ID8 whole exome sequence
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB13730 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The data in this submission relate to whole exome sequencing from murine ovarian cancer cell line ID8. All sequencing was performed by Beckman Coulter Genomics, Grenoble, France in February 2013.
2018-03-31 | E-MTAB-4663 | biostudies-arrayexpress
Project description:Cancer is predominantly a somatic disease. A mutant allele found in cancer cell genome is considered somatic when it is absent in paired normal genome and dbSNP, the most comprehensive public SNP database. However, dbSNP inadequately represents several non-Caucasian populations including that from the Indian subcontinent, posing a limitation in cancer genomic analyses of data from these populations. We present TMC-SNPdb, as the first open source freely accessible (through ANNOVAR), flexible and upgradable SNP database from whole exome data of 62 normal samples derived from cancer patients of Indian origin, representing 114,309 unique germline variants. TMC-SNPdb is presented with a companion subtraction tool that can be executed with command line option or an easy-to-use graphical user interface (GUI) with the ability to deplete additional Indian population specific SNPs over and above that possible with dbSNP and 1000 Genomes databases. Using an institutional generated whole exome data set of 132 samples of Indian origin, we demonstrate that TMC-SNPdb reduced 42%, 33% and 28% false positive somatic events post dbSNP depletion in Indian origin tongue, gallbladder, and cervical cancer samples, respectively. Beyond cancer somatic analyses, we anticipate utility of TMC-SNPdb in several Mendelian germline diseases.
2018-06-06 | E-MTAB-4618 | biostudies-arrayexpress
Project description:whole exome sequening
2023-01-05 | GSE220940 | GEO
Project description:The raw data consist of whole exome sequencing data of cervical squamous samples
2022-02-11 | E-MTAB-11407 | biostudies-arrayexpress
Project description:Whole-exome sequencing of induced murine squamous cell carcinoma. 26 samples are included.
2014-09-11 | E-MTAB-2887 | biostudies-arrayexpress
Project description:Sequencing of mRNA from ID8 tumor cells and ID8 tumor cells harvested from ascites of mice 11 weeks after intra peritoneal inoculation show acquisition of cancer stem cell-like features in ascitic tumor cells.
2020-01-06 | E-MTAB-8140 | biostudies-arrayexpress
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.
2014-08-12 | GSE60254 | GEO
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.
2014-08-12 | GSE60168 | GEO
Project description:Raw data of exome sequencing of 17 paired samples and 1 orphan tumor sample, total 18 samples
2022-02-12 | E-MTAB-9766 | biostudies-arrayexpress