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Auto Tag: Novel algorithm

June 1, 2021

Structural variant detection with low-coverage Pacbio sequencing

Despite amazing progress over the past quarter century in the technology to detect genetic variants, intermediate-sized structural variants (50 bp to 50 kb) have remained difficult to identify. Such variants are too small to detect with array comparative genomic hybridization, but too large to reliably discover with short-read DNA sequencing. Recent de novo assemblies of human genomes have demonstrated the power of PacBio Single Molecule, Real-Time (SMRT) Sequencing to fill this technology gap and sensitively identify structural variants in the human genome. While de novo assembly is the ideal method to identify variants in a genome, it requires high depth of coverage. A structural variant discovery approach that utilizes lower coverage would facilitate evaluation of large patient and population cohorts. Here we introduce such an approach and apply it to 10-fold coverage of several human genomes generated on the PacBio Sequel System. To identify structural variants in low-fold coverage whole genome sequencing data, we apply a reference-based, re-sequencing workflow. First, reads are mapped to the human reference genome with a local aligner. The local alignments often end at structural variant loci. To connect co-linear local alignments across structural variants, we apply a novel algorithm that merges alignments into “chains” and refines the alignment edges. Then, the chained alignments are scanned for windows with an excess of insertions or deletions to identify candidate structural variant loci. Finally, the read support at each putative variant locus is evaluated to produce a variant call. Single nucleotide information is incorporated to phase and evaluate the zygosity of each structural variant. In 10-fold coverage human genome sequence, we identify the vast majority of the structural variants found by de novo assembly, thus demonstrating the power of low-fold coverage SMRT Sequencing to affordably and effectively detect structural variants.

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April 21, 2020

Profiling the genome-wide landscape of tandem repeat expansions.

Tandem repeat (TR) expansions have been implicated in dozens of genetic diseases, including Huntington’s Disease, Fragile X Syndrome, and hereditary ataxias. Furthermore, TRs have recently been implicated in a range of complex traits, including gene expression and cancer risk. While the human genome harbors hundreds of thousands of TRs, analysis of TR expansions has been mainly limited to known pathogenic loci. A major challenge is that expanded repeats are beyond the read length of most next-generation sequencing (NGS) datasets and are not profiled by existing genome-wide tools. We present GangSTR, a novel algorithm for genome-wide genotyping of both short and expanded TRs. GangSTR extracts information from paired-end reads into a unified model to estimate maximum likelihood TR lengths. We validate GangSTR on real and simulated data and show that GangSTR outperforms alternative methods in both accuracy and speed. We apply GangSTR to a deeply sequenced trio to profile the landscape of TR expansions in a healthy family and validate novel expansions using orthogonal technologies. Our analysis reveals that healthy individuals harbor dozens of long TR alleles not captured by current genome-wide methods. GangSTR will likely enable discovery of novel disease-associated variants not currently accessible from NGS. © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.

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April 21, 2020

Assembly of allele-aware, chromosomal-scale autopolyploid genomes based on Hi-C data.

Construction of chromosome-level assembly is a vital step in achieving the goal of a ‘Platinum’ genome, but it remains a major challenge to assemble and anchor sequences to chromosomes in autopolyploid or highly heterozygous genomes. High-throughput chromosome conformation capture (Hi-C) technology serves as a robust tool to dramatically advance chromosome scaffolding; however, existing approaches are mostly designed for diploid genomes and often with the aim of reconstructing a haploid representation, thereby having limited power to reconstruct chromosomes for autopolyploid genomes. We developed a novel algorithm (ALLHiC) that is capable of building allele-aware, chromosomal-scale assembly for autopolyploid genomes using Hi-C paired-end reads with innovative ‘prune’ and ‘optimize’ steps. Application on simulated data showed that ALLHiC can phase allelic contigs and substantially improve ordering and orientation when compared to other mainstream Hi-C assemblers. We applied ALLHiC on an autotetraploid and an autooctoploid sugar-cane genome and successfully constructed the phased chromosomal-level assemblies, revealing allelic variations present in these two genomes. The ALLHiC pipeline enables de novo chromosome-level assembly of autopolyploid genomes, separating each allele. Haplotype chromosome-level assembly of allopolyploid and heterozygous diploid genomes can be achieved using ALLHiC, overcoming obstacles in assembling complex genomes.

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April 21, 2020

MSC: a metagenomic sequence classification algorithm.

Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches follow an alignment-free methodology where the presence of a microbe is predicted using the information about the unique k-mers present in the microbial genomes. However, such approaches suffer from high false positives due to trading off the value of k with the computational resources. In this article, we propose a highly efficient metagenomic sequence classification (MSC) algorithm that is a hybrid of both approaches. Instead of aligning reads to the full genomes, MSC aligns reads onto a set of carefully chosen, shorter and highly discriminating model sequences built from the unique k-mers of each of the reference sequences.Microbiome researchers are generally interested in two objectives of a taxonomic classifier: (i) to detect prevalence, i.e. the taxa present in a sample, and (ii) to estimate their relative abundances. MSC is primarily designed to detect prevalence and experimental results show that MSC is indeed a more effective and efficient algorithm compared to the other state-of-the-art algorithms in terms of accuracy, memory and runtime. Moreover, MSC outputs an approximate estimate of the abundances.The implementations are freely available for non-commercial purposes. They can be downloaded from https://drive.google.com/open?id=1XirkAamkQ3ltWvI1W1igYQFusp9DHtVl. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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April 21, 2020

Long-read sequencing identified intronic repeat expansions in SAMD12 from Chinese pedigrees affected with familial cortical myoclonic tremor with epilepsy.

The locus for familial cortical myoclonic tremor with epilepsy (FCMTE) has long been mapped to 8q24 in linkage studies, but the causative mutations remain unclear. Recently, expansions of intronic TTTCA and TTTTA repeat motifs within SAMD12 were found to be involved in the pathogenesis of FCMTE in Japanese pedigrees. We aim to identify the causative mutations of FCMTE in Chinese pedigrees.We performed genetic linkage analysis by microsatellite markers in a five-generation Chinese pedigree with 55 members. We also used array-comparative genomic hybridisation (CGH) and next-generation sequencing (NGS) technologies (whole-exome sequencing, capture region deep sequencing and whole-genome sequencing) to identify the causative mutations in the disease locus. Recently, we used low-coverage (~10×) long-read genome sequencing (LRS) on the PacBio Sequel and Oxford Nanopore platforms to identify the causative mutations, and used repeat-primed PCR for validation of the repeat expansions.Linkage analysis mapped the disease locus to 8q23.3-24.23. Array-CGH and NGS failed to identify causative mutations in this locus. LRS identified the intronic TTTCA and TTTTA repeat expansions in SAMD12 as the causative mutations, thus corroborating the recently published results in Japanese pedigrees.We identified the pentanucleotide repeat expansion in SAMD12 as the causative mutation in Chinese FCMTE pedigrees. Our study also suggested that LRS is an effective tool for molecular diagnosis of genetic disorders, especially for neurological diseases that cannot be positively diagnosed by conventional clinical microarray and NGS technologies. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.

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April 21, 2020

Haplotype-aware diplotyping from noisy long reads.

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.

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