TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators

Asa Thibodeau,Dong-Guk Shin,Inanc Birol

doi:10.1093/bioinformatics/bty998

Abstract

SummaryCurrent approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels.Availability and implementationThe TriPOINT open source software is freely available at https://github.uconn.edu/ajt06004/TriPOINT under the GPL v3.0 license.Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

Pathway analyses are often utilized to identify pathways that are enriched in differential genes between conditions to gain a better understanding of the biological processes that are affected by the phenotype of interest
Supplementary information: Supplementary data are available at Bioinformatics online
Methods for pathway analysis over the years have fallen into three categories (Khatri et al, 2012): (i) over representation analyses which count the number of differentially expressed genes within a pathway (Huang et al, 2009a, b), (ii) functional class scoring which calculates enrichment scores of pathway gene sets (Subramanian et al, 2005, 2007) and (iii) pathway topology analyses where pathways are translated into directed graphs or networks to incorporate directionality and interaction types such as activation or inhibition (Bokanizad et al, 2016; Martini et al, 2013; Sebastian-Leon et al, 2013; Tarca et al, 2009; Vaske et al, 2010; Zhao et al, 2017)

Summary

Introduction

Pathway analyses are often utilized to identify pathways that are enriched in differential genes between conditions (i.e. cases versus controls) to gain a better understanding of the biological processes that are affected by the phenotype of interest (e.g. a disease). A few pathway analyses have integrated pathways with additional data (Calura et al, 2014). These analyses can lead to the identification of pathways whose functions are affected as a result of a disruption in the processes, e.g. via a single nucleotide polymorphism that might be associated with a disease state. The majority of single nucleotide polymorphisms are located in non-coding regions (Hindorff et al, 2009), where determining their phenotypic outcome is a challenging task. Several assays have been developed, including ChIA-PET (Fullwood et al, 2009), HiC (Lieberman-Aiden et al, 2009) and HiChIP (Mumbach et al, 2016), to identify chromatin loops that bring non-coding regions in close proximity of their target genes’ promoters, which help uncover

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