Literature DB >> 28096075

hppRNA-a Snakemake-based handy parameter-free pipeline for RNA-Seq analysis of numerous samples.

Dapeng Wang1.   

Abstract

RNA-Seq technology has been gradually becoming a routine approach for characterizing the properties of transcriptome in terms of organisms, cell types and conditions and consequently a big burden has been put on the facet of data analysis, which calls for an easy-to-learn workflow to cope with the increased demands from a large number of laboratories across the world. We report a one-in-all solution called hppRNA, composed of four scenarios such as pre-mapping, core-workflow, post-mapping and sequence variation detection, written by a series of individual Perl and R scripts, counting on well-established and preinstalled software, irrespective of single-end or paired-end, unstranded or stranded sequencing method. It features six independent core-workflows comprising the state-of-the-art technology with dozens of popular cutting-edge tools such as Tophat-Cufflink-Cuffdiff, Subread-featureCounts-DESeq2, STAR-RSEM-EBSeq, Bowtie-eXpress-edgeR, kallisto-sleuth, HISAT-StringTie-Ballgown, and embeds itself in Snakemake, which is a modern pipeline management system. The core function of this pipeline is turning the raw fastq files into gene/isoform expression matrix and differentially expressed genes or isoforms as well as the identification of fusion genes, single nucleotide polymorphisms, long noncoding RNAs and circular RNAs. Last but not least, this pipeline is specifically designed for performing the systematic analysis on a huge set of samples in one go, ideally for the researchers who intend to deploy the pipeline on their local servers. The scripts as well as the user manual are freely available at https://sourceforge.net/projects/hpprna/.

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Year:  2018        PMID: 28096075     DOI: 10.1093/bib/bbw143

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  11 in total

1.  ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data.

Authors:  Stephany Orjuela; Ruizhu Huang; Katharina M Hembach; Mark D Robinson; Charlotte Soneson
Journal:  G3 (Bethesda)       Date:  2019-07-09       Impact factor: 3.154

2.  GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure.

Authors:  John A Hadish; Tyler D Biggs; Benjamin T Shealy; M Reed Bender; Coleman B McKnight; Connor Wytko; Melissa C Smith; F Alex Feltus; Loren Honaas; Stephen P Ficklin
Journal:  BMC Bioinformatics       Date:  2022-05-02       Impact factor: 3.307

3.  Upregulated YB-1 protein promotes glioblastoma growth through a YB-1/CCT4/mLST8/mTOR pathway.

Authors:  Jin-Zhu Wang; Hong Zhu; Pu You; Hui Liu; Wei-Kang Wang; Xiaojuan Fan; Yun Yang; Keren Xu; Yingfeng Zhu; Qunyi Li; Ping Wu; Chao Peng; Catherine Cl Wong; Kaicheng Li; Yufeng Shi; Nu Zhang; Xiuxing Wang; Rong Zeng; Ying Huang; Liusong Yang; Zefeng Wang; Jingyi Hui
Journal:  J Clin Invest       Date:  2022-04-15       Impact factor: 19.456

Review 4.  Disease-Associated Circular RNAs: From Biology to Computational Identification.

Authors:  Min Tang; Ling Kui; Guanyi Lu; Wenqiang Chen
Journal:  Biomed Res Int       Date:  2020-08-17       Impact factor: 3.411

5.  The transcription factors TFE3 and TFEB amplify p53 dependent transcriptional programs in response to DNA damage.

Authors:  Owen A Brady; Eutteum Jeong; José A Martina; Mehdi Pirooznia; Ilker Tunc; Rosa Puertollano
Journal:  Elife       Date:  2018-12-06       Impact factor: 8.140

Review 6.  Computational Methods for Mapping, Assembly and Quantification for Coding and Non-coding Transcripts.

Authors:  Isaac A Babarinde; Yuhao Li; Andrew P Hutchins
Journal:  Comput Struct Biotechnol J       Date:  2019-05-07       Impact factor: 7.271

7.  Transcriptome Sequencing Unravels Potential Biomarkers at Different Stages of Cerebral Ischemic Stroke.

Authors:  You Cai; Yufen Zhang; Xiao Ke; Yu Guo; Chengye Yao; Na Tang; Pei Pang; Gangcai Xie; Li Fang; Zhe Zhang; Jincheng Li; Yixian Fan; Ximiao He; Ruojian Wen; Lei Pei; Youming Lu
Journal:  Front Genet       Date:  2019-09-24       Impact factor: 4.599

8.  RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow.

Authors:  Marie Lataretu; Martin Hölzer
Journal:  Genes (Basel)       Date:  2020-12-10       Impact factor: 4.096

9.  VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis.

Authors:  MacIntosh Cornwell; Mahesh Vangala; Len Taing; Zachary Herbert; Johannes Köster; Bo Li; Hanfei Sun; Taiwen Li; Jian Zhang; Xintao Qiu; Matthew Pun; Rinath Jeselsohn; Myles Brown; X Shirley Liu; Henry W Long
Journal:  BMC Bioinformatics       Date:  2018-04-12       Impact factor: 3.169

10.  RASflow: an RNA-Seq analysis workflow with Snakemake.

Authors:  Xiaokang Zhang; Inge Jonassen
Journal:  BMC Bioinformatics       Date:  2020-03-18       Impact factor: 3.169

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