Literature DB >> 33835454

RAP: A Web Tool for RNA-Seq Data Analysis.

Mattia D'Antonio1, Pietro Libro2, Ernesto Picardi3,4,5, Graziano Pesole3,4,5, Tiziana Castrignanò6.   

Abstract

Since 1950 main studies of RNA regarded its role in the protein synthesis. Later insights showed that only a small portion of RNA codes for proteins where the rest could have different functional roles. With the advent of Next Generation Sequencing (NGS) and in particular with RNA-seq technology the cost of sequencing production dropped down. Among the NGS application areas, the transcriptome analysis, that is, the analysis of transcripts in a cell, their quantification for a specific developmental stage or treatment condition, became more and more adopted in the laboratories. As a consequence in the last decade new insights were gained in the understanding of both transcriptome complexity and involvement of RNA molecules in cellular processes. For what concerns computational advances, bioinformatics research developed new methods for analyzing RNA-seq data. The comparison among transcriptome profiles from several samples is often a difficult task for nonexpert programmers. Here, in this chapter, we introduce RAP (RNA-Seq Analysis Pipeline), a completely automated web tool for transcriptome analysis. It is a user-friendly web tool implementing a detailed transcriptome workflow to detect differential expressed genes and transcript, identify spliced junctions and constitutive or alternative polyadenylation sites and predict gene fusion events. Through the web interface the researchers can get all this information without any knowledge of the underlying High Performance Computing infrastructure.

Keywords:  Alternative splicing sites; Bioinformatics; Fusion transcripts; Genomics; HPC; RNA-Seq; Transcriptomics

Year:  2021        PMID: 33835454     DOI: 10.1007/978-1-0716-1307-8_21

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  6 in total

1.  ChimeraScan: a tool for identifying chimeric transcription in sequencing data.

Authors:  Matthew K Iyer; Arul M Chinnaiyan; Christopher A Maher
Journal:  Bioinformatics       Date:  2011-08-11       Impact factor: 6.937

2.  Ligand binding and protein dynamics: a fluorescence depolarization study of aspartate transcarbamylase from Escherichia coli.

Authors:  C A Royer; P Tauc; G Hervé; J C Brochon
Journal:  Biochemistry       Date:  1987-10-06       Impact factor: 3.162

3.  Differential analysis of gene regulation at transcript resolution with RNA-seq.

Authors:  Cole Trapnell; David G Hendrickson; Martin Sauvageau; Loyal Goff; John L Rinn; Lior Pachter
Journal:  Nat Biotechnol       Date:  2012-12-09       Impact factor: 54.908

4.  Differential expression analysis for sequence count data.

Authors:  Simon Anders; Wolfgang Huber
Journal:  Genome Biol       Date:  2010-10-27       Impact factor: 13.583

5.  Big Data: Astronomical or Genomical?

Authors:  Zachary D Stephens; Skylar Y Lee; Faraz Faghri; Roy H Campbell; Chengxiang Zhai; Miles J Efron; Ravishankar Iyer; Michael C Schatz; Saurabh Sinha; Gene E Robinson
Journal:  PLoS Biol       Date:  2015-07-07       Impact factor: 8.029

6.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.

Authors:  Daehwan Kim; Geo Pertea; Cole Trapnell; Harold Pimentel; Ryan Kelley; Steven L Salzberg
Journal:  Genome Biol       Date:  2013-04-25       Impact factor: 13.583

  6 in total

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