COMPSRA: a COMprehensive Platform for Small RNA-Seq data Analysis

被引:0
|
作者
Jiang Li
Alvin T. Kho
Robert P. Chase
Lorena Pantano
Leanna Farnam
Sami S. Amr
Kelan G. Tantisira
机构
[1] The Channing Division of Network Medicine,
[2] Department of Medicine,undefined
[3] Brigham & Women’s Hospital and Harvard Medical School,undefined
[4] Boston Children’s Hospital,undefined
[5] Harvard T.H. Chan School of Public Health,undefined
[6] Partners Personalized Medicine,undefined
[7] Division of Pulmonary and Critical Care Medicine,undefined
[8] Department of Medicine,undefined
[9] Brigham and Women’s Hospital,undefined
[10] and Harvard Medical School,undefined
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Small RNA-Seq is a common means to interrogate the small RNA’ome or the full spectrum of small RNAs (<200 nucleotide length) of a biological system. A pivotal problem in NGS based small RNA analysis is identifying and quantifying the small RNA’ome constituent components. For example, small RNAs in the circulatory system (circulating RNAs) are potential disease biomarkers and their function is being actively investigated. Most existing NGS data analysis tools focus on the microRNA component and a few other small RNA types like piRNA, snRNA and snoRNA. A comprehensive platform is needed to interrogate the full small RNA’ome, a prerequisite for down-stream data analysis. We present COMPSRA, a comprehensive modular stand-alone platform for identifying and quantifying small RNAs from small RNA sequencing data. COMPSRA contains prebuilt customizable standard RNA databases and sequence processing tools to enable turnkey basic small RNA analysis. We evaluated COMPSRA against comparable existing tools on small RNA sequencing data set from serum samples of 12 healthy human controls, and COMPSRA identified a greater diversity and abundance of small RNA molecules. COMPSRA is modular, stand-alone and integrates multiple customizable RNA databases and sequence processing tool and is distributed under the GNU General Public License free to non-commercial registered users at https://github.com/cougarlj/COMPSRA.
引用
下载
收藏
相关论文
共 50 条
  • [41] Oqtans: a multifunctional workbench for RNA-seq data analysis
    Sreedharan, Vipin T.
    Schultheiss, Sebastian J.
    Jean, Geraldine
    Kahles, Andre
    Bohnert, Regina
    Drewe, Philipp
    Mudrakarta, Pramod
    Goernitz, Nico
    Zeller, Georg
    Raetsch, Gunnar
    BMC BIOINFORMATICS, 2014, 15
  • [42] aTAP: automated transcriptome analysis platform for processing RNA-seq data by de novo assembly
    Surachat, Komwit
    Taylor, Todd Duane
    Wattanamatiphot, Wanicbut
    Sukpisit, Sukgamon
    Jeenkeawpiam, Kongpop
    HELIYON, 2022, 8 (08)
  • [43] Improving the Flexibility of RNA-Seq Data Analysis Pipelines
    Phan, John H.
    Wu, Po-Yen
    Wang, May D.
    2012 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS (GENSIPS), 2012, : 70 - 73
  • [44] Differential expression analysis for paired RNA-seq data
    Chung, Lisa M.
    Ferguson, John P.
    Zheng, Wei
    Qian, Feng
    Bruno, Vincent
    Montgomery, Ruth R.
    Zhao, Hongyu
    BMC BIOINFORMATICS, 2013, 14 : 110
  • [45] Computational analysis of alternative polyadenylation from standard RNA-seq and single-cell RNA-seq data
    Gao, Yipeng
    Li, Wei
    MRNA 3' END PROCESSING AND METABOLISM, 2021, 655 : 225 - 243
  • [46] Multivariate approach to the analysis of correlated RNA-seq data
    Park, Hyunjin
    Lee, Seungyeoun
    Kim, Ye Jin
    Choi, Myung-Sook
    Park, Taesung
    2016 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2016, : 1783 - 1786
  • [47] PUseqClust: A Clustering Analysis Method for RNA-Seq Data
    Shi X.-F.
    Liu X.-J.
    Zhang L.
    Ruan Jian Xue Bao/Journal of Software, 2019, 30 (09): : 2857 - 2868
  • [48] Intron Retention as a Mode for RNA-Seq Data Analysis
    Zheng, Jian-Tao
    Lin, Cui-Xiang
    Fang, Zhao-Yu
    Li, Hong-Dong
    FRONTIERS IN GENETICS, 2020, 11
  • [49] Getting the most out of RNA-seq data analysis
    Khang, Tsung Fei
    Lau, Ching Yee
    PEERJ, 2015, 3
  • [50] A survey of best practices for RNA-seq data analysis
    Conesa, Ana
    Madrigal, Pedro
    Tarazona, Sonia
    Gomez-Cabrero, David
    Cervera, Alejandra
    McPherson, Andrew
    Szczesniak, Michal Wojciech
    Gaffney, Daniel J.
    Elo, Laura L.
    Zhang, Xuegong
    Mortazavi, Ali
    GENOME BIOLOGY, 2016, 17