Effect of method of deduplication on estimation of differential gene expression using RNA-seq

PeerJ

article

Anna V. Klepikova¹  Artem S. Kasianov²  Mikhail S. Chesnokov⁴  Natalia L. Lazarevich⁴  Aleksey A. Penin¹  Maria Logacheva¹ 

[1] Institute for Information Transmission Problems of the Russian Academy of Sciences;A. N. Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University;N. I. Vavilov Institute for General Genetics;N.N. Blokhin Russian Cancer Research Center of the Ministry of Health of the Russian Federation;Department of Biology, Lomonosov Moscow State University;Extreme Biology Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University

关键词: RNA-seq;    Differential expression;    Deduplication;    Cancer genomics;    Hepatocarcinoma;   

DOI  :  10.7717/peerj.3091

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundRNA-seq is a useful tool for analysis of gene expression. However, its robustness is greatly affected by a number of artifacts. One of them is the presence of duplicated reads.ResultsTo infer the influence of different methods of removal of duplicated reads on estimation of gene expression in cancer genomics, we analyzed paired samples of hepatocellular carcinoma (HCC) and non-tumor liver tissue. Four protocols of data analysis were applied to each sample: processing without deduplication, deduplication using a method implemented in SAMtools, and deduplication based on one or two molecular indices (MI). We also analyzed the influence of sequencing layout (single read or paired end) and read length. We found that deduplication without MI greatly affects estimated expression values; this effect is the most pronounced for highly expressed genes.ConclusionThe use of unique molecular identifiers greatly improves accuracy of RNA-seq analysis, especially for highly expressed genes. We developed a set of scripts that enable handling of MI and their incorporation into RNA-seq analysis pipelines. Deduplication without MI affects results of differential gene expression analysis, producing a high proportion of false negative results. The absence of duplicate read removal is biased towards false positives. In those cases where using MI is not possible, we recommend using paired-end sequencing layout.

【 授权许可】

CC BY