Biology subjects

Zhou, B.

Publications and source records attributed to Zhou, B..

206 records · Page 12Linked 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↗

bioRxiv2017

A Large-Scale Binding and Functional Map of Human RNA Binding Proteins

Genomes encompass all the information necessary to specify the development and function of an organism. In addition to genes, genomes also contain a myriad of functional elements that control various steps in gene expression. A major class of these elements function only when transcribed into RNA as they serve as the binding sites for RNA binding proteins (RBPs), which act to control post-transcriptional processes including splicing, cleavage and polyadenylation, RNA editing, RNA localization, stability, and translation. Despite the importance of these functional RNA elements encoded in the genome, they have been much less studied than genes and DNA elements. Here, we describe the mapping and characterization of RNA elements recognized by a large collection of human RBPs in K562 and HepG2 cells. These data expand the catalog of functional elements encoded in the human genome by addition of a large set of elements that function at the RNA level through interaction with RBPs.\n\nHighlightsO_LI223 eCLIP datasets for 150 RBPs reveal a wide variety of in vivo RNA target classes.\nC_LIO_LI472 knockdown/RNA-seq profiles of 263 RBPs reveal factor-responsive targets and integration with eCLIP indicates RNA expression and splicing regulatory patterns.\nC_LIO_LI78 RNA Bind-N-Seq profiles of in vitro binding motifs reveal links between in vitro and in vivo binding and indicate that eCLIP peaks that contain in vitro motifs are more strongly associated with regulation.\nC_LIO_LI274 maps of RBP subcellular localization by immunofluorescence indicate widespread organelle-specific RNA processing regulation.\nC_LIO_LI63 ChIP-seq profiles of DNA association suggest broad interconnectivity between chromatin association and RNA processing.\nC_LI

genomics↗

bioRxiv2017

Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds

Traditional theories of sensorimotor learning posit that animals use sensory error signals to find the optimal motor command in the face of Gaussian sensory and motor noise. However, most such theories cannot explain common behavioral observations, for example that smaller sensory errors are more readily corrected than larger errors and that large abrupt (but not gradually introduced) errors lead to weak learning. Here we propose a new theory of sensorimotor learning that explains these observations. The theory posits that the animal learns an entire probability distribution of motor commands rather than trying to arrive at a single optimal command, and that learning arises via Bayesian inference when new sensory information becomes available. We test this theory using data from a songbird, the Bengalese finch, that is adapting the pitch (fundamental frequency) of its song following perturbations of auditory feedback using miniature headphones. We observe the distribution of the sung pitches to have long, non-Gaussian tails, which, within our theory, explains the observed dynamics of learning. Further, the theory makes surprising predictions about the dynamics of the shape of the pitch distribution, which we confirm experimentally.

animal behavior and cognition↗

bioRxiv2017

Genomic variations in paired normal controls for lung adenocarcinomas

Somatic genomic mutations in lung adenocarcinomas (LUADs) have been extensively dissected, but whether the counterpart normal lung tissues that are exposed to ambient air or tobacco smoke as the tumor tissues do, harbor genomic variations, remains unclear. Here, the genome of normal lung tissues and paired tumors of 11 patients with LUAD were sequenced, the genome sequences of counterpart normal controls (CNCs) and tumor tissues of 513 patients were downloaded from TCGA database and analyzed. In the initial screening, genomic alterations were identified in the \"normal\" lung tissues and verified by Sanger capillary sequencing. In CNCs of TCGA datasets, a mean of 0.2721 exonic variations/Mb and 5.2885 altered genes per sample were uncovered. The C:G[->]T:A transitions, a signature of tobacco carcinogen N-methyl-N-nitro-N-nitrosoguanidine, were the predominant nucleotide changes in CNCs. 16 genes had a variant rate of more than 2%, and CNC variations in MUC5B, ZXDB, PLIN4, CCDC144NL, CNTNAP3B, and CCDC180 were associated with poor prognosis whereas alterations in CHD3 and KRTAP5-5 were associated with favorable clinical outcome of the patients. This study identified the genomic alterations in CNC samples of LUADs, and further highlighted the DNA damage effect of tobacco on lung epithelial cells.

cancer biology↗

bioRxiv2017

Resistance genes in global crop breeding networks

Resistance genes are a major tool for managing crop diseases. The crop breeder networks that exchange resistance genes and deploy them in elite varieties help to determine the global landscape of resistance and epidemics, and comprise an important system for maintaining food security. These networks function as complex adaptive systems, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. We evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat, which illustrate a range of public and private configurations. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Principles for maintaining system resilience can be applied to global resistance gene deployment. For example, both diversity and redundancy in the roles played by individual crop breeding groups (public versus private, global versus local) may support societal goals for crop production. Another principle is management of connectivity, where enhanced connectivity among crop breeders may benefit global resistance gene deployment, but increase risks to the durability of resistance genes without effective policies regarding deployment.

genetics↗

bioRxiv2016

Baseline mutation profiling of 1134 samples of circulating cell-free DNA and blood cells from healthy individuals

The molecular alteration in circulating cell-free DNA (cfDNA) in plasma can reflect the status of the human body in a timely manner. Hence, cfDNA has emerged as important biomarkers in clinical diagnostics, particularly in cancer. However, somatic mutations are also commonly found in healthy individuals, which extensively interfere with the diagnostic results in cancer. This study was designed to examine the background somatic mutations in white blood cells (WBC) and cfDNA for healthy controls based on the sequencing data from 1134 samples, to understand the patterns and origin of mutations detected in cfDNA. We determined the mutation frequencies in both the WBC and cfDNA groups of the samples by a panel of 50 cancer-associated genes which covered 20K nucleotide regions using ultra-deep sequencing with average depth >40000 folds. Our results showed that most of mutations in cfDNA originated from WBC. We also observed that NPM1 gene was the most frequently mutant gene in both WBC and cfDNA. Our study highlighted the importance of sequencing both cfDNA and WBC, to improve the sensitivity and accuracy for calling cancer-related mutations from circulating tumor DNA, and shielded light on developing the early cancer diagnosis by cfDNA sequencing.

bioinformatics↗