KPP: KEGG Pathway Painter

Manyam et al. BMC Systems Biology 2015, 9(Suppl 2):S3 http://www.biomedcentral.com/1752-0509/9/S2/S3 RESEARCH Open Access KPP: KEGG Pathway Painter Ganiraju Manyam1,2, Aybike Birerdinc1, Ancha Baranova1,3* From IX International Conference on the Bioinformatics of Genome Regulation and Structure\Systems Biology (BGRS\SB-2014) Novosibirsk, Russia. 23-28 June 2014 Abstract Background: High-throughput technologies became common tools to decipher genome-wide changes of gene expression (GE) patterns. Functional analysis of GE patterns is a daunting task as it requires often recourse to the public repositories of biological knowledge. On the other hand, in many cases researcher’s inquiry can be served by a comprehensive glimpse. The KEGG PATHWAY database is a compilation of manually verified maps of biological interactions represented by the complete set of pathways related to signal transduction and other cellular processes. Rapid mapping of the differentially expressed genes to the KEGG pathways may provide an idea about the functional relevance of the gene lists corresponding to the high-throughput expression data. Results: Here we present a web based graphic tool KEGG Pathway Painter (KPP). KPP paints pathways from the KEGG database using large sets of the candidate genes accompanied by “overexpressed” or “underexpressed” marks, for example, those generated by microarrays or miRNA profilings. Conclusion: KPP provides fast and comprehensive visualization of the global GE changes by consolidating a list of the color-coded candidate genes into the KEGG pathways. KPP is freely available and can be accessed at http:// web.cos.gmu.edu/~gmanyam/kegg/ Background High-throughput technologies became common tools to decipher genome-wide changes of gene expression (GE) patterns or relative protein abundance. Typical output of these large-scale studies is represented by the list comprised of hundreds of gene candidates with attached quantitative labels. Functional analysis of these gene lists is a daunting task as it requires regular recourse to the public repositories of biological knowledge or use of expensive databases of manually curated biological annotation [1,2]. On the other hand, in many cases researcher’s inquiry can be successfully served by a comprehensive glimpse. Functional analysis of markers identified from largescale datasets can be performed using a wide variety of bioinformatics tools. As microarrays became a common tool to decipher global gene expression, centralized * Correspondence: 1 School of Systems Biology, George Mason University, Fairfax, VA - 22030, USA Full list of author information is available at the end of the article systems like Gene Expression Omnibus (GEO), ArrayExpress was developed to congregate the valuable profile data [3,4]. An analysis of combined datasets generated in independent microarray experiments (so-called “microarray meta-analysis”), is often being employed [5], for example, to develop biomarker panels or to extract insights into the pathogenesis of various chronic diseases [6] including human malignancies [7]. Meta-analysis lead to an increase of the complexity in microarray analysis; therefore, sophistication of subsequent functional analysis also increased. Gene Ontology (GO) and other pathwaycentered types of analysis became indispensable [8]. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a compendium of databases covering both annotated genomes and protein interaction networks for all sequenced organisms. Its integral part, KEGG PATHWAY, is a compilation of manually verified pathway maps displaying both the molecular interactions and the biochemical reactions [9]. The recent version of this database includes a complete set of pathways related to signal transduction and other cellular processes [10]. The extensive collection © 2015 Manyam et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Manyam et al. BMC Systems Biology 2015, 9(Suppl 2):S3 http://www.biomedcentral.com/1752-0509/9/S2/S3 of the pathways at KEGG can be utilized for the rapid graphical evaluation of the functional relevance of the observed changes in GE patterns. This will save the precious time of the expert biologists and bioinformatics specialists. Pathways assembled into the KEGG database are displayed as semi-static objects that can be manipulated using tools like KGML and KEGG application programmable interface (API) [11,12]. KEGG API provides a routine that highlights specified genes within the particular metabolic pathway (http://www.genome.jp/kegg/ tool/color_pathway.html). Similar task may be also executed using G-language Genome Analysis Environment [13]. Both approaches work on the pathway by pathway basis. Another tool, Pathway Express, calculates the pathway-wise impact of differentially expressed genes based on normalized fold change and depicts the pathways with differentially expressed genes [14]. However, the fold-change approach and its associated standard t-test statistics usually produce severely over-fitted models. A number of recently developed approaches generate gene rankings dissociated from the fold change estimates [15,16]. An analysis of these gene lists may benefit from the binary graphical mapping of upregulated and downregulated elements within the complete collection of pathway maps. Resulting graphical pictures may be helpful both as tool for a quick assessment of the functional relevance of a gene list and as a set of the snapshots easily convertible into the illustrative material for presentations or manuscript figures. With this notion, here we present a web-based tool, KEGG Pathway Painter (KPP). KPP performs a batch painting of relevant pathways according to the uploaded lists of up-regulated and down-regulated genes in KEGG. KPP returns a set of images that give a holistic perspective to the functional importance of the change in the GE patterns revealed by a given high-throughput experiment and facilitate the extraction of the biological insights. Implementation KPP was implemented using PERL/CGI. Pathways assembled into the KEGG database are displayed as semistatic objects that can be manipulated using tools like KGML (KEGG Markup Language) and KEGG API (Application Programming Interface). The API allows access to the resources stored in KEGG system in an interactive and user-friendly way (http://www.genome.jp/kegg/rest/). KEGG Pathway Painter (KPP) accepts the up-regulated and down-regulated gene lists as two different text files containing the gene identifiers of any sequ (...truncated)