Long-read sequencing (LRS) promises to improve characterization of structural variants (SVs), a major source of genetic diversity. We generated LRS data on 3,622 Icelanders using Oxford Nanopore Technologies, and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions), spanning a median of 10 Mb per haploid genome. We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association with a rare (AF = 0.037%) deletion of the first exon of PCSK9. Carriers of this deletion have 0.93 mmol/L (1.31 SD) lower LDL cholesterol levels than the population average (p-value = 7.0•10 −20 ). We also discovered an association with a multi-allelic SV inside a large repeat region, contained within single long reads, in an exon of ACAN. Within this repeat region we found 11 alleles that differ in the number of a 57 bp-motif repeat, and observed a linear relationship (0.016 SD per motif inserted, p = 6.2•10 −18 ) between the number of repeats carried and height. These results show that SVs can be accurately characterized at population scale using long read sequence data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.Human sequence diversity is partially due to structural variants 1 (SVs); genomic rearrangements affecting at least 50 bp of sequence in forms of insertions, deletions, inversions, or translocations. The number of SVs carried by each individual is less than the number of single nucleotide polymorphisms (SNPs) and short (< 50 bp) insertions and deletions (indels), but their greater size makes them more likely to have a functional role 2 , as evident by their disproportionately large impact on diseases and other traits 2,3 .Extensive characterization of three trios sequenced using several technologies 4 and an annotated set based on one sample (HG002) 5 indicate that humans carry 23-31 thousand SVs .
We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.
Long-read sequencing (LRS) promises to improve characterization of structural variants (SVs), a major source of genetic diversity. We generated LRS data on 1,817 Icelanders using Oxford Nanopore Technologies, and identified a median of 23,111 autosomal structural variants per individual (a median of 11,506 insertions and 11,576 deletions), spanning cumulatively a median of 9.9 Mb. We found that rare SVs are larger in size than common ones and are more likely to impact protein function. We discovered an association with a rare deletion of the first exon ofPCSK9. Carriers of this deletion have 0.93 mmol/L (1.36 sd) lower LDL cholesterol levels than the population average (p-value = 2.4·10−22). We show that SVs can be accurately characterized at population scale using long read sequence data in a genomewide non-targeted fashion and how these variants impact disease.
HighlightsRaptor is a tool to search through large collections of genomic texts Raptor is 12-144 times faster and uses up to 30 times less RAM than COBS or MantisThe Raptor index is 6-50 times faster to build The use of minimizers and Bloom filters makes Raptor very spaceefficient
Motivation
The ever-growing size of sequencing data is a major bottleneck in bioinformatics as the advances of hardware development cannot keep up with the data growth. Therefore, an enormous amount of data is collected but rarely ever reused, because it is nearly impossible to find meaningful experiments in the stream of raw data.
Results
As a solution, we propose Needle, a fast and space-efficient index which can be built for thousands of experiments in less than two hours and can estimate the quantification of a transcript in these experiments in seconds, thereby outperforming its competitors. The basic idea of the Needle index is to create multiple interleaved Bloom filters that each store a set of representative k-mers depending on their multiplicity in the raw data. This is then used to quantify the query.
Supplementary information
Supplementary data are available at Bioinformatics online.
Availability and implementation
https://github.com/seqan/needle
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