84a3a0b2-e91f-4115-9dd3-e2e415c61f1c - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001005478 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:LBC1921 and LBC1936 GVCFs called with GATK's HaplotypeCaller were combined and subject to variant quality score recalibration. This VCF contains the subset of samples (n = 296) from the LBC1921 cohort.
| EGAD00001005477 | 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 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: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.
2012-03-03 | E-GEOD-36217 | biostudies-arrayexpress
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 “recalibrated” (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 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.
2012-03-03 | GSE36217 | GEO
Project description:Using a public reference data set of 82 unique entities, 382 nanopore-sequenced brain tumor samples were classified based on their methylation status through an ad hoc random forest algorithm. As a measure of confidence, score recalibration was performed and platform-specific thresholds were defined.
2022-07-30 | GSE209865 | GEO
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:<p>Genetic analysis of patients with Inherited Retinal Dystrophies (IRDs) was carried out by performing Whole Genome Sequencing (WGS). The main purpose of this study is to identify simple and complex mutations responsible for IRD in patients. </p> <p>WGS was performed on selected affected and unaffected individuals using the Illumina HiSeqX10. The reads were aligned to human genome 19 (hg19) and variant calling was performed using Genome Analysis Toolkit (GATK). The genotyping quality of single nucleotide variants (SNVs) and indels was assessed using the variant quality score recalibration approach implemented in GATK. Copy number variations (CNVs) were called using Genome STRiP and SpeedSeq. </p> <p>This large set of whole genome sequencing data from different ethnicity can be stored and shared through dbGaP. This data could serve as a source for checking frequencies of variants or the pathogenicity of selected variants in different ethnicities. </p>
| phs001619 | dbGaP
Project description:Despite the widespread adoption of ChIP-seq there is still no consensus on quality assessment metrics. No single published metric can reliably discriminate the success or failure of an experiment, thus hampering objectivity and reproducibility of quality control. We introduce a new framework for ChIP-seq data quality assessment that overcomes the limitation of previous solutions. Our tool called "ChIC" incorporates a novel set of quality control metrics integrated into one single score summarizing the sample quality and a reference compendium with thousands of published ChIP-seq samples, for easier evaluation of new data. This test dataset contain an example of succesfull and non-succesfull ChIP-seq sample for mouse H3K27me3.
2024-12-12 | GSE119932 | GEO
Project description:Improvement in detection of minor alleles in next generation sequencing by base quality recalibration
| PRJEB11001 | ENA