Header Leaderboard Ad


Genotyping problem using VarScan 2



No announcement yet.
  • Filter
  • Time
  • Show
Clear All
new posts

  • Genotyping problem using VarScan 2


    Tried to use VarScan 2 to get SNP calls (tumor/normal paired samples). However, I don't think the output makes sense.

    For example, the pileup file for the normal looks like,

    chr1 11765 N 6 GgGGGG [email protected]?AF
    chr1 11766 N 6 TtTTTT AG6:4E
    chr1 11767 N 6 GgGGGG CEBA=F
    chr1 11768 N 6 TtTTTT [email protected];AG

    The VarScan 2 output:

    chrom position ref var normal_reads1 normal_reads2 normal_var_freq normal_gt tumor_reads1
    tumor_reads2 tumor_var_freq tumor_gt somatic_status variant_p_value somatic_p_value tumor_reads1_plus
    tumor_reads1_minus tumor_reads2_plus tumor_reads2_minus

    chr1 11766 N T 0 9 100% T 0 9 100% T Germline 1.101911
    9461502356E-10 1.0 0 0 6 3 0 0 7 2

    Not sure what went wrong when the pileup file show read count is 6 (bold) at location 11766, but the VarScan has a count of 9 (bold).


  • #2
    Any suggestions?

    By the way, the script I used is,

    java -jar VarScan.v2.3.3.jar somatic pileup.file1 pileup.file2 output.file



    • #3

      That's a bit puzzling... it looks like you didn't provide a reference to SAMtools mpileup (-f) when generating the pileup file, which is why the reference base is always "N". This can cause some issues in read counting.

      Further, I can't replicate your result if I use the pileup and command that you provided, at least, not without having the tumor pileup. Would you send me your pileup files for tumor and normal?

      Also, we're kind of moving away from the two individual pileup files model for VarScan. It's still supported, but I think you're better off running a single mpileup command with both samples, and then calling VarScan somatic with the --mpileup 1 option:
      samtools mpileup -f reference.fa normal.bam tumor.bam >both.mpileup
      java -jar VarScan.jar somatic both.mpileup both.varScan.output --mpileup 1


      Latest Articles


      • seqadmin
        A Brief Overview and Common Challenges in Single-cell Sequencing Analysis
        by seqadmin

        ​​​​​​The introduction of single-cell sequencing has advanced the ability to study cell-to-cell heterogeneity. Its use has improved our understanding of somatic mutations1, cell lineages2, cellular diversity and regulation3, and development in multicellular organisms4. Single-cell sequencing encompasses hundreds of techniques with different approaches to studying the genomes, transcriptomes, epigenomes, and other omics of individual cells. The analysis of single-cell sequencing data i...

        01-24-2023, 01:19 PM
      • seqadmin
        Introduction to Single-Cell Sequencing
        by seqadmin
        Single-cell sequencing is a technique used to investigate the genome, transcriptome, epigenome, and other omics of individual cells using high-throughput sequencing. This technology has provided many scientific breakthroughs and continues to be applied across many fields, including microbiology, oncology, immunology, neurobiology, precision medicine, and stem cell research.

        The advancement of single-cell sequencing began in 2009 when Tang et al. investigated the single-cell transcriptomes
        01-09-2023, 03:10 PM