From next-generation sequencing alignments to accurate comparison and validation of single-nucleotide variants: the pibase software

Whole-exome sequencing reveals novel cancer genes and actionable targets in biliary tract cancers in primary sclerosing cholangitis

BACKGROUND

People with primary sclerosing cholangitis (PSC) have a 20% lifetime risk of biliary tract cancer (BTC). Using whole-exome sequencing, we characterized genomic alterations in tissue samples from BTC with underlying PSC.

METHODS

We extracted DNA from formalin-fixed, paraffin-embedded tumor and paired nontumor tissue from 52 resection or biopsy specimens from patients with PSC and BTC and performed whole-exome sequencing. Following copy number analysis, variant calling, and filtering, putative PSC-BTC-associated genes were assessed by pathway analyses and annotated to targeted cancer therapies.

RESULTS

We identified 53 candidate cancer genes with a total of 123 nonsynonymous alterations passing filtering thresholds in 2 or more samples. Of the identified genes, 19% had not previously been implicated in BTC, including CNGA3, KRT28, and EFCAB5. Another subset comprised genes previously implicated in hepato-pancreato-biliary cancer, such as ARID2, ELF3, and PTPRD. Finally, we identified a subset of genes implicated in a wide range of cancers such as the tumor suppressor genes TP53, CDKN2A, SMAD4, and RNF43 and the oncogenes KRAS, ERBB2, and BRAF. Focal copy number variations were found in 51.9% of the samples. Alterations in potential actionable genes, including ERBB2, MDM2, and FGFR3 were identified and alterations in the RTK/RAS (p = 0.036), TP53 (p = 0.04), and PI3K (p = 0.043) pathways were significantly associated with reduced overall survival.

CONCLUSIONS

In this exome-wide characterization of PSC-associated BTC, we delineated both PSC-specific and universal cancer genes. Our findings provide opportunities for a better understanding of the development of BTC in PSC and could be used as a platform to develop personalized treatment approaches.

Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking

BACKGROUND

High-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling.

RESULTS

Our approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants.

CONCLUSIONS

Our findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.

Improving the filtering of false positive single nucleotide variations by combining genomic features with quality metrics

MOTIVATION

Technical errors in sequencing or bioinformatics steps and difficulties in alignment at some genomic sites result in false positive (FP) variants. Filtering based on quality metrics is a common method for detecting FP variants, but setting thresholds to reduce FP rates may reduce the number of true positive variants by overlooking the more complex relationships between features. The goal of this study is to develop a machine learning-based model for identifying FPs that integrates quality metrics with genomic features and with the feature interpretability property to provide insights into model results.

RESULTS

We propose a random forest-based model that utilizes genomic features to improve identification of FPs. Further examination of the features shows that the newly introduced features have an important impact on the prediction of variants misclassified by VEF, GATK-CNN, and GARFIELD, recently introduced FP detection systems. We applied cost-sensitive training to avoid errors in misclassification of true variants and developed a model that provides a robust mechanism against misclassification of true variants while increasing the prediction rate of FP variants. This model can be easily re-trained when factors such as experimental protocols might alter the FP distribution. In addition, it has an interpretability mechanism that allows users to understand the impact of features on the model's predictions.

AVAILABILITY AND IMPLEMENTATION

The software implementation can be found at https://github.com/ideateknoloji/FPDetect.

Stage IV Colorectal Cancer Patients with High Risk Mutation Profiles Survived 16 Months Longer with Individualized Therapies

Personalized treatment vs. standard of care is much debated, especially in clinical practice. Here we investigated whether overall survival differences in metastatic colorectal cancer patients are explained by tumor mutation profiles or by treatment differences in real clinical practice. Our retrospective study of metastatic colorectal cancer patients of confirmed European ancestry comprised 54 Americans and 54 gender-matched Germans. The Americans received standard of care, and on treatment failure, 35 patients received individualized treatments. The German patients received standard of care only. Tumor mutations, tumor mutation burden and microsatellite status were identified by using the FoundationOne assay or the IDT Pan-Cancer assay. High-risk patients were identified according to the mutational classification by Schell and colleagues. Results: Kaplan–Meier estimates show the high-risk patients to survive 16 months longer under individualized treatments than those under only standard of care, in the median (p < 0.001). Tumor mutation profiles stratify patients by risk groups but not by country. Conclusions: High-risk patients appear to survive significantly longer (p < 0.001) if they receive individualized treatments after the exhaustion of standard of care treatments. Secondly, the tumor mutation landscape in Americans and Germans is congruent and thus warrants the transatlantic exchange of successful treatment protocols and the harmonization of guidelines.

Rapid response of stage IV colorectal cancer with APC/TP53/KRAS mutations to FOLFIRI and Bevacizumab combination chemotherapy: a case report of use of liquid biopsy

Background

Liquid biopsies of blood plasma cell free DNA can be used to monitor treatment response and potentially detect mutations that are present in resistant clones in metastatic cancer patients.

Case presentation

In our non-interventional liquid biopsy study, a male patient in his fifties diagnosed with stage IV colorectal cancer and polytope liver metastases rapidly progressed after completing chemotherapy and deceased 8 months after diagnosis. Retrospective cell free DNA testing showed that the APC/TP53/KRAS major clone responded quickly after 3 cycles of FOLFIRI + Bevacizumab. Retrospective exome sequencing of pre-chemotherapy and post-chemotherapy tissue samples including metastases confirmed that the APC/TP53/KRAS and other major clonal mutations (GPR50, SLC5A, ZIC3, SF3A1 and others) were present in all samples. After the last chemotherapy cycle, CT imaging, CEA and CA19–9 markers validated the cfDNA findings of treatment response. However, 5 weeks later, the tumour had rapidly progressed.

Conclusion

As FOLFIRI+Bevacizumab has recently also been associated with sustained complete remission in a APC/TP53/KRAS triple-mutated patient, these driver genes should be tested and monitored in a more in-depth manner in future patients. Patients with metastatic disease should be monitored more closely during and after chemotherapy, ideally using cfDNA.

Identification and characterization of two functional variants in the human longevity gene FOXO3

FOXO3 is consistently annotated as a human longevity gene. However, functional variants and underlying mechanisms for the association remain unknown. Here, we perform resequencing of the FOXO3 locus and single-nucleotide variant (SNV) genotyping in three European populations. We find two FOXO3 SNVs, rs12206094 and rs4946935, to be most significantly associated with longevity and further characterize them functionally. We experimentally validate the in silico predicted allele-dependent binding of transcription factors (CTCF, SRF) to the SNVs. Specifically, in luciferase reporter assays, the longevity alleles of both variants show considerable enhancer activities that are reversed by IGF-1 treatment. An eQTL database search reveals that the alleles are also associated with higher FOXO3 mRNA expression in various human tissues, which is in line with observations in long-lived model organisms. In summary, we present experimental evidence for a functional link between common intronic variants in FOXO3 and human longevity.

FOXO3 is one of the few established longevity genes. Here, the authors fine-map the FOXO3-longevity association to two intronic SNPs and, using luciferase assays and EMSAs, show that these SNPs affect binding of transcription factors CTCF and SRF and associate with FOXO3 expression.

Fast and accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut

BACKGROUND

Detection of somatic mutations is one of the main goals of next generation DNA sequencing. A wide range of experimental systems are available for the study of spontaneous or environmentally induced mutagenic processes. However, most of the routinely used mutation calling algorithms are not optimised for the simultaneous analysis of multiple samples, or for non-human experimental model systems with no reliable databases of common genetic variations. Most standard tools either require numerous in-house post filtering steps with scarce documentation or take an unpractically long time to run. To overcome these problems, we designed the streamlined IsoMut tool which can be readily adapted to experimental scenarios where the goal is the identification of experimentally induced mutations in multiple isogenic samples.

METHODS

Using 30 isogenic samples, reliable cohorts of validated mutations were created for testing purposes. Optimal values of the filtering parameters of IsoMut were determined in a thorough and strict optimization procedure based on these test sets.

RESULTS

We show that IsoMut, when tuned correctly, decreases the false positive rate compared to conventional tools in a 30 sample experimental setup; and detects not only single nucleotide variations, but short insertions and deletions as well. IsoMut can also be run more than a hundred times faster than the most precise state of art tool, due its straightforward and easily understandable filtering algorithm.

CONCLUSIONS

IsoMut has already been successfully applied in multiple recent studies to find unique, treatment induced mutations in sets of isogenic samples with very low false positive rates. These types of studies provide an important contribution to determining the mutagenic effect of environmental agents or genetic defects, and IsoMut turned out to be an invaluable tool in the analysis of such data.

Early-onset Crohn's disease and autoimmunity associated with a variant in CTLA-4

OBJECTIVE

IBD is a group of complex, systemic disorders associated with intestinal inflammation and extraintestinal manifestations. Recent studies revealed Mendelian forms of IBD, which contributed significantly to our understanding of disease pathogenesis and the heritability of IBD.

DESIGN

We performed exome sequencing in a family with Crohn's disease (CD) and severe autoimmunity, analysed immune cell phenotype and function in affected and non-affected individuals, and performed in silico and in vitro analyses of cytotoxic T lymphocyte-associated protein 4 (CTLA-4) structure and function.

RESULTS

A novel missense variant was identified in CTLA4 encoding CTLA-4, a coinhibitory protein expressed by T cells and required for regulation of T cell activation. The residue affected by the mutation, CTLA-4 Tyr60, is evolutionarily highly conserved, and the identified Y60C variant is predicted to affect protein folding and structural stability and demonstrated to cause impaired CTLA-4 dimerisation and CD80 binding. Intestinal inflammation and autoimmunity in carriers of CTLA-4 Y60C exhibit incomplete penetrance with a spectrum of clinical presentations ranging from asymptomatic carrier status to fatal autoimmunity and intestinal inflammation. In a clinically affected CTLA-4 Y60C carrier, T cell proliferation was increased in vitro and associated with an increased ratio of memory to naive T cells in vivo, consistent with impaired regulation of T cell activation.

CONCLUSIONS

Our results support the concept that variants in CTLA4 provide the basis for a novel Mendelian form of early-onset CD associated with systemic autoimmunity. Incomplete penetrance of autoimmunity further indicates the presence of other genetic and/or environmental modifiers.

New technologies for DNA analysis--a review of the READNA Project

The REvolutionary Approaches and Devices for Nucleic Acid analysis (READNA) project received funding from the European Commission for 41/2 years. The objectives of the project revolved around technological developments in nucleic acid analysis. The project partners have discovered, created and developed a huge body of insights into nucleic acid analysis, ranging from improvements and implementation of current technologies to the most promising sequencing technologies that constitute a 3(rd) and 4(th) generation of sequencing methods with nanopores and in situ sequencing, respectively.

Genomics and drug profiling of fatal TCF3-HLF-positive acute lymphoblastic leukemia identifies recurrent mutation patterns and therapeutic options

TCF3-HLF-fusion positive acute lymphoblastic leukemia (ALL) is currently incurable. Employing an integrated approach, we uncovered distinct mutation, gene expression, and drug response profiles in TCF3-HLF-positive and treatment-responsive TCF3-PBX1-positive ALL. Recurrent intragenic deletions of PAX5 or VPREB1 were identified in constellation with TCF3-HLF. Moreover somatic mutations in the non-translocated allele of TCF3 and a reduction of PAX5 gene dosage in TCF3-HLF ALL suggest cooperation within a restricted genetic context. The enrichment for stem cell and myeloid features in the TCF3-HLF signature may reflect reprogramming by TCF3-HLF of a lymphoid-committed cell of origin towards a hybrid, drug-resistant hematopoietic state. Drug response profiling of matched patient-derived xenografts revealed a distinct profile for TCF3-HLF ALL with resistance to conventional chemotherapeutics, but sensitivity towards glucocorticoids, anthracyclines and agents in clinical development. Striking on-target sensitivity was achieved with the BCL2-specific inhibitor venetoclax (ABT-199). This integrated approach thus provides alternative treatment options for this deadly disease.

Vy-PER: eliminating false positive detection of virus integration events in next generation sequencing data

Several pathogenic viruses such as hepatitis B and human immunodeficiency viruses may integrate into the host genome. These virus/host integrations are detectable using paired-end next generation sequencing. However, the low number of expected true virus integrations may be difficult to distinguish from the noise of many false positive candidates. Here, we propose a novel filtering approach that increases specificity without compromising sensitivity for virus/host chimera detection. Our detection pipeline termed Vy-PER (Virus integration detection bY Paired End Reads) outperforms existing similar tools in speed and accuracy. We analysed whole genome data from childhood acute lymphoblastic leukemia (ALL), which is characterised by genomic rearrangements and usually associated with radiation exposure. This analysis was motivated by the recently reported virus integrations at genomic rearrangement sites and association with chromosomal instability in liver cancer. However, as expected, our analysis of 20 tumour and matched germline genomes from ALL patients finds no significant evidence for integrations by known viruses. Nevertheless, our method eliminates 12,800 false positives per genome (80× coverage) and only our method detects singleton human-phiX174-chimeras caused by optical errors of the Illumina HiSeq platform. This high accuracy is useful for detecting low virus integration levels as well as non-integrated viruses.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

Development of a high-resolution NGS-based HLA-typing and analysis pipeline

The human leukocyte antigen (HLA) complex contains the most polymorphic genes in the human genome. The classical HLA class I and II genes define the specificity of adaptive immune responses. Genetic variation at the HLA genes is associated with susceptibility to autoimmune and infectious diseases and plays a major role in transplantation medicine and immunology. Currently, the HLA genes are characterized using Sanger- or next-generation sequencing (NGS) of a limited amplicon repertoire or labeled oligonucleotides for allele-specific sequences. High-quality NGS-based methods are in proprietary use and not publicly available. Here, we introduce the first highly automated open-kit/open-source HLA-typing method for NGS. The method employs in-solution targeted capturing of the classical class I (HLA-A, HLA-B, HLA-C) and class II HLA genes (HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, HLA-DPB1). The calling algorithm allows for highly confident allele-calling to three-field resolution (cDNA nucleotide variants). The method was validated on 357 commercially available DNA samples with known HLA alleles obtained by classical typing. Our results showed on average an accurate allele call rate of 0.99 in a fully automated manner, identifying also errors in the reference data. Finally, our method provides the flexibility to add further enrichment target regions.

Quality control metrics improve repeatability and reproducibility of single-nucleotide variants derived from whole-genome sequencing

Although many quality control (QC) methods have been developed to improve the quality of single-nucleotide variants (SNVs) in SNV-calling, QC methods for use subsequent to single-nucleotide polymorphism-calling have not been reported. We developed five QC metrics to improve the quality of SNVs using the whole-genome-sequencing data of a monozygotic twin pair from the Korean Personal Genome Project. The QC metrics improved both repeatability between the monozygotic twin pair and reproducibility between SNV-calling pipelines. We demonstrated the QC metrics improve reproducibility of SNVs derived from not only whole-genome-sequencing data but also whole-exome-sequencing data. The QC metrics are calculated based on the reference genome used in the alignment without accessing the raw and intermediate data or knowing the SNV-calling details. Therefore, the QC metrics can be easily adopted in downstream association analysis.

Whole genome and exome sequencing of monozygotic twins discordant for Crohn's disease

Background

Crohn’s disease (CD) is an inflammatory bowel disease caused by genetic and environmental factors. More than 160 susceptibility loci have been identified for IBD, yet a large part of the genetic variance remains unexplained. Recent studies have demonstrated genetic differences between monozygotic twins, who were long thought to be genetically completely identical.

Results

We aimed to test if somatic mutations play a role in CD etiology by sequencing the genomes and exomes of directly affected tissue from the bowel and blood samples of one and the blood-derived exomes of two further monozygotic discordant twin pairs. Our goal was the identification of mutations present only in the affected twins, pointing to novel candidates for CD susceptibility loci. We present a thorough genetic characterization of the sequenced individuals but detected no consistent differences within the twin pairs. An estimate of the CD susceptibility based on known CD loci however hinted at a higher mutational load in all three twin pairs compared to 1,920 healthy individuals.

Conclusion

Somatic mosaicism does not seem to play a role in the discordance of monozygotic CD twins. Our study constitutes the first to perform whole genome sequencing for CD twins and therefore provides a valuable reference dataset for future studies. We present an example framework for mosaicism detection and point to the challenges in these types of analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-564) contains supplementary material, which is available to authorized users.

GrabBlur--a framework to facilitate the secure exchange of whole-exome and -genome SNV data using VCF files

Background

Next Generation Sequencing (NGS) of whole exomes or genomes is increasingly being used in human genetic research and diagnostics. Sharing NGS data with third parties can help physicians and researchers to identify causative or predisposing mutations for a specific sample of interest more efficiently. In many cases, however, the exchange of such data may collide with data privacy regulations. GrabBlur is a newly developed tool to aggregate and share NGS-derived single nucleotide variant (SNV) data in a public database, keeping individual samples unidentifiable. In contrast to other currently existing SNV databases, GrabBlur includes phenotypic information and contact details of the submitter of a given database entry. By means of GrabBlur human geneticists can securely and easily share SNV data from resequencing projects. GrabBlur can ease the interpretation of SNV data by offering basic annotations, genotype frequencies and in particular phenotypic information - given that this information was shared - for the SNV of interest.

Tool description

GrabBlur facilitates the combination of phenotypic and NGS data (VCF files) via a local interface or command line operations. Data submissions may include HPO (Human Phenotype Ontology) terms, other trait descriptions, NGS technology information and the identity of the submitter. Most of this information is optional and its provision at the discretion of the submitter. Upon initial intake, GrabBlur merges and aggregates all sample-specific data. If a certain SNV is rare, the sample-specific information is replaced with the submitter identity. Generally, all data in GrabBlur are highly aggregated so that they can be shared with others while ensuring maximum privacy. Thus, it is impossible to reconstruct complete exomes or genomes from the database or to re-identify single individuals. After the individual information has been sufficiently "blurred", the data can be uploaded into a publicly accessible domain where aggregated genotypes are provided alongside phenotypic information. A web interface allows querying the database and the extraction of gene-wise SNV information. If an interesting SNV is found, the interrogator can get in contact with the submitter to exchange further information on the carrier and clarify, for example, whether the latter's phenotype matches with phenotype of their own patient.

Assessment of whole genome amplification for sequence capture and massively parallel sequencing

Exome sequence capture and massively parallel sequencing can be combined to achieve inexpensive and rapid global analyses of the functional sections of the genome. The difficulties of working with relatively small quantities of genetic material, as may be necessary when sharing tumor biopsies between collaborators for instance, can be overcome using whole genome amplification. However, the potential drawbacks of using a whole genome amplification technology based on random primers in combination with sequence capture followed by massively parallel sequencing have not yet been examined in detail, especially in the context of mutation discovery in tumor material. In this work, we compare mutations detected in sequence data for unamplified DNA, whole genome amplified DNA, and RNA originating from the same tumor tissue samples from 16 patients diagnosed with non-small cell lung cancer. The results obtained provide a comprehensive overview of the merits of these techniques for mutation analysis. We evaluated the identified genetic variants, and found that most (74%) of them were observed in both the amplified and the unamplified sequence data. Eighty-nine percent of the variations found by WGA were shared with unamplified DNA. We demonstrate a strategy for avoiding allelic bias by including RNA-sequencing information.

Building a genome analysis pipeline to predict disease risk and prevent disease

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.

Comparing somatic mutation-callers: beyond Venn diagrams

BACKGROUND

Somatic mutation-calling based on DNA from matched tumor-normal patient samples is one of the key tasks carried by many cancer genome projects. One such large-scale project is The Cancer Genome Atlas (TCGA), which is now routinely compiling catalogs of somatic mutations from hundreds of paired tumor-normal DNA exome-sequence data. Nonetheless, mutation calling is still very challenging. TCGA benchmark studies revealed that even relatively recent mutation callers from major centers showed substantial discrepancies. Evaluation of the mutation callers or understanding the sources of discrepancies is not straightforward, since for most tumor studies, validation data based on independent whole-exome DNA sequencing is not available, only partial validation data for a selected (ascertained) subset of sites.

RESULTS

To provide guidelines to comparing outputs from multiple callers, we have analyzed two sets of mutation-calling data from the TCGA benchmark studies and their partial validation data. Various aspects of the mutation-calling outputs were explored to characterize the discrepancies in detail. To assess the performances of multiple callers, we introduce four approaches utilizing the external sequence data to varying degrees, ranging from having independent DNA-seq pairs, RNA-seq for tumor samples only, the original exome-seq pairs only, or none of those.

CONCLUSIONS

Our analyses provide guidelines to visualizing and understanding the discrepancies among the outputs from multiple callers. Furthermore, applying the four evaluation approaches to the whole exome data, we illustrate the challenges and highlight the various circumstances that require extra caution in assessing the performances of multiple callers.

Cancer genome landscapes

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

Cancer genome landscapes

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

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.