GATCI_WXS_germline - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001005916 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Exome sequences were aligned to the GRCH38 reference genome. Aligned sequence was analyzed with GATK/SomaticSniper, to generate somatic variant calls. Somatic variant calls are in VCF format. Details for the mutect call can be found in the vcf header.
| EGAD00001005918 | EGA
Project description:Exome sequences were aligned to the GRCH38 reference genome. Aligned sequence was analyzed with GATK/MuTect, to generate somatic variant calls. Somatic variant calls are in VCF format. Details for the mutect call can be found in the vcf header.
| EGAD00001005917 | EGA
Project description:Sequence was aligned to the GRCH38 reference genome. Aligned sequence was analyzed with GATK Haplotype Caller, to generate germline variant calls across the SureSelect All Exon V5+UTR target region. Variant calls are in VCF format. In total there are samples from 173 donors. 101 donors have calls generated from both normal and tumour samples tumour samples, 94 of which have a matched normal. Details for the call can be found in the vcf headers.
| EGAD00001004127 | EGA
Project description:Sequence was aligned to the GRCH38 reference genome. Aligned sequence was analyzed with GATK/MuTect, to generate somatic variant calls across the SureSelect All Exon V5+UTR target region. Somatic variant calls are in VCF format. In total there are 166 tumour samples, 94 of which have a matched normal. Somatic variants for tumours without a matched normal, were called against a panel of normals. Details for the mutect call can be found in the vcf header.
| EGAD00001004129 | EGA
Project description:This experiment was conducted to generate targeted resequencing data covering a region associated with osteosarcoma in greyhounds. 8 greyhounds diagnosed with osteosarcoma and 7 greyhounds without tumors were sequenced. DNA from the 15 dogs was used to prepare libraries and hybrid capture performed to enrich the region of interest prior to paired-end sequencing using Illumina Genome Analyzer II. The reads were aligned to the dog-genome CanFam2.0 using bwa and pre-processed using Picard and GATK. Variant discovery was performed using GATK. The resulting list of variants were used in the study to finemap the associated region and look for causal variants. We submit the preprocessed BAM-files that still have all reads although some reads are flagged. We also submit the resulting vcf-file with called and filtered variants in all individuals.
2013-11-16 | E-MTAB-1984 | biostudies-arrayexpress
Project description:Sequence data in fastq format was aligned to the GRCh38 reference genome with BWA-MEM and preprocessed with GATK for indel realignment and base quality score recalibration. Aligned sequence was analyzed with GATK HaplotypeCaller to generate germline variant calls. Variant calls are in VCF format. In total, there are 60 tumour samples from 38 patients, all with matched normal. Further details can be found in the vcf headers
| EGAD00001005817 | EGA
Project description:Sequence data in fastq format was aligned to the GRCh38 reference genome with BWA-MEM and preprocessed with GATK for indel realignment and base quality score recalibration. Aligned sequence was analyzed with SomaticSniper to generate somatic variant calls. Variant calls are in VCF format. In total, there are 60 tumour samples from 38 patients, all with matched normal. Further details can be found in the vcf headers
| EGAD00001005818 | EGA
Project description:Sequence data in fastq format was aligned to the GRCh38 reference genome with BWA-MEM and preprocessed with GATK for indel realignment and base quality score recalibration. Aligned sequence was analyzed with MuTect to generate somatic variant calls. Variant calls are in VCF format. In total, there are 60 tumour samples from 38 patients, all with matched normal. Further details can be found in the vcf headers.
| EGAD00001005822 | EGA
Project description:Next generation sequencing platforms have become essential tools for understanding DNA in a wide range of contexts. Their success heavily relies on the accuracy, sensitivity and specificity of methods used to discern differences between the reference genome and genomes under investigation. Here we compare the relative performances of five popular single nucleotide variant callers with and without their associated recommended hard filtering criteria. We compare: FreeBayes; the Genome Analysis Toolkit’s Haplotype Caller and Unified Genotyper; SAMtools; and VarScan. We tailor this comparison to suit smaller projects with modest sample numbers (n = 10) and coverage (~10X) to fill a current gap in the literature. Other comparison studies are generally applicable only to larger projects in model species, where there is access to large amounts of sequencing data and curated callsets for base and variant quality score recalibration. We estimated the accuracy, sensitivity and specificity of each pipeline according to the genotype concordance rate and number with the “truth” dataset for 10 canine samples. The truth dataset was defined as genotypes obtained from the CanineHD BeadChip array. Whole genome sequencing data was performed on the Illumina HiSeq2000 or HiSeq2500 platform as 100-101 base pair, paired end reads to an average sample coverage of 10.3X. Apart from GATK Haplotype Caller, applying recommended hard filters did not improve the performance of genotyping concordance at the tested levels of minimum coverage. The default VarScan pipeline with no additional filters applied (VarScan uses SAMtools mpileup, without base alignment quality computation) generally outperformed other callers in terms of accuracy, sensitivity and specificity. The results of this study demonstrate that hard filtering of variant calls from low-powered genome studies can impair accuracy, sensitivity and specificity of callsets and provides some benchmark performance metrics on a range of low coverage levels.
2018-08-31 | GSE117010 | GEO
Project description:DNA was isolated from whole red blood cells from various lines and crosses of broiler chickens. DNA was genotyped using Axiom genome-wide chicken array and cel files were analyzed using Axiom Analysis Suite Software (version 3.0.1) with Gallus gallus 5.0 using the software's Best Practices for agricultural animals. The results were exported (Genotyping_Data-3-21-2018.vcf) for all genotype calls and text file of all SNPs with >= 97% call rate rate was also produced for filtering the VCF file (ALL_SNPSs_with_Call_Rate_97_Plus_3-21-2018).
2019-05-25 | GSE131764 | GEO