Integrated Genome-Wide Analysis of Gene Expression and DNA Copy Number Variations Highlights Stem Cell-Related Pathways in Small Cell Esophageal Carcinoma.
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ABSTRACT:
SUBMITTER: Liu D
PROVIDER: S-EPMC6136462 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Publications
Integrated Genome-Wide Analysis of Gene Expression and DNA Copy Number Variations Highlights Stem Cell-Related Pathways in Small Cell Esophageal Carcinoma.
Liu Di D Xu Xinyan X Wen Junmiao J Xie Liyi L Zhang Junhua J Shen Yuxin Y Jiang Guoliang G Chen Jiayan J Fan Min M Fan Min M
Stem cells international 20180830
<i>Purpose</i>/<i>Objectives</i>. Primary small cell esophageal carcinoma (SCEC) represents a rare and aggressive malignancy without any prospective clinical trial or established treatment strategy at present. Although previous studies have indicated similarities between SCEC and small cell lung cancer (SCLC) in terms of their clinical manifestations, pathology, and morphology, very little genetic information is available on this highly malignant tumor. At present, patients with SCEC are staged ...[more]
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Project description:BackgroundCopy number variation (CNV) can lead to intra-specific genome variations. It is not only part of normal genetic variation, but also is the source of phenotypic differences. Rice (Oryza sativa L.) is a model organism with a well-annotated genome, but investigation of CNVs in rice lags behind its mammalian counterparts.ResultsWe comprehensively assayed CNVs using high-density array comparative genomic hybridization in a panel of 20 Asian cultivated rice comprising six indica, three aus, two rayada, two aromatic, three tropical japonica, and four temperate japonica varieties. We used a stringent criterion to identify a total of 2886 high-confidence copy number variable regions (CNVRs), which span 10.28 Mb (or 2.69%) of the rice genome, overlapping 1321 genes. These genes were significantly enriched for specific biological functions involved in cell death, protein phosphorylation, and defense response. Transposable elements (TEs) and other repetitive sequences were identified in the majority of CNVRs. Chromosome 11 showed the greatest enrichment for CNVs. Of subspecies-specific CNVRs, 55.75% and 61.96% were observed in only one cultivar of ssp. indica and ssp. japonica, respectively. Some CNVs with high frequency differences among groups resided in genes underlying rice adaptation.ConclusionsHigher recombination rates and the presence of homologous gene clusters are probably predispositions for generation of the higher number of CNVs on chromosome 11 by non-allelic homologous recombination events. The subspecies-specific variants are enriched for rare alleles, which suggests that CNVs are relatively recent events that have arisen within breeding populations. A number of the CNVs identified in this study are candidates for generation of group-specific phenotypes.
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Project description:Discovery of copy number variations (CNVs), a major category of structural variations, have dramatically changed our understanding of differences between individuals and provide an alternate paradigm for the genetic basis of human diseases. CNVs include both copy gain and copy loss events and their detection genome-wide is now possible using high-throughput, low-cost next generation sequencing (NGS) methods. However, accurate detection of CNVs from NGS data is not straightforward due to non-uniform coverage of reads resulting from various systemic biases. We have developed an integrated platform, iCopyDAV, to handle some of these issues in CNV detection in whole genome NGS data. It has a modular framework comprising five major modules: data pre-treatment, segmentation, variant calling, annotation and visualization. An important feature of iCopyDAV is the functional annotation module that enables the user to identify and prioritize CNVs encompassing various functional elements, genomic features and disease-associations. Parallelization of the segmentation algorithms makes the iCopyDAV platform even accessible on a desktop. Here we show the effect of sequencing coverage, read length, bin size, data pre-treatment and segmentation approaches on accurate detection of the complete spectrum of CNVs. Performance of iCopyDAV is evaluated on both simulated data and real data for different sequencing depths. It is an open-source integrated pipeline available at https://github.com/vogetihrsh/icopydav and as Docker's image at http://bioinf.iiit.ac.in/icopydav/.
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