BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification method

Author:

Sun Shanwen1ORCID,Xu Lei2,Zou Quan13ORCID,Wang Guohua34

Affiliation:

1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054 China

2. School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, 518055 China

3. State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, 150040, China

4. College of Information and Computer Engineering, Northeast Forestry University, Harbin, 150040 China

Abstract

Abstract Summary Processing raw reads of RNA-sequencing (RNA-seq) data, no matter public or newly sequenced data, involves a lot of specialized tools and technical configurations that are often unfamiliar and time-consuming to learn for non-bioinformatics researchers. Here, we develop the R package BP4RNAseq, which integrates the state-of-art tools from both alignment-based and alignment-free quantification workflows. The BP4RNAseq package is a highly automated tool using an optimized pipeline to improve the sensitivity and accuracy of RNA-seq analyses. It can take only two non-technical parameters and output six formatted gene expression quantification at gene and transcript levels. The package applies to both retrospective and newly generated bulk RNA-seq data analyses and is also applicable for single-cell RNA-seq analyses. It, therefore, greatly facilitates the application of RNA-seq. Availability and implementation The BP4RNAseq package for R and its documentation are freely available at https://github.com/sunshanwen/BP4RNAseq. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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