GenoNet scores for human genome assembly GRCh37

<p>Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type specific epigenetic annotations to predict functional consequences of non-coding variants.</p> <p><strong>Format</strong></p> <p>The GenoNet scores are stored in the tab-delimited text files.&nbsp;</p> <p>Each row represents a genomic region with 131 columns. Please find the header line in &quot;genonet.header.txt&quot;.&nbsp;</p> <p>The first four columns are chromosome, start coordinate, end coordinate, and a region ID named by positions. Please note that the coordinates are counted in the 0-based UCSC Genome Browser BED format. For example, the following region with a start position 10000 and an end position 10025 includes 25 base pairs within chr1:10001-10025.</p> <p>chr1 &nbsp; &nbsp;10000 &nbsp; &nbsp;10025 &nbsp; &nbsp;chr1_10001_10025</p> <p>Columns 5-131 are the predicted tissue-specific functional effects (GenoNet scores) for the 127 Roadmap tissues. Each column is named by the corresponding epigenome ID. This <a href="https://docs.google.com/spreadsheet/ccc?key=0Am6FxqAtrFDwdHU1UC13ZUxKYy1XVEJPUzV6MEtQOXc&amp;usp=sharing">online spreadsheet</a> includes the information about the 127 Roadmap tissues in detail.</p> <p><strong>Reference</strong><br> Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018.</p> <p><strong>Release</strong></p> <p>GRCh37&nbsp;<a href="https://zenodo.org/record/3336209">https://zenodo.org/record/3336209</a></p> <p>GRCh38 liftover&nbsp;<a href="https://zenodo.org/record/6484230">https://zenodo.org/record/6484230</a></p>