Dataset Information

Improving mapping and SNP-calling performance in multiplexed targeted next-generation sequencing.

ABSTRACT: BACKGROUND: Compared to classical genotyping, targeted next-generation sequencing (tNGS) can be custom-designed to interrogate entire genomic regions of interest, in order to detect novel as well as known variants. To bring down the per-sample cost, one approach is to pool barcoded NGS libraries before sample enrichment. Still, we lack a complete understanding of how this multiplexed tNGS approach and the varying performance of the ever-evolving analytical tools can affect the quality of variant discovery. Therefore, we evaluated the impact of different software tools and analytical approaches on the discovery of single nucleotide polymorphisms (SNPs) in multiplexed tNGS data. To generate our own test model, we combined a sequence capture method with NGS in three experimental stages of increasing complexity (E. coli genes, multiplexed E. coli, and multiplexed HapMap BRCA1/2 regions). RESULTS: We successfully enriched barcoded NGS libraries instead of genomic DNA, achieving reproducible coverage profiles (Pearson correlation coefficients of up to 0.99) across multiplexed samples, with <10% strand bias. However, the SNP calling quality was substantially affected by the choice of tools and mapping strategy. With the aim of reducing computational requirements, we compared conventional whole-genome mapping and SNP-calling with a new faster approach: target-region mapping with subsequent 'read-backmapping' to the whole genome to reduce the false detection rate. Consequently, we developed a combined mapping pipeline, which includes standard tools (BWA, SAMtools, etc.), and tested it on public HiSeq2000 exome data from the 1000 Genomes Project. Our pipeline saved 12 hours of run time per Hiseq2000 exome sample and detected ~5% more SNPs than the conventional whole genome approach. This suggests that more potential novel SNPs may be discovered using both approaches than with just the conventional approach. CONCLUSIONS: We recommend applying our general 'two-step' mapping approach for more efficient SNP discovery in tNGS. Our study has also shown the benefit of computing inter-sample SNP-concordances and inspecting read alignments in order to attain more confident results.

SUBMITTER: Elsharawy A

PROVIDER: S-EPMC3563481 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving mapping and SNP-calling performance in multiplexed targeted next-generation sequencing.

Elsharawy Abdou A Forster Michael M Schracke Nadine N Keller Andreas A Thomsen Ingo I Petersen Britt-Sabina BS Stade Björn B Stähler Peer P Schreiber Stefan S Rosenstiel Philip P Franke Andre A

BMC genomics 20120822

<h4>Background</h4>Compared to classical genotyping, targeted next-generation sequencing (tNGS) can be custom-designed to interrogate entire genomic regions of interest, in order to detect novel as well as known variants. To bring down the per-sample cost, one approach is to pool barcoded NGS libraries before sample enrichment. Still, we lack a complete understanding of how this multiplexed tNGS approach and the varying performance of the ever-evolving analytical tools can affect the quality of ...[more]

PMID: 22913592

Similar Datasets

Project description:Single nucleotide polymorphisms (SNPs) are widely used in genome-wide association studies and population genetics analyses. Next-generation sequencing (NGS) has become convenient, and many SNP-calling pipelines have been developed for human NGS data. We took advantage of a gap knowledge in selecting the appropriated SNP calling pipeline to handle with high-throughput NGS data. To fill this gap, we studied and compared seven SNP calling pipelines, which include 16GT, genome analysis toolkit (GATK), Bcftools-single (Bcftools single sample mode), Bcftools-multiple (Bcftools multiple sample mode), VarScan2-single (VarScan2 single sample mode), VarScan2-multiple (VarScan2 multiple sample mode) and Freebayes pipelines, using 96 NGS data with the different depth gradients of approximately 5X, 10X, 20X, 30X, 40X, and 50X coverage from 16 Rhode Island Red chickens. The sixteen chickens were also genotyped with a 50K SNP array, and the sensitivity and specificity of each pipeline were assessed by comparison to the results of SNP arrays. For each pipeline, except Freebayes, the number of detected SNPs increased as the input read depth increased. In comparison with other pipelines, 16GT, followed by Bcftools-multiple, obtained the most SNPs when the input coverage exceeded 10X, and Bcftools-multiple obtained the most when the input was 5X and 10X. The sensitivity and specificity of each pipeline increased with increasing input. Bcftools-multiple had the highest sensitivity numerically when the input ranged from 5X to 30X, and 16GT showed the highest sensitivity when the input was 40X and 50X. Bcftools-multiple also had the highest specificity, followed by GATK, at almost all input levels. For most calling pipelines, there were no obvious changes in SNP numbers, sensitivities or specificities beyond 20X. In conclusion, (1) if only SNPs were detected, the sequencing depth did not need to exceed 20X; (2) the Bcftools-multiple may be the best choice for detecting SNPs from chicken NGS data, but for a single sample or sequencing depth greater than 20X, 16GT was recommended. Our findings provide a reference for researchers to select suitable pipelines to obtain SNPs from the NGS data of chickens or nonhuman animals.

Project description:BackgroundNext-generation sequencing (NGS) has enabled efficient high-resolution typing of human leukocyte antigen (HLA) genes with minimal ambiguity. Most commercially available assays amplify individual or subgroup of HLA genes by long-range PCR followed by library preparation and sequencing. The AllType assay simplifies the workflow by amplifying 11 transplant-relevant HLA genes in one PCR reaction. Here, we report the performance of this unique workflow evaluated using 218 genetically diverse samples.MethodsFive whole genes (HLA-A/B/C/DQA1/DPA1) and six near-whole genes (HLA-DRB1/DRB345/DQB1/DPB1; excluding exon 1 and part of intron 1) were amplified in a multiplexed, long-range PCR. Manual library preparation was performed per manufacturer's protocol, followed by template preparation and chip loading on the Ion Chef, and sequencing on the Ion S5 sequencer. Pre-specified rules for quality control and repeat testing were followed; technologists were blinded to the reference results. The concordance between AllType and reference results was determined at 2-field resolution. We also describe the ranges of input DNA and library concentrations, read number per sample and per locus, and key health metrics in relation to typing results.ResultsThe concordance rates were 98.6%, 99.8% and 99.9% at the sample (n = 218), genotype (n = 1688), and allele (n = 3376) levels, respectively. Three genotypes were discordant, all of which shared the same G group typing results with the reference. Most ambiguous genotypes (116 out of 144, 80.6%) were due to the lack of exon 1 and intron 1 coverage for HLA-DRB1/DRB345/DQB1/DPB1 genes. A broad range of input DNA concentrations and library concentrations were tolerated. Per sample read numbers were adequate for accurate genotyping. Per locus read numbers showed some inter-lot variations, and a trend toward improved inter-locus balance was observed with later lots of reagents.ConclusionThe AllType assay on the Ion Chef/Ion S5 platform offers a robust and efficient workflow for clinical HLA typing at the 2-field resolution. The multiplex PCR strategy simplifies the laboratory procedure without compromising the typing accuracy.

Dataset Information

Improving mapping and SNP-calling performance in multiplexed targeted next-generation sequencing.

Publications

Improving mapping and SNP-calling performance in multiplexed targeted next-generation sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets