RNA-seq Data Analysis for Differential Expression

Navdeep Gill; Braham Dhillon

doi:10.1007/978-1-0716-1795-3_4

RNA-seq Data Analysis for Differential Expression

Methods Mol Biol. 2022:2391:45-54. doi: 10.1007/978-1-0716-1795-3_4.

Authors

Navdeep Gill¹, Braham Dhillon²

Affiliations

¹ Department of Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR, USA.
² Department of Plant Pathology, University of Florida, Fort Lauderdale Research and Education Center, Davie, FL, USA. dhillonb@ufl.edu.

PMID: 34686975
DOI: 10.1007/978-1-0716-1795-3_4

Abstract

Changes in the surrounding environment are mirrored by changes in the transcript profile of an organism. In the case of a plant pathogen, host colonization would be a challenge that triggers changes in transcript expression patterns. Determining the transcriptional profile could provide valuable clues on how an organism responds to defined stimuli, in this case, how a pathogen colonizes its host. Several robust data analysis methods and pipelines are available that can identify these differentially expressed transcripts. In this chapter we outline the steps and other caveats that are needed to run one such pipeline.

Keywords: DESeq2; Data analysis; Differentially expressed genes; HISAT2; Pipeline; RNA-seq; Splice-aware; StringTie; Transcript profile; Transcriptome.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Data Analysis
Exome Sequencing
Gene Expression Profiling*
RNA-Seq
Sequence Analysis, RNA*
Transcriptome