Biology subjects

Urban, A. E.

Publications and source records attributed to Urban, A. E..

21 records · Page 2Linked to original sources

bioRxiv2017

Comprehensive, Integrated, and Phased Whole-Genome Analysis of the Primary ENCODE Cell Line K562

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and Indels (both corrected for CN in aneuploid regions), loss of heterozygosity, mega-base-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs) including small and large-scale complex SVs and non-reference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we re-analyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

genomics↗

bioRxiv2017

Whole-genome sequencing analysis of genomic copy number variation (CNV) using low-coverage and paired-end strategies is highly efficient and outperforms array based CNV analysis

BackgroundCNV analysis is an integral component to the study of human genomes in both research and clinical settings. Array-based CNV analysis is the current first-tier approach in clinical cytogenetics. Decreasing costs in high-throughput sequencing and cloud computing have opened doors for the development of sequencing-based CNV analysis pipelines with fast turnaround times. We carry out a systematic and quantitative comparative analysis for several low-coverage whole-genome sequencing (WGS) strategies to detect CNV in the human genome.\n\nMethodsWe compared the CNV detection capabilities of WGS strategies (short-insert, 3kb-, and 5kb-insert mate-pair) each at 1x, 3x, and 5x coverages relative to each other and to 17 currently used high-density oligonucleotide arrays. For benchmarking, we used a set of Gold Standard (GS) CNVs generated for the 1000-Genomes-Project CEU subject NA12878.\n\nResultsOverall, low-coverage WGS strategies detect drastically more GS CNVs compared to arrays and are accompanied with smaller percentages of CNV calls without validation. Furthermore, we show that WGS (at [≥]1x coverage) is able to detect all seven GS deletion-CNVs >100 kb in NA12878 whereas only one is detected by most arrays. Lastly, we show that the much larger 15 Mbp Cri-du-chat deletion can be readily detected with short-insert paired-end WGS at even just 1x coverage.\n\nConclusionsCNV analysis using low-coverage WGS is efficient and outperforms the array-based analysis that is currently used for clinical cytogenetics.

genomics↗

bioRxiv2017

Detection of complex structural variation from paired-end sequencing data

Complex structural variants (cxSVs), e.g. inversions with flanking deletions or interspersed inverted duplications, are part of human genetic diversity but their characteristics are not well delineated. Because their structures are difficult to resolve, cxSVs have been largely excluded from genome analysis and population-scale association studies. To permit large-scale detection of cxSVs from paired-end whole-genome sequencing, we developed Automated Reconstruction of Complex Variants (ARC-SV) using a novel probabilistic algorithm and a machine learning approach that leverages the new Human Pangenome Reference Consortium diploid assemblies. Using ARC-SV, we resolved, across 4,262 human genomes spanning all continental super-populations, 8,493 cxSVs belonging to 12 subclasses. Some cxSVs with population-specific signatures are shared with Neanderthals. Overall cxSVs are significantly enriched in regions prone to recombination and germline de novo mutations. Many cxSVs mark phenotypic hotspots (each significantly associated with [≥] 20 traits) identified in genome-wide association studies (GWAS), and 46.4% of all significant GWAS-SNPs catalogued to date reside within {+/-}125 kb of at least one cxSV locus. Common SNPs near cxSVs show significant trait heritability enrichment. Genomic regions affected by cxSVs are enriched for bivalent chromatin states. Rare cxSVs are enriched in neural genes and loci undergoing rapid or accelerated evolution and recently evolved cis-regulatory regions for human corticogenesis. We also identified 41 fixed loci where divergence from our most recent common ancestor is via localized cxSV. Our method and analysis framework allow for the accurate, efficient, and automatic identification of cxSVs for future population-scale studies of human disease and genome biology.

bioinformatics↗