0c1e751423b38dd741875d4cdcc6ffb5d4c4a135 max Tue May 12 07:51:34 2026 -0700 mei: add DeepMEI 1000G subtrack on hg38 91,617 MEIs (68,282 Alu, 16,891 L1, 6,444 SVA) called by DeepMEI on the 3,202 high-coverage 1000 Genomes samples. Same 1-bp anchor convention and Okabe-Ito colors as meiHgsvc3. DeepMEI's symbolic ALT carries no inserted sequence or insertion length, so the bigBed schema is a subset of meiHgsvc3 (no svLen, callerCount, validation flags, insertSeq). Also fixes the INS-svLen:carrierCount label format note in meiHgsvc3.html. refs #37524 diff --git src/hg/makeDb/trackDb/human/meiDeepmei1kg.html src/hg/makeDb/trackDb/human/meiDeepmei1kg.html new file mode 100644 index 00000000000..49e826c3f85 --- /dev/null +++ src/hg/makeDb/trackDb/human/meiDeepmei1kg.html @@ -0,0 +1,141 @@ +<h2>Description</h2> +<p> +This track shows <b>mobile element insertions (MEIs)</b> called by +<a href="https://github.com/xuxif/DeepMEI" target="_blank">DeepMEI</a> +on the 3,202 high-coverage 1000 Genomes Project samples (NYGC +re-sequencing) aligned to GRCh38. At each site, at least one of the +3,202 samples carries a non-reference insertion of an Alu, L1 (LINE-1) +or SVA mobile element. DeepMEI is a convolutional neural-network +caller that scans short-read alignments for the read-pair, split-read +and clipping signatures of a new insertion and classifies each +candidate site as Alu, L1 or SVA. +</p> + +<table class="stdTbl"> +<tr><th>Class</th><th>MEIs</th></tr> +<tr><td>Alu</td><td>68,282</td></tr> +<tr><td>L1</td><td>16,891</td></tr> +<tr><td>SVA</td><td>6,444</td></tr> +<tr><th>Total</th><th>91,617</th></tr> +</table> + +<p> +For each MEI, the track lists the element class, the alt-allele count, +allele number and allele frequency across the 3,202 samples, the number +of carrier samples, and the list of carrier sample IDs. +</p> + +<h2>Display Conventions and Configuration</h2> +<p> +An insertion has zero length on the reference: it attaches between +two adjacent reference bases without replacing any of them. Following +the VCF convention used by DeepMEI and by the other long-read SV +and MEI tracks, each MEI is drawn as a <b>1-bp block sitting on the +anchor base</b> — the reference base immediately to the left of +the insertion attachment point. The inserted mobile element itself is +not present in the reference and is therefore not drawn; the source +VCF uses a symbolic ALT (e.g. <tt><INS:ME:ALU></tt>) and does not +report the inserted sequence or its exact length, so neither is shown +on this track. The item label is <tt>INS-class-carrierCount</tt>. +</p> +<p> +Items are colored by element class: +</p> +<ul> + <li><span style="display:inline-block;background-color:#0072B2;width:18px;height:12px;vertical-align:middle;"></span> <b>Alu</b> — SINE (Short INterspersed Element)</li> + <li><span style="display:inline-block;background-color:#D55E00;width:18px;height:12px;vertical-align:middle;"></span> <b>L1</b> — LINE-1 (Long INterspersed Element-1)</li> + <li><span style="display:inline-block;background-color:#009E73;width:18px;height:12px;vertical-align:middle;"></span> <b>SVA</b> (SINE-VNTR-Alu) — composite retrotransposon</li> +</ul> +<p> +The score column encodes the alt-allele frequency on a 0-1000 scale. +Filters allow restricting to specific element classes, allele frequency +and carrier counts. +</p> + +<h2>Methods</h2> +<p> +DeepMEI is a deep convolutional neural network that detects +non-reference mobile element insertions from short-read whole-genome +sequencing. For every candidate site supported by an anomalous +read-pair, split-read or soft-clip signature, the surrounding alignment +pile-up is encoded as an image and passed through a CNN that classifies +the site as Alu, L1, SVA or background. The model was trained on +labelled MEIs from the 1000 Genomes phase 3 callset and orthogonal +long-read truth sets. For this track, DeepMEI was run on the +high-coverage (~30×) Illumina re-sequencing of all 3,202 +1000 Genomes Project samples produced by the New York Genome Center +(NYGC), giving 6,404 haplotypes per site. See Xu et al. 2023 (bioRxiv) +for full methodological details. +</p> + +<p> +The original VCF was downloaded from the DeepMEI GitHub repository +(file <tt>merge_1000g.latested.vcf.gz</tt> in +<a href="https://github.com/xuxif/DeepMEI/tree/main/DeepMEI/1000g_high_callset" +target="_blank">DeepMEI/1000g_high_callset/</a>) and converted to +bigBed following the steps described in the +<a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/hg38/mei.txt" +target="_blank">makeDoc file</a>. Conversion uses scripts in +<a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/scripts/mei" +target="_blank">src/hg/makeDb/scripts/mei</a>: VCF-style positions +(1-based POS, anchor base) are converted to half-open BED coordinates +(<tt>chromStart = POS - 1</tt>, <tt>chromEnd = chromStart + 1</tt>), +per-sample genotypes are tallied across the 3,202 samples, and items +are colored by mobile element class. +</p> + +<h2>Data Access</h2> +<p> +The data can be explored interactively in table format with the +<a href="../cgi-bin/hgTables">Table Browser</a> or the +<a href="../cgi-bin/hgIntegrator">Data Integrator</a> and exported from +there to spreadsheet or tab-separated tables. From scripts, the data can +be accessed through our <a href="https://api.genome.ucsc.edu">API</a>, +track=<i>meiDeepmei1kg</i>. +</p> +<p> +For automated download and analysis, the genome annotation is stored in +a bigBed file that can be downloaded from +<a href="http://hgdownload.soe.ucsc.edu/gbdb/hg38/mei/" target="_blank"> +our download server</a>. The file for this track is called +<tt>deepmei1kg.bb</tt> in <tt>/gbdb/hg38/mei/</tt>. Individual regions +or the whole genome annotation can be obtained using our tool +<tt>bigBedToBed</tt>, which can be compiled from the source code or +downloaded as a precompiled binary for your system. Instructions for +downloading source code and binaries can be found +<a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads">here</a>. +The tool can also be used to obtain features within a given range, e.g. +<tt>bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg38/mei/deepmei1kg.bb -chrom=chr21 -start=0 -end=100000000 stdout</tt>. +</p> +<p> +The original annotation source data can be downloaded from the +<a href="https://github.com/xuxif/DeepMEI/tree/main/DeepMEI/1000g_high_callset" +target="_blank">DeepMEI GitHub repository</a>. +</p> + +<h2>Credits</h2> +<p> +Thanks to Xiaofei Xu, Fengxiao Bu and colleagues for developing +DeepMEI and releasing the 1000 Genomes MEI callset, and to the New +York Genome Center for producing the underlying high-coverage +1000 Genomes re-sequencing data. +</p> + +<h2>References</h2> +<p> +Xu X, Huang Y, Wang X, Cheng J, Yuan H, Bu F. +<a href="https://doi.org/10.1101/2023.03.07.531451" target="_blank"> +Identification of mobile element insertion from whole genome sequencing +data using deep neural network model</a>. +<em>bioRxiv</em>. 2023 March 8. doi:10.1101/2023.03.07.531451. +</p> +<p> +Byrska-Bishop M, Evani US, Zhao X, Basile AO, Abel HJ, Regier AA, Corvelo A, Clarke WE, Musunuri R, +Nagulapalli K <em>et al</em>. +<a href="https://linkinghub.elsevier.com/retrieve/pii/S0092-8674(22)00991-6" target="_blank"> +High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 +trios</a>. +<em>Cell</em>. 2022 Sep 1;185(18):3426-3440.e19. +PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/36055201" target="_blank">36055201</a>; PMC: <a +href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9439720/" target="_blank">PMC9439720</a> +</p>