Ontology highlight
ABSTRACT:
PROVIDER: PRJNA693418 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13491102.fastq.gz | Fastqsanger.gz | |||
| SRR13494715.fastq.gz | Fastqsanger.gz | |||
| SRR13489924.fastq.gz | Fastqsanger.gz | |||
| SRR13497635.fastq.gz | Fastqsanger.gz | |||
| SRR13497636.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 16 |
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Project description:Interventions: NA:No
Primary outcome(s): Whole exome;PFS
Study Design: Cross-sectional
| 47576 | ecrin-mdr-crc
Project description:Whole exome sequencing (the SureSelectXT Mouse All Exon Kit) was done on leukemias from NP23-NHD13 double transgenic mice with strain background designated “C57Bl6-NIH”.
2020-09-10 | GSE137542 | GEO
Project description:Agilent whole exome hybridisation capture was performed on genomic DNA derived from Chondrosarcoma cancer and matched normal DNA from the same patients. Next Generation sequencing performed on the resulting exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes. Now we aim to re find and validate the findings of those exome libraries using bespoke pulldown methods and sequencing the products.
2016-05-17 | E-ERAD-37 | 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:Single Gland Whole-exome sequencing: building on our prior description of multi-region WES of colorectal tumors and targeted single gland sequencing (E-MTAB-2247), we performed WES of multiple single glands from different sides (right: A and left: B) of two tumors in this study (tumor O and U) on the illumina platform using the Agilent SureSelect 2.0 or illumina Nextera Rapid Capture Exome kit (SureSelect or NRCE, as indicated in the naming of fastq files). Colorectal Cancer Xenograft Whole-exome sequencing: The HCT116 and LoVo Mismatch-Repair-deficient colorectal adenocarcinoma cell lines were obtained from the ATCC and cultured under standard conditions. For both cell lines, a single âfoundingâ cell was cloned and expanded in vitro to ~6M cells. Two aliquots of ~1M cells were subcutaneously injected into opposite flanks (right and left) of a nude mouse and tumors allowed to reach a size of ~1B cells (1cm3) before the animal was sacrificed. Tumor tissue was collected separately from the right and left lesions and DNA was extracted for WES using the illumina TruSeq Exome kit or Nextera Rapid Capture Exome expanded Kits (Truseq or NRCEe), as was DNA from the first passage population (a polyclonal tissue culture for HCT116 and a polyclonal xenograft sample for LoVo), which were employed as a control to study mutation accumulation in culture and post xenotransplantation.
2017-05-01 | E-MTAB-5547 | 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 | GSE60168 | 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:Purpose: DNA methyltransferase 3A (DNMT3A) mediates de novo DNA methylation. Mutations in DNMT3A are associated with hematological malignancies, most frequently acute myeloid leukemia. DNMT3A mutations are hypothesized to establish a pre-leukemic state, rendering cells vulnerable to secondary oncogenic mutations and malignant transformation. However, the mechanisms by which DNMT3A mutations contribute to leukemogenesis are not well-defined. Methods: Whole-exome sequencing of extracted DNA was performed to confirm DNMT3A mutations. Library construction, exon capture, and sequencing was performed by Otogenetics (Atlanta, GA, USA). In brief, paired-end libraries were generated using the Illumina TruSeq DNA sample preparation kit. Exons were enriched using the Agilent Human All Exon V5 (51 Mb) capture system. Illumina HiSeq2500 was used for sequencing with a paired-end sequencing length of 100-125 bp and approximately 70 million reads per sample. Results: the DNMT3A mutations induced by K562 cells were transfected with a plasmid encoding Cas9, a CRISPR guide-RNA (gRNA) targeting the DNMT3A gene, and green fluorescent protein (GFP) followed by single-cell sorting. GFP-positive single cell clones were genotyped to confirm the presence of DNMT3A mutations. MT2-MT5 were verified by analysis of whole-exome sequencing data, and WTblk and WT1 were again confirmed to have no mutations in the DNMT3A gene. Conclusions: CRISPR/Cas9 gene editing allowed the generation of DNMT3A-mutated K562 cells that may be used to model effects of DNMT3A mutations in human cells. Our findings implicate aberrant splicing and induction of genomic instability as potential mechanisms by which DNMT3A mutations might predispose to malignancy.
2018-01-09 | GSE96625 | 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. DNA copy number profiles of mouse squamous cell lung cancer (SCLC) were generated with ENCODER from whole exome sequencing data and compared to results from the NimbleGen array
2014-08-12 | E-GEOD-60254 | 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. DNA copy number profiles of melanoma PDX sample were generated with ENCODER from whole exome sequencing data and compared to results from the SNP6 platform.
2014-08-12 | E-GEOD-60168 | biostudies-arrayexpress