Haplotype-resolved genome sequencing of a Gujarati Indian individual

Abstract

Haplotype information is essential to the complete description and interpretation of genomes 1 , genetic diversity 2 and genetic ancestry 3 . Although individual human genome sequencing is increasingly routine 4 , nearly all such genomes are unresolved with respect to haplotype. Here we combine the throughput of massively parallel sequencing 5 with the contiguity information provided by large-insert cloning 6 to experimentally determine the haplotype-resolved genome of a South Asian individual. A single fosmid library was split into a modest number of pools, each providing ~3% physical coverage of the diploid genome. Sequencing of each pool yielded reads overwhelmingly derived from only one homologous chromosome at any given location. These data were combined with whole-genome shotgun sequence to directly phase 94% of ascertained heterozygous single nucleotide polymorphisms (SNPs) into long haplotype blocks (N50 of 386 kilobases (kbp)). This method also facilitates the analysis of structural variation, for example, to anchor novel insertions 7,8 to specific locations and haplotypes.The high quality of the human reference genome derives from the hierarchical sequencing of large-insert clones, such that the assembly corresponding to each clone represents a single haplotype 9 . One of the first 'personal genomes' exploited clone-based mate pairing and long, accurate Sanger reads to resolve variants into haplotype blocks (N50 of 350 kbp; that is, 50% of resolved sequence is within blocks of at least 350 kbp) 1 . Although new technologies 5 have subsequently enabled >1,000-fold reduction in genome sequencing costs, the short read-lengths and paucity of contiguity information are such that it remains challenging to determine haplotypes at a genome-wide scale. Genomic phase, the assignment of alleles to homologous chromosomes, was determined for SNPs using mate-

De novo assembly and phasing of a Korean human genome

“…Phasing was performed with PacBio long reads, Illumina short reads, 10X Genomics linked reads 4 (30× ), and reads from BACs representing a single haplotype (47× ). Heterozygous SNVs called from these methods are unambiguously assigned to two alternative phases, producing phased blocks with an N50 length of 11.6 Mb, considerably longer than previously reported 4,6,8,15,16 (Table 1). We assessed the accuracy of the phased blocks against the end sequences of BACs, and found a long-range switch error rate to be under 0.3%.…”

The oyster genome reveals stress adaptation and complexity of shell formation

“…1 Tables 5 and 6). Fosmid pooling has been used for re-sequencing 16,17 , and our results show that the combination of fosmid pooling, NGS and hierarchical assembly provides a new, cost-effective alternative for de novo sequencing and assembly of complex genomes.…”

Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing

“…All data from these validation experiments in the tumors (that is, confirmed somatic variants, false-positive and false-negative variants) are accessible in Supplementary Table 1. Functional annotation of SNVs. Several computational tools and databases were used to predict the functional effect of coding and noncoding SNVs, including known or predicted protein coding genes, miRNAs and their target sites 26 , TRANSFAC transcription factor binding sites 44 , OregANNO annotated regulatory sites 45 , Vista Enhancer sites 46 , conservation as indicated by the presence of GERP constraint elements 17 , phastcons conserved elements 47 and repeat elements. The effect of coding mutations was assessed using SIFT 48 , PolyPhen 49 and CanPredict 50 .…”

“…Another commonly used approach is to apply quality filters that are aimed at selectively removing errors. Every whole-genome sequence reported so far has used filtering to some extent: the most commonly used filters being those that remove sequences with a too-low coverage depth, discard variants with a low-confidence score or eliminate variants located within a cluster of variants 3,7,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . Surprisingly, there is little consensus with respect to which filters should be used and at which threshold they should be applied.…”

“…As a result, each reported study developed its own heterogeneous set of filters and applied them at various (suboptimal) thresholds. For instance, in the case of the coverage depth filter, thresholds removing sequences with a coverage depth <4×, <10× or <11× were applied 6,10,11,16,17,23,24 . Additionally, it has not been assessed to which extent filters discard true variants, and how each filter can be optimized in terms of sensitivity and specificity.…”