Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA407971 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR6061300.fastq.gz | Fastqsanger.gz | |||
| SRR6061300_1.fastq.gz | Fastqsanger.gz | |||
| SRR6061300_2.fastq.gz | Fastqsanger.gz | |||
| SRR6061310_1.fastq.gz | Fastqsanger.gz | |||
| SRR6061310_2.fastq.gz | Fastqsanger.gz |
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Project description:Evaluation of copy number variation detection between high-resolution array CGH and low-coverage short-insert and mate-pair whole-genome sequencing
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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.
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Project description:Structural variations (SVs) contribute significantly to the variability of the human genome and extensive genomic rearrangements are a hallmark of cancer. Genomic DNA paired-end-tag (DNA-PET) sequencing is an attractive approach to identify genomic SVs. The current application of PET sequencing with short insert size DNA is insufficient for the comprehensive mapping of SVs in low complexity and repeat-rich genomic regions. We have developed a robust procedure to generate PET sequencing data using large DNA inserts of 10 - 20 kb for the identification of SVs. We compared the characteristics of the large insert libraries with short insert (1 kb) libraries with the same sequencing depths and costs. Although short insert libraries bear an advantage in identifying small deletions, they do not provide a significantly better breakpoint resolution. Large inserts are superior to short inserts in providing higher physical genome coverage and therefore achieve greater sensitivity for the identification of the different types of SVs, including copy number neutral and complex events. Further, large inserts allow the identification of SVs within repetitive sequences which cannot be spanned by short inserts.
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Project description:Structural variations (SVs) contribute significantly to the variability of the human genome and extensive genomic rearrangements are a hallmark of cancer. Genomic DNA paired-end-tag (DNA-PET) sequencing is an attractive approach to identify genomic SVs. The current application of PET sequencing with short insert size DNA is insufficient for the comprehensive mapping of SVs in low complexity and repeat-rich genomic regions. We have developed a robust procedure to generate PET sequencing data using large DNA inserts of 10 - 20 kb for the identification of SVs. We compared the characteristics of the large insert libraries with short insert (1 kb) libraries with the same sequencing depths and costs. Although short insert libraries bear an advantage in identifying small deletions, they do not provide a significantly better breakpoint resolution. Large inserts are superior to short inserts in providing higher physical genome coverage and therefore achieve greater sensitivity for the identification of the different types of SVs, including copy number neutral and complex events. Further, large inserts allow the identification of SVs within repetitive sequences which cannot be spanned by short inserts. Structural variations of three cancer cell lines using short (1 kb) and long (10 kb and 20 kb) insert size DNA fragments
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Project description:The ENCODE projects seeks to identify and characterize functional elements in the human genome. Throughout the scale-up phase of ENCODE, the transcriptome group has generate Long RNA-Seq, Small RNA-Seq, Cap-Analysis of Gene Expression (CAGE), and RNA-PET short read data on the Illumina platform for ~ 40 different human primary and transformed cell lines in replicate. From these data several high-resolution and discrete features/elements have been mined out (5’ caps, splice junctions, polyadenylation sites, small RNAs, etc…). However, because these data are obtained from short-read data, we have only limited “connectivity” information. For example, from the long RNA-Seq data, which was sequenced in mate-pair fashion with average insert sizes ~ 200 bp, we know that the sequence from mate 1 is physically linked to the sequence in mate 2. We don’t know the sequence in between and we don’t know how this mate-pair is connected to other mate-pairs in the context of longer transcripts in vivo. To date, this information is gleaned from models generated in silico: In our case, by the program Cufflinks. Consequently, we have a collection of transcript models exhibiting a vast array of local complexity assembled from short read data that need to be experimentally tested. Alternatively, one can “cut to the chase” and use a more raw/elemental form of the data as a basis for additional experimentation to clone out the longer sequences generated in vivo. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf 454 Data from HepG2, HUVEC, and H1 ES cells
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2023-02-24 | E-MTAB-12670 | biostudies-arrayexpress
Project description:Gene amplifications and deletions frequently contribute to tumorigenesis. Characterization of these DNA copy-number changes is important for both the basic understanding of cancer and its diagnosis. Comparative genomic hybridization (CGH) was developed to survey DNA copy-number variations across a whole genome. With CGH, differentially labelled test and reference genomic DNAs are co-hybridized to normal metaphase chromosomes, and fluorescence ratios along the length of chromosomes provide a cytogenetic representation of DNA copy-number variation. CGH, however, has a limited ( approximately 20 Mb) mapping resolution, and higher-resolution techniques, such as fluorescence in situ hybridization (FISH), are prohibitively labour-intensive on a genomic scale. Array-based CGH, in which fluorescence ratios at arrayed DNA elements provide a locus-by-locus measure of DNA copy-number variation, represents another means of achieving increased mapping resolution. Published array CGH methods have relied on large genomic clone (for example BAC) array targets and have covered only a small fraction of the human genome. cDNAs representing over 30,000 radiation-hybrid (RH)-mapped human genes provide an alternative and readily available genomic resource for mapping DNA copy-number changes. Although cDNA microarrays have been used extensively to characterize variation in human gene expression, human genomic DNA is a far more complex mixture than the mRNA representation of human cells. Therefore, analysis of DNA copy-number variation using cDNA microarrays would require a sensitivity of detection an order of magnitude greater than has been routinely reported. We describe here a cDNA microarray-based CGH method, and its application to DNA copy-number variation analysis in breast cancer cell lines and tumours. This study is described more fully in Pollack JR et al.(1999) Nat Genet 23:41-6 Keywords: other
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