SEAS: A System for SEED-Based Pathway Enrichment Analysis

Pathway enrichment analysis represents a key technique for analyzing high-throughput omic data, and it can help to link individual genes or proteins found to be differentially expressed under specific conditions to well-understood biological pathways. We present here a computational tool, SEAS, for pathway enrichment analysis over a given set of genes in a specified organism against the pathways (or subsystems) in the SEED database, a popular pathway database for bacteria. SEAS maps a given set of genes of a bacterium to pathway genes covered by SEED through gene ID and/or orthology mapping, and then calculates the statistical significance of the enrichment of each relevant SEED pathway by the mapped genes. Our evaluation of SEAS indicates that the program provides highly reliable pathway mapping results and identifies more organism-specific pathways than similar existing programs. SEAS is publicly released under the GPL license agreement and freely available at http://csbl.bmb.uga.edu/~xizeng/research/seas/.

[1]  Rick L. Stevens,et al.  The RAST Server: Rapid Annotations using Subsystems Technology , 2008, BMC Genomics.

[2]  Timothy C. Meredith,et al.  Modification of Lipopolysaccharide with Colanic Acid (M-antigen) Repeats in Escherichia coli* , 2007, Journal of Biological Chemistry.

[3]  Li-Ping Wei,et al.  Transcriptome Profiling, Molecular Biological, and Physiological Studies Reveal a Major Role for Ethylene in Cotton Fiber Cell Elongation[W][OA] , 2006, The Plant Cell Online.

[4]  Alexander R. Pico,et al.  GenMAPP 2: new features and resources for pathway analysis , 2007, BMC Bioinformatics.

[5]  Akiyasu C. Yoshizawa,et al.  KAAS: an automatic genome annotation and pathway reconstruction server , 2007, Environmental health perspectives.

[6]  Jihoon Kim,et al.  ArrayXPath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using Scalable Vector Graphics , 2005, Nucleic Acids Res..

[7]  Jianmin Wu,et al.  KOBAS server: a web-based platform for automated annotation and pathway identification , 2006, Nucleic Acids Res..

[8]  Rafael A. Irizarry,et al.  Bioinformatics and Computational Biology Solutions using R and Bioconductor , 2005 .

[9]  David Botstein,et al.  GO: : TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes , 2004, Bioinform..

[10]  Paul Bertone,et al.  Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. , 2010, RNA.

[11]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[13]  Sayan Mukherjee,et al.  Modeling Cancer Progression via Pathway Dependencies , 2008, PLoS Comput. Biol..

[14]  M. Metzker.  Sequencing technologies — the next generation , 2010, Nature Reviews Genetics.

[15]  Brad T. Sherman,et al.  The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists , 2007, Genome Biology.

[16]  Yuzhen Ye,et al.  A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes , 2009, PLoS Comput. Biol..

[17]  J. Eisen,et al.  Adaptations to Submarine Hydrothermal Environments Exemplified by the Genome of Nautilia profundicola , 2009, PLoS genetics.

[18]  David W. Mount,et al.  Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data , 2004, Bioinform..

[19]  Hubert Hackl,et al.  PathwayExplorer: web service for visualizing high-throughput expression data on biological pathways , 2005, Nucleic Acids Res..

[20]  Alan J Wolfe,et al.  Evidence that acetyl phosphate functions as a global signal during biofilm development , 2003, Molecular microbiology.

[21]  Liping Wei,et al.  Genes and (Common) Pathways Underlying Drug Addiction , 2007, PLoS Comput. Biol..

[22]  Sandrine Dudoit,et al.  Multiple Testing Procedures: the multtest Package and Applications to Genomics , 2005 .

[23]  T. Werner.  Bioinformatics applications for pathway analysis of microarray data. , 2008, Current opinion in biotechnology.

[24]  Naryttza N. Diaz,et al.  The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes , 2005, Nucleic acids research.

[25]  Tao Cai,et al.  Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary , 2005, Bioinform..

[26]  Brad T. Sherman,et al.  DAVID: Database for Annotation, Visualization, and Integrated Discovery , 2003, Genome Biology.

[27]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[28]  Matthew DeJongh,et al.  Gene set analyses for interpreting microarray experiments on prokaryotic organisms , 2008, BMC Bioinformatics.

[29]  Georg F. Weiller,et al.  PathExpress update: the enzyme neighbourhood method of associating gene-expression data with metabolic pathways , 2009, Nucleic Acids Res..

[30]  M. Orešič,et al.  Pathways to the analysis of microarray data. , 2005, Trends in biotechnology.

[31]  Ying Xu,et al.  Mapping of orthologous genes in the context of biological pathways: An application of integer programming , 2006, Proc. Natl. Acad. Sci. USA.

[32]  Y. Komeda,et al.  Transcriptional control of flagellar genes in Escherichia coli K-12 , 1986, Journal of bacteriology.