Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
Project description:We present scNanoATAC-seq (Single-cell Assay for Transposase Accessible Chromatin by Oxford Nanopore Technologies Sequencing), an effective method for simultaneous detection of chromatin accessibility and genetic variation. Long fragments (about 4-5Kb) of single-cell ATAC-seq library were enriched and sequenced by Oxford Nanopore Technologies platform. Ends of long ATAC-seq fragments are regarded as chromatin accessibility signal in downstream analysis.
Project description:We present scNanoATAC-seq (Single-cell Assay for Transposase Accessible Chromatin by Oxford Nanopore Technologies Sequencing), an effective method for simultaneous detection of chromatin accessibility and genetic variation. Long fragments (about 4-5Kb) of single-cell ATAC-seq library were enriched and sequenced by Oxford Nanopore Technologies platform. Ends of long ATAC-seq fragments are regarded as chromatin accessibility signal in downstream analysis.
Project description:The array46 750k study was performed in the DNA sample for mosaicism aneuploidy detection and loss or absence of heterozygosity. Samples include patients with phenotype associated with pigmentary mosaicism The array46 750k study was performed in the DNA sample to discard uniparental disomy (UPD)
Project description:Long-read nanopore sequencing has emerged as a potent tool for studying RNA modifications. However, the detection of N4-acetylcytidine (ac4C) based on nanopore sequencing remains largely unexplored. Here, we introduce ac4Cnet, a deep learning frame utilizing Oxford Nanopore direct RNA sequencing to accurately identify ac4C sites. Our methodology involves training ac4Cnet capable of distinguishing ac4C from unmodified cytidine and 5-methylcytosine (m5C), as well as estimating the modification rate at each ac4C site. We demonstrate the robustness of our approach through validations on independent in vitro datasets and a human cell line, highlighting its versatility and potential for advancing the study of ac4C modifications.
Project description:This SuperSeries is composed of the following subset Series: GSE27940: Methylation detection Oligonucleotide Microarray Analysis: high resolution method for CpG island methylation detection 3. GSE27943: Gene Expression Array of Human Ovarian Cancer. GSE28013: Representational Oligonucleotide Microarray Analysis (ROMA) array for Copy Number Variation Detection. Refer to individual Series
Project description:Despite the widespread adoption of ChIP-seq there is still no consensus on quality assessment metrics. No single published metric can reliably discriminate the success or failure of an experiment, thus hampering objectivity and reproducibility of quality control. We introduce a new framework for ChIP-seq data quality assessment that overcomes the limitation of previous solutions. Our tool called "ChIC" incorporates a novel set of quality control metrics integrated into one single score summarizing the sample quality and a reference compendium with thousands of published ChIP-seq samples, for easier evaluation of new data. This test dataset contain an example of succesfull and non-succesfull ChIP-seq sample for mouse H3K27me3.
