Dataset Information

Tumor Liver vs Normal Liver: ChIP-chip and RNA expression

ABSTRACT: There is widespread interest in efficient characterization of differences between tumor and normal samples. Here, we demonstrate an effective methodology for genome-scale characterization of tumors. Using matched normal and tumor samples from liver cancer patients, as well as non-cancer-related normal liver tissue, we first determined changes in gene expression as monitored on RNA expression arrays. We identified several hundred mRNAs that were consistently changed in the tumor samples. To characterize the mechanisms responsible for creation of the tumor-specific transcriptome, we performed ChIP-chip experiments to assay binding of RNA polymerase II, H3me3K27, and H3me3K9 in 25,000 promoter regions. These experiments identified changes in active and silenced regions of the genome in the tumor cells. Finally, we used a “virtual comparative genomic hybridization” (vCGH) method to identify copy number alterations in the tumor samples. Through comparison of RNA Polymerase II binding, chromatin structure, and copy number changes, we suggest that the major contributor to creation of the liver tumor transcriptome was changes in gene copy number. Keywords: ChIP-chip, gene expression, CGH

ORGANISM(S): Homo sapiens

PROVIDER: GSE10842 | GEO | 2008/06/04

SECONDARY ACCESSION(S): PRJNA107327

REPOSITORIES: GEO

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Project description:Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. DNA copy number profiles generated with a new tool, ENCODER, were compared to DNA copy number profiles from SNP6, NimbleGen and low-coverage Whole Genome Sequencing.

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Tumor Liver vs Normal Liver: ChIP-chip and RNA expression

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