Ontology highlight
ABSTRACT:
PROVIDER: PRJNA579845 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Six samples from two Glioma_CKO mice (one normal brain tissue, two tumor tissues per mouse) were performed Whole Exome Sequencing
2022-01-11 | GSE160659 | GEO
Project description:RNA sequencing and whole-exome sequencing data were obtained from 30 platinum-resistant recurrent ovarian cancer samples.
2023-05-01 | GSE230541 | 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:Whole exome sequencing of 5 MDS/MPN patients to identify the target of chromosome 22 acquired uniparental disomy (22aUPD). For samples E4051 and E6523, peripheral blood leucocytes (tumour) and cultured T-cells (germline) were prepared for exome sequencing using the Agilent SureSelect kit (Agilent Technologies, Palo Alto, CA, USA) (Human All Exon 50 Mb) and then sequenced on an Illumina HiSeq 2000 (Illumina, Great Abington, UK) at the Wellcome Trust Centre for Human Genetics, Oxford, UK. For samples ULSAM1182, ULSAM1242 and ULSAM1356, peripheral blood leukocyte DNA only were exome sequenced by SciLifeLab (Stockholm, Sweden).
2019-01-22 | E-MTAB-7491 | biostudies-arrayexpress
Project description:Illumina human Omni5Exome arrays were used to investigate CNVs in Sѐzary syndrome tumours as part of a larger study involving whole exome sequencing of the same samples and targeted resequencing of a further cohort.
2016-07-22 | GSE80650 | GEO
Project description:Purpose: There are three goals of this study: 1. To compare the genomic, exome and chromatin accessiblity profiles of the specific engineered fallopian tube cells of high-grade serous tubo-ovarian cancer (HGSC) models (this study) using whole-exome, whole-genome and ATAC-seq sequencing. Methods: For whole-exome analysis, genomic DNA was extracted from the cell lines mentioned below. Conclusions: We conclude that whole-exome, whole-genome and ATAC-seq characterization would expedite genetic network analyses and permit the dissection of complex biological functions.
2020-11-17 | GSE159253 | 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 | 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:Whole exome sequencing of 5 HCLc tumor-germline pairs. Genomic DNA from HCLc tumor cells and T-cells for germline was used. Whole exome enrichment was performed with either Agilent SureSelect (50Mb, samples S3G/T, S5G/T, S9G/T) or Roche Nimblegen (44.1Mb, samples S4G/T and S6G/T). The resulting exome libraries were sequenced on the Illumina HiSeq platform with paired-end 100bp reads to an average depth of 120-134x. Bam files were generated using NovoalignMPI (v3.0) to align the raw fastq files to the reference genome sequence (hg19) and picard tools (v1.34) to flag duplicate reads (optical or pcr), unmapped reads, reads mapping to more than one location, and reads failing vendor QC.
2016-02-01 | E-MTAB-4313 | biostudies-arrayexpress