Specific sequence selection and next generation resequencing of 68 E. coli genes using HybSelect™

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

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.

Identification of novel SNPs in glioblastoma using targeted resequencing

High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other “omics” approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens.

Identification of factors required for meristem function in Arabidopsis using a novel next generation sequencing fast forward genetics approach

BACKGROUND

Phenotype-driven forward genetic experiments are powerful approaches for linking phenotypes to genomic elements but they still involve a laborious positional cloning process. Although sequencing of complete genomes now becomes available, discriminating causal mutations from the enormous amounts of background variation remains a major challenge.

METHOD

To improve this, we developed a universal two-step approach, named 'fast forward genetics', which combines traditional bulk segregant techniques with targeted genomic enrichment and next-generation sequencing technology

RESULTS

As a proof of principle we successfully applied this approach to two Arabidopsis mutants and identified a novel factor required for stem cell activity.

CONCLUSION

We demonstrated that the 'fast forward genetics' procedure efficiently identifies a small number of testable candidate mutations. As the approach is independent of genome size, it can be applied to any model system of interest. Furthermore, we show that experiments can be multiplexed and easily scaled for the identification of multiple individual mutants in a single sequencing run.

Comprehensive assessment of sequence variation within the copy number variable defensin cluster on 8p23 by target enriched in-depth 454 sequencing

Background

In highly copy number variable (CNV) regions such as the human defensin gene locus, comprehensive assessment of sequence variations is challenging. PCR approaches are practically restricted to tiny fractions, and next-generation sequencing (NGS) approaches of whole individual genomes e.g. by the 1000 Genomes Project is confined by an affordable sequence depth. Combining target enrichment with NGS may represent a feasible approach.

Results

As a proof of principle, we enriched a ~850 kb section comprising the CNV defensin gene cluster DEFB, the invariable DEFA part and 11 control regions from two genomes by sequence capture and sequenced it by 454 technology. 6,651 differences to the human reference genome were found. Comparison to HapMap genotypes revealed sensitivities and specificities in the range of 94% to 99% for the identification of variations.

Using error probabilities for rigorous filtering revealed 2,886 unique single nucleotide variations (SNVs) including 358 putative novel ones. DEFB CN determinations by haplotype ratios were in agreement with alternative methods.

Conclusion

Although currently labor extensive and having high costs, target enriched NGS provides a powerful tool for the comprehensive assessment of SNVs in highly polymorphic CNV regions of individual genomes. Furthermore, it reveals considerable amounts of putative novel variations and simultaneously allows CN estimation.

Targeted next-generation sequencing for the molecular genetic diagnostics of cardiomyopathies

BACKGROUND

Today, mutations in more than 30 different genes have been found to cause inherited cardiomyopathies, some associated with very poor prognosis. However, because of the genetic heterogeneity and limitations in throughput and scalability of current diagnostic tools up until now, it is hardly possible to genetically characterize patients with cardiomyopathy in a fast, comprehensive, and cost-efficient manner.

METHODS AND RESULTS

We established an array-based subgenomic enrichment followed by next-generation sequencing to detect mutations in patients with hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). With this approach, we show that the genomic region of interest can be enriched by a mean factor of 2169 compared with the coverage of the whole genome, resulting in high sequence coverage of selected disease genes and allowing us to define the genetic pathogenesis of cardiomyopathies in a single sequencing run. In 6 patients, we detected disease-causing mutations, 2 microdeletions, and 4 point mutations. Furthermore, we identified several novel nonsynonymous variants, which are predicted to be harmful, and hence, might be potential disease mutations or modifiers for DCM or HCM.

CONCLUSIONS

The approach presented here allows for the first time a comprehensive genetic screening in patients with hereditary DCM or HCM in a fast and cost-efficient manner.

Targeted high throughput sequencing of a cancer-related exome subset by specific sequence capture with a fully automated microarray platform

Sequence capture methods for targeted next generation sequencing promise to massively reduce cost of genomics projects compared to untargeted sequencing. However, evaluated capture methods specifically dedicated to biologically relevant genomic regions are rare. Whole exome capture has been shown to be a powerful tool to discover the genetic origin of disease and provides a reduction in target size and thus calculative sequencing capacity of >90-fold compared to untargeted whole genome sequencing. For further cost reduction, a valuable complementing approach is the analysis of smaller, relevant gene subsets but involving large cohorts of samples. However, effective adjustment of target sizes and sample numbers is hampered by the limited scalability of enrichment systems. We report a highly scalable and automated method to capture a 480 Kb exome subset of 115 cancer-related genes using microfluidic DNA arrays. The arrays are adaptable from 125 Kb to 1 Mb target size and/or one to eight samples without barcoding strategies, representing a further 26 - 270-fold reduction of calculative sequencing capacity compared to whole exome sequencing. Illumina GAII analysis of a HapMap genome enriched for this exome subset revealed a completeness of >96%. Uniformity was such that >68% of exons had at least half the median depth of coverage. An analysis of reference SNPs revealed a sensitivity of up to 93% and a specificity of 98.2% or higher.