In 2012, NIST convened the Genome in a Bottle Consortium to develop the metrology infrastructure needed to enable confidence in human whole genome variant calls.
The Genome in a Bottle Consortium is developing the reference materials, reference methods , and reference data n
Purpose: Clinical laboratories, research laboratories and technology developers all need DNA samples with reliably known genotypes in order to help validate and improve their methods. The Genome in a Bottle Consortium (genomeinabottle.org) has been developing Reference Materials with high-accuracy whole genome sequences to support these efforts.Methodology: Our pilot reference material is based on Coriell sample NA12878 and was released in May 2015 as NIST RM 8398 (tinyurl.com/giabpilot). To minimize bias and improve accuracy, 11 whole-genome and 3 exome data sets produced using 5 different technologies were integrated using a systematic arbitration method . The Genome in a Bottle Analysis Group is adapting these methods and developing new methods to characterize 2 families, one Asian and one Ashkenazi Jewish from the Personal Genome Project, which are consented for public release of sequencing and phenotype data. We have generated a larger and even more diverse data set on these samples, including high-depth Illumina paired-end and mate-pair, Complete Genomics, and Ion Torrent short-read data, as well as Moleculo, 10X, Oxford Nanopore, PacBio, and BioNano Genomics long-read data. We are analyzing these data to provide an accurate assessment of not just small variants but also large structural variants (SVs) in both “easy” regions of the genome and in some “hard” repetitive regions. We have also made all of the input data sources publicly available for download, analysis, and publication.Results: Our arbitration method produced a reference data set of 2,787,291 single nucleotide variants (SNVs), 365,135 indels, 2744 SVs, and 2.2 billion homozygous reference calls for our pilot genome. We found that our call set is highly sensitive and specific in comparison to independent reference data sets. We have also generated preliminary assemblies and structural variant calls for the next 2 trios from long read data and are currently integrating and validating these.Discussion: We combined the strengths of each of our input datasets to develop a comprehensive and accurate benchmark call set. In the short time it has been available, over 20 published or submitted papers have used our data. Many challenges exist in comparing to our benchmark calls, and thus we have worked with the Global Alliance for Genomics and Health to develop standardized methods, performance metrics, and software to assist in its use. Zook et al, Nat Biotech. 2014.
Structural variant detection with long read sequencing reveals driver and passenger mutations in a melanoma cell line
Past large scale cancer genome sequencing efforts, including The Cancer Genome Atlas and the International Cancer Genome Consortium, have utilized short-read sequencing, which is well-suited for detecting single nucleotide variants (SNVs) but far less reliable for detecting variants larger than 20 base pairs, including insertions, deletions, duplications, inversions and translocations. Recent same-sample comparisons of short- and long-read human reference genome data have revealed that short-read resequencing typically uncovers only ~4,000 structural variants (SVs, =50 bp) per genome and is biased towards deletions, whereas sequencing with PacBio long-reads consistently finds ~20,000 SVs, evenly balanced between insertions and deletions. This discovery has important implications for cancer research, as it is clear that SVs are both common and biologically important in many cancer subtypes, including colorectal, breast and ovarian cancer. Without confident and comprehensive detection of structural variants, it is unlikely we have a sufficiently complete picture of all the genomic changes that impact cancer development, disease progression, treatment response, drug resistance, and relapse. To begin to address this unmet need, we have sequenced the COLO829 tumor and matched normal lymphoblastoid cell lines to 49- and 51-fold coverage, respectively, with PacBio SMRT Sequencing, with the goal of developing a high-confidence structural variant call set that can be used to empirically evaluate cost-effective experimental designs for larger scale studies and develop structural variation calling software suitable for cancer genomics. Structural variant calling revealed over 21,000 deletions and 19,500 insertions larger than 20 bp, nearly four times the number of events detected with short-read sequencing. The vast majority of events are shared between the tumor and normal, with about 100 putative somatic deletions and 400 insertions, primarily in microsatellites. A further 40 rearrangements were detected, nearly exclusively in the tumor. One rearrangement is shared between the tumor and normal, t(5;X) which disrupts the mismatch repeat gene MSH3, and is likely a driver mutation. Generating high-confidence call sets that cover the entire size-spectrum of somatic variants from a range of cancer model systems is the first step in determining what will be the best approach for addressing an ongoing blind spot in our current understanding of cancer genomes. Here the application of PacBio sequencing to a melanoma cancer cell line revealed thousands of previously overlooked variants, including a mutation likely involved in tumorogenesis.
Genomics studies have shown that the insertions, deletions, duplications, translocations, inversions, and tandem repeat expansions in the structural variant (SV) size range (>50 bp) contribute to the evolution of traits and often have significant associations with agronomically important phenotypes. However, most SVs are too small to detect with array comparative genomic hybridization and too large to reliably discover with short-read DNA sequencing. While de novo assembly is the most comprehensive way to identify variants in a genome, recent studies in human genomes show that PacBio SMRT Sequencing sensitively detects structural variants at low coverage. Here we present SV characterization in the major crop species Oryza sativa subsp. indica (rice) with low-fold coverage of long reads. In addition, we provide recommendations for sequencing and analysis for the application of this workflow to other important agricultural species.
Detection and phasing of small variants in Genome in a Bottle samples with highly accurate long reads
Introduction: Long-read PacBio SMRT Sequencing has been applied successfully to assemble genomes and detect structural variants. However, due to high raw read error rates of 10-15%, it has remained difficult to call small variants from long reads. Recent improvements in library preparation, sequencing chemistry, and instrument yield have increased length, accuracy, and throughput of PacBio Circular Consensus (CCS) reads, resulting in 10-20 kb “HiFi” reads with mean read quality above 99%. Materials and Methods: We sequenced 11 kb size-selected libraries from the Genome in a Bottle (GIAB) human reference samples HG001, HG002, and HG005 to approximately 30-fold coverage on the Sequel II System with six SMRT Cells 8M each. The CCS algorithm was used to generate highly accurate (average 99.8%) reads of mean length 10-11 kb, which were then mapped to the hs37d5 reference with pbmm2. We detected small variants using Google DeepVariant and compared these variant calls to GIAB benchmarks. Small variants were then phased with WhatsHap. Results: With these long, highly accurate CCS reads, DeepVariant achieves high SNP and Indel accuracy against the GIAB benchmark truth set for all three reference samples. Using WhatsHap, small variants were phased into haplotype blocks with N50 from 82 to 146 kb. The improved mappability of long reads allows detection of variants in many medically relevant genes such as CYP2D6and PMS2that have proven ‘difficult-to-map’ with short reads. We show that small variant precision and recall remain high down to 15-fold coverage. Conclusions: These highly accurate long reads combine the mappability of noisy long reads with the accuracy and small variant detection utility of short reads, which will allow the detection and phasing of variants in regions that have proven recalcitrant to short read sequencing and variant detection.
Characterization of Reference Materials for Genetic Testing of CYP2D6 Alleles: A GeT-RM Collaborative Project.
Pharmacogenetic testing increasingly is available from clinical and research laboratories. However, only a limited number of quality control and other reference materials currently are available for the complex rearrangements and rare variants that occur in the CYP2D6 gene. To address this need, the Division of Laboratory Systems, CDC-based Genetic Testing Reference Material Coordination Program, in collaboration with members of the pharmacogenetic testing and research communities and the Coriell Cell Repositories (Camden, NJ), has characterized 179 DNA samples derived from Coriell cell lines. Testing included the recharacterization of 137 genomic DNAs that were genotyped in previous Genetic Testing Reference Material Coordination Program studies and 42 additional samples that had not been characterized previously. DNA samples were distributed to volunteer testing laboratories for genotyping using a variety of commercially available and laboratory-developed tests. These publicly available samples will support the quality-assurance and quality-control programs of clinical laboratories performing CYP2D6 testing.Published by Elsevier Inc.
New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls =50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.
Benchmark small variant calls are required for developing, optimizing and assessing the performance of sequencing and bioinformatics methods. Here, as part of the Genome in a Bottle (GIAB) Consortium, we apply a reproducible, cloud-based pipeline to integrate multiple short- and linked-read sequencing datasets and provide benchmark calls for human genomes. We generate benchmark calls for one previously analyzed GIAB sample, as well as six genomes from the Personal Genome Project. These new genomes have broad, open consent, making this a ‘first of its kind’ resource that is available to the community for multiple downstream applications. We produce 17% more benchmark single nucleotide variations, 176% more indels and 12% larger benchmark regions than previously published GIAB benchmarks. We demonstrate that this benchmark reliably identifies errors in existing callsets and highlight challenges in interpreting performance metrics when using benchmarks that are not perfect or comprehensive. Finally, we identify strengths and weaknesses of callsets by stratifying performance according to variant type and genome context.
The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, thus impairing analysis accuracy. Here we present a graph reference genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million insertions and deletions (indels). The pipeline processes one whole-genome sequencing sample in 6.5?h using a system with 36?CPU cores. We show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, with unaffected specificity. Structural variations incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is an important advance toward fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.
Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome.
The DNA sequencing technologies in use today produce either highly accurate short reads or less-accurate long reads. We report the optimization of circular consensus sequencing (CCS) to improve the accuracy of single-molecule real-time (SMRT) sequencing (PacBio) and generate highly accurate (99.8%) long high-fidelity (HiFi) reads with an average length of 13.5?kilobases (kb). We applied our approach to sequence the well-characterized human HG002/NA24385 genome and obtained precision and recall rates of at least 99.91% for single-nucleotide variants (SNVs), 95.98% for insertions and deletions <50 bp (indels) and 95.99% for structural variants. Our CCS method matches or exceeds the ability of short-read sequencing to detect small variants and structural variants. We estimate that 2,434 discordances are correctable mistakes in the 'genome in a bottle' (GIAB) benchmark set. Nearly all (99.64%) variants can be phased into haplotypes, further improving variant detection. De novo genome assembly using CCS reads alone produced a contiguous and accurate genome with a contig N50 of >15?megabases (Mb) and concordance of 99.997%, substantially outperforming assembly with less-accurate long reads.
Corals comprise a biomineralizing cnidarian, dinoflagellate algal symbionts, and associated microbiome of prokaryotes and viruses. Ongoing efforts to conserve coral reefs by identifying the major stress response pathways and thereby laying the foundation to select resistant genotypes rely on a robust genomic foundation. Here we generated and analyzed a high quality long-read based ~886 Mbp nuclear genome assembly and transcriptome data from the dominant rice coral, Montipora capitata from Hawai’i. Our work provides insights into the architecture of coral genomes and shows how they differ in size and gene inventory, putatively due to population size variation. We describe a recent example of foreign gene acquisition via a bacterial gene transfer agent and illustrate the major pathways of stress response that can be used to predict regulatory components of the transcriptional networks in M. capitata. These genomic resources provide insights into the adaptive potential of these sessile, long-lived species in both natural and human influenced environments and facilitate functional and population genomic studies aimed at Hawaiian reef restoration and conservation.
Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.
The DNA base modification N6-methyladenine (m6A) is involved in many pathways related to the survival of bacteria and their interactions with hosts. Nanopore sequencing offers a new, portable method to detect base modifications. Here, we show that a neural network can improve m6A detection at trained sequence contexts compared to previously published methods using deviations between measured and expected current values as each adenine travels through a pore. The model, implemented as the mCaller software package, can be extended to detect known or confirm suspected methyltransferase target motifs based on predictions of methylation at untrained contexts. We use PacBio, Oxford Nanopore, methylated DNA immunoprecipitation sequencing (MeDIP-seq), and whole-genome bisulfite sequencing data to generate and orthogonally validate methylomes for eight microbial reference species. These well-characterized microbial references can serve as controls in the development and evaluation of future methods for the identification of base modifications from single-molecule sequencing data.
Current genotyping approaches for single-nucleotide variations rely on short, accurate reads from second-generation sequencing devices. Presently, third-generation sequencing platforms are rapidly becoming more widespread, yet approaches for leveraging their long but error-prone reads for genotyping are lacking. Here, we introduce a novel statistical framework for the joint inference of haplotypes and genotypes from noisy long reads, which we term diplotyping. Our technique takes full advantage of linkage information provided by long reads. We validate hundreds of thousands of candidate variants that have not yet been included in the high-confidence reference set of the Genome-in-a-Bottle effort.