Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1187591 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR31411900_1.fastq.gz | Fastqsanger.gz | |||
| SRR31411900_2.fastq.gz | Fastqsanger.gz | |||
| SRR31411901_1.fastq.gz | Fastqsanger.gz | |||
| SRR31411901_2.fastq.gz | Fastqsanger.gz | |||
| SRR31411902_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 12 |
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Project description:<p>Recently developed methods that utilize partitioning of long genomic DNA fragments, and barcoding of shorter fragments derived from them, have succeeded in retaining long-range information in short sequencing reads. These so-called read cloud approaches represent a powerful, accurate, and cost-effective alternative to single-molecule long-read sequencing. We developed software, GROC-SVs, that takes advantage of read clouds for structural variant detection and assembly. We apply the method to two 10x Genomics data sets, one chromothriptic sarcoma with several spatially separated samples, and one breast cancer cell line, all Illumina-sequenced to high coverage. Comparison to short-fragment data from the same samples, and validation by mate-pair data from a subset of the sarcoma samples, demonstrate substantial improvement in specificity of breakpoint detection compared to short-fragment sequencing, at comparable sensitivity, and vice versa. The embedded long-range information also facilitates sequence assembly of a large fraction of the breakpoints; importantly, consecutive breakpoints that are closer than the average length of the input DNA molecules can be assembled together and their order and arrangement reconstructed, with some events exhibiting remarkable complexity. These features facilitated an analysis of the structural evolution of the sarcoma. In the chromothripsis, rearrangements occurred before copy number amplifications, and using the phylogenetic tree built from point mutation data, we show that single nucleotide variants and structural variants are not correlated. We predict significant future advances in structural variant science using 10x data analyzed with GROC-SVs and other read cloud-specific methods.</p>
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Project description:Here, we report an enrichment-based ultra-low input cfDNA methylation profiling method using methyl-CpG binding proteins capture, termed cfMBD-seq. We optimized the conditions of cfMBD capture by adjusting the amount of MethylCap protein along with using methylated filler DNA. Our data showed that cfMBD-seq performs equally to the standard MBD-seq (>1000 ng input) even when using 1 ng DNA as the input. cfMBD-seq demonstrated equivalent sequencing data quality as well as similar methylation profile when compared to cfMeDIP-seq. We showed that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of CpG islands. This new bisulfite-free ultra-low input methylation profiling technology has a great potential in non-invasive and cost-effective cancer detection and classification.
2021-01-01 | GSE161331 | GEO
Project description:High-throughput RNA-sequencing has now become the gold standard method for whole-transcriptome gene expression analysis. It is widely used in a number of applications studying various transcriptomes of cells and tissues. It is also being increasingly considered for a number of clinical applications, including expression profiling for diagnostics or alternative transcripts detection. However, RNA sequencing can be challenging in some situations, for instance due to low input quantities or degraded RNA samples. Several protocols have been proposed to overcome some of these challenges, and many are available as commercial kits. Here we perform a comprehensive testing of three recent commercial technologies for RNA-seq library preparation (Truseq, Smarter and Smarter Ultra-Low) on human reference tissue preparations, for standard (1ug), low (100 and 10 ng) and ultra-low (< 1 ng) input quantities, and for mRNA and total RNA, stranded or unstranded. We analyze the results using read quality and alignments metrics, gene detection and differential gene expression metrics. Overall, we show that the Truseq kit performs well at 100 ng input quantity, while the Smarter kit shows degraded performances for 100 and 10 ng input quantities, and that the Smarter Ultra-Low kit performs quite well for input quantities < 1 ng. All the results are discussed in details, and we provide guidelines for the selection of a RNA-seq library preparation kits by biologists.
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Project description:Haematopoetic RNA-seq from ultra low input
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