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Benchmarker for Evaluating the Effectiveness of RNA-Seq Software

eagle-i ID


Resource Type

  1. Algorithmic software component


  1. Resource Description
    "BEERS was designed to benchmark RNA-Seq alignment algorithms and also algorithms that aim to reconstruct different isoforms and alternate splicing from RNA-Seq data. By default BEERS simulates either mouse or human paired-end RNA-Seq data modeled on the illumina platform. It starts with a large number of gene models (approx 500K) taken from about ten different published annotation efforts, and then chooses a fixed number of these genes at random (30,000 by default). This avoids biasing for or against any particular set of annotations. BEERS then introduces substitutions, indels, alternate spice forms, sequencing errors, and intron signal. BEERS can also simulate strand specific reads. BEERS does not simulate quality scores. There are four configuration files required (available on website). BEERS can also be configured to use any set of gene models. Pre-built indexes for human refseq are given on the website. Using these indexes will generate a much tamer set of transcripts."
  2. Additional Name
  3. Contact
    Grant, Gregory R., Ph.D.
  4. Used by
    Penn Center for Bioinformatics
  5. Data Input
    Configuration files: gene models taken from about ten different published annotation efforts
  6. Data Output
    True alignments
  7. Data Output
    Sequence reads
  8. Data Output
    Simulated reads: junctions
  9. Data Output
    Full transcript information for each simulated transcript
  10. Software purpose
    Modeling and simulation objective
  11. Related Publication or Documentation
    Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)
  12. Website(s)
  13. Related Technique
    Bioinformatics analysis
  14. Related Technique
    RNA sequencing
  15. Developed by
    Grant, Gregory R., Ph.D.
  16. Coded in
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Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016