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Title: RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis
Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approachesfor differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.  more » « less
Award ID(s):
1750472
NSF-PAR ID:
10132785
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Annual Review of Biomedical Data Science
Volume:
2
Issue:
1
ISSN:
2574-3414
Page Range / eLocation ID:
139 to 173
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Contact

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    Supplementary information

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