Header Leaderboard Ad

Collapse

6-99bp indels with BWA/GATK

Collapse

Announcement

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

  • 6-99bp indels with BWA/GATK

    Hi,

    I am using BWA and GATK to detect mutations in BRCA1. The BRCA1 sequences have been Sanger validated and contain known mutations. I am achieving a fair degree of accuracy so far, successfully detecting 99% of SNPs and over 90% of Indels. The majority of false negatives are for Indels over 5 bp in size. These range from 6-99bp in length. Can anyone recommend what command line parameters/values could be used to get the aligner to pick up some of the larger indels?

    Thanks in advance.

  • #2
    I am now getting all Indels up to 29bp in length. I achieved this by increasing the maximum number of permitted gap extensions with bwa aln -e 50.

    I will continue to experiment in order to get the larger indels.

    Comment


    • #3
      Do you perform a base recalibration step with GATK before calling indels?

      Comment


      • #4
        Originally posted by genericforms View Post
        Do you perform a base recalibration step with GATK before calling indels?
        Indeed I do.

        Comment


        • #5
          I have been trying to call indels with GATK UnifiedGenotyper from BWA-mapped BAMs for some time now, but with no success.

          Did you have to use anything outside of the default parameters with UnifiedGenotyper or COuntCovariates/TableRecalibration? Others with this problem have found that it could be sequencing error rates in the sample were too high.

          If you dont mind, could you post a couple command lines from your pipeline? I'm particularly interested in your UnifiedGenotyper and base recalibration commands. It would be an immense help.

          Comment


          • #6
            Originally posted by oiiio View Post
            I have been trying to call indels with GATK UnifiedGenotyper from BWA-mapped BAMs for some time now, but with no success.

            Did you have to use anything outside of the default parameters with UnifiedGenotyper or COuntCovariates/TableRecalibration? Others with this problem have found that it could be sequencing error rates in the sample were too high.

            If you dont mind, could you post a couple command lines from your pipeline? I'm particularly interested in your UnifiedGenotyper and base recalibration commands. It would be an immense help.
            I am pretty sure my commands are very standard. Nonetheless, you are welcome to have a look!

            for file in *fastq; do bwa aln -e 50 -f ${file%%.fastq}.sai chr17hg19 ${file}; done

            for file in *sai; do bwa samse chr17hg19 ${file} ${file%%.sai}.fastq > ${file%%.sai}.sam; done

            for file in *bam; do java -Xmx3g -jar /home/goliver/ngs_software/picard-tools-1.53/SortSam.jar I=${file} O=${file%%.bam}_sorted.bam SO=coordinate; done

            for file in *_sorted.bam; do java -Xmx3g -jar /home/goliver/ngs_software/picard-tools-1.53/MarkDuplicates.jar I=${file} O=${file%%.bam}_ndup.bam M=metric TMP_DIR=./tmp REMOVE_DUPLICATES=TRUE VALIDATION_STRINGENCY=LENIENT; done

            for file in *ndup.bam; do java -jar /home/goliver/ngs_software/picard-tools-1.53/AddOrReplaceReadGroups.jar I=${file} O=${file%%.bam}_rg.bam SO=coordinate ID=1 LB=Z PL=illumina PU=Z SM=Z; done

            for file in *rg.bam; do java -Xmx3g -jar /home/goliver/ngs_software/picard-tools-1.53/BuildBamIndex.jar I=${file} O=${file}.bai; done

            for file in *rg.bam; do java -Xmx3g -jar /home/goliver/ngs_software/GenomeAnalysisTK-1.2-24-g6478681/GenomeAnalysisTK.jar -T RealignerTargetCreator -R ../ref_chr17.hg19.fa -o ${file%%.bam}.intervals -I ${file}; done

            for file in *rg.bam; do java -Xmx3g -jar /home/goliver/ngs_software/GenomeAnalysisTK-1.2-24-g6478681/GenomeAnalysisTK.jar -I ${file} -R ../ref_chr17.hg19.fa -T IndelRealigner -o ${file%%.bam}_2.bam -targetIntervals ${file%%.bam}.intervals --known ../GATK/dbsnp_132.b37.vcf; done

            for file in *_2.bam; do java -Xmx20g -jar /home/goliver/ngs_software/GenomeAnalysisTK-1.2-24-g6478681/GenomeAnalysisTK.jar -R ../ref_chr17.hg19.fa -knownSites ../GATK/dbsnp_132.b37.vcf -I ${file} -T CountCovariates -cov QualityScoreCovariate -cov DinucCovariate -cov ReadGroupCovariate -cov CycleCovariate -recalFile ${file%%.bam}.recal.csv --default_read_group 1 --default_platform illumina -nt 4; done

            for file in *_2.bam; do java -Xmx3g -jar /home/goliver/ngs_software/GenomeAnalysisTK-1.2-24-g6478681/GenomeAnalysisTK.jar -l INFO -R ../ref_chr17.hg19.fa -T TableRecalibration -I ${file} -o ${file%%.bam}.final.bam -recalFile ${file%%.bam}.recal.csv --default_read_group 1 --default_platform illumina; done

            for file in *final.bam; do java -Xmx3g -jar /home/goliver/ngs_software/GenomeAnalysisTK-1.2-24-g6478681/GenomeAnalysisTK.jar -T UnifiedGenotyper -glm BOTH -I ${file} -R ../ref_chr17.hg19.fa -o ${file%%.bam}.vcf; done

            Comment


            • #7
              Do you have any paired-end data as opposed to single-ended as you methods suggest? The indel alignment should be better with paired-ends than single ends

              Comment


              • #8
                Originally posted by Jon_Keats View Post
                Do you have any paired-end data as opposed to single-ended as you methods suggest? The indel alignment should be better with paired-ends than single ends
                This particular dataset is all single end. I am pretty certain the larger indels can still be detected though...

                Comment

                Latest Articles

                Collapse

                • 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

                ad_right_rmr

                Collapse
                Working...
                X