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Analysing .ace and .bam files for SNPs

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  • Analysing .ace and .bam files for SNPs

    Hi

    I have imported .ace files from 454 sequencing into both Geneious and Seqman pro. I want to find SNPs in reads and currently the analysis only highlights insertions and deletions. This is because the reads import with gaps (dashed lines) for every single variation, which means that for my ~6kb sequence I have an 18kb contig. How can I remove these gaps so that the SNP analysis works properly?

    I would appreciate any help with this!

    Thanks

  • #2
    It sounds like you are running into the issue where Newbler tends to use a deletion/insertion rather than a mismatch for a SNP. Is that a fair guess?

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    • #3
      Yes that's right. Is there no way to get around this?

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      • #4
        I've found a thread discussing this problem....
        http://seqanswers.com/forums/archive...hp/t-1009.html

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        • #5
          That issue drove me crazy for a bit. We ended up using alternative 454 mappers for alignment, eg CLC genomics workbench, bwa-sw, or others discussed on this forum.

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          • #6
            It has been driving me crazy too! What was your experience from using alternative mappers? Have you attempted to use MIRA, which was mentioned in the other post?

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            • #7
              Originally posted by kirstyn View Post
              It has been driving me crazy too! What was your experience from using alternative mappers? Have you attempted to use MIRA, which was mentioned in the other post?
              Should do the job. Leads you on the Staden/gap5 track.
              If you are familiar with Consed, you could also use it for your mapping of 454 data (it internally uses cross_match).

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              • #8
                Otherwise novoalign should do the job pretty well, and the free version should be fast enough.
                Keep in mind they automatically trim to 160bp (or used to at least), which should be disabled for 454 reads.

                I have always had problems with bwa-sw and segmentation faults.

                Clcgenomics worked well but was very expensive.

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