Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance

Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance

Abstract Background RNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on perform...

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Bibliographic Details
Main Authors: Alyssa Baccarella, Claire R. Williams, Jay Z. Parrish, Charles C. Kim
Format: Article
Language:English
Published: BMC 2018-11-01
Series:BMC Bioinformatics
Subjects:
Monocytes
RNA-sequencing
Gene expression analysis
Read depth
Sample number
Online Access:http://link.springer.com/article/10.1186/s12859-018-2445-2

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http://link.springer.com/article/10.1186/s12859-018-2445-2

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