VESPA: Software to Facilitate Genomic Annotation of Prokaryotic Organisms Through Integration of Proteomic and Transcriptomic Data

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

Peterson, Elena S.; McCue, Lee Ann; Rutledge, Alexandra C.

2012-04-25

Visual Exploration and Statistics to Promote Annotation (VESPA) is an interactive visual analysis software tool that facilitates the discovery of structural mis-annotations in prokaryotic genomes. VESPA integrates high-throughput peptide-centric proteomics data and oligo-centric or RNA-Seq transcriptomics data into a genomic context. The data may be interrogated via visual analysis across multiple levels of genomic resolution, linked searches, exports and interaction with BLAST to rapidly identify location of interest within the genome and evaluate potential mis-annotations.

Genovar: a detection and visualization tool for genomic variants.

PubMed

Jung, Kwang Su; Moon, Sanghoon; Kim, Young Jin; Kim, Bong-Jo; Park, Kiejung

2012-05-08

Along with single nucleotide polymorphisms (SNPs), copy number variation (CNV) is considered an important source of genetic variation associated with disease susceptibility. Despite the importance of CNV, the tools currently available for its analysis often produce false positive results due to limitations such as low resolution of array platforms, platform specificity, and the type of CNV. To resolve this problem, spurious signals must be separated from true signals by visual inspection. None of the previously reported CNV analysis tools support this function and the simultaneous visualization of comparative genomic hybridization arrays (aCGH) and sequence alignment. The purpose of the present study was to develop a useful program for the efficient detection and visualization of CNV regions that enables the manual exclusion of erroneous signals. A JAVA-based stand-alone program called Genovar was developed. To ascertain whether a detected CNV region is a novel variant, Genovar compares the detected CNV regions with previously reported CNV regions using the Database of Genomic Variants (DGV, http://projects.tcag.ca/variation) and the Single Nucleotide Polymorphism Database (dbSNP). The current version of Genovar is capable of visualizing genomic data from sources such as the aCGH data file and sequence alignment format files. Genovar is freely accessible and provides a user-friendly graphic user interface (GUI) to facilitate the detection of CNV regions. The program also provides comprehensive information to help in the elimination of spurious signals by visual inspection, making Genovar a valuable tool for reducing false positive CNV results. http://genovar.sourceforge.net/.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence-Function Space and Genome Context to Discover Novel Functions.

PubMed

Gerlt, John A

2017-08-22

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of "genomic enzymology" web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence-function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence–Function Space and Genome Context to Discover Novel Functions

PubMed Central

2017-01-01

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of “genomic enzymology” web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence–function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems. PMID:28826221

BactoGeNIE: A large-scale comparative genome visualization for big displays

DOE PAGES

Aurisano, Jillian; Reda, Khairi; Johnson, Andrew; ...

2015-08-13

The volume of complete bacterial genome sequence data available to comparative genomics researchers is rapidly increasing. However, visualizations in comparative genomics--which aim to enable analysis tasks across collections of genomes--suffer from visual scalability issues. While large, multi-tiled and high-resolution displays have the potential to address scalability issues, new approaches are needed to take advantage of such environments, in order to enable the effective visual analysis of large genomics datasets. In this paper, we present Bacterial Gene Neighborhood Investigation Environment, or BactoGeNIE, a novel and visually scalable design for comparative gene neighborhood analysis on large display environments. We evaluate BactoGeNIE throughmore » a case study on close to 700 draft Escherichia coli genomes, and present lessons learned from our design process. In conclusion, BactoGeNIE accommodates comparative tasks over substantially larger collections of neighborhoods than existing tools and explicitly addresses visual scalability. Given current trends in data generation, scalable designs of this type may inform visualization design for large-scale comparative research problems in genomics.« less

BactoGeNIE: a large-scale comparative genome visualization for big displays

PubMed Central

2015-01-01

Background The volume of complete bacterial genome sequence data available to comparative genomics researchers is rapidly increasing. However, visualizations in comparative genomics--which aim to enable analysis tasks across collections of genomes--suffer from visual scalability issues. While large, multi-tiled and high-resolution displays have the potential to address scalability issues, new approaches are needed to take advantage of such environments, in order to enable the effective visual analysis of large genomics datasets. Results In this paper, we present Bacterial Gene Neighborhood Investigation Environment, or BactoGeNIE, a novel and visually scalable design for comparative gene neighborhood analysis on large display environments. We evaluate BactoGeNIE through a case study on close to 700 draft Escherichia coli genomes, and present lessons learned from our design process. Conclusions BactoGeNIE accommodates comparative tasks over substantially larger collections of neighborhoods than existing tools and explicitly addresses visual scalability. Given current trends in data generation, scalable designs of this type may inform visualization design for large-scale comparative research problems in genomics. PMID:26329021

BactoGeNIE: A large-scale comparative genome visualization for big displays

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

Aurisano, Jillian; Reda, Khairi; Johnson, Andrew

The volume of complete bacterial genome sequence data available to comparative genomics researchers is rapidly increasing. However, visualizations in comparative genomics--which aim to enable analysis tasks across collections of genomes--suffer from visual scalability issues. While large, multi-tiled and high-resolution displays have the potential to address scalability issues, new approaches are needed to take advantage of such environments, in order to enable the effective visual analysis of large genomics datasets. In this paper, we present Bacterial Gene Neighborhood Investigation Environment, or BactoGeNIE, a novel and visually scalable design for comparative gene neighborhood analysis on large display environments. We evaluate BactoGeNIE throughmore » a case study on close to 700 draft Escherichia coli genomes, and present lessons learned from our design process. In conclusion, BactoGeNIE accommodates comparative tasks over substantially larger collections of neighborhoods than existing tools and explicitly addresses visual scalability. Given current trends in data generation, scalable designs of this type may inform visualization design for large-scale comparative research problems in genomics.« less

CGDV: a webtool for circular visualization of genomics and transcriptomics data.

PubMed

Jha, Vineet; Singh, Gulzar; Kumar, Shiva; Sonawane, Amol; Jere, Abhay; Anamika, Krishanpal

2017-10-24

Interpretation of large-scale data is very challenging and currently there is scarcity of web tools which support automated visualization of a variety of high throughput genomics and transcriptomics data and for a wide variety of model organisms along with user defined karyotypes. Circular plot provides holistic visualization of high throughput large scale data but it is very complex and challenging to generate as most of the available tools need informatics expertise to install and run them. We have developed CGDV (Circos for Genomics and Transcriptomics Data Visualization), a webtool based on Circos, for seamless and automated visualization of a variety of large scale genomics and transcriptomics data. CGDV takes output of analyzed genomics or transcriptomics data of different formats, such as vcf, bed, xls, tab limited matrix text file, CNVnator raw output and Gene fusion raw output, to plot circular view of the sample data. CGDV take cares of generating intermediate files required for circos. CGDV is freely available at https://cgdv-upload.persistent.co.in/cgdv/ . The circular plot for each data type is tailored to gain best biological insights into the data. The inter-relationship between data points, homologous sequences, genes involved in fusion events, differential expression pattern, sequencing depth, types and size of variations and enrichment of DNA binding proteins can be seen using CGDV. CGDV thus helps biologists and bioinformaticians to visualize a variety of genomics and transcriptomics data seamlessly.

GenomeCAT: a versatile tool for the analysis and integrative visualization of DNA copy number variants.

PubMed

Tebel, Katrin; Boldt, Vivien; Steininger, Anne; Port, Matthias; Ebert, Grit; Ullmann, Reinhard

2017-01-06

The analysis of DNA copy number variants (CNV) has increasing impact in the field of genetic diagnostics and research. However, the interpretation of CNV data derived from high resolution array CGH or NGS platforms is complicated by the considerable variability of the human genome. Therefore, tools for multidimensional data analysis and comparison of patient cohorts are needed to assist in the discrimination of clinically relevant CNVs from others. We developed GenomeCAT, a standalone Java application for the analysis and integrative visualization of CNVs. GenomeCAT is composed of three modules dedicated to the inspection of single cases, comparative analysis of multidimensional data and group comparisons aiming at the identification of recurrent aberrations in patients sharing the same phenotype, respectively. Its flexible import options ease the comparative analysis of own results derived from microarray or NGS platforms with data from literature or public depositories. Multidimensional data obtained from different experiment types can be merged into a common data matrix to enable common visualization and analysis. All results are stored in the integrated MySQL database, but can also be exported as tab delimited files for further statistical calculations in external programs. GenomeCAT offers a broad spectrum of visualization and analysis tools that assist in the evaluation of CNVs in the context of other experiment data and annotations. The use of GenomeCAT does not require any specialized computer skills. The various R packages implemented for data analysis are fully integrated into GenomeCATs graphical user interface and the installation process is supported by a wizard. The flexibility in terms of data import and export in combination with the ability to create a common data matrix makes the program also well suited as an interface between genomic data from heterogeneous sources and external software tools. Due to the modular architecture the functionality of GenomeCAT can be easily extended by further R packages or customized plug-ins to meet future requirements.

PanACEA: a bioinformatics tool for the exploration and visualization of bacterial pan-chromosomes.

PubMed

Clarke, Thomas H; Brinkac, Lauren M; Inman, Jason M; Sutton, Granger; Fouts, Derrick E

2018-06-27

Bacterial pan-genomes, comprised of conserved and variable genes across multiple sequenced bacterial genomes, allow for identification of genomic regions that are phylogenetically discriminating or functionally important. Pan-genomes consist of large amounts of data, which can restrict researchers ability to locate and analyze these regions. Multiple software packages are available to visualize pan-genomes, but currently their ability to address these concerns are limited by using only pre-computed data sets, prioritizing core over variable gene clusters, or by not accounting for pan-chromosome positioning in the viewer. We introduce PanACEA (Pan-genome Atlas with Chromosome Explorer and Analyzer), which utilizes locally-computed interactive web-pages to view ordered pan-genome data. It consists of multi-tiered, hierarchical display pages that extend from pan-chromosomes to both core and variable regions to single genes. Regions and genes are functionally annotated to allow for rapid searching and visual identification of regions of interest with the option that user-supplied genomic phylogenies and metadata can be incorporated. PanACEA's memory and time requirements are within the capacities of standard laptops. The capability of PanACEA as a research tool is demonstrated by highlighting a variable region important in differentiating strains of Enterobacter hormaechei. PanACEA can rapidly translate the results of pan-chromosome programs into an intuitive and interactive visual representation. It will empower researchers to visually explore and identify regions of the pan-chromosome that are most biologically interesting, and to obtain publication quality images of these regions.

CAMBerVis: visualization software to support comparative analysis of multiple bacterial strains.

PubMed

Woźniak, Michał; Wong, Limsoon; Tiuryn, Jerzy

2011-12-01

A number of inconsistencies in genome annotations are documented among bacterial strains. Visualization of the differences may help biologists to make correct decisions in spurious cases. We have developed a visualization tool, CAMBerVis, to support comparative analysis of multiple bacterial strains. The software manages simultaneous visualization of multiple bacterial genomes, enabling visual analysis focused on genome structure annotations. The CAMBerVis software is freely available at the project website: http://bioputer.mimuw.edu.pl/camber. Input datasets for Mycobacterium tuberculosis and Staphylocacus aureus are integrated with the software as examples. m.wozniak@mimuw.edu.pl Supplementary data are available at Bioinformatics online.

Genome contact map explorer: a platform for the comparison, interactive visualization and analysis of genome contact maps

PubMed Central

Kumar, Rajendra; Sobhy, Haitham

2017-01-01

Abstract Hi-C experiments generate data in form of large genome contact maps (Hi-C maps). These show that chromosomes are arranged in a hierarchy of three-dimensional compartments. But to understand how these compartments form and by how much they affect genetic processes such as gene regulation, biologists and bioinformaticians need efficient tools to visualize and analyze Hi-C data. However, this is technically challenging because these maps are big. In this paper, we remedied this problem, partly by implementing an efficient file format and developed the genome contact map explorer platform. Apart from tools to process Hi-C data, such as normalization methods and a programmable interface, we made a graphical interface that let users browse, scroll and zoom Hi-C maps to visually search for patterns in the Hi-C data. In the software, it is also possible to browse several maps simultaneously and plot related genomic data. The software is openly accessible to the scientific community. PMID:28973466

Integrative Genomics Viewer (IGV) | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.

A novel bioinformatics method for efficient knowledge discovery by BLSOM from big genomic sequence data.

PubMed

Bai, Yu; Iwasaki, Yuki; Kanaya, Shigehiko; Zhao, Yue; Ikemura, Toshimichi

2014-01-01

With remarkable increase of genomic sequence data of a wide range of species, novel tools are needed for comprehensive analyses of the big sequence data. Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional data such as oligonucleotide composition on one map. By modifying the conventional SOM, we have previously developed Batch-Learning SOM (BLSOM), which allows classification of sequence fragments according to species, solely depending on the oligonucleotide composition. In the present study, we introduce the oligonucleotide BLSOM used for characterization of vertebrate genome sequences. We first analyzed pentanucleotide compositions in 100 kb sequences derived from a wide range of vertebrate genomes and then the compositions in the human and mouse genomes in order to investigate an efficient method for detecting differences between the closely related genomes. BLSOM can recognize the species-specific key combination of oligonucleotide frequencies in each genome, which is called a "genome signature," and the specific regions specifically enriched in transcription-factor-binding sequences. Because the classification and visualization power is very high, BLSOM is an efficient powerful tool for extracting a wide range of information from massive amounts of genomic sequences (i.e., big sequence data).

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

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

King, Zachary A.; Drager, Andreas; Ebrahim, Ali

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics).more » Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.« less

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

PubMed Central

King, Zachary A.; Dräger, Andreas; Ebrahim, Ali; Sonnenschein, Nikolaus; Lewis, Nathan E.; Palsson, Bernhard O.

2015-01-01

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools. PMID:26313928

Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways

DOE PAGES

King, Zachary A.; Drager, Andreas; Ebrahim, Ali; ...

2015-08-27

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics).more » Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.« less

PGSB PlantsDB: updates to the database framework for comparative plant genome research.

PubMed

Spannagl, Manuel; Nussbaumer, Thomas; Bader, Kai C; Martis, Mihaela M; Seidel, Michael; Kugler, Karl G; Gundlach, Heidrun; Mayer, Klaus F X

2016-01-04

PGSB (Plant Genome and Systems Biology: formerly MIPS) PlantsDB (http://pgsb.helmholtz-muenchen.de/plant/index.jsp) is a database framework for the comparative analysis and visualization of plant genome data. The resource has been updated with new data sets and types as well as specialized tools and interfaces to address user demands for intuitive access to complex plant genome data. In its latest incarnation, we have re-worked both the layout and navigation structure and implemented new keyword search options and a new BLAST sequence search functionality. Actively involved in corresponding sequencing consortia, PlantsDB has dedicated special efforts to the integration and visualization of complex triticeae genome data, especially for barley, wheat and rye. We enhanced CrowsNest, a tool to visualize syntenic relationships between genomes, with data from the wheat sub-genome progenitor Aegilops tauschii and added functionality to the PGSB RNASeqExpressionBrowser. GenomeZipper results were integrated for the genomes of barley, rye, wheat and perennial ryegrass and interactive access is granted through PlantsDB interfaces. Data exchange and cross-linking between PlantsDB and other plant genome databases is stimulated by the transPLANT project (http://transplantdb.eu/). © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

CuGene as a tool to view and explore genomic data

NASA Astrophysics Data System (ADS)

Haponiuk, Michał; Pawełkowicz, Magdalena; Przybecki, Zbigniew; Nowak, Robert M.

2017-08-01

Integrated CuGene is an easy-to-use, open-source, on-line tool that can be used to browse, analyze, and query genomic data and annotations. It places annotation tracks beneath genome coordinate positions, allowing rapid visual correlation of different types of information. It also allows users to upload and display their own experimental results or annotation sets. An important functionality of the application is a possibility to find similarity between sequences by applying four different algorithms of different accuracy. The presented tool was tested on real genomic data and is extensively used by Polish Consortium of Cucumber Genome Sequencing.

Visualizing conserved gene location across microbe genomes

NASA Astrophysics Data System (ADS)

Shaw, Chris D.

2009-01-01

This paper introduces an analysis-based zoomable visualization technique for displaying the location of genes across many related species of microbes. The purpose of this visualizatiuon is to enable a biologist to examine the layout of genes in the organism of interest with respect to the gene organization of related organisms. During the genomic annotation process, the ability to observe gene organization in common with previously annotated genomes can help a biologist better confirm the structure and function of newly analyzed microbe DNA sequences. We have developed a visualization and analysis tool that enables the biologist to observe and examine gene organization among genomes, in the context of the primary sequence of interest. This paper describes the visualization and analysis steps, and presents a case study using a number of Rickettsia genomes.

DCODE.ORG Anthology of Comparative Genomic Tools

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

Loots, G G; Ovcharenko, I

2005-01-11

Comparative genomics provides the means to demarcate functional regions in anonymous DNA sequences. The successful application of this method to identifying novel genes is currently shifting to deciphering the noncoding encryption of gene regulation across genomes. To facilitate the use of comparative genomics to practical applications in genetics and genomics we have developed several analytical and visualization tools for the analysis of arbitrary sequences and whole genomes. These tools include two alignment tools: zPicture and Mulan; a phylogenetic shadowing tool: eShadow for identifying lineage- and species-specific functional elements; two evolutionary conserved transcription factor analysis tools: rVista and multiTF; a toolmore » for extracting cis-regulatory modules governing the expression of co-regulated genes, CREME; and a dynamic portal to multiple vertebrate and invertebrate genome alignments, the ECR Browser. Here we briefly describe each one of these tools and provide specific examples on their practical applications. All the tools are publicly available at the http://www.dcode.org/ web site.« less

The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes.

PubMed

Treangen, Todd J; Ondov, Brian D; Koren, Sergey; Phillippy, Adam M

2014-01-01

Whole-genome sequences are now available for many microbial species and clades, however existing whole-genome alignment methods are limited in their ability to perform sequence comparisons of multiple sequences simultaneously. Here we present the Harvest suite of core-genome alignment and visualization tools for the rapid and simultaneous analysis of thousands of intraspecific microbial strains. Harvest includes Parsnp, a fast core-genome multi-aligner, and Gingr, a dynamic visual platform. Together they provide interactive core-genome alignments, variant calls, recombination detection, and phylogenetic trees. Using simulated and real data we demonstrate that our approach exhibits unrivaled speed while maintaining the accuracy of existing methods. The Harvest suite is open-source and freely available from: http://github.com/marbl/harvest.

Visualization of RNA structure models within the Integrative Genomics Viewer.

PubMed

Busan, Steven; Weeks, Kevin M

2017-07-01

Analyses of the interrelationships between RNA structure and function are increasingly important components of genomic studies. The SHAPE-MaP strategy enables accurate RNA structure probing and realistic structure modeling of kilobase-length noncoding RNAs and mRNAs. Existing tools for visualizing RNA structure models are not suitable for efficient analysis of long, structurally heterogeneous RNAs. In addition, structure models are often advantageously interpreted in the context of other experimental data and gene annotation information, for which few tools currently exist. We have developed a module within the widely used and well supported open-source Integrative Genomics Viewer (IGV) that allows visualization of SHAPE and other chemical probing data, including raw reactivities, data-driven structural entropies, and data-constrained base-pair secondary structure models, in context with linear genomic data tracks. We illustrate the usefulness of visualizing RNA structure in the IGV by exploring structure models for a large viral RNA genome, comparing bacterial mRNA structure in cells with its structure under cell- and protein-free conditions, and comparing a noncoding RNA structure modeled using SHAPE data with a base-pairing model inferred through sequence covariation analysis. © 2017 Busan and Weeks; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

GenomeGems: evaluation of genetic variability from deep sequencing data

PubMed Central

2012-01-01

Background Detection of disease-causing mutations using Deep Sequencing technologies possesses great challenges. In particular, organizing the great amount of sequences generated so that mutations, which might possibly be biologically relevant, are easily identified is a difficult task. Yet, for this assignment only limited automatic accessible tools exist. Findings We developed GenomeGems to gap this need by enabling the user to view and compare Single Nucleotide Polymorphisms (SNPs) from multiple datasets and to load the data onto the UCSC Genome Browser for an expanded and familiar visualization. As such, via automatic, clear and accessible presentation of processed Deep Sequencing data, our tool aims to facilitate ranking of genomic SNP calling. GenomeGems runs on a local Personal Computer (PC) and is freely available at http://www.tau.ac.il/~nshomron/GenomeGems. Conclusions GenomeGems enables researchers to identify potential disease-causing SNPs in an efficient manner. This enables rapid turnover of information and leads to further experimental SNP validation. The tool allows the user to compare and visualize SNPs from multiple experiments and to easily load SNP data onto the UCSC Genome browser for further detailed information. PMID:22748151

Dcode.org anthology of comparative genomic tools.

PubMed

Loots, Gabriela G; Ovcharenko, Ivan

2005-07-01

Comparative genomics provides the means to demarcate functional regions in anonymous DNA sequences. The successful application of this method to identifying novel genes is currently shifting to deciphering the non-coding encryption of gene regulation across genomes. To facilitate the practical application of comparative sequence analysis to genetics and genomics, we have developed several analytical and visualization tools for the analysis of arbitrary sequences and whole genomes. These tools include two alignment tools, zPicture and Mulan; a phylogenetic shadowing tool, eShadow for identifying lineage- and species-specific functional elements; two evolutionary conserved transcription factor analysis tools, rVista and multiTF; a tool for extracting cis-regulatory modules governing the expression of co-regulated genes, Creme 2.0; and a dynamic portal to multiple vertebrate and invertebrate genome alignments, the ECR Browser. Here, we briefly describe each one of these tools and provide specific examples on their practical applications. All the tools are publicly available at the http://www.dcode.org/ website.

CircosVCF: circos visualization of whole-genome sequence variations stored in VCF files.

PubMed

Drori, E; Levy, D; Smirin-Yosef, P; Rahimi, O; Salmon-Divon, M

2017-05-01

Visualization of whole-genomic variations in a meaningful manner assists researchers in gaining new insights into the underlying data, especially when it comes in the context of whole genome comparisons. CircosVCF is a web based visualization tool for genome-wide variant data described in VCF files, using circos plots. The user friendly interface of CircosVCF supports an interactive design of the circles in the plot, and the integration of additional information such as experimental data or annotations. The provided visualization capabilities give a broad overview of the genomic relationships between genomes, and allow identification of specific meaningful SNPs regions. CircosVCF was implemented in JavaScript and is available at http://www.ariel.ac.il/research/fbl/software. malisa@ariel.ac.il. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A low-latency, big database system and browser for storage, querying and visualization of 3D genomic data.

PubMed

Butyaev, Alexander; Mavlyutov, Ruslan; Blanchette, Mathieu; Cudré-Mauroux, Philippe; Waldispühl, Jérôme

2015-09-18

Recent releases of genome three-dimensional (3D) structures have the potential to transform our understanding of genomes. Nonetheless, the storage technology and visualization tools need to evolve to offer to the scientific community fast and convenient access to these data. We introduce simultaneously a database system to store and query 3D genomic data (3DBG), and a 3D genome browser to visualize and explore 3D genome structures (3DGB). We benchmark 3DBG against state-of-the-art systems and demonstrate that it is faster than previous solutions, and importantly gracefully scales with the size of data. We also illustrate the usefulness of our 3D genome Web browser to explore human genome structures. The 3D genome browser is available at http://3dgb.cs.mcgill.ca/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

A low-latency, big database system and browser for storage, querying and visualization of 3D genomic data

PubMed Central

Butyaev, Alexander; Mavlyutov, Ruslan; Blanchette, Mathieu; Cudré-Mauroux, Philippe; Waldispühl, Jérôme

2015-01-01

Recent releases of genome three-dimensional (3D) structures have the potential to transform our understanding of genomes. Nonetheless, the storage technology and visualization tools need to evolve to offer to the scientific community fast and convenient access to these data. We introduce simultaneously a database system to store and query 3D genomic data (3DBG), and a 3D genome browser to visualize and explore 3D genome structures (3DGB). We benchmark 3DBG against state-of-the-art systems and demonstrate that it is faster than previous solutions, and importantly gracefully scales with the size of data. We also illustrate the usefulness of our 3D genome Web browser to explore human genome structures. The 3D genome browser is available at http://3dgb.cs.mcgill.ca/. PMID:25990738

Epiviz: a view inside the design of an integrated visual analysis software for genomics

PubMed Central

2015-01-01

Background Computational and visual data analysis for genomics has traditionally involved a combination of tools and resources, of which the most ubiquitous consist of genome browsers, focused mainly on integrative visualization of large numbers of big datasets, and computational environments, focused on data modeling of a small number of moderately sized datasets. Workflows that involve the integration and exploration of multiple heterogeneous data sources, small and large, public and user specific have been poorly addressed by these tools. In our previous work, we introduced Epiviz, which bridges the gap between the two types of tools, simplifying these workflows. Results In this paper we expand on the design decisions behind Epiviz, and introduce a series of new advanced features that further support the type of interactive exploratory workflow we have targeted. We discuss three ways in which Epiviz advances the field of genomic data analysis: 1) it brings code to interactive visualizations at various different levels; 2) takes the first steps in the direction of collaborative data analysis by incorporating user plugins from source control providers, as well as by allowing analysis states to be shared among the scientific community; 3) combines established analysis features that have never before been available simultaneously in a genome browser. In our discussion section, we present security implications of the current design, as well as a series of limitations and future research steps. Conclusions Since many of the design choices of Epiviz are novel in genomics data analysis, this paper serves both as a document of our own approaches with lessons learned, as well as a start point for future efforts in the same direction for the genomics community. PMID:26328750

Genomic analysis and geographic visualization of H5N1 and SARS-CoV.

PubMed

Hill, Andrew W; Alexandrov, Boyan; Guralnick, Robert P; Janies, Daniel

2007-10-11

Emerging infectious diseases and organisms present critical issues of national security public health, and economic welfare. We still understand little about the zoonotic potential of many viruses. To this end, we are developing novel database tools to manage comparative genomic datasets. These tools add value because they allow us to summarize the direction, frequency and order of genomic changes. We will perform numerous real world tests with our tools with both Avian Influenza and Coronaviruses.

GenomePeek—an online tool for prokaryotic genome and metagenome analysis

DOE PAGES

McNair, Katelyn; Edwards, Robert A.

2015-06-16

As increases in prokaryotic sequencing take place, a method to quickly and accurately analyze this data is needed. Previous tools are mainly designed for metagenomic analysis and have limitations; such as long runtimes and significant false positive error rates. The online tool GenomePeek (edwards.sdsu.edu/GenomePeek) was developed to analyze both single genome and metagenome sequencing files, quickly and with low error rates. GenomePeek uses a sequence assembly approach where reads to a set of conserved genes are extracted, assembled and then aligned against the highly specific reference database. GenomePeek was found to be faster than traditional approaches while still keeping errormore » rates low, as well as offering unique data visualization options.« less

Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

PubMed Central

Thorvaldsdóttir, Helga; Mesirov, Jill P.

2013-01-01

Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license. PMID:22517427

Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration.

PubMed

Thorvaldsdóttir, Helga; Robinson, James T; Mesirov, Jill P

2013-03-01

Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today's sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.

Genoviz Software Development Kit: Java tool kit for building genomics visualization applications.

PubMed

Helt, Gregg A; Nicol, John W; Erwin, Ed; Blossom, Eric; Blanchard, Steven G; Chervitz, Stephen A; Harmon, Cyrus; Loraine, Ann E

2009-08-25

Visualization software can expose previously undiscovered patterns in genomic data and advance biological science. The Genoviz Software Development Kit (SDK) is an open source, Java-based framework designed for rapid assembly of visualization software applications for genomics. The Genoviz SDK framework provides a mechanism for incorporating adaptive, dynamic zooming into applications, a desirable feature of genome viewers. Visualization capabilities of the Genoviz SDK include automated layout of features along genetic or genomic axes; support for user interactions with graphical elements (Glyphs) in a map; a variety of Glyph sub-classes that promote experimentation with new ways of representing data in graphical formats; and support for adaptive, semantic zooming, whereby objects change their appearance depending on zoom level and zooming rate adapts to the current scale. Freely available demonstration and production quality applications, including the Integrated Genome Browser, illustrate Genoviz SDK capabilities. Separation between graphics components and genomic data models makes it easy for developers to add visualization capability to pre-existing applications or build new applications using third-party data models. Source code, documentation, sample applications, and tutorials are available at http://genoviz.sourceforge.net/.

Delta: a new web-based 3D genome visualization and analysis platform.

PubMed

Tang, Bixia; Li, Feifei; Li, Jing; Zhao, Wenming; Zhang, Zhihua

2018-04-15

Delta is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes. Delta takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome. Delta features a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs. Finally, by visually comparing the 3D model of the β-globin gene locus and its annotation, we speculated a plausible transitory interaction pattern in the locus. Experimental evidence was found to support this speculation by literature survey. This served as an example of intuitive hypothesis testing with the help of Delta. Delta is freely accessible from http://delta.big.ac.cn, and the source code is available at https://github.com/zhangzhwlab/delta. zhangzhihua@big.ac.cn. Supplementary data are available at Bioinformatics online.

RareVariantVis: new tool for visualization of causative variants in rare monogenic disorders using whole genome sequencing data.

PubMed

Stokowy, Tomasz; Garbulowski, Mateusz; Fiskerstrand, Torunn; Holdhus, Rita; Labun, Kornel; Sztromwasser, Pawel; Gilissen, Christian; Hoischen, Alexander; Houge, Gunnar; Petersen, Kjell; Jonassen, Inge; Steen, Vidar M

2016-10-01

The search for causative genetic variants in rare diseases of presumed monogenic inheritance has been boosted by the implementation of whole exome (WES) and whole genome (WGS) sequencing. In many cases, WGS seems to be superior to WES, but the analysis and visualization of the vast amounts of data is demanding. To aid this challenge, we have developed a new tool-RareVariantVis-for analysis of genome sequence data (including non-coding regions) for both germ line and somatic variants. It visualizes variants along their respective chromosomes, providing information about exact chromosomal position, zygosity and frequency, with point-and-click information regarding dbSNP IDs, gene association and variant inheritance. Rare variants as well as de novo variants can be flagged in different colors. We show the performance of the RareVariantVis tool in the Genome in a Bottle WGS data set. https://www.bioconductor.org/packages/3.3/bioc/html/RareVariantVis.html tomasz.stokowy@k2.uib.no 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.

Genome U-Plot: a whole genome visualization.

PubMed

Gaitatzes, Athanasios; Johnson, Sarah H; Smadbeck, James B; Vasmatzis, George

2018-05-15

The ability to produce and analyze whole genome sequencing (WGS) data from samples with structural variations (SV) generated the need to visualize such abnormalities in simplified plots. Conventional two-dimensional representations of WGS data frequently use either circular or linear layouts. There are several diverse advantages regarding both these representations, but their major disadvantage is that they do not use the two-dimensional space very efficiently. We propose a layout, termed the Genome U-Plot, which spreads the chromosomes on a two-dimensional surface and essentially quadruples the spatial resolution. We present the Genome U-Plot for producing clear and intuitive graphs that allows researchers to generate novel insights and hypotheses by visualizing SVs such as deletions, amplifications, and chromoanagenesis events. The main features of the Genome U-Plot are its layered layout, its high spatial resolution and its improved aesthetic qualities. We compare conventional visualization schemas with the Genome U-Plot using visualization metrics such as number of line crossings and crossing angle resolution measures. Based on our metrics, we improve the readability of the resulting graph by at least 2-fold, making apparent important features and making it easy to identify important genomic changes. A whole genome visualization tool with high spatial resolution and improved aesthetic qualities. An implementation and documentation of the Genome U-Plot is publicly available at https://github.com/gaitat/GenomeUPlot. vasmatzis.george@mayo.edu. Supplementary data are available at Bioinformatics online.

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology

PubMed Central

Latendresse, Mario; Paley, Suzanne M.; Krummenacker, Markus; Ong, Quang D.; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M.; Caspi, Ron

2016-01-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. PMID:26454094

Next generation tools for genomic data generation, distribution, and visualization

PubMed Central

2010-01-01

Background With the rapidly falling cost and availability of high throughput sequencing and microarray technologies, the bottleneck for effectively using genomic analysis in the laboratory and clinic is shifting to one of effectively managing, analyzing, and sharing genomic data. Results Here we present three open-source, platform independent, software tools for generating, analyzing, distributing, and visualizing genomic data. These include a next generation sequencing/microarray LIMS and analysis project center (GNomEx); an application for annotating and programmatically distributing genomic data using the community vetted DAS/2 data exchange protocol (GenoPub); and a standalone Java Swing application (GWrap) that makes cutting edge command line analysis tools available to those who prefer graphical user interfaces. Both GNomEx and GenoPub use the rich client Flex/Flash web browser interface to interact with Java classes and a relational database on a remote server. Both employ a public-private user-group security model enabling controlled distribution of patient and unpublished data alongside public resources. As such, they function as genomic data repositories that can be accessed manually or programmatically through DAS/2-enabled client applications such as the Integrated Genome Browser. Conclusions These tools have gained wide use in our core facilities, research laboratories and clinics and are freely available for non-profit use. See http://sourceforge.net/projects/gnomex/, http://sourceforge.net/projects/genoviz/, and http://sourceforge.net/projects/useq. PMID:20828407

RATT: Rapid Annotation Transfer Tool

PubMed Central

Otto, Thomas D.; Dillon, Gary P.; Degrave, Wim S.; Berriman, Matthew

2011-01-01

Second-generation sequencing technologies have made large-scale sequencing projects commonplace. However, making use of these datasets often requires gene function to be ascribed genome wide. Although tool development has kept pace with the changes in sequence production, for tasks such as mapping, de novo assembly or visualization, genome annotation remains a challenge. We have developed a method to rapidly provide accurate annotation for new genomes using previously annotated genomes as a reference. The method, implemented in a tool called RATT (Rapid Annotation Transfer Tool), transfers annotations from a high-quality reference to a new genome on the basis of conserved synteny. We demonstrate that a Mycobacterium tuberculosis genome or a single 2.5 Mb chromosome from a malaria parasite can be annotated in less than five minutes with only modest computational resources. RATT is available at http://ratt.sourceforge.net. PMID:21306991

Gnome View: A tool for visual representation of human genome data

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

Pelkey, J.E.; Thomas, G.S.; Thurman, D.A.

1993-02-01

GnomeView is a tool for exploring data generated by the Human Gemone Project. GnomeView provides both graphical and textural styles of data presentation: employs an intuitive window-based graphical query interface: and integrates its underlying genome databases in such a way that the user can navigate smoothly across databases and between different levels of data. This paper describes GnomeView and discusses how it addresses various genome informatics issues.

Surveying the maize community for their diversity and pedigree visualization needs to prioritize tool development and curation

USDA-ARS?s Scientific Manuscript database

The Maize Genetics and Genomics Database (MaizeGDB) team prepared a survey to identify breeders’ needs for visualizing pedigrees, diversity data, and haplotypes in order to prioritize tool development and curation efforts at MaizeGDB. The survey was distributed to the maize research community on beh...

Extension modules for storage, visualization and querying of genomic, genetic and breeding data in Tripal databases

PubMed Central

Lee, Taein; Cheng, Chun-Huai; Ficklin, Stephen; Yu, Jing; Humann, Jodi; Main, Dorrie

2017-01-01

Abstract Tripal is an open-source database platform primarily used for development of genomic, genetic and breeding databases. We report here on the release of the Chado Loader, Chado Data Display and Chado Search modules to extend the functionality of the core Tripal modules. These new extension modules provide additional tools for (1) data loading, (2) customized visualization and (3) advanced search functions for supported data types such as organism, marker, QTL/Mendelian Trait Loci, germplasm, map, project, phenotype, genotype and their respective metadata. The Chado Loader module provides data collection templates in Excel with defined metadata and data loaders with front end forms. The Chado Data Display module contains tools to visualize each data type and the metadata which can be used as is or customized as desired. The Chado Search module provides search and download functionality for the supported data types. Also included are the tools to visualize map and species summary. The use of materialized views in the Chado Search module enables better performance as well as flexibility of data modeling in Chado, allowing existing Tripal databases with different metadata types to utilize the module. These Tripal Extension modules are implemented in the Genome Database for Rosaceae (rosaceae.org), CottonGen (cottongen.org), Citrus Genome Database (citrusgenomedb.org), Genome Database for Vaccinium (vaccinium.org) and the Cool Season Food Legume Database (coolseasonfoodlegume.org). Database URL: https://www.citrusgenomedb.org/, https://www.coolseasonfoodlegume.org/, https://www.cottongen.org/, https://www.rosaceae.org/, https://www.vaccinium.org/

SeeGH--a software tool for visualization of whole genome array comparative genomic hybridization data.

PubMed

Chi, Bryan; DeLeeuw, Ronald J; Coe, Bradley P; MacAulay, Calum; Lam, Wan L

2004-02-09

Array comparative genomic hybridization (CGH) is a technique which detects copy number differences in DNA segments. Complete sequencing of the human genome and the development of an array representing a tiling set of tens of thousands of DNA segments spanning the entire human genome has made high resolution copy number analysis throughout the genome possible. Since array CGH provides signal ratio for each DNA segment, visualization would require the reassembly of individual data points into chromosome profiles. We have developed a visualization tool for displaying whole genome array CGH data in the context of chromosomal location. SeeGH is an application that translates spot signal ratio data from array CGH experiments to displays of high resolution chromosome profiles. Data is imported from a simple tab delimited text file obtained from standard microarray image analysis software. SeeGH processes the signal ratio data and graphically displays it in a conventional CGH karyotype diagram with the added features of magnification and DNA segment annotation. In this process, SeeGH imports the data into a database, calculates the average ratio and standard deviation for each replicate spot, and links them to chromosome regions for graphical display. Once the data is displayed, users have the option of hiding or flagging DNA segments based on user defined criteria, and retrieve annotation information such as clone name, NCBI sequence accession number, ratio, base pair position on the chromosome, and standard deviation. SeeGH represents a novel software tool used to view and analyze array CGH data. The software gives users the ability to view the data in an overall genomic view as well as magnify specific chromosomal regions facilitating the precise localization of genetic alterations. SeeGH is easily installed and runs on Microsoft Windows 2000 or later environments.

DNAism: exploring genomic datasets on the web with Horizon Charts.

PubMed

Rio Deiros, David; Gibbs, Richard A; Rogers, Jeffrey

2016-01-27

Computational biologists daily face the need to explore massive amounts of genomic data. New visualization techniques can help researchers navigate and understand these big data. Horizon Charts are a relatively new visualization method that, under the right circumstances, maximizes data density without losing graphical perception. Horizon Charts have been successfully applied to understand multi-metric time series data. We have adapted an existing JavaScript library (Cubism) that implements Horizon Charts for the time series domain so that it works effectively with genomic datasets. We call this new library DNAism. Horizon Charts can be an effective visual tool to explore complex and large genomic datasets. Researchers can use our library to leverage these techniques to extract additional insights from their own datasets.

A survey of tools for variant analysis of next-generation genome sequencing data

PubMed Central

Pabinger, Stephan; Dander, Andreas; Fischer, Maria; Snajder, Rene; Sperk, Michael; Efremova, Mirjana; Krabichler, Birgit; Speicher, Michael R.; Zschocke, Johannes

2014-01-01

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers. PMID:23341494

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology.

PubMed

Karp, Peter D; Latendresse, Mario; Paley, Suzanne M; Krummenacker, Markus; Ong, Quang D; Billington, Richard; Kothari, Anamika; Weaver, Daniel; Lee, Thomas; Subhraveti, Pallavi; Spaulding, Aaron; Fulcher, Carol; Keseler, Ingrid M; Caspi, Ron

2016-09-01

Pathway Tools is a bioinformatics software environment with a broad set of capabilities. The software provides genome-informatics tools such as a genome browser, sequence alignments, a genome-variant analyzer and comparative-genomics operations. It offers metabolic-informatics tools, such as metabolic reconstruction, quantitative metabolic modeling, prediction of reaction atom mappings and metabolic route search. Pathway Tools also provides regulatory-informatics tools, such as the ability to represent and visualize a wide range of regulatory interactions. This article outlines the advances in Pathway Tools in the past 5 years. Major additions include components for metabolic modeling, metabolic route search, computation of atom mappings and estimation of compound Gibbs free energies of formation; addition of editors for signaling pathways, for genome sequences and for cellular architecture; storage of gene essentiality data and phenotype data; display of multiple alignments, and of signaling and electron-transport pathways; and development of Python and web-services application programming interfaces. Scientists around the world have created more than 9800 Pathway/Genome Databases by using Pathway Tools, many of which are curated databases for important model organisms. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

The Pathway Tools software.

PubMed

Karp, Peter D; Paley, Suzanne; Romero, Pedro

2002-01-01

Bioinformatics requires reusable software tools for creating model-organism databases (MODs). The Pathway Tools is a reusable, production-quality software environment for creating a type of MOD called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc (see http://ecocyc.org) integrates our evolving understanding of the genes, proteins, metabolic network, and genetic network of an organism. This paper provides an overview of the four main components of the Pathway Tools: The PathoLogic component supports creation of new PGDBs from the annotated genome of an organism. The Pathway/Genome Navigator provides query, visualization, and Web-publishing services for PGDBs. The Pathway/Genome Editors support interactive updating of PGDBs. The Pathway Tools ontology defines the schema of PGDBs. The Pathway Tools makes use of the Ocelot object database system for data management services for PGDBs. The Pathway Tools has been used to build PGDBs for 13 organisms within SRI and by external users.

CRF: detection of CRISPR arrays using random forest.

PubMed

Wang, Kai; Liang, Chun

2017-01-01

CRISPRs (clustered regularly interspaced short palindromic repeats) are particular repeat sequences found in wide range of bacteria and archaea genomes. Several tools are available for detecting CRISPR arrays in the genomes of both domains. Here we developed a new web-based CRISPR detection tool named CRF (CRISPR Finder by Random Forest). Different from other CRISPR detection tools, a random forest classifier was used in CRF to filter out invalid CRISPR arrays from all putative candidates and accordingly enhanced detection accuracy. In CRF, particularly, triplet elements that combine both sequence content and structure information were extracted from CRISPR repeats for classifier training. The classifier achieved high accuracy and sensitivity. Moreover, CRF offers a highly interactive web interface for robust data visualization that is not available among other CRISPR detection tools. After detection, the query sequence, CRISPR array architecture, and the sequences and secondary structures of CRISPR repeats and spacers can be visualized for visual examination and validation. CRF is freely available at http://bioinfolab.miamioh.edu/crf/home.php.

Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies

PubMed Central

Schatz, Michael C.; Phillippy, Adam M.; Sommer, Daniel D.; Delcher, Arthur L.; Puiu, Daniela; Narzisi, Giuseppe; Salzberg, Steven L.; Pop, Mihai

2013-01-01

Since its launch in 2004, the open-source AMOS project has released several innovative DNA sequence analysis applications including: Hawkeye, a visual analytics tool for inspecting the structure of genome assemblies; the Assembly Forensics and FRCurve pipelines for systematically evaluating the quality of a genome assembly; and AMOScmp, the first comparative genome assembler. These applications have been used to assemble and analyze dozens of genomes ranging in complexity from simple microbial species through mammalian genomes. Recent efforts have been focused on enhancing support for new data characteristics brought on by second- and now third-generation sequencing. This review describes the major components of AMOS in light of these challenges, with an emphasis on methods for assessing assembly quality and the visual analytics capabilities of Hawkeye. These interactive graphical aspects are essential for navigating and understanding the complexities of a genome assembly, from the overall genome structure down to individual bases. Hawkeye and AMOS are available open source at http://amos.sourceforge.net. PMID:22199379

Using the Saccharomyces Genome Database (SGD) for analysis of genomic information

PubMed Central

Skrzypek, Marek S.; Hirschman, Jodi

2011-01-01

Analysis of genomic data requires access to software tools that place the sequence-derived information in the context of biology. The Saccharomyces Genome Database (SGD) integrates functional information about budding yeast genes and their products with a set of analysis tools that facilitate exploring their biological details. This unit describes how the various types of functional data available at SGD can be searched, retrieved, and analyzed. Starting with the guided tour of the SGD Home page and Locus Summary page, this unit highlights how to retrieve data using YeastMine, how to visualize genomic information with GBrowse, how to explore gene expression patterns with SPELL, and how to use Gene Ontology tools to characterize large-scale datasets. PMID:21901739

Maser: one-stop platform for NGS big data from analysis to visualization

PubMed Central

Kinjo, Sonoko; Monma, Norikazu; Misu, Sadahiko; Kitamura, Norikazu; Imoto, Junichi; Yoshitake, Kazutoshi; Gojobori, Takashi; Ikeo, Kazuho

2018-01-01

Abstract A major challenge in analyzing the data from high-throughput next-generation sequencing (NGS) is how to handle the huge amounts of data and variety of NGS tools and visualize the resultant outputs. To address these issues, we developed a cloud-based data analysis platform, Maser (Management and Analysis System for Enormous Reads), and an original genome browser, Genome Explorer (GE). Maser enables users to manage up to 2 terabytes of data to conduct analyses with easy graphical user interface operations and offers analysis pipelines in which several individual tools are combined as a single pipeline for very common and standard analyses. GE automatically visualizes genome assembly and mapping results output from Maser pipelines, without requiring additional data upload. With this function, the Maser pipelines can graphically display the results output from all the embedded tools and mapping results in a web browser. Therefore Maser realized a more user-friendly analysis platform especially for beginners by improving graphical display and providing the selected standard pipelines that work with built-in genome browser. In addition, all the analyses executed on Maser are recorded in the analysis history, helping users to trace and repeat the analyses. The entire process of analysis and its histories can be shared with collaborators or opened to the public. In conclusion, our system is useful for managing, analyzing, and visualizing NGS data and achieves traceability, reproducibility, and transparency of NGS analysis. Database URL: http://cell-innovation.nig.ac.jp/maser/ PMID:29688385

GenPlay Multi-Genome, a tool to compare and analyze multiple human genomes in a graphical interface.

PubMed

Lajugie, Julien; Fourel, Nicolas; Bouhassira, Eric E

2015-01-01

Parallel visualization of multiple individual human genomes is a complex endeavor that is rapidly gaining importance with the increasing number of personal, phased and cancer genomes that are being generated. It requires the display of variants such as SNPs, indels and structural variants that are unique to specific genomes and the introduction of multiple overlapping gaps in the reference sequence. Here, we describe GenPlay Multi-Genome, an application specifically written to visualize and analyze multiple human genomes in parallel. GenPlay Multi-Genome is ideally suited for the comparison of allele-specific expression and functional genomic data obtained from multiple phased genomes in a graphical interface with access to multiple-track operation. It also allows the analysis of data that have been aligned to custom genomes rather than to a standard reference and can be used as a variant calling format file browser and as a tool to compare different genome assembly, such as hg19 and hg38. GenPlay is available under the GNU public license (GPL-3) from http://genplay.einstein.yu.edu. The source code is available at https://github.com/JulienLajugie/GenPlay. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

NASA Astrophysics Data System (ADS)

Mirel, Barbara; Kumar, Anuj; Nong, Paige; Su, Gang; Meng, Fan

2016-02-01

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students' visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students' successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

PubMed Central

Kumar, Anuj; Nong, Paige; Su, Gang; Meng, Fan

2016-01-01

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students’ visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students’ successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules. PMID:26877625

User Guidelines for the Brassica Database: BRAD.

PubMed

Wang, Xiaobo; Cheng, Feng; Wang, Xiaowu

2016-01-01

The genome sequence of Brassica rapa was first released in 2011. Since then, further Brassica genomes have been sequenced or are undergoing sequencing. It is therefore necessary to develop tools that help users to mine information from genomic data efficiently. This will greatly aid scientific exploration and breeding application, especially for those with low levels of bioinformatic training. Therefore, the Brassica database (BRAD) was built to collect, integrate, illustrate, and visualize Brassica genomic datasets. BRAD provides useful searching and data mining tools, and facilitates the search of gene annotation datasets, syntenic or non-syntenic orthologs, and flanking regions of functional genomic elements. It also includes genome-analysis tools such as BLAST and GBrowse. One of the important aims of BRAD is to build a bridge between Brassica crop genomes with the genome of the model species Arabidopsis thaliana, thus transferring the bulk of A. thaliana gene study information for use with newly sequenced Brassica crops.

ABACAS: algorithm-based automatic contiguation of assembled sequences

PubMed Central

Assefa, Samuel; Keane, Thomas M.; Otto, Thomas D.; Newbold, Chris; Berriman, Matthew

2009-01-01

Summary: Due to the availability of new sequencing technologies, we are now increasingly interested in sequencing closely related strains of existing finished genomes. Recently a number of de novo and mapping-based assemblers have been developed to produce high quality draft genomes from new sequencing technology reads. New tools are necessary to take contigs from a draft assembly through to a fully contiguated genome sequence. ABACAS is intended as a tool to rapidly contiguate (align, order, orientate), visualize and design primers to close gaps on shotgun assembled contigs based on a reference sequence. The input to ABACAS is a set of contigs which will be aligned to the reference genome, ordered and orientated, visualized in the ACT comparative browser, and optimal primer sequences are automatically generated. Availability and Implementation: ABACAS is implemented in Perl and is freely available for download from http://abacas.sourceforge.net Contact: sa4@sanger.ac.uk PMID:19497936

UCbase 2.0: ultraconserved sequences database (2014 update)

PubMed Central

Lomonaco, Vincenzo; Martoglia, Riccardo; Mandreoli, Federica; Anderlucci, Laura; Emmett, Warren; Bicciato, Silvio; Taccioli, Cristian

2014-01-01

UCbase 2.0 (http://ucbase.unimore.it) is an update, extension and evolution of UCbase, a Web tool dedicated to the analysis of ultraconserved sequences (UCRs). UCRs are 481 sequences >200 bases sharing 100% identity among human, mouse and rat genomes. They are frequently located in genomic regions known to be involved in cancer or differentially expressed in human leukemias and carcinomas. UCbase 2.0 is a platform-independent Web resource that includes the updated version of the human genome annotation (hg19), information linking disorders to chromosomal coordinates based on the Systematized Nomenclature of Medicine classification, a query tool to search for Single Nucleotide Polymorphisms (SNPs) and a new text box to directly interrogate the database using a MySQL interface. To facilitate the interactive visual interpretation of UCR chromosomal positioning, UCbase 2.0 now includes a graph visualization interface directly linked to UCSC genome browser. Database URL: http://ucbase.unimore.it PMID:24951797

Genomicus update 2015: KaryoView and MatrixView provide a genome-wide perspective to multispecies comparative genomics

PubMed Central

Louis, Alexandra; Nguyen, Nga Thi Thuy; Muffato, Matthieu; Roest Crollius, Hugues

2015-01-01

The Genomicus web server (http://www.genomicus.biologie.ens.fr/genomicus) is a visualization tool allowing comparative genomics in four different phyla (Vertebrate, Fungi, Metazoan and Plants). It provides access to genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants. Here we present the new features available for vertebrate genome with a focus on new graphical tools. The interface to enter the database has been improved, two pairwise genome comparison tools are now available (KaryoView and MatrixView) and the multiple genome comparison tools (PhyloView and AlignView) propose three new kinds of representation and a more intuitive menu. These new developments have been implemented for Genomicus portal dedicated to vertebrates. This allows the analysis of 68 extant animal genomes, as well as 58 ancestral reconstructed genomes. The Genomicus server also provides access to ancestral gene orders, to facilitate evolutionary and comparative genomics studies, as well as computationally predicted regulatory interactions, thanks to the representation of conserved non-coding elements with their putative gene targets. PMID:25378326

The coffee genome hub: a resource for coffee genomes

PubMed Central

Dereeper, Alexis; Bocs, Stéphanie; Rouard, Mathieu; Guignon, Valentin; Ravel, Sébastien; Tranchant-Dubreuil, Christine; Poncet, Valérie; Garsmeur, Olivier; Lashermes, Philippe; Droc, Gaëtan

2015-01-01

The whole genome sequence of Coffea canephora, the perennial diploid species known as Robusta, has been recently released. In the context of the C. canephora genome sequencing project and to support post-genomics efforts, we developed the Coffee Genome Hub (http://coffee-genome.org/), an integrative genome information system that allows centralized access to genomics and genetics data and analysis tools to facilitate translational and applied research in coffee. We provide the complete genome sequence of C. canephora along with gene structure, gene product information, metabolism, gene families, transcriptomics, syntenic blocks, genetic markers and genetic maps. The hub relies on generic software (e.g. GMOD tools) for easy querying, visualizing and downloading research data. It includes a Genome Browser enhanced by a Community Annotation System, enabling the improvement of automatic gene annotation through an annotation editor. In addition, the hub aims at developing interoperability among other existing South Green tools managing coffee data (phylogenomics resources, SNPs) and/or supporting data analyses with the Galaxy workflow manager. PMID:25392413

Integration and visualization of systems biology data in context of the genome

PubMed Central

2010-01-01

Background High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment. Results The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data. A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome. Conclusions Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment. PMID:20642854

Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium.

PubMed

Moskvin, Oleg V; Bolotin, Dmitry; Wang, Andrew; Ivanov, Pavel S; Gomelsky, Mark

2011-02-01

We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (-400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

PhytoPath: an integrative resource for plant pathogen genomics.

PubMed

Pedro, Helder; Maheswari, Uma; Urban, Martin; Irvine, Alistair George; Cuzick, Alayne; McDowall, Mark D; Staines, Daniel M; Kulesha, Eugene; Hammond-Kosack, Kim Elizabeth; Kersey, Paul Julian

2016-01-04

PhytoPath (www.phytopathdb.org) is a resource for genomic and phenotypic data from plant pathogen species, that integrates phenotypic data for genes from PHI-base, an expertly curated catalog of genes with experimentally verified pathogenicity, with the Ensembl tools for data visualization and analysis. The resource is focused on fungi, protists (oomycetes) and bacterial plant pathogens that have genomes that have been sequenced and annotated. Genes with associated PHI-base data can be easily identified across all plant pathogen species using a BioMart-based query tool and visualized in their genomic context on the Ensembl genome browser. The PhytoPath resource contains data for 135 genomic sequences from 87 plant pathogen species, and 1364 genes curated for their role in pathogenicity and as targets for chemical intervention. Support for community annotation of gene models is provided using the WebApollo online gene editor, and we are working with interested communities to improve reference annotation for selected species. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Molecules to maps: tools for visualization and interaction in support of computational biology.

PubMed

Kraemer, E T; Ferrin, T E

1998-01-01

The volume of data produced by genome projects, X-ray crystallography, NMR spectroscopy, and electron and confocal microscopy present the bioinformatics community with new challenges for analyzing, understanding, and exchanging this data. At the 1998 Pacific Symposium on Biocomputing, a track entitled 'Molecules to Maps: Tools for Visualization and Interaction in Computational Biology' provided tool developers and users with the opportunity to discuss advances in tools and techniques to assist scientists in evaluating, absorbing, navigating, and correlating this sea of information, through visualization and user interaction. In this paper we present these advances and discuss some of the challenges that remain to be solved.

CoCoNUT: an efficient system for the comparison and analysis of genomes

PubMed Central

2008-01-01

Background Comparative genomics is the analysis and comparison of genomes from different species. This area of research is driven by the large number of sequenced genomes and heavily relies on efficient algorithms and software to perform pairwise and multiple genome comparisons. Results Most of the software tools available are tailored for one specific task. In contrast, we have developed a novel system CoCoNUT (Computational Comparative geNomics Utility Toolkit) that allows solving several different tasks in a unified framework: (1) finding regions of high similarity among multiple genomic sequences and aligning them, (2) comparing two draft or multi-chromosomal genomes, (3) locating large segmental duplications in large genomic sequences, and (4) mapping cDNA/EST to genomic sequences. Conclusion CoCoNUT is competitive with other software tools w.r.t. the quality of the results. The use of state of the art algorithms and data structures allows CoCoNUT to solve comparative genomics tasks more efficiently than previous tools. With the improved user interface (including an interactive visualization component), CoCoNUT provides a unified, versatile, and easy-to-use software tool for large scale studies in comparative genomics. PMID:19014477

Public data and open source tools for multi-assay genomic investigation of disease.

PubMed

Kannan, Lavanya; Ramos, Marcel; Re, Angela; El-Hachem, Nehme; Safikhani, Zhaleh; Gendoo, Deena M A; Davis, Sean; Gomez-Cabrero, David; Castelo, Robert; Hansen, Kasper D; Carey, Vincent J; Morgan, Martin; Culhane, Aedín C; Haibe-Kains, Benjamin; Waldron, Levi

2016-07-01

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods. © The Author 2015. Published by Oxford University Press.

The MaizeGDB Genome Browser tutorial: one example of database outreach to biologists via video.

PubMed

Harper, Lisa C; Schaeffer, Mary L; Thistle, Jordan; Gardiner, Jack M; Andorf, Carson M; Campbell, Darwin A; Cannon, Ethalinda K S; Braun, Bremen L; Birkett, Scott M; Lawrence, Carolyn J; Sen, Taner Z

2011-01-01

Video tutorials are an effective way for researchers to quickly learn how to use online tools offered by biological databases. At MaizeGDB, we have developed a number of video tutorials that demonstrate how to use various tools and explicitly outline the caveats researchers should know to interpret the information available to them. One such popular video currently available is 'Using the MaizeGDB Genome Browser', which describes how the maize genome was sequenced and assembled as well as how the sequence can be visualized and interacted with via the MaizeGDB Genome Browser. Database

Applications of the pipeline environment for visual informatics and genomics computations

PubMed Central

2011-01-01

Background Contemporary informatics and genomics research require efficient, flexible and robust management of large heterogeneous data, advanced computational tools, powerful visualization, reliable hardware infrastructure, interoperability of computational resources, and detailed data and analysis-protocol provenance. The Pipeline is a client-server distributed computational environment that facilitates the visual graphical construction, execution, monitoring, validation and dissemination of advanced data analysis protocols. Results This paper reports on the applications of the LONI Pipeline environment to address two informatics challenges - graphical management of diverse genomics tools, and the interoperability of informatics software. Specifically, this manuscript presents the concrete details of deploying general informatics suites and individual software tools to new hardware infrastructures, the design, validation and execution of new visual analysis protocols via the Pipeline graphical interface, and integration of diverse informatics tools via the Pipeline eXtensible Markup Language syntax. We demonstrate each of these processes using several established informatics packages (e.g., miBLAST, EMBOSS, mrFAST, GWASS, MAQ, SAMtools, Bowtie) for basic local sequence alignment and search, molecular biology data analysis, and genome-wide association studies. These examples demonstrate the power of the Pipeline graphical workflow environment to enable integration of bioinformatics resources which provide a well-defined syntax for dynamic specification of the input/output parameters and the run-time execution controls. Conclusions The LONI Pipeline environment http://pipeline.loni.ucla.edu provides a flexible graphical infrastructure for efficient biomedical computing and distributed informatics research. The interactive Pipeline resource manager enables the utilization and interoperability of diverse types of informatics resources. The Pipeline client-server model provides computational power to a broad spectrum of informatics investigators - experienced developers and novice users, user with or without access to advanced computational-resources (e.g., Grid, data), as well as basic and translational scientists. The open development, validation and dissemination of computational networks (pipeline workflows) facilitates the sharing of knowledge, tools, protocols and best practices, and enables the unbiased validation and replication of scientific findings by the entire community. PMID:21791102

VCS: Tool for Visualizing Copy Number Variation and Single Nucleotide Polymorphism.

PubMed

Kim, HyoYoung; Sung, Samsun; Cho, Seoae; Kim, Tae-Hun; Seo, Kangseok; Kim, Heebal

2014-12-01

Copy number variation (CNV) or single nucleotide phlyorphism (SNP) is useful genetic resource to aid in understanding complex phenotypes or deseases susceptibility. Although thousands of CNVs and SNPs are currently avaliable in the public databases, they are somewhat difficult to use for analyses without visualization tools. We developed a web-based tool called the VCS (visualization of CNV or SNP) to visualize the CNV or SNP detected. The VCS tool can assist to easily interpret a biological meaning from the numerical value of CNV and SNP. The VCS provides six visualization tools: i) the enrichment of genome contents in CNV; ii) the physical distribution of CNV or SNP on chromosomes; iii) the distribution of log2 ratio of CNVs with criteria of interested; iv) the number of CNV or SNP per binning unit; v) the distribution of homozygosity of SNP genotype; and vi) cytomap of genes within CNV or SNP region.

SEURAT: visual analytics for the integrated analysis of microarray data.

PubMed

Gribov, Alexander; Sill, Martin; Lück, Sonja; Rücker, Frank; Döhner, Konstanze; Bullinger, Lars; Benner, Axel; Unwin, Antony

2010-06-03

In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data.

Explorative visual analytics on interval-based genomic data and their metadata.

PubMed

Jalili, Vahid; Matteucci, Matteo; Masseroli, Marco; Ceri, Stefano

2017-12-04

With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSE under GPLv3 open-source license.

MaGnET: Malaria Genome Exploration Tool.

PubMed

Sharman, Joanna L; Gerloff, Dietlind L

2013-09-15

The Malaria Genome Exploration Tool (MaGnET) is a software tool enabling intuitive 'exploration-style' visualization of functional genomics data relating to the malaria parasite, Plasmodium falciparum. MaGnET provides innovative integrated graphic displays for different datasets, including genomic location of genes, mRNA expression data, protein-protein interactions and more. Any selection of genes to explore made by the user is easily carried over between the different viewers for different datasets, and can be changed interactively at any point (without returning to a search). Free online use (Java Web Start) or download (Java application archive and MySQL database; requires local MySQL installation) at http://malariagenomeexplorer.org joanna.sharman@ed.ac.uk or dgerloff@ffame.org Supplementary data are available at Bioinformatics online.

Gobe: an interactive, web-based tool for comparative genomic visualization.

PubMed

Pedersen, Brent S; Tang, Haibao; Freeling, Michael

2011-04-01

Gobe is a web-based tool for viewing comparative genomic data. It supports viewing multiple genomic regions simultaneously. Its simple text format and flash-based rendering make it an interactive, exploratory research tool. Gobe can be used without installation through our web service, or downloaded and customized with stylesheets and javascript callback functions. Gobe is a flash application that runs in all modern web-browsers. The full source-code, including that for the online web application is available under the MIT license at: http://github.com/brentp/gobe. Sample applications are hosted at http://try-gobe.appspot.com/ and http://synteny.cnr.berkeley.edu/gobe-app/.

Variant Review with the Integrative Genomics Viewer.

PubMed

Robinson, James T; Thorvaldsdóttir, Helga; Wenger, Aaron M; Zehir, Ahmet; Mesirov, Jill P

2017-11-01

Manual review of aligned reads for confirmation and interpretation of variant calls is an important step in many variant calling pipelines for next-generation sequencing (NGS) data. Visual inspection can greatly increase the confidence in calls, reduce the risk of false positives, and help characterize complex events. The Integrative Genomics Viewer (IGV) was one of the first tools to provide NGS data visualization, and it currently provides a rich set of tools for inspection, validation, and interpretation of NGS datasets, as well as other types of genomic data. Here, we present a short overview of IGV's variant review features for both single-nucleotide variants and structural variants, with examples from both cancer and germline datasets. IGV is freely available at https://www.igv.org Cancer Res; 77(21); e31-34. ©2017 AACR . ©2017 American Association for Cancer Research.

CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets.

PubMed

Schofield, E C; Carver, T; Achuthan, P; Freire-Pritchett, P; Spivakov, M; Todd, J A; Burren, O S

2016-08-15

Promoter capture Hi-C (PCHi-C) allows the genome-wide interrogation of physical interactions between distal DNA regulatory elements and gene promoters in multiple tissue contexts. Visual integration of the resultant chromosome interaction maps with other sources of genomic annotations can provide insight into underlying regulatory mechanisms. We have developed Capture HiC Plotter (CHiCP), a web-based tool that allows interactive exploration of PCHi-C interaction maps and integration with both public and user-defined genomic datasets. CHiCP is freely accessible from www.chicp.org and supports most major HTML5 compliant web browsers. Full source code and installation instructions are available from http://github.com/D-I-L/django-chicp ob219@cam.ac.uk. © The Author 2016. Published by Oxford University Press. All rights reserved.

Genomicus update 2015: KaryoView and MatrixView provide a genome-wide perspective to multispecies comparative genomics.

PubMed

Louis, Alexandra; Nguyen, Nga Thi Thuy; Muffato, Matthieu; Roest Crollius, Hugues

2015-01-01

The Genomicus web server (http://www.genomicus.biologie.ens.fr/genomicus) is a visualization tool allowing comparative genomics in four different phyla (Vertebrate, Fungi, Metazoan and Plants). It provides access to genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants. Here we present the new features available for vertebrate genome with a focus on new graphical tools. The interface to enter the database has been improved, two pairwise genome comparison tools are now available (KaryoView and MatrixView) and the multiple genome comparison tools (PhyloView and AlignView) propose three new kinds of representation and a more intuitive menu. These new developments have been implemented for Genomicus portal dedicated to vertebrates. This allows the analysis of 68 extant animal genomes, as well as 58 ancestral reconstructed genomes. The Genomicus server also provides access to ancestral gene orders, to facilitate evolutionary and comparative genomics studies, as well as computationally predicted regulatory interactions, thanks to the representation of conserved non-coding elements with their putative gene targets. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Enhancing knowledge discovery from cancer genomics data with Galaxy

PubMed Central

Albuquerque, Marco A.; Grande, Bruno M.; Ritch, Elie J.; Pararajalingam, Prasath; Jessa, Selin; Krzywinski, Martin; Grewal, Jasleen K.; Shah, Sohrab P.; Boutros, Paul C.

2017-01-01

Abstract The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker. PMID:28327945

Enhancing knowledge discovery from cancer genomics data with Galaxy.

PubMed

Albuquerque, Marco A; Grande, Bruno M; Ritch, Elie J; Pararajalingam, Prasath; Jessa, Selin; Krzywinski, Martin; Grewal, Jasleen K; Shah, Sohrab P; Boutros, Paul C; Morin, Ryan D

2017-05-01

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker. © The Author 2017. Published by Oxford University Press.

The UCSC genome browser and associated tools

PubMed Central

Haussler, David; Kent, W. James

2013-01-01

The UCSC Genome Browser (http://genome.ucsc.edu) is a graphical viewer for genomic data now in its 13th year. Since the early days of the Human Genome Project, it has presented an integrated view of genomic data of many kinds. Now home to assemblies for 58 organisms, the Browser presents visualization of annotations mapped to genomic coordinates. The ability to juxtapose annotations of many types facilitates inquiry-driven data mining. Gene predictions, mRNA alignments, epigenomic data from the ENCODE project, conservation scores from vertebrate whole-genome alignments and variation data may be viewed at any scale from a single base to an entire chromosome. The Browser also includes many other widely used tools, including BLAT, which is useful for alignments from high-throughput sequencing experiments. Private data uploaded as Custom Tracks and Data Hubs in many formats may be displayed alongside the rich compendium of precomputed data in the UCSC database. The Table Browser is a full-featured graphical interface, which allows querying, filtering and intersection of data tables. The Saved Session feature allows users to store and share customized views, enhancing the utility of the system for organizing multiple trains of thought. Binary Alignment/Map (BAM), Variant Call Format and the Personal Genome Single Nucleotide Polymorphisms (SNPs) data formats are useful for visualizing a large sequencing experiment (whole-genome or whole-exome), where the differences between the data set and the reference assembly may be displayed graphically. Support for high-throughput sequencing extends to compact, indexed data formats, such as BAM, bigBed and bigWig, allowing rapid visualization of large datasets from RNA-seq and ChIP-seq experiments via local hosting. PMID:22908213

The UCSC genome browser and associated tools.

PubMed

Kuhn, Robert M; Haussler, David; Kent, W James

2013-03-01

The UCSC Genome Browser (http://genome.ucsc.edu) is a graphical viewer for genomic data now in its 13th year. Since the early days of the Human Genome Project, it has presented an integrated view of genomic data of many kinds. Now home to assemblies for 58 organisms, the Browser presents visualization of annotations mapped to genomic coordinates. The ability to juxtapose annotations of many types facilitates inquiry-driven data mining. Gene predictions, mRNA alignments, epigenomic data from the ENCODE project, conservation scores from vertebrate whole-genome alignments and variation data may be viewed at any scale from a single base to an entire chromosome. The Browser also includes many other widely used tools, including BLAT, which is useful for alignments from high-throughput sequencing experiments. Private data uploaded as Custom Tracks and Data Hubs in many formats may be displayed alongside the rich compendium of precomputed data in the UCSC database. The Table Browser is a full-featured graphical interface, which allows querying, filtering and intersection of data tables. The Saved Session feature allows users to store and share customized views, enhancing the utility of the system for organizing multiple trains of thought. Binary Alignment/Map (BAM), Variant Call Format and the Personal Genome Single Nucleotide Polymorphisms (SNPs) data formats are useful for visualizing a large sequencing experiment (whole-genome or whole-exome), where the differences between the data set and the reference assembly may be displayed graphically. Support for high-throughput sequencing extends to compact, indexed data formats, such as BAM, bigBed and bigWig, allowing rapid visualization of large datasets from RNA-seq and ChIP-seq experiments via local hosting.

CpGAVAS, an integrated web server for the annotation, visualization, analysis, and GenBank submission of completely sequenced chloroplast genome sequences

PubMed Central

2012-01-01

Background The complete sequences of chloroplast genomes provide wealthy information regarding the evolutionary history of species. With the advance of next-generation sequencing technology, the number of completely sequenced chloroplast genomes is expected to increase exponentially, powerful computational tools annotating the genome sequences are in urgent need. Results We have developed a web server CPGAVAS. The server accepts a complete chloroplast genome sequence as input. First, it predicts protein-coding and rRNA genes based on the identification and mapping of the most similar, full-length protein, cDNA and rRNA sequences by integrating results from Blastx, Blastn, protein2genome and est2genome programs. Second, tRNA genes and inverted repeats (IR) are identified using tRNAscan, ARAGORN and vmatch respectively. Third, it calculates the summary statistics for the annotated genome. Fourth, it generates a circular map ready for publication. Fifth, it can create a Sequin file for GenBank submission. Last, it allows the extractions of protein and mRNA sequences for given list of genes and species. The annotation results in GFF3 format can be edited using any compatible annotation editing tools. The edited annotations can then be uploaded to CPGAVAS for update and re-analyses repeatedly. Using known chloroplast genome sequences as test set, we show that CPGAVAS performs comparably to another application DOGMA, while having several superior functionalities. Conclusions CPGAVAS allows the semi-automatic and complete annotation of a chloroplast genome sequence, and the visualization, editing and analysis of the annotation results. It will become an indispensible tool for researchers studying chloroplast genomes. The software is freely accessible from http://www.herbalgenomics.org/cpgavas. PMID:23256920

A web server for mining Comparative Genomic Hybridization (CGH) data

NASA Astrophysics Data System (ADS)

Liu, Jun; Ranka, Sanjay; Kahveci, Tamer

2007-11-01

Advances in cytogenetics and molecular biology has established that chromosomal alterations are critical in the pathogenesis of human cancer. Recurrent chromosomal alterations provide cytological and molecular markers for the diagnosis and prognosis of disease. They also facilitate the identification of genes that are important in carcinogenesis, which in the future may help in the development of targeted therapy. A large amount of publicly available cancer genetic data is now available and it is growing. There is a need for public domain tools that allow users to analyze their data and visualize the results. This chapter describes a web based software tool that will allow researchers to analyze and visualize Comparative Genomic Hybridization (CGH) datasets. It employs novel data mining methodologies for clustering and classification of CGH datasets as well as algorithms for identifying important markers (small set of genomic intervals with aberrations) that are potentially cancer signatures. The developed software will help in understanding the relationships between genomic aberrations and cancer types.

UCbase 2.0: ultraconserved sequences database (2014 update).

PubMed

Lomonaco, Vincenzo; Martoglia, Riccardo; Mandreoli, Federica; Anderlucci, Laura; Emmett, Warren; Bicciato, Silvio; Taccioli, Cristian

2014-01-01

UCbase 2.0 (http://ucbase.unimore.it) is an update, extension and evolution of UCbase, a Web tool dedicated to the analysis of ultraconserved sequences (UCRs). UCRs are 481 sequences >200 bases sharing 100% identity among human, mouse and rat genomes. They are frequently located in genomic regions known to be involved in cancer or differentially expressed in human leukemias and carcinomas. UCbase 2.0 is a platform-independent Web resource that includes the updated version of the human genome annotation (hg19), information linking disorders to chromosomal coordinates based on the Systematized Nomenclature of Medicine classification, a query tool to search for Single Nucleotide Polymorphisms (SNPs) and a new text box to directly interrogate the database using a MySQL interface. To facilitate the interactive visual interpretation of UCR chromosomal positioning, UCbase 2.0 now includes a graph visualization interface directly linked to UCSC genome browser. Database URL: http://ucbase.unimore.it. © The Author(s) 2014. Published by Oxford University Press.

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing

PubMed Central

Nguyen, Nga Thi Thuy; Vincens, Pierre

2018-01-01

Abstract Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView). PMID:29087490

The MaizeGDB Genome Browser tutorial: one example of database outreach to biologists via video

PubMed Central

Harper, Lisa C.; Schaeffer, Mary L.; Thistle, Jordan; Gardiner, Jack M.; Andorf, Carson M.; Campbell, Darwin A.; Cannon, Ethalinda K.S.; Braun, Bremen L.; Birkett, Scott M.; Lawrence, Carolyn J.; Sen, Taner Z.

2011-01-01

Video tutorials are an effective way for researchers to quickly learn how to use online tools offered by biological databases. At MaizeGDB, we have developed a number of video tutorials that demonstrate how to use various tools and explicitly outline the caveats researchers should know to interpret the information available to them. One such popular video currently available is ‘Using the MaizeGDB Genome Browser’, which describes how the maize genome was sequenced and assembled as well as how the sequence can be visualized and interacted with via the MaizeGDB Genome Browser. Database URL: http://www.maizegdb.org/ PMID:21565781

CyanoBase: the cyanobacteria genome database update 2010.

PubMed

Nakao, Mitsuteru; Okamoto, Shinobu; Kohara, Mitsuyo; Fujishiro, Tsunakazu; Fujisawa, Takatomo; Sato, Shusei; Tabata, Satoshi; Kaneko, Takakazu; Nakamura, Yasukazu

2010-01-01

CyanoBase (http://genome.kazusa.or.jp/cyanobase) is the genome database for cyanobacteria, which are model organisms for photosynthesis. The database houses cyanobacteria species information, complete genome sequences, genome-scale experiment data, gene information, gene annotations and mutant information. In this version, we updated these datasets and improved the navigation and the visual display of the data views. In addition, a web service API now enables users to retrieve the data in various formats with other tools, seamlessly.

EGenBio: A Data Management System for Evolutionary Genomics and Biodiversity

PubMed Central

Nahum, Laila A; Reynolds, Matthew T; Wang, Zhengyuan O; Faith, Jeremiah J; Jonna, Rahul; Jiang, Zhi J; Meyer, Thomas J; Pollock, David D

2006-01-01

Background Evolutionary genomics requires management and filtering of large numbers of diverse genomic sequences for accurate analysis and inference on evolutionary processes of genomic and functional change. We developed Evolutionary Genomics and Biodiversity (EGenBio; ) to begin to address this. Description EGenBio is a system for manipulation and filtering of large numbers of sequences, integrating curated sequence alignments and phylogenetic trees, managing evolutionary analyses, and visualizing their output. EGenBio is organized into three conceptual divisions, Evolution, Genomics, and Biodiversity. The Genomics division includes tools for selecting pre-aligned sequences from different genes and species, and for modifying and filtering these alignments for further analysis. Species searches are handled through queries that can be modified based on a tree-based navigation system and saved. The Biodiversity division contains tools for analyzing individual sequences or sequence alignments, whereas the Evolution division contains tools involving phylogenetic trees. Alignments are annotated with analytical results and modification history using our PRAED format. A miscellaneous Tools section and Help framework are also available. EGenBio was developed around our comparative genomic research and a prototype database of mtDNA genomes. It utilizes MySQL-relational databases and dynamic page generation, and calls numerous custom programs. Conclusion EGenBio was designed to serve as a platform for tools and resources to ease combined analysis in evolution, genomics, and biodiversity. PMID:17118150

Grohar: Automated Visualization of Genome-Scale Metabolic Models and Their Pathways.

PubMed

Moškon, Miha; Zimic, Nikolaj; Mraz, Miha

2018-05-01

Genome-scale metabolic models (GEMs) have become a powerful tool for the investigation of the entire metabolism of the organism in silico. These models are, however, often extremely hard to reconstruct and also difficult to apply to the selected problem. Visualization of the GEM allows us to easier comprehend the model, to perform its graphical analysis, to find and correct the faulty relations, to identify the parts of the system with a designated function, etc. Even though several approaches for the automatic visualization of GEMs have been proposed, metabolic maps are still manually drawn or at least require large amount of manual curation. We present Grohar, a computational tool for automatic identification and visualization of GEM (sub)networks and their metabolic fluxes. These (sub)networks can be specified directly by listing the metabolites of interest or indirectly by providing reference metabolic pathways from different sources, such as KEGG, SBML, or Matlab file. These pathways are identified within the GEM using three different pathway alignment algorithms. Grohar also supports the visualization of the model adjustments (e.g., activation or inhibition of metabolic reactions) after perturbations are induced.

OSLay: optimal syntenic layout of unfinished assemblies.

PubMed

Richter, Daniel C; Schuster, Stephan C; Huson, Daniel H

2007-07-01

The whole genome shotgun approach to genome sequencing results in a collection of contigs that must be ordered and oriented to facilitate efficient gap closure. We present a new tool OSLay that uses synteny between matching sequences in a target assembly and a reference assembly to layout the contigs (or scaffolds) in the target assembly. The underlying algorithm is based on maximum weight matching. The tool provides an interactive visualization of the computed layout and the result can be imported into the assembly editing tool Consed to support the design of primer pairs for gap closure. To enhance efficiency in the gap closure phase of a genome project it is crucial to know which contigs are adjacent in the target genome. Related genome sequences can be used to layout contigs in an assembly. OSLay is freely available from: http://www-ab.informatik.unituebingen.de/software/oslay.

MEXPRESS: visualizing expression, DNA methylation and clinical TCGA data.

PubMed

Koch, Alexander; De Meyer, Tim; Jeschke, Jana; Van Criekinge, Wim

2015-08-26

In recent years, increasing amounts of genomic and clinical cancer data have become publically available through large-scale collaborative projects such as The Cancer Genome Atlas (TCGA). However, as long as these datasets are difficult to access and interpret, they are essentially useless for a major part of the research community and their scientific potential will not be fully realized. To address these issues we developed MEXPRESS, a straightforward and easy-to-use web tool for the integration and visualization of the expression, DNA methylation and clinical TCGA data on a single-gene level ( http://mexpress.be ). In comparison to existing tools, MEXPRESS allows researchers to quickly visualize and interpret the different TCGA datasets and their relationships for a single gene, as demonstrated for GSTP1 in prostate adenocarcinoma. We also used MEXPRESS to reveal the differences in the DNA methylation status of the PAM50 marker gene MLPH between the breast cancer subtypes and how these differences were linked to the expression of MPLH. We have created a user-friendly tool for the visualization and interpretation of TCGA data, offering clinical researchers a simple way to evaluate the TCGA data for their genes or candidate biomarkers of interest.

Scribl: an HTML5 Canvas-based graphics library for visualizing genomic data over the web.

PubMed

Miller, Chase A; Anthony, Jon; Meyer, Michelle M; Marth, Gabor

2013-02-01

High-throughput biological research requires simultaneous visualization as well as analysis of genomic data, e.g. read alignments, variant calls and genomic annotations. Traditionally, such integrative analysis required desktop applications operating on locally stored data. Many current terabyte-size datasets generated by large public consortia projects, however, are already only feasibly stored at specialist genome analysis centers. As even small laboratories can afford very large datasets, local storage and analysis are becoming increasingly limiting, and it is likely that most such datasets will soon be stored remotely, e.g. in the cloud. These developments will require web-based tools that enable users to access, analyze and view vast remotely stored data with a level of sophistication and interactivity that approximates desktop applications. As rapidly dropping cost enables researchers to collect data intended to answer questions in very specialized contexts, developers must also provide software libraries that empower users to implement customized data analyses and data views for their particular application. Such specialized, yet lightweight, applications would empower scientists to better answer specific biological questions than possible with general-purpose genome browsers currently available. Using recent advances in core web technologies (HTML5), we developed Scribl, a flexible genomic visualization library specifically targeting coordinate-based data such as genomic features, DNA sequence and genetic variants. Scribl simplifies the development of sophisticated web-based graphical tools that approach the dynamism and interactivity of desktop applications. Software is freely available online at http://chmille4.github.com/Scribl/ and is implemented in JavaScript with all modern browsers supported.

CyanoBase: the cyanobacteria genome database update 2010

PubMed Central

Nakao, Mitsuteru; Okamoto, Shinobu; Kohara, Mitsuyo; Fujishiro, Tsunakazu; Fujisawa, Takatomo; Sato, Shusei; Tabata, Satoshi; Kaneko, Takakazu; Nakamura, Yasukazu

2010-01-01

CyanoBase (http://genome.kazusa.or.jp/cyanobase) is the genome database for cyanobacteria, which are model organisms for photosynthesis. The database houses cyanobacteria species information, complete genome sequences, genome-scale experiment data, gene information, gene annotations and mutant information. In this version, we updated these datasets and improved the navigation and the visual display of the data views. In addition, a web service API now enables users to retrieve the data in various formats with other tools, seamlessly. PMID:19880388

The next generation of melanocyte data: Genetic, epigenetic, and transcriptional resource datasets and analysis tools.

PubMed

Loftus, Stacie K

2018-05-01

The number of melanocyte- and melanoma-derived next generation sequence genome-scale datasets have rapidly expanded over the past several years. This resource guide provides a summary of publicly available sources of melanocyte cell derived whole genome, exome, mRNA and miRNA transcriptome, chromatin accessibility and epigenetic datasets. Also highlighted are bioinformatic resources and tools for visualization and data queries which allow researchers a genome-scale view of the melanocyte. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

SEURAT: Visual analytics for the integrated analysis of microarray data

PubMed Central

2010-01-01

Background In translational cancer research, gene expression data is collected together with clinical data and genomic data arising from other chip based high throughput technologies. Software tools for the joint analysis of such high dimensional data sets together with clinical data are required. Results We have developed an open source software tool which provides interactive visualization capability for the integrated analysis of high-dimensional gene expression data together with associated clinical data, array CGH data and SNP array data. The different data types are organized by a comprehensive data manager. Interactive tools are provided for all graphics: heatmaps, dendrograms, barcharts, histograms, eventcharts and a chromosome browser, which displays genetic variations along the genome. All graphics are dynamic and fully linked so that any object selected in a graphic will be highlighted in all other graphics. For exploratory data analysis the software provides unsupervised data analytics like clustering, seriation algorithms and biclustering algorithms. Conclusions The SEURAT software meets the growing needs of researchers to perform joint analysis of gene expression, genomical and clinical data. PMID:20525257

SPOCS: software for predicting and visualizing orthology/paralogy relationships among genomes.

PubMed

Curtis, Darren S; Phillips, Aaron R; Callister, Stephen J; Conlan, Sean; McCue, Lee Ann

2013-10-15

At the rate that prokaryotic genomes can now be generated, comparative genomics studies require a flexible method for quickly and accurately predicting orthologs among the rapidly changing set of genomes available. SPOCS implements a graph-based ortholog prediction method to generate a simple tab-delimited table of orthologs and in addition, html files that provide a visualization of the predicted ortholog/paralog relationships to which gene/protein expression metadata may be overlaid. A SPOCS web application is freely available at http://cbb.pnnl.gov/portal/tools/spocs.html. Source code for Linux systems is also freely available under an open source license at http://cbb.pnnl.gov/portal/software/spocs.html; the Boost C++ libraries and BLAST are required.

Genomicus 2018: karyotype evolutionary trees and on-the-fly synteny computing.

PubMed

Nguyen, Nga Thi Thuy; Vincens, Pierre; Roest Crollius, Hugues; Louis, Alexandra

2018-01-04

Since 2010, the Genomicus web server is available online at http://genomicus.biologie.ens.fr/genomicus. This graphical browser provides access to comparative genomic analyses in four different phyla (Vertebrate, Plants, Fungi, and non vertebrate Metazoans). Users can analyse genomic information from extant species, as well as ancestral gene content and gene order for vertebrates and flowering plants, in an integrated evolutionary context. New analyses and visualization tools have recently been implemented in Genomicus Vertebrate. Karyotype structures from several genomes can now be compared along an evolutionary pathway (Multi-KaryotypeView), and synteny blocks can be computed and visualized between any two genomes (PhylDiagView). © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Integration of information and volume visualization for analysis of cell lineage and gene expression during embryogenesis

NASA Astrophysics Data System (ADS)

Cedilnik, Andrej; Baumes, Jeffrey; Ibanez, Luis; Megason, Sean; Wylie, Brian

2008-01-01

Dramatic technological advances in the field of genomics have made it possible to sequence the complete genomes of many different organisms. With this overwhelming amount of data at hand, biologists are now confronted with the challenge of understanding the function of the many different elements of the genome. One of the best places to start gaining insight on the mechanisms by which the genome controls an organism is the study of embryogenesis. There are multiple and inter-related layers of information that must be established in order to understand how the genome controls the formation of an organism. One is cell lineage which describes how patterns of cell division give rise to different parts of an organism. Another is gene expression which describes when and where different genes are turned on. Both of these data types can now be acquired using fluorescent laser-scanning (confocal or 2-photon) microscopy of embryos tagged with fluorescent proteins to generate 3D movies of developing embryos. However, analyzing the wealth of resulting images requires tools capable of interactively visualizing several different types of information as well as being scalable to terabytes of data. This paper describes how the combination of existing large data volume visualization and the new Titan information visualization framework of the Visualization Toolkit (VTK) can be applied to the problem of studying the cell lineage of an organism. In particular, by linking the visualization of spatial and temporal gene expression data with novel ways of visualizing cell lineage data, users can study how the genome regulates different aspects of embryonic development.

Genomics Portals: integrative web-platform for mining genomics data.

PubMed

Shinde, Kaustubh; Phatak, Mukta; Johannes, Freudenberg M; Chen, Jing; Li, Qian; Vineet, Joshi K; Hu, Zhen; Ghosh, Krishnendu; Meller, Jaroslaw; Medvedovic, Mario

2010-01-13

A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems. Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis. The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org.

Genomics Portals: integrative web-platform for mining genomics data

PubMed Central

2010-01-01

Background A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems. Results Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis. Conclusion The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org. PMID:20070909

FISH Oracle: a web server for flexible visualization of DNA copy number data in a genomic context.

PubMed

Mader, Malte; Simon, Ronald; Steinbiss, Sascha; Kurtz, Stefan

2011-07-28

The rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Optimally, such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate. We have developed a web-based software FISH Oracle to visualize data from multiple array CGH experiments in a genomic context. Its fast visualization engine and advanced web and database technology supports highly interactive use. FISH Oracle comes with a convenient data import mechanism, powerful search options for genomic elements (e.g. gene names or karyobands), quick navigation and zooming into interesting regions, and mechanisms to export the visualization into different high quality formats. These features make the software especially suitable for the needs of life scientists. FISH Oracle offers a fast and easy to use visualization tool for array CGH and SNP array data. It allows for the identification of genomic regions representing minimal common changes based on data from one or more experiments. FISH Oracle will be instrumental to identify candidate onco and tumor suppressor genes based on the frequency and genomic position of DNA copy number changes. The FISH Oracle application and an installed demo web server are available at http://www.zbh.uni-hamburg.de/fishoracle.

FISH Oracle: a web server for flexible visualization of DNA copy number data in a genomic context

PubMed Central

2011-01-01

Background The rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Optimally, such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate. Results We have developed a web-based software FISH Oracle to visualize data from multiple array CGH experiments in a genomic context. Its fast visualization engine and advanced web and database technology supports highly interactive use. FISH Oracle comes with a convenient data import mechanism, powerful search options for genomic elements (e.g. gene names or karyobands), quick navigation and zooming into interesting regions, and mechanisms to export the visualization into different high quality formats. These features make the software especially suitable for the needs of life scientists. Conclusions FISH Oracle offers a fast and easy to use visualization tool for array CGH and SNP array data. It allows for the identification of genomic regions representing minimal common changes based on data from one or more experiments. FISH Oracle will be instrumental to identify candidate onco and tumor suppressor genes based on the frequency and genomic position of DNA copy number changes. The FISH Oracle application and an installed demo web server are available at http://www.zbh.uni-hamburg.de/fishoracle. PMID:21884636

MaGnET: Malaria Genome Exploration Tool

PubMed Central

Sharman, Joanna L.; Gerloff, Dietlind L.

2013-01-01

Summary: The Malaria Genome Exploration Tool (MaGnET) is a software tool enabling intuitive ‘exploration-style’ visualization of functional genomics data relating to the malaria parasite, Plasmodium falciparum. MaGnET provides innovative integrated graphic displays for different datasets, including genomic location of genes, mRNA expression data, protein–protein interactions and more. Any selection of genes to explore made by the user is easily carried over between the different viewers for different datasets, and can be changed interactively at any point (without returning to a search). Availability and Implementation: Free online use (Java Web Start) or download (Java application archive and MySQL database; requires local MySQL installation) at http://malariagenomeexplorer.org Contact: joanna.sharman@ed.ac.uk or dgerloff@ffame.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23894142

GPU Accelerated Browser for Neuroimaging Genomics.

PubMed

Zigon, Bob; Li, Huang; Yao, Xiaohui; Fang, Shiaofen; Hasan, Mohammad Al; Yan, Jingwen; Moore, Jason H; Saykin, Andrew J; Shen, Li

2018-04-25

Neuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates. The ANOVA algorithm is 110 times faster than the 4-core OpenMP version, while the VEGAS algorithm is 375 times faster than its 4-core OpenMP counter part. This approach lays a solid foundation for researchers to address the challenges of mining large-scale imaging genomics datasets via interactive visual exploration.

DNA Data Visualization (DDV): Software for Generating Web-Based Interfaces Supporting Navigation and Analysis of DNA Sequence Data of Entire Genomes.

PubMed

Neugebauer, Tomasz; Bordeleau, Eric; Burrus, Vincent; Brzezinski, Ryszard

2015-01-01

Data visualization methods are necessary during the exploration and analysis activities of an increasingly data-intensive scientific process. There are few existing visualization methods for raw nucleotide sequences of a whole genome or chromosome. Software for data visualization should allow the researchers to create accessible data visualization interfaces that can be exported and shared with others on the web. Herein, novel software developed for generating DNA data visualization interfaces is described. The software converts DNA data sets into images that are further processed as multi-scale images to be accessed through a web-based interface that supports zooming, panning and sequence fragment selection. Nucleotide composition frequencies and GC skew of a selected sequence segment can be obtained through the interface. The software was used to generate DNA data visualization of human and bacterial chromosomes. Examples of visually detectable features such as short and long direct repeats, long terminal repeats, mobile genetic elements, heterochromatic segments in microbial and human chromosomes, are presented. The software and its source code are available for download and further development. The visualization interfaces generated with the software allow for the immediate identification and observation of several types of sequence patterns in genomes of various sizes and origins. The visualization interfaces generated with the software are readily accessible through a web browser. This software is a useful research and teaching tool for genetics and structural genomics.

Using Galaxy to Perform Large-Scale Interactive Data Analyses

PubMed Central

Hillman-Jackson, Jennifer; Clements, Dave; Blankenberg, Daniel; Taylor, James; Nekrutenko, Anton

2012-01-01

Innovations in biomedical research technologies continue to provide experimental biologists with novel and increasingly large genomic and high-throughput data resources to be analyzed. As creating and obtaining data has become easier, the key decision faced by many researchers is a practical one: where and how should an analysis be performed? Datasets are large and analysis tool set-up and use is riddled with complexities outside of the scope of core research activities. The authors believe that Galaxy (galaxyproject.org) provides a powerful solution that simplifies data acquisition and analysis in an intuitive web-application, granting all researchers access to key informatics tools previously only available to computational specialists working in Unix-based environments. We will demonstrate through a series of biomedically relevant protocols how Galaxy specifically brings together 1) data retrieval from public and private sources, for example, UCSC’s Eukaryote and Microbial Genome Browsers (genome.ucsc.edu), 2) custom tools (wrapped Unix functions, format standardization/conversions, interval operations) and 3rd party analysis tools, for example, Bowtie/Tuxedo Suite (bowtie-bio.sourceforge.net), Lastz (www.bx.psu.edu/~rsharris/lastz/), SAMTools (samtools.sourceforge.net), FASTX-toolkit (hannonlab.cshl.edu/fastx_toolkit), and MACS (liulab.dfci.harvard.edu/MACS), and creates results formatted for visualization in tools such as the Galaxy Track Browser (GTB, galaxyproject.org/wiki/Learn/Visualization), UCSC Genome Browser (genome.ucsc.edu), Ensembl (www.ensembl.org), and GeneTrack (genetrack.bx.psu.edu). Galaxy rapidly has become the most popular choice for integrated next generation sequencing (NGS) analytics and collaboration, where users can perform, document, and share complex analysis within a single interface in an unprecedented number of ways. PMID:18428782

Caryoscope: An Open Source Java application for viewing microarray data in a genomic context

PubMed Central

Awad, Ihab AB; Rees, Christian A; Hernandez-Boussard, Tina; Ball, Catherine A; Sherlock, Gavin

2004-01-01

Background Microarray-based comparative genome hybridization experiments generate data that can be mapped onto the genome. These data are interpreted more easily when represented graphically in a genomic context. Results We have developed Caryoscope, which is an open source Java application for visualizing microarray data from array comparative genome hybridization experiments in a genomic context. Caryoscope can read General Feature Format files (GFF files), as well as comma- and tab-delimited files, that define the genomic positions of the microarray reporters for which data are obtained. The microarray data can be browsed using an interactive, zoomable interface, which helps users identify regions of chromosomal deletion or amplification. The graphical representation of the data can be exported in a number of graphic formats, including publication-quality formats such as PostScript. Conclusion Caryoscope is a useful tool that can aid in the visualization, exploration and interpretation of microarray data in a genomic context. PMID:15488149

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

DOE PAGES

King, Zachary A.; Lu, Justin; Drager, Andreas; ...

2015-10-17

In this study, genome-scale metabolic models are mathematically structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scalemore » metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.« less

BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

PubMed Central

King, Zachary A.; Lu, Justin; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard O.; Lewis, Nathan E.

2016-01-01

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data. PMID:26476456

Bolbase: a comprehensive genomics database for Brassica oleracea.

PubMed

Yu, Jingyin; Zhao, Meixia; Wang, Xiaowu; Tong, Chaobo; Huang, Shunmou; Tehrim, Sadia; Liu, Yumei; Hua, Wei; Liu, Shengyi

2013-09-30

Brassica oleracea is a morphologically diverse species in the family Brassicaceae and contains a group of nutrition-rich vegetable crops, including common heading cabbage, cauliflower, broccoli, kohlrabi, kale, Brussels sprouts. This diversity along with its phylogenetic membership in a group of three diploid and three tetraploid species, and the recent availability of genome sequences within Brassica provide an unprecedented opportunity to study intra- and inter-species divergence and evolution in this species and its close relatives. We have developed a comprehensive database, Bolbase, which provides access to the B. oleracea genome data and comparative genomics information. The whole genome of B. oleracea is available, including nine fully assembled chromosomes and 1,848 scaffolds, with 45,758 predicted genes, 13,382 transposable elements, and 3,581 non-coding RNAs. Comparative genomics information is available, including syntenic regions among B. oleracea, Brassica rapa and Arabidopsis thaliana, synonymous (Ks) and non-synonymous (Ka) substitution rates between orthologous gene pairs, gene families or clusters, and differences in quantity, category, and distribution of transposable elements on chromosomes. Bolbase provides useful search and data mining tools, including a keyword search, a local BLAST server, and a customized GBrowse tool, which can be used to extract annotations of genome components, identify similar sequences and visualize syntenic regions among species. Users can download all genomic data and explore comparative genomics in a highly visual setting. Bolbase is the first resource platform for the B. oleracea genome and for genomic comparisons with its relatives, and thus it will help the research community to better study the function and evolution of Brassica genomes as well as enhance molecular breeding research. This database will be updated regularly with new features, improvements to genome annotation, and new genomic sequences as they become available. Bolbase is freely available at http://ocri-genomics.org/bolbase.

Scribl: an HTML5 Canvas-based graphics library for visualizing genomic data over the web

PubMed Central

Miller, Chase A.; Anthony, Jon; Meyer, Michelle M.; Marth, Gabor

2013-01-01

Motivation: High-throughput biological research requires simultaneous visualization as well as analysis of genomic data, e.g. read alignments, variant calls and genomic annotations. Traditionally, such integrative analysis required desktop applications operating on locally stored data. Many current terabyte-size datasets generated by large public consortia projects, however, are already only feasibly stored at specialist genome analysis centers. As even small laboratories can afford very large datasets, local storage and analysis are becoming increasingly limiting, and it is likely that most such datasets will soon be stored remotely, e.g. in the cloud. These developments will require web-based tools that enable users to access, analyze and view vast remotely stored data with a level of sophistication and interactivity that approximates desktop applications. As rapidly dropping cost enables researchers to collect data intended to answer questions in very specialized contexts, developers must also provide software libraries that empower users to implement customized data analyses and data views for their particular application. Such specialized, yet lightweight, applications would empower scientists to better answer specific biological questions than possible with general-purpose genome browsers currently available. Results: Using recent advances in core web technologies (HTML5), we developed Scribl, a flexible genomic visualization library specifically targeting coordinate-based data such as genomic features, DNA sequence and genetic variants. Scribl simplifies the development of sophisticated web-based graphical tools that approach the dynamism and interactivity of desktop applications. Availability and implementation: Software is freely available online at http://chmille4.github.com/Scribl/ and is implemented in JavaScript with all modern browsers supported. Contact: gabor.marth@bc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23172864

GREAT: a web portal for Genome Regulatory Architecture Tools

PubMed Central

Bouyioukos, Costas; Bucchini, François; Elati, Mohamed; Képès, François

2016-01-01

GREAT (Genome REgulatory Architecture Tools) is a novel web portal for tools designed to generate user-friendly and biologically useful analysis of genome architecture and regulation. The online tools of GREAT are freely accessible and compatible with essentially any operating system which runs a modern browser. GREAT is based on the analysis of genome layout -defined as the respective positioning of co-functional genes- and its relation with chromosome architecture and gene expression. GREAT tools allow users to systematically detect regular patterns along co-functional genomic features in an automatic way consisting of three individual steps and respective interactive visualizations. In addition to the complete analysis of regularities, GREAT tools enable the use of periodicity and position information for improving the prediction of transcription factor binding sites using a multi-view machine learning approach. The outcome of this integrative approach features a multivariate analysis of the interplay between the location of a gene and its regulatory sequence. GREAT results are plotted in web interactive graphs and are available for download either as individual plots, self-contained interactive pages or as machine readable tables for downstream analysis. The GREAT portal can be reached at the following URL https://absynth.issb.genopole.fr/GREAT and each individual GREAT tool is available for downloading. PMID:27151196

PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways.

PubMed

Demir, E; Babur, O; Dogrusoz, U; Gursoy, A; Nisanci, G; Cetin-Atalay, R; Ozturk, M

2002-07-01

Availability of the sequences of entire genomes shifts the scientific curiosity towards the identification of function of the genomes in large scale as in genome studies. In the near future, data produced about cellular processes at molecular level will accumulate with an accelerating rate as a result of proteomics studies. In this regard, it is essential to develop tools for storing, integrating, accessing, and analyzing this data effectively. We define an ontology for a comprehensive representation of cellular events. The ontology presented here enables integration of fragmented or incomplete pathway information and supports manipulation and incorporation of the stored data, as well as multiple levels of abstraction. Based on this ontology, we present the architecture of an integrated environment named Patika (Pathway Analysis Tool for Integration and Knowledge Acquisition). Patika is composed of a server-side, scalable, object-oriented database and client-side editors to provide an integrated, multi-user environment for visualizing and manipulating network of cellular events. This tool features automated pathway layout, functional computation support, advanced querying and a user-friendly graphical interface. We expect that Patika will be a valuable tool for rapid knowledge acquisition, microarray generated large-scale data interpretation, disease gene identification, and drug development. A prototype of Patika is available upon request from the authors.

The Gene Expression Omnibus Database.

PubMed

Clough, Emily; Barrett, Tanya

2016-01-01

The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome-protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/.

RSAT 2015: Regulatory Sequence Analysis Tools

PubMed Central

Medina-Rivera, Alejandra; Defrance, Matthieu; Sand, Olivier; Herrmann, Carl; Castro-Mondragon, Jaime A.; Delerce, Jeremy; Jaeger, Sébastien; Blanchet, Christophe; Vincens, Pierre; Caron, Christophe; Staines, Daniel M.; Contreras-Moreira, Bruno; Artufel, Marie; Charbonnier-Khamvongsa, Lucie; Hernandez, Céline; Thieffry, Denis; Thomas-Chollier, Morgane; van Helden, Jacques

2015-01-01

RSAT (Regulatory Sequence Analysis Tools) is a modular software suite for the analysis of cis-regulatory elements in genome sequences. Its main applications are (i) motif discovery, appropriate to genome-wide data sets like ChIP-seq, (ii) transcription factor binding motif analysis (quality assessment, comparisons and clustering), (iii) comparative genomics and (iv) analysis of regulatory variations. Nine new programs have been added to the 43 described in the 2011 NAR Web Software Issue, including a tool to extract sequences from a list of coordinates (fetch-sequences from UCSC), novel programs dedicated to the analysis of regulatory variants from GWAS or population genomics (retrieve-variation-seq and variation-scan), a program to cluster motifs and visualize the similarities as trees (matrix-clustering). To deal with the drastic increase of sequenced genomes, RSAT public sites have been reorganized into taxon-specific servers. The suite is well-documented with tutorials and published protocols. The software suite is available through Web sites, SOAP/WSDL Web services, virtual machines and stand-alone programs at http://www.rsat.eu/. PMID:25904632

PathFinder: reconstruction and dynamic visualization of metabolic pathways.

PubMed

Goesmann, Alexander; Haubrock, Martin; Meyer, Folker; Kalinowski, Jörn; Giegerich, Robert

2002-01-01

Beyond methods for a gene-wise annotation and analysis of sequenced genomes new automated methods for functional analysis on a higher level are needed. The identification of realized metabolic pathways provides valuable information on gene expression and regulation. Detection of incomplete pathways helps to improve a constantly evolving genome annotation or discover alternative biochemical pathways. To utilize automated genome analysis on the level of metabolic pathways new methods for the dynamic representation and visualization of pathways are needed. PathFinder is a tool for the dynamic visualization of metabolic pathways based on annotation data. Pathways are represented as directed acyclic graphs, graph layout algorithms accomplish the dynamic drawing and visualization of the metabolic maps. A more detailed analysis of the input data on the level of biochemical pathways helps to identify genes and detect improper parts of annotations. As an Relational Database Management System (RDBMS) based internet application PathFinder reads a list of EC-numbers or a given annotation in EMBL- or Genbank-format and dynamically generates pathway graphs.

Update on Genomic Databases and Resources at the National Center for Biotechnology Information.

PubMed

Tatusova, Tatiana

2016-01-01

The National Center for Biotechnology Information (NCBI), as a primary public repository of genomic sequence data, collects and maintains enormous amounts of heterogeneous data. Data for genomes, genes, gene expressions, gene variation, gene families, proteins, and protein domains are integrated with the analytical, search, and retrieval resources through the NCBI website, text-based search and retrieval system, provides a fast and easy way to navigate across diverse biological databases.Comparative genome analysis tools lead to further understanding of evolution processes quickening the pace of discovery. Recent technological innovations have ignited an explosion in genome sequencing that has fundamentally changed our understanding of the biology of living organisms. This huge increase in DNA sequence data presents new challenges for the information management system and the visualization tools. New strategies have been designed to bring an order to this genome sequence shockwave and improve the usability of associated data.

VirtualPlant: A Software Platform to Support Systems Biology Research1[W][OA

PubMed Central

Katari, Manpreet S.; Nowicki, Steve D.; Aceituno, Felipe F.; Nero, Damion; Kelfer, Jonathan; Thompson, Lee Parnell; Cabello, Juan M.; Davidson, Rebecca S.; Goldberg, Arthur P.; Shasha, Dennis E.; Coruzzi, Gloria M.; Gutiérrez, Rodrigo A.

2010-01-01

Data generation is no longer the limiting factor in advancing biological research. In addition, data integration, analysis, and interpretation have become key bottlenecks and challenges that biologists conducting genomic research face daily. To enable biologists to derive testable hypotheses from the increasing amount of genomic data, we have developed the VirtualPlant software platform. VirtualPlant enables scientists to visualize, integrate, and analyze genomic data from a systems biology perspective. VirtualPlant integrates genome-wide data concerning the known and predicted relationships among genes, proteins, and molecules, as well as genome-scale experimental measurements. VirtualPlant also provides visualization techniques that render multivariate information in visual formats that facilitate the extraction of biological concepts. Importantly, VirtualPlant helps biologists who are not trained in computer science to mine lists of genes, microarray experiments, and gene networks to address questions in plant biology, such as: What are the molecular mechanisms by which internal or external perturbations affect processes controlling growth and development? We illustrate the use of VirtualPlant with three case studies, ranging from querying a gene of interest to the identification of gene networks and regulatory hubs that control seed development. Whereas the VirtualPlant software was developed to mine Arabidopsis (Arabidopsis thaliana) genomic data, its data structures, algorithms, and visualization tools are designed in a species-independent way. VirtualPlant is freely available at www.virtualplant.org. PMID:20007449

Methods, Tools and Current Perspectives in Proteogenomics *

PubMed Central

Ruggles, Kelly V.; Krug, Karsten; Wang, Xiaojing; Clauser, Karl R.; Wang, Jing; Payne, Samuel H.; Fenyö, David; Zhang, Bing; Mani, D. R.

2017-01-01

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications. PMID:28456751

The Plant Genome Integrative Explorer Resource: PlantGenIE.org.

PubMed

Sundell, David; Mannapperuma, Chanaka; Netotea, Sergiu; Delhomme, Nicolas; Lin, Yao-Cheng; Sjödin, Andreas; Van de Peer, Yves; Jansson, Stefan; Hvidsten, Torgeir R; Street, Nathaniel R

2015-12-01

Accessing and exploring large-scale genomics data sets remains a significant challenge to researchers without specialist bioinformatics training. We present the integrated PlantGenIE.org platform for exploration of Populus, conifer and Arabidopsis genomics data, which includes expression networks and associated visualization tools. Standard features of a model organism database are provided, including genome browsers, gene list annotation, Blast homology searches and gene information pages. Community annotation updating is supported via integration of WebApollo. We have produced an RNA-sequencing (RNA-Seq) expression atlas for Populus tremula and have integrated these data within the expression tools. An updated version of the ComPlEx resource for performing comparative plant expression analyses of gene coexpression network conservation between species has also been integrated. The PlantGenIE.org platform provides intuitive access to large-scale and genome-wide genomics data from model forest tree species, facilitating both community contributions to annotation improvement and tools supporting use of the included data resources to inform biological insight. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

GREAT: a web portal for Genome Regulatory Architecture Tools.

PubMed

Bouyioukos, Costas; Bucchini, François; Elati, Mohamed; Képès, François

2016-07-08

GREAT (Genome REgulatory Architecture Tools) is a novel web portal for tools designed to generate user-friendly and biologically useful analysis of genome architecture and regulation. The online tools of GREAT are freely accessible and compatible with essentially any operating system which runs a modern browser. GREAT is based on the analysis of genome layout -defined as the respective positioning of co-functional genes- and its relation with chromosome architecture and gene expression. GREAT tools allow users to systematically detect regular patterns along co-functional genomic features in an automatic way consisting of three individual steps and respective interactive visualizations. In addition to the complete analysis of regularities, GREAT tools enable the use of periodicity and position information for improving the prediction of transcription factor binding sites using a multi-view machine learning approach. The outcome of this integrative approach features a multivariate analysis of the interplay between the location of a gene and its regulatory sequence. GREAT results are plotted in web interactive graphs and are available for download either as individual plots, self-contained interactive pages or as machine readable tables for downstream analysis. The GREAT portal can be reached at the following URL https://absynth.issb.genopole.fr/GREAT and each individual GREAT tool is available for downloading. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

MobilomeFINDER: web-based tools for in silico and experimental discovery of bacterial genomic islands

PubMed Central

Ou, Hong-Yu; He, Xinyi; Harrison, Ewan M.; Kulasekara, Bridget R.; Thani, Ali Bin; Kadioglu, Aras; Lory, Stephen; Hinton, Jay C. D.; Barer, Michael R.; Rajakumar, Kumar

2007-01-01

MobilomeFINDER (http://mml.sjtu.edu.cn/MobilomeFINDER) is an interactive online tool that facilitates bacterial genomic island or ‘mobile genome’ (mobilome) discovery; it integrates the ArrayOme and tRNAcc software packages. ArrayOme utilizes a microarray-derived comparative genomic hybridization input data set to generate ‘inferred contigs’ produced by merging adjacent genes classified as ‘present’. Collectively these ‘fragments’ represent a hypothetical ‘microarray-visualized genome (MVG)’. ArrayOme permits recognition of discordances between physical genome and MVG sizes, thereby enabling identification of strains rich in microarray-elusive novel genes. Individual tRNAcc tools facilitate automated identification of genomic islands by comparative analysis of the contents and contexts of tRNA sites and other integration hotspots in closely related sequenced genomes. Accessory tools facilitate design of hotspot-flanking primers for in silico and/or wet-science-based interrogation of cognate loci in unsequenced strains and analysis of islands for features suggestive of foreign origins; island-specific and genome-contextual features are tabulated and represented in schematic and graphical forms. To date we have used MobilomeFINDER to analyse several Enterobacteriaceae, Pseudomonas aeruginosa and Streptococcus suis genomes. MobilomeFINDER enables high-throughput island identification and characterization through increased exploitation of emerging sequence data and PCR-based profiling of unsequenced test strains; subsequent targeted yeast recombination-based capture permits full-length sequencing and detailed functional studies of novel genomic islands. PMID:17537813

BioQ: tracing experimental origins in public genomic databases using a novel data provenance model.

PubMed

Saccone, Scott F; Quan, Jiaxi; Jones, Peter L

2012-04-15

Public genomic databases, which are often used to guide genetic studies of human disease, are now being applied to genomic medicine through in silico integrative genomics. These databases, however, often lack tools for systematically determining the experimental origins of the data. We introduce a new data provenance model that we have implemented in a public web application, BioQ, for assessing the reliability of the data by systematically tracing its experimental origins to the original subjects and biologics. BioQ allows investigators to both visualize data provenance as well as explore individual elements of experimental process flow using precise tools for detailed data exploration and documentation. It includes a number of human genetic variation databases such as the HapMap and 1000 Genomes projects. BioQ is freely available to the public at http://bioq.saclab.net.

Bolbase: a comprehensive genomics database for Brassica oleracea

PubMed Central

2013-01-01

Background Brassica oleracea is a morphologically diverse species in the family Brassicaceae and contains a group of nutrition-rich vegetable crops, including common heading cabbage, cauliflower, broccoli, kohlrabi, kale, Brussels sprouts. This diversity along with its phylogenetic membership in a group of three diploid and three tetraploid species, and the recent availability of genome sequences within Brassica provide an unprecedented opportunity to study intra- and inter-species divergence and evolution in this species and its close relatives. Description We have developed a comprehensive database, Bolbase, which provides access to the B. oleracea genome data and comparative genomics information. The whole genome of B. oleracea is available, including nine fully assembled chromosomes and 1,848 scaffolds, with 45,758 predicted genes, 13,382 transposable elements, and 3,581 non-coding RNAs. Comparative genomics information is available, including syntenic regions among B. oleracea, Brassica rapa and Arabidopsis thaliana, synonymous (Ks) and non-synonymous (Ka) substitution rates between orthologous gene pairs, gene families or clusters, and differences in quantity, category, and distribution of transposable elements on chromosomes. Bolbase provides useful search and data mining tools, including a keyword search, a local BLAST server, and a customized GBrowse tool, which can be used to extract annotations of genome components, identify similar sequences and visualize syntenic regions among species. Users can download all genomic data and explore comparative genomics in a highly visual setting. Conclusions Bolbase is the first resource platform for the B. oleracea genome and for genomic comparisons with its relatives, and thus it will help the research community to better study the function and evolution of Brassica genomes as well as enhance molecular breeding research. This database will be updated regularly with new features, improvements to genome annotation, and new genomic sequences as they become available. Bolbase is freely available at http://ocri-genomics.org/bolbase. PMID:24079801

SpirPep: an in silico digestion-based platform to assist bioactive peptides discovery from a genome-wide database.

PubMed

Anekthanakul, Krittima; Hongsthong, Apiradee; Senachak, Jittisak; Ruengjitchatchawalya, Marasri

2018-04-20

Bioactive peptides, including biological sources-derived peptides with different biological activities, are protein fragments that influence the functions or conditions of organisms, in particular humans and animals. Conventional methods of identifying bioactive peptides are time-consuming and costly. To quicken the processes, several bioinformatics tools are recently used to facilitate screening of the potential peptides prior their activity assessment in vitro and/or in vivo. In this study, we developed an efficient computational method, SpirPep, which offers many advantages over the currently available tools. The SpirPep web application tool is a one-stop analysis and visualization facility to assist bioactive peptide discovery. The tool is equipped with 15 customized enzymes and 1-3 miscleavage options, which allows in silico digestion of protein sequences encoded by protein-coding genes from single, multiple, or genome-wide scaling, and then directly classifies the peptides by bioactivity using an in-house database that contains bioactive peptides collected from 13 public databases. With this tool, the resulting peptides are categorized by each selected enzyme, and shown in a tabular format where the peptide sequences can be tracked back to their original proteins. The developed tool and webpages are coded in PHP and HTML with CSS/JavaScript. Moreover, the tool allows protein-peptide alignment visualization by Generic Genome Browser (GBrowse) to display the region and details of the proteins and peptides within each parameter, while considering digestion design for the desirable bioactivity. SpirPep is efficient; it takes less than 20 min to digest 3000 proteins (751,860 amino acids) with 15 enzymes and three miscleavages for each enzyme, and only a few seconds for single enzyme digestion. Obviously, the tool identified more bioactive peptides than that of the benchmarked tool; an example of validated pentapeptide (FLPIL) from LC-MS/MS was demonstrated. The web and database server are available at http://spirpepapp.sbi.kmutt.ac.th . SpirPep, a web-based bioactive peptide discovery application, is an in silico-based tool with an overview of the results. The platform is a one-stop analysis and visualization facility; and offers advantages over the currently available tools. This tool may be useful for further bioactivity analysis and the quantitative discovery of desirable peptides.

Oncogenomic portals for the visualization and analysis of genome-wide cancer data

PubMed Central

Klonowska, Katarzyna; Czubak, Karol; Wojciechowska, Marzena; Handschuh, Luiza; Zmienko, Agnieszka; Figlerowicz, Marek; Dams-Kozlowska, Hanna; Kozlowski, Piotr

2016-01-01

Somatically acquired genomic alterations that drive oncogenic cellular processes are of great scientific and clinical interest. Since the initiation of large-scale cancer genomic projects (e.g., the Cancer Genome Project, The Cancer Genome Atlas, and the International Cancer Genome Consortium cancer genome projects), a number of web-based portals have been created to facilitate access to multidimensional oncogenomic data and assist with the interpretation of the data. The portals provide the visualization of small-size mutations, copy number variations, methylation, and gene/protein expression data that can be correlated with the available clinical, epidemiological, and molecular features. Additionally, the portals enable to analyze the gathered data with the use of various user-friendly statistical tools. Herein, we present a highly illustrated review of seven portals, i.e., Tumorscape, UCSC Cancer Genomics Browser, ICGC Data Portal, COSMIC, cBioPortal, IntOGen, and BioProfiling.de. All of the selected portals are user-friendly and can be exploited by scientists from different cancer-associated fields, including those without bioinformatics background. It is expected that the use of the portals will contribute to a better understanding of cancer molecular etiology and will ultimately accelerate the translation of genomic knowledge into clinical practice. PMID:26484415

Oncogenomic portals for the visualization and analysis of genome-wide cancer data.

PubMed

Klonowska, Katarzyna; Czubak, Karol; Wojciechowska, Marzena; Handschuh, Luiza; Zmienko, Agnieszka; Figlerowicz, Marek; Dams-Kozlowska, Hanna; Kozlowski, Piotr

2016-01-05

Somatically acquired genomic alterations that drive oncogenic cellular processes are of great scientific and clinical interest. Since the initiation of large-scale cancer genomic projects (e.g., the Cancer Genome Project, The Cancer Genome Atlas, and the International Cancer Genome Consortium cancer genome projects), a number of web-based portals have been created to facilitate access to multidimensional oncogenomic data and assist with the interpretation of the data. The portals provide the visualization of small-size mutations, copy number variations, methylation, and gene/protein expression data that can be correlated with the available clinical, epidemiological, and molecular features. Additionally, the portals enable to analyze the gathered data with the use of various user-friendly statistical tools. Herein, we present a highly illustrated review of seven portals, i.e., Tumorscape, UCSC Cancer Genomics Browser, ICGC Data Portal, COSMIC, cBioPortal, IntOGen, and BioProfiling.de. All of the selected portals are user-friendly and can be exploited by scientists from different cancer-associated fields, including those without bioinformatics background. It is expected that the use of the portals will contribute to a better understanding of cancer molecular etiology and will ultimately accelerate the translation of genomic knowledge into clinical practice.

BiQ Analyzer HT: locus-specific analysis of DNA methylation by high-throughput bisulfite sequencing

PubMed Central

Lutsik, Pavlo; Feuerbach, Lars; Arand, Julia; Lengauer, Thomas; Walter, Jörn; Bock, Christoph

2011-01-01

Bisulfite sequencing is a widely used method for measuring DNA methylation in eukaryotic genomes. The assay provides single-base pair resolution and, given sufficient sequencing depth, its quantitative accuracy is excellent. High-throughput sequencing of bisulfite-converted DNA can be applied either genome wide or targeted to a defined set of genomic loci (e.g. using locus-specific PCR primers or DNA capture probes). Here, we describe BiQ Analyzer HT (http://biq-analyzer-ht.bioinf.mpi-inf.mpg.de/), a user-friendly software tool that supports locus-specific analysis and visualization of high-throughput bisulfite sequencing data. The software facilitates the shift from time-consuming clonal bisulfite sequencing to the more quantitative and cost-efficient use of high-throughput sequencing for studying locus-specific DNA methylation patterns. In addition, it is useful for locus-specific visualization of genome-wide bisulfite sequencing data. PMID:21565797

RSAT 2015: Regulatory Sequence Analysis Tools.

PubMed

Medina-Rivera, Alejandra; Defrance, Matthieu; Sand, Olivier; Herrmann, Carl; Castro-Mondragon, Jaime A; Delerce, Jeremy; Jaeger, Sébastien; Blanchet, Christophe; Vincens, Pierre; Caron, Christophe; Staines, Daniel M; Contreras-Moreira, Bruno; Artufel, Marie; Charbonnier-Khamvongsa, Lucie; Hernandez, Céline; Thieffry, Denis; Thomas-Chollier, Morgane; van Helden, Jacques

2015-07-01

RSAT (Regulatory Sequence Analysis Tools) is a modular software suite for the analysis of cis-regulatory elements in genome sequences. Its main applications are (i) motif discovery, appropriate to genome-wide data sets like ChIP-seq, (ii) transcription factor binding motif analysis (quality assessment, comparisons and clustering), (iii) comparative genomics and (iv) analysis of regulatory variations. Nine new programs have been added to the 43 described in the 2011 NAR Web Software Issue, including a tool to extract sequences from a list of coordinates (fetch-sequences from UCSC), novel programs dedicated to the analysis of regulatory variants from GWAS or population genomics (retrieve-variation-seq and variation-scan), a program to cluster motifs and visualize the similarities as trees (matrix-clustering). To deal with the drastic increase of sequenced genomes, RSAT public sites have been reorganized into taxon-specific servers. The suite is well-documented with tutorials and published protocols. The software suite is available through Web sites, SOAP/WSDL Web services, virtual machines and stand-alone programs at http://www.rsat.eu/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Visualization of Genome Diversity in German Shepherd Dogs.

PubMed

Mortlock, Sally-Anne; Booth, Rachel; Mazrier, Hamutal; Khatkar, Mehar S; Williamson, Peter

2015-01-01

A loss of genetic diversity may lead to increased disease risks in subpopulations of dogs. The canine breed structure has contributed to relatively small effective population size in many breeds and can limit the options for selective breeding strategies to maintain diversity. With the completion of the canine genome sequencing project, and the subsequent reduction in the cost of genotyping on a genomic scale, evaluating diversity in dogs has become much more accurate and accessible. This provides a potential tool for advising dog breeders and developing breeding programs within a breed. A challenge in doing this is to present complex relationship data in a form that can be readily utilized. Here, we demonstrate the use of a pipeline, known as NetView, to visualize the network of relationships in a subpopulation of German Shepherd Dogs.

Image Analysis of DNA Fiber and Nucleus in Plants.

PubMed

Ohmido, Nobuko; Wako, Toshiyuki; Kato, Seiji; Fukui, Kiichi

2016-01-01

Advances in cytology have led to the application of a wide range of visualization methods in plant genome studies. Image analysis methods are indispensable tools where morphology, density, and color play important roles in the biological systems. Visualization and image analysis methods are useful techniques in the analyses of the detailed structure and function of extended DNA fibers (EDFs) and interphase nuclei. The EDF is the highest in the spatial resolving power to reveal genome structure and it can be used for physical mapping, especially for closely located genes and tandemly repeated sequences. One the other hand, analyzing nuclear DNA and proteins would reveal nuclear structure and functions. In this chapter, we describe the image analysis protocol for quantitatively analyzing different types of plant genome, EDFs and interphase nuclei.

iScreen: Image-Based High-Content RNAi Screening Analysis Tools.

PubMed

Zhong, Rui; Dong, Xiaonan; Levine, Beth; Xie, Yang; Xiao, Guanghua

2015-09-01

High-throughput RNA interference (RNAi) screening has opened up a path to investigating functional genomics in a genome-wide pattern. However, such studies are often restricted to assays that have a single readout format. Recently, advanced image technologies have been coupled with high-throughput RNAi screening to develop high-content screening, in which one or more cell image(s), instead of a single readout, were generated from each well. This image-based high-content screening technology has led to genome-wide functional annotation in a wider spectrum of biological research studies, as well as in drug and target discovery, so that complex cellular phenotypes can be measured in a multiparametric format. Despite these advances, data analysis and visualization tools are still largely lacking for these types of experiments. Therefore, we developed iScreen (image-Based High-content RNAi Screening Analysis Tool), an R package for the statistical modeling and visualization of image-based high-content RNAi screening. Two case studies were used to demonstrate the capability and efficiency of the iScreen package. iScreen is available for download on CRAN (http://cran.cnr.berkeley.edu/web/packages/iScreen/index.html). The user manual is also available as a supplementary document. © 2014 Society for Laboratory Automation and Screening.

CFGP: a web-based, comparative fungal genomics platform.

PubMed

Park, Jongsun; Park, Bongsoo; Jung, Kyongyong; Jang, Suwang; Yu, Kwangyul; Choi, Jaeyoung; Kong, Sunghyung; Park, Jaejin; Kim, Seryun; Kim, Hyojeong; Kim, Soonok; Kim, Jihyun F; Blair, Jaime E; Lee, Kwangwon; Kang, Seogchan; Lee, Yong-Hwan

2008-01-01

Since the completion of the Saccharomyces cerevisiae genome sequencing project in 1996, the genomes of over 80 fungal species have been sequenced or are currently being sequenced. Resulting data provide opportunities for studying and comparing fungal biology and evolution at the genome level. To support such studies, the Comparative Fungal Genomics Platform (CFGP; http://cfgp.snu.ac.kr), a web-based multifunctional informatics workbench, was developed. The CFGP comprises three layers, including the basal layer, middleware and the user interface. The data warehouse in the basal layer contains standardized genome sequences of 65 fungal species. The middleware processes queries via six analysis tools, including BLAST, ClustalW, InterProScan, SignalP 3.0, PSORT II and a newly developed tool named BLASTMatrix. The BLASTMatrix permits the identification and visualization of genes homologous to a query across multiple species. The Data-driven User Interface (DUI) of the CFGP was built on a new concept of pre-collecting data and post-executing analysis instead of the 'fill-in-the-form-and-press-SUBMIT' user interfaces utilized by most bioinformatics sites. A tool termed Favorite, which supports the management of encapsulated sequence data and provides a personalized data repository to users, is another novel feature in the DUI.

Alview: Portable Software for Viewing Sequence Reads in BAM Formatted Files.

PubMed

Finney, Richard P; Chen, Qing-Rong; Nguyen, Cu V; Hsu, Chih Hao; Yan, Chunhua; Hu, Ying; Abawi, Massih; Bian, Xiaopeng; Meerzaman, Daoud M

2015-01-01

The name Alview is a contraction of the term Alignment Viewer. Alview is a compiled to native architecture software tool for visualizing the alignment of sequencing data. Inputs are files of short-read sequences aligned to a reference genome in the SAM/BAM format and files containing reference genome data. Outputs are visualizations of these aligned short reads. Alview is written in portable C with optional graphical user interface (GUI) code written in C, C++, and Objective-C. The application can run in three different ways: as a web server, as a command line tool, or as a native, GUI program. Alview is compatible with Microsoft Windows, Linux, and Apple OS X. It is available as a web demo at https://cgwb.nci.nih.gov/cgi-bin/alview. The source code and Windows/Mac/Linux executables are available via https://github.com/NCIP/alview.

Strategies to explore functional genomics data sets in NCBI's GEO database.

PubMed

Wilhite, Stephen E; Barrett, Tanya

2012-01-01

The Gene Expression Omnibus (GEO) database is a major repository that stores high-throughput functional genomics data sets that are generated using both microarray-based and sequence-based technologies. Data sets are submitted to GEO primarily by researchers who are publishing their results in journals that require original data to be made freely available for review and analysis. In addition to serving as a public archive for these data, GEO has a suite of tools that allow users to identify, analyze, and visualize data relevant to their specific interests. These tools include sample comparison applications, gene expression profile charts, data set clusters, genome browser tracks, and a powerful search engine that enables users to construct complex queries.

Strategies to Explore Functional Genomics Data Sets in NCBI’s GEO Database

PubMed Central

Wilhite, Stephen E.; Barrett, Tanya

2012-01-01

The Gene Expression Omnibus (GEO) database is a major repository that stores high-throughput functional genomics data sets that are generated using both microarray-based and sequence-based technologies. Data sets are submitted to GEO primarily by researchers who are publishing their results in journals that require original data to be made freely available for review and analysis. In addition to serving as a public archive for these data, GEO has a suite of tools that allow users to identify, analyze and visualize data relevant to their specific interests. These tools include sample comparison applications, gene expression profile charts, data set clusters, genome browser tracks, and a powerful search engine that enables users to construct complex queries. PMID:22130872

A Guide to the PLAZA 3.0 Plant Comparative Genomic Database.

PubMed

Vandepoele, Klaas

2017-01-01

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

Microreact: visualizing and sharing data for genomic epidemiology and phylogeography

PubMed Central

Argimón, Silvia; Abudahab, Khalil; Goater, Richard J. E.; Fedosejev, Artemij; Bhai, Jyothish; Glasner, Corinna; Feil, Edward J.; Holden, Matthew T. G.; Yeats, Corin A.; Grundmann, Hajo; Spratt, Brian G.

2016-01-01

Visualization is frequently used to aid our interpretation of complex datasets. Within microbial genomics, visualizing the relationships between multiple genomes as a tree provides a framework onto which associated data (geographical, temporal, phenotypic and epidemiological) are added to generate hypotheses and to explore the dynamics of the system under investigation. Selected static images are then used within publications to highlight the key findings to a wider audience. However, these images are a very inadequate way of exploring and interpreting the richness of the data. There is, therefore, a need for flexible, interactive software that presents the population genomic outputs and associated data in a user-friendly manner for a wide range of end users, from trained bioinformaticians to front-line epidemiologists and health workers. Here, we present Microreact, a web application for the easy visualization of datasets consisting of any combination of trees, geographical, temporal and associated metadata. Data files can be uploaded to Microreact directly via the web browser or by linking to their location (e.g. from Google Drive/Dropbox or via API), and an integrated visualization via trees, maps, timelines and tables provides interactive querying of the data. The visualization can be shared as a permanent web link among collaborators, or embedded within publications to enable readers to explore and download the data. Microreact can act as an end point for any tool or bioinformatic pipeline that ultimately generates a tree, and provides a simple, yet powerful, visualization method that will aid research and discovery and the open sharing of datasets. PMID:28348833

PSAT: A web tool to compare genomic neighborhoods of multiple prokaryotic genomes

PubMed Central

Fong, Christine; Rohmer, Laurence; Radey, Matthew; Wasnick, Michael; Brittnacher, Mitchell J

2008-01-01

Background The conservation of gene order among prokaryotic genomes can provide valuable insight into gene function, protein interactions, or events by which genomes have evolved. Although some tools are available for visualizing and comparing the order of genes between genomes of study, few support an efficient and organized analysis between large numbers of genomes. The Prokaryotic Sequence homology Analysis Tool (PSAT) is a web tool for comparing gene neighborhoods among multiple prokaryotic genomes. Results PSAT utilizes a database that is preloaded with gene annotation, BLAST hit results, and gene-clustering scores designed to help identify regions of conserved gene order. Researchers use the PSAT web interface to find a gene of interest in a reference genome and efficiently retrieve the sequence homologs found in other bacterial genomes. The tool generates a graphic of the genomic neighborhood surrounding the selected gene and the corresponding regions for its homologs in each comparison genome. Homologs in each region are color coded to assist users with analyzing gene order among various genomes. In contrast to common comparative analysis methods that filter sequence homolog data based on alignment score cutoffs, PSAT leverages gene context information for homologs, including those with weak alignment scores, enabling a more sensitive analysis. Features for constraining or ordering results are designed to help researchers browse results from large numbers of comparison genomes in an organized manner. PSAT has been demonstrated to be useful for helping to identify gene orthologs and potential functional gene clusters, and detecting genome modifications that may result in loss of function. Conclusion PSAT allows researchers to investigate the order of genes within local genomic neighborhoods of multiple genomes. A PSAT web server for public use is available for performing analyses on a growing set of reference genomes through any web browser with no client side software setup or installation required. Source code is freely available to researchers interested in setting up a local version of PSAT for analysis of genomes not available through the public server. Access to the public web server and instructions for obtaining source code can be found at . PMID:18366802

Open source bioimage informatics for cell biology.

PubMed

Swedlow, Jason R; Eliceiri, Kevin W

2009-11-01

Significant technical advances in imaging, molecular biology and genomics have fueled a revolution in cell biology, in that the molecular and structural processes of the cell are now visualized and measured routinely. Driving much of this recent development has been the advent of computational tools for the acquisition, visualization, analysis and dissemination of these datasets. These tools collectively make up a new subfield of computational biology called bioimage informatics, which is facilitated by open source approaches. We discuss why open source tools for image informatics in cell biology are needed, some of the key general attributes of what make an open source imaging application successful, and point to opportunities for further operability that should greatly accelerate future cell biology discovery.

AGORA : Organellar genome annotation from the amino acid and nucleotide references.

PubMed

Jung, Jaehee; Kim, Jong Im; Jeong, Young-Sik; Yi, Gangman

2018-03-29

Next-generation sequencing (NGS) technologies have led to the accumulation of highthroughput sequence data from various organisms in biology. To apply gene annotation of organellar genomes for various organisms, more optimized tools for functional gene annotation are required. Almost all gene annotation tools are mainly focused on the chloroplast genome of land plants or the mitochondrial genome of animals.We have developed a web application AGORA for the fast, user-friendly, and improved annotations of organellar genomes. AGORA annotates genes based on a BLAST-based homology search and clustering with selected reference sequences from the NCBI database or user-defined uploaded data. AGORA can annotate the functional genes in almost all mitochondrion and plastid genomes of eukaryotes. The gene annotation of a genome with an exon-intron structure within a gene or inverted repeat region is also available. It provides information of start and end positions of each gene, BLAST results compared with the reference sequence, and visualization of gene map by OGDRAW. Users can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/gene_project/AGORA/.The main module of the tool is implemented by the python and php, and the web page is built by the HTML and CSS to support all browsers. gangman@dongguk.edu.

Comparative map and trait viewer (CMTV): an integrated bioinformatic tool to construct consensus maps and compare QTL and functional genomics data across genomes and experiments.

PubMed

Sawkins, M C; Farmer, A D; Hoisington, D; Sullivan, J; Tolopko, A; Jiang, Z; Ribaut, J-M

2004-10-01

In the past few decades, a wealth of genomic data has been produced in a wide variety of species using a diverse array of functional and molecular marker approaches. In order to unlock the full potential of the information contained in these independent experiments, researchers need efficient and intuitive means to identify common genomic regions and genes involved in the expression of target phenotypic traits across diverse conditions. To address this need, we have developed a Comparative Map and Trait Viewer (CMTV) tool that can be used to construct dynamic aggregations of a variety of types of genomic datasets. By algorithmically determining correspondences between sets of objects on multiple genomic maps, the CMTV can display syntenic regions across taxa, combine maps from separate experiments into a consensus map, or project data from different maps into a common coordinate framework using dynamic coordinate translations between source and target maps. We present a case study that illustrates the utility of the tool for managing large and varied datasets by integrating data collected by CIMMYT in maize drought tolerance research with data from public sources. This example will focus on one of the visualization features for Quantitative Trait Locus (QTL) data, using likelihood ratio (LR) files produced by generic QTL analysis software and displaying the data in a unique visual manner across different combinations of traits, environments and crosses. Once a genomic region of interest has been identified, the CMTV can search and display additional QTLs meeting a particular threshold for that region, or other functional data such as sets of differentially expressed genes located in the region; it thus provides an easily used means for organizing and manipulating data sets that have been dynamically integrated under the focus of the researcher's specific hypothesis.

Assocplots: a Python package for static and interactive visualization of multiple-group GWAS results.

PubMed

Khramtsova, Ekaterina A; Stranger, Barbara E

2017-02-01

Over the last decade, genome-wide association studies (GWAS) have generated vast amounts of analysis results, requiring development of novel tools for data visualization. Quantile–quantile (QQ) plots and Manhattan plots are classical tools which have been utilized to visually summarize GWAS results and identify genetic variants significantly associated with traits of interest. However, static visualizations are limiting in the information that can be shown. Here, we present Assocplots, a Python package for viewing and exploring GWAS results not only using classic static Manhattan and QQ plots, but also through a dynamic extension which allows to interactively visualize the relationships between GWAS results from multiple cohorts or studies. The Assocplots package is open source and distributed under the MIT license via GitHub (https://github.com/khramts/assocplots) along with examples, documentation and installation instructions. ekhramts@medicine.bsd.uchicago.edu or bstranger@medicine.bsd.uchicago.edu

The PathoYeastract database: an information system for the analysis of gene and genomic transcription regulation in pathogenic yeasts.

PubMed

Monteiro, Pedro Tiago; Pais, Pedro; Costa, Catarina; Manna, Sauvagya; Sá-Correia, Isabel; Teixeira, Miguel Cacho

2017-01-04

We present the PATHOgenic YEAst Search for Transcriptional Regulators And Consensus Tracking (PathoYeastract - http://pathoyeastract.org) database, a tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in the pathogenic yeasts Candida albicans and C. glabrata Upon data retrieval from hundreds of publications, followed by curation, the database currently includes 28 000 unique documented regulatory associations between transcription factors (TF) and target genes and 107 DNA binding sites, considering 134 TFs in both species. Following the structure used for the YEASTRACT database, PathoYeastract makes available bioinformatics tools that enable the user to exploit the existing information to predict the TFs involved in the regulation of a gene or genome-wide transcriptional response, while ranking those TFs in order of their relative importance. Each search can be filtered based on the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. Promoter analysis tools and interactive visualization tools for the representation of TF regulatory networks are also provided. The PathoYeastract database further provides simple tools for the prediction of gene and genomic regulation based on orthologous regulatory associations described for other yeast species, a comparative genomics setup for the study of cross-species evolution of regulatory networks. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

CAS-viewer: web-based tool for splicing-guided integrative analysis of multi-omics cancer data.

PubMed

Han, Seonggyun; Kim, Dongwook; Kim, Youngjun; Choi, Kanghoon; Miller, Jason E; Kim, Dokyoon; Lee, Younghee

2018-04-20

The Cancer Genome Atlas (TCGA) project is a public resource that provides transcriptomic, DNA sequence, methylation, and clinical data for 33 cancer types. Transforming the large size and high complexity of TCGA cancer genome data into integrated knowledge can be useful to promote cancer research. Alternative splicing (AS) is a key regulatory mechanism of genes in human cancer development and in the interaction with epigenetic factors. Therefore, AS-guided integration of existing TCGA data sets will make it easier to gain insight into the genetic architecture of cancer risk and related outcomes. There are already existing tools analyzing and visualizing alternative mRNA splicing patterns for large-scale RNA-seq experiments. However, these existing web-based tools are limited to the analysis of individual TCGA data sets at a time, such as only transcriptomic information. We implemented CAS-viewer (integrative analysis of Cancer genome data based on Alternative Splicing), a web-based tool leveraging multi-cancer omics data from TCGA. It illustrates alternative mRNA splicing patterns along with methylation, miRNAs, and SNPs, and then provides an analysis tool to link differential transcript expression ratio to methylation, miRNA, and splicing regulatory elements for 33 cancer types. Moreover, one can analyze AS patterns with clinical data to identify potential transcripts associated with different survival outcome for each cancer. CAS-viewer is a web-based application for transcript isoform-driven integration of multi-omics data in multiple cancer types and will aid in the visualization and possible discovery of biomarkers for cancer by integrating multi-omics data from TCGA.

Segtor: Rapid Annotation of Genomic Coordinates and Single Nucleotide Variations Using Segment Trees

PubMed Central

Renaud, Gabriel; Neves, Pedro; Folador, Edson Luiz; Ferreira, Carlos Gil; Passetti, Fabio

2011-01-01

Various research projects often involve determining the relative position of genomic coordinates, intervals, single nucleotide variations (SNVs), insertions, deletions and translocations with respect to genes and their potential impact on protein translation. Due to the tremendous increase in throughput brought by the use of next-generation sequencing, investigators are routinely faced with the need to annotate very large datasets. We present Segtor, a tool to annotate large sets of genomic coordinates, intervals, SNVs, indels and translocations. Our tool uses segment trees built using the start and end coordinates of the genomic features the user wishes to use instead of storing them in a database management system. The software also produces annotation statistics to allow users to visualize how many coordinates were found within various portions of genes. Our system currently can be made to work with any species available on the UCSC Genome Browser. Segtor is a suitable tool for groups, especially those with limited access to programmers or with interest to analyze large amounts of individual genomes, who wish to determine the relative position of very large sets of mapped reads and subsequently annotate observed mutations between the reads and the reference. Segtor (http://lbbc.inca.gov.br/segtor/) is an open-source tool that can be freely downloaded for non-profit use. We also provide a web interface for testing purposes. PMID:22069465

ColorTree: a batch customization tool for phylogenic trees

PubMed Central

Chen, Wei-Hua; Lercher, Martin J

2009-01-01

Background Genome sequencing projects and comparative genomics studies typically aim to trace the evolutionary history of large gene sets, often requiring human inspection of hundreds of phylogenetic trees. If trees are checked for compatibility with an explicit null hypothesis (e.g., the monophyly of certain groups), this daunting task is greatly facilitated by an appropriate coloring scheme. Findings In this note, we introduce ColorTree, a simple yet powerful batch customization tool for phylogenic trees. Based on pattern matching rules, ColorTree applies a set of customizations to an input tree file, e.g., coloring labels or branches. The customized trees are saved to an output file, which can then be viewed and further edited by Dendroscope (a freely available tree viewer). ColorTree runs on any Perl installation as a stand-alone command line tool, and its application can thus be easily automated. This way, hundreds of phylogenic trees can be customized for easy visual inspection in a matter of minutes. Conclusion ColorTree allows efficient and flexible visual customization of large tree sets through the application of a user-supplied configuration file to multiple tree files. PMID:19646243

ColorTree: a batch customization tool for phylogenic trees.

PubMed

Chen, Wei-Hua; Lercher, Martin J

2009-07-31

Genome sequencing projects and comparative genomics studies typically aim to trace the evolutionary history of large gene sets, often requiring human inspection of hundreds of phylogenetic trees. If trees are checked for compatibility with an explicit null hypothesis (e.g., the monophyly of certain groups), this daunting task is greatly facilitated by an appropriate coloring scheme. In this note, we introduce ColorTree, a simple yet powerful batch customization tool for phylogenic trees. Based on pattern matching rules, ColorTree applies a set of customizations to an input tree file, e.g., coloring labels or branches. The customized trees are saved to an output file, which can then be viewed and further edited by Dendroscope (a freely available tree viewer). ColorTree runs on any Perl installation as a stand-alone command line tool, and its application can thus be easily automated. This way, hundreds of phylogenic trees can be customized for easy visual inspection in a matter of minutes. ColorTree allows efficient and flexible visual customization of large tree sets through the application of a user-supplied configuration file to multiple tree files.

Novel integrative genomic tool for interrogating lithium response in bipolar disorder

PubMed Central

Hunsberger, J G; Chibane, F L; Elkahloun, A G; Henderson, R; Singh, R; Lawson, J; Cruceanu, C; Nagarajan, V; Turecki, G; Squassina, A; Medeiros, C D; Del Zompo, M; Rouleau, G A; Alda, M; Chuang, D-M

2015-01-01

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery. PMID:25646593

Novel integrative genomic tool for interrogating lithium response in bipolar disorder.

PubMed

Hunsberger, J G; Chibane, F L; Elkahloun, A G; Henderson, R; Singh, R; Lawson, J; Cruceanu, C; Nagarajan, V; Turecki, G; Squassina, A; Medeiros, C D; Del Zompo, M; Rouleau, G A; Alda, M; Chuang, D-M

2015-02-03

We developed a novel integrative genomic tool called GRANITE (Genetic Regulatory Analysis of Networks Investigational Tool Environment) that can effectively analyze large complex data sets to generate interactive networks. GRANITE is an open-source tool and invaluable resource for a variety of genomic fields. Although our analysis is confined to static expression data, GRANITE has the capability of evaluating time-course data and generating interactive networks that may shed light on acute versus chronic treatment, as well as evaluating dose response and providing insight into mechanisms that underlie therapeutic versus sub-therapeutic doses or toxic doses. As a proof-of-concept study, we investigated lithium (Li) response in bipolar disorder (BD). BD is a severe mood disorder marked by cycles of mania and depression. Li is one of the most commonly prescribed and decidedly effective treatments for many patients (responders), although its mode of action is not yet fully understood, nor is it effective in every patient (non-responders). In an in vitro study, we compared vehicle versus chronic Li treatment in patient-derived lymphoblastoid cells (LCLs) (derived from either responders or non-responders) using both microRNA (miRNA) and messenger RNA gene expression profiling. We present both Li responder and non-responder network visualizations created by our GRANITE analysis in BD. We identified by network visualization that the Let-7 family is consistently downregulated by Li in both groups where this miRNA family has been implicated in neurodegeneration, cell survival and synaptic development. We discuss the potential of this analysis for investigating treatment response and even providing clinicians with a tool for predicting treatment response in their patients, as well as for providing the industry with a tool for identifying network nodes as targets for novel drug discovery.

Enhanced annotations and features for comparing thousands of Pseudomonas genomes in the Pseudomonas genome database.

PubMed

Winsor, Geoffrey L; Griffiths, Emma J; Lo, Raymond; Dhillon, Bhavjinder K; Shay, Julie A; Brinkman, Fiona S L

2016-01-04

The Pseudomonas Genome Database (http://www.pseudomonas.com) is well known for the application of community-based annotation approaches for producing a high-quality Pseudomonas aeruginosa PAO1 genome annotation, and facilitating whole-genome comparative analyses with other Pseudomonas strains. To aid analysis of potentially thousands of complete and draft genome assemblies, this database and analysis platform was upgraded to integrate curated genome annotations and isolate metadata with enhanced tools for larger scale comparative analysis and visualization. Manually curated gene annotations are supplemented with improved computational analyses that help identify putative drug targets and vaccine candidates or assist with evolutionary studies by identifying orthologs, pathogen-associated genes and genomic islands. The database schema has been updated to integrate isolate metadata that will facilitate more powerful analysis of genomes across datasets in the future. We continue to place an emphasis on providing high-quality updates to gene annotations through regular review of the scientific literature and using community-based approaches including a major new Pseudomonas community initiative for the assignment of high-quality gene ontology terms to genes. As we further expand from thousands of genomes, we plan to provide enhancements that will aid data visualization and analysis arising from whole-genome comparative studies including more pan-genome and population-based approaches. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

BioQ: tracing experimental origins in public genomic databases using a novel data provenance model

PubMed Central

Saccone, Scott F.; Quan, Jiaxi; Jones, Peter L.

2012-01-01

Motivation: Public genomic databases, which are often used to guide genetic studies of human disease, are now being applied to genomic medicine through in silico integrative genomics. These databases, however, often lack tools for systematically determining the experimental origins of the data. Results: We introduce a new data provenance model that we have implemented in a public web application, BioQ, for assessing the reliability of the data by systematically tracing its experimental origins to the original subjects and biologics. BioQ allows investigators to both visualize data provenance as well as explore individual elements of experimental process flow using precise tools for detailed data exploration and documentation. It includes a number of human genetic variation databases such as the HapMap and 1000 Genomes projects. Availability and implementation: BioQ is freely available to the public at http://bioq.saclab.net Contact: ssaccone@wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22426342

MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

PubMed

Kelley, James J; Maor, Shay; Kim, Min Kyung; Lane, Anatoliy; Lun, Desmond S

2017-08-15

Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites; and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii). MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. dslun@rutgers.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

GAC: Gene Associations with Clinical, a web based application.

PubMed

Zhang, Xinyan; Rupji, Manali; Kowalski, Jeanne

2017-01-01

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

RPAN: rice pan-genome browser for ∼3000 rice genomes.

PubMed

Sun, Chen; Hu, Zhiqiang; Zheng, Tianqing; Lu, Kuangchen; Zhao, Yue; Wang, Wensheng; Shi, Jianxin; Wang, Chunchao; Lu, Jinyuan; Zhang, Dabing; Li, Zhikang; Wei, Chaochun

2017-01-25

A pan-genome is the union of the gene sets of all the individuals of a clade or a species and it provides a new dimension of genome complexity with the presence/absence variations (PAVs) of genes among these genomes. With the progress of sequencing technologies, pan-genome study is becoming affordable for eukaryotes with large-sized genomes. The Asian cultivated rice, Oryza sativa L., is one of the major food sources for the world and a model organism in plant biology. Recently, the 3000 Rice Genome Project (3K RGP) sequenced more than 3000 rice genomes with a mean sequencing depth of 14.3×, which provided a tremendous resource for rice research. In this paper, we present a genome browser, Rice Pan-genome Browser (RPAN), as a tool to search and visualize the rice pan-genome derived from 3K RGP. RPAN contains a database of the basic information of 3010 rice accessions, including genomic sequences, gene annotations, PAV information and gene expression data of the rice pan-genome. At least 12 000 novel genes absent in the reference genome were included. RPAN also provides multiple search and visualization functions. RPAN can be a rich resource for rice biology and rice breeding. It is available at http://cgm.sjtu.edu.cn/3kricedb/ or http://www.rmbreeding.cn/pan3k. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Integrated network analysis and effective tools in plant systems biology

PubMed Central

Fukushima, Atsushi; Kanaya, Shigehiko; Nishida, Kozo

2014-01-01

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms. PMID:25408696

The Saccharomyces Genome Database Variant Viewer

PubMed Central

Sheppard, Travis K.; Hitz, Benjamin C.; Engel, Stacia R.; Song, Giltae; Balakrishnan, Rama; Binkley, Gail; Costanzo, Maria C.; Dalusag, Kyla S.; Demeter, Janos; Hellerstedt, Sage T.; Karra, Kalpana; Nash, Robert S.; Paskov, Kelley M.; Skrzypek, Marek S.; Weng, Shuai; Wong, Edith D.; Cherry, J. Michael

2016-01-01

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is the authoritative community resource for the Saccharomyces cerevisiae reference genome sequence and its annotation. In recent years, we have moved toward increased representation of sequence variation and allelic differences within S. cerevisiae. The publication of numerous additional genomes has motivated the creation of new tools for their annotation and analysis. Here we present the Variant Viewer: a dynamic open-source web application for the visualization of genomic and proteomic differences. Multiple sequence alignments have been constructed across high quality genome sequences from 11 different S. cerevisiae strains and stored in the SGD. The alignments and summaries are encoded in JSON and used to create a two-tiered dynamic view of the budding yeast pan-genome, available at http://www.yeastgenome.org/variant-viewer. PMID:26578556

Visualization of A- and B-genome chromosomes in wheat (Triticum aestivum L.) x jointed goatgrass (Aegilops cylindrica Host) backcross progenies.

PubMed

Wang, Z N; Hang, A; Hansen, J; Burton, C; Mallory-Smith, C A; Zemetra, R S

2000-12-01

Wheat (Triticum aestivum) and jointed goatgrass (Aegilops cylindrica) can cross with each other, and their self-fertile backcross progenies frequently have extra chromosomes and chromosome segments, presumably retained from wheat, raising the possibility that a herbicide resistance gene might transfer from wheat to jointed goatgrass. Genomic in situ hybridization (GISH) was used to clarify the origin of these extra chromosomes. By using T. durum DNA (AABB genome) as a probe and jointed goatgrass DNA (CCDD genome) as blocking DNA, one, two, and three A- or B-genome chromosomes were identified in three BC2S2 individuals where 2n = 29, 30, and 31 chromosomes, respectively. A translocation between wheat and jointed goatgrass chromosomes was also detected in an individual with 30 chromosomes. In pollen mother cells with meiotic configuration of 14 II + 2 I, the two univalents were identified as being retained from the A or B genome of wheat. By using Ae. markgrafii DNA (CC genome) as a probe and wheat DNA (AABBDD genome) as blocking DNA. 14 C-genome chromosomes were visualized in all BC2S2 individuals. The GISH procedure provides a powerful tool to detect the A or B-genome chromatin in a jointed goatgrass background, making it possible to assess the risk of transfer of herbicide resistance genes located on the A or B genome of wheat to jointed goatgrass.

CFGP: a web-based, comparative fungal genomics platform

PubMed Central

Park, Jongsun; Park, Bongsoo; Jung, Kyongyong; Jang, Suwang; Yu, Kwangyul; Choi, Jaeyoung; Kong, Sunghyung; Park, Jaejin; Kim, Seryun; Kim, Hyojeong; Kim, Soonok; Kim, Jihyun F.; Blair, Jaime E.; Lee, Kwangwon; Kang, Seogchan; Lee, Yong-Hwan

2008-01-01

Since the completion of the Saccharomyces cerevisiae genome sequencing project in 1996, the genomes of over 80 fungal species have been sequenced or are currently being sequenced. Resulting data provide opportunities for studying and comparing fungal biology and evolution at the genome level. To support such studies, the Comparative Fungal Genomics Platform (CFGP; http://cfgp.snu.ac.kr), a web-based multifunctional informatics workbench, was developed. The CFGP comprises three layers, including the basal layer, middleware and the user interface. The data warehouse in the basal layer contains standardized genome sequences of 65 fungal species. The middleware processes queries via six analysis tools, including BLAST, ClustalW, InterProScan, SignalP 3.0, PSORT II and a newly developed tool named BLASTMatrix. The BLASTMatrix permits the identification and visualization of genes homologous to a query across multiple species. The Data-driven User Interface (DUI) of the CFGP was built on a new concept of pre-collecting data and post-executing analysis instead of the ‘fill-in-the-form-and-press-SUBMIT’ user interfaces utilized by most bioinformatics sites. A tool termed Favorite, which supports the management of encapsulated sequence data and provides a personalized data repository to users, is another novel feature in the DUI. PMID:17947331

Open source bioimage informatics for cell biology

PubMed Central

Swedlow, Jason R.; Eliceiri, Kevin W.

2009-01-01

Significant technical advances in imaging, molecular biology and genomics have fueled a revolution in cell biology, in that the molecular and structural processes of the cell are now visualized and measured routinely. Driving much of this recent development has been the advent of computational tools for the acquisition, visualization, analysis and dissemination of these datasets. These tools collectively make up a new subfield of computational biology called bioimage informatics, which is facilitated by open source approaches. We discuss why open source tools for image informatics in cell biology are needed, some of the key general attributes of what make an open source imaging application successful, and point to opportunities for further operability that should greatly accelerate future cell biology discovery. PMID:19833518

Informing the Design of Direct-to-Consumer Interactive Personal Genomics Reports

PubMed Central

Shaer, Orit; Okerlund, Johanna; Balestra, Martina; Stowell, Elizabeth; Ascher, Laura; Bi, Joanna; Schlenker, Claire; Ball, Madeleine

2015-01-01

Background In recent years, people who sought direct-to-consumer genetic testing services have been increasingly confronted with an unprecedented amount of personal genomic information, which influences their decisions, emotional state, and well-being. However, these users of direct-to-consumer genetic services, who vary in their education and interests, frequently have little relevant experience or tools for understanding, reasoning about, and interacting with their personal genomic data. Online interactive techniques can play a central role in making personal genomic data useful for these users. Objective We sought to (1) identify the needs of diverse users as they make sense of their personal genomic data, (2) consequently develop effective interactive visualizations of genomic trait data to address these users’ needs, and (3) evaluate the effectiveness of the developed visualizations in facilitating comprehension. Methods The first two user studies, conducted with 63 volunteers in the Personal Genome Project and with 36 personal genomic users who participated in a design workshop, respectively, employed surveys and interviews to identify the needs and expectations of diverse users. Building on the two initial studies, the third study was conducted with 730 Amazon Mechanical Turk users and employed a controlled experimental design to examine the effectiveness of different design interventions on user comprehension. Results The first two studies identified searching, comparing, sharing, and organizing data as fundamental to users’ understanding of personal genomic data. The third study demonstrated that interactive and visual design interventions could improve the understandability of personal genomic reports for consumers. In particular, results showed that a new interactive bubble chart visualization designed for the study resulted in the highest comprehension scores, as well as the highest perceived comprehension scores. These scores were significantly higher than scores received using the industry standard tabular reports currently used for communicating personal genomic information. Conclusions Drawing on multiple research methods and populations, the findings of the studies reported in this paper offer deep understanding of users’ needs and practices, and demonstrate that interactive online design interventions can improve the understandability of personal genomic reports for consumers. We discuss implications for designers and researchers. PMID:26070951

Informing the Design of Direct-to-Consumer Interactive Personal Genomics Reports.

PubMed

Shaer, Orit; Nov, Oded; Okerlund, Johanna; Balestra, Martina; Stowell, Elizabeth; Ascher, Laura; Bi, Joanna; Schlenker, Claire; Ball, Madeleine

2015-06-12

In recent years, people who sought direct-to-consumer genetic testing services have been increasingly confronted with an unprecedented amount of personal genomic information, which influences their decisions, emotional state, and well-being. However, these users of direct-to-consumer genetic services, who vary in their education and interests, frequently have little relevant experience or tools for understanding, reasoning about, and interacting with their personal genomic data. Online interactive techniques can play a central role in making personal genomic data useful for these users. We sought to (1) identify the needs of diverse users as they make sense of their personal genomic data, (2) consequently develop effective interactive visualizations of genomic trait data to address these users' needs, and (3) evaluate the effectiveness of the developed visualizations in facilitating comprehension. The first two user studies, conducted with 63 volunteers in the Personal Genome Project and with 36 personal genomic users who participated in a design workshop, respectively, employed surveys and interviews to identify the needs and expectations of diverse users. Building on the two initial studies, the third study was conducted with 730 Amazon Mechanical Turk users and employed a controlled experimental design to examine the effectiveness of different design interventions on user comprehension. The first two studies identified searching, comparing, sharing, and organizing data as fundamental to users' understanding of personal genomic data. The third study demonstrated that interactive and visual design interventions could improve the understandability of personal genomic reports for consumers. In particular, results showed that a new interactive bubble chart visualization designed for the study resulted in the highest comprehension scores, as well as the highest perceived comprehension scores. These scores were significantly higher than scores received using the industry standard tabular reports currently used for communicating personal genomic information. Drawing on multiple research methods and populations, the findings of the studies reported in this paper offer deep understanding of users' needs and practices, and demonstrate that interactive online design interventions can improve the understandability of personal genomic reports for consumers. We discuss implications for designers and researchers.

Ensembl Plants: Integrating Tools for Visualizing, Mining, and Analyzing Plant Genomic Data.

PubMed

Bolser, Dan M; Staines, Daniel M; Perry, Emily; Kersey, Paul J

2017-01-01

Ensembl Plants ( http://plants.ensembl.org ) is an integrative resource presenting genome-scale information for 39 sequenced plant species. Available data includes genome sequence, gene models, functional annotation, and polymorphic loci; for the latter, additional information including population structure, individual genotypes, linkage, and phenotype data is available for some species. Comparative data is also available, including genomic alignments and "gene trees," which show the inferred evolutionary history of each gene family represented in the resource. Access to the data is provided through a genome browser, which incorporates many specialist interfaces for different data types, through a variety of programmatic interfaces, and via a specialist data mining tool supporting rapid filtering and retrieval of bulk data. Genomic data from many non-plant species, including those of plant pathogens, pests, and pollinators, is also available via the same interfaces through other divisions of Ensembl.Ensembl Plants is updated 4-6 times a year and is developed in collaboration with our international partners in the Gramene ( http://www.gramene.org ) and transPLANT projects ( http://www.transplantdb.eu ).

A Drosophila Toolkit for the Visualization and Quantification of Viral Replication Launched from Transgenic Genomes

PubMed Central

Wernet, Mathias F.; Klovstad, Martha; Clandinin, Thomas R.

2014-01-01

Arthropod RNA viruses pose a serious threat to human health, yet many aspects of their replication cycle remain incompletely understood. Here we describe a versatile Drosophila toolkit of transgenic, self-replicating genomes (‘replicons’) from Sindbis virus that allow rapid visualization and quantification of viral replication in vivo. We generated replicons expressing Luciferase for the quantification of viral replication, serving as useful new tools for large-scale genetic screens for identifying cellular pathways that influence viral replication. We also present a new binary system in which replication-deficient viral genomes can be activated ‘in trans’, through co-expression of an intact replicon contributing an RNA-dependent RNA polymerase. The utility of this toolkit for studying virus biology is demonstrated by the observation of stochastic exclusion between replicons expressing different fluorescent proteins, when co-expressed under control of the same cellular promoter. This process is analogous to ‘superinfection exclusion’ between virus particles in cell culture, a process that is incompletely understood. We show that viral polymerases strongly prefer to replicate the genome that encoded them, and that almost invariably only a single virus genome is stochastically chosen for replication in each cell. Our in vivo system now makes this process amenable to detailed genetic dissection. Thus, this toolkit allows the cell-type specific, quantitative study of viral replication in a genetic model organism, opening new avenues for molecular, genetic and pharmacological dissection of virus biology and tool development. PMID:25386852

Efficient CRISPR/Cas9-based genome editing in carrot cells.

PubMed

Klimek-Chodacka, Magdalena; Oleszkiewicz, Tomasz; Lowder, Levi G; Qi, Yiping; Baranski, Rafal

2018-04-01

The first report presenting successful and efficient carrot genome editing using CRISPR/Cas9 system. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas9) is a powerful genome editing tool that has been widely adopted in model organisms recently, but has not been used in carrot-a model species for in vitro culture studies and an important health-promoting crop grown worldwide. In this study, for the first time, we report application of the CRISPR/Cas9 system for efficient targeted mutagenesis of the carrot genome. Multiplexing CRISPR/Cas9 vectors expressing two single-guide RNA (gRNAs) targeting the carrot flavanone-3-hydroxylase (F3H) gene were tested for blockage of the anthocyanin biosynthesis in a model purple-colored callus using Agrobacterium-mediated genetic transformation. This approach allowed fast and visual comparison of three codon-optimized Cas9 genes and revealed that the most efficient one in generating F3H mutants was the Arabidopsis codon-optimized AteCas9 gene with up to 90% efficiency. Knockout of F3H gene resulted in the discoloration of calli, validating the functional role of this gene in the anthocyanin biosynthesis in carrot as well as providing a visual marker for screening successfully edited events. Most resulting mutations were small Indels, but long chromosome fragment deletions of 116-119 nt were also generated with simultaneous cleavage mediated by two gRNAs. The results demonstrate successful site-directed mutagenesis in carrot with CRISPR/Cas9 and the usefulness of a model callus culture to validate genome editing systems. Given that the carrot genome has been sequenced recently, our timely study sheds light on the promising application of genome editing tools for boosting basic and translational research in this important vegetable crop.

Surveying the Maize community for their diversity and pedigree visualization needs to prioritize tool development and curation

PubMed Central

Braun, Bremen L.; Schott, David A.; Portwood, II, John L.; Schaeffer, Mary L.; Harper, Lisa C.; Gardiner, Jack M.; Cannon, Ethalinda K.; Andorf, Carson M.

2017-01-01

Abstract The Maize Genetics and Genomics Database (MaizeGDB) team prepared a survey to identify breeders’ needs for visualizing pedigrees, diversity data and haplotypes in order to prioritize tool development and curation efforts at MaizeGDB. The survey was distributed to the maize research community on behalf of the Maize Genetics Executive Committee in Summer 2015. The survey garnered 48 responses from maize researchers, of which more than half were self-identified as breeders. The survey showed that the maize researchers considered their top priorities for visualization as: (i) displaying single nucleotide polymorphisms in a given region for a given list of lines, (ii) showing haplotypes for a given list of lines and (iii) presenting pedigree relationships visually. The survey also asked which populations would be most useful to display. The following two populations were on top of the list: (i) 3000 publicly available maize inbred lines used in Romay et al. (Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol, 2013;14:R55) and (ii) maize lines with expired Plant Variety Protection Act (ex-PVP) certificates. Driven by this strong stakeholder input, MaizeGDB staff are currently working in four areas to improve its interface and web-based tools: (i) presenting immediate progenies of currently available stocks at the MaizeGDB Stock pages, (ii) displaying the most recent ex-PVP lines described in the Germplasm Resources Information Network (GRIN) on the MaizeGDB Stock pages, (iii) developing network views of pedigree relationships and (iv) visualizing genotypes from SNP-based diversity datasets. These survey results can help other biological databases to direct their efforts according to user preferences as they serve similar types of data sets for their communities. Database URL: https://www.maizegdb.org PMID:28605768

Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction.

PubMed

Dhanasekaran, A Ranjitha; Pearson, Jon L; Ganesan, Balasubramanian; Weimer, Bart C

2015-02-25

Mass spectrometric analysis of microbial metabolism provides a long list of possible compounds. Restricting the identification of the possible compounds to those produced by the specific organism would benefit the identification process. Currently, identification of mass spectrometry (MS) data is commonly done using empirically derived compound databases. Unfortunately, most databases contain relatively few compounds, leaving long lists of unidentified molecules. Incorporating genome-encoded metabolism enables MS output identification that may not be included in databases. Using an organism's genome as a database restricts metabolite identification to only those compounds that the organism can produce. To address the challenge of metabolomic analysis from MS data, a web-based application to directly search genome-constructed metabolic databases was developed. The user query returns a genome-restricted list of possible compound identifications along with the putative metabolic pathways based on the name, formula, SMILES structure, and the compound mass as defined by the user. Multiple queries can be done simultaneously by submitting a text file created by the user or obtained from the MS analysis software. The user can also provide parameters specific to the experiment's MS analysis conditions, such as mass deviation, adducts, and detection mode during the query so as to provide additional levels of evidence to produce the tentative identification. The query results are provided as an HTML page and downloadable text file of possible compounds that are restricted to a specific genome. Hyperlinks provided in the HTML file connect the user to the curated metabolic databases housed in ProCyc, a Pathway Tools platform, as well as the KEGG Pathway database for visualization and metabolic pathway analysis. Metabolome Searcher, a web-based tool, facilitates putative compound identification of MS output based on genome-restricted metabolic capability. This enables researchers to rapidly extend the possible identifications of large data sets for metabolites that are not in compound databases. Putative compound names with their associated metabolic pathways from metabolomics data sets are returned to the user for additional biological interpretation and visualization. This novel approach enables compound identification by restricting the possible masses to those encoded in the genome.

Visual ModuleOrganizer: a graphical interface for the detection and comparative analysis of repeat DNA modules

PubMed Central

2014-01-01

Background DNA repeats, such as transposable elements, minisatellites and palindromic sequences, are abundant in sequences and have been shown to have significant and functional roles in the evolution of the host genomes. In a previous study, we introduced the concept of a repeat DNA module, a flexible motif present in at least two occurences in the sequences. This concept was embedded into ModuleOrganizer, a tool allowing the detection of repeat modules in a set of sequences. However, its implementation remains difficult for larger sequences. Results Here we present Visual ModuleOrganizer, a Java graphical interface that enables a new and optimized version of the ModuleOrganizer tool. To implement this version, it was recoded in C++ with compressed suffix tree data structures. This leads to less memory usage (at least 120-fold decrease in average) and decreases by at least four the computation time during the module detection process in large sequences. Visual ModuleOrganizer interface allows users to easily choose ModuleOrganizer parameters and to graphically display the results. Moreover, Visual ModuleOrganizer dynamically handles graphical results through four main parameters: gene annotations, overlapping modules with known annotations, location of the module in a minimal number of sequences, and the minimal length of the modules. As a case study, the analysis of FoldBack4 sequences clearly demonstrated that our tools can be extended to comparative and evolutionary analyses of any repeat sequence elements in a set of genomic sequences. With the increasing number of sequences available in public databases, it is now possible to perform comparative analyses of repeated DNA modules in a graphic and friendly manner within a reasonable time period. Availability Visual ModuleOrganizer interface and the new version of the ModuleOrganizer tool are freely available at: http://lcb.cnrs-mrs.fr/spip.php?rubrique313. PMID:24678954

The Gene Expression Omnibus database

PubMed Central

Clough, Emily; Barrett, Tanya

2016-01-01

The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome–protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/. PMID:27008011

ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology[OPEN

PubMed Central

Waese, Jamie; Fan, Jim; Yu, Hans; Fucile, Geoffrey; Shi, Ruian; Cumming, Matthew; Town, Chris; Stuerzlinger, Wolfgang

2017-01-01

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this article and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an “app” on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research. PMID:28808136

StreptoBase: An Oral Streptococcus mitis Group Genomic Resource and Analysis Platform.

PubMed

Zheng, Wenning; Tan, Tze King; Paterson, Ian C; Mutha, Naresh V R; Siow, Cheuk Chuen; Tan, Shi Yang; Old, Lesley A; Jakubovics, Nicholas S; Choo, Siew Woh

2016-01-01

The oral streptococci are spherical Gram-positive bacteria categorized under the phylum Firmicutes which are among the most common causative agents of bacterial infective endocarditis (IE) and are also important agents in septicaemia in neutropenic patients. The Streptococcus mitis group is comprised of 13 species including some of the most common human oral colonizers such as S. mitis, S. oralis, S. sanguinis and S. gordonii as well as species such as S. tigurinus, S. oligofermentans and S. australis that have only recently been classified and are poorly understood at present. We present StreptoBase, which provides a specialized free resource focusing on the genomic analyses of oral species from the mitis group. It currently hosts 104 S. mitis group genomes including 27 novel mitis group strains that we sequenced using the high throughput Illumina HiSeq technology platform, and provides a comprehensive set of genome sequences for analyses, particularly comparative analyses and visualization of both cross-species and cross-strain characteristics of S. mitis group bacteria. StreptoBase incorporates sophisticated in-house designed bioinformatics web tools such as Pairwise Genome Comparison (PGC) tool and Pathogenomic Profiling Tool (PathoProT), which facilitate comparative pathogenomics analysis of Streptococcus strains. Examples are provided to demonstrate how StreptoBase can be employed to compare genome structure of different S. mitis group bacteria and putative virulence genes profile across multiple streptococcal strains. In conclusion, StreptoBase offers access to a range of streptococci genomic resources as well as analysis tools and will be an invaluable platform to accelerate research in streptococci. Database URL: http://streptococcus.um.edu.my.

Pathway Analysis and Omics Data Visualization Using Pathway Genome Databases: FragariaCyc, a Case Study.

PubMed

Naithani, Sushma; Jaiswal, Pankaj

2017-01-01

The species-specific plant Pathway Genome Databases (PGDBs) based on the BioCyc platform provide a conceptual model of the cellular metabolic network of an organism. Such frameworks allow analysis of the genome-scale expression data to understand changes in the overall metabolisms of an organism (or organs, tissues, and cells) in response to various extrinsic (e.g. developmental and differentiation) and/or extrinsic signals (e.g. pathogens and abiotic stresses) from the surrounding environment. Using FragariaCyc, a pathway database for the diploid strawberry Fragaria vesca, we show (1) the basic navigation across a PGDB; (2) a case study of pathway comparison across plant species; and (3) an example of RNA-Seq data analysis using Omics Viewer tool. The protocols described here generally apply to other Pathway Tools-based PGDBs.

WGE: a CRISPR database for genome engineering.

PubMed

Hodgkins, Alex; Farne, Anna; Perera, Sajith; Grego, Tiago; Parry-Smith, David J; Skarnes, William C; Iyer, Vivek

2015-09-15

The rapid development of CRISPR-Cas9 mediated genome editing techniques has given rise to a number of online and stand-alone tools to find and score CRISPR sites for whole genomes. Here we describe the Wellcome Trust Sanger Institute Genome Editing database (WGE), which uses novel methods to compute, visualize and select optimal CRISPR sites in a genome browser environment. The WGE database currently stores single and paired CRISPR sites and pre-calculated off-target information for CRISPRs located in the mouse and human exomes. Scoring and display of off-target sites is simple, and intuitive, and filters can be applied to identify high-quality CRISPR sites rapidly. WGE also provides a tool for the design and display of gene targeting vectors in the same genome browser, along with gene models, protein translation and variation tracks. WGE is open, extensible and can be set up to compute and present CRISPR sites for any genome. The WGE database is freely available at www.sanger.ac.uk/htgt/wge : vvi@sanger.ac.uk or skarnes@sanger.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Recent updates and developments to plant genome size databases

PubMed Central

Garcia, Sònia; Leitch, Ilia J.; Anadon-Rosell, Alba; Canela, Miguel Á.; Gálvez, Francisco; Garnatje, Teresa; Gras, Airy; Hidalgo, Oriane; Johnston, Emmeline; Mas de Xaxars, Gemma; Pellicer, Jaume; Siljak-Yakovlev, Sonja; Vallès, Joan; Vitales, Daniel; Bennett, Michael D.

2014-01-01

Two plant genome size databases have been recently updated and/or extended: the Plant DNA C-values database (http://data.kew.org/cvalues), and GSAD, the Genome Size in Asteraceae database (http://www.asteraceaegenomesize.com). While the first provides information on nuclear DNA contents across land plants and some algal groups, the second is focused on one of the largest and most economically important angiosperm families, Asteraceae. Genome size data have numerous applications: they can be used in comparative studies on genome evolution, or as a tool to appraise the cost of whole-genome sequencing programs. The growing interest in genome size and increasing rate of data accumulation has necessitated the continued update of these databases. Currently, the Plant DNA C-values database (Release 6.0, Dec. 2012) contains data for 8510 species, while GSAD has 1219 species (Release 2.0, June 2013), representing increases of 17 and 51%, respectively, in the number of species with genome size data, compared with previous releases. Here we provide overviews of the most recent releases of each database, and outline new features of GSAD. The latter include (i) a tool to visually compare genome size data between species, (ii) the option to export data and (iii) a webpage containing information about flow cytometry protocols. PMID:24288377

Setting Up the JBrowse Genome Browser

PubMed Central

Skinner, Mitchell E; Holmes, Ian H

2010-01-01

JBrowse is a web-based tool for visualizing genomic data. Unlike most other web-based genome browsers, JBrowse exploits the capabilities of the user's web browser to make scrolling and zooming fast and smooth. It supports the browsers used by almost all internet users, and is relatively simple to install. JBrowse can utilize multiple types of data in a variety of common genomic data formats, including genomic feature data in bioperl databases, GFF files, and BED files, and quantitative data in wiggle files. This unit describes how to obtain the JBrowse software, set it up on a Linux or Mac OS X computer running as a web server and incorporate genome annotation data from multiple sources into JBrowse. After completing the protocols described in this unit, the reader will have a web site that other users can visit to browse the genomic data. PMID:21154710

RefSeq microbial genomes database: new representation and annotation strategy.

PubMed

Tatusova, Tatiana; Ciufo, Stacy; Fedorov, Boris; O'Neill, Kathleen; Tolstoy, Igor

2014-01-01

The source of the microbial genomic sequences in the RefSeq collection is the set of primary sequence records submitted to the International Nucleotide Sequence Database public archives. These can be accessed through the Entrez search and retrieval system at http://www.ncbi.nlm.nih.gov/genome. Next-generation sequencing has enabled researchers to perform genomic sequencing at rates that were unimaginable in the past. Microbial genomes can now be sequenced in a matter of hours, which has led to a significant increase in the number of assembled genomes deposited in the public archives. This huge increase in DNA sequence data presents new challenges for the annotation, analysis and visualization bioinformatics tools. New strategies have been developed for the annotation and representation of reference genomes and sequence variations derived from population studies and clinical outbreaks.

CGI: Java Software for Mapping and Visualizing Data from Array-based Comparative Genomic Hybridization and Expression Profiling

PubMed Central

Gu, Joyce Xiuweu-Xu; Wei, Michael Yang; Rao, Pulivarthi H.; Lau, Ching C.; Behl, Sanjiv; Man, Tsz-Kwong

2007-01-01

With the increasing application of various genomic technologies in biomedical research, there is a need to integrate these data to correlate candidate genes/regions that are identified by different genomic platforms. Although there are tools that can analyze data from individual platforms, essential software for integration of genomic data is still lacking. Here, we present a novel Java-based program called CGI (Cytogenetics-Genomics Integrator) that matches the BAC clones from array-based comparative genomic hybridization (aCGH) to genes from RNA expression profiling datasets. The matching is computed via a fast, backend MySQL database containing UCSC Genome Browser annotations. This program also provides an easy-to-use graphical user interface for visualizing and summarizing the correlation of DNA copy number changes and RNA expression patterns from a set of experiments. In addition, CGI uses a Java applet to display the copy number values of a specific BAC clone in aCGH experiments side by side with the expression levels of genes that are mapped back to that BAC clone from the microarray experiments. The CGI program is built on top of extensible, reusable graphic components specifically designed for biologists. It is cross-platform compatible and the source code is freely available under the General Public License. PMID:19936083

CGI: Java software for mapping and visualizing data from array-based comparative genomic hybridization and expression profiling.

PubMed

Gu, Joyce Xiuweu-Xu; Wei, Michael Yang; Rao, Pulivarthi H; Lau, Ching C; Behl, Sanjiv; Man, Tsz-Kwong

2007-10-06

With the increasing application of various genomic technologies in biomedical research, there is a need to integrate these data to correlate candidate genes/regions that are identified by different genomic platforms. Although there are tools that can analyze data from individual platforms, essential software for integration of genomic data is still lacking. Here, we present a novel Java-based program called CGI (Cytogenetics-Genomics Integrator) that matches the BAC clones from array-based comparative genomic hybridization (aCGH) to genes from RNA expression profiling datasets. The matching is computed via a fast, backend MySQL database containing UCSC Genome Browser annotations. This program also provides an easy-to-use graphical user interface for visualizing and summarizing the correlation of DNA copy number changes and RNA expression patterns from a set of experiments. In addition, CGI uses a Java applet to display the copy number values of a specific BAC clone in aCGH experiments side by side with the expression levels of genes that are mapped back to that BAC clone from the microarray experiments. The CGI program is built on top of extensible, reusable graphic components specifically designed for biologists. It is cross-platform compatible and the source code is freely available under the General Public License.

Application of Genomic In Situ Hybridization in Horticultural Science

PubMed Central

Ramzan, Fahad; Lim, Ki-Byung

2017-01-01

Molecular cytogenetic techniques, such as in situ hybridization methods, are admirable tools to analyze the genomic structure and function, chromosome constituents, recombination patterns, alien gene introgression, genome evolution, aneuploidy, and polyploidy and also genome constitution visualization and chromosome discrimination from different genomes in allopolyploids of various horticultural crops. Using GISH advancement as multicolor detection is a significant approach to analyze the small and numerous chromosomes in fruit species, for example, Diospyros hybrids. This analytical technique has proved to be the most exact and effective way for hybrid status confirmation and helps remarkably to distinguish donor parental genomes in hybrids such as Clivia, Rhododendron, and Lycoris ornamental hybrids. The genome characterization facilitates in hybrid selection having potential desirable characteristics during the early hybridization breeding, as this technique expedites to detect introgressed sequence chromosomes. This review study epitomizes applications and advancements of genomic in situ hybridization (GISH) techniques in horticultural plants. PMID:28459054

EuPathDB: the eukaryotic pathogen genomics database resource

PubMed Central

Aurrecoechea, Cristina; Barreto, Ana; Basenko, Evelina Y.; Brestelli, John; Brunk, Brian P.; Cade, Shon; Crouch, Kathryn; Doherty, Ryan; Falke, Dave; Fischer, Steve; Gajria, Bindu; Harb, Omar S.; Heiges, Mark; Hertz-Fowler, Christiane; Hu, Sufen; Iodice, John; Kissinger, Jessica C.; Lawrence, Cris; Li, Wei; Pinney, Deborah F.; Pulman, Jane A.; Roos, David S.; Shanmugasundram, Achchuthan; Silva-Franco, Fatima; Steinbiss, Sascha; Stoeckert, Christian J.; Spruill, Drew; Wang, Haiming; Warrenfeltz, Susanne; Zheng, Jie

2017-01-01

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions. PMID:27903906

Update on RefSeq microbial genomes resources.

PubMed

Tatusova, Tatiana; Ciufo, Stacy; Federhen, Scott; Fedorov, Boris; McVeigh, Richard; O'Neill, Kathleen; Tolstoy, Igor; Zaslavsky, Leonid

2015-01-01

NCBI RefSeq genome collection http://www.ncbi.nlm.nih.gov/genome represents all three major domains of life: Eukarya, Bacteria and Archaea as well as Viruses. Prokaryotic genome sequences are the most rapidly growing part of the collection. During the year of 2014 more than 10,000 microbial genome assemblies have been publicly released bringing the total number of prokaryotic genomes close to 30,000. We continue to improve the quality and usability of the microbial genome resources by providing easy access to the data and the results of the pre-computed analysis, and improving analysis and visualization tools. A number of improvements have been incorporated into the Prokaryotic Genome Annotation Pipeline. Several new features have been added to RefSeq prokaryotic genomes data processing pipeline including the calculation of genome groups (clades) and the optimization of protein clusters generation using pan-genome approach. Published by Oxford University Press on behalf of Nucleic Acids Research 2014. This work is written by US Government employees and is in the public domain in the US.

GenePattern | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

GenePattern is a genomic analysis platform that provides access to hundreds of tools for the analysis and visualization of multiple data types. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research. A new GenePattern Notebook environment allows users to combine GenePattern analyses with text, graphics, and code to create complete reproducible research narratives.

GAC: Gene Associations with Clinical, a web based application

PubMed Central

Zhang, Xinyan; Rupji, Manali; Kowalski, Jeanne

2018-01-01

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC. PMID:29263780

G23D: Online tool for mapping and visualization of genomic variants on 3D protein structures.

PubMed

Solomon, Oz; Kunik, Vered; Simon, Amos; Kol, Nitzan; Barel, Ortal; Lev, Atar; Amariglio, Ninette; Somech, Raz; Rechavi, Gidi; Eyal, Eran

2016-08-26

Evaluation of the possible implications of genomic variants is an increasingly important task in the current high throughput sequencing era. Structural information however is still not routinely exploited during this evaluation process. The main reasons can be attributed to the partial structural coverage of the human proteome and the lack of tools which conveniently convert genomic positions, which are the frequent output of genomic pipelines, to proteins and structure coordinates. We present G23D, a tool for conversion of human genomic coordinates to protein coordinates and protein structures. G23D allows mapping of genomic positions/variants on evolutionary related (and not only identical) protein three dimensional (3D) structures as well as on theoretical models. By doing so it significantly extends the space of variants for which structural insight is feasible. To facilitate interpretation of the variant consequence, pathogenic variants, functional sites and polymorphism sites are displayed on protein sequence and structure diagrams alongside the input variants. G23D also provides modeling of the mutant structure, analysis of intra-protein contacts and instant access to functional predictions and predictions of thermo-stability changes. G23D is available at http://www.sheba-cancer.org.il/G23D . G23D extends the fraction of variants for which structural analysis is applicable and provides better and faster accessibility for structural data to biologists and geneticists who routinely work with genomic information.

ArrayExpress update--trends in database growth and links to data analysis tools.

PubMed

Rustici, Gabriella; Kolesnikov, Nikolay; Brandizi, Marco; Burdett, Tony; Dylag, Miroslaw; Emam, Ibrahim; Farne, Anna; Hastings, Emma; Ison, Jon; Keays, Maria; Kurbatova, Natalja; Malone, James; Mani, Roby; Mupo, Annalisa; Pedro Pereira, Rui; Pilicheva, Ekaterina; Rung, Johan; Sharma, Anjan; Tang, Y Amy; Ternent, Tobias; Tikhonov, Andrew; Welter, Danielle; Williams, Eleanor; Brazma, Alvis; Parkinson, Helen; Sarkans, Ugis

2013-01-01

The ArrayExpress Archive of Functional Genomics Data (http://www.ebi.ac.uk/arrayexpress) is one of three international functional genomics public data repositories, alongside the Gene Expression Omnibus at NCBI and the DDBJ Omics Archive, supporting peer-reviewed publications. It accepts data generated by sequencing or array-based technologies and currently contains data from almost a million assays, from over 30 000 experiments. The proportion of sequencing-based submissions has grown significantly over the last 2 years and has reached, in 2012, 15% of all new data. All data are available from ArrayExpress in MAGE-TAB format, which allows robust linking to data analysis and visualization tools, including Bioconductor and GenomeSpace. Additionally, R objects, for microarray data, and binary alignment format files, for sequencing data, have been generated for a significant proportion of ArrayExpress data.

CRISPR-Cas Genome Surgery in Ophthalmology

PubMed Central

DiCarlo, James E.; Sengillo, Jesse D.; Justus, Sally; Cabral, Thiago; Tsang, Stephen H.; Mahajan, Vinit B.

2017-01-01

Genetic disease affecting vision can significantly impact patient quality of life. Gene therapy seeks to slow the progression of these diseases by treating the underlying etiology at the level of the genome. Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated systems (Cas) represent powerful tools for studying diseases through the creation of model organisms generated by targeted modification and by the correction of disease mutations for therapeutic purposes. CRISPR-Cas systems have been applied successfully to the visual sciences and study of ophthalmic disease – from the modification of zebrafish and mammalian models of eye development and disease, to the correction of pathogenic mutations in patient-derived stem cells. Recent advances in CRISPR-Cas delivery and optimization boast improved functionality that continues to enhance genome-engineering applications in the eye. This review provides a synopsis of the recent implementations of CRISPR-Cas tools in the field of ophthalmology. PMID:28573077

GPFrontend and GPGraphics: graphical analysis tools for genetic association studies.

PubMed

Uebe, Steffen; Pasutto, Francesca; Krumbiegel, Mandy; Schanze, Denny; Ekici, Arif B; Reis, André

2010-09-21

Most software packages for whole genome association studies are non-graphical, purely text based programs originally designed to run with UNIX-like operating systems. Graphical output is often not intended or supposed to be performed with other command line tools, e.g. gnuplot. Using the Microsoft .NET 2.0 platform and Visual Studio 2005, we have created a graphical software package to analyze data from microarray whole genome association studies, both for a DNA-pooling based approach as well as regular single sample data. Part of this package was made to integrate with GenePool 0.8.2, a previously existing software suite for GNU/Linux systems, which we have modified to run in a Microsoft Windows environment. Further modifications cause it to generate some additional data. This enables GenePool to interact with the .NET parts created by us. The programs we developed are GPFrontend, a graphical user interface and frontend to use GenePool and create metadata files for it, and GPGraphics, a program to further analyze and graphically evaluate output of different WGA analysis programs, among them also GenePool. Our programs enable regular MS Windows users without much experience in bioinformatics to easily visualize whole genome data from a variety of sources.

JNSViewer—A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures

PubMed Central

Dong, Min; Graham, Mitchell; Yadav, Nehul

2017-01-01

Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html. PMID:28582416

The Saccharomyces Genome Database Variant Viewer.

PubMed

Sheppard, Travis K; Hitz, Benjamin C; Engel, Stacia R; Song, Giltae; Balakrishnan, Rama; Binkley, Gail; Costanzo, Maria C; Dalusag, Kyla S; Demeter, Janos; Hellerstedt, Sage T; Karra, Kalpana; Nash, Robert S; Paskov, Kelley M; Skrzypek, Marek S; Weng, Shuai; Wong, Edith D; Cherry, J Michael

2016-01-04

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is the authoritative community resource for the Saccharomyces cerevisiae reference genome sequence and its annotation. In recent years, we have moved toward increased representation of sequence variation and allelic differences within S. cerevisiae. The publication of numerous additional genomes has motivated the creation of new tools for their annotation and analysis. Here we present the Variant Viewer: a dynamic open-source web application for the visualization of genomic and proteomic differences. Multiple sequence alignments have been constructed across high quality genome sequences from 11 different S. cerevisiae strains and stored in the SGD. The alignments and summaries are encoded in JSON and used to create a two-tiered dynamic view of the budding yeast pan-genome, available at http://www.yeastgenome.org/variant-viewer. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

CartograTree: connecting tree genomes, phenotypes and environment.

PubMed

Vasquez-Gross, Hans A; Yu, John J; Figueroa, Ben; Gessler, Damian D G; Neale, David B; Wegrzyn, Jill L

2013-05-01

Today, researchers spend a tremendous amount of time gathering, formatting, filtering and visualizing data collected from disparate sources. Under the umbrella of forest tree biology, we seek to provide a platform and leverage modern technologies to connect biotic and abiotic data. Our goal is to provide an integrated web-based workspace that connects environmental, genomic and phenotypic data via geo-referenced coordinates. Here, we connect the genomic query web-based workspace, DiversiTree and a novel geographical interface called CartograTree to data housed on the TreeGenes database. To accomplish this goal, we implemented Simple Semantic Web Architecture and Protocol to enable the primary genomics database, TreeGenes, to communicate with semantic web services regardless of platform or back-end technologies. The novelty of CartograTree lies in the interactive workspace that allows for geographical visualization and engagement of high performance computing (HPC) resources. The application provides a unique tool set to facilitate research on the ecology, physiology and evolution of forest tree species. CartograTree can be accessed at: http://dendrome.ucdavis.edu/cartogratree. © 2013 Blackwell Publishing Ltd.

ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases.

PubMed

Shen, Li; Shao, Ningyi; Liu, Xiaochuan; Nestler, Eric

2014-04-15

Understanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge. We have developed ngs.plot - a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready. We conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data.

ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases

PubMed Central

2014-01-01

Background Understanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge. Results We have developed ngs.plot – a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready. Conclusions We conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data. PMID:24735413

A reference guide for tree analysis and visualization

PubMed Central

2010-01-01

The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis. PMID:20175922

Genome image programs: visualization and interpretation of Escherichia coli microarray experiments.

PubMed

Zimmer, Daniel P; Paliy, Oleg; Thomas, Brian; Gyaneshwar, Prasad; Kustu, Sydney

2004-08-01

We have developed programs to facilitate analysis of microarray data in Escherichia coli. They fall into two categories: manipulation of microarray images and identification of known biological relationships among lists of genes. A program in the first category arranges spots from glass-slide DNA microarrays according to their position in the E. coli genome and displays them compactly in genome order. The resulting genome image is presented in a web browser with an image map that allows the user to identify genes in the reordered image. Another program in the first category aligns genome images from two or more experiments. These images assist in visualizing regions of the genome with common transcriptional control. Such regions include multigene operons and clusters of operons, which are easily identified as strings of adjacent, similarly colored spots. The images are also useful for assessing the overall quality of experiments. The second category of programs includes a database and a number of tools for displaying biological information about many E. coli genes simultaneously rather than one gene at a time, which facilitates identifying relationships among them. These programs have accelerated and enhanced our interpretation of results from E. coli DNA microarray experiments. Examples are given. Copyright 2004 Genetics Society of America

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks.

PubMed

Thibodeau, Asa; Márquez, Eladio J; Luo, Oscar; Ruan, Yijun; Menghi, Francesca; Shin, Dong-Guk; Stitzel, Michael L; Vera-Licona, Paola; Ucar, Duygu

2016-06-01

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

ePlant: Visualizing and Exploring Multiple Levels of Data for Hypothesis Generation in Plant Biology.

PubMed

Waese, Jamie; Fan, Jim; Pasha, Asher; Yu, Hans; Fucile, Geoffrey; Shi, Ruian; Cumming, Matthew; Kelley, Lawrence A; Sternberg, Michael J; Krishnakumar, Vivek; Ferlanti, Erik; Miller, Jason; Town, Chris; Stuerzlinger, Wolfgang; Provart, Nicholas J

2017-08-01

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this article and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an "app" on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research. © 2017 American Society of Plant Biologists. All rights reserved.

IMG/M: integrated genome and metagenome comparative data analysis system

DOE PAGES

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; ...

2016-10-13

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support formore » examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review(ER) companion system (IMG/M ER: https://img.jgi.doe.gov/ mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.« less

IMG/M: integrated genome and metagenome comparative data analysis system

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

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support formore » examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review(ER) companion system (IMG/M ER: https://img.jgi.doe.gov/ mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.« less

IMG/M: integrated genome and metagenome comparative data analysis system

PubMed Central

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; Palaniappan, Krishna; Szeto, Ernest; Pillay, Manoj; Ratner, Anna; Huang, Jinghua; Andersen, Evan; Huntemann, Marcel; Varghese, Neha; Hadjithomas, Michalis; Tennessen, Kristin; Nielsen, Torben; Ivanova, Natalia N.; Kyrpides, Nikos C.

2017-01-01

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support for examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review (ER) companion system (IMG/M ER: https://img.jgi.doe.gov/mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system. PMID:27738135

Analysis and Visualization of ChIP-Seq and RNA-Seq Sequence Alignments Using ngs.plot.

PubMed

Loh, Yong-Hwee Eddie; Shen, Li

2016-01-01

The continual maturation and increasing applications of next-generation sequencing technology in scientific research have yielded ever-increasing amounts of data that need to be effectively and efficiently analyzed and innovatively mined for new biological insights. We have developed ngs.plot-a quick and easy-to-use bioinformatics tool that performs visualizations of the spatial relationships between sequencing alignment enrichment and specific genomic features or regions. More importantly, ngs.plot is customizable beyond the use of standard genomic feature databases to allow the analysis and visualization of user-specified regions of interest generated by the user's own hypotheses. In this protocol, we demonstrate and explain the use of ngs.plot using command line executions, as well as a web-based workflow on the Galaxy framework. We replicate the underlying commands used in the analysis of a true biological dataset that we had reported and published earlier and demonstrate how ngs.plot can easily generate publication-ready figures. With ngs.plot, users would be able to efficiently and innovatively mine their own datasets without having to be involved in the technical aspects of sequence coverage calculations and genomic databases.

3DScapeCS: application of three dimensional, parallel, dynamic network visualization in Cytoscape

PubMed Central

2013-01-01

Background The exponential growth of gigantic biological data from various sources, such as protein-protein interaction (PPI), genome sequences scaffolding, Mass spectrometry (MS) molecular networking and metabolic flux, demands an efficient way for better visualization and interpretation beyond the conventional, two-dimensional visualization tools. Results We developed a 3D Cytoscape Client/Server (3DScapeCS) plugin, which adopted Cytoscape in interpreting different types of data, and UbiGraph for three-dimensional visualization. The extra dimension is useful in accommodating, visualizing, and distinguishing large-scale networks with multiple crossed connections in five case studies. Conclusions Evaluation on several experimental data using 3DScapeCS and its special features, including multilevel graph layout, time-course data animation, and parallel visualization has proven its usefulness in visualizing complex data and help to make insightful conclusions. PMID:24225050

An interactive environment for agile analysis and visualization of ChIP-sequencing data.

PubMed

Lerdrup, Mads; Johansen, Jens Vilstrup; Agrawal-Singh, Shuchi; Hansen, Klaus

2016-04-01

To empower experimentalists with a means for fast and comprehensive chromatin immunoprecipitation sequencing (ChIP-seq) data analyses, we introduce an integrated computational environment, EaSeq. The software combines the exploratory power of genome browsers with an extensive set of interactive and user-friendly tools for genome-wide abstraction and visualization. It enables experimentalists to easily extract information and generate hypotheses from their own data and public genome-wide datasets. For demonstration purposes, we performed meta-analyses of public Polycomb ChIP-seq data and established a new screening approach to analyze more than 900 datasets from mouse embryonic stem cells for factors potentially associated with Polycomb recruitment. EaSeq, which is freely available and works on a standard personal computer, can substantially increase the throughput of many analysis workflows, facilitate transparency and reproducibility by automatically documenting and organizing analyses, and enable a broader group of scientists to gain insights from ChIP-seq data.

MVisAGe Identifies Concordant and Discordant Genomic Alterations of Driver Genes in Squamous Tumors.

PubMed

Walter, Vonn; Du, Ying; Danilova, Ludmila; Hayward, Michele C; Hayes, D Neil

2018-06-15

Integrated analyses of multiple genomic datatypes are now common in cancer profiling studies. Such data present opportunities for numerous computational experiments, yet analytic pipelines are limited. Tools such as the cBioPortal and Regulome Explorer, although useful, are not easy to access programmatically or to implement locally. Here, we introduce the MVisAGe R package, which allows users to quantify gene-level associations between two genomic datatypes to investigate the effect of genomic alterations (e.g., DNA copy number changes on gene expression). Visualizing Pearson/Spearman correlation coefficients according to the genomic positions of the underlying genes provides a powerful yet novel tool for conducting exploratory analyses. We demonstrate its utility by analyzing three publicly available cancer datasets. Our approach highlights canonical oncogenes in chr11q13 that displayed the strongest associations between expression and copy number, including CCND1 and CTTN , genes not identified by copy number analysis in the primary reports. We demonstrate highly concordant usage of shared oncogenes on chr3q, yet strikingly diverse oncogene usage on chr11q as a function of HPV infection status. Regions of chr19 that display remarkable associations between methylation and gene expression were identified, as were previously unreported miRNA-gene expression associations that may contribute to the epithelial-to-mesenchymal transition. Significance: This study presents an important bioinformatics tool that will enable integrated analyses of multiple genomic datatypes. Cancer Res; 78(12); 3375-85. ©2018 AACR . ©2018 American Association for Cancer Research.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

PubMed Central

Caspi, Ron; Altman, Tomer; Dale, Joseph M.; Dreher, Kate; Fulcher, Carol A.; Gilham, Fred; Kaipa, Pallavi; Karthikeyan, Athikkattuvalasu S.; Kothari, Anamika; Krummenacker, Markus; Latendresse, Mario; Mueller, Lukas A.; Paley, Suzanne; Popescu, Liviu; Pujar, Anuradha; Shearer, Alexander G.; Zhang, Peifen; Karp, Peter D.

2010-01-01

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. With more than 1400 pathways, MetaCyc is the largest collection of metabolic pathways currently available. Pathways reactions are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes, and literature citations. BioCyc (BioCyc.org) is a collection of more than 500 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs also contain additional features, such as predicted operons, transport systems, and pathway hole-fillers. The BioCyc Web site offers several tools for the analysis of the PGDBs, including Omics Viewers that enable visualization of omics datasets on two different genome-scale diagrams and tools for comparative analysis. The BioCyc PGDBs generated by SRI are offered for adoption by any party interested in curation of metabolic, regulatory, and genome-related information about an organism. PMID:19850718

Gee Fu: a sequence version and web-services database tool for genomic assembly, genome feature and NGS data.

PubMed

Ramirez-Gonzalez, Ricardo; Caccamo, Mario; MacLean, Daniel

2011-10-01

Scientists now use high-throughput sequencing technologies and short-read assembly methods to create draft genome assemblies in just days. Tools and pipelines like the assembler, and the workflow management environments make it easy for a non-specialist to implement complicated pipelines to produce genome assemblies and annotations very quickly. Such accessibility results in a proliferation of assemblies and associated files, often for many organisms. These assemblies get used as a working reference by lots of different workers, from a bioinformatician doing gene prediction or a bench scientist designing primers for PCR. Here we describe Gee Fu, a database tool for genomic assembly and feature data, including next-generation sequence alignments. Gee Fu is an instance of a Ruby-On-Rails web application on a feature database that provides web and console interfaces for input, visualization of feature data via AnnoJ, access to data through a web-service interface, an API for direct data access by Ruby scripts and access to feature data stored in BAM files. Gee Fu provides a platform for storing and sharing different versions of an assembly and associated features that can be accessed and updated by bench biologists and bioinformaticians in ways that are easy and useful for each. http://tinyurl.com/geefu dan.maclean@tsl.ac.uk.

chimeraviz: a tool for visualizing chimeric RNA.

PubMed

Lågstad, Stian; Zhao, Sen; Hoff, Andreas M; Johannessen, Bjarne; Lingjærde, Ole Christian; Skotheim, Rolf I

2017-09-15

Advances in high-throughput RNA sequencing have enabled more efficient detection of fusion transcripts, but the technology and associated software used for fusion detection from sequencing data often yield a high false discovery rate. Good prioritization of the results is important, and this can be helped by a visualization framework that automatically integrates RNA data with known genomic features. Here we present chimeraviz , a Bioconductor package that automates the creation of chimeric RNA visualizations. The package supports input from nine different fusion-finder tools: deFuse, EricScript, InFusion, JAFFA, FusionCatcher, FusionMap, PRADA, SOAPfuse and STAR-FUSION. chimeraviz is an R package available via Bioconductor ( https://bioconductor.org/packages/release/bioc/html/chimeraviz.html ) under Artistic-2.0. Source code and support is available at GitHub ( https://github.com/stianlagstad/chimeraviz ). rolf.i.skotheim@rr-research.no. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

iCanPlot: Visual Exploration of High-Throughput Omics Data Using Interactive Canvas Plotting

PubMed Central

Sinha, Amit U.; Armstrong, Scott A.

2012-01-01

Increasing use of high throughput genomic scale assays requires effective visualization and analysis techniques to facilitate data interpretation. Moreover, existing tools often require programming skills, which discourages bench scientists from examining their own data. We have created iCanPlot, a compelling platform for visual data exploration based on the latest technologies. Using the recently adopted HTML5 Canvas element, we have developed a highly interactive tool to visualize tabular data and identify interesting patterns in an intuitive fashion without the need of any specialized computing skills. A module for geneset overlap analysis has been implemented on the Google App Engine platform: when the user selects a region of interest in the plot, the genes in the region are analyzed on the fly. The visualization and analysis are amalgamated for a seamless experience. Further, users can easily upload their data for analysis—which also makes it simple to share the analysis with collaborators. We illustrate the power of iCanPlot by showing an example of how it can be used to interpret histone modifications in the context of gene expression. PMID:22393367

Apollo2Go: a web service adapter for the Apollo genome viewer to enable distributed genome annotation.

PubMed

Klee, Kathrin; Ernst, Rebecca; Spannagl, Manuel; Mayer, Klaus F X

2007-08-30

Apollo, a genome annotation viewer and editor, has become a widely used genome annotation and visualization tool for distributed genome annotation projects. When using Apollo for annotation, database updates are carried out by uploading intermediate annotation files into the respective database. This non-direct database upload is laborious and evokes problems of data synchronicity. To overcome these limitations we extended the Apollo data adapter with a generic, configurable web service client that is able to retrieve annotation data in a GAME-XML-formatted string and pass it on to Apollo's internal input routine. This Apollo web service adapter, Apollo2Go, simplifies the data exchange in distributed projects and aims to render the annotation process more comfortable. The Apollo2Go software is freely available from ftp://ftpmips.gsf.de/plants/apollo_webservice.

Apollo2Go: a web service adapter for the Apollo genome viewer to enable distributed genome annotation

PubMed Central

Klee, Kathrin; Ernst, Rebecca; Spannagl, Manuel; Mayer, Klaus FX

2007-01-01

Background Apollo, a genome annotation viewer and editor, has become a widely used genome annotation and visualization tool for distributed genome annotation projects. When using Apollo for annotation, database updates are carried out by uploading intermediate annotation files into the respective database. This non-direct database upload is laborious and evokes problems of data synchronicity. Results To overcome these limitations we extended the Apollo data adapter with a generic, configurable web service client that is able to retrieve annotation data in a GAME-XML-formatted string and pass it on to Apollo's internal input routine. Conclusion This Apollo web service adapter, Apollo2Go, simplifies the data exchange in distributed projects and aims to render the annotation process more comfortable. The Apollo2Go software is freely available from . PMID:17760972

At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana

PubMed Central

Laubinger, Sascha; Zeller, Georg; Henz, Stefan R; Sachsenberg, Timo; Widmer, Christian K; Naouar, Naïra; Vuylsteke, Marnik; Schölkopf, Bernhard; Rätsch, Gunnar; Weigel, Detlef

2008-01-01

Gene expression maps for model organisms, including Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas, Arabidopsis thaliana Tiling Array Express (At-TAX), which is based on whole-genome tiling arrays. We demonstrate that tiling arrays are accurate tools for gene expression analysis and identified more than 1,000 unannotated transcribed regions. Visualizations of gene expression estimates, transcribed regions, and tiling probe measurements are accessible online at the At-TAX homepage. PMID:18613972

MSeqDR: A Centralized Knowledge Repository and Bioinformatics Web Resource to Facilitate Genomic Investigations in Mitochondrial Disease.

PubMed

Shen, Lishuang; Diroma, Maria Angela; Gonzalez, Michael; Navarro-Gomez, Daniel; Leipzig, Jeremy; Lott, Marie T; van Oven, Mannis; Wallace, Douglas C; Muraresku, Colleen Clarke; Zolkipli-Cunningham, Zarazuela; Chinnery, Patrick F; Attimonelli, Marcella; Zuchner, Stephan; Falk, Marni J; Gai, Xiaowu

2016-06-01

MSeqDR is the Mitochondrial Disease Sequence Data Resource, a centralized and comprehensive genome and phenome bioinformatics resource built by the mitochondrial disease community to facilitate clinical diagnosis and research investigations of individual patient phenotypes, genomes, genes, and variants. A central Web portal (https://mseqdr.org) integrates community knowledge from expert-curated databases with genomic and phenotype data shared by clinicians and researchers. MSeqDR also functions as a centralized application server for Web-based tools to analyze data across both mitochondrial and nuclear DNA, including investigator-driven whole exome or genome dataset analyses through MSeqDR-Genesis. MSeqDR-GBrowse genome browser supports interactive genomic data exploration and visualization with custom tracks relevant to mtDNA variation and mitochondrial disease. MSeqDR-LSDB is a locus-specific database that currently manages 178 mitochondrial diseases, 1,363 genes associated with mitochondrial biology or disease, and 3,711 pathogenic variants in those genes. MSeqDR Disease Portal allows hierarchical tree-style disease exploration to evaluate their unique descriptions, phenotypes, and causative variants. Automated genomic data submission tools are provided that capture ClinVar compliant variant annotations. PhenoTips will be used for phenotypic data submission on deidentified patients using human phenotype ontology terminology. The development of a dynamic informed patient consent process to guide data access is underway to realize the full potential of these resources. © 2016 WILEY PERIODICALS, INC.

MIPS PlantsDB: a database framework for comparative plant genome research.

PubMed

Nussbaumer, Thomas; Martis, Mihaela M; Roessner, Stephan K; Pfeifer, Matthias; Bader, Kai C; Sharma, Sapna; Gundlach, Heidrun; Spannagl, Manuel

2013-01-01

The rapidly increasing amount of plant genome (sequence) data enables powerful comparative analyses and integrative approaches and also requires structured and comprehensive information resources. Databases are needed for both model and crop plant organisms and both intuitive search/browse views and comparative genomics tools should communicate the data to researchers and help them interpret it. MIPS PlantsDB (http://mips.helmholtz-muenchen.de/plant/genomes.jsp) was initially described in NAR in 2007 [Spannagl,M., Noubibou,O., Haase,D., Yang,L., Gundlach,H., Hindemitt, T., Klee,K., Haberer,G., Schoof,H. and Mayer,K.F. (2007) MIPSPlantsDB-plant database resource for integrative and comparative plant genome research. Nucleic Acids Res., 35, D834-D840] and was set up from the start to provide data and information resources for individual plant species as well as a framework for integrative and comparative plant genome research. PlantsDB comprises database instances for tomato, Medicago, Arabidopsis, Brachypodium, Sorghum, maize, rice, barley and wheat. Building up on that, state-of-the-art comparative genomics tools such as CrowsNest are integrated to visualize and investigate syntenic relationships between monocot genomes. Results from novel genome analysis strategies targeting the complex and repetitive genomes of triticeae species (wheat and barley) are provided and cross-linked with model species. The MIPS Repeat Element Database (mips-REdat) and Catalog (mips-REcat) as well as tight connections to other databases, e.g. via web services, are further important components of PlantsDB.

MIPS PlantsDB: a database framework for comparative plant genome research

PubMed Central

Nussbaumer, Thomas; Martis, Mihaela M.; Roessner, Stephan K.; Pfeifer, Matthias; Bader, Kai C.; Sharma, Sapna; Gundlach, Heidrun; Spannagl, Manuel

2013-01-01

The rapidly increasing amount of plant genome (sequence) data enables powerful comparative analyses and integrative approaches and also requires structured and comprehensive information resources. Databases are needed for both model and crop plant organisms and both intuitive search/browse views and comparative genomics tools should communicate the data to researchers and help them interpret it. MIPS PlantsDB (http://mips.helmholtz-muenchen.de/plant/genomes.jsp) was initially described in NAR in 2007 [Spannagl,M., Noubibou,O., Haase,D., Yang,L., Gundlach,H., Hindemitt, T., Klee,K., Haberer,G., Schoof,H. and Mayer,K.F. (2007) MIPSPlantsDB–plant database resource for integrative and comparative plant genome research. Nucleic Acids Res., 35, D834–D840] and was set up from the start to provide data and information resources for individual plant species as well as a framework for integrative and comparative plant genome research. PlantsDB comprises database instances for tomato, Medicago, Arabidopsis, Brachypodium, Sorghum, maize, rice, barley and wheat. Building up on that, state-of-the-art comparative genomics tools such as CrowsNest are integrated to visualize and investigate syntenic relationships between monocot genomes. Results from novel genome analysis strategies targeting the complex and repetitive genomes of triticeae species (wheat and barley) are provided and cross-linked with model species. The MIPS Repeat Element Database (mips-REdat) and Catalog (mips-REcat) as well as tight connections to other databases, e.g. via web services, are further important components of PlantsDB. PMID:23203886

Gene context analysis in the Integrated Microbial Genomes (IMG) data management system.

PubMed

Mavromatis, Konstantinos; Chu, Ken; Ivanova, Natalia; Hooper, Sean D; Markowitz, Victor M; Kyrpides, Nikos C

2009-11-24

Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG's regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov.

Aligning the unalignable: bacteriophage whole genome alignments.

PubMed

Bérard, Sèverine; Chateau, Annie; Pompidor, Nicolas; Guertin, Paul; Bergeron, Anne; Swenson, Krister M

2016-01-13

In recent years, many studies focused on the description and comparison of large sets of related bacteriophage genomes. Due to the peculiar mosaic structure of these genomes, few informative approaches for comparing whole genomes exist: dot plots diagrams give a mostly qualitative assessment of the similarity/dissimilarity between two or more genomes, and clustering techniques are used to classify genomes. Multiple alignments are conspicuously absent from this scene. Indeed, whole genome aligners interpret lack of similarity between sequences as an indication of rearrangements, insertions, or losses. This behavior makes them ill-prepared to align bacteriophage genomes, where even closely related strains can accomplish the same biological function with highly dissimilar sequences. In this paper, we propose a multiple alignment strategy that exploits functional collinearity shared by related strains of bacteriophages, and uses partial orders to capture mosaicism of sets of genomes. As classical alignments do, the computed alignments can be used to predict that genes have the same biological function, even in the absence of detectable similarity. The Alpha aligner implements these ideas in visual interactive displays, and is used to compute several examples of alignments of Staphylococcus aureus and Mycobacterium bacteriophages, involving up to 29 genomes. Using these datasets, we prove that Alpha alignments are at least as good as those computed by standard aligners. Comparison with the progressive Mauve aligner - which implements a partial order strategy, but whose alignments are linearized - shows a greatly improved interactive graphic display, while avoiding misalignments. Multiple alignments of whole bacteriophage genomes work, and will become an important conceptual and visual tool in comparative genomics of sets of related strains. A python implementation of Alpha, along with installation instructions for Ubuntu and OSX, is available on bitbucket (https://bitbucket.org/thekswenson/alpha).

GDR (Genome Database for Rosaceae): integrated web-database for Rosaceae genomics and genetics data

PubMed Central

Jung, Sook; Staton, Margaret; Lee, Taein; Blenda, Anna; Svancara, Randall; Abbott, Albert; Main, Dorrie

2008-01-01

The Genome Database for Rosaceae (GDR) is a central repository of curated and integrated genetics and genomics data of Rosaceae, an economically important family which includes apple, cherry, peach, pear, raspberry, rose and strawberry. GDR contains annotated databases of all publicly available Rosaceae ESTs, the genetically anchored peach physical map, Rosaceae genetic maps and comprehensively annotated markers and traits. The ESTs are assembled to produce unigene sets of each genus and the entire Rosaceae. Other annotations include putative function, microsatellites, open reading frames, single nucleotide polymorphisms, gene ontology terms and anchored map position where applicable. Most of the published Rosaceae genetic maps can be viewed and compared through CMap, the comparative map viewer. The peach physical map can be viewed using WebFPC/WebChrom, and also through our integrated GDR map viewer, which serves as a portal to the combined genetic, transcriptome and physical mapping information. ESTs, BACs, markers and traits can be queried by various categories and the search result sites are linked to the mapping visualization tools. GDR also provides online analysis tools such as a batch BLAST/FASTA server for the GDR datasets, a sequence assembly server and microsatellite and primer detection tools. GDR is available at http://www.rosaceae.org. PMID:17932055

The ReproGenomics Viewer: an integrative cross-species toolbox for the reproductive science community

PubMed Central

Darde, Thomas A.; Sallou, Olivier; Becker, Emmanuelle; Evrard, Bertrand; Monjeaud, Cyril; Le Bras, Yvan; Jégou, Bernard; Collin, Olivier; Rolland, Antoine D.; Chalmel, Frédéric

2015-01-01

We report the development of the ReproGenomics Viewer (RGV), a multi- and cross-species working environment for the visualization, mining and comparison of published omics data sets for the reproductive science community. The system currently embeds 15 published data sets related to gametogenesis from nine model organisms. Data sets have been curated and conveniently organized into broad categories including biological topics, technologies, species and publications. RGV's modular design for both organisms and genomic tools enables users to upload and compare their data with that from the data sets embedded in the system in a cross-species manner. The RGV is freely available at http://rgv.genouest.org. PMID:25883147

Real-time imaging of specific genomic loci in eukaryotic cells using the ANCHOR DNA labelling system.

PubMed

Germier, Thomas; Sylvain, Audibert; Silvia, Kocanova; David, Lane; Kerstin, Bystricky

2018-06-01

Spatio-temporal organization of the cell nucleus adapts to and regulates genomic processes. Microscopy approaches that enable direct monitoring of specific chromatin sites in single cells and in real time are needed to better understand the dynamics involved. In this chapter, we describe the principle and development of ANCHOR, a novel tool for DNA labelling in eukaryotic cells. Protocols for use of ANCHOR to visualize a single genomic locus in eukaryotic cells are presented. We describe an approach for live cell imaging of a DNA locus during the entire cell cycle in human breast cancer cells. Copyright © 2018 Elsevier Inc. All rights reserved.

Advances in Engineering the Fly Genome with the CRISPR-Cas System

PubMed Central

Bier, Ethan; Harrison, Melissa M.; O’Connor-Giles, Kate M.; Wildonger, Jill

2018-01-01

Drosophila has long been a premier model for the development and application of cutting-edge genetic approaches. The CRISPR-Cas system now adds the ability to manipulate the genome with ease and precision, providing a rich toolbox to interrogate relationships between genotype and phenotype, to delineate and visualize how the genome is organized, to illuminate and manipulate RNA, and to pioneer new gene drive technologies. Myriad transformative approaches have already originated from the CRISPR-Cas system, which will likely continue to spark the creation of tools with diverse applications. Here, we provide an overview of how CRISPR-Cas gene editing has revolutionized genetic analysis in Drosophila and highlight key areas for future advances. PMID:29301946

arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays

PubMed Central

Menten, Björn; Pattyn, Filip; De Preter, Katleen; Robbrecht, Piet; Michels, Evi; Buysse, Karen; Mortier, Geert; De Paepe, Anne; van Vooren, Steven; Vermeesch, Joris; Moreau, Yves; De Moor, Bart; Vermeulen, Stefan; Speleman, Frank; Vandesompele, Jo

2005-01-01

Background The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH). One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment. Results We have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment) supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser. Conclusion ArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at . PMID:15910681

TreeQ-VISTA: An Interactive Tree Visualization Tool withFunctional Annotation Query Capabilities

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

Gu, Shengyin; Anderson, Iain; Kunin, Victor

2007-05-07

Summary: We describe a general multiplatform exploratorytool called TreeQ-Vista, designed for presenting functional annotationsin a phylogenetic context. Traits, such as phenotypic and genomicproperties, are interactively queried from a relational database with auser-friendly interface which provides a set of tools for users with orwithout SQL knowledge. The query results are projected onto aphylogenetic tree and can be displayed in multiple color groups. A richset of browsing, grouping and query tools are provided to facilitatetrait exploration, comparison and analysis.Availability: The program,detailed tutorial and examples are available online athttp://genome-test.lbl.gov/vista/TreeQVista.

QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks

PubMed Central

Thibodeau, Asa; Márquez, Eladio J.; Luo, Oscar; Ruan, Yijun; Shin, Dong-Guk; Stitzel, Michael L.; Ucar, Duygu

2016-01-01

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/. PMID:27336171

GenomeGraphs: integrated genomic data visualization with R.

PubMed

Durinck, Steffen; Bullard, James; Spellman, Paul T; Dudoit, Sandrine

2009-01-06

Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

PubMed Central

Paley, Suzanne M.; Krummenacker, Markus; Latendresse, Mario; Dale, Joseph M.; Lee, Thomas J.; Kaipa, Pallavi; Gilham, Fred; Spaulding, Aaron; Popescu, Liviu; Altman, Tomer; Paulsen, Ian; Keseler, Ingrid M.; Caspi, Ron

2010-01-01

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry. PMID:19955237

QuadBase2: web server for multiplexed guanine quadruplex mining and visualization

PubMed Central

Dhapola, Parashar; Chowdhury, Shantanu

2016-01-01

DNA guanine quadruplexes or G4s are non-canonical DNA secondary structures which affect genomic processes like replication, transcription and recombination. G4s are computationally identified by specific nucleotide motifs which are also called putative G4 (PG4) motifs. Despite the general relevance of these structures, there is currently no tool available that can allow batch queries and genome-wide analysis of these motifs in a user-friendly interface. QuadBase2 (quadbase.igib.res.in) presents a completely reinvented web server version of previously published QuadBase database. QuadBase2 enables users to mine PG4 motifs in up to 178 eukaryotes through the EuQuad module. This module interfaces with Ensembl Compara database, to allow users mine PG4 motifs in the orthologues of genes of interest across eukaryotes. PG4 motifs can be mined across genes and their promoter sequences in 1719 prokaryotes through ProQuad module. This module includes a feature that allows genome-wide mining of PG4 motifs and their visualization as circular histograms. TetraplexFinder, the module for mining PG4 motifs in user-provided sequences is now capable of handling up to 20 MB of data. QuadBase2 is a comprehensive PG4 motif mining tool that further expands the configurations and algorithms for mining PG4 motifs in a user-friendly way. PMID:27185890

Base-By-Base: single nucleotide-level analysis of whole viral genome alignments.

PubMed

Brodie, Ryan; Smith, Alex J; Roper, Rachel L; Tcherepanov, Vasily; Upton, Chris

2004-07-14

With ever increasing numbers of closely related virus genomes being sequenced, it has become desirable to be able to compare two genomes at a level more detailed than gene content because two strains of an organism may share the same set of predicted genes but still differ in their pathogenicity profiles. For example, detailed comparison of multiple isolates of the smallpox virus genome (each approximately 200 kb, with 200 genes) is not feasible without new bioinformatics tools. A software package, Base-By-Base, has been developed that provides visualization tools to enable researchers to 1) rapidly identify and correct alignment errors in large, multiple genome alignments; and 2) generate tabular and graphical output of differences between the genomes at the nucleotide level. Base-By-Base uses detailed annotation information about the aligned genomes and can list each predicted gene with nucleotide differences, display whether variations occur within promoter regions or coding regions and whether these changes result in amino acid substitutions. Base-By-Base can connect to our mySQL database (Virus Orthologous Clusters; VOCs) to retrieve detailed annotation information about the aligned genomes or use information from text files. Base-By-Base enables users to quickly and easily compare large viral genomes; it highlights small differences that may be responsible for important phenotypic differences such as virulence. It is available via the Internet using Java Web Start and runs on Macintosh, PC and Linux operating systems with the Java 1.4 virtual machine.

MutScan: fast detection and visualization of target mutations by scanning FASTQ data.

PubMed

Chen, Shifu; Huang, Tanxiao; Wen, Tiexiang; Li, Hong; Xu, Mingyan; Gu, Jia

2018-01-22

Some types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines. Typically, this process can be executed using alignment visualization tools such as IGV or GenomeBrowse. However, these tools are too heavy and therefore unsuitable for validating mutations in ultra-deep sequencing data. We developed MutScan to address problems of sensitive detection and efficient validation for target mutations. MutScan involves highly optimized string-searching algorithms, which can scan input FASTQ files to grab all reads that support target mutations. The collected supporting reads for each target mutation will be piled up and visualized using web technologies such as HTML and JavaScript. Algorithms such as rolling hash and bloom filter are applied to accelerate scanning and make MutScan applicable to detect or visualize target mutations in a very fast way. MutScan is a tool for the detection and visualization of target mutations by only scanning FASTQ raw data directly. Compared to conventional pipelines, this offers a very high performance, executing about 20 times faster, and offering maximal sensitivity since it can grab mutations with even one single supporting read. MutScan visualizes detected mutations by generating interactive pile-ups using web technologies. These can serve to validate target mutations, thus avoiding false positives. Furthermore, MutScan can visualize all mutation records in a VCF file to HTML pages for cloud-friendly VCF validation. MutScan is an open source tool available at GitHub: https://github.com/OpenGene/MutScan.

Genome dynamics of the human embryonic kidney 293 lineage in response to cell biology manipulations.

PubMed

Lin, Yao-Cheng; Boone, Morgane; Meuris, Leander; Lemmens, Irma; Van Roy, Nadine; Soete, Arne; Reumers, Joke; Moisse, Matthieu; Plaisance, Stéphane; Drmanac, Radoje; Chen, Jason; Speleman, Frank; Lambrechts, Diether; Van de Peer, Yves; Tavernier, Jan; Callewaert, Nico

2014-09-03

The HEK293 human cell lineage is widely used in cell biology and biotechnology. Here we use whole-genome resequencing of six 293 cell lines to study the dynamics of this aneuploid genome in response to the manipulations used to generate common 293 cell derivatives, such as transformation and stable clone generation (293T); suspension growth adaptation (293S); and cytotoxic lectin selection (293SG). Remarkably, we observe that copy number alteration detection could identify the genomic region that enabled cell survival under selective conditions (i.c. ricin selection). Furthermore, we present methods to detect human/vector genome breakpoints and a user-friendly visualization tool for the 293 genome data. We also establish that the genome structure composition is in steady state for most of these cell lines when standard cell culturing conditions are used. This resource enables novel and more informed studies with 293 cells, and we will distribute the sequenced cell lines to this effect.

UCSC genome browser: deep support for molecular biomedical research.

PubMed

Mangan, Mary E; Williams, Jennifer M; Lathe, Scott M; Karolchik, Donna; Lathe, Warren C

2008-01-01

The volume and complexity of genomic sequence data, and the additional experimental data required for annotation of the genomic context, pose a major challenge for display and access for biomedical researchers. Genome browsers organize this data and make it available in various ways to extract useful information to advance research projects. The UCSC Genome Browser is one of these resources. The official sequence data for a given species forms the framework to display many other types of data such as expression, variation, cross-species comparisons, and more. Visual representations of the data are available for exploration. Data can be queried with sequences. Complex database queries are also easily achieved with the Table Browser interface. Associated tools permit additional query types or access to additional data sources such as images of in situ localizations. Support for solving researcher's issues is provided with active discussion mailing lists and by providing updated training materials. The UCSC Genome Browser provides a source of deep support for a wide range of biomedical molecular research (http://genome.ucsc.edu).

Family genome browser: visualizing genomes with pedigree information.

PubMed

Juan, Liran; Liu, Yongzhuang; Wang, Yongtian; Teng, Mingxiang; Zang, Tianyi; Wang, Yadong

2015-07-15

Families with inherited diseases are widely used in Mendelian/complex disease studies. Owing to the advances in high-throughput sequencing technologies, family genome sequencing becomes more and more prevalent. Visualizing family genomes can greatly facilitate human genetics studies and personalized medicine. However, due to the complex genetic relationships and high similarities among genomes of consanguineous family members, family genomes are difficult to be visualized in traditional genome visualization framework. How to visualize the family genome variants and their functions with integrated pedigree information remains a critical challenge. We developed the Family Genome Browser (FGB) to provide comprehensive analysis and visualization for family genomes. The FGB can visualize family genomes in both individual level and variant level effectively, through integrating genome data with pedigree information. Family genome analysis, including determination of parental origin of the variants, detection of de novo mutations, identification of potential recombination events and identical-by-decent segments, etc., can be performed flexibly. Diverse annotations for the family genome variants, such as dbSNP memberships, linkage disequilibriums, genes, variant effects, potential phenotypes, etc., are illustrated as well. Moreover, the FGB can automatically search de novo mutations and compound heterozygous variants for a selected individual, and guide investigators to find high-risk genes with flexible navigation options. These features enable users to investigate and understand family genomes intuitively and systematically. The FGB is available at http://mlg.hit.edu.cn/FGB/. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

GEnomes Management Application (GEM.app): a new software tool for large-scale collaborative genome analysis.

PubMed

Gonzalez, Michael A; Lebrigio, Rafael F Acosta; Van Booven, Derek; Ulloa, Rick H; Powell, Eric; Speziani, Fiorella; Tekin, Mustafa; Schüle, Rebecca; Züchner, Stephan

2013-06-01

Novel genes are now identified at a rapid pace for many Mendelian disorders, and increasingly, for genetically complex phenotypes. However, new challenges have also become evident: (1) effectively managing larger exome and/or genome datasets, especially for smaller labs; (2) direct hands-on analysis and contextual interpretation of variant data in large genomic datasets; and (3) many small and medium-sized clinical and research-based investigative teams around the world are generating data that, if combined and shared, will significantly increase the opportunities for the entire community to identify new genes. To address these challenges, we have developed GEnomes Management Application (GEM.app), a software tool to annotate, manage, visualize, and analyze large genomic datasets (https://genomics.med.miami.edu/). GEM.app currently contains ∼1,600 whole exomes from 50 different phenotypes studied by 40 principal investigators from 15 different countries. The focus of GEM.app is on user-friendly analysis for nonbioinformaticians to make next-generation sequencing data directly accessible. Yet, GEM.app provides powerful and flexible filter options, including single family filtering, across family/phenotype queries, nested filtering, and evaluation of segregation in families. In addition, the system is fast, obtaining results within 4 sec across ∼1,200 exomes. We believe that this system will further enhance identification of genetic causes of human disease. © 2013 Wiley Periodicals, Inc.

MSeqDR: A Centralized Knowledge Repository and Bioinformatics Web Resource to Facilitate Genomic Investigations in Mitochondrial Disease

PubMed Central

Shen, Lishuang; Diroma, Maria Angela; Gonzalez, Michael; Navarro-Gomez, Daniel; Leipzig, Jeremy; Lott, Marie T.; van Oven, Mannis; Wallace, Douglas C.; Muraresku, Colleen Clarke; Zolkipli-Cunningham, Zarazuela; Chinnery, Patrick F.; Attimonelli, Marcella; Zuchner, Stephan

2016-01-01

MSeqDR is the Mitochondrial Disease Sequence Data Resource, a centralized and comprehensive genome and phenome bioinformatics resource built by the mitochondrial disease community to facilitate clinical diagnosis and research investigations of individual patient phenotypes, genomes, genes, and variants. A central Web portal (https://mseqdr.org) integrates community knowledge from expert-curated databases with genomic and phenotype data shared by clinicians and researchers. MSeqDR also functions as a centralized application server for Web-based tools to analyze data across both mitochondrial and nuclear DNA, including investigator-driven whole exome or genome dataset analyses through MSeqDR-Genesis. MSeqDR-GBrowse supports interactive genomic data exploration and visualization with custom tracks relevant to mtDNA variation and disease. MSeqDR-LSDB is a locus specific database that currently manages 178 mitochondrial diseases, 1,363 genes associated with mitochondrial biology or disease, and 3,711 pathogenic variants in those genes. MSeqDR Disease Portal allows hierarchical tree-style disease exploration to evaluate their unique descriptions, phenotypes, and causative variants. Automated genomic data submission tools are provided that capture ClinVar-compliant variant annotations. PhenoTips is used for phenotypic data submission on de-identified patients using human phenotype ontology terminology. Development of a dynamic informed patient consent process to guide data access is underway to realize the full potential of these resources. PMID:26919060

The Papillomavirus Episteme: a major update to the papillomavirus sequence database.

PubMed

Van Doorslaer, Koenraad; Li, Zhiwen; Xirasagar, Sandhya; Maes, Piet; Kaminsky, David; Liou, David; Sun, Qiang; Kaur, Ramandeep; Huyen, Yentram; McBride, Alison A

2017-01-04

The Papillomavirus Episteme (PaVE) is a database of curated papillomavirus genomic sequences, accompanied by web-based sequence analysis tools. This update describes the addition of major new features. The papillomavirus genomes within PaVE have been further annotated, and now includes the major spliced mRNA transcripts. Viral genes and transcripts can be visualized on both linear and circular genome browsers. Evolutionary relationships among PaVE reference protein sequences can be analysed using multiple sequence alignments and phylogenetic trees. To assist in viral discovery, PaVE offers a typing tool; a simplified algorithm to determine whether a newly sequenced virus is novel. PaVE also now contains an image library containing gross clinical and histopathological images of papillomavirus infected lesions. Database URL: https://pave.niaid.nih.gov/. Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Non-B DB v2.0: a database of predicted non-B DNA-forming motifs and its associated tools.

PubMed

Cer, Regina Z; Donohue, Duncan E; Mudunuri, Uma S; Temiz, Nuri A; Loss, Michael A; Starner, Nathan J; Halusa, Goran N; Volfovsky, Natalia; Yi, Ming; Luke, Brian T; Bacolla, Albino; Collins, Jack R; Stephens, Robert M

2013-01-01

The non-B DB, available at http://nonb.abcc.ncifcrf.gov, catalogs predicted non-B DNA-forming sequence motifs, including Z-DNA, G-quadruplex, A-phased repeats, inverted repeats, mirror repeats, direct repeats and their corresponding subsets: cruciforms, triplexes and slipped structures, in several genomes. Version 2.0 of the database revises and re-implements the motif discovery algorithms to better align with accepted definitions and thresholds for motifs, expands the non-B DNA-forming motifs coverage by including short tandem repeats and adds key visualization tools to compare motif locations relative to other genomic annotations. Non-B DB v2.0 extends the ability for comparative genomics by including re-annotation of the five organisms reported in non-B DB v1.0, human, chimpanzee, dog, macaque and mouse, and adds seven additional organisms: orangutan, rat, cow, pig, horse, platypus and Arabidopsis thaliana. Additionally, the non-B DB v2.0 provides an overall improved graphical user interface and faster query performance.

MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource based on the first complete plant genome

PubMed Central

Schoof, Heiko; Zaccaria, Paolo; Gundlach, Heidrun; Lemcke, Kai; Rudd, Stephen; Kolesov, Grigory; Arnold, Roland; Mewes, H. W.; Mayer, Klaus F. X.

2002-01-01

Arabidopsis thaliana is the first plant for which the complete genome has been sequenced and published. Annotation of complex eukaryotic genomes requires more than the assignment of genetic elements to the sequence. Besides completing the list of genes, we need to discover their cellular roles, their regulation and their interactions in order to understand the workings of the whole plant. The MIPS Arabidopsis thaliana Database (MAtDB; http://mips.gsf.de/proj/thal/db) started out as a repository for genome sequence data in the European Scientists Sequencing Arabidopsis (ESSA) project and the Arabidopsis Genome Initiative. Our aim is to transform MAtDB into an integrated biological knowledge resource by integrating diverse data, tools, query and visualization capabilities and by creating a comprehensive resource for Arabidopsis as a reference model for other species, including crop plants. PMID:11752263

Phylo-VISTA: Interactive visualization of multiple DNA sequence alignments

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

Shah, Nameeta; Couronne, Olivier; Pennacchio, Len A.

The power of multi-sequence comparison for biological discovery is well established. The need for new capabilities to visualize and compare cross-species alignment data is intensified by the growing number of genomic sequence datasets being generated for an ever-increasing number of organisms. To be efficient these visualization algorithms must support the ability to accommodate consistently a wide range of evolutionary distances in a comparison framework based upon phylogenetic relationships. Results: We have developed Phylo-VISTA, an interactive tool for analyzing multiple alignments by visualizing a similarity measure for multiple DNA sequences. The complexity of visual presentation is effectively organized using a frameworkmore » based upon interspecies phylogenetic relationships. The phylogenetic organization supports rapid, user-guided interspecies comparison. To aid in navigation through large sequence datasets, Phylo-VISTA leverages concepts from VISTA that provide a user with the ability to select and view data at varying resolutions. The combination of multiresolution data visualization and analysis, combined with the phylogenetic framework for interspecies comparison, produces a highly flexible and powerful tool for visual data analysis of multiple sequence alignments. Availability: Phylo-VISTA is available at http://www-gsd.lbl. gov/phylovista. It requires an Internet browser with Java Plugin 1.4.2 and it is integrated into the global alignment program LAGAN at http://lagan.stanford.edu« less

The UCSC Genome Browser database: extensions and updates 2013.

PubMed

Meyer, Laurence R; Zweig, Ann S; Hinrichs, Angie S; Karolchik, Donna; Kuhn, Robert M; Wong, Matthew; Sloan, Cricket A; Rosenbloom, Kate R; Roe, Greg; Rhead, Brooke; Raney, Brian J; Pohl, Andy; Malladi, Venkat S; Li, Chin H; Lee, Brian T; Learned, Katrina; Kirkup, Vanessa; Hsu, Fan; Heitner, Steve; Harte, Rachel A; Haeussler, Maximilian; Guruvadoo, Luvina; Goldman, Mary; Giardine, Belinda M; Fujita, Pauline A; Dreszer, Timothy R; Diekhans, Mark; Cline, Melissa S; Clawson, Hiram; Barber, Galt P; Haussler, David; Kent, W James

2013-01-01

The University of California Santa Cruz (UCSC) Genome Browser (http://genome.ucsc.edu) offers online public access to a growing database of genomic sequence and annotations for a wide variety of organisms. The Browser is an integrated tool set for visualizing, comparing, analysing and sharing both publicly available and user-generated genomic datasets. As of September 2012, genomic sequence and a basic set of annotation 'tracks' are provided for 63 organisms, including 26 mammals, 13 non-mammal vertebrates, 3 invertebrate deuterostomes, 13 insects, 6 worms, yeast and sea hare. In the past year 19 new genome assemblies have been added, and we anticipate releasing another 28 in early 2013. Further, a large number of annotation tracks have been either added, updated by contributors or remapped to the latest human reference genome. Among these are an updated UCSC Genes track for human and mouse assemblies. We have also introduced several features to improve usability, including new navigation menus. This article provides an update to the UCSC Genome Browser database, which has been previously featured in the Database issue of this journal.

SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree.

PubMed

Stevens, John R; Jones, Todd R; Lefevre, Michael; Ganesan, Balasubramanian; Weimer, Bart C

2017-01-01

Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significant consensus response (in terms of operational taxonomic unit abundance) to the intervention. We present the R software package SigTree , a collection of flexible tools that make use of meta-analysis methods and regular expressions to identify and visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting for multiple comparisons.

The Reactome pathway Knowledgebase

PubMed Central

Fabregat, Antonio; Sidiropoulos, Konstantinos; Garapati, Phani; Gillespie, Marc; Hausmann, Kerstin; Haw, Robin; Jassal, Bijay; Jupe, Steven; Korninger, Florian; McKay, Sheldon; Matthews, Lisa; May, Bruce; Milacic, Marija; Rothfels, Karen; Shamovsky, Veronica; Webber, Marissa; Weiser, Joel; Williams, Mark; Wu, Guanming; Stein, Lincoln; Hermjakob, Henning; D'Eustachio, Peter

2016-01-01

The Reactome Knowledgebase (www.reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations—an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently. PMID:26656494

Lotus Base: An integrated information portal for the model legume Lotus japonicus

PubMed Central

Mun, Terry; Bachmann, Asger; Gupta, Vikas; Stougaard, Jens; Andersen, Stig U.

2016-01-01

Lotus japonicus is a well-characterized model legume widely used in the study of plant-microbe interactions. However, datasets from various Lotus studies are poorly integrated and lack interoperability. We recognize the need for a comprehensive repository that allows comprehensive and dynamic exploration of Lotus genomic and transcriptomic data. Equally important are user-friendly in-browser tools designed for data visualization and interpretation. Here, we present Lotus Base, which opens to the research community a large, established LORE1 insertion mutant population containing an excess of 120,000 lines, and serves the end-user tightly integrated data from Lotus, such as the reference genome, annotated proteins, and expression profiling data. We report the integration of expression data from the L. japonicus gene expression atlas project, and the development of tools to cluster and export such data, allowing users to construct, visualize, and annotate co-expression gene networks. Lotus Base takes advantage of modern advances in browser technology to deliver powerful data interpretation for biologists. Its modular construction and publicly available application programming interface enable developers to tap into the wealth of integrated Lotus data. Lotus Base is freely accessible at: https://lotus.au.dk. PMID:28008948

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

PubMed Central

Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

2013-01-01

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production. PMID:23555215

Gene Graphics: a genomic neighborhood data visualization web application.

PubMed

Harrison, Katherine J; Crécy-Lagard, Valérie de; Zallot, Rémi

2018-04-15

The examination of gene neighborhood is an integral part of comparative genomics but no tools to produce publication quality graphics of gene clusters are available. Gene Graphics is a straightforward web application for creating such visuals. Supported inputs include National Center for Biotechnology Information gene and protein identifiers with automatic fetching of neighboring information, GenBank files and data extracted from the SEED database. Gene representations can be customized for many parameters including gene and genome names, colors and sizes. Gene attributes can be copied and pasted for rapid and user-friendly customization of homologous genes between species. In addition to Portable Network Graphics and Scalable Vector Graphics, produced representations can be exported as Tagged Image File Format or Encapsulated PostScript, formats that are standard for publication. Hands-on tutorials with real life examples inspired from publications are available for training. Gene Graphics is freely available at https://katlabs.cc/genegraphics/ and source code is hosted at https://github.com/katlabs/genegraphics. katherinejh@ufl.edu or remizallot@ufl.edu. Supplementary data are available at Bioinformatics online.

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

PATRIC, the bacterial bioinformatics database and analysis resource.

PubMed

Wattam, Alice R; Abraham, David; Dalay, Oral; Disz, Terry L; Driscoll, Timothy; Gabbard, Joseph L; Gillespie, Joseph J; Gough, Roger; Hix, Deborah; Kenyon, Ronald; Machi, Dustin; Mao, Chunhong; Nordberg, Eric K; Olson, Robert; Overbeek, Ross; Pusch, Gordon D; Shukla, Maulik; Schulman, Julie; Stevens, Rick L; Sullivan, Daniel E; Vonstein, Veronika; Warren, Andrew; Will, Rebecca; Wilson, Meredith J C; Yoo, Hyun Seung; Zhang, Chengdong; Zhang, Yan; Sobral, Bruno W

2014-01-01

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10,000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue.

PATRIC, the bacterial bioinformatics database and analysis resource

PubMed Central

Wattam, Alice R.; Abraham, David; Dalay, Oral; Disz, Terry L.; Driscoll, Timothy; Gabbard, Joseph L.; Gillespie, Joseph J.; Gough, Roger; Hix, Deborah; Kenyon, Ronald; Machi, Dustin; Mao, Chunhong; Nordberg, Eric K.; Olson, Robert; Overbeek, Ross; Pusch, Gordon D.; Shukla, Maulik; Schulman, Julie; Stevens, Rick L.; Sullivan, Daniel E.; Vonstein, Veronika; Warren, Andrew; Will, Rebecca; Wilson, Meredith J.C.; Yoo, Hyun Seung; Zhang, Chengdong; Zhang, Yan; Sobral, Bruno W.

2014-01-01

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein–protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10 000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue. PMID:24225323

NemaPath: online exploration of KEGG-based metabolic pathways for nematodes

PubMed Central

Wylie, Todd; Martin, John; Abubucker, Sahar; Yin, Yong; Messina, David; Wang, Zhengyuan; McCarter, James P; Mitreva, Makedonka

2008-01-01

Background Nematode.net is a web-accessible resource for investigating gene sequences from parasitic and free-living nematode genomes. Beyond the well-characterized model nematode C. elegans, over 500,000 expressed sequence tags (ESTs) and nearly 600,000 genome survey sequences (GSSs) have been generated from 36 nematode species as part of the Parasitic Nematode Genomics Program undertaken by the Genome Center at Washington University School of Medicine. However, these sequencing data are not present in most publicly available protein databases, which only include sequences in Swiss-Prot. Swiss-Prot, in turn, relies on GenBank/Embl/DDJP for predicted proteins from complete genomes or full-length proteins. Description Here we present the NemaPath pathway server, a web-based pathway-level visualization tool for navigating putative metabolic pathways for over 30 nematode species, including 27 parasites. The NemaPath approach consists of two parts: 1) a backend tool to align and evaluate nematode genomic sequences (curated EST contigs) against the annotated Kyoto Encyclopedia of Genes and Genomes (KEGG) protein database; 2) a web viewing application that displays annotated KEGG pathway maps based on desired confidence levels of primary sequence similarity as defined by a user. NemaPath also provides cross-referenced access to nematode genome information provided by other tools available on Nematode.net, including: detailed NemaGene EST cluster information; putative translations; GBrowse EST cluster views; links from nematode data to external databases for corresponding synonymous C. elegans counterparts, subject matches in KEGG's gene database, and also KEGG Ontology (KO) identification. Conclusion The NemaPath server hosts metabolic pathway mappings for 30 nematode species and is available on the World Wide Web at . The nematode source sequences used for the metabolic pathway mappings are available via FTP , as provided by the Genome Center at Washington University School of Medicine. PMID:18983679

GenColors-based comparative genome databases for small eukaryotic genomes.

PubMed

Felder, Marius; Romualdi, Alessandro; Petzold, Andreas; Platzer, Matthias; Sühnel, Jürgen; Glöckner, Gernot

2013-01-01

Many sequence data repositories can give a quick and easily accessible overview on genomes and their annotations. Less widespread is the possibility to compare related genomes with each other in a common database environment. We have previously described the GenColors database system (http://gencolors.fli-leibniz.de) and its applications to a number of bacterial genomes such as Borrelia, Legionella, Leptospira and Treponema. This system has an emphasis on genome comparison. It combines data from related genomes and provides the user with an extensive set of visualization and analysis tools. Eukaryote genomes are normally larger than prokaryote genomes and thus pose additional challenges for such a system. We have, therefore, adapted GenColors to also handle larger datasets of small eukaryotic genomes and to display eukaryotic gene structures. Further recent developments include whole genome views, genome list options and, for bacterial genome browsers, the display of horizontal gene transfer predictions. Two new GenColors-based databases for two fungal species (http://fgb.fli-leibniz.de) and for four social amoebas (http://sacgb.fli-leibniz.de) were set up. Both new resources open up a single entry point for related genomes for the amoebozoa and fungal research communities and other interested users. Comparative genomics approaches are greatly facilitated by these resources.

IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes

DOE PAGES

Hadjithomas, Michalis; Chen, I-Min A.; Chu, Ken; ...

2016-11-29

Secondary metabolites produced by microbes have diverse biological functions, which makes them a great potential source of biotechnologically relevant compounds with antimicrobial, anti-cancer and other activities. The proteins needed to synthesize these natural products are often encoded by clusters of co-located genes called biosynthetic gene clusters (BCs). In order to advance the exploration of microbial secondary metabolism, we developed the largest publically available database of experimentally verified and predicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc/). Here, we describe an update of IMG-ABC, which includes ClusterScout, a tool for targeted identification of custom biosynthetic genemore » clusters across 40 000 isolate microbial genomes, and a new search capability to query more than 700 000 BCs from isolate genomes for clusters with similar Pfam composition. Additional features enable fast exploration and analysis of BCs through two new interactive visualization features, a BC function heatmap and a BC similarity network graph. These new tools and features add to the value of IMG-ABC's vast body of BC data, facilitating their in-depth analysis and accelerating secondary metabolite discovery.« less

IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes

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

Hadjithomas, Michalis; Chen, I-Min A.; Chu, Ken

Secondary metabolites produced by microbes have diverse biological functions, which makes them a great potential source of biotechnologically relevant compounds with antimicrobial, anti-cancer and other activities. The proteins needed to synthesize these natural products are often encoded by clusters of co-located genes called biosynthetic gene clusters (BCs). In order to advance the exploration of microbial secondary metabolism, we developed the largest publically available database of experimentally verified and predicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc/). Here, we describe an update of IMG-ABC, which includes ClusterScout, a tool for targeted identification of custom biosynthetic genemore » clusters across 40 000 isolate microbial genomes, and a new search capability to query more than 700 000 BCs from isolate genomes for clusters with similar Pfam composition. Additional features enable fast exploration and analysis of BCs through two new interactive visualization features, a BC function heatmap and a BC similarity network graph. These new tools and features add to the value of IMG-ABC's vast body of BC data, facilitating their in-depth analysis and accelerating secondary metabolite discovery.« less

Integration of genomic and medical data into a 3D atlas of human anatomy.

PubMed

Turinsky, Andrei L; Fanea, Elena; Trinh, Quang; Dong, Xiaoli; Stromer, Julie N; Shu, Xueling; Wat, Stephen; Hallgrímsson, Benedikt; Hill, Jonathan W; Edwards, Carol; Grosenick, Brenda; Yajima, Masumi; Sensen, Christoph W

2008-01-01

We have developed a framework for the visual integration and exploration of multi-scale biomedical data, which includes anatomical and molecular components. We have also created a Java-based software system that integrates molecular information, such as gene expression data, into a three-dimensional digital atlas of the male adult human anatomy. Our atlas is structured according to the Terminologia Anatomica. The underlying data-indexing mechanism uses open standards and semantic ontology-processing tools to establish the associations between heterogeneous data types. The software system makes an extensive use of virtual reality visualization.

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.

An integrative model for in-silico clinical-genomics discovery science.

PubMed

Lussier, Yves A; Sarkar, Indra Nell; Cantor, Michael

2002-01-01

Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

Coordinates and intervals in graph-based reference genomes.

PubMed

Rand, Knut D; Grytten, Ivar; Nederbragt, Alexander J; Storvik, Geir O; Glad, Ingrid K; Sandve, Geir K

2017-05-18

It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes. We formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures. We show the advantage of our methods by representing genes on a graph-based representation of the newest assembly of the human genome (GRCh38) and its alternative loci for regions that are highly variable. More complex reference genomes, containing alternative loci, require methods to represent genomic data on these structures. Our proposed notation for genomic intervals makes it possible to fully utilize the alternative loci of the GRCh38 assembly and potential future graph-based reference genomes. We have made a Python package for representing such intervals on offset-based coordinate systems, available at https://github.com/uio-cels/offsetbasedgraph . An interactive web-tool using this Python package to visualize genes on a graph created from GRCh38 is available at https://github.com/uio-cels/genomicgraphcoords .

CSAR-web: a web server of contig scaffolding using algebraic rearrangements.

PubMed

Chen, Kun-Tze; Lu, Chin Lung

2018-05-04

CSAR-web is a web-based tool that allows the users to efficiently and accurately scaffold (i.e. order and orient) the contigs of a target draft genome based on a complete or incomplete reference genome from a related organism. It takes as input a target genome in multi-FASTA format and a reference genome in FASTA or multi-FASTA format, depending on whether the reference genome is complete or incomplete, respectively. In addition, it requires the users to choose either 'NUCmer on nucleotides' or 'PROmer on translated amino acids' for CSAR-web to identify conserved genomic markers (i.e. matched sequence regions) between the target and reference genomes, which are used by the rearrangement-based scaffolding algorithm in CSAR-web to order and orient the contigs of the target genome based on the reference genome. In the output page, CSAR-web displays its scaffolding result in a graphical mode (i.e. scalable dotplot) allowing the users to visually validate the correctness of scaffolded contigs and in a tabular mode allowing the users to view the details of scaffolds. CSAR-web is available online at http://genome.cs.nthu.edu.tw/CSAR-web.

COGNAT: a web server for comparative analysis of genomic neighborhoods.

PubMed

Klimchuk, Olesya I; Konovalov, Kirill A; Perekhvatov, Vadim V; Skulachev, Konstantin V; Dibrova, Daria V; Mulkidjanian, Armen Y

2017-11-22

In prokaryotic genomes, functionally coupled genes can be organized in conserved gene clusters enabling their coordinated regulation. Such clusters could contain one or several operons, which are groups of co-transcribed genes. Those genes that evolved from a common ancestral gene by speciation (i.e. orthologs) are expected to have similar genomic neighborhoods in different organisms, whereas those copies of the gene that are responsible for dissimilar functions (i.e. paralogs) could be found in dissimilar genomic contexts. Comparative analysis of genomic neighborhoods facilitates the prediction of co-regulated genes and helps to discern different functions in large protein families. We intended, building on the attribution of gene sequences to the clusters of orthologous groups of proteins (COGs), to provide a method for visualization and comparative analysis of genomic neighborhoods of evolutionary related genes, as well as a respective web server. Here we introduce the COmparative Gene Neighborhoods Analysis Tool (COGNAT), a web server for comparative analysis of genomic neighborhoods. The tool is based on the COG database, as well as the Pfam protein families database. As an example, we show the utility of COGNAT in identifying a new type of membrane protein complex that is formed by paralog(s) of one of the membrane subunits of the NADH:quinone oxidoreductase of type 1 (COG1009) and a cytoplasmic protein of unknown function (COG3002). This article was reviewed by Drs. Igor Zhulin, Uri Gophna and Igor Rogozin.

The TTSMI database: a catalog of triplex target DNA sites associated with genes and regulatory elements in the human genome.

PubMed

Jenjaroenpun, Piroon; Chew, Chee Siang; Yong, Tai Pang; Choowongkomon, Kiattawee; Thammasorn, Wimada; Kuznetsov, Vladimir A

2015-01-01

A triplex target DNA site (TTS), a stretch of DNA that is composed of polypurines, is able to form a triple-helix (triplex) structure with triplex-forming oligonucleotides (TFOs) and is able to influence the site-specific modulation of gene expression and/or the modification of genomic DNA. The co-localization of a genomic TTS with gene regulatory signals and functional genome structures suggests that TFOs could potentially be exploited in antigene strategies for the therapy of cancers and other genetic diseases. Here, we present the TTS Mapping and Integration (TTSMI; http://ttsmi.bii.a-star.edu.sg) database, which provides a catalog of unique TTS locations in the human genome and tools for analyzing the co-localization of TTSs with genomic regulatory sequences and signals that were identified using next-generation sequencing techniques and/or predicted by computational models. TTSMI was designed as a user-friendly tool that facilitates (i) fast searching/filtering of TTSs using several search terms and criteria associated with sequence stability and specificity, (ii) interactive filtering of TTSs that co-localize with gene regulatory signals and non-B DNA structures, (iii) exploration of dynamic combinations of the biological signals of specific TTSs and (iv) visualization of a TTS simultaneously with diverse annotation tracks via the UCSC genome browser. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Repetitive element signature-based visualization, distance computation, and classification of 1766 microbial genomes.

PubMed

Lee, Kang-Hoon; Shin, Kyung-Seop; Lim, Debora; Kim, Woo-Chan; Chung, Byung Chang; Han, Gyu-Bum; Roh, Jeongkyu; Cho, Dong-Ho; Cho, Kiho

2015-07-01

The genomes of living organisms are populated with pleomorphic repetitive elements (REs) of varying densities. Our hypothesis that genomic RE landscapes are species/strain/individual-specific was implemented into the Genome Signature Imaging system to visualize and compute the RE-based signatures of any genome. Following the occurrence profiling of 5-nucleotide REs/words, the information from top-50 frequency words was transformed into a genome-specific signature and visualized as Genome Signature Images (GSIs), using a CMYK scheme. An algorithm for computing distances among GSIs was formulated using the GSIs' variables (word identity, frequency, and frequency order). The utility of the GSI-distance computation system was demonstrated with control genomes. GSI-based computation of genome-relatedness among 1766 microbes (117 archaea and 1649 bacteria) identified their clustering patterns; although the majority paralleled the established classification, some did not. The Genome Signature Imaging system, with its visualization and distance computation functions, enables genome-scale evolutionary studies involving numerous genomes with varying sizes. Copyright © 2015 Elsevier Inc. All rights reserved.

GENEASE: Real time bioinformatics tool for multi-omics and disease ontology exploration, analysis and visualization.

PubMed

Ghandikota, Sudhir; Hershey, Gurjit K Khurana; Mersha, Tesfaye B

2018-03-24

Advances in high-throughput sequencing technologies have made it possible to generate multiple omics data at an unprecedented rate and scale. The accumulation of these omics data far outpaces the rate at which biologists can mine and generate new hypothesis to test experimentally. There is an urgent need to develop a myriad of powerful tools to efficiently and effectively search and filter these resources to address specific post-GWAS functional genomics questions. However, to date, these resources are scattered across several databases and often lack a unified portal for data annotation and analytics. In addition, existing tools to analyze and visualize these databases are highly fragmented, resulting researchers to access multiple applications and manual interventions for each gene or variant in an ad hoc fashion until all the questions are answered. In this study, we present GENEASE, a web-based one-stop bioinformatics tool designed to not only query and explore multi-omics and phenotype databases (e.g., GTEx, ClinVar, dbGaP, GWAS Catalog, ENCODE, Roadmap Epigenomics, KEGG, Reactome, Gene and Phenotype Ontology) in a single web interface but also to perform seamless post genome-wide association downstream functional and overlap analysis for non-coding regulatory variants. GENEASE accesses over 50 different databases in public domain including model organism-specific databases to facilitate gene/variant and disease exploration, enrichment and overlap analysis in real time. It is a user-friendly tool with point-and-click interface containing links for support information including user manual and examples. GENEASE can be accessed freely at http://research.cchmc.org/mershalab/genease_new/login.html. Tesfaye.Mersha@cchmc.org, Sudhir.Ghandikota@cchmc.org. Supplementary data are available at Bioinformatics online.

YouGenMap: a web platform for dynamic multi-comparative mapping and visualization of genetic maps

Treesearch

Keith Batesole; Kokulapalan Wimalanathan; Lin Liu; Fan Zhang; Craig S. Echt; Chun Liang

2014-01-01

Comparative genetic maps are used in examination of genome organization, detection of conserved gene order, and exploration of marker order variations. YouGenMap is an open-source web tool that offers dynamic comparative mapping capability of users' own genetic mapping between 2 or more map sets. Users' genetic map data and optional gene annotations are...

DaMold: A data-mining platform for variant annotation and visualization in molecular diagnostics research.

PubMed

Pandey, Ram Vinay; Pabinger, Stephan; Kriegner, Albert; Weinhäusel, Andreas

2017-07-01

Next-generation sequencing (NGS) has become a powerful and efficient tool for routine mutation screening in clinical research. As each NGS test yields hundreds of variants, the current challenge is to meaningfully interpret the data and select potential candidates. Analyzing each variant while manually investigating several relevant databases to collect specific information is a cumbersome and time-consuming process, and it requires expertise and familiarity with these databases. Thus, a tool that can seamlessly annotate variants with clinically relevant databases under one common interface would be of great help for variant annotation, cross-referencing, and visualization. This tool would allow variants to be processed in an automated and high-throughput manner and facilitate the investigation of variants in several genome browsers. Several analysis tools are available for raw sequencing-read processing and variant identification, but an automated variant filtering, annotation, cross-referencing, and visualization tool is still lacking. To fulfill these requirements, we developed DaMold, a Web-based, user-friendly tool that can filter and annotate variants and can access and compile information from 37 resources. It is easy to use, provides flexible input options, and accepts variants from NGS and Sanger sequencing as well as hotspots in VCF and BED formats. DaMold is available as an online application at http://damold.platomics.com/index.html, and as a Docker container and virtual machine at https://sourceforge.net/projects/damold/. © 2017 Wiley Periodicals, Inc.

Toppar: an interactive browser for viewing association study results.

PubMed

Juliusdottir, Thorhildur; Banasik, Karina; Robertson, Neil R; Mott, Richard; McCarthy, Mark I

2018-06-01

Data integration and visualization help geneticists make sense of large amounts of data. To help facilitate interpretation of genetic association data we developed Toppar, a customizable visualization tool that stores results from association studies and enables browsing over multiple results, by combining features from existing tools and linking to appropriate external databases. Detailed information on Toppar's features and functionality are on our website http://mccarthy.well.ox.ac.uk/toppar/docs along with instructions on how to download, install and run Toppar. Our online version of Toppar is accessible from the website and can be test-driven using Firefox, Safari or Chrome on sub-sets of publicly available genome-wide association study anthropometric waist and body mass index data (Locke et al., 2015; Shungin et al., 2015) from the Genetic Investigation of ANthropometric Traits consortium. totajuliusd@gmail.com.

SigWin-detector: a Grid-enabled workflow for discovering enriched windows of genomic features related to DNA sequences.

PubMed

Inda, Márcia A; van Batenburg, Marinus F; Roos, Marco; Belloum, Adam S Z; Vasunin, Dmitry; Wibisono, Adianto; van Kampen, Antoine H C; Breit, Timo M

2008-08-08

Chromosome location is often used as a scaffold to organize genomic information in both the living cell and molecular biological research. Thus, ever-increasing amounts of data about genomic features are stored in public databases and can be readily visualized by genome browsers. To perform in silico experimentation conveniently with this genomics data, biologists need tools to process and compare datasets routinely and explore the obtained results interactively. The complexity of such experimentation requires these tools to be based on an e-Science approach, hence generic, modular, and reusable. A virtual laboratory environment with workflows, workflow management systems, and Grid computation are therefore essential. Here we apply an e-Science approach to develop SigWin-detector, a workflow-based tool that can detect significantly enriched windows of (genomic) features in a (DNA) sequence in a fast and reproducible way. For proof-of-principle, we utilize a biological use case to detect regions of increased and decreased gene expression (RIDGEs and anti-RIDGEs) in human transcriptome maps. We improved the original method for RIDGE detection by replacing the costly step of estimation by random sampling with a faster analytical formula for computing the distribution of the null hypothesis being tested and by developing a new algorithm for computing moving medians. SigWin-detector was developed using the WS-VLAM workflow management system and consists of several reusable modules that are linked together in a basic workflow. The configuration of this basic workflow can be adapted to satisfy the requirements of the specific in silico experiment. As we show with the results from analyses in the biological use case on RIDGEs, SigWin-detector is an efficient and reusable Grid-based tool for discovering windows enriched for features of a particular type in any sequence of values. Thus, SigWin-detector provides the proof-of-principle for the modular e-Science based concept of integrative bioinformatics experimentation.

Ensembl Plants: Integrating Tools for Visualizing, Mining, and Analyzing Plant Genomics Data.

PubMed

Bolser, Dan; Staines, Daniel M; Pritchard, Emily; Kersey, Paul

2016-01-01

Ensembl Plants ( http://plants.ensembl.org ) is an integrative resource presenting genome-scale information for a growing number of sequenced plant species (currently 33). Data provided includes genome sequence, gene models, functional annotation, and polymorphic loci. Various additional information are provided for variation data, including population structure, individual genotypes, linkage, and phenotype data. In each release, comparative analyses are performed on whole genome and protein sequences, and genome alignments and gene trees are made available that show the implied evolutionary history of each gene family. Access to the data is provided through a genome browser incorporating many specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These access routes are consistent with those offered through the Ensembl interface for the genomes of non-plant species, including those of plant pathogens, pests, and pollinators.Ensembl Plants is updated 4-5 times a year and is developed in collaboration with our international partners in the Gramene ( http://www.gramene.org ) and transPLANT projects ( http://www.transplantdb.org ).

Groups: knowledge spreadsheets for symbolic biocomputing.

PubMed

Travers, Michael; Paley, Suzanne M; Shrager, Jeff; Holland, Timothy A; Karp, Peter D

2013-01-01

Knowledge spreadsheets (KSs) are a visual tool for interactive data analysis and exploration. They differ from traditional spreadsheets in that rather than being oriented toward numeric data, they work with symbolic knowledge representation structures and provide operations that take into account the semantics of the application domain. 'Groups' is an implementation of KSs within the Pathway Tools system. Groups allows Pathway Tools users to define a group of objects (e.g. groups of genes or metabolites) from a Pathway/Genome Database. Groups can be transformed (e.g. by transforming a metabolite group to the group of pathways in which those metabolites are substrates); combined through set operations; analysed (e.g. through enrichment analysis); and visualized (e.g. by painting onto a metabolic map diagram). Users of the Pathway Tools-based BioCyc.org website have made extensive use of Groups, and an informal survey of Groups users suggests that Groups has achieved the goal of allowing biologists themselves to perform some data manipulations that previously would have required the assistance of a programmer. Database URL: BioCyc.org.

Data on the genome-wide identification of CNL R-genes in Setaria italica (L.) P. Beauv.

PubMed

Andersen, Ethan J; Nepal, Madhav P

2017-08-01

We report data associated with the identification of 242 disease resistance genes (R-genes) in the genome of Setaria italica as presented in "Genetic diversity of disease resistance genes in foxtail millet ( Setaria italica L.)" (Andersen and Nepal, 2017) [1]. Our data describe the structure and evolution of the Coiled-coil, Nucleotide-binding site, Leucine-rich repeat (CNL) R-genes in foxtail millet. The CNL genes were identified through rigorous extraction and analysis of recently available plant genome sequences using cutting-edge analytical software. Data visualization includes gene structure diagrams, chromosomal syntenic maps, a chromosomal density plot, and a maximum-likelihood phylogenetic tree comparing Sorghum bicolor , Panicum virgatum , Setaria italica , and Arabidopsis thaliana . Compilation of InterProScan annotations, Gene Ontology (GO) annotations, and Basic Local Alignment Search Tool (BLAST) results for the 242 R-genes identified in the foxtail millet genome are also included in tabular format.

Integrated genome browser: visual analytics platform for genomics.

PubMed

Freese, Nowlan H; Norris, David C; Loraine, Ann E

2016-07-15

Genome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser. Here we describe multiple updates to IGB, including all-new capabilities to display and interact with data from high-throughput sequencing experiments. To demonstrate, we describe example visualizations and analyses of datasets from RNA-Seq, ChIP-Seq and bisulfite sequencing experiments. Understanding results from genome-scale experiments requires viewing the data in the context of reference genome annotations and other related datasets. To facilitate this, we enhanced IGB's ability to consume data from diverse sources, including Galaxy, Distributed Annotation and IGB-specific Quickload servers. To support future visualization needs as new genome-scale assays enter wide use, we transformed the IGB codebase into a modular, extensible platform for developers to create and deploy all-new visualizations of genomic data. IGB is open source and is freely available from http://bioviz.org/igb aloraine@uncc.edu. © The Author 2016. Published by Oxford University Press.

NCBI GEO: archive for functional genomics data sets--update.

PubMed

Barrett, Tanya; Wilhite, Stephen E; Ledoux, Pierre; Evangelista, Carlos; Kim, Irene F; Tomashevsky, Maxim; Marshall, Kimberly A; Phillippy, Katherine H; Sherman, Patti M; Holko, Michelle; Yefanov, Andrey; Lee, Hyeseung; Zhang, Naigong; Robertson, Cynthia L; Serova, Nadezhda; Davis, Sean; Soboleva, Alexandra

2013-01-01

The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.

CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database

PubMed Central

Jia, Baofeng; Raphenya, Amogelang R.; Alcock, Brian; Waglechner, Nicholas; Guo, Peiyao; Tsang, Kara K.; Lago, Briony A.; Dave, Biren M.; Pereira, Sheldon; Sharma, Arjun N.; Doshi, Sachin; Courtot, Mélanie; Lo, Raymond; Williams, Laura E.; Frye, Jonathan G.; Elsayegh, Tariq; Sardar, Daim; Westman, Erin L.; Pawlowski, Andrew C.; Johnson, Timothy A.; Brinkman, Fiona S.L.; Wright, Gerard D.; McArthur, Andrew G.

2017-01-01

The Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca) is a manually curated resource containing high quality reference data on the molecular basis of antimicrobial resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR. CARD is ontologically structured, model centric, and spans the breadth of AMR drug classes and resistance mechanisms, including intrinsic, mutation-driven and acquired resistance. It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Its design allows the development of novel genome analysis tools, such as the Resistance Gene Identifier (RGI) for resistome prediction from raw genome sequence. Recent improvements include extensive curation of additional reference sequences and mutations, development of a unique Model Ontology and accompanying AMR detection models to power sequence analysis, new visualization tools, and expansion of the RGI for detection of emergent AMR threats. CARD curation is updated monthly based on an interplay of manual literature curation, computational text mining, and genome analysis. PMID:27789705

SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care.

PubMed

Warner, Jeremy L; Rioth, Matthew J; Mandl, Kenneth D; Mandel, Joshua C; Kreda, David A; Kohane, Isaac S; Carbone, Daniel; Oreto, Ross; Wang, Lucy; Zhu, Shilin; Yao, Heming; Alterovitz, Gil

2016-07-01

Precision cancer medicine (PCM) will require ready access to genomic data within the clinical workflow and tools to assist clinical interpretation and enable decisions. Since most electronic health record (EHR) systems do not yet provide such functionality, we developed an EHR-agnostic, clinico-genomic mobile app to demonstrate several features that will be needed for point-of-care conversations. Our prototype, called Substitutable Medical Applications and Reusable Technology (SMART)® PCM, visualizes genomic information in real time, comparing a patient's diagnosis-specific somatic gene mutations detected by PCR-based hotspot testing to a population-level set of comparable data. The initial prototype works for patient specimens with 0 or 1 detected mutation. Genomics extensions were created for the Health Level Seven® Fast Healthcare Interoperability Resources (FHIR)® standard; otherwise, the prototype is a normal SMART on FHIR app. The PCM prototype can rapidly present a visualization that compares a patient's somatic genomic alterations against a distribution built from more than 3000 patients, along with context-specific links to external knowledge bases. Initial evaluation by oncologists provided important feedback about the prototype's strengths and weaknesses. We added several requested enhancements and successfully demonstrated the app at the inaugural American Society of Clinical Oncology Interoperability Demonstration; we have also begun to expand visualization capabilities to include cancer specimens with multiple mutations. PCM is open-source software for clinicians to present the individual patient within the population-level spectrum of cancer somatic mutations. The app can be implemented on any SMART on FHIR-enabled EHRs, and future versions of PCM should be able to evolve in parallel with external knowledge bases. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Genomes as geography: using GIS technology to build interactive genome feature maps

PubMed Central

Dolan, Mary E; Holden, Constance C; Beard, M Kate; Bult, Carol J

2006-01-01

Background Many commonly used genome browsers display sequence annotations and related attributes as horizontal data tracks that can be toggled on and off according to user preferences. Most genome browsers use only simple keyword searches and limit the display of detailed annotations to one chromosomal region of the genome at a time. We have employed concepts, methodologies, and tools that were developed for the display of geographic data to develop a Genome Spatial Information System (GenoSIS) for displaying genomes spatially, and interacting with genome annotations and related attribute data. In contrast to the paradigm of horizontally stacked data tracks used by most genome browsers, GenoSIS uses the concept of registered spatial layers composed of spatial objects for integrated display of diverse data. In addition to basic keyword searches, GenoSIS supports complex queries, including spatial queries, and dynamically generates genome maps. Our adaptation of the geographic information system (GIS) model in a genome context supports spatial representation of genome features at multiple scales with a versatile and expressive query capability beyond that supported by existing genome browsers. Results We implemented an interactive genome sequence feature map for the mouse genome in GenoSIS, an application that uses ArcGIS, a commercially available GIS software system. The genome features and their attributes are represented as spatial objects and data layers that can be toggled on and off according to user preferences or displayed selectively in response to user queries. GenoSIS supports the generation of custom genome maps in response to complex queries about genome features based on both their attributes and locations. Our example application of GenoSIS to the mouse genome demonstrates the powerful visualization and query capability of mature GIS technology applied in a novel domain. Conclusion Mapping tools developed specifically for geographic data can be exploited to display, explore and interact with genome data. The approach we describe here is organism independent and is equally useful for linear and circular chromosomes. One of the unique capabilities of GenoSIS compared to existing genome browsers is the capacity to generate genome feature maps dynamically in response to complex attribute and spatial queries. PMID:16984652

Treelink: data integration, clustering and visualization of phylogenetic trees.

PubMed

Allende, Christian; Sohn, Erik; Little, Cedric

2015-12-29

Phylogenetic trees are central to a wide range of biological studies. In many of these studies, tree nodes need to be associated with a variety of attributes. For example, in studies concerned with viral relationships, tree nodes are associated with epidemiological information, such as location, age and subtype. Gene trees used in comparative genomics are usually linked with taxonomic information, such as functional annotations and events. A wide variety of tree visualization and annotation tools have been developed in the past, however none of them are intended for an integrative and comparative analysis. Treelink is a platform-independent software for linking datasets and sequence files to phylogenetic trees. The application allows an automated integration of datasets to trees for operations such as classifying a tree based on a field or showing the distribution of selected data attributes in branches and leafs. Genomic and proteonomic sequences can also be linked to the tree and extracted from internal and external nodes. A novel clustering algorithm to simplify trees and display the most divergent clades was also developed, where validation can be achieved using the data integration and classification function. Integrated geographical information allows ancestral character reconstruction for phylogeographic plotting based on parsimony and likelihood algorithms. Our software can successfully integrate phylogenetic trees with different data sources, and perform operations to differentiate and visualize those differences within a tree. File support includes the most popular formats such as newick and csv. Exporting visualizations as images, cluster outputs and genomic sequences is supported. Treelink is available as a web and desktop application at http://www.treelinkapp.com .

NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology.

PubMed

Wei, Qing; Khan, Ishita K; Ding, Ziyun; Yerneni, Satwica; Kihara, Daisuke

2017-03-20

The number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion. NaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions. We developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo .

Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies.

PubMed

de Brevern, Alexandre G; Meyniel, Jean-Philippe; Fairhead, Cécile; Neuvéglise, Cécile; Malpertuy, Alain

2015-01-01

Sequencing the human genome began in 1994, and 10 years of work were necessary in order to provide a nearly complete sequence. Nowadays, NGS technologies allow sequencing of a whole human genome in a few days. This deluge of data challenges scientists in many ways, as they are faced with data management issues and analysis and visualization drawbacks due to the limitations of current bioinformatics tools. In this paper, we describe how the NGS Big Data revolution changes the way of managing and analysing data. We present how biologists are confronted with abundance of methods, tools, and data formats. To overcome these problems, focus on Big Data Information Technology innovations from web and business intelligence. We underline the interest of NoSQL databases, which are much more efficient than relational databases. Since Big Data leads to the loss of interactivity with data during analysis due to high processing time, we describe solutions from the Business Intelligence that allow one to regain interactivity whatever the volume of data is. We illustrate this point with a focus on the Amadea platform. Finally, we discuss visualization challenges posed by Big Data and present the latest innovations with JavaScript graphic libraries.

Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies

PubMed Central

de Brevern, Alexandre G.; Meyniel, Jean-Philippe; Fairhead, Cécile; Neuvéglise, Cécile; Malpertuy, Alain

2015-01-01

Sequencing the human genome began in 1994, and 10 years of work were necessary in order to provide a nearly complete sequence. Nowadays, NGS technologies allow sequencing of a whole human genome in a few days. This deluge of data challenges scientists in many ways, as they are faced with data management issues and analysis and visualization drawbacks due to the limitations of current bioinformatics tools. In this paper, we describe how the NGS Big Data revolution changes the way of managing and analysing data. We present how biologists are confronted with abundance of methods, tools, and data formats. To overcome these problems, focus on Big Data Information Technology innovations from web and business intelligence. We underline the interest of NoSQL databases, which are much more efficient than relational databases. Since Big Data leads to the loss of interactivity with data during analysis due to high processing time, we describe solutions from the Business Intelligence that allow one to regain interactivity whatever the volume of data is. We illustrate this point with a focus on the Amadea platform. Finally, we discuss visualization challenges posed by Big Data and present the latest innovations with JavaScript graphic libraries. PMID:26125026

Ensembl BioMarts: a hub for data retrieval across taxonomic space.

PubMed

Kinsella, Rhoda J; Kähäri, Andreas; Haider, Syed; Zamora, Jorge; Proctor, Glenn; Spudich, Giulietta; Almeida-King, Jeff; Staines, Daniel; Derwent, Paul; Kerhornou, Arnaud; Kersey, Paul; Flicek, Paul

2011-01-01

For a number of years the BioMart data warehousing system has proven to be a valuable resource for scientists seeking a fast and versatile means of accessing the growing volume of genomic data provided by the Ensembl project. The launch of the Ensembl Genomes project in 2009 complemented the Ensembl project by utilizing the same visualization, interactive and programming tools to provide users with a means for accessing genome data from a further five domains: protists, bacteria, metazoa, plants and fungi. The Ensembl and Ensembl Genomes BioMarts provide a point of access to the high-quality gene annotation, variation data, functional and regulatory annotation and evolutionary relationships from genomes spanning the taxonomic space. This article aims to give a comprehensive overview of the Ensembl and Ensembl Genomes BioMarts as well as some useful examples and a description of current data content and future objectives. Database URLs: http://www.ensembl.org/biomart/martview/; http://metazoa.ensembl.org/biomart/martview/; http://plants.ensembl.org/biomart/martview/; http://protists.ensembl.org/biomart/martview/; http://fungi.ensembl.org/biomart/martview/; http://bacteria.ensembl.org/biomart/martview/.

A New Approach to Dissect Nuclear Organization: TALE-Mediated Genome Visualization (TGV).

PubMed

Miyanari, Yusuke

2016-01-01

Spatiotemporal organization of chromatin within the nucleus has so far remained elusive. Live visualization of nuclear remodeling could be a promising approach to understand its functional relevance in genome functions and mechanisms regulating genome architecture. Recent technological advances in live imaging of chromosomes begun to explore the biological roles of the movement of the chromatin within the nucleus. Here I describe a new technique, called TALE-mediated genome visualization (TGV), which allows us to visualize endogenous repetitive sequence including centromeric, pericentromeric, and telomeric repeats in living cells.

CASFISH: CRISPR/Cas9-mediated in situ labeling of genomic loci in fixed cells.

PubMed

Deng, Wulan; Shi, Xinghua; Tjian, Robert; Lionnet, Timothée; Singer, Robert H

2015-09-22

Direct visualization of genomic loci in the 3D nucleus is important for understanding the spatial organization of the genome and its association with gene expression. Various DNA FISH methods have been developed in the past decades, all involving denaturing dsDNA and hybridizing fluorescent nucleic acid probes. Here we report a novel approach that uses in vitro constituted nuclease-deficient clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated caspase 9 (Cas9) complexes as probes to label sequence-specific genomic loci fluorescently without global DNA denaturation (Cas9-mediated fluorescence in situ hybridization, CASFISH). Using fluorescently labeled nuclease-deficient Cas9 (dCas9) protein assembled with various single-guide RNA (sgRNA), we demonstrated rapid and robust labeling of repetitive DNA elements in pericentromere, centromere, G-rich telomere, and coding gene loci. Assembling dCas9 with an array of sgRNAs tiling arbitrary target loci, we were able to visualize nonrepetitive genomic sequences. The dCas9/sgRNA binary complex is stable and binds its target DNA with high affinity, allowing sequential or simultaneous probing of multiple targets. CASFISH assays using differently colored dCas9/sgRNA complexes allow multicolor labeling of target loci in cells. In addition, the CASFISH assay is remarkably rapid under optimal conditions and is applicable for detection in primary tissue sections. This rapid, robust, less disruptive, and cost-effective technology adds a valuable tool for basic research and genetic diagnosis.

An integrated 3-Dimensional Genome Modeling Engine for data-driven simulation of spatial genome organization.

PubMed

Szałaj, Przemysław; Tang, Zhonghui; Michalski, Paul; Pietal, Michal J; Luo, Oscar J; Sadowski, Michał; Li, Xingwang; Radew, Kamen; Ruan, Yijun; Plewczynski, Dariusz

2016-12-01

ChIA-PET is a high-throughput mapping technology that reveals long-range chromatin interactions and provides insights into the basic principles of spatial genome organization and gene regulation mediated by specific protein factors. Recently, we showed that a single ChIA-PET experiment provides information at all genomic scales of interest, from the high-resolution locations of binding sites and enriched chromatin interactions mediated by specific protein factors, to the low resolution of nonenriched interactions that reflect topological neighborhoods of higher-order chromosome folding. This multilevel nature of ChIA-PET data offers an opportunity to use multiscale 3D models to study structural-functional relationships at multiple length scales, but doing so requires a structural modeling platform. Here, we report the development of 3D-GNOME (3-Dimensional Genome Modeling Engine), a complete computational pipeline for 3D simulation using ChIA-PET data. 3D-GNOME consists of three integrated components: a graph-distance-based heat map normalization tool, a 3D modeling platform, and an interactive 3D visualization tool. Using ChIA-PET and Hi-C data derived from human B-lymphocytes, we demonstrate the effectiveness of 3D-GNOME in building 3D genome models at multiple levels, including the entire genome, individual chromosomes, and specific segments at megabase (Mb) and kilobase (kb) resolutions of single average and ensemble structures. Further incorporation of CTCF-motif orientation and high-resolution looping patterns in 3D simulation provided additional reliability of potential biologically plausible topological structures. © 2016 Szałaj et al.; Published by Cold Spring Harbor Laboratory Press.

aCNViewer: Comprehensive genome-wide visualization of absolute copy number and copy neutral variations.

PubMed

Renault, Victor; Tost, Jörg; Pichon, Fabien; Wang-Renault, Shu-Fang; Letouzé, Eric; Imbeaud, Sandrine; Zucman-Rossi, Jessica; Deleuze, Jean-François; How-Kit, Alexandre

2017-01-01

Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information. To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B). aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub https://github.com/FJD-CEPH/aCNViewer and Docker https://hub.docker.com/r/fjdceph/acnviewer/. aCNViewer@cephb.fr.

Exploring variation-aware contig graphs for (comparative) metagenomics using MaryGold

PubMed Central

Nijkamp, Jurgen F.; Pop, Mihai; Reinders, Marcel J. T.; de Ridder, Dick

2013-01-01

Motivation: Although many tools are available to study variation and its impact in single genomes, there is a lack of algorithms for finding such variation in metagenomes. This hampers the interpretation of metagenomics sequencing datasets, which are increasingly acquired in research on the (human) microbiome, in environmental studies and in the study of processes in the production of foods and beverages. Existing algorithms often depend on the use of reference genomes, which pose a problem when a metagenome of a priori unknown strain composition is studied. In this article, we develop a method to perform reference-free detection and visual exploration of genomic variation, both within a single metagenome and between metagenomes. Results: We present the MaryGold algorithm and its implementation, which efficiently detects bubble structures in contig graphs using graph decomposition. These bubbles represent variable genomic regions in closely related strains in metagenomic samples. The variation found is presented in a condensed Circos-based visualization, which allows for easy exploration and interpretation of the found variation. We validated the algorithm on two simulated datasets containing three respectively seven Escherichia coli genomes and showed that finding allelic variation in these genomes improves assemblies. Additionally, we applied MaryGold to publicly available real metagenomic datasets, enabling us to find within-sample genomic variation in the metagenomes of a kimchi fermentation process, the microbiome of a premature infant and in microbial communities living on acid mine drainage. Moreover, we used MaryGold for between-sample variation detection and exploration by comparing sequencing data sampled at different time points for both of these datasets. Availability: MaryGold has been written in C++ and Python and can be downloaded from http://bioinformatics.tudelft.nl/software Contact: d.deridder@tudelft.nl PMID:24058058

Microbial genomic island discovery, visualization and analysis.

PubMed

Bertelli, Claire; Tilley, Keith E; Brinkman, Fiona S L

2018-06-03

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes.

PubMed

Hadjithomas, Michalis; Chen, I-Min A; Chu, Ken; Huang, Jinghua; Ratner, Anna; Palaniappan, Krishna; Andersen, Evan; Markowitz, Victor; Kyrpides, Nikos C; Ivanova, Natalia N

2017-01-04

Secondary metabolites produced by microbes have diverse biological functions, which makes them a great potential source of biotechnologically relevant compounds with antimicrobial, anti-cancer and other activities. The proteins needed to synthesize these natural products are often encoded by clusters of co-located genes called biosynthetic gene clusters (BCs). In order to advance the exploration of microbial secondary metabolism, we developed the largest publically available database of experimentally verified and predicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc/). Here, we describe an update of IMG-ABC, which includes ClusterScout, a tool for targeted identification of custom biosynthetic gene clusters across 40 000 isolate microbial genomes, and a new search capability to query more than 700 000 BCs from isolate genomes for clusters with similar Pfam composition. Additional features enable fast exploration and analysis of BCs through two new interactive visualization features, a BC function heatmap and a BC similarity network graph. These new tools and features add to the value of IMG-ABC's vast body of BC data, facilitating their in-depth analysis and accelerating secondary metabolite discovery. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

PetriScape - A plugin for discrete Petri net simulations in Cytoscape.

PubMed

Almeida, Diogo; Azevedo, Vasco; Silva, Artur; Baumbach, Jan

2016-06-04

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

PetriScape - A plugin for discrete Petri net simulations in Cytoscape.

PubMed

Almeida, Diogo; Azevedo, Vasco; Silva, Artur; Baumbach, Jan

2016-03-01

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.

Theories of Visual Rhetoric: Looking at the Human Genome.

ERIC Educational Resources Information Center

Rosner, Mary

2001-01-01

Considers how visuals are constructions that are products of a writer's interpretation with its own "power-laden agenda." Reviews the current approach taken by composition scholars, surveys richer interdisciplinary work on visuals, and (by using visuals connected with the Human Genome Project) models an analysis of visuals as rhetoric.…

Dynamix: dynamic visualization by automatic selection of informative tracks from hundreds of genomic datasets.

PubMed

Monfort, Matthias; Furlong, Eileen E M; Girardot, Charles

2017-07-15

Visualization of genomic data is fundamental for gaining insights into genome function. Yet, co-visualization of a large number of datasets remains a challenge in all popular genome browsers and the development of new visualization methods is needed to improve the usability and user experience of genome browsers. We present Dynamix, a JBrowse plugin that enables the parallel inspection of hundreds of genomic datasets. Dynamix takes advantage of a priori knowledge to automatically display data tracks with signal within a genomic region of interest. As the user navigates through the genome, Dynamix automatically updates data tracks and limits all manual operations otherwise needed to adjust the data visible on screen. Dynamix also introduces a new carousel view that optimizes screen utilization by enabling users to independently scroll through groups of tracks. Dynamix is hosted at http://furlonglab.embl.de/Dynamix . charles.girardot@embl.de. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

NCBI GEO: archive for functional genomics data sets—update

PubMed Central

Barrett, Tanya; Wilhite, Stephen E.; Ledoux, Pierre; Evangelista, Carlos; Kim, Irene F.; Tomashevsky, Maxim; Marshall, Kimberly A.; Phillippy, Katherine H.; Sherman, Patti M.; Holko, Michelle; Yefanov, Andrey; Lee, Hyeseung; Zhang, Naigong; Robertson, Cynthia L.; Serova, Nadezhda; Davis, Sean; Soboleva, Alexandra

2013-01-01

The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data. PMID:23193258

Ensembl Genomes 2013: scaling up access to genome-wide data.

PubMed

Kersey, Paul Julian; Allen, James E; Christensen, Mikkel; Davis, Paul; Falin, Lee J; Grabmueller, Christoph; Hughes, Daniel Seth Toney; Humphrey, Jay; Kerhornou, Arnaud; Khobova, Julia; Langridge, Nicholas; McDowall, Mark D; Maheswari, Uma; Maslen, Gareth; Nuhn, Michael; Ong, Chuang Kee; Paulini, Michael; Pedro, Helder; Toneva, Iliana; Tuli, Mary Ann; Walts, Brandon; Williams, Gareth; Wilson, Derek; Youens-Clark, Ken; Monaco, Marcela K; Stein, Joshua; Wei, Xuehong; Ware, Doreen; Bolser, Daniel M; Howe, Kevin Lee; Kulesha, Eugene; Lawson, Daniel; Staines, Daniel Michael

2014-01-01

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species. The project exploits and extends technologies for genome annotation, analysis and dissemination, developed in the context of the vertebrate-focused Ensembl project, and provides a complementary set of resources for non-vertebrate species through a consistent set of programmatic and interactive interfaces. These provide access to data including reference sequence, gene models, transcriptional data, polymorphisms and comparative analysis. This article provides an update to the previous publications about the resource, with a focus on recent developments. These include the addition of important new genomes (and related data sets) including crop plants, vectors of human disease and eukaryotic pathogens. In addition, the resource has scaled up its representation of bacterial genomes, and now includes the genomes of over 9000 bacteria. Specific extensions to the web and programmatic interfaces have been developed to support users in navigating these large data sets. Looking forward, analytic tools to allow targeted selection of data for visualization and download are likely to become increasingly important in future as the number of available genomes increases within all domains of life, and some of the challenges faced in representing bacterial data are likely to become commonplace for eukaryotes in future.

An integrated network visualization framework towards metabolic engineering applications.

PubMed

Noronha, Alberto; Vilaça, Paulo; Rocha, Miguel

2014-12-30

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential. In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks. The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

FISH Oracle 2: a web server for integrative visualization of genomic data in cancer research.

PubMed

Mader, Malte; Simon, Ronald; Kurtz, Stefan

2014-03-31

A comprehensive view on all relevant genomic data is instrumental for understanding the complex patterns of molecular alterations typically found in cancer cells. One of the most effective ways to rapidly obtain an overview of genomic alterations in large amounts of genomic data is the integrative visualization of genomic events. We developed FISH Oracle 2, a web server for the interactive visualization of different kinds of downstream processed genomics data typically available in cancer research. A powerful search interface and a fast visualization engine provide a highly interactive visualization for such data. High quality image export enables the life scientist to easily communicate their results. A comprehensive data administration allows to keep track of the available data sets. We applied FISH Oracle 2 to published data and found evidence that, in colorectal cancer cells, the gene TTC28 may be inactivated in two different ways, a fact that has not been published before. The interactive nature of FISH Oracle 2 and the possibility to store, select and visualize large amounts of downstream processed data support life scientists in generating hypotheses. The export of high quality images supports explanatory data visualization, simplifying the communication of new biological findings. A FISH Oracle 2 demo server and the software is available at http://www.zbh.uni-hamburg.de/fishoracle.

The ENCODE Project at UC Santa Cruz.

PubMed

Thomas, Daryl J; Rosenbloom, Kate R; Clawson, Hiram; Hinrichs, Angie S; Trumbower, Heather; Raney, Brian J; Karolchik, Donna; Barber, Galt P; Harte, Rachel A; Hillman-Jackson, Jennifer; Kuhn, Robert M; Rhead, Brooke L; Smith, Kayla E; Thakkapallayil, Archana; Zweig, Ann S; Haussler, David; Kent, W James

2007-01-01

The goal of the Encyclopedia Of DNA Elements (ENCODE) Project is to identify all functional elements in the human genome. The pilot phase is for comparison of existing methods and for the development of new methods to rigorously analyze a defined 1% of the human genome sequence. Experimental datasets are focused on the origin of replication, DNase I hypersensitivity, chromatin immunoprecipitation, promoter function, gene structure, pseudogenes, non-protein-coding RNAs, transcribed RNAs, multiple sequence alignment and evolutionarily constrained elements. The ENCODE project at UCSC website (http://genome.ucsc.edu/ENCODE) is the primary portal for the sequence-based data produced as part of the ENCODE project. In the pilot phase of the project, over 30 labs provided experimental results for a total of 56 browser tracks supported by 385 database tables. The site provides researchers with a number of tools that allow them to visualize and analyze the data as well as download data for local analyses. This paper describes the portal to the data, highlights the data that has been made available, and presents the tools that have been developed within the ENCODE project. Access to the data and types of interactive analysis that are possible are illustrated through supplemental examples.

GAMOLA2, a Comprehensive Software Package for the Annotation and Curation of Draft and Complete Microbial Genomes

PubMed Central

Altermann, Eric; Lu, Jingli; McCulloch, Alan

2017-01-01

Expert curated annotation remains one of the critical steps in achieving a reliable biological relevant annotation. Here we announce the release of GAMOLA2, a user friendly and comprehensive software package to process, annotate and curate draft and complete bacterial, archaeal, and viral genomes. GAMOLA2 represents a wrapping tool to combine gene model determination, functional Blast, COG, Pfam, and TIGRfam analyses with structural predictions including detection of tRNAs, rRNA genes, non-coding RNAs, signal protein cleavage sites, transmembrane helices, CRISPR repeats and vector sequence contaminations. GAMOLA2 has already been validated in a wide range of bacterial and archaeal genomes, and its modular concept allows easy addition of further functionality in future releases. A modified and adapted version of the Artemis Genome Viewer (Sanger Institute) has been developed to leverage the additional features and underlying information provided by the GAMOLA2 analysis, and is part of the software distribution. In addition to genome annotations, GAMOLA2 features, among others, supplemental modules that assist in the creation of custom Blast databases, annotation transfers between genome versions, and the preparation of Genbank files for submission via the NCBI Sequin tool. GAMOLA2 is intended to be run under a Linux environment, whereas the subsequent visualization and manual curation in Artemis is mobile and platform independent. The development of GAMOLA2 is ongoing and community driven. New functionality can easily be added upon user requests, ensuring that GAMOLA2 provides information relevant to microbiologists. The software is available free of charge for academic use. PMID:28386247

GAMOLA2, a Comprehensive Software Package for the Annotation and Curation of Draft and Complete Microbial Genomes.

PubMed

Altermann, Eric; Lu, Jingli; McCulloch, Alan

2017-01-01

Expert curated annotation remains one of the critical steps in achieving a reliable biological relevant annotation. Here we announce the release of GAMOLA2, a user friendly and comprehensive software package to process, annotate and curate draft and complete bacterial, archaeal, and viral genomes. GAMOLA2 represents a wrapping tool to combine gene model determination, functional Blast, COG, Pfam, and TIGRfam analyses with structural predictions including detection of tRNAs, rRNA genes, non-coding RNAs, signal protein cleavage sites, transmembrane helices, CRISPR repeats and vector sequence contaminations. GAMOLA2 has already been validated in a wide range of bacterial and archaeal genomes, and its modular concept allows easy addition of further functionality in future releases. A modified and adapted version of the Artemis Genome Viewer (Sanger Institute) has been developed to leverage the additional features and underlying information provided by the GAMOLA2 analysis, and is part of the software distribution. In addition to genome annotations, GAMOLA2 features, among others, supplemental modules that assist in the creation of custom Blast databases, annotation transfers between genome versions, and the preparation of Genbank files for submission via the NCBI Sequin tool. GAMOLA2 is intended to be run under a Linux environment, whereas the subsequent visualization and manual curation in Artemis is mobile and platform independent. The development of GAMOLA2 is ongoing and community driven. New functionality can easily be added upon user requests, ensuring that GAMOLA2 provides information relevant to microbiologists. The software is available free of charge for academic use.

BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

PubMed Central

2010-01-01

Background Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use. Description We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest. Conclusions BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at http://bigg.ucsd.edu. PMID:20426874

MIPS Arabidopsis thaliana Database (MAtDB): an integrated biological knowledge resource for plant genomics

PubMed Central

Schoof, Heiko; Ernst, Rebecca; Nazarov, Vladimir; Pfeifer, Lukas; Mewes, Hans-Werner; Mayer, Klaus F. X.

2004-01-01

Arabidopsis thaliana is the most widely studied model plant. Functional genomics is intensively underway in many laboratories worldwide. Beyond the basic annotation of the primary sequence data, the annotated genetic elements of Arabidopsis must be linked to diverse biological data and higher order information such as metabolic or regulatory pathways. The MIPS Arabidopsis thaliana database MAtDB aims to provide a comprehensive resource for Arabidopsis as a genome model that serves as a primary reference for research in plants and is suitable for transfer of knowledge to other plants, especially crops. The genome sequence as a common backbone serves as a scaffold for the integration of data, while, in a complementary effort, these data are enhanced through the application of state-of-the-art bioinformatics tools. This information is visualized on a genome-wide and a gene-by-gene basis with access both for web users and applications. This report updates the information given in a previous report and provides an outlook on further developments. The MAtDB web interface can be accessed at http://mips.gsf.de/proj/thal/db. PMID:14681437

The Human Ageing Genomic Resources: online databases and tools for biogerontologists

PubMed Central

de Magalhães, João Pedro; Budovsky, Arie; Lehmann, Gilad; Costa, Joana; Li, Yang; Fraifeld, Vadim; Church, George M.

2009-01-01

Summary Ageing is a complex, challenging phenomenon that will require multiple, interdisciplinary approaches to unravel its puzzles. To assist basic research on ageing, we developed the Human Ageing Genomic Resources (HAGR). This work provides an overview of the databases and tools in HAGR and describes how the gerontology research community can employ them. Several recent changes and improvements to HAGR are also presented. The two centrepieces in HAGR are GenAge and AnAge. GenAge is a gene database featuring genes associated with ageing and longevity in model organisms, a curated database of genes potentially associated with human ageing, and a list of genes tested for their association with human longevity. A myriad of biological data and information is included for hundreds of genes, making GenAge a reference for research that reflects our current understanding of the genetic basis of ageing. GenAge can also serve as a platform for the systems biology of ageing, and tools for the visualization of protein-protein interactions are also included. AnAge is a database of ageing in animals, featuring over 4,000 species, primarily assembled as a resource for comparative and evolutionary studies of ageing. Longevity records, developmental and reproductive traits, taxonomic information, basic metabolic characteristics, and key observations related to ageing are included in AnAge. Software is also available to aid researchers in the form of Perl modules to automate numerous tasks and as an SPSS script to analyse demographic mortality data. The Human Ageing Genomic Resources are available online at http://genomics.senescence.info. PMID:18986374

Construction and completion of flux balance models from pathway databases.

PubMed

Latendresse, Mario; Krummenacker, Markus; Trupp, Miles; Karp, Peter D

2012-02-01

Flux balance analysis (FBA) is a well-known technique for genome-scale modeling of metabolic flux. Typically, an FBA formulation requires the accurate specification of four sets: biochemical reactions, biomass metabolites, nutrients and secreted metabolites. The development of FBA models can be time consuming and tedious because of the difficulty in assembling completely accurate descriptions of these sets, and in identifying errors in the composition of these sets. For example, the presence of a single non-producible metabolite in the biomass will make the entire model infeasible. Other difficulties in FBA modeling are that model distributions, and predicted fluxes, can be cryptic and difficult to understand. We present a multiple gap-filling method to accelerate the development of FBA models using a new tool, called MetaFlux, based on mixed integer linear programming (MILP). The method suggests corrections to the sets of reactions, biomass metabolites, nutrients and secretions. The method generates FBA models directly from Pathway/Genome Databases. Thus, FBA models developed in this framework are easily queried and visualized using the Pathway Tools software. Predicted fluxes are more easily comprehended by visualizing them on diagrams of individual metabolic pathways or of metabolic maps. MetaFlux can also remove redundant high-flux loops, solve FBA models once they are generated and model the effects of gene knockouts. MetaFlux has been validated through construction of FBA models for Escherichia coli and Homo sapiens. Pathway Tools with MetaFlux is freely available to academic users, and for a fee to commercial users. Download from: biocyc.org/download.shtml. mario.latendresse@sri.com Supplementary data are available at Bioinformatics online.

CisSERS: Customizable in silico sequence evaluation for restriction sites

DOE PAGES

Sharpe, Richard M.; Koepke, Tyson; Harper, Artemus; ...

2016-04-12

High-throughput sequencing continues to produce an immense volume of information that is processed and assembled into mature sequence data. Here, data analysis tools are urgently needed that leverage the embedded DNA sequence polymorphisms and consequent changes to restriction sites or sequence motifs in a high-throughput manner to enable biological experimentation. CisSERS was developed as a standalone open source tool to analyze sequence datasets and provide biologists with individual or comparative genome organization information in terms of presence and frequency of patterns or motifs such as restriction enzymes. Predicted agarose gel visualization of the custom analyses results was also integrated tomore » enhance the usefulness of the software. CisSERS offers several novel functionalities, such as handling of large and multiple datasets in parallel, multiple restriction enzyme site detection and custom motif detection features, which are seamlessly integrated with real time agarose gel visualization. Using a simple fasta-formatted file as input, CisSERS utilizes the REBASE enzyme database. Results from CisSERSenable the user to make decisions for designing genotyping by sequencing experiments, reduced representation sequencing, 3’UTR sequencing, and cleaved amplified polymorphic sequence (CAPS) molecular markers for large sample sets. CisSERS is a java based graphical user interface built around a perl backbone. Several of the applications of CisSERS including CAPS molecular marker development were successfully validated using wet-lab experimentation. Here, we present the tool CisSERSand results from in-silico and corresponding wet-lab analyses demonstrating that CisSERS is a technology platform solution that facilitates efficient data utilization in genomics and genetics studies.« less

CisSERS: Customizable in silico sequence evaluation for restriction sites

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

Sharpe, Richard M.; Koepke, Tyson; Harper, Artemus

High-throughput sequencing continues to produce an immense volume of information that is processed and assembled into mature sequence data. Here, data analysis tools are urgently needed that leverage the embedded DNA sequence polymorphisms and consequent changes to restriction sites or sequence motifs in a high-throughput manner to enable biological experimentation. CisSERS was developed as a standalone open source tool to analyze sequence datasets and provide biologists with individual or comparative genome organization information in terms of presence and frequency of patterns or motifs such as restriction enzymes. Predicted agarose gel visualization of the custom analyses results was also integrated tomore » enhance the usefulness of the software. CisSERS offers several novel functionalities, such as handling of large and multiple datasets in parallel, multiple restriction enzyme site detection and custom motif detection features, which are seamlessly integrated with real time agarose gel visualization. Using a simple fasta-formatted file as input, CisSERS utilizes the REBASE enzyme database. Results from CisSERSenable the user to make decisions for designing genotyping by sequencing experiments, reduced representation sequencing, 3’UTR sequencing, and cleaved amplified polymorphic sequence (CAPS) molecular markers for large sample sets. CisSERS is a java based graphical user interface built around a perl backbone. Several of the applications of CisSERS including CAPS molecular marker development were successfully validated using wet-lab experimentation. Here, we present the tool CisSERSand results from in-silico and corresponding wet-lab analyses demonstrating that CisSERS is a technology platform solution that facilitates efficient data utilization in genomics and genetics studies.« less

Sequence-Based Mapping and Genome Editing Reveal Mutations in Stickleback Hps5 Cause Oculocutaneous Albinism and the casper Phenotype.

PubMed

Hart, James C; Miller, Craig T

2017-09-07

Here, we present and characterize the spontaneous X-linked recessive mutation casper , which causes oculocutaneous albinism in threespine sticklebacks ( Gasterosteus aculeatus ). In humans, Hermansky-Pudlak syndrome results in pigmentation defects due to disrupted formation of the melanin-containing lysosomal-related organelle (LRO), the melanosome. casper mutants display not only reduced pigmentation of melanosomes in melanophores, but also reductions in the iridescent silver color from iridophores, while the yellow pigmentation from xanthophores appears unaffected. We mapped casper using high-throughput sequencing of genomic DNA from bulked casper mutants to a region of the stickleback X chromosome (chromosome 19) near the stickleback ortholog of Hermansky-Pudlak syndrome 5 ( Hps5 ). casper mutants have an insertion of a single nucleotide in the sixth exon of Hps5 , predicted to generate an early frameshift. Genome editing using CRISPR/Cas9 induced lesions in Hps5 and phenocopied the casper mutation. Injecting single or paired Hps5 guide RNAs revealed higher incidences of genomic deletions from paired guide RNAs compared to single gRNAs. Stickleback Hps5 provides a genetic system where a hemizygous locus in XY males and a diploid locus in XX females can be used to generate an easily scored visible phenotype, facilitating quantitative studies of different genome editing approaches. Lastly, we show the ability to better visualize patterns of fluorescent transgenic reporters in Hps5 mutant fish. Thus, Hps5 mutations present an opportunity to study pigmented LROs in the emerging stickleback model system, as well as a tool to aid in assaying genome editing and visualizing enhancer activity in transgenic fish. Copyright © 2017 Hart and Milller.

WebStruct and VisualStruct: Web interfaces and visualization for Structure software implemented in a cluster environment.

PubMed

Jayashree, B; Rajgopal, S; Hoisington, D; Prasanth, V P; Chandra, S

2008-09-24

Structure, is a widely used software tool to investigate population genetic structure with multi-locus genotyping data. The software uses an iterative algorithm to group individuals into "K" clusters, representing possibly K genetically distinct subpopulations. The serial implementation of this programme is processor-intensive even with small datasets. We describe an implementation of the program within a parallel framework. Speedup was achieved by running different replicates and values of K on each node of the cluster. A web-based user-oriented GUI has been implemented in PHP, through which the user can specify input parameters for the programme. The number of processors to be used can be specified in the background command. A web-based visualization tool "Visualstruct", written in PHP (HTML and Java script embedded), allows for the graphical display of population clusters output from Structure, where each individual may be visualized as a line segment with K colors defining its possible genomic composition with respect to the K genetic sub-populations. The advantage over available programs is in the increased number of individuals that can be visualized. The analyses of real datasets indicate a speedup of up to four, when comparing the speed of execution on clusters of eight processors with the speed of execution on one desktop. The software package is freely available to interested users upon request.

CRAVE: a database, middleware and visualization system for phenotype ontologies.

PubMed

Gkoutos, Georgios V; Green, Eain C J; Greenaway, Simon; Blake, Andrew; Mallon, Ann-Marie; Hancock, John M

2005-04-01

A major challenge in modern biology is to link genome sequence information to organismal function. In many organisms this is being done by characterizing phenotypes resulting from mutations. Efficiently expressing phenotypic information requires combinatorial use of ontologies. However tools are not currently available to visualize combinations of ontologies. Here we describe CRAVE (Concept Relation Assay Value Explorer), a package allowing storage, active updating and visualization of multiple ontologies. CRAVE is a web-accessible JAVA application that accesses an underlying MySQL database of ontologies via a JAVA persistent middleware layer (Chameleon). This maps the database tables into discrete JAVA classes and creates memory resident, interlinked objects corresponding to the ontology data. These JAVA objects are accessed via calls through the middleware's application programming interface. CRAVE allows simultaneous display and linking of multiple ontologies and searching using Boolean and advanced searches.

Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

PubMed Central

Gutman, David A; Cobb, Jake; Somanna, Dhananjaya; Park, Yuna; Wang, Fusheng; Kurc, Tahsin; Saltz, Joel H; Brat, Daniel J; Cooper, Lee A D

2013-01-01

Background The integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies of The Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed in whole-slide images, outcome, and genetic events. The pairing of genomics and rich clinical descriptions with whole-slide imaging provided by TCGA presents a unique opportunity to perform these correlative studies. However, better tools are needed to integrate the vast and disparate data types. Objective To build an integrated web-based platform supporting whole-slide pathology image visualization and data integration. Materials and methods All images and genomic data were directly obtained from the TCGA and National Cancer Institute (NCI) websites. Results The Cancer Digital Slide Archive (CDSA) produced is accessible to the public (http://cancer.digitalslidearchive.net) and currently hosts more than 20 000 whole-slide images from 22 cancer types. Discussion The capabilities of CDSA are demonstrated using TCGA datasets to integrate pathology imaging with associated clinical, genomic and MRI measurements in glioblastomas and can be extended to other tumor types. CDSA also allows URL-based sharing of whole-slide images, and has preliminary support for directly sharing regions of interest and other annotations. Images can also be selected on the basis of other metadata, such as mutational profile, patient age, and other relevant characteristics. Conclusions With the increasing availability of whole-slide scanners, analysis of digitized pathology images will become increasingly important in linking morphologic observations with genomic and clinical endpoints. PMID:23893318

An evaluation of copy number variation detection tools for cancer using whole exome sequencing data.

PubMed

Zare, Fatima; Dow, Michelle; Monteleone, Nicholas; Hosny, Abdelrahman; Nabavi, Sheida

2017-05-31

Recently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors' complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer. In this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly. The limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

HEDD: Human Enhancer Disease Database

PubMed Central

Wang, Zhen; Zhang, Quanwei; Zhang, Wen; Lin, Jhih-Rong; Cai, Ying; Mitra, Joydeep

2018-01-01

Abstract Enhancers, as specialized genomic cis-regulatory elements, activate transcription of their target genes and play an important role in pathogenesis of many human complex diseases. Despite recent systematic identification of them in the human genome, currently there is an urgent need for comprehensive annotation databases of human enhancers with a focus on their disease connections. In response, we built the Human Enhancer Disease Database (HEDD) to facilitate studies of enhancers and their potential roles in human complex diseases. HEDD currently provides comprehensive genomic information for ∼2.8 million human enhancers identified by ENCODE, FANTOM5 and RoadMap with disease association scores based on enhancer–gene and gene–disease connections. It also provides Web-based analytical tools to visualize enhancer networks and score enhancers given a set of selected genes in a specific gene network. HEDD is freely accessible at http://zdzlab.einstein.yu.edu/1/hedd.php. PMID:29077884

Rapid Sequencing of Complete env Genes from Primary HIV-1 Samples.

PubMed

Laird Smith, Melissa; Murrell, Ben; Eren, Kemal; Ignacio, Caroline; Landais, Elise; Weaver, Steven; Phung, Pham; Ludka, Colleen; Hepler, Lance; Caballero, Gemma; Pollner, Tristan; Guo, Yan; Richman, Douglas; Poignard, Pascal; Paxinos, Ellen E; Kosakovsky Pond, Sergei L; Smith, Davey M

2016-07-01

The ability to study rapidly evolving viral populations has been constrained by the read length of next-generation sequencing approaches and the sampling depth of single-genome amplification methods. Here, we develop and characterize a method using Pacific Biosciences' Single Molecule, Real-Time (SMRT®) sequencing technology to sequence multiple, intact full-length human immunodeficiency virus-1 env genes amplified from viral RNA populations circulating in blood, and provide computational tools for analyzing and visualizing these data.

The Yak genome database: an integrative database for studying yak biology and high-altitude adaption

PubMed Central

2012-01-01

Background The yak (Bos grunniens) is a long-haired bovine that lives at high altitudes and is an important source of milk, meat, fiber and fuel. The recent sequencing, assembly and annotation of its genome are expected to further our understanding of the means by which it has adapted to life at high altitudes and its ecologically important traits. Description The Yak Genome Database (YGD) is an internet-based resource that provides access to genomic sequence data and predicted functional information concerning the genes and proteins of Bos grunniens. The curated data stored in the YGD includes genome sequences, predicted genes and associated annotations, non-coding RNA sequences, transposable elements, single nucleotide variants, and three-way whole-genome alignments between human, cattle and yak. YGD offers useful searching and data mining tools, including the ability to search for genes by name or using function keywords as well as GBrowse genome browsers and/or BLAST servers, which can be used to visualize genome regions and identify similar sequences. Sequence data from the YGD can also be downloaded to perform local searches. Conclusions A new yak genome database (YGD) has been developed to facilitate studies on high-altitude adaption and bovine genomics. The database will be continuously updated to incorporate new information such as transcriptome data and population resequencing data. The YGD can be accessed at http://me.lzu.edu.cn/yak. PMID:23134687

GIANT API: an application programming interface for functional genomics

PubMed Central

Roberts, Andrew M.; Wong, Aaron K.; Fisk, Ian; Troyanskaya, Olga G.

2016-01-01

GIANT API provides biomedical researchers programmatic access to tissue-specific and global networks in humans and model organisms, and associated tools, which includes functional re-prioritization of existing genome-wide association study (GWAS) data. Using tissue-specific interaction networks, researchers are able to predict relationships between genes specific to a tissue or cell lineage, identify the changing roles of genes across tissues and uncover disease-gene associations. Additionally, GIANT API enables computational tools like NetWAS, which leverages tissue-specific networks for re-prioritization of GWAS results. The web services covered by the API include 144 tissue-specific functional gene networks in human, global functional networks for human and six common model organisms and the NetWAS method. GIANT API conforms to the REST architecture, which makes it stateless, cacheable and highly scalable. It can be used by a diverse range of clients including web browsers, command terminals, programming languages and standalone apps for data analysis and visualization. The API is freely available for use at http://giant-api.princeton.edu. PMID:27098035

IPRStats: visualization of the functional potential of an InterProScan run.

PubMed

Kelly, Ryan J; Vincent, David E; Friedberg, Iddo

2010-12-21

InterPro is a collection of protein signatures for the classification and automated annotation of proteins. Interproscan is a software tool that scans protein sequences against Interpro member databases using a variety of profile-based, hidden markov model and positional specific score matrix methods. It not only combines a set of analysis tools, but also performs data look-up from various sources, as well as some redundancy removal. Interproscan is robust and scalable, able to perform on any machine from a netbook to a large cluster. However, when performing whole-genome or metagenome analysis, there is a need for a fast statistical visualization of the results to have good initial grasp on the functional potential of the sequences in the analyzed data set. This is especially important when analyzing and comparing metagenomic or metaproteomic data-sets. IPRStats is a tool for the visualization of Interproscan results. Interproscan results are parsed from the Interproscan XML or EBIXML file into an SQLite or MySQL database. The results for each signature database scan are read and displayed as pie-charts or bar charts as summary statistics. A table is also provided, where each entry is a signature (e.g. a Pfam entry) accompanied by one or more Gene Ontology terms, if Interproscan was run using the Gene Ontology option. We present an platform-independent, open source licensed tool that is useful for Interproscan users who wish to view the summary of their results in a rapid and concise fashion.

FISH Oracle 2: a web server for integrative visualization of genomic data in cancer research

PubMed Central

2014-01-01

Background A comprehensive view on all relevant genomic data is instrumental for understanding the complex patterns of molecular alterations typically found in cancer cells. One of the most effective ways to rapidly obtain an overview of genomic alterations in large amounts of genomic data is the integrative visualization of genomic events. Results We developed FISH Oracle 2, a web server for the interactive visualization of different kinds of downstream processed genomics data typically available in cancer research. A powerful search interface and a fast visualization engine provide a highly interactive visualization for such data. High quality image export enables the life scientist to easily communicate their results. A comprehensive data administration allows to keep track of the available data sets. We applied FISH Oracle 2 to published data and found evidence that, in colorectal cancer cells, the gene TTC28 may be inactivated in two different ways, a fact that has not been published before. Conclusions The interactive nature of FISH Oracle 2 and the possibility to store, select and visualize large amounts of downstream processed data support life scientists in generating hypotheses. The export of high quality images supports explanatory data visualization, simplifying the communication of new biological findings. A FISH Oracle 2 demo server and the software is available at http://www.zbh.uni-hamburg.de/fishoracle. PMID:24684958

Ferret: a user-friendly Java tool to extract data from the 1000 Genomes Project.

PubMed

Limou, Sophie; Taverner, Andrew M; Winkler, Cheryl A

2016-07-15

The 1000 Genomes (1KG) Project provides a near-comprehensive resource on human genetic variation in worldwide reference populations. 1KG variants can be accessed through a browser and through the raw and annotated data that are regularly released on an ftp server. We developed Ferret, a user-friendly Java tool, to easily extract genetic variation information from these large and complex data files. From a locus, gene(s) or SNP(s) of interest, Ferret retrieves genotype data for 1KG SNPs and indels, and computes allelic frequencies for 1KG populations and optionally, for the Exome Sequencing Project populations. By converting the 1KG data into files that can be imported into popular pre-existing tools (e.g. PLINK and HaploView), Ferret offers a straightforward way, even for non-bioinformatics specialists, to manipulate, explore and merge 1KG data with the user's dataset, as well as visualize linkage disequilibrium pattern, infer haplotypes and design tagSNPs. Ferret tool and source code are publicly available at http://limousophie35.github.io/Ferret/ ferret@nih.gov Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

ENGINES: exploring single nucleotide variation in entire human genomes.

PubMed

Amigo, Jorge; Salas, Antonio; Phillips, Christopher

2011-04-19

Next generation ultra-sequencing technologies are starting to produce extensive quantities of data from entire human genome or exome sequences, and therefore new software is needed to present and analyse this vast amount of information. The 1000 Genomes project has recently released raw data for 629 complete genomes representing several human populations through their Phase I interim analysis and, although there are certain public tools available that allow exploration of these genomes, to date there is no tool that permits comprehensive population analysis of the variation catalogued by such data. We have developed a genetic variant site explorer able to retrieve data for Single Nucleotide Variation (SNVs), population by population, from entire genomes without compromising future scalability and agility. ENGINES (ENtire Genome INterface for Exploring SNVs) uses data from the 1000 Genomes Phase I to demonstrate its capacity to handle large amounts of genetic variation (>7.3 billion genotypes and 28 million SNVs), as well as deriving summary statistics of interest for medical and population genetics applications. The whole dataset is pre-processed and summarized into a data mart accessible through a web interface. The query system allows the combination and comparison of each available population sample, while searching by rs-number list, chromosome region, or genes of interest. Frequency and FST filters are available to further refine queries, while results can be visually compared with other large-scale Single Nucleotide Polymorphism (SNP) repositories such as HapMap or Perlegen. ENGINES is capable of accessing large-scale variation data repositories in a fast and comprehensive manner. It allows quick browsing of whole genome variation, while providing statistical information for each variant site such as allele frequency, heterozygosity or FST values for genetic differentiation. Access to the data mart generating scripts and to the web interface is granted from http://spsmart.cesga.es/engines.php. © 2011 Amigo et al; licensee BioMed Central Ltd.

gmos: Rapid Detection of Genome Mosaicism over Short Evolutionary Distances.

PubMed

Domazet-Lošo, Mirjana; Domazet-Lošo, Tomislav

2016-01-01

Prokaryotic and viral genomes are often altered by recombination and horizontal gene transfer. The existing methods for detecting recombination are primarily aimed at viral genomes or sets of loci, since the expensive computation of underlying statistical models often hinders the comparison of complete prokaryotic genomes. As an alternative, alignment-free solutions are more efficient, but cannot map (align) a query to subject genomes. To address this problem, we have developed gmos (Genome MOsaic Structure), a new program that determines the mosaic structure of query genomes when compared to a set of closely related subject genomes. The program first computes local alignments between query and subject genomes and then reconstructs the query mosaic structure by choosing the best local alignment for each query region. To accomplish the analysis quickly, the program mostly relies on pairwise alignments and constructs multiple sequence alignments over short overlapping subject regions only when necessary. This fine-tuned implementation achieves an efficiency comparable to an alignment-free tool. The program performs well for simulated and real data sets of closely related genomes and can be used for fast recombination detection; for instance, when a new prokaryotic pathogen is discovered. As an example, gmos was used to detect genome mosaicism in a pathogenic Enterococcus faecium strain compared to seven closely related genomes. The analysis took less than two minutes on a single 2.1 GHz processor. The output is available in fasta format and can be visualized using an accessory program, gmosDraw (freely available with gmos).

gmos: Rapid Detection of Genome Mosaicism over Short Evolutionary Distances

PubMed Central

Domazet-Lošo, Mirjana; Domazet-Lošo, Tomislav

2016-01-01

Prokaryotic and viral genomes are often altered by recombination and horizontal gene transfer. The existing methods for detecting recombination are primarily aimed at viral genomes or sets of loci, since the expensive computation of underlying statistical models often hinders the comparison of complete prokaryotic genomes. As an alternative, alignment-free solutions are more efficient, but cannot map (align) a query to subject genomes. To address this problem, we have developed gmos (Genome MOsaic Structure), a new program that determines the mosaic structure of query genomes when compared to a set of closely related subject genomes. The program first computes local alignments between query and subject genomes and then reconstructs the query mosaic structure by choosing the best local alignment for each query region. To accomplish the analysis quickly, the program mostly relies on pairwise alignments and constructs multiple sequence alignments over short overlapping subject regions only when necessary. This fine-tuned implementation achieves an efficiency comparable to an alignment-free tool. The program performs well for simulated and real data sets of closely related genomes and can be used for fast recombination detection; for instance, when a new prokaryotic pathogen is discovered. As an example, gmos was used to detect genome mosaicism in a pathogenic Enterococcus faecium strain compared to seven closely related genomes. The analysis took less than two minutes on a single 2.1 GHz processor. The output is available in fasta format and can be visualized using an accessory program, gmosDraw (freely available with gmos). PMID:27846272

ExpressionDB: An open source platform for distributing genome-scale datasets.

PubMed

Hughes, Laura D; Lewis, Scott A; Hughes, Michael E

2017-01-01

RNA-sequencing (RNA-seq) and microarrays are methods for measuring gene expression across the entire transcriptome. Recent advances have made these techniques practical and affordable for essentially any laboratory with experience in molecular biology. A variety of computational methods have been developed to decrease the amount of bioinformatics expertise necessary to analyze these data. Nevertheless, many barriers persist which discourage new labs from using functional genomics approaches. Since high-quality gene expression studies have enduring value as resources to the entire research community, it is of particular importance that small labs have the capacity to share their analyzed datasets with the research community. Here we introduce ExpressionDB, an open source platform for visualizing RNA-seq and microarray data accommodating virtually any number of different samples. ExpressionDB is based on Shiny, a customizable web application which allows data sharing locally and online with customizable code written in R. ExpressionDB allows intuitive searches based on gene symbols, descriptions, or gene ontology terms, and it includes tools for dynamically filtering results based on expression level, fold change, and false-discovery rates. Built-in visualization tools include heatmaps, volcano plots, and principal component analysis, ensuring streamlined and consistent visualization to all users. All of the scripts for building an ExpressionDB with user-supplied data are freely available on GitHub, and the Creative Commons license allows fully open customization by end-users. We estimate that a demo database can be created in under one hour with minimal programming experience, and that a new database with user-supplied expression data can be completed and online in less than one day.

GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle.

PubMed

Lardenois, Aurélie; Gattiker, Alexandre; Collin, Olivier; Chalmel, Frédéric; Primig, Michael

2010-01-01

GermOnline 4.0 is a cross-species database portal focusing on high-throughput expression data relevant for germline development, the meiotic cell cycle and mitosis in healthy versus malignant cells. It is thus a source of information for life scientists as well as clinicians who are interested in gene expression and regulatory networks. The GermOnline gateway provides unlimited access to information produced with high-density oligonucleotide microarrays (3'-UTR GeneChips), genome-wide protein-DNA binding assays and protein-protein interaction studies in the context of Ensembl genome annotation. Samples used to produce high-throughput expression data and to carry out genome-wide in vivo DNA binding assays are annotated via the MIAME-compliant Multiomics Information Management and Annotation System (MIMAS 3.0). Furthermore, the Saccharomyces Genomics Viewer (SGV) was developed and integrated into the gateway. SGV is a visualization tool that outputs genome annotation and DNA-strand specific expression data produced with high-density oligonucleotide tiling microarrays (Sc_tlg GeneChips) which cover the complete budding yeast genome on both DNA strands. It facilitates the interpretation of expression levels and transcript structures determined for various cell types cultured under different growth and differentiation conditions. Database URL: www.germonline.org/

GermOnline 4.0 is a genomics gateway for germline development, meiosis and the mitotic cell cycle

PubMed Central

Lardenois, Aurélie; Gattiker, Alexandre; Collin, Olivier; Chalmel, Frédéric; Primig, Michael

2010-01-01

GermOnline 4.0 is a cross-species database portal focusing on high-throughput expression data relevant for germline development, the meiotic cell cycle and mitosis in healthy versus malignant cells. It is thus a source of information for life scientists as well as clinicians who are interested in gene expression and regulatory networks. The GermOnline gateway provides unlimited access to information produced with high-density oligonucleotide microarrays (3′-UTR GeneChips), genome-wide protein–DNA binding assays and protein–protein interaction studies in the context of Ensembl genome annotation. Samples used to produce high-throughput expression data and to carry out genome-wide in vivo DNA binding assays are annotated via the MIAME-compliant Multiomics Information Management and Annotation System (MIMAS 3.0). Furthermore, the Saccharomyces Genomics Viewer (SGV) was developed and integrated into the gateway. SGV is a visualization tool that outputs genome annotation and DNA-strand specific expression data produced with high-density oligonucleotide tiling microarrays (Sc_tlg GeneChips) which cover the complete budding yeast genome on both DNA strands. It facilitates the interpretation of expression levels and transcript structures determined for various cell types cultured under different growth and differentiation conditions. Database URL: www.germonline.org/ PMID:21149299

PLACNETw: a web-based tool for plasmid reconstruction from bacterial genomes.

PubMed

Vielva, Luis; de Toro, María; Lanza, Val F; de la Cruz, Fernando

2017-12-01

PLACNET is a graph-based tool for reconstruction of plasmids from next generation sequence pair-end datasets. PLACNET graphs contain two types of nodes (assembled contigs and reference genomes) and two types of edges (scaffold links and homology to references). Manual pruning of the graphs is a necessary requirement in PLACNET, but this is difficult for users without solid bioinformatic background. PLACNETw, a webtool based on PLACNET, provides an interactive graphic interface, automates BLAST searches, and extracts the relevant information for decision making. It allows a user with domain expertise to visualize the scaffold graphs and related information of contigs as well as reference sequences, so that the pruning operations can be done interactively from a personal computer without the need for additional tools. After successful pruning, each plasmid becomes a separate connected component subgraph. The resulting data are automatically downloaded by the user. PLACNETw is freely available at https://castillo.dicom.unican.es/upload/. delacruz@unican.es. A tutorial video and several solved examples are available at https://castillo.dicom.unican.es/placnetw_video/ and https://castillo.dicom.unican.es/examples/. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

IVAG: An Integrative Visualization Application for Various Types of Genomic Data Based on R-Shiny and the Docker Platform.

PubMed

Lee, Tae-Rim; Ahn, Jin Mo; Kim, Gyuhee; Kim, Sangsoo

2017-12-01

Next-generation sequencing (NGS) technology has become a trend in the genomics research area. There are many software programs and automated pipelines to analyze NGS data, which can ease the pain for traditional scientists who are not familiar with computer programming. However, downstream analyses, such as finding differentially expressed genes or visualizing linkage disequilibrium maps and genome-wide association study (GWAS) data, still remain a challenge. Here, we introduce a dockerized web application written in R using the Shiny platform to visualize pre-analyzed RNA sequencing and GWAS data. In addition, we have integrated a genome browser based on the JBrowse platform and an automated intermediate parsing process required for custom track construction, so that users can easily build and navigate their personal genome tracks with in-house datasets. This application will help scientists perform series of downstream analyses and obtain a more integrative understanding about various types of genomic data by interactively visualizing them with customizable options.

Unipro UGENE: a unified bioinformatics toolkit.

PubMed

Okonechnikov, Konstantin; Golosova, Olga; Fursov, Mikhail

2012-04-15

Unipro UGENE is a multiplatform open-source software with the main goal of assisting molecular biologists without much expertise in bioinformatics to manage, analyze and visualize their data. UGENE integrates widely used bioinformatics tools within a common user interface. The toolkit supports multiple biological data formats and allows the retrieval of data from remote data sources. It provides visualization modules for biological objects such as annotated genome sequences, Next Generation Sequencing (NGS) assembly data, multiple sequence alignments, phylogenetic trees and 3D structures. Most of the integrated algorithms are tuned for maximum performance by the usage of multithreading and special processor instructions. UGENE includes a visual environment for creating reusable workflows that can be launched on local resources or in a High Performance Computing (HPC) environment. UGENE is written in C++ using the Qt framework. The built-in plugin system and structured UGENE API make it possible to extend the toolkit with new functionality. UGENE binaries are freely available for MS Windows, Linux and Mac OS X at http://ugene.unipro.ru/download.html. UGENE code is licensed under the GPLv2; the information about the code licensing and copyright of integrated tools can be found in the LICENSE.3rd_party file provided with the source bundle.

pong: fast analysis and visualization of latent clusters in population genetic data.

PubMed

Behr, Aaron A; Liu, Katherine Z; Liu-Fang, Gracie; Nakka, Priyanka; Ramachandran, Sohini

2016-09-15

A series of methods in population genetics use multilocus genotype data to assign individuals membership in latent clusters. These methods belong to a broad class of mixed-membership models, such as latent Dirichlet allocation used to analyze text corpora. Inference from mixed-membership models can produce different output matrices when repeatedly applied to the same inputs, and the number of latent clusters is a parameter that is often varied in the analysis pipeline. For these reasons, quantifying, visualizing, and annotating the output from mixed-membership models are bottlenecks for investigators across multiple disciplines from ecology to text data mining. We introduce pong, a network-graphical approach for analyzing and visualizing membership in latent clusters with a native interactive D3.js visualization. pong leverages efficient algorithms for solving the Assignment Problem to dramatically reduce runtime while increasing accuracy compared with other methods that process output from mixed-membership models. We apply pong to 225 705 unlinked genome-wide single-nucleotide variants from 2426 unrelated individuals in the 1000 Genomes Project, and identify previously overlooked aspects of global human population structure. We show that pong outpaces current solutions by more than an order of magnitude in runtime while providing a customizable and interactive visualization of population structure that is more accurate than those produced by current tools. pong is freely available and can be installed using the Python package management system pip. pong's source code is available at https://github.com/abehr/pong aaron_behr@alumni.brown.edu or sramachandran@brown.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Rapid Sequencing of Complete env Genes from Primary HIV-1 Samples

PubMed Central

Eren, Kemal; Ignacio, Caroline; Landais, Elise; Weaver, Steven; Phung, Pham; Ludka, Colleen; Hepler, Lance; Caballero, Gemma; Pollner, Tristan; Guo, Yan; Richman, Douglas; Poignard, Pascal; Paxinos, Ellen E.; Kosakovsky Pond, Sergei L.

2016-01-01

Abstract The ability to study rapidly evolving viral populations has been constrained by the read length of next-generation sequencing approaches and the sampling depth of single-genome amplification methods. Here, we develop and characterize a method using Pacific Biosciences’ Single Molecule, Real-Time (SMRT®) sequencing technology to sequence multiple, intact full-length human immunodeficiency virus-1 env genes amplified from viral RNA populations circulating in blood, and provide computational tools for analyzing and visualizing these data. PMID:29492273

A Method for Visualization of Incoming Adenovirus Chromatin Complexes in Fixed and Living Cells

PubMed Central

Komatsu, Tetsuro; Dacheux, Denis; Kreppel, Florian; Nagata, Kyosuke; Wodrich, Harald

2015-01-01

Inside the adenovirus virion, the genome forms a chromatin-like structure with viral basic core proteins. Core protein VII is the major DNA binding protein and was shown to remain associated with viral genomes upon virus entry even after nuclear delivery. It has been suggested that protein VII plays a regulatory role in viral gene expression and is a functional component of viral chromatin complexes in host cells. As such, protein VII could be used as a maker to track adenoviral chromatin complexes in vivo. In this study, we characterize a new monoclonal antibody against protein VII that stains incoming viral chromatin complexes following nuclear import. Furthermore, we describe the development of a novel imaging system that uses Template Activating Factor-I (TAF-I/SET), a cellular chromatin protein tightly bound to protein VII upon infection. This setup allows us not only to rapidly visualize protein VII foci in fixed cells but also to monitor their movement in living cells. These powerful tools can provide novel insights into the spatio-temporal regulation of incoming adenoviral chromatin complexes. PMID:26332038

High-Performance Integrated Virtual Environment (HIVE) Tools and Applications for Big Data Analysis.

PubMed

Simonyan, Vahan; Mazumder, Raja

2014-09-30

The High-performance Integrated Virtual Environment (HIVE) is a high-throughput cloud-based infrastructure developed for the storage and analysis of genomic and associated biological data. HIVE consists of a web-accessible interface for authorized users to deposit, retrieve, share, annotate, compute and visualize Next-generation Sequencing (NGS) data in a scalable and highly efficient fashion. The platform contains a distributed storage library and a distributed computational powerhouse linked seamlessly. Resources available through the interface include algorithms, tools and applications developed exclusively for the HIVE platform, as well as commonly used external tools adapted to operate within the parallel architecture of the system. HIVE is composed of a flexible infrastructure, which allows for simple implementation of new algorithms and tools. Currently, available HIVE tools include sequence alignment and nucleotide variation profiling tools, metagenomic analyzers, phylogenetic tree-building tools using NGS data, clone discovery algorithms, and recombination analysis algorithms. In addition to tools, HIVE also provides knowledgebases that can be used in conjunction with the tools for NGS sequence and metadata analysis.

High-Performance Integrated Virtual Environment (HIVE) Tools and Applications for Big Data Analysis

PubMed Central

Simonyan, Vahan; Mazumder, Raja

2014-01-01

The High-performance Integrated Virtual Environment (HIVE) is a high-throughput cloud-based infrastructure developed for the storage and analysis of genomic and associated biological data. HIVE consists of a web-accessible interface for authorized users to deposit, retrieve, share, annotate, compute and visualize Next-generation Sequencing (NGS) data in a scalable and highly efficient fashion. The platform contains a distributed storage library and a distributed computational powerhouse linked seamlessly. Resources available through the interface include algorithms, tools and applications developed exclusively for the HIVE platform, as well as commonly used external tools adapted to operate within the parallel architecture of the system. HIVE is composed of a flexible infrastructure, which allows for simple implementation of new algorithms and tools. Currently, available HIVE tools include sequence alignment and nucleotide variation profiling tools, metagenomic analyzers, phylogenetic tree-building tools using NGS data, clone discovery algorithms, and recombination analysis algorithms. In addition to tools, HIVE also provides knowledgebases that can be used in conjunction with the tools for NGS sequence and metadata analysis. PMID:25271953

RGAugury: a pipeline for genome-wide prediction of resistance gene analogs (RGAs) in plants.

PubMed

Li, Pingchuan; Quan, Xiande; Jia, Gaofeng; Xiao, Jin; Cloutier, Sylvie; You, Frank M

2016-11-02

Resistance gene analogs (RGAs), such as NBS-encoding proteins, receptor-like protein kinases (RLKs) and receptor-like proteins (RLPs), are potential R-genes that contain specific conserved domains and motifs. Thus, RGAs can be predicted based on their conserved structural features using bioinformatics tools. Computer programs have been developed for the identification of individual domains and motifs from the protein sequences of RGAs but none offer a systematic assessment of the different types of RGAs. A user-friendly and efficient pipeline is needed for large-scale genome-wide RGA predictions of the growing number of sequenced plant genomes. An integrative pipeline, named RGAugury, was developed to automate RGA prediction. The pipeline first identifies RGA-related protein domains and motifs, namely nucleotide binding site (NB-ARC), leucine rich repeat (LRR), transmembrane (TM), serine/threonine and tyrosine kinase (STTK), lysin motif (LysM), coiled-coil (CC) and Toll/Interleukin-1 receptor (TIR). RGA candidates are identified and classified into four major families based on the presence of combinations of these RGA domains and motifs: NBS-encoding, TM-CC, and membrane associated RLP and RLK. All time-consuming analyses of the pipeline are paralleled to improve performance. The pipeline was evaluated using the well-annotated Arabidopsis genome. A total of 98.5, 85.2, and 100 % of the reported NBS-encoding genes, membrane associated RLPs and RLKs were validated, respectively. The pipeline was also successfully applied to predict RGAs for 50 sequenced plant genomes. A user-friendly web interface was implemented to ease command line operations, facilitate visualization and simplify result management for multiple datasets. RGAugury is an efficiently integrative bioinformatics tool for large scale genome-wide identification of RGAs. It is freely available at Bitbucket: https://bitbucket.org/yaanlpc/rgaugury .

CNV Workshop: an integrated platform for high-throughput copy number variation discovery and clinical diagnostics.

PubMed

Gai, Xiaowu; Perin, Juan C; Murphy, Kevin; O'Hara, Ryan; D'arcy, Monica; Wenocur, Adam; Xie, Hongbo M; Rappaport, Eric F; Shaikh, Tamim H; White, Peter S

2010-02-04

Recent studies have shown that copy number variations (CNVs) are frequent in higher eukaryotes and associated with a substantial portion of inherited and acquired risk for various human diseases. The increasing availability of high-resolution genome surveillance platforms provides opportunity for rapidly assessing research and clinical samples for CNV content, as well as for determining the potential pathogenicity of identified variants. However, few informatics tools for accurate and efficient CNV detection and assessment currently exist. We developed a suite of software tools and resources (CNV Workshop) for automated, genome-wide CNV detection from a variety of SNP array platforms. CNV Workshop includes three major components: detection, annotation, and presentation of structural variants from genome array data. CNV detection utilizes a robust and genotype-specific extension of the Circular Binary Segmentation algorithm, and the use of additional detection algorithms is supported. Predicted CNVs are captured in a MySQL database that supports cohort-based projects and incorporates a secure user authentication layer and user/admin roles. To assist with determination of pathogenicity, detected CNVs are also annotated automatically for gene content, known disease loci, and gene-based literature references. Results are easily queried, sorted, filtered, and visualized via a web-based presentation layer that includes a GBrowse-based graphical representation of CNV content and relevant public data, integration with the UCSC Genome Browser, and tabular displays of genomic attributes for each CNV. To our knowledge, CNV Workshop represents the first cohesive and convenient platform for detection, annotation, and assessment of the biological and clinical significance of structural variants. CNV Workshop has been successfully utilized for assessment of genomic variation in healthy individuals and disease cohorts and is an ideal platform for coordinating multiple associated projects. Available on the web at: http://sourceforge.net/projects/cnv.

Analysis and visualization of chromosomal abnormalities in SNP data with SNPscan

PubMed Central

Ting, Jason C; Ye, Ying; Thomas, George H; Ruczinski, Ingo; Pevsner, Jonathan

2006-01-01

Background A variety of diseases are caused by chromosomal abnormalities such as aneuploidies (having an abnormal number of chromosomes), microdeletions, microduplications, and uniparental disomy. High density single nucleotide polymorphism (SNP) microarrays provide information on chromosomal copy number changes, as well as genotype (heterozygosity and homozygosity). SNP array studies generate multiple types of data for each SNP site, some with more than 100,000 SNPs represented on each array. The identification of different classes of anomalies within SNP data has been challenging. Results We have developed SNPscan, a web-accessible tool to analyze and visualize high density SNP data. It enables researchers (1) to visually and quantitatively assess the quality of user-generated SNP data relative to a benchmark data set derived from a control population, (2) to display SNP intensity and allelic call data in order to detect chromosomal copy number anomalies (duplications and deletions), (3) to display uniparental isodisomy based on loss of heterozygosity (LOH) across genomic regions, (4) to compare paired samples (e.g. tumor and normal), and (5) to generate a file type for viewing SNP data in the University of California, Santa Cruz (UCSC) Human Genome Browser. SNPscan accepts data exported from Affymetrix Copy Number Analysis Tool as its input. We validated SNPscan using data generated from patients with known deletions, duplications, and uniparental disomy. We also inspected previously generated SNP data from 90 apparently normal individuals from the Centre d'Étude du Polymorphisme Humain (CEPH) collection, and identified three cases of uniparental isodisomy, four females having an apparently mosaic X chromosome, two mislabelled SNP data sets, and one microdeletion on chromosome 2 with mosaicism from an apparently normal female. These previously unrecognized abnormalities were all detected using SNPscan. The microdeletion was independently confirmed by fluorescence in situ hybridization, and a region of homozygosity in a UPD case was confirmed by sequencing of genomic DNA. Conclusion SNPscan is useful to identify chromosomal abnormalities based on SNP intensity (such as chromosomal copy number changes) and heterozygosity data (including regions of LOH and some cases of UPD). The program and source code are available at the SNPscan website . PMID:16420694

aCNViewer: Comprehensive genome-wide visualization of absolute copy number and copy neutral variations

PubMed Central

Wang-Renault, Shu-Fang; Letouzé, Eric; Imbeaud, Sandrine; Zucman-Rossi, Jessica; Deleuze, Jean-François; How-Kit, Alexandre

2017-01-01

Motivation Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information. Results To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B). Availability and implementation aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub https://github.com/FJD-CEPH/aCNViewer and Docker https://hub.docker.com/r/fjdceph/acnviewer/. Contact aCNViewer@cephb.fr PMID:29261730

Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration.

PubMed

Arneson, Douglas; Bhattacharya, Anindya; Shu, Le; Mäkinen, Ville-Petteri; Yang, Xia

2016-09-09

Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.

Characterization of virus-like particles by atomic force microscopy in ambient conditions

NASA Astrophysics Data System (ADS)

Oropesa, Reinier; Ramos, Jorge R.; Falcón, Viviana; Felipe, Ariel

2013-06-01

Recombinant virus-like particles (VLPs) are attractive candidates for vaccine design since they resemble native viroids in size and morphology, but they are non-infectious due to the absence of a viral genome. The visualization of surface morphologies and structures can be used to deepen the understanding of physical, chemical, and biological phenomena. Atomic force microscopy (AFM) is a useful tool for the visualization of soft biological samples in a nanoscale resolution. In this work we have investigated the morphology of recombinant surface antigens of hepatitis B (rHBsAg) VLPs from Cuban vaccine against hepatitis B. The rHBsAg VLPs sizes estimated by AFM between 15 and 30 nm are similar to those reported on previous transmission electron microscopy (TEM) studies.

Visual Exploration of Genetic Association with Voxel-based Imaging Phenotypes in an MCI/AD Study

PubMed Central

Kim, Sungeun; Shen, Li; Saykin, Andrew J.; West, John D.

2010-01-01

Neuroimaging genomics is a new transdisciplinary research field, which aims to examine genetic effects on brain via integrated analyses of high throughput neuroimaging and genomic data. We report our recent work on (1) developing an imaging genomic browsing system that allows for whole genome and entire brain analyses based on visual exploration and (2) applying the system to the imaging genomic analysis of an existing MCI/AD cohort. Voxel-based morphometry is used to define imaging phenotypes. ANCOVA is employed to evaluate the effect of the interaction of genotypes and diagnosis in relation to imaging phenotypes while controlling for relevant covariates. Encouraging experimental results suggest that the proposed system has substantial potential for enabling discovery of imaging genomic associations through visual evaluation and for localizing candidate imaging regions and genomic regions for refined statistical modeling. PMID:19963597

Visualization portal for genetic variation (VizGVar): a tool for interactive visualization of SNPs and somatic mutations in exons, genes and protein domains.

PubMed

Solano-Román, Antonio; Alfaro-Arias, Verónica; Cruz-Castillo, Carlos; Orozco-Solano, Allan

2018-03-15

VizGVar was designed to meet the growing need of the research community for improved genomic and proteomic data viewers that benefit from better information visualization. We implemented a new information architecture and applied user centered design principles to provide a new improved way of visualizing genetic information and protein data related to human disease. VizGVar connects the entire database of Ensembl protein motifs, domains, genes and exons with annotated SNPs and somatic variations from PharmGKB and COSMIC. VizGVar precisely represents genetic variations and their respective location by colored curves to designate different types of variations. The structured hierarchy of biological data is reflected in aggregated patterns through different levels, integrating several layers of information at once. VizGVar provides a new interactive, web-based JavaScript visualization of somatic mutations and protein variation, enabling fast and easy discovery of clinically relevant variation patterns. VizGVar is accessible at http://vizport.io/vizgvar; http://vizport.io/vizgvar/doc/. asolano@broadinstitute.org or allan.orozcosolano@ucr.ac.cr.

STAR: an integrated solution to management and visualization of sequencing data.

PubMed

Wang, Tao; Liu, Jie; Shen, Li; Tonti-Filippini, Julian; Zhu, Yun; Jia, Haiyang; Lister, Ryan; Whitaker, John W; Ecker, Joseph R; Millar, A Harvey; Ren, Bing; Wang, Wei

2013-12-15

Easily visualization of complex data features is a necessary step to conduct studies on next-generation sequencing (NGS) data. We developed STAR, an integrated web application that enables online management, visualization and track-based analysis of NGS data. STAR is a multilayer web service system. On the client side, STAR leverages JavaScript, HTML5 Canvas and asynchronous communications to deliver a smoothly scrolling desktop-like graphical user interface with a suite of in-browser analysis tools that range from providing simple track configuration controls to sophisticated feature detection within datasets. On the server side, STAR supports private session state retention via an account management system and provides data management modules that enable collection, visualization and analysis of third-party sequencing data from the public domain with over thousands of tracks hosted to date. Overall, STAR represents a next-generation data exploration solution to match the requirements of NGS data, enabling both intuitive visualization and dynamic analysis of data. STAR browser system is freely available on the web at http://wanglab.ucsd.edu/star/browser and https://github.com/angell1117/STAR-genome-browser.

Domino: Extracting, Comparing, and Manipulating Subsets across Multiple Tabular Datasets

PubMed Central

Gratzl, Samuel; Gehlenborg, Nils; Lex, Alexander; Pfister, Hanspeter; Streit, Marc

2016-01-01

Answering questions about complex issues often requires analysts to take into account information contained in multiple interconnected datasets. A common strategy in analyzing and visualizing large and heterogeneous data is dividing it into meaningful subsets. Interesting subsets can then be selected and the associated data and the relationships between the subsets visualized. However, neither the extraction and manipulation nor the comparison of subsets is well supported by state-of-the-art techniques. In this paper we present Domino, a novel multiform visualization technique for effectively representing subsets and the relationships between them. By providing comprehensive tools to arrange, combine, and extract subsets, Domino allows users to create both common visualization techniques and advanced visualizations tailored to specific use cases. In addition to the novel technique, we present an implementation that enables analysts to manage the wide range of options that our approach offers. Innovative interactive features such as placeholders and live previews support rapid creation of complex analysis setups. We introduce the technique and the implementation using a simple example and demonstrate scalability and effectiveness in a use case from the field of cancer genomics. PMID:26356916

Cloud Based Resource for Data Hosting, Visualization and Analysis Using UCSC Cancer Genomics Browser | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The Cancer Analysis Virtual Machine (CAVM) project will leverage cloud technology, the UCSC Cancer Genomics Browser, and the Galaxy analysis workflow system to provide investigators with a flexible, scalable platform for hosting, visualizing and analyzing their own genomic data.

Purdue ionomics information management system. An integrated functional genomics platform.

PubMed

Baxter, Ivan; Ouzzani, Mourad; Orcun, Seza; Kennedy, Brad; Jandhyala, Shrinivas S; Salt, David E

2007-02-01

The advent of high-throughput phenotyping technologies has created a deluge of information that is difficult to deal with without the appropriate data management tools. These data management tools should integrate defined workflow controls for genomic-scale data acquisition and validation, data storage and retrieval, and data analysis, indexed around the genomic information of the organism of interest. To maximize the impact of these large datasets, it is critical that they are rapidly disseminated to the broader research community, allowing open access for data mining and discovery. We describe here a system that incorporates such functionalities developed around the Purdue University high-throughput ionomics phenotyping platform. The Purdue Ionomics Information Management System (PiiMS) provides integrated workflow control, data storage, and analysis to facilitate high-throughput data acquisition, along with integrated tools for data search, retrieval, and visualization for hypothesis development. PiiMS is deployed as a World Wide Web-enabled system, allowing for integration of distributed workflow processes and open access to raw data for analysis by numerous laboratories. PiiMS currently contains data on shoot concentrations of P, Ca, K, Mg, Cu, Fe, Zn, Mn, Co, Ni, B, Se, Mo, Na, As, and Cd in over 60,000 shoot tissue samples of Arabidopsis (Arabidopsis thaliana), including ethyl methanesulfonate, fast-neutron and defined T-DNA mutants, and natural accession and populations of recombinant inbred lines from over 800 separate experiments, representing over 1,000,000 fully quantitative elemental concentrations. PiiMS is accessible at www.purdue.edu/dp/ionomics.

CloVR-Comparative: automated, cloud-enabled comparative microbial genome sequence analysis pipeline.

PubMed

Agrawal, Sonia; Arze, Cesar; Adkins, Ricky S; Crabtree, Jonathan; Riley, David; Vangala, Mahesh; Galens, Kevin; Fraser, Claire M; Tettelin, Hervé; White, Owen; Angiuoli, Samuel V; Mahurkar, Anup; Fricke, W Florian

2017-04-27

The benefit of increasing genomic sequence data to the scientific community depends on easy-to-use, scalable bioinformatics support. CloVR-Comparative combines commonly used bioinformatics tools into an intuitive, automated, and cloud-enabled analysis pipeline for comparative microbial genomics. CloVR-Comparative runs on annotated complete or draft genome sequences that are uploaded by the user or selected via a taxonomic tree-based user interface and downloaded from NCBI. CloVR-Comparative runs reference-free multiple whole-genome alignments to determine unique, shared and core coding sequences (CDSs) and single nucleotide polymorphisms (SNPs). Output includes short summary reports and detailed text-based results files, graphical visualizations (phylogenetic trees, circular figures), and a database file linked to the Sybil comparative genome browser. Data up- and download, pipeline configuration and monitoring, and access to Sybil are managed through CloVR-Comparative web interface. CloVR-Comparative and Sybil are distributed as part of the CloVR virtual appliance, which runs on local computers or the Amazon EC2 cloud. Representative datasets (e.g. 40 draft and complete Escherichia coli genomes) are processed in <36 h on a local desktop or at a cost of <$20 on EC2. CloVR-Comparative allows anybody with Internet access to run comparative genomics projects, while eliminating the need for on-site computational resources and expertise.

eXframe: reusable framework for storage, analysis and visualization of genomics experiments

PubMed Central

2011-01-01

Background Genome-wide experiments are routinely conducted to measure gene expression, DNA-protein interactions and epigenetic status. Structured metadata for these experiments is imperative for a complete understanding of experimental conditions, to enable consistent data processing and to allow retrieval, comparison, and integration of experimental results. Even though several repositories have been developed for genomics data, only a few provide annotation of samples and assays using controlled vocabularies. Moreover, many of them are tailored for a single type of technology or measurement and do not support the integration of multiple data types. Results We have developed eXframe - a reusable web-based framework for genomics experiments that provides 1) the ability to publish structured data compliant with accepted standards 2) support for multiple data types including microarrays and next generation sequencing 3) query, analysis and visualization integration tools (enabled by consistent processing of the raw data and annotation of samples) and is available as open-source software. We present two case studies where this software is currently being used to build repositories of genomics experiments - one contains data from hematopoietic stem cells and another from Parkinson's disease patients. Conclusion The web-based framework eXframe offers structured annotation of experiments as well as uniform processing and storage of molecular data from microarray and next generation sequencing platforms. The framework allows users to query and integrate information across species, technologies, measurement types and experimental conditions. Our framework is reusable and freely modifiable - other groups or institutions can deploy their own custom web-based repositories based on this software. It is interoperable with the most important data formats in this domain. We hope that other groups will not only use eXframe, but also contribute their own useful modifications. PMID:22103807

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

PubMed

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

2018-04-12

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

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

PubMed Central

Zhang, Bing; Schmoyer, Denise; Kirov, Stefan; Snoddy, Jay

2004-01-01

Background Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets. Results We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at . Conclusion GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets. PMID:14975175

Genome Editing Tools in Plants

PubMed Central

Mohanta, Tapan Kumar; Bashir, Tufail; Hashem, Abeer; Bae, Hanhong

2017-01-01

Genome editing tools have the potential to change the genomic architecture of a genome at precise locations, with desired accuracy. These tools have been efficiently used for trait discovery and for the generation of plants with high crop yields and resistance to biotic and abiotic stresses. Due to complex genomic architecture, it is challenging to edit all of the genes/genomes using a particular genome editing tool. Therefore, to overcome this challenging task, several genome editing tools have been developed to facilitate efficient genome editing. Some of the major genome editing tools used to edit plant genomes are: Homologous recombination (HR), zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), pentatricopeptide repeat proteins (PPRs), the CRISPR/Cas9 system, RNA interference (RNAi), cisgenesis, and intragenesis. In addition, site-directed sequence editing and oligonucleotide-directed mutagenesis have the potential to edit the genome at the single-nucleotide level. Recently, adenine base editors (ABEs) have been developed to mutate A-T base pairs to G-C base pairs. ABEs use deoxyadeninedeaminase (TadA) with catalytically impaired Cas9 nickase to mutate A-T base pairs to G-C base pairs. PMID:29257124

Gigwa-Genotype investigator for genome-wide analyses.

PubMed

Sempéré, Guilhem; Philippe, Florian; Dereeper, Alexis; Ruiz, Manuel; Sarah, Gautier; Larmande, Pierre

2016-06-06

Exploring the structure of genomes and analyzing their evolution is essential to understanding the ecological adaptation of organisms. However, with the large amounts of data being produced by next-generation sequencing, computational challenges arise in terms of storage, search, sharing, analysis and visualization. This is particularly true with regards to studies of genomic variation, which are currently lacking scalable and user-friendly data exploration solutions. Here we present Gigwa, a web-based tool that provides an easy and intuitive way to explore large amounts of genotyping data by filtering it not only on the basis of variant features, including functional annotations, but also on genotype patterns. The data storage relies on MongoDB, which offers good scalability properties. Gigwa can handle multiple databases and may be deployed in either single- or multi-user mode. In addition, it provides a wide range of popular export formats. The Gigwa application is suitable for managing large amounts of genomic variation data. Its user-friendly web interface makes such processing widely accessible. It can either be simply deployed on a workstation or be used to provide a shared data portal for a given community of researchers.

GenomicTools: a computational platform for developing high-throughput analytics in genomics.

PubMed

Tsirigos, Aristotelis; Haiminen, Niina; Bilal, Erhan; Utro, Filippo

2012-01-15

Recent advances in sequencing technology have resulted in the dramatic increase of sequencing data, which, in turn, requires efficient management of computational resources, such as computing time, memory requirements as well as prototyping of computational pipelines. We present GenomicTools, a flexible computational platform, comprising both a command-line set of tools and a C++ API, for the analysis and manipulation of high-throughput sequencing data such as DNA-seq, RNA-seq, ChIP-seq and MethylC-seq. GenomicTools implements a variety of mathematical operations between sets of genomic regions thereby enabling the prototyping of computational pipelines that can address a wide spectrum of tasks ranging from pre-processing and quality control to meta-analyses. Additionally, the GenomicTools platform is designed to analyze large datasets of any size by minimizing memory requirements. In practical applications, where comparable, GenomicTools outperforms existing tools in terms of both time and memory usage. The GenomicTools platform (version 2.0.0) was implemented in C++. The source code, documentation, user manual, example datasets and scripts are available online at http://code.google.com/p/ibm-cbc-genomic-tools.

viRome: an R package for the visualization and analysis of viral small RNA sequence datasets.

PubMed

Watson, Mick; Schnettler, Esther; Kohl, Alain

2013-08-01

RNA interference (RNAi) is known to play an important part in defence against viruses in a range of species. Second-generation sequencing technologies allow us to assay these systems and the small RNAs that play a key role with unprecedented depth. However, scientists need access to tools that can condense, analyse and display the resulting data. Here, we present viRome, a package for R that takes aligned sequence data and produces a range of essential plots and reports. viRome is released under the BSD license as a package for R available for both Windows and Linux http://virome.sf.net. Additional information and a tutorial is available on the ARK-Genomics website: http://www.ark-genomics.org/bioinformatics/virome. mick.watson@roslin.ed.ac.uk.

VIZARD: analysis of Affymetrix Arabidopsis GeneChip data

NASA Technical Reports Server (NTRS)

Moseyko, Nick; Feldman, Lewis J.

2002-01-01

SUMMARY: The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array. AVAILABILITY: VIZARD is available free of charge for educational, research, and not-for-profit purposes, and can be downloaded at http://www.anm.f2s.com/research/vizard/ CONTACT: moseyko@uclink4.berkeley.edu.

Evaluation of somatic copy number estimation tools for whole-exome sequencing data.

PubMed

Nam, Jae-Yong; Kim, Nayoung K D; Kim, Sang Cheol; Joung, Je-Gun; Xi, Ruibin; Lee, Semin; Park, Peter J; Park, Woong-Yang

2016-03-01

Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

An automated graphics tool for comparative genomics: the Coulson plot generator

PubMed Central

2013-01-01

Background Comparative analysis is an essential component to biology. When applied to genomics for example, analysis may require comparisons between the predicted presence and absence of genes in a group of genomes under consideration. Frequently, genes can be grouped into small categories based on functional criteria, for example membership of a multimeric complex, participation in a metabolic or signaling pathway or shared sequence features and/or paralogy. These patterns of retention and loss are highly informative for the prediction of function, and hence possible biological context, and can provide great insights into the evolutionary history of cellular functions. However, representation of such information in a standard spreadsheet is a poor visual means from which to extract patterns within a dataset. Results We devised the Coulson Plot, a new graphical representation that exploits a matrix of pie charts to display comparative genomics data. Each pie is used to describe a complex or process from a separate taxon, and is divided into sectors corresponding to the number of proteins (subunits) in a complex/process. The predicted presence or absence of proteins in each complex are delineated by occupancy of a given sector; this format is visually highly accessible and makes pattern recognition rapid and reliable. A key to the identity of each subunit, plus hierarchical naming of taxa and coloring are included. A java-based application, the Coulson plot generator (CPG) automates graphic production, with a tab or comma-delineated text file as input and generating an editable portable document format or svg file. Conclusions CPG software may be used to rapidly convert spreadsheet data to a graphical matrix pie chart format. The representation essentially retains all of the information from the spreadsheet but presents a graphically rich format making comparisons and identification of patterns significantly clearer. While the Coulson plot format is highly useful in comparative genomics, its original purpose, the software can be used to visualize any dataset where entity occupancy is compared between different classes. Availability CPG software is available at sourceforge http://sourceforge.net/projects/coulson and http://dl.dropbox.com/u/6701906/Web/Sites/Labsite/CPG.html PMID:23621955

D-Light on promoters: a client-server system for the analysis and visualization of cis-regulatory elements

PubMed Central

2013-01-01

Background The binding of transcription factors to DNA plays an essential role in the regulation of gene expression. Numerous experiments elucidated binding sequences which subsequently have been used to derive statistical models for predicting potential transcription factor binding sites (TFBS). The rapidly increasing number of genome sequence data requires sophisticated computational approaches to manage and query experimental and predicted TFBS data in the context of other epigenetic factors and across different organisms. Results We have developed D-Light, a novel client-server software package to store and query large amounts of TFBS data for any number of genomes. Users can add small-scale data to the server database and query them in a large scale, genome-wide promoter context. The client is implemented in Java and provides simple graphical user interfaces and data visualization. Here we also performed a statistical analysis showing what a user can expect for certain parameter settings and we illustrate the usage of D-Light with the help of a microarray data set. Conclusions D-Light is an easy to use software tool to integrate, store and query annotation data for promoters. A public D-Light server, the client and server software for local installation and the source code under GNU GPL license are available at http://biwww.che.sbg.ac.at/dlight. PMID:23617301

GenomeFingerprinter: the genome fingerprint and the universal genome fingerprint analysis for systematic comparative genomics.

PubMed

Ai, Yuncan; Ai, Hannan; Meng, Fanmei; Zhao, Lei

2013-01-01

No attention has been paid on comparing a set of genome sequences crossing genetic components and biological categories with far divergence over large size range. We define it as the systematic comparative genomics and aim to develop the methodology. First, we create a method, GenomeFingerprinter, to unambiguously produce a set of three-dimensional coordinates from a sequence, followed by one three-dimensional plot and six two-dimensional trajectory projections, to illustrate the genome fingerprint of a given genome sequence. Second, we develop a set of concepts and tools, and thereby establish a method called the universal genome fingerprint analysis (UGFA). Particularly, we define the total genetic component configuration (TGCC) (including chromosome, plasmid, and phage) for describing a strain as a systematic unit, the universal genome fingerprint map (UGFM) of TGCC for differentiating strains as a universal system, and the systematic comparative genomics (SCG) for comparing a set of genomes crossing genetic components and biological categories. Third, we construct a method of quantitative analysis to compare two genomes by using the outcome dataset of genome fingerprint analysis. Specifically, we define the geometric center and its geometric mean for a given genome fingerprint map, followed by the Euclidean distance, the differentiate rate, and the weighted differentiate rate to quantitatively describe the difference between two genomes of comparison. Moreover, we demonstrate the applications through case studies on various genome sequences, giving tremendous insights into the critical issues in microbial genomics and taxonomy. We have created a method, GenomeFingerprinter, for rapidly computing, geometrically visualizing, intuitively comparing a set of genomes at genome fingerprint level, and hence established a method called the universal genome fingerprint analysis, as well as developed a method of quantitative analysis of the outcome dataset. These have set up the methodology of systematic comparative genomics based on the genome fingerprint analysis.

The i5k Workspace@NAL – enabling genomic data access, visualization, and curation for the i5k community

USDA-ARS?s Scientific Manuscript database

The 5,000 arthropod genomes initiative (i5k) has tasked itself with coordinating the sequencing of 5,000 insect or related arthropod genomes. The resulting influx of data, mostly from small research groups or communities with little bioinformatics experience, will require visualization, disseminatio...

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform

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

Li, Po-E; Lo, Chien -Chi; Anderson, Joseph J.

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the easemore » of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. As a result, this bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research.« less

Biomedical informatics: development of a comprehensive data warehouse for clinical and genomic breast cancer research.

PubMed

Hu, Hai; Brzeski, Henry; Hutchins, Joe; Ramaraj, Mohan; Qu, Long; Xiong, Richard; Kalathil, Surendran; Kato, Rand; Tenkillaya, Santhosh; Carney, Jerry; Redd, Rosann; Arkalgudvenkata, Sheshkumar; Shahzad, Kashif; Scott, Richard; Cheng, Hui; Meadow, Stephen; McMichael, John; Sheu, Shwu-Lin; Rosendale, David; Kvecher, Leonid; Ahern, Stephen; Yang, Song; Zhang, Yonghong; Jordan, Rick; Somiari, Stella B; Hooke, Jeffrey; Shriver, Craig D; Somiari, Richard I; Liebman, Michael N

2004-10-01

The Windber Research Institute is an integrated high-throughput research center employing clinical, genomic and proteomic platforms to produce terabyte levels of data. We use biomedical informatics technologies to integrate all of these operations. This report includes information on a multi-year, multi-phase hybrid data warehouse project currently under development in the Institute. The purpose of the warehouse is to host the terabyte-level of internal experimentally generated data as well as data from public sources. We have previously reported on the phase I development, which integrated limited internal data sources and selected public databases. Currently, we are completing phase II development, which integrates our internal automated data sources and develops visualization tools to query across these data types. This paper summarizes our clinical and experimental operations, the data warehouse development, and the challenges we have faced. In phase III we plan to federate additional manual internal and public data sources and then to develop and adapt more data analysis and mining tools. We expect that the final implementation of the data warehouse will greatly facilitate biomedical informatics research.

Analyzing and interpreting genome data at the network level with ConsensusPathDB.

PubMed

Herwig, Ralf; Hardt, Christopher; Lienhard, Matthias; Kamburov, Atanas

2016-10-01

ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform

PubMed Central

Li, Po-E; Lo, Chien-Chi; Anderson, Joseph J.; Davenport, Karen W.; Bishop-Lilly, Kimberly A.; Xu, Yan; Ahmed, Sanaa; Feng, Shihai; Mokashi, Vishwesh P.; Chain, Patrick S.G.

2017-01-01

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the ease of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. This bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research. PMID:27899609

GIANT API: an application programming interface for functional genomics.

PubMed

Roberts, Andrew M; Wong, Aaron K; Fisk, Ian; Troyanskaya, Olga G

2016-07-08

GIANT API provides biomedical researchers programmatic access to tissue-specific and global networks in humans and model organisms, and associated tools, which includes functional re-prioritization of existing genome-wide association study (GWAS) data. Using tissue-specific interaction networks, researchers are able to predict relationships between genes specific to a tissue or cell lineage, identify the changing roles of genes across tissues and uncover disease-gene associations. Additionally, GIANT API enables computational tools like NetWAS, which leverages tissue-specific networks for re-prioritization of GWAS results. The web services covered by the API include 144 tissue-specific functional gene networks in human, global functional networks for human and six common model organisms and the NetWAS method. GIANT API conforms to the REST architecture, which makes it stateless, cacheable and highly scalable. It can be used by a diverse range of clients including web browsers, command terminals, programming languages and standalone apps for data analysis and visualization. The API is freely available for use at http://giant-api.princeton.edu. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform

DOE PAGES

Li, Po-E; Lo, Chien -Chi; Anderson, Joseph J.; ...

2016-11-24

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the easemore » of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. As a result, this bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research.« less

A geographically-diverse collection of 418 human gut microbiome pathway genome databases

PubMed Central

Hahn, Aria S.; Altman, Tomer; Konwar, Kishori M.; Hanson, Niels W.; Kim, Dongjae; Relman, David A.; Dill, David L.; Hallam, Steven J.

2017-01-01

Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GutCyc, a compendium of environmental pathway genome databases (ePGDBs) constructed from 418 assembled human microbiome datasets using MetaPathways, enabling reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the Pathway Tools software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GutCyc provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GutCyc data products are searchable online, or may be downloaded and explored locally using MetaPathways and Pathway Tools. PMID:28398290

LENS: web-based lens for enrichment and network studies of human proteins

PubMed Central

2015-01-01

Background Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition. Results We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia. Conclusion LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS. PMID:26680011

Arabidopsis Gene Family Profiler (aGFP)--user-oriented transcriptomic database with easy-to-use graphic interface.

PubMed

Dupl'áková, Nikoleta; Renák, David; Hovanec, Patrik; Honysová, Barbora; Twell, David; Honys, David

2007-07-23

Microarray technologies now belong to the standard functional genomics toolbox and have undergone massive development leading to increased genome coverage, accuracy and reliability. The number of experiments exploiting microarray technology has markedly increased in recent years. In parallel with the rapid accumulation of transcriptomic data, on-line analysis tools are being introduced to simplify their use. Global statistical data analysis methods contribute to the development of overall concepts about gene expression patterns and to query and compose working hypotheses. More recently, these applications are being supplemented with more specialized products offering visualization and specific data mining tools. We present a curated gene family-oriented gene expression database, Arabidopsis Gene Family Profiler (aGFP; http://agfp.ueb.cas.cz), which gives the user access to a large collection of normalised Affymetrix ATH1 microarray datasets. The database currently contains NASC Array and AtGenExpress transcriptomic datasets for various tissues at different developmental stages of wild type plants gathered from nearly 350 gene chips. The Arabidopsis GFP database has been designed as an easy-to-use tool for users needing an easily accessible resource for expression data of single genes, pre-defined gene families or custom gene sets, with the further possibility of keyword search. Arabidopsis Gene Family Profiler presents a user-friendly web interface using both graphic and text output. Data are stored at the MySQL server and individual queries are created in PHP script. The most distinguishable features of Arabidopsis Gene Family Profiler database are: 1) the presentation of normalized datasets (Affymetrix MAS algorithm and calculation of model-based gene-expression values based on the Perfect Match-only model); 2) the choice between two different normalization algorithms (Affymetrix MAS4 or MAS5 algorithms); 3) an intuitive interface; 4) an interactive "virtual plant" visualizing the spatial and developmental expression profiles of both gene families and individual genes. Arabidopsis GFP gives users the possibility to analyze current Arabidopsis developmental transcriptomic data starting with simple global queries that can be expanded and further refined to visualize comparative and highly selective gene expression profiles.

Opportunities for CRISPR/Cas9 Gene Editing in Retinal Regeneration Research

PubMed Central

Campbell, Leah J.; Hyde, David R.

2017-01-01

While retinal degeneration and disease results in permanent damage and vision loss in humans, the severely damaged zebrafish retina has a high capacity to regenerate lost neurons and restore visual behaviors. Advancements in understanding the molecular and cellular basis of this regeneration response give hope that strategies and therapeutics may be developed to restore sight to blind and visually-impaired individuals. Our current understanding has been facilitated by the amenability of zebrafish to molecular tools, imaging techniques, and forward and reverse genetic approaches. Accordingly, the zebrafish research community has developed a diverse array of research tools for use in developing and adult animals, including toolkits for facilitating the generation of transgenic animals, systems for inducible, cell-specific transgene expression, and the creation of knockout alleles for nearly every protein coding gene. As CRISPR/Cas9 genome editing has begun to revolutionize molecular biology research, the zebrafish community has responded in stride by developing CRISPR/Cas9 techniques for the zebrafish as well as incorporating CRISPR/Cas9 into available toolsets. The application of CRISPR/Cas9 to retinal regeneration research will undoubtedly bring us closer to understanding the mechanisms underlying retinal repair and vision restoration in the zebrafish, as well as developing therapeutic approaches that will restore vision to blind and visually-impaired individuals. This review focuses on how CRISPR/Cas9 has been integrated into zebrafish research toolsets and how this new tool will revolutionize the field of retinal regeneration research. PMID:29218308

First genome sequences of Achromobacter phages reveal new members of the N4 family.

PubMed

Wittmann, Johannes; Dreiseikelmann, Brigitte; Rohde, Manfred; Meier-Kolthoff, Jan P; Bunk, Boyke; Rohde, Christine

2014-01-27

Multi-resistant Achromobacter xylosoxidans has been recognized as an emerging pathogen causing nosocomially acquired infections during the last years. Phages as natural opponents could be an alternative to fight such infections. Bacteriophages against this opportunistic pathogen were isolated in a recent study. This study shows a molecular analysis of two podoviruses and reveals first insights into the genomic structure of Achromobacter phages so far. Growth curve experiments and adsorption kinetics were performed for both phages. Adsorption and propagation in cells were visualized by electron microscopy. Both phage genomes were sequenced with the PacBio RS II system based on single molecule, real-time (SMRT) technology and annotated with several bioinformatic tools. To further elucidate the evolutionary relationships between the phage genomes, a phylogenomic analysis was conducted using the genome Blast Distance Phylogeny approach (GBDP). In this study, we present the first detailed analysis of genome sequences of two Achromobacter phages so far. Phages JWAlpha and JWDelta were isolated from two different waste water treatment plants in Germany. Both phages belong to the Podoviridae and contain linear, double-stranded DNA with a length of 72329 bp and 73659 bp, respectively. 92 and 89 putative open reading frames were identified for JWAlpha and JWDelta, respectively, by bioinformatic analysis with several tools. The genomes have nearly the same organization and could be divided into different clusters for transcription, replication, host interaction, head and tail structure and lysis. Detailed annotation via protein comparisons with BLASTP revealed strong similarities to N4-like phages. Analysis of the genomes of Achromobacter phages JWAlpha and JWDelta and comparisons of different gene clusters with other phages revealed that they might be strongly related to other N4-like phages, especially of the Escherichia group. Although all these phages show a highly conserved genomic structure and partially strong similarities at the amino acid level, some differences could be identified. Those differences, e.g. the existence of specific genes for replication or host interaction in some N4-like phages, seem to be interesting targets for further examination of function and specific mechanisms, which might enlighten the mechanism of phage establishment in the host cell after infection.

Sockeye: A 3D Environment for Comparative Genomics

PubMed Central

Montgomery, Stephen B.; Astakhova, Tamara; Bilenky, Mikhail; Birney, Ewan; Fu, Tony; Hassel, Maik; Melsopp, Craig; Rak, Marcin; Robertson, A. Gordon; Sleumer, Monica; Siddiqui, Asim S.; Jones, Steven J.M.

2004-01-01

Comparative genomics techniques are used in bioinformatics analyses to identify the structural and functional properties of DNA sequences. As the amount of available sequence data steadily increases, the ability to perform large-scale comparative analyses has become increasingly relevant. In addition, the growing complexity of genomic feature annotation means that new approaches to genomic visualization need to be explored. We have developed a Java-based application called Sockeye that uses three-dimensional (3D) graphics technology to facilitate the visualization of annotation and conservation across multiple sequences. This software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization. PMID:15123592

Genome-Enabled Molecular Tools for Reductive Dehalogenation

DTIC Science & Technology

2011-11-01

Genome-Enabled Molecular Tools for Reductive Dehalogenation - A Shift in Paradigm for Bioremediation - Alfred M. Spormann Departments of Chemical...Genome-Enabled Molecular Tools for Reductive Dehalogenation 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d...Applications Technical Session No. 3D C-77 GENOME-ENABLED MOLECULAR TOOLS FOR REDUCTIVE DEHALOGENATION PROFESSOR ALFRED SPORMANN Stanford

InCHlib - interactive cluster heatmap for web applications.

PubMed

Skuta, Ctibor; Bartůněk, Petr; Svozil, Daniel

2014-12-01

Hierarchical clustering is an exploratory data analysis method that reveals the groups (clusters) of similar objects. The result of the hierarchical clustering is a tree structure called dendrogram that shows the arrangement of individual clusters. To investigate the row/column hierarchical cluster structure of a data matrix, a visualization tool called 'cluster heatmap' is commonly employed. In the cluster heatmap, the data matrix is displayed as a heatmap, a 2-dimensional array in which the colour of each element corresponds to its value. The rows/columns of the matrix are ordered such that similar rows/columns are near each other. The ordering is given by the dendrogram which is displayed on the side of the heatmap. We developed InCHlib (Interactive Cluster Heatmap Library), a highly interactive and lightweight JavaScript library for cluster heatmap visualization and exploration. InCHlib enables the user to select individual or clustered heatmap rows, to zoom in and out of clusters or to flexibly modify heatmap appearance. The cluster heatmap can be augmented with additional metadata displayed in a different colour scale. In addition, to further enhance the visualization, the cluster heatmap can be interconnected with external data sources or analysis tools. Data clustering and the preparation of the input file for InCHlib is facilitated by the Python utility script inchlib_clust . The cluster heatmap is one of the most popular visualizations of large chemical and biomedical data sets originating, e.g., in high-throughput screening, genomics or transcriptomics experiments. The presented JavaScript library InCHlib is a client-side solution for cluster heatmap exploration. InCHlib can be easily deployed into any modern web application and configured to cooperate with external tools and data sources. Though InCHlib is primarily intended for the analysis of chemical or biological data, it is a versatile tool which application domain is not limited to the life sciences only.

Purdue Ionomics Information Management System. An Integrated Functional Genomics Platform1[C][W][OA

PubMed Central

Baxter, Ivan; Ouzzani, Mourad; Orcun, Seza; Kennedy, Brad; Jandhyala, Shrinivas S.; Salt, David E.

2007-01-01

The advent of high-throughput phenotyping technologies has created a deluge of information that is difficult to deal with without the appropriate data management tools. These data management tools should integrate defined workflow controls for genomic-scale data acquisition and validation, data storage and retrieval, and data analysis, indexed around the genomic information of the organism of interest. To maximize the impact of these large datasets, it is critical that they are rapidly disseminated to the broader research community, allowing open access for data mining and discovery. We describe here a system that incorporates such functionalities developed around the Purdue University high-throughput ionomics phenotyping platform. The Purdue Ionomics Information Management System (PiiMS) provides integrated workflow control, data storage, and analysis to facilitate high-throughput data acquisition, along with integrated tools for data search, retrieval, and visualization for hypothesis development. PiiMS is deployed as a World Wide Web-enabled system, allowing for integration of distributed workflow processes and open access to raw data for analysis by numerous laboratories. PiiMS currently contains data on shoot concentrations of P, Ca, K, Mg, Cu, Fe, Zn, Mn, Co, Ni, B, Se, Mo, Na, As, and Cd in over 60,000 shoot tissue samples of Arabidopsis (Arabidopsis thaliana), including ethyl methanesulfonate, fast-neutron and defined T-DNA mutants, and natural accession and populations of recombinant inbred lines from over 800 separate experiments, representing over 1,000,000 fully quantitative elemental concentrations. PiiMS is accessible at www.purdue.edu/dp/ionomics. PMID:17189337

Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

PubMed Central

Trapnell, Cole; Roberts, Adam; Goff, Loyal; Pertea, Geo; Kim, Daehwan; Kelley, David R; Pimentel, Harold; Salzberg, Steven L; Rinn, John L; Pachter, Lior

2012-01-01

Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ~1 h of hands-on time. PMID:22383036

Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data.

PubMed

Greenwald, William W; Li, He; Smith, Erin N; Benaglio, Paola; Nariai, Naoki; Frazer, Kelly A

2017-04-07

Genomic interaction studies use next-generation sequencing (NGS) to examine the interactions between two loci on the genome, with subsequent bioinformatics analyses typically including annotation, intersection, and merging of data from multiple experiments. While many file types and analysis tools exist for storing and manipulating single locus NGS data, there is currently no file standard or analysis tool suite for manipulating and storing paired-genomic-loci: the data type resulting from "genomic interaction" studies. As genomic interaction sequencing data are becoming prevalent, a standard file format and tools for working with these data conveniently and efficiently are needed. This article details a file standard and novel software tool suite for working with paired-genomic-loci data. We present the paired-genomic-loci (PGL) file standard for genomic-interactions data, and the accompanying analysis tool suite "pgltools": a cross platform, pypy compatible python package available both as an easy-to-use UNIX package, and as a python module, for integration into pipelines of paired-genomic-loci analyses. Pgltools is a freely available, open source tool suite for manipulating paired-genomic-loci data. Source code, an in-depth manual, and a tutorial are available publicly at www.github.com/billgreenwald/pgltools , and a python module of the operations can be installed from PyPI via the PyGLtools module.

Plant Reactome: a resource for plant pathways and comparative analysis

PubMed Central

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

2017-01-01

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

Three-dimensional optical coherence tomography of the embryonic murine cardiovascular system

NASA Astrophysics Data System (ADS)

Luo, Wei; Marks, Daniel L.; Ralston, Tyler S.; Boppart, Stephen A.

2006-03-01

Optical coherence tomography (OCT) is an emerging high-resolution real-time biomedical imaging technology that has potential as a novel investigational tool in developmental biology and functional genomics. In this study, murine embryos and embryonic hearts are visualized with an OCT system capable of 2-µm axial and 15-µm lateral resolution and with real-time acquisition rates. We present, to our knowledge, the first sets of high-resolution 2- and 3-D OCT images that reveal the internal structures of the mammalian (murine) embryo (E10.5) and embryonic (E14.5 and E17.5) cardiovascular system. Strong correlations are observed between OCT images and corresponding hematoxylin- and eosin-stained histological sections. Real-time in vivo embryonic (E10.5) heart activity is captured by spectral-domain optical coherence tomography, processed, and displayed at a continuous rate of five frames per second. With the ability to obtain not only high-resolution anatomical data but also functional information during cardiovascular development, the OCT technology has the potential to visualize and quantify changes in murine development and in congenital and induced heart disease, as well as enable a wide range of basic in vitro and in vivo research studies in functional genomics.

A reporter model to visualize imprinting stability at the Dlk1 locus during mouse development and in pluripotent cells.

PubMed

Swanzey, Emily; Stadtfeld, Matthias

2016-11-15

Genomic imprinting results in the monoallelic expression of genes that encode important regulators of growth and proliferation. Dysregulation of imprinted genes, such as those within the Dlk1-Dio3 locus, is associated with developmental syndromes and specific diseases. Our ability to interrogate causes of imprinting instability has been hindered by the absence of suitable model systems. Here, we describe a Dlk1 knock-in reporter mouse that enables single-cell visualization of allele-specific expression and prospective isolation of cells, simultaneously. We show that this 'imprinting reporter mouse' can be used to detect tissue-specific Dlk1 expression patterns in developing embryos. We also apply this system to pluripotent cell culture and demonstrate that it faithfully indicates DNA methylation changes induced upon cellular reprogramming. Finally, the reporter system reveals the role of elevated oxygen levels in eroding imprinted Dlk1 expression during prolonged culture and in vitro differentiation. The possibility to study allele-specific expression in different contexts makes our reporter system a useful tool to dissect the regulation of genomic imprinting in normal development and disease. © 2016. Published by The Company of Biologists Ltd.

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

Karp, Peter D.

Pathway Tools is a systems-biology software package written by SRI International (SRI) that produces Pathway/Genome Databases (PGDBs) for organisms with a sequenced genome. Pathway Tools also provides a wide range of capabilities for analyzing predicted metabolic networks and user-generated omics data. More than 5,000 academic, industrial, and government groups have licensed Pathway Tools. This user community includes researchers at all three DOE bioenergy centers, as well as academic and industrial metabolic engineering (ME) groups. An integral part of the Pathway Tools software is MetaCyc, a large, multiorganism database of metabolic pathways and enzymes that SRI and its academic collaborators manuallymore » curate. This project included two main goals: I. Enhance the MetaCyc content of bioenergy-related enzymes and pathways. II. Develop computational tools for engineering metabolic pathways that satisfy specified design goals, in particular for bioenergy-related pathways. In part I, SRI proposed to significantly expand the coverage of bioenergy-related metabolic information in MetaCyc, followed by the generation of organism-specific PGDBs for all energy-relevant organisms sequenced at the DOE Joint Genome Institute (JGI). Part I objectives included: 1: Expand the content of MetaCyc to include bioenergy-related enzymes and pathways. 2: Enhance the Pathway Tools software to enable display of complex polymer degradation processes. 3: Create new PGDBs for the energy-related organisms sequenced by JGI, update existing PGDBs with new MetaCyc content, and make these data available to JBEI via the BioCyc website. In part II, SRI proposed to develop an efficient computational tool for the engineering of metabolic pathways. Part II objectives included: 4: Develop computational tools for generating metabolic pathways that satisfy specified design goals, enabling users to specify parameters such as starting and ending compounds, and preferred or disallowed intermediate compounds. The pathways were to be generated using metabolic reactions from a reference database (DB). 5: Develop computational tools for ranking the pathways generated in objective (4) according to their optimality. The ranking criteria include stoichiometric yield, the number and cost of additional inputs and the cofactor compounds required by the pathway, pathway length, and pathway energetics. 6: Develop tools for visualizing generated pathways to facilitate the evaluation of a large space of generated pathways.« less

MAJIQ-SPEL: Web-tool to interrogate classical and complex splicing variations from RNA-Seq data.

PubMed

Green, Christopher J; Gazzara, Matthew R; Barash, Yoseph

2017-09-11

Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret, and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis. Program and code will be available at http://majiq.biociphers.org/majiq-spel. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Association of common genetic variants in GPCPD1 with scaling of visual cortical surface area in humans.

PubMed

Bakken, Trygve E; Roddey, J Cooper; Djurovic, Srdjan; Akshoomoff, Natacha; Amaral, David G; Bloss, Cinnamon S; Casey, B J; Chang, Linda; Ernst, Thomas M; Gruen, Jeffrey R; Jernigan, Terry L; Kaufmann, Walter E; Kenet, Tal; Kennedy, David N; Kuperman, Joshua M; Murray, Sarah S; Sowell, Elizabeth R; Rimol, Lars M; Mattingsdal, Morten; Melle, Ingrid; Agartz, Ingrid; Andreassen, Ole A; Schork, Nicholas J; Dale, Anders M; Weiner, Michael; Aisen, Paul; Petersen, Ronald; Jack, Clifford R; Jagust, William; Trojanowki, John Q; Toga, Arthur W; Beckett, Laurel; Green, Robert C; Saykin, Andrew J; Morris, John; Liu, Enchi; Montine, Tom; Gamst, Anthony; Thomas, Ronald G; Donohue, Michael; Walter, Sarah; Gessert, Devon; Sather, Tamie; Harvey, Danielle; Kornak, John; Dale, Anders; Bernstein, Matthew; Felmlee, Joel; Fox, Nick; Thompson, Paul; Schuff, Norbert; Alexander, Gene; DeCarli, Charles; Bandy, Dan; Koeppe, Robert A; Foster, Norm; Reiman, Eric M; Chen, Kewei; Mathis, Chet; Cairns, Nigel J; Taylor-Reinwald, Lisa; Trojanowki, J Q; Shaw, Les; Lee, Virginia M Y; Korecka, Magdalena; Crawford, Karen; Neu, Scott; Foroud, Tatiana M; Potkin, Steven; Shen, Li; Kachaturian, Zaven; Frank, Richard; Snyder, Peter J; Molchan, Susan; Kaye, Jeffrey; Quinn, Joseph; Lind, Betty; Dolen, Sara; Schneider, Lon S; Pawluczyk, Sonia; Spann, Bryan M; Brewer, James; Vanderswag, Helen; Heidebrink, Judith L; Lord, Joanne L; Johnson, Kris; Doody, Rachelle S; Villanueva-Meyer, Javier; Chowdhury, Munir; Stern, Yaakov; Honig, Lawrence S; Bell, Karen L; Morris, John C; Ances, Beau; Carroll, Maria; Leon, Sue; Mintun, Mark A; Schneider, Stacy; Marson, Daniel; Griffith, Randall; Clark, David; Grossman, Hillel; Mitsis, Effie; Romirowsky, Aliza; deToledo-Morrell, Leyla; Shah, Raj C; Duara, Ranjan; Varon, Daniel; Roberts, Peggy; Albert, Marilyn; Onyike, Chiadi; Kielb, Stephanie; Rusinek, Henry; de Leon, Mony J; Glodzik, Lidia; De Santi, Susan; Doraiswamy, P Murali; Petrella, Jeffrey R; Coleman, R Edward; Arnold, Steven E; Karlawish, Jason H; Wolk, David; Smith, Charles D; Jicha, Greg; Hardy, Peter; Lopez, Oscar L; Oakley, MaryAnn; Simpson, Donna M; Porsteinsson, Anton P; Goldstein, Bonnie S; Martin, Kim; Makino, Kelly M; Ismail, M Saleem; Brand, Connie; Mulnard, Ruth A; Thai, Gaby; Mc-Adams-Ortiz, Catherine; Womack, Kyle; Mathews, Dana; Quiceno, Mary; Diaz-Arrastia, Ramon; King, Richard; Weiner, Myron; Martin-Cook, Kristen; DeVous, Michael; Levey, Allan I; Lah, James J; Cellar, Janet S; Burns, Jeffrey M; Anderson, Heather S; Swerdlow, Russell H; Apostolova, Liana; Lu, Po H; Bartzokis, George; Silverman, Daniel H S; Graff-Radford, Neill R; Parfitt, Francine; Johnson, Heather; Farlow, Martin R; Hake, Ann Marie; Matthews, Brandy R; Herring, Scott; van Dyck, Christopher H; Carson, Richard E; MacAvoy, Martha G; Chertkow, Howard; Bergman, Howard; Hosein, Chris; Black, Sandra; Stefanovic, Bojana; Caldwell, Curtis; Ging-Yuek; Hsiung, Robin; Feldman, Howard; Mudge, Benita; Assaly, Michele; Kertesz, Andrew; Rogers, John; Trost, Dick; Bernick, Charles; Munic, Donna; Kerwin, Diana; Mesulam, Marek-Marsel; Lipowski, Kristina; Wu, Chuang-Kuo; Johnson, Nancy; Sadowsky, Carl; Martinez, Walter; Villena, Teresa; Turner, Raymond Scott; Johnson, Kathleen; Reynolds, Brigid; Sperling, Reisa A; Johnson, Keith A; Marshall, Gad; Frey, Meghan; Yesavage, Jerome; Taylor, Joy L; Lane, Barton; Rosen, Allyson; Tinklenberg, Jared; Sabbagh, Marwan; Belden, Christine; Jacobson, Sandra; Kowall, Neil; Killiany, Ronald; Budson, Andrew E; Norbash, Alexander; Johnson, Patricia Lynn; Obisesan, Thomas O; Wolday, Saba; Bwayo, Salome K; Lerner, Alan; Hudson, Leon; Ogrocki, Paula; Fletcher, Evan; Carmichael, Owen; Olichney, John; Kittur, Smita; Borrie, Michael; Lee, T-Y; Bartha, Rob; Johnson, Sterling; Asthana, Sanjay; Carlsson, Cynthia M; Potkin, Steven G; Preda, Adrian; Nguyen, Dana; Tariot, Pierre; Fleisher, Adam; Reeder, Stephanie; Bates, Vernice; Capote, Horacio; Rainka, Michelle; Scharre, Douglas W; Kataki, Maria; Zimmerman, Earl A; Celmins, Dzintra; Brown, Alice D; Pearlson, Godfrey D; Blank, Karen; Anderson, Karen; Santulli, Robert B; Schwartz, Eben S; Sink, Kaycee M; Williamson, Jeff D; Garg, Pradeep; Watkins, Franklin; Ott, Brian R; Querfurth, Henry; Tremont, Geoffrey; Salloway, Stephen; Malloy, Paul; Correia, Stephen; Rosen, Howard J; Miller, Bruce L; Mintzer, Jacobo; Longmire, Crystal Flynn; Spicer, Kenneth; Finger, Elizabether; Rachinsky, Irina; Drost, Dick; Jernigan, Terry; McCabe, Connor; Grant, Ellen; Ernst, Thomas; Kuperman, Josh; Chung, Yoon; Murray, Sarah; Bloss, Cinnamon; Darst, Burcu; Pritchett, Lexi; Saito, Ashley; Amaral, David; DiNino, Mishaela; Eyngorina, Bella; Sowell, Elizabeth; Houston, Suzanne; Soderberg, Lindsay; Kaufmann, Walter; van Zijl, Peter; Rizzo-Busack, Hilda; Javid, Mohsin; Mehta, Natasha; Ruberry, Erika; Powers, Alisa; Rosen, Bruce; Gebhard, Nitzah; Manigan, Holly; Frazier, Jean; Kennedy, David; Yakutis, Lauren; Hill, Michael; Gruen, Jeffrey; Bosson-Heenan, Joan; Carlson, Heatherly

2012-03-06

Visual cortical surface area varies two- to threefold between human individuals, is highly heritable, and has been correlated with visual acuity and visual perception. However, it is still largely unknown what specific genetic and environmental factors contribute to normal variation in the area of visual cortex. To identify SNPs associated with the proportional surface area of visual cortex, we performed a genome-wide association study followed by replication in two independent cohorts. We identified one SNP (rs6116869) that replicated in both cohorts and had genome-wide significant association (P(combined) = 3.2 × 10(-8)). Furthermore, a metaanalysis of imputed SNPs in this genomic region identified a more significantly associated SNP (rs238295; P = 6.5 × 10(-9)) that was in strong linkage disequilibrium with rs6116869. These SNPs are located within 4 kb of the 5' UTR of GPCPD1, glycerophosphocholine phosphodiesterase GDE1 homolog (Saccharomyces cerevisiae), which in humans, is more highly expressed in occipital cortex compared with the remainder of cortex than 99.9% of genes genome-wide. Based on these findings, we conclude that this common genetic variation contributes to the proportional area of human visual cortex. We suggest that identifying genes that contribute to normal cortical architecture provides a first step to understanding genetic mechanisms that underlie visual perception.

STAR: an integrated solution to management and visualization of sequencing data

PubMed Central

Wang, Tao; Liu, Jie; Shen, Li; Tonti-Filippini, Julian; Zhu, Yun; Jia, Haiyang; Lister, Ryan; Whitaker, John W.; Ecker, Joseph R.; Millar, A. Harvey; Ren, Bing; Wang, Wei

2013-01-01

Motivation: Easily visualization of complex data features is a necessary step to conduct studies on next-generation sequencing (NGS) data. We developed STAR, an integrated web application that enables online management, visualization and track-based analysis of NGS data. Results: STAR is a multilayer web service system. On the client side, STAR leverages JavaScript, HTML5 Canvas and asynchronous communications to deliver a smoothly scrolling desktop-like graphical user interface with a suite of in-browser analysis tools that range from providing simple track configuration controls to sophisticated feature detection within datasets. On the server side, STAR supports private session state retention via an account management system and provides data management modules that enable collection, visualization and analysis of third-party sequencing data from the public domain with over thousands of tracks hosted to date. Overall, STAR represents a next-generation data exploration solution to match the requirements of NGS data, enabling both intuitive visualization and dynamic analysis of data. Availability and implementation: STAR browser system is freely available on the web at http://wanglab.ucsd.edu/star/browser and https://github.com/angell1117/STAR-genome-browser. Contact: wei-wang@ucsd.edu PMID:24078702

MOSAIC: an online database dedicated to the comparative genomics of bacterial strains at the intra-species level.

PubMed

Chiapello, Hélène; Gendrault, Annie; Caron, Christophe; Blum, Jérome; Petit, Marie-Agnès; El Karoui, Meriem

2008-11-27

The recent availability of complete sequences for numerous closely related bacterial genomes opens up new challenges in comparative genomics. Several methods have been developed to align complete genomes at the nucleotide level but their use and the biological interpretation of results are not straightforward. It is therefore necessary to develop new resources to access, analyze, and visualize genome comparisons. Here we present recent developments on MOSAIC, a generalist comparative bacterial genome database. This database provides the bacteriologist community with easy access to comparisons of complete bacterial genomes at the intra-species level. The strategy we developed for comparison allows us to define two types of regions in bacterial genomes: backbone segments (i.e., regions conserved in all compared strains) and variable segments (i.e., regions that are either specific to or variable in one of the aligned genomes). Definition of these segments at the nucleotide level allows precise comparative and evolutionary analyses of both coding and non-coding regions of bacterial genomes. Such work is easily performed using the MOSAIC Web interface, which allows browsing and graphical visualization of genome comparisons. The MOSAIC database now includes 493 pairwise comparisons and 35 multiple maximal comparisons representing 78 bacterial species. Genome conserved regions (backbones) and variable segments are presented in various formats for further analysis. A graphical interface allows visualization of aligned genomes and functional annotations. The MOSAIC database is available online at http://genome.jouy.inra.fr/mosaic.

Reconstruction of Ancestral Genomes in Presence of Gene Gain and Loss.

PubMed

Avdeyev, Pavel; Jiang, Shuai; Aganezov, Sergey; Hu, Fei; Alekseyev, Max A

2016-03-01

Since most dramatic genomic changes are caused by genome rearrangements as well as gene duplications and gain/loss events, it becomes crucial to understand their mechanisms and reconstruct ancestral genomes of the given genomes. This problem was shown to be NP-complete even in the "simplest" case of three genomes, thus calling for heuristic rather than exact algorithmic solutions. At the same time, a larger number of input genomes may actually simplify the problem in practice as it was earlier illustrated with MGRA, a state-of-the-art software tool for reconstruction of ancestral genomes of multiple genomes. One of the key obstacles for MGRA and other similar tools is presence of breakpoint reuses when the same breakpoint region is broken by several different genome rearrangements in the course of evolution. Furthermore, such tools are often limited to genomes composed of the same genes with each gene present in a single copy in every genome. This limitation makes these tools inapplicable for many biological datasets and degrades the resolution of ancestral reconstructions in diverse datasets. We address these deficiencies by extending the MGRA algorithm to genomes with unequal gene contents. The developed next-generation tool MGRA2 can handle gene gain/loss events and shares the ability of MGRA to reconstruct ancestral genomes uniquely in the case of limited breakpoint reuse. Furthermore, MGRA2 employs a number of novel heuristics to cope with higher breakpoint reuse and process datasets inaccessible for MGRA. In practical experiments, MGRA2 shows superior performance for simulated and real genomes as compared to other ancestral genome reconstruction tools.

Genomics Analogy Model for Educators (GAME): Fuzzy DNA Model to Enable the Learning of Gene Sequencing by Visually-Impaired and Blind Students

ERIC Educational Resources Information Center

Butler, Charles; Bello, Julia; York, Alan; Orvis, Kathryn; Pittendrigh, Barry R.

2008-01-01

Much of the general population is aware of terms such as biotechnology, genetic engineering, and genomics. However, there is a lack of understanding concerning these fields among many secondary school students. Few teaching models exist to explain concepts behind genomics and even less are available for teaching the visually impaired and blind.…

Chromatibody, a novel non-invasive molecular tool to explore and manipulate chromatin in living cells

PubMed Central

Jullien, Denis; Vignard, Julien; Fedor, Yoann; Béry, Nicolas; Olichon, Aurélien; Crozatier, Michèle; Erard, Monique; Cassard, Hervé; Ducommun, Bernard; Salles, Bernard

2016-01-01

ABSTRACT Chromatin function is involved in many cellular processes, its visualization or modification being essential in many developmental or cellular studies. Here, we present the characterization of chromatibody, a chromatin-binding single-domain, and explore its use in living cells. This non-intercalating tool specifically binds the heterodimer of H2A–H2B histones and displays a versatile reactivity, specifically labeling chromatin from yeast to mammals. We show that this genetically encoded probe, when fused to fluorescent proteins, allows non-invasive real-time chromatin imaging. Chromatibody is a dynamic chromatin probe that can be modulated. Finally, chromatibody is an efficient tool to target an enzymatic activity to the nucleosome, such as the DNA damage-dependent H2A ubiquitylation, which can modify this epigenetic mark at the scale of the genome and result in DNA damage signaling and repair defects. Taken together, these results identify chromatibody as a universal non-invasive tool for either in vivo chromatin imaging or to manipulate the chromatin landscape. PMID:27206857

PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics.

PubMed

Fernandez-Ricaud, Luciano; Kourtchenko, Olga; Zackrisson, Martin; Warringer, Jonas; Blomberg, Anders

2016-06-23

Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology. To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis. PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data.

PubMed

Ambrosini, Giovanna; Dreos, René; Kumar, Sunil; Bucher, Philipp

2016-11-18

ChIP-seq and related high-throughput chromatin profilig assays generate ever increasing volumes of highly valuable biological data. To make sense out of it, biologists need versatile, efficient and user-friendly tools for access, visualization and itegrative analysis of such data. Here we present the ChIP-Seq command line tools and web server, implementing basic algorithms for ChIP-seq data analysis starting with a read alignment file. The tools are optimized for memory-efficiency and speed thus allowing for processing of large data volumes on inexpensive hardware. The web interface provides access to a large database of public data. The ChIP-Seq tools have a modular and interoperable design in that the output from one application can serve as input to another one. Complex and innovative tasks can thus be achieved by running several tools in a cascade. The various ChIP-Seq command line tools and web services either complement or compare favorably to related bioinformatics resources in terms of computational efficiency, ease of access to public data and interoperability with other web-based tools. The ChIP-Seq server is accessible at http://ccg.vital-it.ch/chipseq/ .

Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal

PubMed Central

Gao, Jianjiong; Aksoy, Bülent Arman; Dogrusoz, Ugur; Dresdner, Gideon; Gross, Benjamin; Sumer, S. Onur; Sun, Yichao; Jacobsen, Anders; Sinha, Rileen; Larsson, Erik; Cerami, Ethan; Sander, Chris; Schultz, Nikolaus

2014-01-01

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics. PMID:23550210

HiView: an integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants.

PubMed

Xu, Zheng; Zhang, Guosheng; Duan, Qing; Chai, Shengjie; Zhang, Baqun; Wu, Cong; Jin, Fulai; Yue, Feng; Li, Yun; Hu, Ming

2016-03-11

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits and diseases. However, most of them are located in the non-protein coding regions, and therefore it is challenging to hypothesize the functions of these non-coding GWAS variants. Recent large efforts such as the ENCODE and Roadmap Epigenomics projects have predicted a large number of regulatory elements. However, the target genes of these regulatory elements remain largely unknown. Chromatin conformation capture based technologies such as Hi-C can directly measure the chromatin interactions and have generated an increasingly comprehensive catalog of the interactome between the distal regulatory elements and their potential target genes. Leveraging such information revealed by Hi-C holds the promise of elucidating the functions of genetic variants in human diseases. In this work, we present HiView, the first integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants. HiView is able to display Hi-C data and statistical evidence for chromatin interactions in genomic regions surrounding any given GWAS variant, enabling straightforward visualization and interpretation. We believe that as the first GWAS variants-centered Hi-C genome browser, HiView is a useful tool guiding post-GWAS functional genomics studies. HiView is freely accessible at: http://www.unc.edu/~yunmli/HiView .

PoMaMo--a comprehensive database for potato genome data.

PubMed

Meyer, Svenja; Nagel, Axel; Gebhardt, Christiane

2005-01-01

A database for potato genome data (PoMaMo, Potato Maps and More) was established. The database contains molecular maps of all twelve potato chromosomes with about 1000 mapped elements, sequence data, putative gene functions, results from BLAST analysis, SNP and InDel information from different diploid and tetraploid potato genotypes, publication references, links to other public databases like GenBank (http://www.ncbi.nlm.nih.gov/) or SGN (Solanaceae Genomics Network, http://www.sgn.cornell.edu/), etc. Flexible search and data visualization interfaces enable easy access to the data via internet (https://gabi.rzpd.de/PoMaMo.html). The Java servlet tool YAMB (Yet Another Map Browser) was designed to interactively display chromosomal maps. Maps can be zoomed in and out, and detailed information about mapped elements can be obtained by clicking on an element of interest. The GreenCards interface allows a text-based data search by marker-, sequence- or genotype name, by sequence accession number, gene function, BLAST Hit or publication reference. The PoMaMo database is a comprehensive database for different potato genome data, and to date the only database containing SNP and InDel data from diploid and tetraploid potato genotypes.

PoMaMo—a comprehensive database for potato genome data

PubMed Central

Meyer, Svenja; Nagel, Axel; Gebhardt, Christiane

2005-01-01

A database for potato genome data (PoMaMo, Potato Maps and More) was established. The database contains molecular maps of all twelve potato chromosomes with about 1000 mapped elements, sequence data, putative gene functions, results from BLAST analysis, SNP and InDel information from different diploid and tetraploid potato genotypes, publication references, links to other public databases like GenBank (http://www.ncbi.nlm.nih.gov/) or SGN (Solanaceae Genomics Network, http://www.sgn.cornell.edu/), etc. Flexible search and data visualization interfaces enable easy access to the data via internet (https://gabi.rzpd.de/PoMaMo.html). The Java servlet tool YAMB (Yet Another Map Browser) was designed to interactively display chromosomal maps. Maps can be zoomed in and out, and detailed information about mapped elements can be obtained by clicking on an element of interest. The GreenCards interface allows a text-based data search by marker-, sequence- or genotype name, by sequence accession number, gene function, BLAST Hit or publication reference. The PoMaMo database is a comprehensive database for different potato genome data, and to date the only database containing SNP and InDel data from diploid and tetraploid potato genotypes. PMID:15608284

VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment.

PubMed

Habegger, Lukas; Balasubramanian, Suganthi; Chen, David Z; Khurana, Ekta; Sboner, Andrea; Harmanci, Arif; Rozowsky, Joel; Clarke, Declan; Snyder, Michael; Gerstein, Mark

2012-09-01

The functional annotation of variants obtained through sequencing projects is generally assumed to be a simple intersection of genomic coordinates with genomic features. However, complexities arise for several reasons, including the differential effects of a variant on alternatively spliced transcripts, as well as the difficulty in assessing the impact of small insertions/deletions and large structural variants. Taking these factors into consideration, we developed the Variant Annotation Tool (VAT) to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals. VAT also allows visualization of the effects of different variants, integrates allele frequencies and genotype data from the underlying individuals and facilitates comparative analysis between different groups of individuals. VAT can either be run through a command-line interface or as a web application. Finally, in order to enable on-demand access and to minimize unnecessary transfers of large data files, VAT can be run as a virtual machine in a cloud-computing environment. VAT is implemented in C and PHP. The VAT web service, Amazon Machine Image, source code and detailed documentation are available at vat.gersteinlab.org.

OrthoVenn: a web server for genome wide comparison and annotation of orthologous clusters across multiple species.

PubMed

Wang, Yi; Coleman-Derr, Devin; Chen, Guoping; Gu, Yong Q

2015-07-01

Genome wide analysis of orthologous clusters is an important component of comparative genomics studies. Identifying the overlap among orthologous clusters can enable us to elucidate the function and evolution of proteins across multiple species. Here, we report a web platform named OrthoVenn that is useful for genome wide comparisons and visualization of orthologous clusters. OrthoVenn provides coverage of vertebrates, metazoa, protists, fungi, plants and bacteria for the comparison of orthologous clusters and also supports uploading of customized protein sequences from user-defined species. An interactive Venn diagram, summary counts, and functional summaries of the disjunction and intersection of clusters shared between species are displayed as part of the OrthoVenn result. OrthoVenn also includes in-depth views of the clusters using various sequence analysis tools. Furthermore, OrthoVenn identifies orthologous clusters of single copy genes and allows for a customized search of clusters of specific genes through key words or BLAST. OrthoVenn is an efficient and user-friendly web server freely accessible at http://probes.pw.usda.gov/OrthoVenn or http://aegilops.wheat.ucdavis.edu/OrthoVenn. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

NCBI GEO: archive for functional genomics data sets--10 years on.

PubMed

Barrett, Tanya; Troup, Dennis B; Wilhite, Stephen E; Ledoux, Pierre; Evangelista, Carlos; Kim, Irene F; Tomashevsky, Maxim; Marshall, Kimberly A; Phillippy, Katherine H; Sherman, Patti M; Muertter, Rolf N; Holko, Michelle; Ayanbule, Oluwabukunmi; Yefanov, Andrey; Soboleva, Alexandra

2011-01-01

A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20,000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.

CAR: contig assembly of prokaryotic draft genomes using rearrangements.

PubMed

Lu, Chin Lung; Chen, Kun-Tze; Huang, Shih-Yuan; Chiu, Hsien-Tai

2014-11-28

Next generation sequencing technology has allowed efficient production of draft genomes for many organisms of interest. However, most draft genomes are just collections of independent contigs, whose relative positions and orientations along the genome being sequenced are unknown. Although several tools have been developed to order and orient the contigs of draft genomes, more accurate tools are still needed. In this study, we present a novel reference-based contig assembly (or scaffolding) tool, named as CAR, that can efficiently and more accurately order and orient the contigs of a prokaryotic draft genome based on a reference genome of a related organism. Given a set of contigs in multi-FASTA format and a reference genome in FASTA format, CAR can output a list of scaffolds, each of which is a set of ordered and oriented contigs. For validation, we have tested CAR on a real dataset composed of several prokaryotic genomes and also compared its performance with several other reference-based contig assembly tools. Consequently, our experimental results have shown that CAR indeed performs better than all these other reference-based contig assembly tools in terms of sensitivity, precision and genome coverage. CAR serves as an efficient tool that can more accurately order and orient the contigs of a prokaryotic draft genome based on a reference genome. The web server of CAR is freely available at http://genome.cs.nthu.edu.tw/CAR/ and its stand-alone program can also be downloaded from the same website.

JUICE: a data management system that facilitates the analysis of large volumes of information in an EST project workflow.

PubMed

Latorre, Mariano; Silva, Herman; Saba, Juan; Guziolowski, Carito; Vizoso, Paula; Martinez, Veronica; Maldonado, Jonathan; Morales, Andrea; Caroca, Rodrigo; Cambiazo, Veronica; Campos-Vargas, Reinaldo; Gonzalez, Mauricio; Orellana, Ariel; Retamales, Julio; Meisel, Lee A

2006-11-23

Expressed sequence tag (EST) analyses provide a rapid and economical means to identify candidate genes that may be involved in a particular biological process. These ESTs are useful in many Functional Genomics studies. However, the large quantity and complexity of the data generated during an EST sequencing project can make the analysis of this information a daunting task. In an attempt to make this task friendlier, we have developed JUICE, an open source data management system (Apache + PHP + MySQL on Linux), which enables the user to easily upload, organize, visualize and search the different types of data generated in an EST project pipeline. In contrast to other systems, the JUICE data management system allows a branched pipeline to be established, modified and expanded, during the course of an EST project. The web interfaces and tools in JUICE enable the users to visualize the information in a graphical, user-friendly manner. The user may browse or search for sequences and/or sequence information within all the branches of the pipeline. The user can search using terms associated with the sequence name, annotation or other characteristics stored in JUICE and associated with sequences or sequence groups. Groups of sequences can be created by the user, stored in a clipboard and/or downloaded for further analyses. Different user profiles restrict the access of each user depending upon their role in the project. The user may have access exclusively to visualize sequence information, access to annotate sequences and sequence information, or administrative access. JUICE is an open source data management system that has been developed to aid users in organizing and analyzing the large amount of data generated in an EST Project workflow. JUICE has been used in one of the first functional genomics projects in Chile, entitled "Functional Genomics in nectarines: Platform to potentiate the competitiveness of Chile in fruit exportation". However, due to its ability to organize and visualize data from external pipelines, JUICE is a flexible data management system that should be useful for other EST/Genome projects. The JUICE data management system is released under the Open Source GNU Lesser General Public License (LGPL). JUICE may be downloaded from http://genoma.unab.cl/juice_system/ or http://www.genomavegetal.cl/juice_system/.

JUICE: a data management system that facilitates the analysis of large volumes of information in an EST project workflow

PubMed Central

Latorre, Mariano; Silva, Herman; Saba, Juan; Guziolowski, Carito; Vizoso, Paula; Martinez, Veronica; Maldonado, Jonathan; Morales, Andrea; Caroca, Rodrigo; Cambiazo, Veronica; Campos-Vargas, Reinaldo; Gonzalez, Mauricio; Orellana, Ariel; Retamales, Julio; Meisel, Lee A

2006-01-01

Background Expressed sequence tag (EST) analyses provide a rapid and economical means to identify candidate genes that may be involved in a particular biological process. These ESTs are useful in many Functional Genomics studies. However, the large quantity and complexity of the data generated during an EST sequencing project can make the analysis of this information a daunting task. Results In an attempt to make this task friendlier, we have developed JUICE, an open source data management system (Apache + PHP + MySQL on Linux), which enables the user to easily upload, organize, visualize and search the different types of data generated in an EST project pipeline. In contrast to other systems, the JUICE data management system allows a branched pipeline to be established, modified and expanded, during the course of an EST project. The web interfaces and tools in JUICE enable the users to visualize the information in a graphical, user-friendly manner. The user may browse or search for sequences and/or sequence information within all the branches of the pipeline. The user can search using terms associated with the sequence name, annotation or other characteristics stored in JUICE and associated with sequences or sequence groups. Groups of sequences can be created by the user, stored in a clipboard and/or downloaded for further analyses. Different user profiles restrict the access of each user depending upon their role in the project. The user may have access exclusively to visualize sequence information, access to annotate sequences and sequence information, or administrative access. Conclusion JUICE is an open source data management system that has been developed to aid users in organizing and analyzing the large amount of data generated in an EST Project workflow. JUICE has been used in one of the first functional genomics projects in Chile, entitled "Functional Genomics in nectarines: Platform to potentiate the competitiveness of Chile in fruit exportation". However, due to its ability to organize and visualize data from external pipelines, JUICE is a flexible data management system that should be useful for other EST/Genome projects. The JUICE data management system is released under the Open Source GNU Lesser General Public License (LGPL). JUICE may be downloaded from or . PMID:17123449

SpliceRover: Interpretable Convolutional Neural: Networks for Improved Splice Site Prediction.

PubMed

Zuallaert, Jasper; Godin, Fréderic; Kim, Mijung; Soete, Arne; Saeys, Yvan; De Neve, Wesley

2018-06-21

During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding the biological signals that are hallmarks of gene function and regulation is currently mostly done using automated genome annotation platforms, which mainly rely on integrated machine learning frameworks to identify different functional sites of interest, including splice sites. Splicing is an essential step in the gene regulation process, and the correct identification of splice sites is a major cornerstone in a genome annotation system. In this paper, we present SpliceRover, a predictive deep learning approach that outperforms the state-of-the-art in splice site prediction. SpliceRover uses convolutional neural networks (CNNs), which have been shown to obtain cutting edge performance on a wide variety of prediction tasks. We adapted this approach to deal with genomic sequence inputs, and show it consistently outperforms already existing approaches, with relative improvements in prediction effectiveness of up to 80.9% when measured in terms of false discovery rate. However, a major criticism of CNNs concerns their "black box" nature, as mechanisms to obtain insight into their reasoning processes are limited. To facilitate interpretability of the SpliceRover models, we introduce an approach to visualize the biologically relevant information learnt. We show that our visualization approach is able to recover features known to be important for splice site prediction (binding motifs around the splice site, presence of polypyrimidine tracts and branch points), as well as reveal new features (e.g., several types of exclusion patterns near splice sites). SpliceRover is available as a web service. The prediction tool and instructions can be found at http://bioit2.irc.ugent.be/splicerover/. Supplementary materials are available at Bioinformatics online.

Applying Genomic and Genetic Tools to Understand and Mitigate Damage from Exposure to Toxins

DTIC Science & Technology

2013-10-01

sequences to the human genome . Genome Biol 10, R25 (2009). 26 Award number: W81XWH-09-1-0715 Title: Applying Genomic and Genetic Tools to Understand...utilizing the high-throughput technology of mRNA-seq. BODY The goal of our research program (W81XWH-09-1-0715) was to utilize genetic and genomic ...also acquired the achetf222a * * * * * 5 Award number: W81XWH-09-1-0715 Title: Applying Genomic and Genetic Tools to Understand and Mitigate

Haptic over visual information in the distribution of visual attention after tool-use in near and far space.

PubMed

Park, George D; Reed, Catherine L

2015-10-01

Despite attentional prioritization for grasping space near the hands, tool-use appears to transfer attentional bias to the tool's end/functional part. The contributions of haptic and visual inputs to attentional distribution along a tool were investigated as a function of tool-use in near (Experiment 1) and far (Experiment 2) space. Visual attention was assessed with a 50/50, go/no-go, target discrimination task, while a tool was held next to targets appearing near the tool-occupied hand or tool-end. Target response times (RTs) and sensitivity (d-prime) were measured at target locations, before and after functional tool practice for three conditions: (1) open-tool: tool-end visible (visual + haptic inputs), (2) hidden-tool: tool-end visually obscured (haptic input only), and (3) short-tool: stick missing tool's length/end (control condition: hand occupied but no visual/haptic input). In near space, both open- and hidden-tool groups showed a tool-end, attentional bias (faster RTs toward tool-end) before practice; after practice, RTs near the hand improved. In far space, the open-tool group showed no bias before practice; after practice, target RTs near the tool-end improved. However, the hidden-tool group showed a consistent tool-end bias despite practice. Lack of short-tool group results suggested that hidden-tool group results were specific to haptic inputs. In conclusion, (1) allocation of visual attention along a tool due to tool practice differs in near and far space, and (2) visual attention is drawn toward the tool's end even when visually obscured, suggesting haptic input provides sufficient information for directing attention along the tool.

WGSSAT: A High-Throughput Computational Pipeline for Mining and Annotation of SSR Markers From Whole Genomes.

PubMed

Pandey, Manmohan; Kumar, Ravindra; Srivastava, Prachi; Agarwal, Suyash; Srivastava, Shreya; Nagpure, Naresh S; Jena, Joy K; Kushwaha, Basdeo

2018-03-16

Mining and characterization of Simple Sequence Repeat (SSR) markers from whole genomes provide valuable information about biological significance of SSR distribution and also facilitate development of markers for genetic analysis. Whole genome sequencing (WGS)-SSR Annotation Tool (WGSSAT) is a graphical user interface pipeline developed using Java Netbeans and Perl scripts which facilitates in simplifying the process of SSR mining and characterization. WGSSAT takes input in FASTA format and automates the prediction of genes, noncoding RNA (ncRNA), core genes, repeats and SSRs from whole genomes followed by mapping of the predicted SSRs onto a genome (classified according to genes, ncRNA, repeats, exonic, intronic, and core gene region) along with primer identification and mining of cross-species markers. The program also generates a detailed statistical report along with visualization of mapped SSRs, genes, core genes, and RNAs. The features of WGSSAT were demonstrated using Takifugu rubripes data. This yielded a total of 139 057 SSR, out of which 113 703 SSR primer pairs were uniquely amplified in silico onto a T. rubripes (fugu) genome. Out of 113 703 mined SSRs, 81 463 were from coding region (including 4286 exonic and 77 177 intronic), 7 from RNA, 267 from core genes of fugu, whereas 105 641 SSR and 601 SSR primer pairs were uniquely mapped onto the medaka genome. WGSSAT is tested under Ubuntu Linux. The source code, documentation, user manual, example dataset and scripts are available online at https://sourceforge.net/projects/wgssat-nbfgr.

GenomeDiagram: a python package for the visualization of large-scale genomic data.

PubMed

Pritchard, Leighton; White, Jennifer A; Birch, Paul R J; Toth, Ian K

2006-03-01

We present GenomeDiagram, a flexible, open-source Python module for the visualization of large-scale genomic, comparative genomic and other data with reference to a single chromosome or other biological sequence. GenomeDiagram may be used to generate publication-quality vector graphics, rastered images and in-line streamed graphics for webpages. The package integrates with datatypes from the BioPython project, and is available for Windows, Linux and Mac OS X systems. GenomeDiagram is freely available as source code (under GNU Public License) at http://bioinf.scri.ac.uk/lp/programs.html, and requires Python 2.3 or higher, and recent versions of the ReportLab and BioPython packages. A user manual, example code and images are available at http://bioinf.scri.ac.uk/lp/programs.html.

Approaches to Fungal Genome Annotation

PubMed Central

Haas, Brian J.; Zeng, Qiandong; Pearson, Matthew D.; Cuomo, Christina A.; Wortman, Jennifer R.

2011-01-01

Fungal genome annotation is the starting point for analysis of genome content. This generally involves the application of diverse methods to identify features on a genome assembly such as protein-coding and non-coding genes, repeats and transposable elements, and pseudogenes. Here we describe tools and methods leveraged for eukaryotic genome annotation with a focus on the annotation of fungal nuclear and mitochondrial genomes. We highlight the application of the latest technologies and tools to improve the quality of predicted gene sets. The Broad Institute eukaryotic genome annotation pipeline is described as one example of how such methods and tools are integrated into a sequencing center’s production genome annotation environment. PMID:22059117

iSyTE 2.0: a database for expression-based gene discovery in the eye

PubMed Central

Kakrana, Atul; Yang, Andrian; Anand, Deepti; Djordjevic, Djordje; Ramachandruni, Deepti; Singh, Abhyudai; Huang, Hongzhan

2018-01-01

Abstract Although successful in identifying new cataract-linked genes, the previous version of the database iSyTE (integrated Systems Tool for Eye gene discovery) was based on expression information on just three mouse lens stages and was functionally limited to visualization by only UCSC-Genome Browser tracks. To increase its efficacy, here we provide an enhanced iSyTE version 2.0 (URL: http://research.bioinformatics.udel.edu/iSyTE) based on well-curated, comprehensive genome-level lens expression data as a one-stop portal for the effective visualization and analysis of candidate genes in lens development and disease. iSyTE 2.0 includes all publicly available lens Affymetrix and Illumina microarray datasets representing a broad range of embryonic and postnatal stages from wild-type and specific gene-perturbation mouse mutants with eye defects. Further, we developed a new user-friendly web interface for direct access and cogent visualization of the curated expression data, which supports convenient searches and a range of downstream analyses. The utility of these new iSyTE 2.0 features is illustrated through examples of established genes associated with lens development and pathobiology, which serve as tutorials for its application by the end-user. iSyTE 2.0 will facilitate the prioritization of eye development and disease-linked candidate genes in studies involving transcriptomics or next-generation sequencing data, linkage analysis and GWAS approaches. PMID:29036527

YersiniaBase: a genomic resource and analysis platform for comparative analysis of Yersinia.

PubMed

Tan, Shi Yang; Dutta, Avirup; Jakubovics, Nicholas S; Ang, Mia Yang; Siow, Cheuk Chuen; Mutha, Naresh Vr; Heydari, Hamed; Wee, Wei Yee; Wong, Guat Jah; Choo, Siew Woh

2015-01-16

Yersinia is a Gram-negative bacteria that includes serious pathogens such as the Yersinia pestis, which causes plague, Yersinia pseudotuberculosis, Yersinia enterocolitica. The remaining species are generally considered non-pathogenic to humans, although there is evidence that at least some of these species can cause occasional infections using distinct mechanisms from the more pathogenic species. With the advances in sequencing technologies, many genomes of Yersinia have been sequenced. However, there is currently no specialized platform to hold the rapidly-growing Yersinia genomic data and to provide analysis tools particularly for comparative analyses, which are required to provide improved insights into their biology, evolution and pathogenicity. To facilitate the ongoing and future research of Yersinia, especially those generally considered non-pathogenic species, a well-defined repository and analysis platform is needed to hold the Yersinia genomic data and analysis tools for the Yersinia research community. Hence, we have developed the YersiniaBase, a robust and user-friendly Yersinia resource and analysis platform for the analysis of Yersinia genomic data. YersiniaBase has a total of twelve species and 232 genome sequences, of which the majority are Yersinia pestis. In order to smooth the process of searching genomic data in a large database, we implemented an Asynchronous JavaScript and XML (AJAX)-based real-time searching system in YersiniaBase. Besides incorporating existing tools, which include JavaScript-based genome browser (JBrowse) and Basic Local Alignment Search Tool (BLAST), YersiniaBase also has in-house developed tools: (1) Pairwise Genome Comparison tool (PGC) for comparing two user-selected genomes; (2) Pathogenomics Profiling Tool (PathoProT) for comparative pathogenomics analysis of Yersinia genomes; (3) YersiniaTree for constructing phylogenetic tree of Yersinia. We ran analyses based on the tools and genomic data in YersiniaBase and the preliminary results showed differences in virulence genes found in Yersinia pestis and Yersinia pseudotuberculosis compared to other Yersinia species, and differences between Yersinia enterocolitica subsp. enterocolitica and Yersinia enterocolitica subsp. palearctica. YersiniaBase offers free access to wide range of genomic data and analysis tools for the analysis of Yersinia. YersiniaBase can be accessed at http://yersinia.um.edu.my .

ESCAPE: database for integrating high-content published data collected from human and mouse embryonic stem cells.

PubMed

Xu, Huilei; Baroukh, Caroline; Dannenfelser, Ruth; Chen, Edward Y; Tan, Christopher M; Kou, Yan; Kim, Yujin E; Lemischka, Ihor R; Ma'ayan, Avi

2013-01-01

High content studies that profile mouse and human embryonic stem cells (m/hESCs) using various genome-wide technologies such as transcriptomics and proteomics are constantly being published. However, efforts to integrate such data to obtain a global view of the molecular circuitry in m/hESCs are lagging behind. Here, we present an m/hESC-centered database called Embryonic Stem Cell Atlas from Pluripotency Evidence integrating data from many recent diverse high-throughput studies including chromatin immunoprecipitation followed by deep sequencing, genome-wide inhibitory RNA screens, gene expression microarrays or RNA-seq after knockdown (KD) or overexpression of critical factors, immunoprecipitation followed by mass spectrometry proteomics and phosphoproteomics. The database provides web-based interactive search and visualization tools that can be used to build subnetworks and to identify known and novel regulatory interactions across various regulatory layers. The web-interface also includes tools to predict the effects of combinatorial KDs by additive effects controlled by sliders, or through simulation software implemented in MATLAB. Overall, the Embryonic Stem Cell Atlas from Pluripotency Evidence database is a comprehensive resource for the stem cell systems biology community. Database URL: http://www.maayanlab.net/ESCAPE

Metabolic network visualization eliminating node redundance and preserving metabolic pathways

PubMed Central

Bourqui, Romain; Cottret, Ludovic; Lacroix, Vincent; Auber, David; Mary, Patrick; Sagot, Marie-France; Jourdan, Fabien

2007-01-01

Background The tools that are available to draw and to manipulate the representations of metabolism are usually restricted to metabolic pathways. This limitation becomes problematic when studying processes that span several pathways. The various attempts that have been made to draw genome-scale metabolic networks are confronted with two shortcomings: 1- they do not use contextual information which leads to dense, hard to interpret drawings, 2- they impose to fit to very constrained standards, which implies, in particular, duplicating nodes making topological analysis considerably more difficult. Results We propose a method, called MetaViz, which enables to draw a genome-scale metabolic network and that also takes into account its structuration into pathways. This method consists in two steps: a clustering step which addresses the pathway overlapping problem and a drawing step which consists in drawing the clustered graph and each cluster. Conclusion The method we propose is original and addresses new drawing issues arising from the no-duplication constraint. We do not propose a single drawing but rather several alternative ways of presenting metabolism depending on the pathway on which one wishes to focus. We believe that this provides a valuable tool to explore the pathway structure of metabolism. PMID:17608928

Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform.

PubMed

Li, Po-E; Lo, Chien-Chi; Anderson, Joseph J; Davenport, Karen W; Bishop-Lilly, Kimberly A; Xu, Yan; Ahmed, Sanaa; Feng, Shihai; Mokashi, Vishwesh P; Chain, Patrick S G

2017-01-09

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the ease of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. This bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Summary of talks and papers at ISCB-Asia/SCCG 2012

PubMed Central

2013-01-01

The second ISCB-Asia conference of the International Society for Computational Biology took place December 17-19, 2012, in Shenzhen, China. The conference was co-hosted by BGI as the first Shenzhen Conference on Computational Genomics (SCCG). 45 talks were presented at ISCB-Asia/SCCG 2012. The topics covered included software tools, reproducible computing, next-generation sequencing data analysis, transcription and mRNA regulation, protein structure and function, cancer genomics and personalized medicine. Nine of the proceedings track talks are included as full papers in this supplement. In this report we first give a short overview of the conference by listing some statistics and visualizing the talk abstracts as word clouds. Then we group the talks by topic and briefly summarize each one, providing references to related publications whenever possible. Finally, we close with a few comments on the success of this conference.

An integrated SNP mining and utilization (ISMU) pipeline for next generation sequencing data.

PubMed

Azam, Sarwar; Rathore, Abhishek; Shah, Trushar M; Telluri, Mohan; Amindala, BhanuPrakash; Ruperao, Pradeep; Katta, Mohan A V S K; Varshney, Rajeev K

2014-01-01

Open source single nucleotide polymorphism (SNP) discovery pipelines for next generation sequencing data commonly requires working knowledge of command line interface, massive computational resources and expertise which is a daunting task for biologists. Further, the SNP information generated may not be readily used for downstream processes such as genotyping. Hence, a comprehensive pipeline has been developed by integrating several open source next generation sequencing (NGS) tools along with a graphical user interface called Integrated SNP Mining and Utilization (ISMU) for SNP discovery and their utilization by developing genotyping assays. The pipeline features functionalities such as pre-processing of raw data, integration of open source alignment tools (Bowtie2, BWA, Maq, NovoAlign and SOAP2), SNP prediction (SAMtools/SOAPsnp/CNS2snp and CbCC) methods and interfaces for developing genotyping assays. The pipeline outputs a list of high quality SNPs between all pairwise combinations of genotypes analyzed, in addition to the reference genome/sequence. Visualization tools (Tablet and Flapjack) integrated into the pipeline enable inspection of the alignment and errors, if any. The pipeline also provides a confidence score or polymorphism information content value with flanking sequences for identified SNPs in standard format required for developing marker genotyping (KASP and Golden Gate) assays. The pipeline enables users to process a range of NGS datasets such as whole genome re-sequencing, restriction site associated DNA sequencing and transcriptome sequencing data at a fast speed. The pipeline is very useful for plant genetics and breeding community with no computational expertise in order to discover SNPs and utilize in genomics, genetics and breeding studies. The pipeline has been parallelized to process huge datasets of next generation sequencing. It has been developed in Java language and is available at http://hpc.icrisat.cgiar.org/ISMU as a standalone free software.

TDR Targets: a chemogenomics resource for neglected diseases.

PubMed

Magariños, María P; Carmona, Santiago J; Crowther, Gregory J; Ralph, Stuart A; Roos, David S; Shanmugam, Dhanasekaran; Van Voorhis, Wesley C; Agüero, Fernán

2012-01-01

The TDR Targets Database (http://tdrtargets.org) has been designed and developed as an online resource to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data (e.g. expression, phylogeny, essentiality) for genes collected from various sources, including literature curation. This information can be browsed and queried using an extensive web interface with functionalities for combining, saving, exporting and sharing the query results. Target genes can be ranked and prioritized using numerical weights assigned to the criteria used for querying. In this report we describe recent updates to the TDR Targets database, including the addition of new genomes (specifically helminths), and integration of chemical structure, property and bioactivity information for biological ligands, drugs and inhibitors and cheminformatic tools for querying and visualizing these chemical data. These changes greatly facilitate exploration of linkages (both known and predicted) between genes and small molecules, yielding insight into whether particular proteins may be druggable, effectively allowing the navigation of chemical space in a genomics context.

TDR Targets: a chemogenomics resource for neglected diseases

PubMed Central

Magariños, María P.; Carmona, Santiago J.; Crowther, Gregory J.; Ralph, Stuart A.; Roos, David S.; Shanmugam, Dhanasekaran; Van Voorhis, Wesley C.; Agüero, Fernán

2012-01-01

The TDR Targets Database (http://tdrtargets.org) has been designed and developed as an online resource to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data (e.g. expression, phylogeny, essentiality) for genes collected from various sources, including literature curation. This information can be browsed and queried using an extensive web interface with functionalities for combining, saving, exporting and sharing the query results. Target genes can be ranked and prioritized using numerical weights assigned to the criteria used for querying. In this report we describe recent updates to the TDR Targets database, including the addition of new genomes (specifically helminths), and integration of chemical structure, property and bioactivity information for biological ligands, drugs and inhibitors and cheminformatic tools for querying and visualizing these chemical data. These changes greatly facilitate exploration of linkages (both known and predicted) between genes and small molecules, yielding insight into whether particular proteins may be druggable, effectively allowing the navigation of chemical space in a genomics context. PMID:22116064

MutationAligner: a resource of recurrent mutation hotspots in protein domains in cancer

PubMed Central

Gauthier, Nicholas Paul; Reznik, Ed; Gao, Jianjiong; Sumer, Selcuk Onur; Schultz, Nikolaus; Sander, Chris; Miller, Martin L.

2016-01-01

The MutationAligner web resource, available at http://www.mutationaligner.org, enables discovery and exploration of somatic mutation hotspots identified in protein domains in currently (mid-2015) more than 5000 cancer patient samples across 22 different tumor types. Using multiple sequence alignments of protein domains in the human genome, we extend the principle of recurrence analysis by aggregating mutations in homologous positions across sets of paralogous genes. Protein domain analysis enhances the statistical power to detect cancer-relevant mutations and links mutations to the specific biological functions encoded in domains. We illustrate how the MutationAligner database and interactive web tool can be used to explore, visualize and analyze mutation hotspots in protein domains across genes and tumor types. We believe that MutationAligner will be an important resource for the cancer research community by providing detailed clues for the functional importance of particular mutations, as well as for the design of functional genomics experiments and for decision support in precision medicine. MutationAligner is slated to be periodically updated to incorporate additional analyses and new data from cancer genomics projects. PMID:26590264

Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies

PubMed Central

Viennas, Emmanouil; Komianou, Angeliki; Mizzi, Clint; Stojiljkovic, Maja; Mitropoulou, Christina; Muilu, Juha; Vihinen, Mauno; Grypioti, Panagiota; Papadaki, Styliani; Pavlidis, Cristiana; Zukic, Branka; Katsila, Theodora; van der Spek, Peter J.; Pavlovic, Sonja; Tzimas, Giannis; Patrinos, George P.

2017-01-01

FINDbase (http://www.findbase.org) is a comprehensive data repository that records the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants leading mostly to monogenic disorders and pharmacogenomics biomarkers. The database also records the incidence of rare genetic diseases in various populations, all in well-distinct data modules. Here, we report extensive data content updates in all data modules, with direct implications to clinical pharmacogenomics. Also, we report significant new developments in FINDbase, namely (i) the release of a new version of the ETHNOS software that catalyzes development curation of national/ethnic genetic databases, (ii) the migration of all FINDbase data content into 90 distinct national/ethnic mutation databases, all built around Microsoft's PivotViewer (http://www.getpivot.com) software (iii) new data visualization tools and (iv) the interrelation of FINDbase with DruGeVar database with direct implications in clinical pharmacogenomics. The abovementioned updates further enhance the impact of FINDbase, as a key resource for Genomic Medicine applications. PMID:27924022

CRISPR/Cas9-Mediated Fluorescent Tagging of Endogenous Proteins in Human Pluripotent Stem Cells.

PubMed

Sharma, Arun; Toepfer, Christopher N; Ward, Tarsha; Wasson, Lauren; Agarwal, Radhika; Conner, David A; Hu, Johnny H; Seidman, Christine E

2018-01-24

Human induced pluripotent stem cells (hiPSCs) can be used to mass produce surrogates of human tissues, enabling new advances in drug screening, disease modeling, and cell therapy. Recent developments in clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 genome editing technology use homology-directed repair (HDR) to efficiently generate custom hiPSC lines harboring a variety of genomic insertions and deletions. Thus, hiPSCs that encode an endogenous protein fused to a fluorescent reporter protein can be rapidly created by employing CRISPR/Cas9 genome editing, enhancing HDR efficiency and optimizing homology arm length. These fluorescently tagged hiPSCs can be used to visualize protein function and dynamics in real time as cells proliferate and differentiate. Given that nearly any intracellular protein can be fluorescently tagged, this system serves as a powerful tool to facilitate new discoveries across many biological disciplines. In this unit, we present protocols for the design, generation, and monoclonal expansion of genetically customized hiPSCs encoding fluorescently tagged endogenous proteins. © 2018 by John Wiley & Sons, Inc. Copyright © 2018 John Wiley & Sons, Inc.

A Web Geographic Information System to share data and explorative analysis tools: The application to West Nile disease in the Mediterranean basin.

PubMed

Savini, Lara; Tora, Susanna; Di Lorenzo, Alessio; Cioci, Daniela; Monaco, Federica; Polci, Andrea; Orsini, Massimiliano; Calistri, Paolo; Conte, Annamaria

2018-01-01

In the last decades an increasing number of West Nile Disease cases was observed in equines and humans in the Mediterranean basin and surveillance systems are set up in numerous countries to manage and control the disease. The collection, storage and distribution of information on the spread of the disease becomes important for a shared intervention and control strategy. To this end, a Web Geographic Information System has been developed and disease data, climatic and environmental remote sensed data, full genome sequences of selected isolated strains are made available. This paper describes the Disease Monitoring Dashboard (DMD) web system application, the tools available for the preliminary analysis on climatic and environmental factors and the other interactive tools for epidemiological analysis. WNV occurrence data are collected from multiple official and unofficial sources. Whole genome sequences and metadata of WNV strains are retrieved from public databases or generated in the framework of the Italian surveillance activities. Climatic and environmental data are provided by NASA website. The Geographical Information System is composed by Oracle 10g Database and ESRI ArcGIS Server 10.03; the web mapping client application is developed with the ArcGIS API for Javascript and Phylocanvas library to facilitate and optimize the mash-up approach. ESRI ArcSDE 10.1 has been used to store spatial data. The DMD application is accessible through a generic web browser at https://netmed.izs.it/networkMediterraneo/. The system collects data through on-line forms and automated procedures and visualizes data as interactive graphs, maps and tables. The spatial and temporal dynamic visualization of disease events is managed by a time slider that returns results on both map and epidemiological curve. Climatic and environmental data can be associated to cases through python procedures and downloaded as Excel files. The system compiles multiple datasets through user-friendly web tools; it integrates entomological, veterinary and human surveillance, molecular information on pathogens and environmental and climatic data. The principal result of the DMD development is the transfer and dissemination of knowledge and technologies to develop strategies for integrated prevention and control measures of animal and human diseases.

Inferring transposons activity chronology by TRANScendence - TEs database and de-novo mining tool.

PubMed

Startek, Michał Piotr; Nogły, Jakub; Gromadka, Agnieszka; Grzebelus, Dariusz; Gambin, Anna

2017-10-16

The constant progress in sequencing technology leads to ever increasing amounts of genomic data. In the light of current evidence transposable elements (TEs for short) are becoming useful tools for learning about the evolution of host genome. Therefore the software for genome-wide detection and analysis of TEs is of great interest. Here we describe the computational tool for mining, classifying and storing TEs from newly sequenced genomes. This is an online, web-based, user-friendly service, enabling users to upload their own genomic data, and perform de-novo searches for TEs. The detected TEs are automatically analyzed, compared to reference databases, annotated, clustered into families, and stored in TEs repository. Also, the genome-wide nesting structure of found elements are detected and analyzed by new method for inferring evolutionary history of TEs. We illustrate the functionality of our tool by performing a full-scale analyses of TE landscape in Medicago truncatula genome. TRANScendence is an effective tool for the de-novo annotation and classification of transposable elements in newly-acquired genomes. Its streamlined interface makes it well-suited for evolutionary studies.

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

PubMed

Vadigepalli, Rajanikanth; Chakravarthula, Praveen; Zak, Daniel E; Schwaber, James S; Gonye, Gregory E

2003-01-01

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

A Tool for Multiple Targeted Genome Deletions that Is Precise, Scar-Free, and Suitable for Automation.

PubMed

Aubrey, Wayne; Riley, Michael C; Young, Michael; King, Ross D; Oliver, Stephen G; Clare, Amanda

2015-01-01

Many advances in synthetic biology require the removal of a large number of genomic elements from a genome. Most existing deletion methods leave behind markers, and as there are a limited number of markers, such methods can only be applied a fixed number of times. Deletion methods that recycle markers generally are either imprecise (remove untargeted sequences), or leave scar sequences which can cause genome instability and rearrangements. No existing marker recycling method is automation-friendly. We have developed a novel openly available deletion tool that consists of: 1) a method for deleting genomic elements that can be repeatedly used without limit, is precise, scar-free, and suitable for automation; and 2) software to design the method's primers. Our tool is sequence agnostic and could be used to delete large numbers of coding sequences, promoter regions, transcription factor binding sites, terminators, etc in a single genome. We have validated our tool on the deletion of non-essential open reading frames (ORFs) from S. cerevisiae. The tool is applicable to arbitrary genomes, and we provide primer sequences for the deletion of: 90% of the ORFs from the S. cerevisiae genome, 88% of the ORFs from S. pombe genome, and 85% of the ORFs from the L. lactis genome.

A Tool for Multiple Targeted Genome Deletions that Is Precise, Scar-Free, and Suitable for Automation

PubMed Central

Aubrey, Wayne; Riley, Michael C.; Young, Michael; King, Ross D.; Oliver, Stephen G.; Clare, Amanda

2015-01-01

Many advances in synthetic biology require the removal of a large number of genomic elements from a genome. Most existing deletion methods leave behind markers, and as there are a limited number of markers, such methods can only be applied a fixed number of times. Deletion methods that recycle markers generally are either imprecise (remove untargeted sequences), or leave scar sequences which can cause genome instability and rearrangements. No existing marker recycling method is automation-friendly. We have developed a novel openly available deletion tool that consists of: 1) a method for deleting genomic elements that can be repeatedly used without limit, is precise, scar-free, and suitable for automation; and 2) software to design the method’s primers. Our tool is sequence agnostic and could be used to delete large numbers of coding sequences, promoter regions, transcription factor binding sites, terminators, etc in a single genome. We have validated our tool on the deletion of non-essential open reading frames (ORFs) from S. cerevisiae. The tool is applicable to arbitrary genomes, and we provide primer sequences for the deletion of: 90% of the ORFs from the S. cerevisiae genome, 88% of the ORFs from S. pombe genome, and 85% of the ORFs from the L. lactis genome. PMID:26630677

JBrowse: a dynamic web platform for genome visualization and analysis

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

Buels, Robert; Yao, Eric; Diesh, Colin M.

JBrowse is a fast and full-featured genome browser built with JavaScript and HTML5. It is easily embedded into websites or apps but can also be served as a standalone web page. Overall improvements to speed and scalability are accompanied by specific enhancements that support complex interactive queries on large track sets. Analysis functions can readily be added using the plugin framework; most visual aspects of tracks can also be customized, along with clicks, mouseovers, menus, and popup boxes. JBrowse can also be used to browse local annotation files offline and to generate high-resolution figures for publication. JBrowse is a maturemore » web application suitable for genome visualization and analysis.« less

JBrowse: a dynamic web platform for genome visualization and analysis.

PubMed

Buels, Robert; Yao, Eric; Diesh, Colin M; Hayes, Richard D; Munoz-Torres, Monica; Helt, Gregg; Goodstein, David M; Elsik, Christine G; Lewis, Suzanna E; Stein, Lincoln; Holmes, Ian H

2016-04-12

JBrowse is a fast and full-featured genome browser built with JavaScript and HTML5. It is easily embedded into websites or apps but can also be served as a standalone web page. Overall improvements to speed and scalability are accompanied by specific enhancements that support complex interactive queries on large track sets. Analysis functions can readily be added using the plugin framework; most visual aspects of tracks can also be customized, along with clicks, mouseovers, menus, and popup boxes. JBrowse can also be used to browse local annotation files offline and to generate high-resolution figures for publication. JBrowse is a mature web application suitable for genome visualization and analysis.

A Novel System for Visualizing Alphavirus Assembly

PubMed Central

Steel, J. Jordan; Geiss, Brian J.

2015-01-01

Alphaviruses are small, enveloped RNA viruses that form infectious particles by budding through the cellular plasma membrane. To help visualize and understand the intracellular assembly of alphavirus virions we have developed a bimolecular fluorescence complementation-based system (BiFC) that allows visualization of capsid and E2 subcellular localization and association in live cells. In this system, N- or C-terminal Venus fluorescent protein fragments (VN- and VC-) are fused to the N-terminus of the capsid protein on the Sindbis virus structural polyprotein, which results in the formation of fluorescent capsid-like structures in the absence of viral genomes that associate with the plasma membrane of cells. Mutation of the capsid autoprotease active site blocks structural polyprotein processing and alters the subcellular distribution of capsid fluorescence. Incorporating mCherry into the extracellular domain of the E2 glycoprotein allows the visualization of E2 glycoprotein localization and showed a close association of the E2 and capsid proteins at the plasma membrane as expected. These results suggest that this system is a useful new tool to study alphavirus assembly in live cells and may be useful in identifying molecules that inhibit alphavirus virion formation. PMID:26122073

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

PubMed

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

2017-01-04

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

A genomic overview of the population structure of Salmonella.

PubMed

Alikhan, Nabil-Fareed; Zhou, Zhemin; Sergeant, Martin J; Achtman, Mark

2018-04-01

For many decades, Salmonella enterica has been subdivided by serological properties into serovars or further subdivided for epidemiological tracing by a variety of diagnostic tests with higher resolution. Recently, it has been proposed that so-called eBurst groups (eBGs) based on the alleles of seven housekeeping genes (legacy multilocus sequence typing [MLST]) corresponded to natural populations and could replace serotyping. However, this approach lacks the resolution needed for epidemiological tracing and the existence of natural populations had not been independently validated by independent criteria. Here, we describe EnteroBase, a web-based platform that assembles draft genomes from Illumina short reads in the public domain or that are uploaded by users. EnteroBase implements legacy MLST as well as ribosomal gene MLST (rMLST), core genome MLST (cgMLST), and whole genome MLST (wgMLST) and currently contains over 100,000 assembled genomes from Salmonella. It also provides graphical tools for visual interrogation of these genotypes and those based on core single nucleotide polymorphisms (SNPs). eBGs based on legacy MLST are largely consistent with eBGs based on rMLST, thus demonstrating that these correspond to natural populations. rMLST also facilitated the selection of representative genotypes for SNP analyses of the entire breadth of diversity within Salmonella. In contrast, cgMLST provides the resolution needed for epidemiological investigations. These observations show that genomic genotyping, with the assistance of EnteroBase, can be applied at all levels of diversity within the Salmonella genus.

Tripal: a construction toolkit for online genome databases.

PubMed

Ficklin, Stephen P; Sanderson, Lacey-Anne; Cheng, Chun-Huai; Staton, Margaret E; Lee, Taein; Cho, Il-Hyung; Jung, Sook; Bett, Kirstin E; Main, Doreen

2011-01-01

As the availability, affordability and magnitude of genomics and genetics research increases so does the need to provide online access to resulting data and analyses. Availability of a tailored online database is the desire for many investigators or research communities; however, managing the Information Technology infrastructure needed to create such a database can be an undesired distraction from primary research or potentially cost prohibitive. Tripal provides simplified site development by merging the power of Drupal, a popular web Content Management System with that of Chado, a community-derived database schema for storage of genomic, genetic and other related biological data. Tripal provides an interface that extends the content management features of Drupal to the data housed in Chado. Furthermore, Tripal provides a web-based Chado installer, genomic data loaders, web-based editing of data for organisms, genomic features, biological libraries, controlled vocabularies and stock collections. Also available are Tripal extensions that support loading and visualizations of NCBI BLAST, InterPro, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses, as well as an extension that provides integration of Tripal with GBrowse, a popular GMOD tool. An Application Programming Interface is available to allow creation of custom extensions by site developers, and the look-and-feel of the site is completely customizable through Drupal-based PHP template files. Addition of non-biological content and user-management is afforded through Drupal. Tripal is an open source and freely available software package found at http://tripal.sourceforge.net.

Exploring the possibilities and limitations of a nanomaterials genome.

PubMed

Qian, Chenxi; Siler, Todd; Ozin, Geoffrey A

2015-01-07

What are we going to do with the cornucopia of nanomaterials appearing in the open and patent literature, every day? Imagine the benefits of an intelligent and convenient means of categorizing, organizing, sifting, sorting, connecting, and utilizing this information in scientifically and technologically innovative ways by building a Nanomaterials Genome founded upon an all-purpose Periodic Table of Nanomaterials. In this Concept article, inspired by work on the Human Genome project, which began in 1989 together with motivation from the recent emergence of the Materials Genome project initiated in 2011 and the Nanoinformatics Roadmap 2020 instigated in 2010, we envision the development of a Nanomaterials Genome (NMG) database with the most advanced data-mining tools that leverage inference engines to help connect and interpret patterns of nanomaterials information. It will be equipped with state-of-the-art visualization techniques that rapidly organize and picture, categorize and interrelate the inherited behavior of complex nanomatter from the information programmed in its constituent nanomaterials building blocks. A Nanomaterials Genome Initiative (NMGI) of the type imagined herein has the potential to serve the global nanoscience community with an opportunity to speed up the development continuum of nanomaterials through the innovation process steps of discovery, structure determination and property optimization, functionality elucidation, system design and integration, certification and manufacturing to deployment in technologies that apply these versatile nanomaterials in environmentally responsible ways. The possibilities and limitations of this concept are critically evaluated in this article. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Tripal: a construction toolkit for online genome databases

PubMed Central

Sanderson, Lacey-Anne; Cheng, Chun-Huai; Staton, Margaret E.; Lee, Taein; Cho, Il-Hyung; Jung, Sook; Bett, Kirstin E.; Main, Doreen

2011-01-01

As the availability, affordability and magnitude of genomics and genetics research increases so does the need to provide online access to resulting data and analyses. Availability of a tailored online database is the desire for many investigators or research communities; however, managing the Information Technology infrastructure needed to create such a database can be an undesired distraction from primary research or potentially cost prohibitive. Tripal provides simplified site development by merging the power of Drupal, a popular web Content Management System with that of Chado, a community-derived database schema for storage of genomic, genetic and other related biological data. Tripal provides an interface that extends the content management features of Drupal to the data housed in Chado. Furthermore, Tripal provides a web-based Chado installer, genomic data loaders, web-based editing of data for organisms, genomic features, biological libraries, controlled vocabularies and stock collections. Also available are Tripal extensions that support loading and visualizations of NCBI BLAST, InterPro, Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses, as well as an extension that provides integration of Tripal with GBrowse, a popular GMOD tool. An Application Programming Interface is available to allow creation of custom extensions by site developers, and the look-and-feel of the site is completely customizable through Drupal-based PHP template files. Addition of non-biological content and user-management is afforded through Drupal. Tripal is an open source and freely available software package found at http://tripal.sourceforge.net PMID:21959868

phyloXML: XML for evolutionary biology and comparative genomics

PubMed Central

Han, Mira V; Zmasek, Christian M

2009-01-01

Background Evolutionary trees are central to a wide range of biological studies. In many of these studies, tree nodes and branches need to be associated (or annotated) with various attributes. For example, in studies concerned with organismal relationships, tree nodes are associated with taxonomic names, whereas tree branches have lengths and oftentimes support values. Gene trees used in comparative genomics or phylogenomics are usually annotated with taxonomic information, genome-related data, such as gene names and functional annotations, as well as events such as gene duplications, speciations, or exon shufflings, combined with information related to the evolutionary tree itself. The data standards currently used for evolutionary trees have limited capacities to incorporate such annotations of different data types. Results We developed a XML language, named phyloXML, for describing evolutionary trees, as well as various associated data items. PhyloXML provides elements for commonly used items, such as branch lengths, support values, taxonomic names, and gene names and identifiers. By using "property" elements, phyloXML can be adapted to novel and unforeseen use cases. We also developed various software tools for reading, writing, conversion, and visualization of phyloXML formatted data. Conclusion PhyloXML is an XML language defined by a complete schema in XSD that allows storing and exchanging the structures of evolutionary trees as well as associated data. More information about phyloXML itself, the XSD schema, as well as tools implementing and supporting phyloXML, is available at . PMID:19860910

Refinement of light-responsive transcript lists using rice oligonucleotide arrays: evaluation of gene-redundancy.

PubMed

Jung, Ki-Hong; Dardick, Christopher; Bartley, Laura E; Cao, Peijian; Phetsom, Jirapa; Canlas, Patrick; Seo, Young-Su; Shultz, Michael; Ouyang, Shu; Yuan, Qiaoping; Frank, Bryan C; Ly, Eugene; Zheng, Li; Jia, Yi; Hsia, An-Ping; An, Kyungsook; Chou, Hui-Hsien; Rocke, David; Lee, Geun Cheol; Schnable, Patrick S; An, Gynheung; Buell, C Robin; Ronald, Pamela C

2008-10-06

Studies of gene function are often hampered by gene-redundancy, especially in organisms with large genomes such as rice (Oryza sativa). We present an approach for using transcriptomics data to focus functional studies and address redundancy. To this end, we have constructed and validated an inexpensive and publicly available rice oligonucleotide near-whole genome array, called the rice NSF45K array. We generated expression profiles for light- vs. dark-grown rice leaf tissue and validated the biological significance of the data by analyzing sources of variation and confirming expression trends with reverse transcription polymerase chain reaction. We examined trends in the data by evaluating enrichment of gene ontology terms at multiple false discovery rate thresholds. To compare data generated with the NSF45K array with published results, we developed publicly available, web-based tools (www.ricearray.org). The Oligo and EST Anatomy Viewer enables visualization of EST-based expression profiling data for all genes on the array. The Rice Multi-platform Microarray Search Tool facilitates comparison of gene expression profiles across multiple rice microarray platforms. Finally, we incorporated gene expression and biochemical pathway data to reduce the number of candidate gene products putatively participating in the eight steps of the photorespiration pathway from 52 to 10, based on expression levels of putatively functionally redundant genes. We confirmed the efficacy of this method to cope with redundancy by correctly predicting participation in photorespiration of a gene with five paralogs. Applying these methods will accelerate rice functional genomics.

WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update.

PubMed

Ye, Jia; Zhang, Yong; Cui, Huihai; Liu, Jiawei; Wu, Yuqing; Cheng, Yun; Xu, Huixing; Huang, Xingxin; Li, Shengting; Zhou, An; Zhang, Xiuqing; Bolund, Lars; Chen, Qiang; Wang, Jian; Yang, Huanming; Fang, Lin; Shi, Chunmei

2018-05-18

WEGO (Web Gene Ontology Annotation Plot), created in 2006, is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. Owing largely to the rapid development of high-throughput sequencing and the increasing acceptance of GO, WEGO has benefitted from outstanding performance regarding the number of users and citations in recent years, which motivated us to update to version 2.0. WEGO uses the GO annotation results as input. Based on GO's standardized DAG (Directed Acyclic Graph) structured vocabulary system, the number of genes corresponding to each GO ID is calculated and shown in a graphical format. WEGO 2.0 updates have targeted four aspects, aiming to provide a more efficient and up-to-date approach for comparative genomic analyses. First, the number of input files, previously limited to three, is now unlimited, allowing WEGO to analyze multiple datasets. Also added in this version are the reference datasets of nine model species that can be adopted as baselines in genomic comparative analyses. Furthermore, in the analyzing processes each Chi-square test is carried out for multiple datasets instead of every two samples. At last, WEGO 2.0 provides an additional output graph along with the traditional WEGO histogram, displaying the sorted P-values of GO terms and indicating their significant differences. At the same time, WEGO 2.0 features an entirely new user interface. WEGO is available for free at http://wego.genomics.org.cn.

Visualization of DNA Replication in the Vertebrate Model System DT40 using the DNA Fiber Technique

PubMed Central

Schwab, Rebekka A.V.; Niedzwiedz, Wojciech

2011-01-01

Maintenance of replication fork stability is of utmost importance for dividing cells to preserve viability and prevent disease. The processes involved not only ensure faithful genome duplication in the face of endogenous and exogenous DNA damage but also prevent genomic instability, a recognized causative factor in tumor development. Here, we describe a simple and cost-effective fluorescence microscopy-based method to visualize DNA replication in the avian B-cell line DT40. This cell line provides a powerful tool to investigate protein function in vivo by reverse genetics in vertebrate cells1. DNA fiber fluorography in DT40 cells lacking a specific gene allows one to elucidate the function of this gene product in DNA replication and genome stability. Traditional methods to analyze replication fork dynamics in vertebrate cells rely on measuring the overall rate of DNA synthesis in a population of pulse-labeled cells. This is a quantitative approach and does not allow for qualitative analysis of parameters that influence DNA synthesis. In contrast, the rate of movement of active forks can be followed directly when using the DNA fiber technique2-4. In this approach, nascent DNA is labeled in vivo by incorporation of halogenated nucleotides (Fig 1A). Subsequently, individual fibers are stretched onto a microscope slide, and the labeled DNA replication tracts are stained with specific antibodies and visualized by fluorescence microscopy (Fig 1B). Initiation of replication as well as fork directionality is determined by the consecutive use of two differently modified analogues. Furthermore, the dual-labeling approach allows for quantitative analysis of parameters that influence DNA synthesis during the S-phase, i.e. replication structures such as ongoing and stalled forks, replication origin density as well as fork terminations. Finally, the experimental procedure can be accomplished within a day, and requires only general laboratory equipment and a fluorescence microscope. PMID:22064662

PCOGR: phylogenetic COG ranking as an online tool to judge the specificity of COGs with respect to freely definable groups of organisms.

PubMed

Meereis, Florian; Kaufmann, Michael

2004-10-15

The rapidly increasing number of completely sequenced genomes led to the establishment of the COG-database which, based on sequence homologies, assigns similar proteins from different organisms to clusters of orthologous groups (COGs). There are several bioinformatic studies that made use of this database to determine (hyper)thermophile-specific proteins by searching for COGs containing (almost) exclusively proteins from (hyper)thermophilic genomes. However, public software to perform individually definable group-specific searches is not available. The tool described here exactly fills this gap. The software is accessible at http://www.uni-wh.de/pcogr and is linked to the COG-database. The user can freely define two groups of organisms by selecting for each of the (current) 66 organisms to belong either to groupA, to the reference groupB or to be ignored by the algorithm. Then, for all COGs a specificity index is calculated with respect to the specificity to groupA, i. e. high scoring COGs contain proteins from the most of groupA organisms while proteins from the most organisms assigned to groupB are absent. In addition to ranking all COGs according to the user defined specificity criteria, a graphical visualization shows the distribution of all COGs by displaying their abundance as a function of their specificity indexes. This software allows detecting COGs specific to a predefined group of organisms. All COGs are ranked in the order of their specificity and a graphical visualization allows recognizing (i) the presence and abundance of such COGs and (ii) the phylogenetic relationship between groupA- and groupB-organisms. The software also allows detecting putative protein-protein interactions, novel enzymes involved in only partially known biochemical pathways, and alternate enzymes originated by convergent evolution.

PlantAPA: A Portal for Visualization and Analysis of Alternative Polyadenylation in Plants

PubMed Central

Wu, Xiaohui; Zhang, Yumin; Li, Qingshun Q.

2016-01-01

Alternative polyadenylation (APA) is an important layer of gene regulation that produces mRNAs that have different 3′ ends and/or encode diverse protein isoforms. Up to 70% of annotated genes in plants undergo APA. Increasing numbers of poly(A) sites collected in various plant species demand new methods and tools to access and mine these data. We have created an open-access web service called PlantAPA (http://bmi.xmu.edu.cn/plantapa) to visualize and analyze genome-wide poly(A) sites in plants. PlantAPA provides various interactive and dynamic graphics and seamlessly integrates a genome browser that can profile heterogeneous cleavage sites and quantify expression patterns of poly(A) sites across different conditions. Particularly, through PlantAPA, users can analyze poly(A) sites in extended 3′ UTR regions, intergenic regions, and ambiguous regions owing to alternative transcription or RNA processing. In addition, it also provides tools for analyzing poly(A) site selections, 3′ UTR lengthening or shortening, non-canonical APA site switching, and differential gene expression between conditions, making it more powerful for the study of APA-mediated gene expression regulation. More importantly, PlantAPA offers a bioinformatics pipeline that allows users to upload their own short reads or ESTs for poly(A) site extraction, enabling users to further explore poly(A) site selection using stored PlantAPA poly(A) sites together with their own poly(A) site datasets. To date, PlantAPA hosts the largest database of APA sites in plants, including Oryza sativa, Arabidopsis thaliana, Medicago truncatula, and Chlamydomonas reinhardtii. As a user-friendly web service, PlantAPA will be a valuable addition to the community of biologists studying APA mechanisms and gene expression regulation in plants. PMID:27446120

The salinity tolerant poplar database (STPD): a comprehensive database for studying tree salt-tolerant adaption and poplar genomics.

PubMed

Ma, Yazhen; Xu, Ting; Wan, Dongshi; Ma, Tao; Shi, Sheng; Liu, Jianquan; Hu, Quanjun

2015-03-17

Soil salinity is a significant factor that impairs plant growth and agricultural productivity, and numerous efforts are underway to enhance salt tolerance of economically important plants. Populus species are widely cultivated for diverse uses. Especially, they grow in different habitats, from salty soil to mesophytic environment, and are therefore used as a model genus for elucidating physiological and molecular mechanisms of stress tolerance in woody plants. The Salinity Tolerant Poplar Database (STPD) is an integrative database for salt-tolerant poplar genome biology. Currently the STPD contains Populus euphratica genome and its related genetic resources. P. euphratica, with a preference of the salty habitats, has become a valuable genetic resource for the exploitation of tolerance characteristics in trees. This database contains curated data including genomic sequence, genes and gene functional information, non-coding RNA sequences, transposable elements, simple sequence repeats and single nucleotide polymorphisms information of P. euphratica, gene expression data between P. euphratica and Populus tomentosa, and whole-genome alignments between Populus trichocarpa, P. euphratica and Salix suchowensis. The STPD provides useful searching and data mining tools, including GBrowse genome browser, BLAST servers and genome alignments viewer, which can be used to browse genome regions, identify similar sequences and visualize genome alignments. Datasets within the STPD can also be downloaded to perform local searches. A new Salinity Tolerant Poplar Database has been developed to assist studies of salt tolerance in trees and poplar genomics. The database will be continuously updated to incorporate new genome-wide data of related poplar species. This database will serve as an infrastructure for researches on the molecular function of genes, comparative genomics, and evolution in closely related species as well as promote advances in molecular breeding within Populus. The STPD can be accessed at http://me.lzu.edu.cn/stpd/ .

A simple and efficient method to visualize and quantify the efficiency of chromosomal mutations from genome editing

PubMed Central

Fu, Liezhen; Wen, Luan; Luu, Nga; Shi, Yun-Bo

2016-01-01

Genome editing with designer nucleases such as TALEN and CRISPR/Cas enzymes has broad applications. Delivery of these designer nucleases into organisms induces various genetic mutations including deletions, insertions and nucleotide substitutions. Characterizing those mutations is critical for evaluating the efficacy and specificity of targeted genome editing. While a number of methods have been developed to identify the mutations, none other than sequencing allows the identification of the most desired mutations, i.e., out-of-frame insertions/deletions that disrupt genes. Here we report a simple and efficient method to visualize and quantify the efficiency of genomic mutations induced by genome-editing. Our approach is based on the expression of a two-color fusion protein in a vector that allows the insertion of the edited region in the genome in between the two color moieties. We show that our approach not only easily identifies developing animals with desired mutations but also efficiently quantifies the mutation rate in vivo. Furthermore, by using LacZα and GFP as the color moieties, our approach can even eliminate the need for a fluorescent microscope, allowing the analysis with simple bright field visualization. Such an approach will greatly simplify the screen for effective genome-editing enzymes and identify the desired mutant cells/animals. PMID:27748423

IDEA: Interactive Display for Evolutionary Analyses.

PubMed

Egan, Amy; Mahurkar, Anup; Crabtree, Jonathan; Badger, Jonathan H; Carlton, Jane M; Silva, Joana C

2008-12-08

The availability of complete genomic sequences for hundreds of organisms promises to make obtaining genome-wide estimates of substitution rates, selective constraints and other molecular evolution variables of interest an increasingly important approach to addressing broad evolutionary questions. Two of the programs most widely used for this purpose are codeml and baseml, parts of the PAML (Phylogenetic Analysis by Maximum Likelihood) suite. A significant drawback of these programs is their lack of a graphical user interface, which can limit their user base and considerably reduce their efficiency. We have developed IDEA (Interactive Display for Evolutionary Analyses), an intuitive graphical input and output interface which interacts with PHYLIP for phylogeny reconstruction and with codeml and baseml for molecular evolution analyses. IDEA's graphical input and visualization interfaces eliminate the need to edit and parse text input and output files, reducing the likelihood of errors and improving processing time. Further, its interactive output display gives the user immediate access to results. Finally, IDEA can process data in parallel on a local machine or computing grid, allowing genome-wide analyses to be completed quickly. IDEA provides a graphical user interface that allows the user to follow a codeml or baseml analysis from parameter input through to the exploration of results. Novel options streamline the analysis process, and post-analysis visualization of phylogenies, evolutionary rates and selective constraint along protein sequences simplifies the interpretation of results. The integration of these functions into a single tool eliminates the need for lengthy data handling and parsing, significantly expediting access to global patterns in the data.

IDEA: Interactive Display for Evolutionary Analyses

PubMed Central

Egan, Amy; Mahurkar, Anup; Crabtree, Jonathan; Badger, Jonathan H; Carlton, Jane M; Silva, Joana C

2008-01-01

Background The availability of complete genomic sequences for hundreds of organisms promises to make obtaining genome-wide estimates of substitution rates, selective constraints and other molecular evolution variables of interest an increasingly important approach to addressing broad evolutionary questions. Two of the programs most widely used for this purpose are codeml and baseml, parts of the PAML (Phylogenetic Analysis by Maximum Likelihood) suite. A significant drawback of these programs is their lack of a graphical user interface, which can limit their user base and considerably reduce their efficiency. Results We have developed IDEA (Interactive Display for Evolutionary Analyses), an intuitive graphical input and output interface which interacts with PHYLIP for phylogeny reconstruction and with codeml and baseml for molecular evolution analyses. IDEA's graphical input and visualization interfaces eliminate the need to edit and parse text input and output files, reducing the likelihood of errors and improving processing time. Further, its interactive output display gives the user immediate access to results. Finally, IDEA can process data in parallel on a local machine or computing grid, allowing genome-wide analyses to be completed quickly. Conclusion IDEA provides a graphical user interface that allows the user to follow a codeml or baseml analysis from parameter input through to the exploration of results. Novel options streamline the analysis process, and post-analysis visualization of phylogenies, evolutionary rates and selective constraint along protein sequences simplifies the interpretation of results. The integration of these functions into a single tool eliminates the need for lengthy data handling and parsing, significantly expediting access to global patterns in the data. PMID:19061522

The Role of Motor Learning in Spatial Adaptation near a Tool

PubMed Central

Brown, Liana E.; Doole, Robert; Malfait, Nicole

2011-01-01

Some visual-tactile (bimodal) cells have visual receptive fields (vRFs) that overlap and extend moderately beyond the skin of the hand. Neurophysiological evidence suggests, however, that a vRF will grow to encompass a hand-held tool following active tool use but not after passive holding. Why does active tool use, and not passive holding, lead to spatial adaptation near a tool? We asked whether spatial adaptation could be the result of motor or visual experience with the tool, and we distinguished between these alternatives by isolating motor from visual experience with the tool. Participants learned to use a novel, weighted tool. The active training group received both motor and visual experience with the tool, the passive training group received visual experience with the tool, but no motor experience, and finally, a no-training control group received neither visual nor motor experience using the tool. After training, we used a cueing paradigm to measure how quickly participants detected targets, varying whether the tool was placed near or far from the target display. Only the active training group detected targets more quickly when the tool was placed near, rather than far, from the target display. This effect of tool location was not present for either the passive-training or control groups. These results suggest that motor learning influences how visual space around the tool is represented. PMID:22174944

VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment

PubMed Central

Habegger, Lukas; Balasubramanian, Suganthi; Chen, David Z.; Khurana, Ekta; Sboner, Andrea; Harmanci, Arif; Rozowsky, Joel; Clarke, Declan; Snyder, Michael; Gerstein, Mark

2012-01-01

Summary: The functional annotation of variants obtained through sequencing projects is generally assumed to be a simple intersection of genomic coordinates with genomic features. However, complexities arise for several reasons, including the differential effects of a variant on alternatively spliced transcripts, as well as the difficulty in assessing the impact of small insertions/deletions and large structural variants. Taking these factors into consideration, we developed the Variant Annotation Tool (VAT) to functionally annotate variants from multiple personal genomes at the transcript level as well as obtain summary statistics across genes and individuals. VAT also allows visualization of the effects of different variants, integrates allele frequencies and genotype data from the underlying individuals and facilitates comparative analysis between different groups of individuals. VAT can either be run through a command-line interface or as a web application. Finally, in order to enable on-demand access and to minimize unnecessary transfers of large data files, VAT can be run as a virtual machine in a cloud-computing environment. Availability and Implementation: VAT is implemented in C and PHP. The VAT web service, Amazon Machine Image, source code and detailed documentation are available at vat.gersteinlab.org. Contact: lukas.habegger@yale.edu or mark.gerstein@yale.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:22743228

NCBI GEO: archive for functional genomics data sets—10 years on

PubMed Central

Barrett, Tanya; Troup, Dennis B.; Wilhite, Stephen E.; Ledoux, Pierre; Evangelista, Carlos; Kim, Irene F.; Tomashevsky, Maxim; Marshall, Kimberly A.; Phillippy, Katherine H.; Sherman, Patti M.; Muertter, Rolf N.; Holko, Michelle; Ayanbule, Oluwabukunmi; Yefanov, Andrey; Soboleva, Alexandra

2011-01-01

A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20 000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/. PMID:21097893

SolEST database: a "one-stop shop" approach to the study of Solanaceae transcriptomes.

PubMed

D'Agostino, Nunzio; Traini, Alessandra; Frusciante, Luigi; Chiusano, Maria Luisa

2009-11-30

Since no genome sequences of solanaceous plants have yet been completed, expressed sequence tag (EST) collections represent a reliable tool for broad sampling of Solanaceae transcriptomes, an attractive route for understanding Solanaceae genome functionality and a powerful reference for the structural annotation of emerging Solanaceae genome sequences. We describe the SolEST database http://biosrv.cab.unina.it/solestdb which integrates different EST datasets from both cultivated and wild Solanaceae species and from two species of the genus Coffea. Background as well as processed data contained in the database, extensively linked to external related resources, represent an invaluable source of information for these plant families. Two novel features differentiate SolEST from other resources: i) the option of accessing and then visualizing Solanaceae EST/TC alignments along the emerging tomato and potato genome sequences; ii) the opportunity to compare different Solanaceae assemblies generated by diverse research groups in the attempt to address a common complaint in the SOL community. Different databases have been established worldwide for collecting Solanaceae ESTs and are related in concept, content and utility to the one presented herein. However, the SolEST database has several distinguishing features that make it appealing for the research community and facilitates a "one-stop shop" for the study of Solanaceae transcriptomes.

TSVdb: a web-tool for TCGA splicing variants analysis.

PubMed

Sun, Wenjie; Duan, Ting; Ye, Panmeng; Chen, Kelie; Zhang, Guanling; Lai, Maode; Zhang, Honghe

2018-05-29

Collaborative projects such as The Cancer Genome Atlas (TCGA) have generated various -omics and clinical data on cancer. Many computational tools have been developed to facilitate the study of the molecular characterization of tumors using data from the TCGA. Alternative splicing of a gene produces splicing variants, and accumulating evidence has revealed its essential role in cancer-related processes, implying the urgent need to discover tumor-specific isoforms and uncover their potential functions in tumorigenesis. We developed TSVdb, a web-based tool, to explore alternative splicing based on TCGA samples with 30 clinical variables from 33 tumors. TSVdb has an integrated and well-proportioned interface for visualization of the clinical data, gene expression, usage of exons/junctions and splicing patterns. Researchers can interpret the isoform expression variations between or across clinical subgroups and estimate the relationships between isoforms and patient prognosis. TSVdb is available at http://www.tsvdb.com , and the source code is available at https://github.com/wenjie1991/TSVdb . TSVdb will inspire oncologists and accelerate isoform-level advances in cancer research.

PGP repository: a plant phenomics and genomics data publication infrastructure

PubMed Central

Arend, Daniel; Junker, Astrid; Scholz, Uwe; Schüler, Danuta; Wylie, Juliane; Lange, Matthias

2016-01-01

Plant genomics and phenomics represents the most promising tools for accelerating yield gains and overcoming emerging crop productivity bottlenecks. However, accessing this wealth of plant diversity requires the characterization of this material using state-of-the-art genomic, phenomic and molecular technologies and the release of subsequent research data via a long-term stable, open-access portal. Although several international consortia and public resource centres offer services for plant research data management, valuable digital assets remains unpublished and thus inaccessible to the scientific community. Recently, the Leibniz Institute of Plant Genetics and Crop Plant Research and the German Plant Phenotyping Network have jointly initiated the Plant Genomics and Phenomics Research Data Repository (PGP) as infrastructure to comprehensively publish plant research data. This covers in particular cross-domain datasets that are not being published in central repositories because of its volume or unsupported data scope, like image collections from plant phenotyping and microscopy, unfinished genomes, genotyping data, visualizations of morphological plant models, data from mass spectrometry as well as software and documents. The repository is hosted at Leibniz Institute of Plant Genetics and Crop Plant Research using e!DAL as software infrastructure and a Hierarchical Storage Management System as data archival backend. A novel developed data submission tool was made available for the consortium that features a high level of automation to lower the barriers of data publication. After an internal review process, data are published as citable digital object identifiers and a core set of technical metadata is registered at DataCite. The used e!DAL-embedded Web frontend generates for each dataset a landing page and supports an interactive exploration. PGP is registered as research data repository at BioSharing.org, re3data.org and OpenAIRE as valid EU Horizon 2020 open data archive. Above features, the programmatic interface and the support of standard metadata formats, enable PGP to fulfil the FAIR data principles—findable, accessible, interoperable, reusable. Database URL: http://edal.ipk-gatersleben.de/repos/pgp/ PMID:27087305

Genome Annotation Generator: a simple tool for generating and correcting WGS annotation tables for NCBI submission.

PubMed

Geib, Scott M; Hall, Brian; Derego, Theodore; Bremer, Forest T; Cannoles, Kyle; Sim, Sheina B

2018-04-01

One of the most overlooked, yet critical, components of a whole genome sequencing (WGS) project is the submission and curation of the data to a genomic repository, most commonly the National Center for Biotechnology Information (NCBI). While large genome centers or genome groups have developed software tools for post-annotation assembly filtering, annotation, and conversion into the NCBI's annotation table format, these tools typically require back-end setup and connection to an Structured Query Language (SQL) database and/or some knowledge of programming (Perl, Python) to implement. With WGS becoming commonplace, genome sequencing projects are moving away from the genome centers and into the ecology or biology lab, where fewer resources are present to support the process of genome assembly curation. To fill this gap, we developed software to assess, filter, and transfer annotation and convert a draft genome assembly and annotation set into the NCBI annotation table (.tbl) format, facilitating submission to the NCBI Genome Assembly database. This software has no dependencies, is compatible across platforms, and utilizes a simple command to perform a variety of simple and complex post-analysis, pre-NCBI submission WGS project tasks. The Genome Annotation Generator is a consistent and user-friendly bioinformatics tool that can be used to generate a .tbl file that is consistent with the NCBI submission pipeline. The Genome Annotation Generator achieves the goal of providing a publicly available tool that will facilitate the submission of annotated genome assemblies to the NCBI. It is useful for any individual researcher or research group that wishes to submit a genome assembly of their study system to the NCBI.

Genome Annotation Generator: a simple tool for generating and correcting WGS annotation tables for NCBI submission

PubMed Central

Hall, Brian; Derego, Theodore; Bremer, Forest T; Cannoles, Kyle

2018-01-01

Abstract Background One of the most overlooked, yet critical, components of a whole genome sequencing (WGS) project is the submission and curation of the data to a genomic repository, most commonly the National Center for Biotechnology Information (NCBI). While large genome centers or genome groups have developed software tools for post-annotation assembly filtering, annotation, and conversion into the NCBI’s annotation table format, these tools typically require back-end setup and connection to an Structured Query Language (SQL) database and/or some knowledge of programming (Perl, Python) to implement. With WGS becoming commonplace, genome sequencing projects are moving away from the genome centers and into the ecology or biology lab, where fewer resources are present to support the process of genome assembly curation. To fill this gap, we developed software to assess, filter, and transfer annotation and convert a draft genome assembly and annotation set into the NCBI annotation table (.tbl) format, facilitating submission to the NCBI Genome Assembly database. This software has no dependencies, is compatible across platforms, and utilizes a simple command to perform a variety of simple and complex post-analysis, pre-NCBI submission WGS project tasks. Findings The Genome Annotation Generator is a consistent and user-friendly bioinformatics tool that can be used to generate a .tbl file that is consistent with the NCBI submission pipeline Conclusions The Genome Annotation Generator achieves the goal of providing a publicly available tool that will facilitate the submission of annotated genome assemblies to the NCBI. It is useful for any individual researcher or research group that wishes to submit a genome assembly of their study system to the NCBI. PMID:29635297

JBrowse: A dynamic web platform for genome visualization and analysis

DOE PAGES

Buels, Robert; Yao, Eric; Diesh, Colin M.; ...

2016-04-12

Background: JBrowse is a fast and full-featured genome browser built with JavaScript and HTML5. It is easily embedded into websites or apps but can also be served as a standalone web page. Results: Overall improvements to speed and scalability are accompanied by specific enhancements that support complex interactive queries on large track sets. Analysis functions can readily be added using the plugin framework; most visual aspects of tracks can also be customized, along with clicks, mouseovers, menus, and popup boxes. JBrowse can also be used to browse local annotation files offline and to generate high-resolution figures for publication. Conclusions: JBrowsemore » is a mature web application suitable for genome visualization and analysis.« less

Visualization of genome signatures of eukaryote genomes by batch-learning self-organizing map with a special emphasis on Drosophila genomes.

PubMed

Abe, Takashi; Hamano, Yuta; Ikemura, Toshimichi

2014-01-01

A strategy of evolutionary studies that can compare vast numbers of genome sequences is becoming increasingly important with the remarkable progress of high-throughput DNA sequencing methods. We previously established a sequence alignment-free clustering method "BLSOM" for di-, tri-, and tetranucleotide compositions in genome sequences, which can characterize sequence characteristics (genome signatures) of a wide range of species. In the present study, we generated BLSOMs for tetra- and pentanucleotide compositions in approximately one million sequence fragments derived from 101 eukaryotes, for which almost complete genome sequences were available. BLSOM recognized phylotype-specific characteristics (e.g., key combinations of oligonucleotide frequencies) in the genome sequences, permitting phylotype-specific clustering of the sequences without any information regarding the species. In our detailed examination of 12 Drosophila species, the correlation between their phylogenetic classification and the classification on the BLSOMs was observed to visualize oligonucleotides diagnostic for species-specific clustering.

Dereplication, Aggregation and Scoring Tool (DAS Tool) v1.0

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

SIEBER, CHRISTIAN

Communities of uncultivated microbes are critical to ecosystem function and microorganism health, and a key objective of metagenomic studies is to analyze organism-specific metabolic pathways and reconstruct community interaction networks. This requires accurate assignment of genes to genomes, yet existing binning methods often fail to predict a reasonable number of genomes and report many bins of low quality and completeness. Furthermore, the performance of existing algorithms varies between samples and biotypes. Here, we present a dereplication, aggregation and scoring strategy, DAS Tool, that combines the strengths of a flexible set of established binning algorithms. DAS Tools applied to a constructedmore » community generated more accurate bins than any automated method. Further, when applied to samples of different complexity, including soil, natural oil seeps, and the human gut, DAS Tool recovered substantially more near-complete genomes than any single binning method alone. Included were three genomes from a novel lineage . The ability to reconstruct many near-complete genomes from metagenomics data will greatly advance genome-centric analyses of ecosystems.« less

GeneSeqToFamily: a Galaxy workflow to find gene families based on the Ensembl Compara GeneTrees pipeline.

PubMed

Thanki, Anil S; Soranzo, Nicola; Haerty, Wilfried; Davey, Robert P

2018-03-01

Gene duplication is a major factor contributing to evolutionary novelty, and the contraction or expansion of gene families has often been associated with morphological, physiological, and environmental adaptations. The study of homologous genes helps us to understand the evolution of gene families. It plays a vital role in finding ancestral gene duplication events as well as identifying genes that have diverged from a common ancestor under positive selection. There are various tools available, such as MSOAR, OrthoMCL, and HomoloGene, to identify gene families and visualize syntenic information between species, providing an overview of syntenic regions evolution at the family level. Unfortunately, none of them provide information about structural changes within genes, such as the conservation of ancestral exon boundaries among multiple genomes. The Ensembl GeneTrees computational pipeline generates gene trees based on coding sequences, provides details about exon conservation, and is used in the Ensembl Compara project to discover gene families. A certain amount of expertise is required to configure and run the Ensembl Compara GeneTrees pipeline via command line. Therefore, we converted this pipeline into a Galaxy workflow, called GeneSeqToFamily, and provided additional functionality. This workflow uses existing tools from the Galaxy ToolShed, as well as providing additional wrappers and tools that are required to run the workflow. GeneSeqToFamily represents the Ensembl GeneTrees pipeline as a set of interconnected Galaxy tools, so they can be run interactively within the Galaxy's user-friendly workflow environment while still providing the flexibility to tailor the analysis by changing configurations and tools if necessary. Additional tools allow users to subsequently visualize the gene families produced by the workflow, using the Aequatus.js interactive tool, which has been developed as part of the Aequatus software project.

CRCDA—Comprehensive resources for cancer NGS data analysis

PubMed Central

Thangam, Manonanthini; Gopal, Ramesh Kumar

2015-01-01

Next generation sequencing (NGS) innovations put a compelling landmark in life science and changed the direction of research in clinical oncology with its productivity to diagnose and treat cancer. The aim of our portal comprehensive resources for cancer NGS data analysis (CRCDA) is to provide a collection of different NGS tools and pipelines under diverse classes with cancer pathways and databases and furthermore, literature information from PubMed. The literature data was constrained to 18 most common cancer types such as breast cancer, colon cancer and other cancers that exhibit in worldwide population. NGS-cancer tools for the convenience have been categorized into cancer genomics, cancer transcriptomics, cancer epigenomics, quality control and visualization. Pipelines for variant detection, quality control and data analysis were listed to provide out-of-the box solution for NGS data analysis, which may help researchers to overcome challenges in selecting and configuring individual tools for analysing exome, whole genome and transcriptome data. An extensive search page was developed that can be queried by using (i) type of data [literature, gene data and sequence read archive (SRA) data] and (ii) type of cancer (selected based on global incidence and accessibility of data). For each category of analysis, variety of tools are available and the biggest challenge is in searching and using the right tool for the right application. The objective of the work is collecting tools in each category available at various places and arranging the tools and other data in a simple and user-friendly manner for biologists and oncologists to find information easier. To the best of our knowledge, we have collected and presented a comprehensive package of most of the resources available in cancer for NGS data analysis. Given these factors, we believe that this website will be an useful resource to the NGS research community working on cancer. Database URL: http://bioinfo.au-kbc.org.in/ngs/ngshome.html. PMID:26450948

Genomic prediction using different estimation methodology, blending and cross-validation techniques for growth traits and visual scores in Hereford and Braford cattle.

PubMed

Campos, G S; Reimann, F A; Cardoso, L L; Ferreira, C E R; Junqueira, V S; Schmidt, P I; Braccini Neto, J; Yokoo, M J I; Sollero, B P; Boligon, A A; Cardoso, F F

2018-05-07

The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variances components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into four or five groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyzes using the historical pedigree and phenotypes contributed additional information to calculate the GEBV and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection.

Mitochondrial Disease Sequence Data Resource (MSeqDR): a global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities.

PubMed

Falk, Marni J; Shen, Lishuang; Gonzalez, Michael; Leipzig, Jeremy; Lott, Marie T; Stassen, Alphons P M; Diroma, Maria Angela; Navarro-Gomez, Daniel; Yeske, Philip; Bai, Renkui; Boles, Richard G; Brilhante, Virginia; Ralph, David; DaRe, Jeana T; Shelton, Robert; Terry, Sharon F; Zhang, Zhe; Copeland, William C; van Oven, Mannis; Prokisch, Holger; Wallace, Douglas C; Attimonelli, Marcella; Krotoski, Danuta; Zuchner, Stephan; Gai, Xiaowu

2015-03-01

Success rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires the establishment of robust data resources to enable data sharing that informs accurate understanding of genes, variants, and phenotypes. The "Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium" is a grass-roots effort facilitated by the United Mitochondrial Disease Foundation to identify and prioritize specific genomic data analysis needs of the global mitochondrial disease clinical and research community. A central Web portal (https://mseqdr.org) facilitates the coherent compilation, organization, annotation, and analysis of sequence data from both nuclear and mitochondrial genomes of individuals and families with suspected mitochondrial disease. This Web portal provides users with a flexible and expandable suite of resources to enable variant-, gene-, and exome-level sequence analysis in a secure, Web-based, and user-friendly fashion. Users can also elect to share data with other MSeqDR Consortium members, or even the general public, either by custom annotation tracks or through the use of a convenient distributed annotation system (DAS) mechanism. A range of data visualization and analysis tools are provided to facilitate user interrogation and understanding of genomic, and ultimately phenotypic, data of relevance to mitochondrial biology and disease. Currently available tools for nuclear and mitochondrial gene analyses include an MSeqDR GBrowse instance that hosts optimized mitochondrial disease and mitochondrial DNA (mtDNA) specific annotation tracks, as well as an MSeqDR locus-specific database (LSDB) that curates variant data on more than 1300 genes that have been implicated in mitochondrial disease and/or encode mitochondria-localized proteins. MSeqDR is integrated with a diverse array of mtDNA data analysis tools that are both freestanding and incorporated into an online exome-level dataset curation and analysis resource (GEM.app) that is being optimized to support needs of the MSeqDR community. In addition, MSeqDR supports mitochondrial disease phenotyping and ontology tools, and provides variant pathogenicity assessment features that enable community review, feedback, and integration with the public ClinVar variant annotation resource. A centralized Web-based informed consent process is being developed, with implementation of a Global Unique Identifier (GUID) system to integrate data deposited on a given individual from different sources. Community-based data deposition into MSeqDR has already begun. Future efforts will enhance capabilities to incorporate phenotypic data that enhance genomic data analyses. MSeqDR will fill the existing void in bioinformatics tools and centralized knowledge that are necessary to enable efficient nuclear and mtDNA genomic data interpretation by a range of shareholders across both clinical diagnostic and research settings. Ultimately, MSeqDR is focused on empowering the global mitochondrial disease community to better define and explore mitochondrial diseases. Copyright © 2014 Elsevier Inc. All rights reserved.

Mitochondrial Disease Sequence Data Resource (MSeqDR): A global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities

PubMed Central

Falk, Marni J.; Shen, Lishuang; Gonzalez, Michael; Leipzig, Jeremy; Lott, Marie T.; Stassen, Alphons P.M.; Diroma, Maria Angela; Navarro-Gomez, Daniel; Yeske, Philip; Bai, Renkui; Boles, Richard G.; Brilhante, Virginia; Ralph, David; DaRe, Jeana T.; Shelton, Robert; Terry, Sharon; Zhang, Zhe; Copeland, William C.; van Oven, Mannis; Prokisch, Holger; Wallace, Douglas C.; Attimonelli, Marcella; Krotoski, Danuta; Zuchner, Stephan; Gai, Xiaowu

2014-01-01

Success rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires the establishment of robust data resources to enable data sharing that informs accurate understanding of genes, variants, and phenotypes. The “Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium” is a grass-roots effort facilitated by the United Mitochondrial Disease Foundation to identify and prioritize specific genomic data analysis needs of the global mitochondrial disease clinical and research community. A central Web portal (https://mseqdr.org) facilitates the coherent compilation, organization, annotation, and analysis of sequence data from both nuclear and mitochondrial genomes of individuals and families with suspected mitochondrial disease. This Web portal provides users with a flexible and expandable suite of resources to enable variant-, gene-, and exome-level sequence analysis in a secure, Web-based, and user-friendly fashion. Users can also elect to share data with other MSeqDR Consortium members, or even the general public, either by custom annotation tracks or through use of a convenient distributed annotation system (DAS) mechanism. A range of data visualization and analysis tools are provided to facilitate user interrogation and understanding of genomic, and ultimately phenotypic, data of relevance to mitochondrial biology and disease. Currently available tools for nuclear and mitochondrial gene analyses include an MSeqDR GBrowse instance that hosts optimized mitochondrial disease and mitochondrial DNA (mtDNA) specific annotation tracks, as well as an MSeqDR locus-specific database (LSDB) that curates variant data on more than 1,300 genes that have been implicated in mitochondrial disease and/or encode mitochondria-localized proteins. MSeqDR is integrated with a diverse array of mtDNA data analysis tools that are both freestanding and incorporated into an online exome-level dataset curation and analysis resource (GEM.app) that is being optimized to support needs of the MSeqDR community. In addition, MSeqDR supports mitochondrial disease phenotyping and ontology tools, and provides variant pathogenicity assessment features that enable community review, feedback, and integration with the public ClinVar variant annotation resource. A centralized Web-based informed consent process is being developed, with implementation of a Global Unique Identifier (GUID) system to integrate data deposited on a given individual from different sources. Community-based data deposition into MSeqDR has already begun. Future efforts will enhance capabilities to incorporate phenotypic data that enhance genomic data analyses. MSeqDR will fill the existing void in bioinformatics tools and centralized knowledge that are necessary to enable efficient nuclear and mtDNA genomic data interpretation by a range of shareholders across both clinical diagnostic and research settings. Ultimately, MSeqDR is focused on empowering the global mitochondrial disease community to better define and explore mitochondrial disease. PMID:25542617

Pathogen metadata platform: software for accessing and analyzing pathogen strain information.

PubMed

Chang, Wenling E; Peterson, Matthew W; Garay, Christopher D; Korves, Tonia

2016-09-15

Pathogen metadata includes information about where and when a pathogen was collected and the type of environment it came from. Along with genomic nucleotide sequence data, this metadata is growing rapidly and becoming a valuable resource not only for research but for biosurveillance and public health. However, current freely available tools for analyzing this data are geared towards bioinformaticians and/or do not provide summaries and visualizations needed to readily interpret results. We designed a platform to easily access and summarize data about pathogen samples. The software includes a PostgreSQL database that captures metadata useful for disease outbreak investigations, and scripts for downloading and parsing data from NCBI BioSample and BioProject into the database. The software provides a user interface to query metadata and obtain standardized results in an exportable, tab-delimited format. To visually summarize results, the user interface provides a 2D histogram for user-selected metadata types and mapping of geolocated entries. The software is built on the LabKey data platform, an open-source data management platform, which enables developers to add functionalities. We demonstrate the use of the software in querying for a pathogen serovar and for genome sequence identifiers. This software enables users to create a local database for pathogen metadata, populate it with data from NCBI, easily query the data, and obtain visual summaries. Some of the components, such as the database, are modular and can be incorporated into other data platforms. The source code is freely available for download at https://github.com/wchangmitre/bioattribution .

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

PubMed

Yu, Bowen; Doraiswamy, Harish; Chen, Xi; Miraldi, Emily; Arrieta-Ortiz, Mario Luis; Hafemeister, Christoph; Madar, Aviv; Bonneau, Richard; Silva, Cláudio T

2014-12-01

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

PanWeb: A web interface for pan-genomic analysis.

PubMed

Pantoja, Yan; Pinheiro, Kenny; Veras, Allan; Araújo, Fabrício; Lopes de Sousa, Ailton; Guimarães, Luis Carlos; Silva, Artur; Ramos, Rommel T J

2017-01-01

With increased production of genomic data since the advent of next-generation sequencing (NGS), there has been a need to develop new bioinformatics tools and areas, such as comparative genomics. In comparative genomics, the genetic material of an organism is directly compared to that of another organism to better understand biological species. Moreover, the exponentially growing number of deposited prokaryote genomes has enabled the investigation of several genomic characteristics that are intrinsic to certain species. Thus, a new approach to comparative genomics, termed pan-genomics, was developed. In pan-genomics, various organisms of the same species or genus are compared. Currently, there are many tools that can perform pan-genomic analyses, such as PGAP (Pan-Genome Analysis Pipeline), Panseq (Pan-Genome Sequence Analysis Program) and PGAT (Prokaryotic Genome Analysis Tool). Among these software tools, PGAP was developed in the Perl scripting language and its reliance on UNIX platform terminals and its requirement for an extensive parameterized command line can become a problem for users without previous computational knowledge. Thus, the aim of this study was to develop a web application, known as PanWeb, that serves as a graphical interface for PGAP. In addition, using the output files of the PGAP pipeline, the application generates graphics using custom-developed scripts in the R programming language. PanWeb is freely available at http://www.computationalbiology.ufpa.br/panweb.

Strategies and tools for whole genome alignments

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

Couronne, Olivier; Poliakov, Alexander; Bray, Nicolas

2002-11-25

The availability of the assembled mouse genome makespossible, for the first time, an alignment and comparison of two largevertebrate genomes. We have investigated different strategies ofalignment for the subsequent analysis of conservation of genomes that areeffective for different quality assemblies. These strategies were appliedto the comparison of the working draft of the human genome with the MouseGenome Sequencing Consortium assembly, as well as other intermediatemouse assemblies. Our methods are fast and the resulting alignmentsexhibit a high degree of sensitivity, covering more than 90 percent ofknown coding exons in the human genome. We have obtained such coveragewhile preserving specificity. With amore » view towards the end user, we havedeveloped a suite of tools and websites for automatically aligning, andsubsequently browsing and working with whole genome comparisons. Wedescribe the use of these tools to identify conserved non-coding regionsbetween the human and mouse genomes, some of which have not beenidentified by other methods.« less

The CRISPR-Cas system - from bacterial immunity to genome engineering.

PubMed

Czarnek, Maria; Bereta, Joanna

2016-09-01

Precise and efficient genome modifications present a great value in attempts to comprehend the roles of particular genes and other genetic elements in biological processes as well as in various pathologies. In recent years novel methods of genome modification known as genome editing, which utilize so called "programmable" nucleases, came into use. A true revolution in genome editing has been brought about by the introduction of the CRISP-Cas (clustered regularly interspaced short palindromic repeats-CRISPR associated) system, in which one of such nucleases, i.e. Cas9, plays a major role. This system is based on the elements of the bacterial and archaeal mechanism responsible for acquired immunity against phage infections and transfer of foreign genetic material. Microorganisms incorporate fragments of foreign DNA into CRISPR loci present in their genomes, which enables fast recognition and elimination of future infections. There are several types of CRISPR-Cas systems among prokaryotes but only elements of CRISPR type II are employed in genome engineering. CRISPR-Cas type II utilizes small RNA molecules (crRNA and tracrRNA) to precisely direct the effector nuclease - Cas9 - to a specific site in the genome, i.e. to the sequence complementary to crRNA. Cas9 may be used to: (i) introduce stable changes into genomes e.g. in the process of generation of knock-out and knock-in animals and cell lines, (ii) activate or silence the expression of a gene of interest, and (iii) visualize specific sites in genomes of living cells. The CRISPR-Cas-based tools have been successfully employed for generation of animal and cell models of a number of diseases, e.g. specific types of cancer. In the future, the genome editing by programmable nucleases may find wide application in medicine e.g. in the therapies of certain diseases of genetic origin and in the therapy of HIV-infected patients.

Tomato functional genomics database (TFGD): a comprehensive collection and analysis package for tomato functional genomics

USDA-ARS?s Scientific Manuscript database

Tomato Functional Genomics Database (TFGD; http://ted.bti.cornell.edu) provides a comprehensive systems biology resource to store, mine, analyze, visualize and integrate large-scale tomato functional genomics datasets. The database is expanded from the previously described Tomato Expression Database...

GenomicusPlants: a web resource to study genome evolution in flowering plants.

PubMed

Louis, Alexandra; Murat, Florent; Salse, Jérôme; Crollius, Hugues Roest

2015-01-01

Comparative genomics combined with phylogenetic reconstructions are powerful approaches to study the evolution of genes and genomes. However, the current rapid expansion of the volume of genomic information makes it increasingly difficult to interrogate, integrate and synthesize comparative genome data while taking into account the maximum breadth of information available. GenomicusPlants (http://www.genomicus.biologie.ens.fr/genomicus-plants) is an extension of the Genomicus webserver that addresses this issue by allowing users to explore flowering plant genomes in an intuitive way, across the broadest evolutionary scales. Extant genomes of 26 flowering plants can be analyzed, as well as 23 ancestral reconstructed genomes. Ancestral gene order provides a long-term chronological view of gene order evolution, greatly facilitating comparative genomics and evolutionary studies. Four main interfaces ('views') are available where: (i) PhyloView combines phylogenetic trees with comparisons of genomic loci across any number of genomes; (ii) AlignView projects loci of interest against all other genomes to visualize its topological conservation; (iii) MatrixView compares two genomes in a classical dotplot representation; and (iv) Karyoview visualizes chromosome karyotypes 'painted' with colours of another genome of interest. All four views are interconnected and benefit from many customizable features. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

ReportingTools: an automated result processing and presentation toolkit for high-throughput genomic analyses.

PubMed

Huntley, Melanie A; Larson, Jessica L; Chaivorapol, Christina; Becker, Gabriel; Lawrence, Michael; Hackney, Jason A; Kaminker, Joshua S

2013-12-15

It is common for computational analyses to generate large amounts of complex data that are difficult to process and share with collaborators. Standard methods are needed to transform such data into a more useful and intuitive format. We present ReportingTools, a Bioconductor package, that automatically recognizes and transforms the output of many common Bioconductor packages into rich, interactive, HTML-based reports. Reports are not generic, but have been individually designed to reflect content specific to the result type detected. Tabular output included in reports is sortable, filterable and searchable and contains context-relevant hyperlinks to external databases. Additionally, in-line graphics have been developed for specific analysis types and are embedded by default within table rows, providing a useful visual summary of underlying raw data. ReportingTools is highly flexible and reports can be easily customized for specific applications using the well-defined API. The ReportingTools package is implemented in R and available from Bioconductor (version ≥ 2.11) at the URL: http://bioconductor.org/packages/release/bioc/html/ReportingTools.html. Installation instructions and usage documentation can also be found at the above URL.

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.

A web-based genomic sequence database for the Streptomycetaceae: a tool for systematics and genome mining

USDA-ARS?s Scientific Manuscript database

The ARS Microbial Genome Sequence Database (http://199.133.98.43), a web-based database server, was established utilizing the BIGSdb (Bacterial Isolate Genomics Sequence Database) software package, developed at Oxford University, as a tool to manage multi-locus sequence data for the family Streptomy...

Pre-genomic, genomic and post-genomic study of microbial communities involved in bioenergy.

PubMed

Rittmann, Bruce E; Krajmalnik-Brown, Rosa; Halden, Rolf U

2008-08-01

Microorganisms can produce renewable energy in large quantities and without damaging the environment or disrupting food supply. The microbial communities must be robust and self-stabilizing, and their essential syntrophies must be managed. Pre-genomic, genomic and post-genomic tools can provide crucial information about the structure and function of these microbial communities. Applying these tools will help accelerate the rate at which microbial bioenergy processes move from intriguing science to real-world practice.

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

SPIKE – a database, visualization and analysis tool of cellular signaling pathways

PubMed Central

Elkon, Ran; Vesterman, Rita; Amit, Nira; Ulitsky, Igor; Zohar, Idan; Weisz, Mali; Mass, Gilad; Orlev, Nir; Sternberg, Giora; Blekhman, Ran; Assa, Jackie; Shiloh, Yosef; Shamir, Ron

2008-01-01

Background Biological signaling pathways that govern cellular physiology form an intricate web of tightly regulated interlocking processes. Data on these regulatory networks are accumulating at an unprecedented pace. The assimilation, visualization and interpretation of these data have become a major challenge in biological research, and once met, will greatly boost our ability to understand cell functioning on a systems level. Results To cope with this challenge, we are developing the SPIKE knowledge-base of signaling pathways. SPIKE contains three main software components: 1) A database (DB) of biological signaling pathways. Carefully curated information from the literature and data from large public sources constitute distinct tiers of the DB. 2) A visualization package that allows interactive graphic representations of regulatory interactions stored in the DB and superposition of functional genomic and proteomic data on the maps. 3) An algorithmic inference engine that analyzes the networks for novel functional interplays between network components. SPIKE is designed and implemented as a community tool and therefore provides a user-friendly interface that allows registered users to upload data to SPIKE DB. Our vision is that the DB will be populated by a distributed and highly collaborative effort undertaken by multiple groups in the research community, where each group contributes data in its field of expertise. Conclusion The integrated capabilities of SPIKE make it a powerful platform for the analysis of signaling networks and the integration of knowledge on such networks with omics data. PMID:18289391

LoRTE: Detecting transposon-induced genomic variants using low coverage PacBio long read sequences.

PubMed

Disdero, Eric; Filée, Jonathan

2017-01-01

Population genomic analysis of transposable elements has greatly benefited from recent advances of sequencing technologies. However, the short size of the reads and the propensity of transposable elements to nest in highly repeated regions of genomes limits the efficiency of bioinformatic tools when Illumina or 454 technologies are used. Fortunately, long read sequencing technologies generating read length that may span the entire length of full transposons are now available. However, existing TE population genomic softwares were not designed to handle long reads and the development of new dedicated tools is needed. LoRTE is the first tool able to use PacBio long read sequences to identify transposon deletions and insertions between a reference genome and genomes of different strains or populations. Tested against simulated and genuine Drosophila melanogaster PacBio datasets, LoRTE appears to be a reliable and broadly applicable tool to study the dynamic and evolutionary impact of transposable elements using low coverage, long read sequences. LoRTE is an efficient and accurate tool to identify structural genomic variants caused by TE insertion or deletion. LoRTE is available for download at http://www.egce.cnrs-gif.fr/?p=6422.

A Novel Bioinformatics Strategy to Analyze Microbial Big Sequence Data for Efficient Knowledge Discovery: Batch-Learning Self-Organizing Map (BLSOM).

PubMed

Iwasaki, Yuki; Abe, Takashi; Wada, Kennosuke; Wada, Yoshiko; Ikemura, Toshimichi

2013-11-20

With the remarkable increase of genomic sequence data of microorganisms, novel tools are needed for comprehensive analyses of the big sequence data available. The self-organizing map (SOM) is an effective tool for clustering and visualizing high-dimensional data, such as oligonucleotide composition on one map. By modifying the conventional SOM, we developed batch-learning SOM (BLSOM), which allowed classification of sequence fragments (e.g., 1 kb) according to phylotypes, solely depending on oligonucleotide composition. Metagenomics studies of uncultivable microorganisms in clinical and environmental samples should allow extensive surveys of genes important in life sciences. BLSOM is most suitable for phylogenetic assignment of metagenomic sequences, because fragmental sequences can be clustered according to phylotypes, solely depending on oligonucleotide composition. We first constructed oligonucleotide BLSOMs for all available sequences from genomes of known species, and by mapping metagenomic sequences on these large-scale BLSOMs, we can predict phylotypes of individual metagenomic sequences, revealing a microbial community structure of uncultured microorganisms, including viruses. BLSOM has shown that influenza viruses isolated from humans and birds clearly differ in oligonucleotide composition. Based on this host-dependent oligonucleotide composition, we have proposed strategies for predicting directional changes of virus sequences and for surveilling potentially hazardous strains when introduced into humans from non-human sources.

TeachEnG: a Teaching Engine for Genomics.

PubMed

Kim, Minji; Kim, Yeonsung; Qian, Lei; Song, Jun S

2017-10-15

Bioinformatics is a rapidly growing field that has emerged from the synergy of computer science, statistics and biology. Given the interdisciplinary nature of bioinformatics, many students from diverse fields struggle with grasping bioinformatic concepts only from classroom lectures. Interactive tools for helping students reinforce their learning would be thus desirable. Here, we present an interactive online educational tool called TeachEnG (acronym for Teaching Engine for Genomics) for reinforcing key concepts in sequence alignment and phylogenetic tree reconstruction. Our instructional games allow students to align sequences by hand, fill out the dynamic programming matrix in the Needleman-Wunsch global sequence alignment algorithm, and reconstruct phylogenetic trees via the maximum parsimony, Unweighted Pair Group Method with Arithmetic mean (UPGMA) and Neighbor-Joining algorithms. With an easily accessible interface and instant visual feedback, TeachEnG will help promote active learning in bioinformatics. TeachEnG is freely available at http://teacheng.illinois.edu. The source code is available from https://github.com/KnowEnG/TeachEnG under the Artistic License 2.0. It is written in JavaScript and compatible with Firefox, Safari, Chrome and Microsoft Edge. songj@illinois.edu. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource

PubMed Central

Sun, Shulei; Chen, Jing; Li, Weizhong; Altintas, Ilkay; Lin, Abel; Peltier, Steve; Stocks, Karen; Allen, Eric E.; Ellisman, Mark; Grethe, Jeffrey; Wooley, John

2011-01-01

The Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA, http://camera.calit2.net/) is a database and associated computational infrastructure that provides a single system for depositing, locating, analyzing, visualizing and sharing data about microbial biology through an advanced web-based analysis portal. CAMERA collects and links metadata relevant to environmental metagenome data sets with annotation in a semantically-aware environment allowing users to write expressive semantic queries against the database. To meet the needs of the research community, users are able to query metadata categories such as habitat, sample type, time, location and other environmental physicochemical parameters. CAMERA is compliant with the standards promulgated by the Genomic Standards Consortium (GSC), and sustains a role within the GSC in extending standards for content and format of the metagenomic data and metadata and its submission to the CAMERA repository. To ensure wide, ready access to data and annotation, CAMERA also provides data submission tools to allow researchers to share and forward data to other metagenomics sites and community data archives such as GenBank. It has multiple interfaces for easy submission of large or complex data sets, and supports pre-registration of samples for sequencing. CAMERA integrates a growing list of tools and viewers for querying, analyzing, annotating and comparing metagenome and genome data. PMID:21045053

Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource.

PubMed

Sun, Shulei; Chen, Jing; Li, Weizhong; Altintas, Ilkay; Lin, Abel; Peltier, Steve; Stocks, Karen; Allen, Eric E; Ellisman, Mark; Grethe, Jeffrey; Wooley, John

2011-01-01

The Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA, http://camera.calit2.net/) is a database and associated computational infrastructure that provides a single system for depositing, locating, analyzing, visualizing and sharing data about microbial biology through an advanced web-based analysis portal. CAMERA collects and links metadata relevant to environmental metagenome data sets with annotation in a semantically-aware environment allowing users to write expressive semantic queries against the database. To meet the needs of the research community, users are able to query metadata categories such as habitat, sample type, time, location and other environmental physicochemical parameters. CAMERA is compliant with the standards promulgated by the Genomic Standards Consortium (GSC), and sustains a role within the GSC in extending standards for content and format of the metagenomic data and metadata and its submission to the CAMERA repository. To ensure wide, ready access to data and annotation, CAMERA also provides data submission tools to allow researchers to share and forward data to other metagenomics sites and community data archives such as GenBank. It has multiple interfaces for easy submission of large or complex data sets, and supports pre-registration of samples for sequencing. CAMERA integrates a growing list of tools and viewers for querying, analyzing, annotating and comparing metagenome and genome data.

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.

MutationAligner: a resource of recurrent mutation hotspots in protein domains in cancer.

PubMed

Gauthier, Nicholas Paul; Reznik, Ed; Gao, Jianjiong; Sumer, Selcuk Onur; Schultz, Nikolaus; Sander, Chris; Miller, Martin L

2016-01-04

The MutationAligner web resource, available at http://www.mutationaligner.org, enables discovery and exploration of somatic mutation hotspots identified in protein domains in currently (mid-2015) more than 5000 cancer patient samples across 22 different tumor types. Using multiple sequence alignments of protein domains in the human genome, we extend the principle of recurrence analysis by aggregating mutations in homologous positions across sets of paralogous genes. Protein domain analysis enhances the statistical power to detect cancer-relevant mutations and links mutations to the specific biological functions encoded in domains. We illustrate how the MutationAligner database and interactive web tool can be used to explore, visualize and analyze mutation hotspots in protein domains across genes and tumor types. We believe that MutationAligner will be an important resource for the cancer research community by providing detailed clues for the functional importance of particular mutations, as well as for the design of functional genomics experiments and for decision support in precision medicine. MutationAligner is slated to be periodically updated to incorporate additional analyses and new data from cancer genomics projects. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

The Salmonella In Silico Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies.

PubMed

Yoshida, Catherine E; Kruczkiewicz, Peter; Laing, Chad R; Lingohr, Erika J; Gannon, Victor P J; Nash, John H E; Taboada, Eduardo N

2016-01-01

For nearly 100 years serotyping has been the gold standard for the identification of Salmonella serovars. Despite the increasing adoption of DNA-based subtyping approaches, serotype information remains a cornerstone in food safety and public health activities aimed at reducing the burden of salmonellosis. At the same time, recent advances in whole-genome sequencing (WGS) promise to revolutionize our ability to perform advanced pathogen characterization in support of improved source attribution and outbreak analysis. We present the Salmonella In Silico Typing Resource (SISTR), a bioinformatics platform for rapidly performing simultaneous in silico analyses for several leading subtyping methods on draft Salmonella genome assemblies. In addition to performing serovar prediction by genoserotyping, this resource integrates sequence-based typing analyses for: Multi-Locus Sequence Typing (MLST), ribosomal MLST (rMLST), and core genome MLST (cgMLST). We show how phylogenetic context from cgMLST analysis can supplement the genoserotyping analysis and increase the accuracy of in silico serovar prediction to over 94.6% on a dataset comprised of 4,188 finished genomes and WGS draft assemblies. In addition to allowing analysis of user-uploaded whole-genome assemblies, the SISTR platform incorporates a database comprising over 4,000 publicly available genomes, allowing users to place their isolates in a broader phylogenetic and epidemiological context. The resource incorporates several metadata driven visualizations to examine the phylogenetic, geospatial and temporal distribution of genome-sequenced isolates. As sequencing of Salmonella isolates at public health laboratories around the world becomes increasingly common, rapid in silico analysis of minimally processed draft genome assemblies provides a powerful approach for molecular epidemiology in support of public health investigations. Moreover, this type of integrated analysis using multiple sequence-based methods of sub-typing allows for continuity with historical serotyping data as we transition towards the increasing adoption of genomic analyses in epidemiology. The SISTR platform is freely available on the web at https://lfz.corefacility.ca/sistr-app/.

RoBuST: an integrated genomics resource for the root and bulb crop families Apiaceae and Alliaceae

PubMed Central

2010-01-01

Background Root and bulb vegetables (RBV) include carrots, celeriac (root celery), parsnips (Apiaceae), onions, garlic, and leek (Alliaceae)—food crops grown globally and consumed worldwide. Few data analysis platforms are currently available where data collection, annotation and integration initiatives are focused on RBV plant groups. Scientists working on RBV include breeders, geneticists, taxonomists, plant pathologists, and plant physiologists who use genomic data for a wide range of activities including the development of molecular genetic maps, delineation of taxonomic relationships, and investigation of molecular aspects of gene expression in biochemical pathways and disease responses. With genomic data coming from such diverse areas of plant science, availability of a community resource focused on these RBV data types would be of great interest to this scientific community. Description The RoBuST database has been developed to initiate a platform for collecting and organizing genomic information useful for RBV researchers. The current release of RoBuST contains genomics data for 294 Alliaceae and 816 Apiaceae plant species and has the following features: (1) comprehensive sequence annotations of 3663 genes 5959 RNAs, 22,723 ESTs and 11,438 regulatory sequence elements from Apiaceae and Alliaceae plant families; (2) graphical tools for visualization and analysis of sequence data; (3) access to traits, biosynthetic pathways, genetic linkage maps and molecular taxonomy data associated with Alliaceae and Apiaceae plants; and (4) comprehensive plant splice signal repository of 659,369 splice signals collected from 6015 plant species for comparative analysis of plant splicing patterns. Conclusions RoBuST, available at http://robust.genome.com, provides an integrated platform for researchers to effortlessly explore and analyze genomic data associated with root and bulb vegetables. PMID:20691054

The CanOE strategy: integrating genomic and metabolic contexts across multiple prokaryote genomes to find candidate genes for orphan enzymes.

PubMed

Smith, Adam Alexander Thil; Belda, Eugeni; Viari, Alain; Medigue, Claudine; Vallenet, David

2012-05-01

Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes), a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short). The first step locates "genomic metabolons", i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12.

Exploring human disease using the Rat Genome Database.

PubMed

Shimoyama, Mary; Laulederkind, Stanley J F; De Pons, Jeff; Nigam, Rajni; Smith, Jennifer R; Tutaj, Marek; Petri, Victoria; Hayman, G Thomas; Wang, Shur-Jen; Ghiasvand, Omid; Thota, Jyothi; Dwinell, Melinda R

2016-10-01

Rattus norvegicus, the laboratory rat, has been a crucial model for studies of the environmental and genetic factors associated with human diseases for over 150 years. It is the primary model organism for toxicology and pharmacology studies, and has features that make it the model of choice in many complex-disease studies. Since 1999, the Rat Genome Database (RGD; http://rgd.mcw.edu) has been the premier resource for genomic, genetic, phenotype and strain data for the laboratory rat. The primary role of RGD is to curate rat data and validate orthologous relationships with human and mouse genes, and make these data available for incorporation into other major databases such as NCBI, Ensembl and UniProt. RGD also provides official nomenclature for rat genes, quantitative trait loci, strains and genetic markers, as well as unique identifiers. The RGD team adds enormous value to these basic data elements through functional and disease annotations, the analysis and visual presentation of pathways, and the integration of phenotype measurement data for strains used as disease models. Because much of the rat research community focuses on understanding human diseases, RGD provides a number of datasets and software tools that allow users to easily explore and make disease-related connections among these datasets. RGD also provides comprehensive human and mouse data for comparative purposes, illustrating the value of the rat in translational research. This article introduces RGD and its suite of tools and datasets to researchers - within and beyond the rat community - who are particularly interested in leveraging rat-based insights to understand human diseases. © 2016. Published by The Company of Biologists Ltd.

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

PubMed

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

2016-01-01

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

Genome Calligrapher: A Web Tool for Refactoring Bacterial Genome Sequences for de Novo DNA Synthesis.

PubMed

Christen, Matthias; Deutsch, Samuel; Christen, Beat

2015-08-21

Recent advances in synthetic biology have resulted in an increasing demand for the de novo synthesis of large-scale DNA constructs. Any process improvement that enables fast and cost-effective streamlining of digitized genetic information into fabricable DNA sequences holds great promise to study, mine, and engineer genomes. Here, we present Genome Calligrapher, a computer-aided design web tool intended for whole genome refactoring of bacterial chromosomes for de novo DNA synthesis. By applying a neutral recoding algorithm, Genome Calligrapher optimizes GC content and removes obstructive DNA features known to interfere with the synthesis of double-stranded DNA and the higher order assembly into large DNA constructs. Subsequent bioinformatics analysis revealed that synthesis constraints are prevalent among bacterial genomes. However, a low level of codon replacement is sufficient for refactoring bacterial genomes into easy-to-synthesize DNA sequences. To test the algorithm, 168 kb of synthetic DNA comprising approximately 20 percent of the synthetic essential genome of the cell-cycle bacterium Caulobacter crescentus was streamlined and then ordered from a commercial supplier of low-cost de novo DNA synthesis. The successful assembly into eight 20 kb segments indicates that Genome Calligrapher algorithm can be efficiently used to refactor difficult-to-synthesize DNA. Genome Calligrapher is broadly applicable to recode biosynthetic pathways, DNA sequences, and whole bacterial genomes, thus offering new opportunities to use synthetic biology tools to explore the functionality of microbial diversity. The Genome Calligrapher web tool can be accessed at https://christenlab.ethz.ch/GenomeCalligrapher  .

In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration

PubMed Central

Suzuki, Keiichiro; Tsunekawa, Yuji; Hernandez-Benitez, Reyna; Wu, Jun; Zhu, Jie; Kim, Euiseok J.; Hatanaka, Fumiyuki; Yamamoto, Mako; Araoka, Toshikazu; Li, Zhe; Kurita, Masakazu; Hishida, Tomoaki; Li, Mo; Aizawa, Emi; Guo, Shicheng; Chen, Song; Goebl, April; Soligalla, Rupa Devi; Qu, Jing; Jiang, Tingshuai; Fu, Xin; Jafari, Maryam; Esteban, Concepcion Rodriguez; Berggren, W. Travis; Lajara, Jeronimo; Nuñez-Delicado, Estrella; Guillen, Pedro; Campistol, Josep M.; Matsuzaki, Fumio; Liu, Guang-Hui; Magistretti, Pierre; Zhang, Kun; Callaway, Edward M.; Zhang, Kang; Belmonte, Juan Carlos Izpisua

2017-01-01

Targeted genome editing via engineered nucleases is an exciting area of biomedical research and holds potential for clinical applications. Despite rapid advances in the field, in vivo targeted transgene integration is still infeasible because current tools are inefficient1, especially for non-dividing cells, which compose most adult tissues. This poses a barrier for uncovering fundamental biological principles and developing treatments for a broad range of genetic disorders2. Based on clustered regularly interspaced short palindromic repeat/Cas9 (CRISPR/Cas9)3,4 technology, here we devise a homology-independent targeted integration (HITI) strategy, which allows for robust DNA knock-in in both dividing and non-dividing cells in vitro and, more importantly, in vivo (for example, in neurons of postnatal mammals). As a proof of concept of its therapeutic potential, we demonstrate the efficacy of HITI in improving visual function using a rat model of the retinal degeneration condition retinitis pigmentosa. The HITI method presented here establishes new avenues for basic research and targeted gene therapies. PMID:27851729

New technology and resources for cryptococcal research

PubMed Central

Zhang, Nannan; Park, Yoon-Dong; Williamson, Peter R.

2014-01-01

Rapid advances in molecular biology and genome sequencing have enabled the generation of new technology and resources for cryptococcal research. RNAi-mediated specific gene knock down has become routine and more efficient by utilizing modified shRNA plasmids and convergent promoter RNAi constructs. This system was recently applied in a high-throughput screen to identify genes involved in host-pathogen interactions. Gene deletion efficiencies have also been improved by increasing rates of homologous recombination through a number of approaches, including a combination of double-joint PCR with split-marker transformation, the use of dominant selectable markers and the introduction of Cre-Loxp systems into Cryptococcus. Moreover, visualization of cryptococcal proteins has become more facile using fusions with codon-optimized fluorescent tags, such as green or red fluorescent proteins or, mCherry. Using recent genome-wide analytical tools, new transcriptional factors and regulatory proteins have been identified in novel virulence-related signaling pathways by employing microarray analysis, RNA-sequencing and proteomic analysis. PMID:25460849

Optimizing and evaluating the reconstruction of Metagenome-assembled microbial genomes.

PubMed

Papudeshi, Bhavya; Haggerty, J Matthew; Doane, Michael; Morris, Megan M; Walsh, Kevin; Beattie, Douglas T; Pande, Dnyanada; Zaeri, Parisa; Silva, Genivaldo G Z; Thompson, Fabiano; Edwards, Robert A; Dinsdale, Elizabeth A

2017-11-28

Microbiome/host interactions describe characteristics that affect the host's health. Shotgun metagenomics includes sequencing a random subset of the microbiome to analyze its taxonomic and metabolic potential. Reconstruction of DNA fragments into genomes from metagenomes (called metagenome-assembled genomes) assigns unknown fragments to taxa/function and facilitates discovery of novel organisms. Genome reconstruction incorporates sequence assembly and sorting of assembled sequences into bins, characteristic of a genome. However, the microbial community composition, including taxonomic and phylogenetic diversity may influence genome reconstruction. We determine the optimal reconstruction method for four microbiome projects that had variable sequencing platforms (IonTorrent and Illumina), diversity (high or low), and environment (coral reefs and kelp forests), using a set of parameters to select for optimal assembly and binning tools. We tested the effects of the assembly and binning processes on population genome reconstruction using 105 marine metagenomes from 4 projects. Reconstructed genomes were obtained from each project using 3 assemblers (IDBA, MetaVelvet, and SPAdes) and 2 binning tools (GroopM and MetaBat). We assessed the efficiency of assemblers using statistics that including contig continuity and contig chimerism and the effectiveness of binning tools using genome completeness and taxonomic identification. We concluded that SPAdes, assembled more contigs (143,718 ± 124 contigs) of longer length (N50 = 1632 ± 108 bp), and incorporated the most sequences (sequences-assembled = 19.65%). The microbial richness and evenness were maintained across the assembly, suggesting low contig chimeras. SPAdes assembly was responsive to the biological and technological variations within the project, compared with other assemblers. Among binning tools, we conclude that MetaBat produced bins with less variation in GC content (average standard deviation: 1.49), low species richness (4.91 ± 0.66), and higher genome completeness (40.92 ± 1.75) across all projects. MetaBat extracted 115 bins from the 4 projects of which 66 bins were identified as reconstructed metagenome-assembled genomes with sequences belonging to a specific genus. We identified 13 novel genomes, some of which were 100% complete, but show low similarity to genomes within databases. In conclusion, we present a set of biologically relevant parameters for evaluation to select for optimal assembly and binning tools. For the tools we tested, SPAdes assembler and MetaBat binning tools reconstructed quality metagenome-assembled genomes for the four projects. We also conclude that metagenomes from microbial communities that have high coverage of phylogenetically distinct, and low taxonomic diversity results in highest quality metagenome-assembled genomes.

Sequencing Single Cell Microbial Genomes with Microfluidic Amplifications Tools (MICW - Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

ScienceCinema

Quake, Steve

2018-02-02

Stanford University's Steve Quake on "Sequencing Single Cell Microbial Genomes with Microfluidic Amplification Tools" at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.

Sequencing Single Cell Microbial Genomes with Microfluidic Amplifications Tools (MICW - Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

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

Quake, Steve

2011-10-12

Stanford University's Steve Quake on "Sequencing Single Cell Microbial Genomes with Microfluidic Amplification Tools" at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.

Iterating between Tools to Create and Edit Visualizations.

PubMed

Bigelow, Alex; Drucker, Steven; Fisher, Danyel; Meyer, Miriah

2017-01-01

A common workflow for visualization designers begins with a generative tool, like D3 or Processing, to create the initial visualization; and proceeds to a drawing tool, like Adobe Illustrator or Inkscape, for editing and cleaning. Unfortunately, this is typically a one-way process: once a visualization is exported from the generative tool into a drawing tool, it is difficult to make further, data-driven changes. In this paper, we propose a bridge model to allow designers to bring their work back from the drawing tool to re-edit in the generative tool. Our key insight is to recast this iteration challenge as a merge problem - similar to when two people are editing a document and changes between them need to reconciled. We also present a specific instantiation of this model, a tool called Hanpuku, which bridges between D3 scripts and Illustrator. We show several examples of visualizations that are iteratively created using Hanpuku in order to illustrate the flexibility of the approach. We further describe several hypothetical tools that bridge between other visualization tools to emphasize the generality of the model.