Anchored pseudo-de novo assembly of human genomes identifies extensive sequence variation from unmapped sequence reads

Population-scale detection of non-reference sequence variants using colored de Bruijn graphs

MOTIVATION

With the increasing throughput of sequencing technologies, structural variant (SV) detection has become possible across tens of thousands of genomes. Non-reference sequence (NRS) variants have drawn less attention compared with other types of SVs due to the computational complexity of detecting them. When using short-read data, the detection of NRS variants inevitably involves a de novo assembly which requires high-quality sequence data at high coverage. Previous studies have demonstrated how sequence data of multiple genomes can be combined for the reliable detection of NRS variants. However, the algorithms proposed in these studies have limited scalability to larger sets of genomes.

RESULTS

We introduce PopIns2, a tool to discover and characterize NRS variants in many genomes, which scales to considerably larger numbers of genomes than its predecessor PopIns. In this article, we briefly outline the PopIns2 workflow and highlight our novel algorithmic contributions. We developed an entirely new approach for merging contig assemblies of unaligned reads from many genomes into a single set of NRS using a colored de Bruijn graph. Our tests on simulated data indicate that the new merging algorithm ranks among the best approaches in terms of quality and reliability and that PopIns2 shows the best precision for a growing number of genomes processed. Results on the Polaris Diversity Cohort and a set of 1000 Icelandic human genomes demonstrate unmatched scalability for the application on population-scale datasets.

AVAILABILITY AND IMPLEMENTATION

The source code of PopIns2 is available from https://github.com/kehrlab/PopIns2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pan-genomics in the human genome era

Since the early days of the genome era, the scientific community has relied on a single 'reference' genome for each species, which is used as the basis for a wide range of genetic analyses, including studies of variation within and across species. As sequencing costs have dropped, thousands of new genomes have been sequenced, and scientists have come to realize that a single reference genome is inadequate for many purposes. By sampling a diverse set of individuals, one can begin to assemble a pan-genome: a collection of all the DNA sequences that occur in a species. Here we review efforts to create pan-genomes for a range of species, from bacteria to humans, and we further consider the computational methods that have been proposed in order to capture, interpret and compare pan-genome data. As scientists continue to survey and catalogue the genomic variation across human populations and begin to assemble a human pan-genome, these efforts will increase our power to connect variation to human diversity, disease and beyond.

HUPAN: a pan-genome analysis pipeline for human genomes

The human reference genome is still incomplete, especially for those population-specific or individual-specific regions, which may have important functions. Here, we developed a HUman Pan-genome ANalysis (HUPAN) system to build the human pan-genome. We applied it to 185 deep sequencing and 90 assembled Han Chinese genomes and detected 29.5 Mb novel genomic sequences and at least 188 novel protein-coding genes missing in the human reference genome (GRCh38). It can be an important resource for the human genome-related biomedical studies, such as cancer genome analysis. HUPAN is freely available at http://cgm.sjtu.edu.cn/hupan/ and https://github.com/SJTU-CGM/HUPAN .

Assembly and Analysis of Unmapped Genome Sequence Reads Reveal Novel Sequence and Variation in Dogs

Dogs are excellent animal models for human disease. They have extensive veterinary histories, pedigrees, and a unique genetic system due to breeding practices. Despite these advantages, one factor limiting their usefulness is the canine genome reference (CGR) which was assembled using a single purebred Boxer. Although a common practice, this results in many high-quality reads remaining unmapped. To address this whole-genome sequence data from three breeds, Border Collie (n = 26), Bearded Collie (n = 7), and Entlebucher Sennenhund (n = 8), were analyzed to identify novel, non-CGR genomic contigs using the previously validated pseudo-de novo assembly pipeline. We identified 256,957 novel contigs and paired-end relationships together with BLAT scores provided 126,555 (49%) high-quality contigs with genomic coordinates containing 4.6 Mb of novel sequence absent from the CGR. These contigs close 12,503 known gaps, including 2.4 Mb containing partially missing sequences for 11.5% of Ensembl, 16.4% of RefSeq and 12.2% of canFam3.1+ CGR annotated genes and 1,748 unmapped contigs containing 2,366 novel gene variants. Examples for six disease-associated genes (SCARF2, RD3, COL9A3, FAM161A, RASGRP1 and DLX6) containing gaps or alternate splice variants missing from the CGR are also presented. These findings from non-reference breeds support the need for improvement of the current Boxer-only CGR to avoid missing important biological information. The inclusion of the missing gene sequences into the CGR will facilitate identification of putative disease mutations across diverse breeds and phenotypes.

The Genomic Scrapheap Challenge; Extracting Relevant Data from Unmapped Whole Genome Sequencing Reads, Including Strain Specific Genomic Segments, in Rats

Unmapped next-generation sequencing reads are typically ignored while they contain biologically relevant information. We systematically analyzed unmapped reads from whole genome sequencing of 33 inbred rat strains. High quality reads were selected and enriched for biologically relevant sequences; similarity-based analysis revealed clustering similar to previously reported phylogenetic trees. Our results demonstrate that on average 20% of all unmapped reads harbor sequences that can be used to improve reference genomes and generate hypotheses on potential genotype-phenotype relationships. Analysis pipelines would benefit from incorporating the described methods and reference genomes would benefit from inclusion of the genomic segments obtained through these efforts.