Network‐Based Approaches for Pathway Level Analysis. Issue 1 (9th April 2018)

• Record Type: Journal Article
• Authors: Nguyen, Tin; Mitrea, Cristina; Draghici, Sorin
• Editors: Baxevanis, Andreas D.; Petsko, Gregory A.; Stein, Lincoln D.; Stormo, Gary D.
• Is Part Of: Current protocols in bioinformatics. Volume 61:Issue 1(2018)
• Page Start: 8.25.1
• Page End: 8.25.24
• Publication Date: 2018-04-09
• Subjects: systems biology -- pathway -- topology -- gene network -- survey -- pathway analysis; Bioinformatics -- Laboratory manuals; Nucleotide sequence -- Laboratory manuals; Amino acid sequence -- Laboratory manuals; Base Sequence; Amino Acid Sequence; Computational Biology -- methods; Databases, Genetic; Proteins -- analysis; Sequence Analysis -- methods; Sequence Homology; Amino acid sequence; Bioinformatics; Nucleotide sequence; Laboratory Manuals; Laboratory manuals; 570.285
• DOI: 10.1002/cpbi.42
• Languages: English
• ISSNs: 1934-3396

Abstract

Abstract: Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology‐based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is present in most existing methods. © 2018 by John Wiley & Sons, Inc.