Ontology highlight
ABSTRACT:
PROVIDER: PRJNA884087 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Bambusa tuldoides f. swolleninternode Raw sequence reads
| PRJNA892021 | ENA
Project description:We performed shallow whole genome sequencing (WGS) on circulating free (cf)DNA extracted from plasma or cerebrospinal fluid (CSF), and shallow WGS on the tissue DNA extracted from the biopsy in order to evaluate the correlation between the two biomaterials. After library construction and sequencing (Hiseq3000 or Ion Proton), copy number variations were called with WisecondorX.
2021-11-17 | E-MTAB-10548 | biostudies-arrayexpress
Project description:Whole genome sequencing (WGS) of tongue cancer samples and cell line was performed to identify the fusion gene translocation breakpoint. WGS raw data was aligned to human reference genome (GRCh38.p12) using BWA-MEM (v0.7.17). The BAM files generated were further analysed using SvABA (v1.1.3) tool to identify translocation breakpoints. The translocation breakpoints were annotated using custom scripts, using the reference GENCODE GTF (v30). The fusion breakpoints identified in the SvABA analysis were additionally confirmed using MANTA tool (v1.6.0).
2024-02-17 | E-MTAB-12534 | biostudies-arrayexpress
Project description:In principle, whole-genome sequencing (WGS) of the human genome even at low coverage offers higher resolution for genomic copy number variation (CNV) detection compared to array-based technologies, which is currently the first-tier approach in clinical cytogenetics. There are, however, obstacles in replacing array-based CNV detection with that of low-coverage WGS such as cost, turnaround time, and lack of systematic performance comparisons. With technological advances in WGS in terms of library preparation, instrument platforms, and data analysis algorithms, obstacles imposed by cost and turnaround time are fading. However, a systematic performance comparison between array and low-coverage WGS-based CNV detection has yet to be performed. Here, we compared the CNV detection capabilities between WGS (short-insert, 3kb-, and 5kb-mate-pair libraries) at 1X, 3X, and 5X coverages and standardly used high-resolution arrays in the genome of 1000-Genomes-Project CEU genome NA12878. CNV detection was performed using standard analysis methods, and the results were then compared to a list of Gold Standard NA12878 CNVs distilled from the 1000-Genomes Project. Overall, low-coverage WGS is able to detect drastically more (approximately 5 fold more on average) Gold Standard CNVs compared to arrays and is accompanied with fewer CNV calls without secondary validation. Furthermore, we also show that WGS (at ≥1X coverage) is able to detect all seven validated deletions larger than 100 kb in the NA12878 genome whereas only one of such deletions is detected in most arrays. Finally, we show that the much larger 15 Mbp Cri-du-chat deletion can be clearly seen at even 1X coverage from short-insert WGS.
2018-06-26 | GSE105092 | GEO
Project description:We evaluated linked-read whole genome sequencing (WGS) for detection of structural chromosomal rearrangements in primary samples of varying DNA quality from 12 patients diagnosed with ALL. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements, aneuploidy assessment and gene deletions. Additional RNA-sequencing and copy number aberrations (CNA) data from Illumina Infinium arrays were also generated and assessed against the linked-read WGS data. RNA-sequencing data was used to support structural chromosomal rearrangements detected in the linked-read WGS data by detecting expressed fusion genes as a consequence of the rearrangements. Illumina Infinium arrays (450k array and/or SNP array) were used to assess CNA status to further support the findings in the linked-read WGS data. The processed CNA data from the primary ALL patient samples has been deposited to GEO. RNA-sequencing, linked-read WGS data, and raw SNP array data from the primary ALL patient samples will not be deposited because the patient/parent consent does not cover depositing data that may be used for large-scale determination of germline variants in a repository. The ALL samples were collected 10-20 years ago from pediatric patients aged 2-15 years, some whom have deceased. The linked-read WGS data and the RNA-sequencing data sets generated in the study are available upon reasonable request from the corresponding author Jessica.Nordlund@medsci.uu.se.
2020-03-02 | GSE116057 | GEO
Project description:We evaluated linked-read whole genome sequencing (WGS) for detection of structural chromosomal rearrangements in primary samples of varying DNA quality from 12 patients diagnosed with ALL. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements, aneuploidy assessment and gene deletions. Additional RNA-sequencing and copy number aberrations (CNA) data from Illumina Infinium arrays were also generated and assessed against the linked-read WGS data. RNA-sequencing data was used to support structural chromosomal rearrangements detected in the linked-read WGS data by detecting expressed fusion genes as a consequence of the rearrangements. Illumina Infinium arrays (450k array and/or SNP array) were used to assess CNA status to further support the findings in the linked-read WGS data. The processed CNA data from the primary ALL patient samples has been deposited to GEO. RNA-sequencing, linked-read WGS data, and raw SNP array data from the primary ALL patient samples will not be deposited because the patient/parent consent does not cover depositing data that may be used for large-scale determination of germline variants in a repository. The ALL samples were collected 10-20 years ago from pediatric patients aged 2-15 years, some whom have deceased. The linked-read WGS data and the RNA-sequencing data sets generated in the study are available upon reasonable request from the corresponding author Jessica.Nordlund@medsci.uu.se.
2020-03-02 | GSE117693 | GEO