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Clinical applications of precision oncology require accurate tests that can distinguish cancer-specific mutations from errors introduced at each step of next generation sequencing (NGS). For NGS to successfully improve patient lives, discriminating between true mutations and artifacts is crucial. To date, no study has addressed the effects of cross site reproducibility together with the potentially influential interactions between biological, technical, and computational factors on the accurate identification of variants. Here we systematically interrogated somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy. Different types of samples with varying input amount and tumor purity were processed using multiple library construction protocols. Whole-genome (WGS) and whole-exome sequencing (WES) were carried out at six sequencing centers followed by processing with nine bioinformatics pipelines to evaluate reproducibility. We identified artifacts of C>A mutations in WES due to sample and library processing and highlighted limitations of bioinformatics tools for artifact detection and removal. By examining the interactions and effects of NGS platforms, library preparation protocol, tumor content, read coverage, and bioinformatics processes concomitantly, here we recommend actionable practices for cancer mutation detection experiments and analysis using NGS technologies.

Benchmark small variant calls from the Genome in a Bottle Consortium (GIAB) for the CEPH/HapMap genome NA12878 (HG001) have been used extensively for developing, optimizing, and demonstrating performance of sequencing and bioinformatics methods. Here, we improve and simplify the methods we use to integrate multiple sequencing datasets, with the intention of deploying a reproducible cloud-based pipeline for application to arbitrary human genomes. We use these reproducible methods to form high-confidence calls with respect to GRCh37 and GRCh38 for HG001 and 4 additional broadly-consented genomes from the Personal Genome Project that are available as NIST Reference Materials. Our new methods produce 17% more SNPs and 176% more indels than our previously published calls for HG001. We also phase 99.5% of the variants in HG001 and call about 90% of the reference genome with high-confidence, increased from 78% previously. Our calls only contain 108 differences from the Illumina Platinum Genomes calls in GRCh37, only 14 of which are ambiguous or likely to be errors in our calls. By comparing several callsets to our new calls, our previously published calls, and Illumina Platinum Genomes calls, we highlight challenges in interpreting performance metrics when benchmarking against imperfect high-confidence calls. Our new calls address some of these challenges, but performance metrics should always be interpreted carefully. Benchmarking tools from the Global Alliance for Genomics and Health are useful for stratifying performance metrics by variant type and genome context to elucidate strengths and weaknesses of a method. We also explore differences between comparing to high-confidence calls for the 5 GIAB genomes, and show that performance metrics for one pipeline are largely similar but not identical when comparing to the 5 genomes. Finally, to explore applicability of our methods for genomes that have fewer datasets, we form high-confidence calls using only Illumina and 10x Genomics, and find that they have more high-confidence calls but have a higher error rate. These newly characterized genomes have a broad, open consent with few restrictions availability of samples and data, enabling a uniquely diverse array of applications.

On autopsy, a patient is found to have hypertrophic cardiomyopathy. The patient’s family pursues genetic testing that shows a “likely pathogenic” variant for the condition on the basis of a study in an original research publication. Given the dominant inheritance of the condition and the risk of sudden cardiac death, other family members are tested for the genetic variant to determine their risk. Several family members test negative and are told that they are not at risk for hypertrophic cardiomyopathy and sudden cardiac death, and those who test positive are told that they need to be regularly monitored for cardiomyopathy on echocardiography. Five years later, during a routine clinic visit of one of the genotype-positive family members, the cardiologist queries a database for current knowledge on the genetic variant and discovers that the variant is now interpreted as “likely benign” by another laboratory that uses more recently derived population-frequency data. A newly available testing panel for additional genes that are implicated in hypertrophic cardiomyopathy is initiated on an affected family member, and a different variant is found that is determined to be pathogenic. Family members are retested, and one member who previously tested negative is now found to be positive for this new variant. An immediate clinical workup detects evidence of cardiomyopathy, and an intracardiac defibrillator is implanted to reduce the risk of sudden cardiac death.

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. The Entrez system provides search and retrieval operations for most of these data from 37 distinct databases. The E-utilities serve as the programming interface for the Entrez system. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. New resources released in the past year include iCn3D, MutaBind, and the Antimicrobial Resistance Gene Reference Database; and resources that were updated in the past year include My Bibliography, SciENcv, the Pathogen Detection Project, Assembly, Genome, the Genome Data Viewer, BLAST and PubChem. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk

The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank(®) nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. Additional NCBI resources focus on literature (Bookshelf, PubMed Central (PMC) and PubReader); medical genetics (ClinVar, dbMHC, the Genetic Testing Registry, HIV-1/Human Protein Interaction Database and MedGen); genes and genomics (BioProject, BioSample, dbSNP, dbVar, Epigenomics, Gene, Gene Expression Omnibus (GEO), Genome, HomoloGene, the Map Viewer, Nucleotide, PopSet, Probe, RefSeq, Sequence Read Archive, the Taxonomy Browser, Trace Archive and UniGene); and proteins and chemicals (Biosystems, COBALT, the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART), the Molecular Modeling Database (MMDB), Protein Clusters, Protein and the PubChem suite of small molecule databases). The Entrez system provides search and retrieval operations for many of these databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of these resources can be accessed through the NCBI home page at http://www.ncbi.nlm.nih.gov.

The 1000 Genomes Project was launched as one of the largest distributed data collection and analysis projects ever undertaken in biology. In addition to the primary scientific goals of creating both a deep catalogue of human genetic variation and extensive methods to accurately discover and characterize variation using new sequencing technologies, the project makes all of its data publicly available for community use. The project data coordination center has developed and deployed several tools to enable widespread data access.

Evolution, Gene Number, and Disease Slight variations in the numbers of copies of genes influence human disease and other characters. Variants can be hard to detect when they lie in heavily duplicated and widely similar regions of sequence known as “dark matter.” Sudmant et al. (p. 641 ) have methods to tease apart the duplicated regions to reveal singly unique nucleotide identifiers. These have turned out to be among the most variable seen in different human population groups—most notably among genes for neurodevelopment and neurological diseases. Such polymorphisms can be genotyped with specificity and may help us understand how variation in copy number may affect human evolution and disease.

Genomics resources that use samples from identified populations raise scientific, social and ethical issues that are, in many ways, inextricably linked. Scientific decisions about which populations to sample to produce the HapMap, an international genetic variation resource, have raised questions about the relationships between the social identities used to recruit participants and the biological findings of studies that will use the HapMap. The sometimes problematic implications of those complex relationships have led to questions about how to conduct genetic variation research that uses identified populations in an ethical way, including how to involve members of a population in evaluating the risks and benefits posed for everyone who shares that identity. The ways in which these issues are linked is increasingly drawing the scientific and ethical spheres of genomics research closer together.