Liguo Wang1, Shengqin Wang, Wei Li. 1. Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. WL1@bcm.edu
Abstract
MOTIVATION: RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed. RESULTS: We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments. AVAILABILITY AND IMPLEMENTATION: RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/.
MOTIVATION: RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed. RESULTS: We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments. AVAILABILITY AND IMPLEMENTATION: RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/.
Authors: Sheng Li; Scott W Tighe; Charles M Nicolet; Deborah Grove; Shawn Levy; William Farmerie; Agnes Viale; Chris Wright; Peter A Schweitzer; Yuan Gao; Dewey Kim; Joe Boland; Belynda Hicks; Ryan Kim; Sagar Chhangawala; Nadereh Jafari; Nalini Raghavachari; Jorge Gandara; Natàlia Garcia-Reyero; Cynthia Hendrickson; David Roberson; Jeffrey Rosenfeld; Todd Smith; Jason G Underwood; May Wang; Paul Zumbo; Don A Baldwin; George S Grills; Christopher E Mason Journal: Nat Biotechnol Date: 2014-08-24 Impact factor: 54.908
Authors: Jung H Kim; Maureen A Sartor; Laura S Rozek; Christopher Faulk; Olivia S Anderson; Tamara R Jones; Muna S Nahar; Dana C Dolinoy Journal: BMC Genomics Date: 2014-01-17 Impact factor: 3.969
Authors: Aurélie A G Gabriel; Emilie Mathian; Lise Mangiante; Catherine Voegele; Vincent Cahais; Akram Ghantous; James D McKay; Nicolas Alcala; Lynnette Fernandez-Cuesta; Matthieu Foll Journal: Gigascience Date: 2020-10-30 Impact factor: 6.524