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

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.

[1]  Anna Majer,et al.  A user-friendly computational workflow for the analysis of microRNA deep sequencing data. , 2013, Methods in molecular biology.

[2]  N. Rajewsky,et al.  Discovering microRNAs from deep sequencing data using miRDeep , 2008, Nature Biotechnology.

[3]  Matthew D. Young,et al.  From RNA-seq reads to differential expression results , 2010, Genome Biology.

[4]  Chi-Ching Lee,et al.  DSAP: deep-sequencing small RNA analysis pipeline , 2010, Nucleic Acids Res..

[5]  Chris Williams,et al.  RNA-SeQC: RNA-seq metrics for quality control and process optimization , 2012, Bioinform..

[6]  Alicia Oshlack,et al.  miRNA-Seq normalization comparisons need improvement. , 2013, RNA.

[7]  Johan T den Dunnen,et al.  New methods for next generation sequencing based microRNA expression profiling , 2010, BMC Genomics.

[8]  John D. McPherson,et al.  Optimization of miRNA-seq data preprocessing , 2015, Briefings Bioinform..

[9]  J. Monod,et al.  Genetic regulatory mechanisms in the synthesis of proteins. , 1961, Journal of molecular biology.

[10]  Michael W. Pfaffl,et al.  Normalization Strategies for Microrna Profiling Experiments: a 'normal' Way to a Hidden Layer of Complexity? , 2010 .

[11]  Nicolas Servant,et al.  A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis , 2013, Briefings Bioinform..

[12]  Antonio Rinaldi,et al.  iMir: An integrated pipeline for high-throughput analysis of small non-coding RNA data obtained by smallRNA-Seq , 2013, BMC Bioinformatics.

[13]  Jeffrey G. Reid,et al.  Expression profiling of microRNAs by deep sequencing , 2009, Briefings Bioinform..

[14]  Zhifu Sun,et al.  CAP-miRSeq: a comprehensive analysis pipeline for microRNA sequencing data , 2014, BMC Genomics.

[15]  A. Hill,et al.  Small RNA deep sequencing reveals a distinct miRNA signature released in exosomes from prion-infected neuronal cells , 2012, Nucleic acids research.

[16]  J. Lötvall,et al.  Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells , 2007, Nature Cell Biology.

[17]  N. Rajewsky,et al.  Cell-type-specific signatures of microRNAs on target mRNA expression. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Shawn Hoon,et al.  Biopipe: a flexible framework for protocol-based bioinformatics analysis. , 2003, Genome research.

[19]  S. Thibodeau,et al.  eRNA: a graphic user interface-based tool optimized for large data analysis from high-throughput RNA sequencing , 2014, BMC Genomics.

[20]  E. Lai.  Micro RNAs are complementary to 3′ UTR sequence motifs that mediate negative post-transcriptional regulation , 2002, Nature Genetics.

[21]  Shankar Subramaniam,et al.  Evaluation of normalization methods in mammalian microRNA-Seq data. , 2012, RNA.

[22]  Aaron R. Quinlan,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2022 .

[23]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[24]  Patricia P. Chan,et al.  GtRNAdb: a database of transfer RNA genes detected in genomic sequence , 2008, Nucleic Acids Res..

[25]  Raymond F. Gesteland,et al.  Life Before DNA. (Book Reviews: The RNA World. The Nature of Modern RNA Suggests a Prebiotic RNA World.) , 1993 .

[26]  Lesley Cheng,et al.  The detection of microRNA associated with Alzheimer's disease in biological fluids using next-generation sequencing technologies , 2013, Front. Genet..

[27]  Sandrine Dudoit,et al.  Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.

[28]  F. Denoeud,et al.  Annotating genomes with massive-scale RNA sequencing , 2008, Genome Biology.

[29]  Zhiping Weng,et al.  MicroRNAs Located in the Hox Gene Clusters Are Implicated in Huntington's Disease Pathogenesis , 2014, PLoS genetics.

[30]  A. Antonini,et al.  Identification of circulating microRNAs for the differential diagnosis of Parkinson's disease and Multiple System Atrophy , 2014, Front. Cell. Neurosci..

[31]  Lesley Cheng,et al.  Exosomes provide a protective and enriched source of miRNA for biomarker profiling compared to intracellular and cell-free blood , 2014, Journal of extracellular vesicles.

[32]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[33]  C. Nelson,et al.  miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data , 2012, Nucleic acids research.

[34]  Stijn van Dongen,et al.  miRBase: microRNA sequences, targets and gene nomenclature , 2005, Nucleic Acids Res..

[35]  M. Tewari,et al.  MicroRNA profiling: approaches and considerations , 2012, Nature Reviews Genetics.

[36]  Alessandra Carbone,et al.  MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data , 2010, Bioinform..

[37]  J. Mattick,et al.  Non-coding RNA. , 2006, Human molecular genetics.

[38]  Bairong Shen,et al.  Performance comparison and evaluation of software tools for microRNA deep-sequencing data analysis , 2012, Nucleic acids research.

[39]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[40]  Thomas Tuschl,et al.  Comprehensive profiling of circulating microRNA via small RNA sequencing of cDNA libraries reveals biomarker potential and limitations , 2013, Proceedings of the National Academy of Sciences.

[41]  Qinghua Shi,et al.  mirTools 2.0 for non-coding RNA discovery, profiling, and functional annotation based on high-throughput sequencing , 2013, RNA biology.

[42]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[43]  Barry Merriman,et al.  Progress in Ion Torrent semiconductor chip based sequencing , 2012, Electrophoresis.

[44]  Gang Xu,et al.  mirTools: microRNA profiling and discovery based on high-throughput sequencing , 2010, Nucleic Acids Res..

[45]  Hsien-Da Huang,et al.  miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression , 2009, BMC Bioinformatics.

[46]  Eran Halperin,et al.  miRNAkey: a software for microRNA deep sequencing analysis , 2010, Bioinform..

[47]  J. Cooper,et al.  Systemic exosomal siRNA delivery reduced alpha-synuclein aggregates in brains of transgenic mice , 2014, Movement disorders : official journal of the Movement Disorder Society.

[48]  B. Meyers,et al.  Experimental design, preprocessing, normalization and differential expression analysis of small RNA sequencing experiments , 2011, Silence.

[49]  L. Vella.  The Emerging Role of Exosomes in Epithelial–Mesenchymal-Transition in Cancer , 2014, Front. Oncol..

[50]  Sai Lakshmi Subramanian,et al.  piRNABank: a web resource on classified and clustered Piwi-interacting RNAs , 2007, Nucleic Acids Res..

[51]  C. Rowe,et al.  Prognostic serum miRNA biomarkers associated with Alzheimer’s disease shows concordance with neuropsychological and neuroimaging assessment , 2014, Molecular Psychiatry.

[52]  A. Pasquinelli,et al.  Regulation by let-7 and lin-4 miRNAs Results in Target mRNA Degradation , 2005, Cell.

[53]  S. Thibodeau,et al.  Characterization of human plasma-derived exosomal RNAs by deep sequencing , 2013, BMC Genomics.

[54]  Charity W. Law,et al.  voom: precision weights unlock linear model analysis tools for RNA-seq read counts , 2014, Genome Biology.

[55]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[56]  E. Cuppen,et al.  Limitations and possibilities of small RNA digital gene expression profiling , 2009, Nature Methods.

[57]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

[58]  Ana M. Aransay,et al.  miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments , 2009, Nucleic Acids Res..

[59]  S. Caboche,et al.  Comparison of mapping algorithms used in high-throughput sequencing: application to Ion Torrent data , 2014, BMC Genomics.

[60]  N. Kosaka,et al.  Exosomes from bone marrow mesenchymal stem cells contain a microRNA that promotes dormancy in metastatic breast cancer cells , 2014, Science Signaling.

[61]  Daniel J. Blankenberg,et al.  Galaxy: a platform for interactive large-scale genome analysis. , 2005, Genome research.

[62]  Leo Goodstadt,et al.  Ruffus: a lightweight Python library for computational pipelines , 2010, Bioinform..

[63]  S. Lim,et al.  Hypoxic Tumor Cell Modulates Its Microenvironment to Enhance Angiogenic and Metastatic Potential by Secretion of Proteins and Exosomes* , 2010, Molecular & Cellular Proteomics.

[64]  D. Humphreys,et al.  miRspring: a compact standalone research tool for analyzing miRNA-seq data , 2013, Nucleic acids research.

[65]  Daniel J. Blankenberg,et al.  Galaxy: A Web‐Based Genome Analysis Tool for Experimentalists , 2010, Current protocols in molecular biology.

[66]  Laurent Gil,et al.  Ensembl 2013 , 2012, Nucleic Acids Res..

[67]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[68]  Paul Flicek,et al.  Sense from sequence reads: methods for alignment and assembly , 2009, Nature Methods.

[69]  M. Robinson,et al.  A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.

[70]  Wolfgang Huber,et al.  Love MI, Huber W, Anders S.. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biol 15: 550 , 2014 .

[71]  Joel Dudley,et al.  Bioinformatics software for biologists in the genomics era , 2007, Bioinform..

[72]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[73]  Athanasios K. Tsakalidis,et al.  Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role , 2013, J. Biomed. Informatics.

[74]  W. Jiang,et al.  Differentiation of tumour-promoting stromal myofibroblasts by cancer exosomes , 2014, Oncogene.

[75]  A. Hill,et al.  Prion‐infected cells regulate the release of exosomes with distinct ultrastructural features , 2012, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.