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  • Mudal
    Junior Member
    • Feb 2014
    • 2

    #1

    Reliable variant calling

    Hello!

    I'm new to PacBio and NGS sequence analysis, so I hope someone here can point me in the right direction.
    I have 1 SMRT cell worth of sequencing data (just a pilot for now).

    The sample is a mix of libraries (amplicons) of about 800 bp. Each of these libraries is derived from one and the same gene, but they've been through a mutagenic process and should carry some diversity = 1-10 SNPs per molecule, relative to the reference.
    We have a non-mutagenized wild-type library in the sample as well.

    All of these libraries are barcoded. The amplicon structure is:
    Barcode - Amplicon - Universal_primer.

    Even though barcoding was not exactly done according to PacBio barcoding recommendations, I feel resolving barcodes will not be a huge problem. However, calling variants might be difficult. Does anyone here have experience with reliably detecting SNPs in samples with similar characteristics?
  • phenotype
    Junior Member
    • Sep 2008
    • 8

    #2
    Hi Mudal,

    Here is a good place to start:

    GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.


    I think you might be able to use SMRT Analysis to do this. If not, the tutorial also walks you through command-line steps. Which ever you choose, you'll still need to download and install SMRT Analysis http://www.pacbiodevnet.com on some sort of linux server (https://github.com/PacificBioscience...-Analysis%3F):

    Comment

    • IonTom
      Member
      • Apr 2014
      • 32

      #3
      Maybe try the deepSNV library:
      This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC.

      Comment

      • Mudal
        Junior Member
        • Feb 2014
        • 2

        #4
        Originally posted by IonTom View Post
        Thanks. I aligned my reads to the reference using BLASR from the SMRTAnalysis software. From there, it was easy to demultiplex based on barcodes and get separate BAM files.
        I ended up using deepSNV and freebayes for variant calling.

        Comment

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