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  • Has anyone tried RUM for aligning/counting Illumina RNA-Seq data?

    2



    The publication for this has just arrived: http://rna-seqblog.com/data-analysis...ed-mapper-rum/

    Based on speed and accuracy, it looks like a contender. Comparable to GSNAP in the accuracy stakes whilst being considerably faster.

    I'm, wondering if anyone has trialled it yet? I ran a simulated data set through it just to see how it ran and it went both fast and smoothly. I am unsure as to how the read counts are calculated for closely related isoforms (i.e. how it distinguishes between them). I contacted the author about it but got no response.

    Has anyone else looked into this? Can you offer any thoughts?

    Thanks in advance.

  • #2
    Hi Fabrice,

    I am the developer of GSNAP. Regarding the speed of GSNAP, the latest version (starting with 2011-08-15) is about 4-8 times faster than the one that was tested in that Bioinformatics paper. The latest version of GSNAP uses k-mer sizes of 15 by default, which accounts for the speed up over the previous k-mer size of 12.

    In addition, the latest version is more accurate, at least for paired-end reads, since I have incorporated the GMAP algorithm to handle difficult cases involving multiple indels or splicing.

    As always, you can download the latest source code for free at http://research-pub.gene.com/gmap.

    Regards,

    Thomas Wu

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    • #3
      Hi Thomas,

      I'm trying to look at both RUM and gsnap for RNA-seq alignments and compare to TopHat. gsnap looks like really nice software and I noticed user lh3 recommended it! What command line options would you recommend to align/map RNA-seq to human/mouse genomes for 1) Illumina 36bp single-end reads and 2) Illumina 76bp paired-end reads.

      Thanks, Chris

      Comment


      • #4
        Hi Chris,

        I tend to run GSNAP myself with mostly default options. However, for RNA-Seq, you need to tell the program to find novel splices with "-N 1", otherwise the program will think you have DNA-Seq data. You will also get better results at the ends of reads if you prepare a known splicesites file (see the README file for details), and use that with the "-s" flag to make use of known splice sites.

        You also may want to provide some reasonable value for an allowable number of mismatches with the "-m" flag. If you don't provide it, then GSNAP will pick a value that allows it to compute quickly, but it might pick a value too low for 36-mers. You might therefore select "-m 3" or "-m 4" or so for the 36-mers. For 76-mers, GSNAP will explore up to "-m 4", which is probably fine, or you could specify "-m 5".

        Regards,

        Tom

        Comment


        • #5
          Thanks for that Tom,

          A bit later, I also found some other links where other users that had different command line options:



          Application of sequencing to RNA analysis (RNA-Seq, whole transcriptome, SAGE, expression analysis, novel organism mining, splice variants)


          Chris

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