VARUS

VARUS is a software tool that automates selecting, downloading, and aligning RNA-Seq data from NCBI's Sequence Read Archive (SRA) for a given species and genome. Its main features and advantages include:

1. Automated data selection: VARUS chooses the most suitable RNA-Seq runs from the SRA, eliminating the need for manual selection.

2. Efficient data sampling: The tool employs an online algorithm to randomly select subsets of reads, aiming to cover a large number of transcripts adequately while considering limited network bandwidth and computing resources.

3. Improved sensitivity and specificity: VARUS achieved better sensitivity and specificity in most tested species with fewer downloaded reads compared to manual run selection.

4. Compatibility with genome annotation tools: RNA-Seq data sampled with VARUS is well-suited for fully automatic genome annotation using tools like BRAKER.

5. Automation of genome annotation: VARUS enables the complete automation of the genome annotation process, including the selection and quality control of RNA-Seq data, without compromising accuracy compared to manually supervised annotation.

Topic

RNA-Seq;Gene transcripts;Model organisms;Transcriptomics;Sequence assembly

Detail

  • Operation: Deposition;De-novo assembly;Transcriptome assembly;Genome annotation

  • Software interface: Command-line user interface

  • Language: C++,Perl

  • License: GNU General Public License, version 3

  • Cost: Free of charge with restrictions

  • Version name: v1.0.0

  • Credit: Deutsche Forschungsgemeinschaft (DFG), US National Institutes of Health.

  • Input: -

  • Output: -

  • Contact: Mario Stanke mario.stanke@uni-greifswald.de

  • Collection: -

  • Maturity: -

Publications

  • VARUS: sampling complementary RNA reads from the sequence read archive.
  • Stanke M, et al. VARUS: sampling complementary RNA reads from the sequence read archive. VARUS: sampling complementary RNA reads from the sequence read archive. 2019; 20:558. doi: 10.1186/s12859-019-3182-x
  • https://doi.org/10.1186/s12859-019-3182-x
  • PMID: 31703556
  • PMC: PMC6842140

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