Protocol for identifying key genes using network-based approach as an alternative to differential expression analysis

In a variety of biological contexts, characterizing genes associated with disease etiology and mediating global transcriptomic change is a key initial step. Here, we present a protocol to identify such key genes using our tool "PathExt," a tool that implements a network-based approach. We describe steps for installing libraries, preparing input data and detailed procedures for running PathExt, and characterizing differential pathways and key genes based on ripple centrality scores. For complete details on the use and execution of this protocol, please refer to Agrawal et al.<sup>1</sup><sup>,</sup><sup>2</sup>.

Authors

Institutions

Publication Details

Journal
STAR Protocols
Published
2024-12-01
DOI
https://doi.org/10.1016/j.xpro.2024.103472
Citations
1
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.23

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Protocol for identifying key genes using network-based approach as an alternative to differential expression analysis

Sridhar Hannenhalli, Piyush Agrawal
1 citations
STAR Protocols
Bioinformatics and Genomic Networks
0.23
article

Protocol for identifying key genes using network-based approach as an alternative to differential expression analysis

Sridhar Hannenhalli, Piyush Agrawal
article en
1 citations

Abstract

In a variety of biological contexts, characterizing genes associated with disease etiology and mediating global transcriptomic change is a key initial step. Here, we present a protocol to identify such key genes using our tool "PathExt," a tool that implements a network-based approach. We describe steps for installing libraries, preparing input data and detailed procedures for running PathExt, and characterizing differential pathways and key genes based on ripple centrality scores. For complete details on the use and execution of this protocol, please refer to Agrawal et al.<sup>1</sup><sup>,</sup><sup>2</sup>.

STAR ProtocolsVol. 5(4)
SRM Institute of Science and Technology (IN), National Cancer Institute (US)
National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 40%
Bioinformatics and Genomic Networks
0.23
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.