The data come from an H3K27ac ChIP-seq experiment which was aligned to the human reference genome (hg19), aligned read counts were used to produce the coverage data; looking at these data in a genome browser was used to produce the labels. ChIP-seq means Chromatin Immunoprecipitation followed by high-throughput DNA sequencing; it is an assay used to characterize genome-wide DNA-protein interactions. In this experiment the protein of interest is histone H3, with the specific modification K27ac (hence the name H3K27ac). Large counts (peaks) therefore indicate regions of the reference genome with high likelihood of interaction between DNA and that specific protein, in the specific Monocyte sample tested.
A list of 2 data.tables: coverage has 4 columns (chrom, chromStart, chromEnd, count=number of aligned reads at each position on chrom:chromStart-chromEnd); labels has 4 columns (chrom, chromStart, chromEnd, annotation=label at chrom:chromStart-chromEnd). chrom refers to the chromosome on whcih the data were gathered (chr11), chromStart is the 0-based position before the first base of the data/label, chromEnd is the 1-based position which is the last base of the data/label. Therefore, each chromEnd on each row should be equal to the chromStart of the next row.
UCI Machine Learning Repository, chipseq data set, problem directory H3K27ac-H3K4me3_TDHAM_BP/samples/Mono1_H3K27ac/S001YW_NCMLS/problems/chr11:60000-580000 Links: https://archive.ics.uci.edu/ml/datasets/chipseq for the UCI web page; https://github.com/tdhock/feature-learning-benchmark for a more detailed explanation.
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