Grape

Grape analyzes RNA sequencing (RNA-Seq) data from Next Generation Sequencing (NGS) platforms to perform alignment, gene and transcript expression quantification, exon inclusion estimation, and novel transcript discovery for transcriptome characterization.


Key Features:

  • Versatile Input Support: Accepts raw reads in FASTA or FASTQ formats and prealigned reads in SAM/BAM format from various NGS platforms.
  • Modular Pipeline Design: Performs quality control and, for non-prealigned reads, aligns to a reference genome using integrated mapping tools and supports integration of alternative mapping and quantification tools that use common data interchange formats.
  • Comprehensive Analysis Capabilities: Estimates gene and transcript expression levels, calculates exon inclusion levels, and identifies novel transcripts.
  • Scalability: Operates on single computers and parallel computing clusters to accommodate large-scale studies.
  • Required Inputs: Accepts raw sequencing reads or prealigned reads, a reference genome, and corresponding gene and transcript annotations.

Scientific Applications:

  • Differential expression analysis: Enables gene- and transcript-level quantification for differential expression studies.
  • Alternative splicing studies: Provides exon inclusion level calculations to study alternative splicing events.
  • Novel transcript discovery: Detects previously unannotated transcripts within RNA-Seq datasets.

Methodology:

Computational steps explicitly include quality control of raw reads; mapping reads to a reference genome for non-prealigned data using integrated mapping tools; quantification of gene and transcript expression using integrated tools; calculation of exon inclusion levels; and detection of novel transcripts.

Topics

Details

Tool Type:
command-line tool
Operating Systems:
Linux
Programming Languages:
R, Java, Perl, Python
Added:
8/3/2017
Last Updated:
11/24/2024

Operations

Publications

Knowles DG, Röder M, Merkel A, Guigó R. Grape RNA-Seq analysis pipeline environment. Bioinformatics. 2013;29(5):614-621. doi:10.1093/bioinformatics/btt016. PMID:23329413. PMCID:PMC3582270.

Documentation

Links