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High quality variant calls from multiple dog genome project - Run1

ABSTRACT: High quality variant discovery using multiple dog breeds - Run1: Paired-end fastq files were aligned to Canis lupus familaris reference genome version 3.1 using bwa mem. Duplicates were marked using Picardtools Markdup. Local realignment around indels was performed using GATK tools RealignerTargetCreator and IndelRealigner. Variant calling was done using GATK Unified Genotyper. Filters were applied based GATK best practice. These filters included remove variants with 2 or more alleles, remove variants never observed on forward or reverse strands, remove variants with overall quality less than phred score 20, remove variants with mapping quality less than phred score 30, remove variants with the same base pair position, remove lower quality indel when indels closer than 10 base pairs, remove lower quality variant where variants closer than 3 base pairs, remove SNP within 5bp of an indel. An intersect set containing those variants concordant between Samtools and GATK predictions was extracted from this union set using GATK SelectVariants to produce the final VCF files. (Software: bwa-0.7.12,GATK-3.6,samtools-1.3+htslib-1.2.1)

INSTRUMENT(S): Illumina HiSeq 2500

ORGANISM(S): Canis Lupus Familiaris

SUBMITTER: Unibe - Institute of Genetics

PROVIDER: PRJEB24066 | EVA | 2018-01-10

REPOSITORIES: EVA

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Dataset's files

Source:

			Action	DRS
	dogs.557.publicSamples.ann.vcf.gz	Vcf
	dogs.557.publicSamples.ann.vcf.gz.tbi	Vcf

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Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.

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High quality variant calls from multiple dog genome project - Run1

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