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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
Published on: September 18, 2021
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Impact of human gene annotations on RNA-seq differential expression analysis
Yu Hamaguchi1, Chao Zeng2,3, Michiaki Hamada4,5,6,7
1Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555, Japan. yh549848@aoni.waseda.jp.
BMC Genomics
|October 9, 2021
Summary
Gene annotation complexity impacts RNA-sequencing (RNA-seq) differential expression (DE) analysis. Higher mappability in gene annotations improves DE analysis performance, suggesting removal of unnecessary gene models enhances results.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- RNA-sequencing (RNA-seq) differential expression (DE) analysis relies on gene annotations.
- Human genome annotations are complex and continually updated, impacting DE analysis.
- The effect of annotation complexity on DE analysis performance is not well understood.
Purpose of the Study:
- To assess the impact of gene annotation complexity on DE analysis.
- To evaluate the role of mappability in DE analysis performance.
Main Methods:
- Compared three human gene annotations (GENCODE, RefSeq, NONCODE) using mappability.
- Analyzed the relationship between mappability and DE analysis performance.
Main Results:
- Mappability significantly differs across human gene annotations.
- Increased mappability positively correlates with improved DE analysis performance.
- Mappability's impact is most pronounced during quantification and propagates downstream.
Conclusions:
- Gene annotation complexity negatively affects DE analysis.
- Excluding unnecessary gene models from annotations can enhance DE analysis performance.
- Mappability is a critical metric for evaluating gene annotation quality for DE analysis.
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