; Umrao, Zara; Kalea, Anastasia Z; Acquaah, Vanessa; Martin, Maria-Jesus; Mayr, Manuel; Lovering, Ruth C

2018-06-05

MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult for researchers to find and analyse microRNA-related experimental data and define further research projects. We are addressing this problem by providing two new bioinformatics datasets that contain experimentally verified functional information for mammalian microRNAs involved in cardiovascular-relevant, and other, processes. To date, our resource provides over 3,900 Gene Ontology annotations associated with almost 500 miRNAs from human, mouse and rat and over 2,200 experimentally validated miRNA:target interactions. We illustrate how this resource can be used to create miRNA-focused interaction networks with a biological context using the known biological role of miRNAs and the mRNAs they regulate, enabling discovery of associations between gene products, biological pathways and, ultimately, diseases. This data will be crucial in advancing the field of microRNA bioinformatics and will establish consistent datasets for reproducible functional analysis of microRNAs across all biological research areas. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Better bioinformatics through usability analysis.

PubMed

Bolchini, Davide; Finkelstein, Anthony; Perrone, Vito; Nagl, Sylvia

2009-02-01

Improving the usability of bioinformatics resources enables researchers to find, interact with, share, compare and manipulate important information more effectively and efficiently. It thus enables researchers to gain improved insights into biological processes with the potential, ultimately, of yielding new scientific results. Usability 'barriers' can pose significant obstacles to a satisfactory user experience and force researchers to spend unnecessary time and effort to complete their tasks. The number of online biological databases available is growing and there is an expanding community of diverse users. In this context there is an increasing need to ensure the highest standards of usability. Using 'state-of-the-art' usability evaluation methods, we have identified and characterized a sample of usability issues potentially relevant to web bioinformatics resources, in general. These specifically concern the design of the navigation and search mechanisms available to the user. The usability issues we have discovered in our substantial case studies are undermining the ability of users to find the information they need in their daily research activities. In addition to characterizing these issues, specific recommendations for improvements are proposed leveraging proven practices from web and usability engineering. The methods and approach we exemplify can be readily adopted by the developers of bioinformatics resources.

Using EMBL-EBI services via Web interface and programmatically via Web Services

PubMed Central

Lopez, Rodrigo; Cowley, Andrew; Li, Weizhong; McWilliam, Hamish

2015-01-01

The European Bioinformatics Institute (EMBL-EBI) provides access to a wide range of databases and analysis tools that are of key importance in bioinformatics. As well as providing Web interfaces to these resources, Web Services are available using SOAP and REST protocols that enable programmatic access to our resources and allow their integration into other applications and analytical workflows. This unit describes the various options available to a typical researcher or bioinformatician who wishes to use our resources via Web interface or programmatically via a range of programming languages. PMID:25501941

Toward a complete dataset of drug-drug interaction information from publicly available sources.

PubMed

Ayvaz, Serkan; Horn, John; Hassanzadeh, Oktie; Zhu, Qian; Stan, Johann; Tatonetti, Nicholas P; Vilar, Santiago; Brochhausen, Mathias; Samwald, Matthias; Rastegar-Mojarad, Majid; Dumontier, Michel; Boyce, Richard D

2015-06-01

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Cancer Bioinformatics for Updating Anticancer Drug Developments and Personalized Therapeutics.

PubMed

Lu, Da-Yong; Qu, Rong-Xin; Lu, Ting-Ren; Wu, Hong-Ying

2017-01-01

Last two to three decades, this world witnesses a rapid progress of biomarkers and bioinformatics technologies. Cancer bioinformatics is one of such important omics branches for experimental/clinical studies and applications. Same as other biological techniques or systems, bioinformatics techniques will be widely used. But they are presently not omni-potent. Despite great popularity and improvements, cancer bioinformatics has its own limitations and shortcomings at this stage of technical advancements. This article will offer a panorama of bioinformatics in cancer researches and clinical therapeutic applications-possible advantages and limitations relating to cancer therapeutics. A lot of beneficial capabilities and outcomes have been described. As a result, a successful new era for cancer bioinformatics is waiting for us if we can adhere on scientific studies of cancer bioinformatics in malignant- origin mining, medical verifications and clinical diagnostic applications. Cancer bioinformatics gave a great significance in disease diagnosis and therapeutic predictions. Many creative ideas and future perspectives are highlighted. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

R-Based Software for the Integration of Pathway Data into Bioinformatic Algorithms

PubMed Central

Kramer, Frank; Bayerlová, Michaela; Beißbarth, Tim

2014-01-01

Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools. PMID:24833336

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.

Robust enzyme design: bioinformatic tools for improved protein stability.

PubMed

Suplatov, Dmitry; Voevodin, Vladimir; Švedas, Vytas

2015-03-01

The ability of proteins and enzymes to maintain a functionally active conformation under adverse environmental conditions is an important feature of biocatalysts, vaccines, and biopharmaceutical proteins. From an evolutionary perspective, robust stability of proteins improves their biological fitness and allows for further optimization. Viewed from an industrial perspective, enzyme stability is crucial for the practical application of enzymes under the required reaction conditions. In this review, we analyze bioinformatic-driven strategies that are used to predict structural changes that can be applied to wild type proteins in order to produce more stable variants. The most commonly employed techniques can be classified into stochastic approaches, empirical or systematic rational design strategies, and design of chimeric proteins. We conclude that bioinformatic analysis can be efficiently used to study large protein superfamilies systematically as well as to predict particular structural changes which increase enzyme stability. Evolution has created a diversity of protein properties that are encoded in genomic sequences and structural data. Bioinformatics has the power to uncover this evolutionary code and provide a reproducible selection of hotspots - key residues to be mutated in order to produce more stable and functionally diverse proteins and enzymes. Further development of systematic bioinformatic procedures is needed to organize and analyze sequences and structures of proteins within large superfamilies and to link them to function, as well as to provide knowledge-based predictions for experimental evaluation. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Developing library bioinformatics services in context: the Purdue University Libraries bioinformationist program

PubMed Central

Rein, Diane C.

2006-01-01

Setting: Purdue University is a major agricultural, engineering, biomedical, and applied life science research institution with an increasing focus on bioinformatics research that spans multiple disciplines and campus academic units. The Purdue University Libraries (PUL) hired a molecular biosciences specialist to discover, engage, and support bioinformatics needs across the campus. Program Components: After an extended period of information needs assessment and environmental scanning, the specialist developed a week of focused bioinformatics instruction (Bioinformatics Week) to launch system-wide, library-based bioinformatics services. Evaluation Mechanisms: The specialist employed a two-tiered approach to assess user information requirements and expectations. The first phase involved careful observation and collection of information needs in-context throughout the campus, attending laboratory meetings, interviewing department chairs and individual researchers, and engaging in strategic planning efforts. Based on the information gathered during the integration phase, several survey instruments were developed to facilitate more critical user assessment and the recovery of quantifiable data prior to planning. Next Steps/Future Directions: Given information gathered while working with clients and through formal needs assessments, as well as the success of instructional approaches used in Bioinformatics Week, the specialist is developing bioinformatics support services for the Purdue community. The specialist is also engaged in training PUL faculty librarians in bioinformatics to provide a sustaining culture of library-based bioinformatics support and understanding of Purdue's bioinformatics-related decision and policy making. PMID:16888666

Developing library bioinformatics services in context: the Purdue University Libraries bioinformationist program.

PubMed

Rein, Diane C

2006-07-01

Purdue University is a major agricultural, engineering, biomedical, and applied life science research institution with an increasing focus on bioinformatics research that spans multiple disciplines and campus academic units. The Purdue University Libraries (PUL) hired a molecular biosciences specialist to discover, engage, and support bioinformatics needs across the campus. After an extended period of information needs assessment and environmental scanning, the specialist developed a week of focused bioinformatics instruction (Bioinformatics Week) to launch system-wide, library-based bioinformatics services. The specialist employed a two-tiered approach to assess user information requirements and expectations. The first phase involved careful observation and collection of information needs in-context throughout the campus, attending laboratory meetings, interviewing department chairs and individual researchers, and engaging in strategic planning efforts. Based on the information gathered during the integration phase, several survey instruments were developed to facilitate more critical user assessment and the recovery of quantifiable data prior to planning. Given information gathered while working with clients and through formal needs assessments, as well as the success of instructional approaches used in Bioinformatics Week, the specialist is developing bioinformatics support services for the Purdue community. The specialist is also engaged in training PUL faculty librarians in bioinformatics to provide a sustaining culture of library-based bioinformatics support and understanding of Purdue's bioinformatics-related decision and policy making.

Explorative search of distributed bio-data to answer complex biomedical questions

PubMed Central

2014-01-01

Background The huge amount of biomedical-molecular data increasingly produced is providing scientists with potentially valuable information. Yet, such data quantity makes difficult to find and extract those data that are most reliable and most related to the biomedical questions to be answered, which are increasingly complex and often involve many different biomedical-molecular aspects. Such questions can be addressed only by comprehensively searching and exploring different types of data, which frequently are ordered and provided by different data sources. Search Computing has been proposed for the management and integration of ranked results from heterogeneous search services. Here, we present its novel application to the explorative search of distributed biomedical-molecular data and the integration of the search results to answer complex biomedical questions. Results A set of available bioinformatics search services has been modelled and registered in the Search Computing framework, and a Bioinformatics Search Computing application (Bio-SeCo) using such services has been created and made publicly available at http://www.bioinformatics.deib.polimi.it/bio-seco/seco/. It offers an integrated environment which eases search, exploration and ranking-aware combination of heterogeneous data provided by the available registered services, and supplies global results that can support answering complex multi-topic biomedical questions. Conclusions By using Bio-SeCo, scientists can explore the very large and very heterogeneous biomedical-molecular data available. They can easily make different explorative search attempts, inspect obtained results, select the most appropriate, expand or refine them and move forward and backward in the construction of a global complex biomedical query on multiple distributed sources that could eventually find the most relevant results. Thus, it provides an extremely useful automated support for exploratory integrated bio search, which is fundamental for Life Science data driven knowledge discovery. PMID:24564278

Harnessing pain heterogeneity and RNA transcriptome to identify blood–based pain biomarkers: a novel correlational study design and bioinformatics approach in a graded chronic constriction injury model

PubMed Central

Grace, Peter M.; Hurley, Daniel; Barratt, Daniel T.; Tsykin, Anna; Watkins, Linda R.; Rolan, Paul E.; Hutchinson, Mark R.

2017-01-01

A quantitative, peripherally accessible biomarker for neuropathic pain has great potential to improve clinical outcomes. Based on the premise that peripheral and central immunity contribute to neuropathic pain mechanisms, we hypothesized that biomarkers could be identified from the whole blood of adult male rats, by integrating graded chronic constriction injury (CCI), ipsilateral lumbar dorsal quadrant (iLDQ) and whole blood transcriptomes, and pathway analysis with pain behavior. Correlational bioinformatics identified a range of putative biomarker genes for allodynia intensity, many encoding for proteins with a recognized role in immune/nociceptive mechanisms. A selection of these genes was validated in a separate replication study. Pathway analysis of the iLDQ transcriptome identified Fcγ and Fcε signaling pathways, among others. This study is the first to employ the whole blood transcriptome to identify pain biomarker panels. The novel correlational bioinformatics, developed here, selected such putative biomarkers based on a correlation with pain behavior and formation of signaling pathways with iLDQ genes. Future studies may demonstrate the predictive ability of these biomarker genes across other models and additional variables. PMID:22697386

Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends.

PubMed

Geerts, Hugo; Hofmann-Apitius, Martin; Anastasio, Thomas J

2017-11-01

Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

SeqReporter: automating next-generation sequencing result interpretation and reporting workflow in a clinical laboratory.

PubMed

Roy, Somak; Durso, Mary Beth; Wald, Abigail; Nikiforov, Yuri E; Nikiforova, Marina N

2014-01-01

A wide repertoire of bioinformatics applications exist for next-generation sequencing data analysis; however, certain requirements of the clinical molecular laboratory limit their use: i) comprehensive report generation, ii) compatibility with existing laboratory information systems and computer operating system, iii) knowledgebase development, iv) quality management, and v) data security. SeqReporter is a web-based application developed using ASP.NET framework version 4.0. The client-side was designed using HTML5, CSS3, and Javascript. The server-side processing (VB.NET) relied on interaction with a customized SQL server 2008 R2 database. Overall, 104 cases (1062 variant calls) were analyzed by SeqReporter. Each variant call was classified into one of five report levels: i) known clinical significance, ii) uncertain clinical significance, iii) pending pathologists' review, iv) synonymous and deep intronic, and v) platform and panel-specific sequence errors. SeqReporter correctly annotated and classified 99.9% (859 of 860) of sequence variants, including 68.7% synonymous single-nucleotide variants, 28.3% nonsynonymous single-nucleotide variants, 1.7% insertions, and 1.3% deletions. One variant of potential clinical significance was re-classified after pathologist review. Laboratory information system-compatible clinical reports were generated automatically. SeqReporter also facilitated quality management activities. SeqReporter is an example of a customized and well-designed informatics solution to optimize and automate the downstream analysis of clinical next-generation sequencing data. We propose it as a model that may envisage the development of a comprehensive clinical informatics solution. Copyright © 2014 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

The GMOseek matrix: a decision support tool for optimizing the detection of genetically modified plants.

PubMed

Block, Annette; Debode, Frédéric; Grohmann, Lutz; Hulin, Julie; Taverniers, Isabel; Kluga, Linda; Barbau-Piednoir, Elodie; Broeders, Sylvia; Huber, Ingrid; Van den Bulcke, Marc; Heinze, Petra; Berben, Gilbert; Busch, Ulrich; Roosens, Nancy; Janssen, Eric; Žel, Jana; Gruden, Kristina; Morisset, Dany

2013-08-22

Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs' molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information. The GMOseek matrix was built as a comprehensive, online open-access tabulated database which provides a reliable, comprehensive and user-friendly overview of 328 GMO events and 247 different genetic elements (status: 18/07/2013). The GMOseek matrix is aiming to facilitate GMO detection from plant origin at different phases of the analysis. It assists in selecting the targets for a screening analysis, interpreting the screening results, checking the occurrence of a screening element in a group of selected GMOs, identifying gaps in the available pool of GMO detection methods, and designing a decision tree. The GMOseek matrix is an independent database with effective functionalities in a format facilitating transferability to other platforms. Data were collected from all available sources and experimentally tested where detection methods and certified reference materials (CRMs) were available. The GMOseek matrix is currently a unique and very valuable tool with reliable information on GMOs from plant origin and their present genetic elements that enables further development of appropriate strategies for GMO detection. It is flexible enough to be further updated with new information and integrated in different applications and platforms.

The GMOseek matrix: a decision support tool for optimizing the detection of genetically modified plants

PubMed Central

2013-01-01

Background Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs’ molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information. Description The GMOseek matrix was built as a comprehensive, online open-access tabulated database which provides a reliable, comprehensive and user-friendly overview of 328 GMO events and 247 different genetic elements (status: 18/07/2013). The GMOseek matrix is aiming to facilitate GMO detection from plant origin at different phases of the analysis. It assists in selecting the targets for a screening analysis, interpreting the screening results, checking the occurrence of a screening element in a group of selected GMOs, identifying gaps in the available pool of GMO detection methods, and designing a decision tree. The GMOseek matrix is an independent database with effective functionalities in a format facilitating transferability to other platforms. Data were collected from all available sources and experimentally tested where detection methods and certified reference materials (CRMs) were available. Conclusions The GMOseek matrix is currently a unique and very valuable tool with reliable information on GMOs from plant origin and their present genetic elements that enables further development of appropriate strategies for GMO detection. It is flexible enough to be further updated with new information and integrated in different applications and platforms. PMID:23965170

BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments

PubMed Central

2012-01-01

Background It is known from recent studies that more than 90% of human multi-exon genes are subject to Alternative Splicing (AS), a key molecular mechanism in which multiple transcripts may be generated from a single gene. It is widely recognized that a breakdown in AS mechanisms plays an important role in cellular differentiation and pathologies. Polymerase Chain Reactions, microarrays and sequencing technologies have been applied to the study of transcript diversity arising from alternative expression. Last generation Affymetrix GeneChip Human Exon 1.0 ST Arrays offer a more detailed view of the gene expression profile providing information on the AS patterns. The exon array technology, with more than five million data points, can detect approximately one million exons, and it allows performing analyses at both gene and exon level. In this paper we describe BEAT, an integrated user-friendly bioinformatics framework to store, analyze and visualize exon arrays datasets. It combines a data warehouse approach with some rigorous statistical methods for assessing the AS of genes involved in diseases. Meta statistics are proposed as a novel approach to explore the analysis results. BEAT is available at http://beat.ba.itb.cnr.it. Results BEAT is a web tool which allows uploading and analyzing exon array datasets using standard statistical methods and an easy-to-use graphical web front-end. BEAT has been tested on a dataset with 173 samples and tuned using new datasets of exon array experiments from 28 colorectal cancer and 26 renal cell cancer samples produced at the Medical Genetics Unit of IRCCS Casa Sollievo della Sofferenza. To highlight all possible AS events, alternative names, accession Ids, Gene Ontology terms and biochemical pathways annotations are integrated with exon and gene level expression plots. The user can customize the results choosing custom thresholds for the statistical parameters and exploiting the available clinical data of the samples for a multivariate AS analysis. Conclusions Despite exon array chips being widely used for transcriptomics studies, there is a lack of analysis tools offering advanced statistical features and requiring no programming knowledge. BEAT provides a user-friendly platform for a comprehensive study of AS events in human diseases, displaying the analysis results with easily interpretable and interactive tables and graphics. PMID:22536968

Seahawk: moving beyond HTML in Web-based bioinformatics analysis.

PubMed

Gordon, Paul M K; Sensen, Christoph W

2007-06-18

Traditional HTML interfaces for input to and output from Bioinformatics analysis on the Web are highly variable in style, content and data formats. Combining multiple analyses can therefore be an onerous task for biologists. Semantic Web Services allow automated discovery of conceptual links between remote data analysis servers. A shared data ontology and service discovery/execution framework is particularly attractive in Bioinformatics, where data and services are often both disparate and distributed. Instead of biologists copying, pasting and reformatting data between various Web sites, Semantic Web Service protocols such as MOBY-S hold out the promise of seamlessly integrating multi-step analysis. We have developed a program (Seahawk) that allows biologists to intuitively and seamlessly chain together Web Services using a data-centric, rather than the customary service-centric approach. The approach is illustrated with a ferredoxin mutation analysis. Seahawk concentrates on lowering entry barriers for biologists: no prior knowledge of the data ontology, or relevant services is required. In stark contrast to other MOBY-S clients, in Seahawk users simply load Web pages and text files they already work with. Underlying the familiar Web-browser interaction is an XML data engine based on extensible XSLT style sheets, regular expressions, and XPath statements which import existing user data into the MOBY-S format. As an easily accessible applet, Seahawk moves beyond standard Web browser interaction, providing mechanisms for the biologist to concentrate on the analytical task rather than on the technical details of data formats and Web forms. As the MOBY-S protocol nears a 1.0 specification, we expect more biologists to adopt these new semantic-oriented ways of doing Web-based analysis, which empower them to do more complicated, ad hoc analysis workflow creation without the assistance of a programmer.

Seahawk: moving beyond HTML in Web-based bioinformatics analysis

PubMed Central

Gordon, Paul MK; Sensen, Christoph W

2007-01-01

Background Traditional HTML interfaces for input to and output from Bioinformatics analysis on the Web are highly variable in style, content and data formats. Combining multiple analyses can therfore be an onerous task for biologists. Semantic Web Services allow automated discovery of conceptual links between remote data analysis servers. A shared data ontology and service discovery/execution framework is particularly attractive in Bioinformatics, where data and services are often both disparate and distributed. Instead of biologists copying, pasting and reformatting data between various Web sites, Semantic Web Service protocols such as MOBY-S hold out the promise of seamlessly integrating multi-step analysis. Results We have developed a program (Seahawk) that allows biologists to intuitively and seamlessly chain together Web Services using a data-centric, rather than the customary service-centric approach. The approach is illustrated with a ferredoxin mutation analysis. Seahawk concentrates on lowering entry barriers for biologists: no prior knowledge of the data ontology, or relevant services is required. In stark contrast to other MOBY-S clients, in Seahawk users simply load Web pages and text files they already work with. Underlying the familiar Web-browser interaction is an XML data engine based on extensible XSLT style sheets, regular expressions, and XPath statements which import existing user data into the MOBY-S format. Conclusion As an easily accessible applet, Seahawk moves beyond standard Web browser interaction, providing mechanisms for the biologist to concentrate on the analytical task rather than on the technical details of data formats and Web forms. As the MOBY-S protocol nears a 1.0 specification, we expect more biologists to adopt these new semantic-oriented ways of doing Web-based analysis, which empower them to do more complicated, ad hoc analysis workflow creation without the assistance of a programmer. PMID:17577405

Integration of bioinformatics into an undergraduate biology curriculum and the impact on development of mathematical skills.

PubMed

Wightman, Bruce; Hark, Amy T

2012-01-01

The development of fields such as bioinformatics and genomics has created new challenges and opportunities for undergraduate biology curricula. Students preparing for careers in science, technology, and medicine need more intensive study of bioinformatics and more sophisticated training in the mathematics on which this field is based. In this study, we deliberately integrated bioinformatics instruction at multiple course levels into an existing biology curriculum. Students in an introductory biology course, intermediate lab courses, and advanced project-oriented courses all participated in new course components designed to sequentially introduce bioinformatics skills and knowledge, as well as computational approaches that are common to many bioinformatics applications. In each course, bioinformatics learning was embedded in an existing disciplinary instructional sequence, as opposed to having a single course where all bioinformatics learning occurs. We designed direct and indirect assessment tools to follow student progress through the course sequence. Our data show significant gains in both student confidence and ability in bioinformatics during individual courses and as course level increases. Despite evidence of substantial student learning in both bioinformatics and mathematics, students were skeptical about the link between learning bioinformatics and learning mathematics. While our approach resulted in substantial learning gains, student "buy-in" and engagement might be better in longer project-based activities that demand application of skills to research problems. Nevertheless, in situations where a concentrated focus on project-oriented bioinformatics is not possible or desirable, our approach of integrating multiple smaller components into an existing curriculum provides an alternative. Copyright © 2012 Wiley Periodicals, Inc.

Incorporating Genomics and Bioinformatics across the Life Sciences Curriculum

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

Ditty, Jayna L.; Kvaal, Christopher A.; Goodner, Brad

Undergraduate life sciences education needs an overhaul, as clearly described in the National Research Council of the National Academies publication BIO 2010: Transforming Undergraduate Education for Future Research Biologists. Among BIO 2010's top recommendations is the need to involve students in working with real data and tools that reflect the nature of life sciences research in the 21st century. Education research studies support the importance of utilizing primary literature, designing and implementing experiments, and analyzing results in the context of a bona fide scientific question in cultivating the analytical skills necessary to become a scientist. Incorporating these basic scientific methodologiesmore » in undergraduate education leads to increased undergraduate and post-graduate retention in the sciences. Toward this end, many undergraduate teaching organizations offer training and suggestions for faculty to update and improve their teaching approaches to help students learn as scientists, through design and discovery (e.g., Council of Undergraduate Research [www.cur.org] and Project Kaleidoscope [www.pkal.org]). With the advent of genome sequencing and bioinformatics, many scientists now formulate biological questions and interpret research results in the context of genomic information. Just as the use of bioinformatic tools and databases changed the way scientists investigate problems, it must change how scientists teach to create new opportunities for students to gain experiences reflecting the influence of genomics, proteomics, and bioinformatics on modern life sciences research. Educators have responded by incorporating bioinformatics into diverse life science curricula. While these published exercises in, and guidelines for, bioinformatics curricula are helpful and inspirational, faculty new to the area of bioinformatics inevitably need training in the theoretical underpinnings of the algorithms. Moreover, effectively integrating bioinformatics into courses or independent research projects requires infrastructure for organizing and assessing student work. Here, we present a new platform for faculty to keep current with the rapidly changing field of bioinformatics, the Integrated Microbial Genomes Annotation Collaboration Toolkit (IMG-ACT). It was developed by instructors from both research-intensive and predominately undergraduate institutions in collaboration with the Department of Energy-Joint Genome Institute (DOE-JGI) as a means to innovate and update undergraduate education and faculty development. The IMG-ACT program provides a cadre of tools, including access to a clearinghouse of genome sequences, bioinformatics databases, data storage, instructor course management, and student notebooks for organizing the results of their bioinformatic investigations. In the process, IMG-ACT makes it feasible to provide undergraduate research opportunities to a greater number and diversity of students, in contrast to the traditional mentor-to-student apprenticeship model for undergraduate research, which can be too expensive and time-consuming to provide for every undergraduate. The IMG-ACT serves as the hub for the network of faculty and students that use the system for microbial genome analysis. Open access of the IMG-ACT infrastructure to participating schools ensures that all types of higher education institutions can utilize it. With the infrastructure in place, faculty can focus their efforts on the pedagogy of bioinformatics, involvement of students in research, and use of this tool for their own research agenda. What the original faculty members of the IMG-ACT development team present here is an overview of how the IMG-ACT program has affected our development in terms of teaching and research with the hopes that it will inspire more faculty to get involved.« less

Bi-PROF

PubMed Central

Gries, Jasmin; Schumacher, Dirk; Arand, Julia; Lutsik, Pavlo; Markelova, Maria Rivera; Fichtner, Iduna; Walter, Jörn; Sers, Christine; Tierling, Sascha

2013-01-01

The use of next generation sequencing has expanded our view on whole mammalian methylome patterns. In particular, it provides a genome-wide insight of local DNA methylation diversity at single nucleotide level and enables the examination of single chromosome sequence sections at a sufficient statistical power. We describe a bisulfite-based sequence profiling pipeline, Bi-PROF, which is based on the 454 GS-FLX Titanium technology that allows to obtain up to one million sequence stretches at single base pair resolution without laborious subcloning. To illustrate the performance of the experimental workflow connected to a bioinformatics program pipeline (BiQ Analyzer HT) we present a test analysis set of 68 different epigenetic marker regions (amplicons) in five individual patient-derived xenograft tissue samples of colorectal cancer and one healthy colon epithelium sample as a control. After the 454 GS-FLX Titanium run, sequence read processing and sample decoding, the obtained alignments are quality controlled and statistically evaluated. Comprehensive methylation pattern interpretation (profiling) assessed by analyzing 102-104 sequence reads per amplicon allows an unprecedented deep view on pattern formation and methylation marker heterogeneity in tissues concerned by complex diseases like cancer. PMID:23803588

iFeature: a python package and web server for features extraction and selection from protein and peptide sequences.

PubMed

Chen, Zhen; Zhao, Pei; Li, Fuyi; Leier, André; Marquez-Lago, Tatiana T; Wang, Yanan; Webb, Geoffrey I; Smith, A Ian; Daly, Roger J; Chou, Kuo-Chen; Song, Jiangning

2018-03-08

Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection, and dimensionality reduction algorithms, greatly facilitating training, analysis, and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. jiangning.song@monash.edu; kcchou@gordonlifescience.org; roger.daly@monash.edu. Supplementary data are available at Bioinformatics online.

SIRT3 mediates multi-tissue coupling for metabolic fuel switching.

PubMed

Dittenhafer-Reed, Kristin E; Richards, Alicia L; Fan, Jing; Smallegan, Michael J; Fotuhi Siahpirani, Alireza; Kemmerer, Zachary A; Prolla, Tomas A; Roy, Sushmita; Coon, Joshua J; Denu, John M

2015-04-07

SIRT3 is a member of the Sirtuin family of NAD(+)-dependent deacylases and plays a critical role in metabolic regulation. Organism-wide SIRT3 loss manifests in metabolic alterations; however, the coordinating role of SIRT3 among metabolically distinct tissues is unknown. Using multi-tissue quantitative proteomics comparing fasted wild-type mice to mice lacking SIRT3, innovative bioinformatic analysis, and biochemical validation, we provide a comprehensive view of mitochondrial acetylation and SIRT3 function. We find SIRT3 regulates the acetyl-proteome in core mitochondrial processes common to brain, heart, kidney, liver, and skeletal muscle, but differentially regulates metabolic pathways in fuel-producing and fuel-utilizing tissues. We propose an additional maintenance function for SIRT3 in liver and kidney where SIRT3 expression is elevated to reduce the acetate load on mitochondrial proteins. We provide evidence that SIRT3 impacts ketone body utilization in the brain and reveal a pivotal role for SIRT3 in the coordination between tissues required for metabolic homeostasis. Copyright © 2015 Elsevier Inc. All rights reserved.

A Metabolic Profiling Strategy for the Dissection of Plant Defense against Fungal Pathogens

PubMed Central

Aliferis, Konstantinos A.; Faubert, Denis; Jabaji, Suha

2014-01-01

Here we present a metabolic profiling strategy employing direct infusion Orbitrap mass spectrometry (MS) and gas chromatography-mass spectrometry (GC/MS) for the monitoring of soybean's (Glycine max L.) global metabolism regulation in response to Rhizoctonia solani infection in a time-course. Key elements in the approach are the construction of a comprehensive metabolite library for soybean, which accelerates the steps of metabolite identification and biological interpretation of results, and bioinformatics tools for the visualization and analysis of its metabolome. The study of metabolic networks revealed that infection results in the mobilization of carbohydrates, disturbance of the amino acid pool, and activation of isoflavonoid, α-linolenate, and phenylpropanoid biosynthetic pathways of the plant. Components of these pathways include phytoalexins, coumarins, flavonoids, signaling molecules, and hormones, many of which exhibit antioxidant properties and bioactivity helping the plant to counterattack the pathogen's invasion. Unraveling the biochemical mechanism operating during soybean-Rhizoctonia interaction, in addition to its significance towards the understanding of the plant's metabolism regulation under biotic stress, provides valuable insights with potential for applications in biotechnology, crop breeding, and agrochemical and food industries. PMID:25369450

A Multi-layered Quantitative In Vivo Expression Atlas of the Podocyte Unravels Kidney Disease Candidate Genes.

PubMed

Rinschen, Markus M; Gödel, Markus; Grahammer, Florian; Zschiedrich, Stefan; Helmstädter, Martin; Kretz, Oliver; Zarei, Mostafa; Braun, Daniela A; Dittrich, Sebastian; Pahmeyer, Caroline; Schroder, Patricia; Teetzen, Carolin; Gee, HeonYung; Daouk, Ghaleb; Pohl, Martin; Kuhn, Elisa; Schermer, Bernhard; Küttner, Victoria; Boerries, Melanie; Busch, Hauke; Schiffer, Mario; Bergmann, Carsten; Krüger, Marcus; Hildebrandt, Friedhelm; Dengjel, Joern; Benzing, Thomas; Huber, Tobias B

2018-05-22

Damage to and loss of glomerular podocytes has been identified as the culprit lesion in progressive kidney diseases. Here, we combine mass spectrometry-based proteomics with mRNA sequencing, bioinformatics, and hypothesis-driven studies to provide a comprehensive and quantitative map of mammalian podocytes that identifies unanticipated signaling pathways. Comparison of the in vivo datasets with proteomics data from podocyte cell cultures showed a limited value of available cell culture models. Moreover, in vivo stable isotope labeling by amino acids uncovered surprisingly rapid synthesis of mitochondrial proteins under steady-state conditions that was perturbed under autophagy-deficient, disease-susceptible conditions. Integration of acquired omics dimensions suggested FARP1 as a candidate essential for podocyte function, which could be substantiated by genetic analysis in humans and knockdown experiments in zebrafish. This work exemplifies how the integration of multi-omics datasets can identify a framework of cell-type-specific features relevant for organ health and disease. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics

PubMed Central

Deutsch, Eric W.; Mendoza, Luis; Shteynberg, David; Slagel, Joseph; Sun, Zhi; Moritz, Robert L.

2015-01-01

Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include mass spectrometry to define protein sequence, protein:protein interactions, and protein post-translational modifications. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative mass spectrometry proteomics. It supports all major operating systems and instrument vendors via open data formats. Here we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of tandem mass spectrometry datasets, as well as some major upcoming features. PMID:25631240

Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics.

PubMed

Deutsch, Eric W; Mendoza, Luis; Shteynberg, David; Slagel, Joseph; Sun, Zhi; Moritz, Robert L

2015-08-01

Democratization of genomics technologies has enabled the rapid determination of genotypes. More recently the democratization of comprehensive proteomics technologies is enabling the determination of the cellular phenotype and the molecular events that define its dynamic state. Core proteomic technologies include MS to define protein sequence, protein:protein interactions, and protein PTMs. Key enabling technologies for proteomics are bioinformatic pipelines to identify, quantitate, and summarize these events. The Trans-Proteomics Pipeline (TPP) is a robust open-source standardized data processing pipeline for large-scale reproducible quantitative MS proteomics. It supports all major operating systems and instrument vendors via open data formats. Here, we provide a review of the overall proteomics workflow supported by the TPP, its major tools, and how it can be used in its various modes from desktop to cloud computing. We describe new features for the TPP, including data visualization functionality. We conclude by describing some common perils that affect the analysis of MS/MS datasets, as well as some major upcoming features. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Combinatory annotation of cell membrane receptors and signalling pathways of Bombyx mori prothoracic glands

PubMed Central

Moulos, Panagiotis; Samiotaki, Martina; Panayotou, George; Dedos, Skarlatos G.

2016-01-01

The cells of prothoracic glands (PG) are the main site of synthesis and secretion of ecdysteroids, the biochemical products of cholesterol conversion to steroids that shape the morphogenic development of insects. Despite the availability of genome sequences from several insect species and the extensive knowledge of certain signalling pathways that underpin ecdysteroidogenesis, the spectrum of signalling molecules and ecdysteroidogenic cascades is still not fully comprehensive. To fill this gap and obtain the complete list of cell membrane receptors expressed in PG cells, we used combinatory bioinformatic, proteomic and transcriptomic analysis and quantitative PCR to annotate and determine the expression profiles of genes identified as putative cell membrane receptors of the model insect species, Bombyx mori, and subsequently enrich the repertoire of signalling pathways that are present in its PG cells. The genome annotation dataset we report here highlights modules and pathways that may be directly involved in ecdysteroidogenesis and aims to disseminate data and assist other researchers in the discovery of the role of such receptors and their ligands. PMID:27576083

Condenser: a statistical aggregation tool for multi-sample quantitative proteomic data from Matrix Science Mascot Distiller™.

PubMed

Knudsen, Anders Dahl; Bennike, Tue; Kjeldal, Henrik; Birkelund, Svend; Otzen, Daniel Erik; Stensballe, Allan

2014-05-30

We describe Condenser, a freely available, comprehensive open-source tool for merging multidimensional quantitative proteomics data from the Matrix Science Mascot Distiller Quantitation Toolbox into a common format ready for subsequent bioinformatic analysis. A number of different relative quantitation technologies, such as metabolic (15)N and amino acid stable isotope incorporation, label-free and chemical-label quantitation are supported. The program features multiple options for curative filtering of the quantified peptides, allowing the user to choose data quality thresholds appropriate for the current dataset, and ensure the quality of the calculated relative protein abundances. Condenser also features optional global normalization, peptide outlier removal, multiple testing and calculation of t-test statistics for highlighting and evaluating proteins with significantly altered relative protein abundances. Condenser provides an attractive addition to the gold-standard quantitative workflow of Mascot Distiller, allowing easy handling of larger multi-dimensional experiments. Source code, binaries, test data set and documentation are available at http://condenser.googlecode.com/. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Exploring the molecular mechanisms of Traditional Chinese Medicine components using gene expression signatures and connectivity map.

PubMed

Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kim, Jihye; Kang, Jaewoo; Tan, Aik Choon

2018-04-04

Traditional Chinese Medicine (TCM) has been practiced over thousands of years in China and other Asian countries for treating various symptoms and diseases. However, the underlying molecular mechanisms of TCM are poorly understood, partly due to the "multi-component, multi-target" nature of TCM. To uncover the molecular mechanisms of TCM, we perform comprehensive gene expression analysis using connectivity map. We interrogated gene expression signatures obtained 102 TCM components using the next generation Connectivity Map (CMap) resource. We performed systematic data mining and analysis on the mechanism of action (MoA) of these TCM components based on the CMap results. We clustered the 102 TCM components into four groups based on their MoAs using next generation CMap resource. We performed gene set enrichment analysis on these components to provide additional supports for explaining these molecular mechanisms. We also provided literature evidence to validate the MoAs identified through this bioinformatics analysis. Finally, we developed the Traditional Chinese Medicine Drug Repurposing Hub (TCM Hub) - a connectivity map resource to facilitate the elucidation of TCM MoA for drug repurposing research. TCMHub is freely available in http://tanlab.ucdenver.edu/TCMHub. Molecular mechanisms of TCM could be uncovered by using gene expression signatures and connectivity map. Through this analysis, we identified many of the TCM components possess diverse MoAs, this may explain the applications of TCM in treating various symptoms and diseases. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Comprehensive functional characterization of the Glycoside Hydrolase Family 3 enzymes from Cellvibrio japonicus reveals unique metabolic roles in biomass saccharification

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

Nelson, Cassandra E.; Attia, Mohamed A.; Rogowski, Artur

Here, lignocellulose degradation is central to the carbon cycle and renewable biotechnologies. The xyloglucan (XyG), β(1!3)/β(1!4) mixed-linkage glucan (MLG), and β(1!3) glucan components of lignocellulose represent significant carbohydrate energy sources for saprophytic microorganisms. The bacterium Cellvibrio japonicus has a robust capacity for plant polysaccharide degradation, due to a genome encoding a large contingent of Carbohydrate-Active Enzymes (CAZymes), many of whose specific functions remain unknown. Using a comprehensive genetic and biochemical approach we have delineated the physiological roles of the four C. japonicus Glycoside Hydrolase Family 3 (GH3) members on diverse β-glucans. Despite high protein sequence similarity and partially overlapping activitymore » profiles on disaccharides, these β-glucosidases are not functionally equivalent. Bgl3A has a major role in MLG and sophorose utilization, and supports β(1!3) glucan utilization, while Bgl3B underpins cellulose utilization and supports MLG utilization. Bgl3C drives β(1!3) glucan utilization. Finally, Bgl3D is the crucial β-glucosidase for XyG utilization. This study not only sheds the light on the metabolic machinery of C. japonicus, but also expands the repertoire of characterized CAZymes for future deployment in biotechnological applications. In particular, the precise functional analysis provided here serves as a reference for informed bioinformatics on the genomes of other Cellvibrio and related species.« less

Comprehensive functional characterization of the Glycoside Hydrolase Family 3 enzymes from Cellvibrio japonicus reveals unique metabolic roles in biomass saccharification

DOE PAGES

Nelson, Cassandra E.; Attia, Mohamed A.; Rogowski, Artur; ...

2017-10-20

Here, lignocellulose degradation is central to the carbon cycle and renewable biotechnologies. The xyloglucan (XyG), β(1!3)/β(1!4) mixed-linkage glucan (MLG), and β(1!3) glucan components of lignocellulose represent significant carbohydrate energy sources for saprophytic microorganisms. The bacterium Cellvibrio japonicus has a robust capacity for plant polysaccharide degradation, due to a genome encoding a large contingent of Carbohydrate-Active Enzymes (CAZymes), many of whose specific functions remain unknown. Using a comprehensive genetic and biochemical approach we have delineated the physiological roles of the four C. japonicus Glycoside Hydrolase Family 3 (GH3) members on diverse β-glucans. Despite high protein sequence similarity and partially overlapping activitymore » profiles on disaccharides, these β-glucosidases are not functionally equivalent. Bgl3A has a major role in MLG and sophorose utilization, and supports β(1!3) glucan utilization, while Bgl3B underpins cellulose utilization and supports MLG utilization. Bgl3C drives β(1!3) glucan utilization. Finally, Bgl3D is the crucial β-glucosidase for XyG utilization. This study not only sheds the light on the metabolic machinery of C. japonicus, but also expands the repertoire of characterized CAZymes for future deployment in biotechnological applications. In particular, the precise functional analysis provided here serves as a reference for informed bioinformatics on the genomes of other Cellvibrio and related species.« less

Comprehensive functional characterization of the glycoside hydrolase family 3 enzymes from Cellvibrio japonicus reveals unique metabolic roles in biomass saccharification.

PubMed

Nelson, Cassandra E; Attia, Mohamed A; Rogowski, Artur; Morland, Carl; Brumer, Harry; Gardner, Jeffrey G

2017-12-01

Lignocellulose degradation is central to the carbon cycle and renewable biotechnologies. The xyloglucan (XyG), β(1→3)/β(1→4) mixed-linkage glucan (MLG) and β(1→3) glucan components of lignocellulose represent significant carbohydrate energy sources for saprophytic microorganisms. The bacterium Cellvibrio japonicus has a robust capacity for plant polysaccharide degradation, due to a genome encoding a large contingent of Carbohydrate-Active enZymes (CAZymes), many of whose specific functions remain unknown. Using a comprehensive genetic and biochemical approach, we have delineated the physiological roles of the four C. japonicus glycoside hydrolase family 3 (GH3) members on diverse β-glucans. Despite high protein sequence similarity and partially overlapping activity profiles on disaccharides, these β-glucosidases are not functionally equivalent. Bgl3A has a major role in MLG and sophorose utilization, and supports β(1→3) glucan utilization, while Bgl3B underpins cellulose utilization and supports MLG utilization. Bgl3C drives β(1→3) glucan utilization. Finally, Bgl3D is the crucial β-glucosidase for XyG utilization. This study not only sheds the light on the metabolic machinery of C. japonicus, but also expands the repertoire of characterized CAZymes for future deployment in biotechnological applications. In particular, the precise functional analysis provided here serves as a reference for informed bioinformatics on the genomes of other Cellvibrio and related species. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.

web cellHTS2: a web-application for the analysis of high-throughput screening data.

PubMed

Pelz, Oliver; Gilsdorf, Moritz; Boutros, Michael

2010-04-12

The analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2. The software guides the user through the configuration steps that are required for the analysis of single or multi-channel experiments. The web-application provides options for various standardization and normalization methods, annotation of data sets and a comprehensive HTML report of the screening data analysis, including a ranked hit list. Sessions can be saved and restored for later re-analysis. The web frontend for the cellHTS2 R/Bioconductor package interacts with it through an R-server implementation that enables highly parallel analysis of screening data sets. web cellHTS2 further provides a file import and configuration module for common file formats. The implemented web-application facilitates the analysis of high-throughput data sets and provides a user-friendly interface. web cellHTS2 is accessible online at http://web-cellHTS2.dkfz.de. A standalone version as a virtual appliance and source code for platforms supporting Java 1.5.0 can be downloaded from the web cellHTS2 page. web cellHTS2 is freely distributed under GPL.

High-Content Microscopy Analysis of Subcellular Structures: Assay Development and Application to Focal Adhesion Quantification.

PubMed

Kroll, Torsten; Schmidt, David; Schwanitz, Georg; Ahmad, Mubashir; Hamann, Jana; Schlosser, Corinne; Lin, Yu-Chieh; Böhm, Konrad J; Tuckermann, Jan; Ploubidou, Aspasia

2016-07-01

High-content analysis (HCA) converts raw light microscopy images to quantitative data through the automated extraction, multiparametric analysis, and classification of the relevant information content. Combined with automated high-throughput image acquisition, HCA applied to the screening of chemicals or RNAi-reagents is termed high-content screening (HCS). Its power in quantifying cell phenotypes makes HCA applicable also to routine microscopy. However, developing effective HCA and bioinformatic analysis pipelines for acquisition of biologically meaningful data in HCS is challenging. Here, the step-by-step development of an HCA assay protocol and an HCS bioinformatics analysis pipeline are described. The protocol's power is demonstrated by application to focal adhesion (FA) detection, quantitative analysis of multiple FA features, and functional annotation of signaling pathways regulating FA size, using primary data of a published RNAi screen. The assay and the underlying strategy are aimed at researchers performing microscopy-based quantitative analysis of subcellular features, on a small scale or in large HCS experiments. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

EcoCyc: a comprehensive database resource for Escherichia coli

PubMed Central

Keseler, Ingrid M.; Collado-Vides, Julio; Gama-Castro, Socorro; Ingraham, John; Paley, Suzanne; Paulsen, Ian T.; Peralta-Gil, Martín; Karp, Peter D.

2005-01-01

The EcoCyc database (http://EcoCyc.org/) is a comprehensive source of information on the biology of the prototypical model organism Escherichia coli K12. The mission for EcoCyc is to contain both computable descriptions of, and detailed comments describing, all genes, proteins, pathways and molecular interactions in E.coli. Through ongoing manual curation, extensive information such as summary comments, regulatory information, literature citations and evidence types has been extracted from 8862 publications and added to Version 8.5 of the EcoCyc database. The EcoCyc database can be accessed through a World Wide Web interface, while the downloadable Pathway Tools software and data files enable computational exploration of the data and provide enhanced querying capabilities that web interfaces cannot support. For example, EcoCyc contains carefully curated information that can be used as training sets for bioinformatics prediction of entities such as promoters, operons, genetic networks, transcription factor binding sites, metabolic pathways, functionally related genes, protein complexes and protein–ligand interactions. PMID:15608210

Functional and bioinformatics analysis of an exopolysaccharide-related gene (epsN) from Lactobacillus kefiranofaciens ZW3.

PubMed

Wang, Jingrui; Tang, Wei; Zheng, Yongna; Xing, Zhuqing; Wang, Yanping

2016-09-01

A novel lactic acid bacteria strain Lactobacillus kefiranofaciens ZW3 exhibited the characteristics of high production of exopolysaccharide (EPS). The epsN gene, located in the eps gene cluster of this strain, is associated with EPS biosynthesis. Bioinformatics analysis of this gene was performed. The conserved domain analysis showed that the EpsN protein contained MATE-Wzx-like domains. Then the epsN gene was amplified to construct the recombinant expression vector pMG36e-epsN. The results showed that the EPS yields of the recombinants were significantly improved. By determining the yields of EPS and intracellular polysaccharide, it was considered that epsN gene could play its Wzx flippase role in the EPS biosynthesis. This is the first time to prove the effect of EpsN on L. kefiranofaciens EPS biosynthesis and further prove its functional property.

IonGAP: integrative bacterial genome analysis for Ion Torrent sequence data.

PubMed

Baez-Ortega, Adrian; Lorenzo-Diaz, Fabian; Hernandez, Mariano; Gonzalez-Vila, Carlos Ignacio; Roda-Garcia, Jose Luis; Colebrook, Marcos; Flores, Carlos

2015-09-01

We introduce IonGAP, a publicly available Web platform designed for the analysis of whole bacterial genomes using Ion Torrent sequence data. Besides assembly, it integrates a variety of comparative genomics, annotation and bacterial classification routines, based on the widely used FASTQ, BAM and SRA file formats. Benchmarking with different datasets evidenced that IonGAP is a fast, powerful and simple-to-use bioinformatics tool. By releasing this platform, we aim to translate low-cost bacterial genome analysis for microbiological prevention and control in healthcare, agroalimentary and pharmaceutical industry applications. IonGAP is hosted by the ITER's Teide-HPC supercomputer and is freely available on the Web for non-commercial use at http://iongap.hpc.iter.es. mcolesan@ull.edu.es or cflores@ull.edu.es 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.

Bioinformatics analysis of the phytoene synthase gene in cabbage (Brassica oleracea var. capitata)

NASA Astrophysics Data System (ADS)

Sun, Bo; Jiang, Min; Xue, Shengling; Zheng, Aihong; Zhang, Fen; Tang, Haoru

2018-04-01

Phytoene Synthase (PSY) is an important enzyme in carotenoid biosynthesis. Here, the Brassica oleracea var. capitata PSY (BocPSY) gene sequences were obtained from Brassica database (BRAD), and preformed for bioinformatics analysis. The BocPSY1, BocPSY2 and BocPSY3 genes mapped to chromosomes 2,3 and 9, and contains an open reading frame of 1,248 bp, 1,266 bp and 1,275 bp that encodes a 415, 421, 424 amino acid protein, respectively. Subcellular localization predicted all BocPSY genes were in the chloroplast. The conserved domain of the BocPSY protein is PLN02632. Homology analysis indicates that the levels of identity among BocPSYs were all more than 85%, and the PSY protein is apparently conserved during plant evolution. The findings of the present study provide a molecular basis for the elucidation of PSY gene function in cabbage.

Continuing Education Workshops in Bioinformatics Positively Impact Research and Careers

PubMed Central

Brazas, Michelle D.; Ouellette, B. F. Francis

2016-01-01

Bioinformatics.ca has been hosting continuing education programs in introductory and advanced bioinformatics topics in Canada since 1999 and has trained more than 2,000 participants to date. These workshops have been adapted over the years to keep pace with advances in both science and technology as well as the changing landscape in available learning modalities and the bioinformatics training needs of our audience. Post-workshop surveys have been a mandatory component of each workshop and are used to ensure appropriate adjustments are made to workshops to maximize learning. However, neither bioinformatics.ca nor others offering similar training programs have explored the long-term impact of bioinformatics continuing education training. Bioinformatics.ca recently initiated a look back on the impact its workshops have had on the career trajectories, research outcomes, publications, and collaborations of its participants. Using an anonymous online survey, bioinformatics.ca analyzed responses from those surveyed and discovered its workshops have had a positive impact on collaborations, research, publications, and career progression. PMID:27281025

Continuing Education Workshops in Bioinformatics Positively Impact Research and Careers.

PubMed

Brazas, Michelle D; Ouellette, B F Francis

2016-06-01

Bioinformatics.ca has been hosting continuing education programs in introductory and advanced bioinformatics topics in Canada since 1999 and has trained more than 2,000 participants to date. These workshops have been adapted over the years to keep pace with advances in both science and technology as well as the changing landscape in available learning modalities and the bioinformatics training needs of our audience. Post-workshop surveys have been a mandatory component of each workshop and are used to ensure appropriate adjustments are made to workshops to maximize learning. However, neither bioinformatics.ca nor others offering similar training programs have explored the long-term impact of bioinformatics continuing education training. Bioinformatics.ca recently initiated a look back on the impact its workshops have had on the career trajectories, research outcomes, publications, and collaborations of its participants. Using an anonymous online survey, bioinformatics.ca analyzed responses from those surveyed and discovered its workshops have had a positive impact on collaborations, research, publications, and career progression.