FUN-LDA scores for human genome assembly GRCh37

<p>FUN-LDA is based on a Latent Dirichlet Allocation (LDA) model for predicting functional effects of non-coding genetic variants in a cell type and tissue-specific way by integrating diverse epigenetic annotations for specific cell types and tissues from large-scale genomics projects such as ENCODE and Roadmap Epigenomics. Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome for 127 tissues and cell types in ENCODE and Roadmap Epigenomics.&nbsp;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>Format</strong></p> <p>The FUN-LDA scores are stored in the UCSC Genome Browser bigWig Track Format.&nbsp;</p> <p>To extract FUN-LDA scores, the bigWigAverageOverBed utility is required. It can be downloaded from the Genome Browser website at&nbsp;<a href="https://hgdownload.cse.ucsc.edu/admin/exe">https://hgdownload.cse.ucsc.edu/admin/exe</a>.</p> <p>User should prepare a UCSC Genome Browser bed file, a tab-separated four-column file. The first column is the chromosome; the second is the zero-based coordinate of the position&nbsp;of interest; the third is that zero-based coordinate plus one; and the fourth is a unique identifier for the position. The command below is an example&nbsp;of extracting FUN-LDA scores in tissue E007 for the positions defined in a bed file, &quot;input.bed&quot;.</p> <pre><code class="language-bash">bigWigAverageOverBed E007.valley9.c89.bigwig input.bed output.tab</code></pre> <p>It produces a six-column file, output.tab.&nbsp;The first column includes the unique position id defined in the input bed file.&nbsp;The last column, average over the covered bases, is the score for this position.</p> <p><strong>Reference</strong></p> <p>Daniel Backenroth, Zihuai He, Krzysztof Kiryluk, Valentina Boeva, Lynn Pethukova, Ekta Khurana, Angela Christiano, Joseph Buxbaum, Iuliana Ionita-Laza. FUN-LDA: A latent Dirichlet allocation model for predicting tissue-specific functional effects of noncoding variation: Methods and applications. American Journal of Human Genetics, 2018.<br> &nbsp;</p> <p>&nbsp;</p>