Fast and accurate genomic analyses using genome graphs

Abstract

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, which impairs read alignment and downstream analysis accuracy. Reference genome structures incorporating known genetic variation have been shown to improve the accuracy of genomic analyses, but have so far remained computationally prohibitive for routine large-scale use. Here we present a graph genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million indels. Our Graph Genome Pipeline requires 6.5 hours to process a 30x coverage WGS sample on a system with 36 CPU cores compared with 11 hours required by the GATK Best Practices pipeline.Using complementary benchmarking experiments based on real and simulated data, we show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, or about 20,000 additional variants being detected per sample, while variant calling specificity is unaffected. Structural variations (SVs) 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 a significant advance towards fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

Benchmarking challenging small variants with linked and long reads

“…We present the first diploid small variant benchmark that uses short-, linked-, and long-reads to confidently characterize a broad spectrum of genomic contexts, including non-repetitive regions as well as repetitive regions such as many segmental duplications, difficult to map regions, homopolymers, and tandem repeats. We demonstrated that the benchmark reliably identifies false positives and false negatives in more challenging regions across many short-, linked-, and long-read technologies and variant callers based on traditional methods, deep learning, 8,9 graph-based references, 10 and diploid assembly. 12 We designed this benchmark to cover as much of the human genome as possible with current technologies, as long as the benchmark genome sequence is structurally similar to the GRCh37 or GRCh38 reference.…”

“…[2][3][4] The Global Alliance for Genomics and Health (GA4GH) Benchmarking Team develop tools and best practices to use these benchmarks. 5 These benchmarks and benchmarking tools helped enable the development and optimization of new technologies and bioinformatics approaches, including linked reads, 6 highly accurate long reads, 7 deep learning-based variant callers, 8,9 graph-based variant callers, 10 and de novo assembly. 11,12 However, these benchmarks did not cover some challenging regions that these new methods could access, including many known medically relevant genes.…”

Recovery of non-reference sequences missing from the human reference genome

“…There is growing interest in using genetic variants to augment the reference genome into a graph genome [18][19][20]. To create a representative graph genome, the full spectrum of structural variations, including the alternate alleles, should be understood clearly.…”

Time for a paradigm shift in the use of plant genetic resources

“…SVs are abundant in plant genomes (Torkamaneh et al 2018) and play an important role in phenotypic variation among germplasm collections (Wang et al 2018). We think that this problem will be overcome thanks to long-read sequencing along with the development of graph-based reference genomes (Rakocevic et al 2019).…”