Biology subjects

Chong, Z.

Publications and source records attributed to Chong, Z..

4 recordsLinked to original sources

bioRxiv2018

Identification of meiotic recombination through gamete genome reconstruction using whole genome linked-reads

Meiotic recombination (MR), which transmits exchanged genetic materials between homologous chromosomes to offspring, plays a crucial role in shaping genomic diversity in eukaryotic organisms. In humans, thousands of meiotic recombination hotspots have been mapped by population genetics approaches. However, direct identification of MR events for individuals is still challenging due to the difficulty in resolving the haplotypes of homologous chromosomes and reconstructing the gamete genome. Whole genome linked-read sequencing (lrWGS) can generate haplotype sequences of mega-base pairs (N50 ~2.5Mb) after computational phasing. However, the haplotype information is still isolated in a large number of fragmented genomic regions and limited by switch errors, impeding its further application in the chromosome-scale analysis. In this study, we developed a tool MRLR (Meiotic Recombination identification by Linked-Read sequencing) for the analysis of individual MR events. By leveraging trio pedigree information with lrWGS haplotypes, our pipeline is sufficient to reconstruct the whole human gamete genome with 99.8% haplotyping accuracy. By analyzing the haplotype exchange between homologous chromosomes, MRLR identified 462 high-resolution MR events in 6 human trio samples from the Genome In A Bottle (GIAB) and the Human Genome Structural Variation Consortium (HGSVC). In three datasets of the HGSVC, our results recapitulated 149 (92%) previously identified high-confident MR events and discovered 85 novel events. About half (40) of the new events are supported by single-cell template strand sequencing (Strand-seq) results. We found that 332 (71.9%) MR events co-localize with recombination hotspots (>10 cM/Mb) in human populations, and MR breakpoint regions are enriched in PRDM9 and DMC1 binding sites. In addition, 48% (221) breakpoint regions were detected inside a gene, indicating these MRs can directly affect the haplotype diversity of genic regions. Taken together, our approach provides new opportunities in the haplotype-based genomic analysis of individual meiotic recombination. The MRLR software is implemented in Perl and is freely available at https://github.com/ChongLab/MRLR.

genomics↗

bioRxiv2017

Combining accurate tumour genome simulation with crowd sourcing to benchmark somatic structural variant detection

BackgroundThe phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries, however there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold-standards, extensive resource requirements and difficulties arising from the need to share personal genomic information.\n\nResultsTo facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowd-sourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error-profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.\n\nConclusionsThe synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon.

bioinformatics↗

bioRxiv2017

Multi-platform discovery of haplotype-resolved structural variation in human genomes

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent-child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs ([≥]50 bp) per human genome. We also discover 156 inversions per genome--most of which previously escaped detection. Fifty-eight of the inversions we discovered intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The method and the dataset serve as a gold standard for the scientific community and we make specific recommendations for maximizing structural variation sensitivity for future large-scale genome sequencing studies.

genomics↗

bioRxiv2017

Pan-cancer analysis of whole genomes reveals driver rearrangements promoted by LINE-1 retrotransposition in human tumours

About half of all cancers have somatic integrations of retrotransposons. To characterize their role in oncogenesis, we analyzed the patterns and mechanisms of somatic retrotransposition in 2,954 cancer genomes from 37 histological cancer subtypes. We identified 19,166 somatically acquired retrotransposition events, affecting 35% of samples, and spanning a range of event types. L1 insertions emerged as the first most frequent type of somatic structural variation in esophageal adenocarcinoma, and the second most frequent in head-and-neck and colorectal cancers. Aberrant L1 integrations can delete megabase-scale regions of a chromosome, sometimes removing tumour suppressor genes, as well as inducing complex translocations and large-scale duplications. Somatic retrotranspositions can also initiate breakage-fusion-bridge cycles, leading to high-level amplification of oncogenes. These observations illuminate a relevant role of L1 retrotransposition in remodeling the cancer genome, with potential implications in the development of human tumours.

genomics↗