Getting the most out of RNA-seq data analysis

PeerJ
Tsung Fei Khang, Ching Yee Lau

Abstract

Background. A common research goal in transcriptome projects is to find genes that are differentially expressed in different phenotype classes. Biologists might wish to validate such gene candidates experimentally, or use them for downstream systems biology analysis. Producing a coherent differential gene expression analysis from RNA-seq count data requires an understanding of how numerous sources of variation such as the replicate size, the hypothesized biological effect size, and the specific method for making differential expression calls interact. We believe an explicit demonstration of such interactions in real RNA-seq data sets is of practical interest to biologists. Results. Using two large public RNA-seq data sets-one representing strong, and another mild, biological effect size-we simulated different replicate size scenarios, and tested the performance of several commonly-used methods for calling differentially expressed genes in each of them. We found that, when biological effect size was mild, RNA-seq experiments should focus on experimental validation of differentially expressed gene candidates. Importantly, at least triplicates must be used, and the differentially expressed genes should be called using methods with...Continue Reading

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Citations

Sep 23, 2020·Animal Biotechnology·Jinbo WuZhu Li
Apr 9, 2019·PeerJ·Preethy Sasidharan NairIrma Järvelä

Related Concepts

Cell Count
Gene Expression
Genes
Research
Evaluation
Biologist (General)
Size
Research Study
Sequence Determinations, RNA
Analysis

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