WEGO: a web tool for plotting GO annotations
TL;DR: WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO annotation results, designed to deal with the directed acyclic graph structure of GO to facilitate histogram creation of Go annotation results.
Abstract: Unified, structured vocabularies and classifications freely provided by the Gene Ontology (GO) Consortium are widely accepted in most of the large scale gene annotation projects. Consequently, many tools have been created for use with the GO ontologies. WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO annotation results. Different from other commercial software for creating chart, WEGO is designed to deal with the directed acyclic graph structure of GO to facilitate histogram creation of GO annotation results. WEGO has been used widely in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the daily tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. WEGO, along with the two other tools, namely External to GO Query and GO Archive Query, are freely available for all users at http://wego.genomics.org.cn. There are two available mirror sites at http://wego2.genomics.org.cn and http://wego.genomics.com.cn. Any suggestions are welcome at wego@genomics.org.cn.
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TL;DR: The survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
Abstract: Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
13,102 citations
Cites background from "WEGO: a web tool for plotting GO an..."
...Given this situation, several enrichment tools were specifically designed for these less popular species, such as WEGO for rice (54); easyGO for crops (66); FINA for prokaryotes (58); CLENCH for Arabidopsis (21); JProGo for prokaryotes (48); BayGo for Xylella fastidiosa (52)....
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TL;DR: The evolution of knowledge base–driven pathway analysis over its first decade is discussed, distinctly divided into three generations, and a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods are identified.
Abstract: Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base-driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis.
1,357 citations
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TL;DR: In this review, techniques, software, and statistical analyses used in label‐free quantitative proteomics studies for area under the curve and spectral counting approaches are examined and it is concluded that label‐ free quantitative proteochemistry is a reliable, versatile, and cost‐effective alternative to labelled quantitation.
Abstract: In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.
659 citations
Cites methods from "WEGO: a web tool for plotting GO an..."
...The study by Gammulla et al. used WEGO in combination with an inhouse program that performed cluster analyses of differentially expressed proteins; however, other comprehensive functional categorisation programs are available, such as DAVID [125]....
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...In a recent study involving temperature stress in rice, Web Gene Ontology Annotation Plot (WEGO) [124] was used to functionally categorise the GO annotations from differentially expressed proteins that were found by ANOVA to be statistically significant [46]....
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...[124] Ye, J., Fang, L., Zheng, H., Zhang, Y. et al., WEGO: a web tool for plotting GO annotations....
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...Using spectral counting for quantitation and WEGO for functional categorisation, the authors were able to discover classical and alternate sucrose metabolism pathway switching in rice plants that have been exposed to extreme temperature stresses....
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TL;DR: Highly integrated maps of the epigenome, mRNA, and small RNA transcriptomes of two rice subspecies and their reciprocal hybrids reveal that gene activity was correlated with DNA methylation and both active and repressive histone modifications in transcribed regions.
Abstract: The behavior of transcriptomes and epigenomes in hybrids of heterotic parents is of fundamental interest. Here, we report highly integrated maps of the epigenome, mRNA, and small RNA transcriptomes of two rice (Oryza sativa) subspecies and their reciprocal hybrids. We found that gene activity was correlated with DNA methylation and both active and repressive histone modifications in transcribed regions. Differential epigenetic modifications correlated with changes in transcript levels among hybrids and parental lines. Distinct patterns in gene expression and epigenetic modifications in reciprocal hybrids were observed. Through analyses of single nucleotide polymorphisms from our sequence data, we observed a high correlation of allelic bias of epigenetic modifications or gene expression in reciprocal hybrids with their differences in the parental lines. The abundance of distinct small RNA size classes differed between the parents, and more small RNAs were downregulated than upregulated in the reciprocal hybrids. Together, our data reveal a comprehensive overview of transcriptional and epigenetic trends in heterotic rice crosses and provide a useful resource for the rice community.
503 citations
TL;DR: The results show that characterization of the maize B73 transcriptome is far from complete, and that maize gene expression is more complex than previously thought.
Abstract: Zea mays is an important genetic model for elucidating transcriptional networks. Uncertainties about the complete structure of mRNA transcripts limit the progress of research in this system. Here, using single-molecule sequencing technology, we produce 111,151 transcripts from 6 tissues capturing ∼70% of the genes annotated in maize RefGen_v3 genome. A large proportion of transcripts (57%) represent novel, sometimes tissue-specific, isoforms of known genes and 3% correspond to novel gene loci. In other cases, the identified transcripts have improved existing gene models. Averaging across all six tissues, 90% of the splice junctions are supported by short reads from matched tissues. In addition, we identified a large number of novel long non-coding RNAs and fusion transcripts and found that DNA methylation plays an important role in generating various isoforms. Our results show that characterization of the maize B73 transcriptome is far from complete, and that maize gene expression is more complex than previously thought.
466 citations
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TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
35,225 citations
"WEGO: a web tool for plotting GO an..." refers background in this paper
...However, the GO terms are structured in the form of directed acyclic graph (DAG) to represent a network of complex relationships of ‘child’ and ‘parent’ (1)....
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TL;DR: A draft sequence of the rice genome for the most widely cultivated subspecies in China, Oryza sativa L. ssp.indica, by whole-genome shotgun sequencing is produced, with a large proportion of rice genes with no recognizable homologs due to a gradient in the GC content of rice coding sequences.
Abstract: We have produced a draft sequence of the rice genome for the most widely cultivated subspecies in China, Oryza sativa L. ssp. indica, by whole-genome shotgun sequencing. The genome was 466 megabases in size, with an estimated 46,022 to 55,615 genes. Functional coverage in the assembled sequences was 92.0%. About 42.2% of the genome was in exact 20-nucleotide oligomer repeats, and most of the transposons were in the intergenic regions between genes. Although 80.6% of predicted Arabidopsis thaliana genes had a homolog in rice, only 49.4% of predicted rice genes had a homolog in A. thaliana. The large proportion of rice genes with no recognizable homologs is due to a gradient in the GC-content of rice coding sequences.
4,064 citations
"WEGO: a web tool for plotting GO an..." refers background in this paper
...WEGO has been applied in many important biological research studies, such as the comparative genomics study between the rice genome and the Arabidopsis genome ( 14 ,15) and the silkworm genome analysis (16)....
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Wellcome Trust1, University of Cambridge2, University of California, Berkeley3, Stanford University4, Princeton University5, Carnegie Institution for Science6, Northwestern University7, California Institute of Technology8, Wellcome Trust Sanger Institute9, Medical College of Wisconsin10, Cornell University11, Iowa State University12, Incyte13
TL;DR: The Gene Ontology (GO) project as discussed by the authors provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences.
Abstract: The Gene Ontology (GO) project (http://www.geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.
3,565 citations
TL;DR: GO::TermFinder comprises a set of object-oriented Perl modules for accessing Gene Ontology information and evaluating and visualizing the collective annotation of a list of genes to GO terms, which can be used to draw conclusions from microarray and other biological data.
Abstract: Summary: GO::TermFinder comprises a set of object-oriented Perl modules for accessing Gene Ontology (GO) information and evaluating and visualizing the collective annotation of a list of genes to GO terms. It can be used to draw conclusions from microarray and other biological data, calculating the statistical significance of each annotation. GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script.
Availability: The full source code and documentation for GO::TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/
1,869 citations
01 Jan 2001
TL;DR: The Gene Ontology project seeks to provide a set of structured vocabularies for specific biological domains that can be used to describe gene products in any organism, which includes building three extensive ontologies to describe molecular function, biological process, and cellular component.
Abstract: The exponential growth in the volume of accessible biological information has generated a confusion of voices surrounding the annotation of molecular information about genes and their products. The Gene Ontology (GO) project seeks to provide a set of structured vocabularies for specific biological domains that can be used to describe gene products in any organism. This work includes building three extensive ontologies to describe molecular function, biological process, and cellular component, and providing a community database resource that supports the use of these ontologies. The GO Consortium was initiated by scientists associated with three model organism databases: SGD, the Saccharomyces Genome database; FlyBase, the Drosophila genome database; and MGD/GXD, the Mouse Genome Informatics databases. Additional model organism database groups are joining the project. Each of these model organism information systems is annotating genes and gene products using GO vocabulary terms and incorporating these annotations into their respective model organism databases. Each database contributes its annotation files to a shared GO data resource accessible to the public at http://www.geneontology.org/. The GO site can be used by the community both to recover the GO vocabularies and to access the annotated gene product data sets from the model organism databases. The GO Consortium supports the development of the GO database resource and provides tools enabling curators and researchers to query and manipulate the vocabularies. We believe that the shared development of this molecular annotation resource will contribute to the unification of biological information.
1,034 citations