Identifying Significantly Impacted Pathways and Putative Mechanisms with iPathwayGuide

Sidra Ahsan1, Sorin Drăghici2

1 Advaita Bioinformatics, Plymouth, Michigan, 2 Wayne State University, Department of Obstetrics and Gynecology, Detroit, Michigan
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 7.15
DOI:  10.1002/cpbi.24
Online Posting Date:  June, 2017
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Abstract

iPathwayGuide is a gene expression analysis tool that provides biological context and inferences from data generated by high‐throughput sequencing. iPathwayGuide utilizes a systems biology approach to identify significantly impacted signaling pathways, Gene Ontology terms, disease processes, predicted microRNAs, and putative mechanisms based on the given differential expression signature. By using a novel analytical approach called Impact Analysis, iPathwayGuide considers the role, position, and relationships of each gene within a pathway, which results in a significant reduction in false positives, as well as a better ability to identify the truly impacted pathways and putative mechanisms that can explain all measured gene expression changes. It is a Web‐based, user‐friendly, interactive tool that does not require prior training in bioinformatics. The protocols in this unit describe how to use iPathwayGuide to analyze a single contrast between two phenotypes (any number of samples), and provide guidance on how to interpret the results obtained from iPathwayGuide. Even though iPathwayGuide has powerful meta‐analysis capabilities, these are not covered in this unit. © 2017 by John Wiley & Sons, Inc.

Keywords: gene expression analysis; microarrays; RNA‐seq; pathway analysis; differential expression analysis

     
 
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Table of Contents

  • Introduction
  • Strategic Planning
  • Basic Protocol 1: Using iPathwayGuide to Analyze a Differential Expression Dataset that Compares Two Experimental Conditions
  • Alternate Protocol 1: Using iPathwayGuide to Analyze Microarray Data Generated from Affymetrix Platforms
  • Commentary
  • Literature Cited
  • Figures
  • Tables
     
 
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Materials

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Literature Cited

Literature Cited
  Ackermann, M., & Strimmer, K. (2009). A general modular framework for gene set enrichment analysis. BMC Bioinformatics, 10, 47. doi: 10.1186/1471‐2105‐10‐47
  Alexa, A., Rahnenfuhrer, J., & Lengauer, T. (2006). Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics, 22, 1600–1607. doi: 10.1093/bioinformatics/btl140
  Damian, D., & Gorfine, M. (2004) Statistical concerns about the GSEA procedure. Nature Genetics, 36, 663. doi: 10.1038/ng0704‐663a
  Donato, M., & Drăghici, S. (2010). Signaling pathways coupling phenomena. In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1‐6). Barcelona, Spain: IEEE. doi: 10.1109/IJCNN.2010.5596743
  Donato, M., Xu, Z., Tomoiaga, A., Granneman, J. G., MacKenzie, R. G., Bao, R., … Drăghici, S. (2013). Analysis and correction of crosstalk effects in pathway analysis. Genome Research, 23, 1885–1893. doi: 10.1101/gr.153551.112
  Donato, M., Zhu, Z., Tomoiaga, A., Westfall, P., Romero, R., & Drăghici, S. (2012). A method for analysis and correction of cross‐talk effects in pathway analysis. In The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1‐7). Brisbane, Australia: IEEE.
  Drăghici, S. (2003). Data analysis tools for DNA microarrays. Boca Raton, Fla.: Chapman and Hall/CRC Press.
  Drăghici, S. (2011). Statistics and data analysis for microarrays using R and bioconductor. Boca Raton, Fla.: Chapman and Hall/CRC Press.
  Drăghici, S., Khatri, P., Bhavsar, P., Shah, A., Krawetz, S. A., & Tainsky, M. A. (2003). Onto‐Tools, the toolkit of the modern biologist: Onto‐express, onto‐compare, onto‐design and onto‐ translate. Nucleic Acids Research, 31, 3775–3781. doi: 10.1093/nar/gkg624
  Drăghici, S., Khatri, P., Martins, R. P., Ostermeier, G. C., & Krawetz, S. A. (2003). Global functional profiling of gene expression. Genomics, 81, 98–104. doi: 10.1016/S0888‐7543(02)00021‐6
  Drăghici, S., Khatri, P., Tarca, A. L., Amin, K., Done, A., Voichiisţa, C., … Romero, R. (2007). A systems biology approach for pathway level analysis. Genome Research, 17, 1537–1545. doi: 10.1101/gr.6202607
  Friedman, J. M., & Jones, P. A. (2009). MicroRNAs: Critical mediators of differentiation, development and disease. Swiss Medical Weekly, 139, 466.
  Giorgi, F. M., Bolger, A. M., Lohse, M., & Usadel, B. (2010). Algorithm‐driven artifacts in median polish summarization of microarray data. BMC Bioinformatics, 11, 1. doi: 10.1186/1471‐2105‐11‐1
  Goeman, J. J., & Bühlmann, P. (2007). Analyzing gene expression data in terms of gene sets: Methodological issues. Bioinformatics, 23, 980–987. doi: 10.1093/bioinformatics/btm051
  Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015). Advanced applications of RNA sequencing and challenges. Bioinformatics and Biology Insights, 9, 29. doi: 10.4137/BBI.S28991
  Harris, M.A., Clark J., Ireland, A., Lomax, J., Ashburner, M., Foulger, R, Eilbeck, K., …Gene Ontology Consortium. (2004). The Gene Ontology (GO) database and informatics resource. Nucleic Acids Research, 32, D258–D261. doi: 10.1093/nar/gkh036
  Khatri, P., Drăghici, S., Ostermeier, G. C., & Krawetz, S. A. (2002). Profiling gene expression using Onto‐Express. Genomics, 79, 266–270. doi: 10.1006/geno.2002.6698
  Khatri, P., Drăghici, S., Tarca, A. L., Hassan, S. S., & Romero, R. (2007). A system biology approach for the steady‐state analysis of gene signaling networks. In CIARP’07 Proceedings of the Congress on Pattern Recognition, 12th Iberoamerican Conference on Progress in Pattern Recognition, Image Analysis and Applications (pp. 32–41). Valparaiso, Chile: ACM.
  Khatri, P., Sirota, M., & Butte, A. J. (2012). Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Computational Biology, 8, e1002375. doi: 10.1371/journal.pcbi.1002375
  Mitrea, C., Taghavi, Z., Bokanizad, B., Hanoudi, S., Tagett, R., Donato, M., … Drăghici, S. (2013). Methods and approaches in the topology‐based analysis of biological pathways. Frontiers in Physiology, 4, 278. doi: 10.3389/fphys.2013.00278
  Rhee, Y. S., Wood, V., Dolinski, K., & Drăghici, S. (2008). Use and misuse of the Gene Ontology annotations. Nature Reviews Genetics, 9, 509–515. doi: 10.1038/nrg2363
  Schurch, N. J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., … Barton, G. J. (2016). How many biological replicates are needed in an RNA‐seq experiment and which differential expression tool should you use? RNA, 22, 839–851. doi: 10.1261/rna.053959.115
  Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., ¼ Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge‐based approach for interpreting genome‐wide expression profiles. Proceeding of The National Academy of Sciences of the United States of America, 102), 15545–15550. doi: 10.1073/pnas.0506580102
  Tarca, A. L., Drăghici, S., Khatri, P., Hassan, S. S., Mittal, P., Kim, J.‐S., … Romero, R. (2009). A novel signaling pathway impact analysis (SPIA). Bioinformatics, 25, 75–82. doi: 10.1093/bioinformatics/btn577
  Wu, Z., Irizarry, R., Gentleman, R., Murillo, F., & Spencer, F. (2003). A model based background adjustment for oligonucleotide expression arrays. Journal of the American Statistical Association, 99, 909–917.
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