iSRAP - a one-touch research tool for rapid profiling of small RNA-seq data

被引:11
|
作者
Quek, Camelia [1 ]
Jung, Chol-hee [2 ]
Bellingham, Shayne A. [1 ]
Lonie, Andrew [2 ]
Hill, Andrew F. [1 ,3 ]
机构
[1] Univ Melbourne, Dept Biochem & Mol Biol, Mol Sci & Biotechnol Inst Bio21, Melbourne, Vic, Australia
[2] Univ Melbourne, VLSCI, Melbourne, Vic, Australia
[3] La Trobe Univ, La Trobe Inst Mol Sci, Dept Biochem & Genet, Melbourne, Vic 3083, Australia
基金
澳大利亚研究理事会; 英国医学研究理事会;
关键词
small RNA; non-coding; exosomes; next-generation sequencing; pipeline;
D O I
10.3402/jev.v4.29454
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
Small non-coding RNAs have been significantly recognized as the key modulators in many biological processes, and are emerging as promising biomarkers for several diseases. These RNA species are transcribed in cells and can be packaged in extracellular vesicles, which are small vesicles released from many biotypes, and are involved in intercellular communication. Currently, the advent of next-generation sequencing (NGS) technology for high-throughput profiling has further advanced the biological insights of non-coding RNA on a genome-wide scale and has become the preferred approach for the discovery and quantification of non-coding RNA species. Despite the routine practice of NGS, the processing of large data sets poses difficulty for analysis before conducting downstream experiments. Often, the current analysis tools are designed for specific RNA species, such as microRNA, and are limited in flexibility for modifying parameters for optimization. An analysis tool that allows for maximum control of different software is essential for drawing concrete conclusions for differentially expressed transcripts. Here, we developed a one-touch integrated small RNA analysis pipeline (iSRAP) research tool that is composed of widely used tools for rapid profiling of small RNAs. The performance test of iSRAP using publicly and in-house available data sets shows its ability of comprehensive profiling of small RNAs of various classes, and analysis of differentially expressed small RNAs. iSRAP offers comprehensive analysis of small RNA sequencing data that leverage informed decisions on the downstream analyses of small RNA studies, including extracellular vesicles such as exosomes.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes
    Srivastava, Avi
    Sarkar, Hirak
    Gupta, Nitish
    Patro, Rob
    BIOINFORMATICS, 2016, 32 (12) : 192 - 200
  • [22] MICRORNA PROFILING USING SMALL RNA-SEQ IN PAEDIATRIC LOW GRADE GLIOMAS
    Jeyapalan, Jennie N.
    Jones, Tania A.
    Tatevossian, Ruth G.
    Qaddoumi, Ibrahim
    Ellison, DavidW.
    Sheer, Denise
    NEURO-ONCOLOGY, 2014, 16
  • [23] iREAD: a tool for intron retention detection from RNA-seq data
    Hong-Dong Li
    Cory C. Funk
    Nathan D. Price
    BMC Genomics, 21
  • [24] piRNA analysis framework from small RNA-Seq data by a novel cluster prediction tool - PILFER
    Ray, Rishav
    Pandey, Priyanka
    GENOMICS, 2018, 110 (06) : 355 - 365
  • [25] iREAD: a tool for intron retention detection from RNA-seq data
    Li, Hong-Dong
    Funk, Cory C.
    Price, Nathan D.
    BMC GENOMICS, 2020, 21 (01)
  • [26] PingPongPro: a tool for the detection of piRNA-mediated transposon-silencing in small RNA-Seq data
    Uhrig, Sebastian
    Klein, Holger
    BIOINFORMATICS, 2019, 35 (02) : 335 - 336
  • [27] ARMT: An automatic RNA-seq data mining tool based on comprehensive and integrative analysis in cancer research
    Huang, Guanda
    Zhang, Haibo
    Qu, Yimo
    Huang, Kaitang
    Gong, Xiaocheng
    Wei, Jinfen
    Du, Hongli
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2021, 19 : 4426 - 4434
  • [28] Profiling Alternative 3′ Untranslated Regions in Sorghum using RNA-seq Data
    Tu, Min
    Li, Yin
    FRONTIERS IN GENETICS, 2020, 11
  • [29] Oasis 2: improved online analysis of small RNA-seq data
    Raza-Ur Rahman
    Abhivyakti Gautam
    Jörn Bethune
    Abdul Sattar
    Maksims Fiosins
    Daniel Sumner Magruder
    Vincenzo Capece
    Orr Shomroni
    Stefan Bonn
    BMC Bioinformatics, 19
  • [30] SCRAP: a bioinformatic pipeline for the analysis of small chimeric RNA-seq data
    Mills, William T.
    Eadara, Sreenivas
    Jaffe, Andrew E.
    Meffert, Mollie K.
    RNA, 2023, 29 (01) : 1 - 17