Impact of human gene annotations on RNA-seq differential expression analysis

Yu Hamaguchi*, Chao Zeng, Michiaki Hamada

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear. Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically. Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

本文言語English
論文番号730
ジャーナルBMC Genomics
22
1
DOI
出版ステータスPublished - 2021 12月

ASJC Scopus subject areas

  • バイオテクノロジー
  • 遺伝学

フィンガープリント

「Impact of human gene annotations on RNA-seq differential expression analysis」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル