Bias and Correction in RNA-seq Data for Marine Species

被引:0
|
作者
Kai Song
Li Li
Guofan Zhang
机构
[1] Chinese Academy of Sciences,Key Laboratory of Experimental Marine Biology, Institute of Oceanology
[2] National & Local Joint Engineering Laboratory of Ecological Mariculture,Laboratory for Marine Fisheries and Aquaculture
[3] Qingdao National Laboratory for Marine Science and Technology,Laboratory for Marine Biology and Biotechnology
[4] Qingdao National Laboratory for Marine Science and Technology,undefined
来源
Marine Biotechnology | 2017年 / 19卷
关键词
Transcriptome profiling; RNA-seq analysis bias; Gene expression; GC content;
D O I
暂无
中图分类号
学科分类号
摘要
RNA-seq is a recently developed approach widely used for transcriptome profiling in biological analyses that use next-generation sequencing technologies. Accurate estimation of gene expression levels is critical for answering biological questions. Here, we show that the commonly used measure of gene expression levels, fragments per kilobase of transcript per million mapped reads (FPKM), is biased in transcript length, GC content, and dinucleotide frequencies in the RNA-seq analysis of marine species. We used a generalized linear model to correct the observed biases of FPKM. We used RNA-seq data sets from eight species obtained by different sequencing methods to evaluate the correction methods. Our work contributes to the understanding of potential technical artifacts in RNA-seq experiments for marine species, and presents a means by which more accurate gene expression measures can be obtained.
引用
收藏
页码:541 / 550
页数:9
相关论文
共 50 条
  • [21] SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
    Tao Peng
    Qin Zhu
    Penghang Yin
    Kai Tan
    Genome Biology, 20
  • [22] Modeling Exon-Specific Bias Distribution Improves the Analysis of RNA-Seq Data
    Liu, Xuejun
    Zhang, Li
    Chen, Songcan
    PLOS ONE, 2015, 10 (10):
  • [23] RNA editing in the human ENCODE RNA-seq data
    Park, Eddie
    Williams, Brian
    Wold, Barbara J.
    Mortazavi, Ali
    GENOME RESEARCH, 2012, 22 (09) : 1626 - 1633
  • [24] RNA-Seq UD: A bioinformatics plattform for RNA-Seq analysis
    Ramirez, Miguel
    Alejandro Rojas-Quintero, Cristian
    Enrique Vera-Parra, Nelson
    2015 10TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI), 2015,
  • [26] Improving RNA-Seq expression estimates by correcting for fragment bias
    Roberts, Adam
    Trapnell, Cole
    Donaghey, Julie
    Rinn, John L.
    Pachter, Lior
    GENOME BIOLOGY, 2011, 12 (03):
  • [27] Improving RNA-Seq expression estimates by correcting for fragment bias
    Adam Roberts
    Cole Trapnell
    Julie Donaghey
    John L Rinn
    Lior Pachter
    Genome Biology, 12
  • [28] Pathogen detection in RNA-seq data with Pathonoia
    Liebhoff, Anna-Maria
    Menden, Kevin
    Laschtowitz, Alena
    Franke, Andre
    Schramm, Christoph
    Bonn, Stefan
    BMC BIOINFORMATICS, 2023, 24 (01)
  • [29] An integrative method to normalize RNA-Seq data
    Cyril Filloux
    Meersseman Cédric
    Philippe Romain
    Forestier Lionel
    Klopp Christophe
    Rocha Dominique
    Maftah Abderrahman
    Petit Daniel
    BMC Bioinformatics, 15
  • [30] Comparison of normalization methods for RNA-Seq data
    Aghababazadeh, Farnoosh A.
    Li, Qian
    Fridley, Brooke L.
    GENETIC EPIDEMIOLOGY, 2018, 42 (07) : 684 - 684