31e95f0d1dd4ca08feba9b081a356527bd4b45e6 markd Sat Sep 26 20:36:20 2026 -0700 Drop the hand-built Files column from the TSS faceted tables. refs #35528 UCSC will generate the download links in the faceted table, so the Files column each composite built for itself is redundant. Remove downloadCell, the Files header and cell, the Files entry in subtrackUrls, and the DOWNLOAD constant that only fed them. The table is now Tissue, Sample class, Experiment, Cell line on all three composites. writeMetadata no longer needs db or track; the metadata files it writes are byte-identical. Lead each Data Access section with the link to the hgdownload directory, since that is now the way to a single file, and keep the naming convention beside it. Drop the paragraph describing the Files column. diff --git src/hg/makeDb/trackDb/human/proCapNet.html src/hg/makeDb/trackDb/human/proCapNet.html index b6e37bcc9ea..4dc96db59b2 100644 --- src/hg/makeDb/trackDb/human/proCapNet.html +++ src/hg/makeDb/trackDb/human/proCapNet.html @@ -1,205 +1,202 @@ <h2>Description</h2> <p> ProCapNet is a neural network trained to predict PRO-cap signal from DNA sequence alone. PRO-cap is a run-on assay that captures the 5' end of each nascent RNA, so it reports the exact base and strand at which RNA polymerase II started transcribing, including at enhancers and at unstable transcripts that RNA-seq and CAGE miss. Six separate models were trained, one on each of six cell lines with ENCODE PRO-cap data. </p> <p> This track holds two kinds of output from those models: </p> <ul> <li><b>Predicted PRO-cap</b>: what the model expects the PRO-cap signal to be, at every base of the genome, on both strands. The sequence rules that govern where initiation happens are largely shared between cell types, so any one model highlights sequence capable of driving initiation, including at regions where no PRO-cap experiment has been done.</li> <li><b>Sequence contribution scores</b>: how much each individual base pushed the model's prediction up or down. Bases inside a functional element such as a TATA box or an initiator carry high scores, and the pattern of high-scoring bases often spells out the recognition sequence of a promoter-associated transcription factor. These are computed only around MANE Select transcription start sites, so they cover about 1% of the genome and the track is empty everywhere else.</li> </ul> <p> The predictions are available on GRCh38/hg38 and T2T-CHM13/hs1. The contribution scores are available on GRCh38/hg38 only, because they are computed at MANE Select transcription start sites and MANE is not defined for T2T-CHM13. </p> <p> Predictions are not measurements: they say what the sequence looks capable of, not what a given cell is doing. The matching experimental data is in the PRO-cap track, available on GRCh38/hg38. </p> <h2>Display Conventions and Configuration</h2> <p> Cell lines are listed in the table on this page, one row each, with a checkbox per data type. Use the <b>Sample class</b> facet to narrow the list, and the <b>Group tracks by</b> buttons to order the browser by sample or by data type. </p> <p> Each predicted PRO-cap track is an overlay of the two strands: plus strand predictions are drawn upward and minus strand predictions downward. The y axis is the predicted number of PRO-cap reads at that base. </p> <p> Contribution scores are drawn as a sequence logo when zoomed in far enough to show individual bases: the letter of the reference base is scaled by its score, so a run of tall letters is a motif the model relied on. At lower zoom the same values are drawn as a wiggle. Scores can be negative, meaning the base argued against initiation being placed where it was. Scores exist only in the roughly 2 kb window around each MANE Select transcription start site, about 38.7 Mb of the genome. Everywhere else the track is empty, which is not the same as a score of zero. </p> <p> Read depth differs between the six PRO-cap experiments the models were trained on, and both predicted signal and contribution scores scale with it, so the y axis is not comparable between cell lines. Tracks are colored by the cell line the model was trained on: </p> <ul> <li><span style="display:inline-block; background-color:#0072B2; width:18px; height:12px; vertical-align:middle;"></span> <b>A673</b> Ewing sarcoma</li> <li><span style="display:inline-block; background-color:#D55E00; width:18px; height:12px; vertical-align:middle;"></span> <b>Caco-2</b> colorectal adenocarcinoma</li> <li><span style="display:inline-block; background-color:#009E73; width:18px; height:12px; vertical-align:middle;"></span> <b>Calu3</b> lung adenocarcinoma</li> <li><span style="display:inline-block; background-color:#CC79A7; width:18px; height:12px; vertical-align:middle;"></span> <b>HUVEC</b> umbilical vein endothelial cells</li> <li><span style="display:inline-block; background-color:#E69F00; width:18px; height:12px; vertical-align:middle;"></span> <b>K562</b> chronic myelogenous leukemia</li> <li><span style="display:inline-block; background-color:#56B4E9; width:18px; height:12px; vertical-align:middle;"></span> <b>MCF10A</b> non-tumorigenic breast epithelium</li> </ul> <h2>Methods</h2> <p> ProCapNet adapts the BPNet architecture: it reads 2,114 bp of sequence and predicts a base-resolution initiation profile over the central 1,000 bp on both strands, together with the total number of initiation events in that window. One model was trained per cell line on ENCODE PRO-cap data, using all PRO-cap peaks in that cell line plus sampled DNase-hypersensitive sites as background, with 7-fold cross-validation split by chromosome. Full details are in Cochran <em>et al.</em>, 2024. </p> <p> The Kundaje lab generated the genome-wide predictions by applying each model in 2,114 bp windows at a stride of 250 bp, then averaging across the seven cross-validation models and across the forward and reverse-complemented sequence. On hg38 no prediction was made where most of a window was unresolved (N) in the reference. Contribution scores were computed with DeepSHAP, which contrasts the model's output on the real sequence against its output on 25 dinucleotide shuffles of it, and were computed only at MANE Select transcription start sites. </p> <p> The ProCapNet model implementation is at <a href="https://github.com/kundajelab/ProCapNet" target="_blank">kundajelab/ProCapNet</a>. The steps that turned the published files into these tracks are recorded in <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/doc/$db/transcriptionStart.txt" target="_blank">doc/$db/transcriptionStart.txt</a>, the scripts they run are in <a href="https://github.com/ucscGenomeBrowser/kent/tree/master/src/hg/makeDb/outside/proCapNet" target="_blank">makeDb/outside/proCapNet</a>, and the track configuration is in <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/trackDb/human/$db/transcriptionStart.ra" target="_blank">trackDb/human/$db/transcriptionStart.ra</a>. </p> <h2>Data Access</h2> +<p> +The bigWig files are on our +<a href="http://hgdownload.soe.ucsc.edu/gbdb/$db/proCapNet/" target="_blank">download +server</a>. Predictions are under <tt>pred/</tt> and are named for the cell line, +the model and the strand, for example <tt>K562.proCapNet.pos.bw</tt> and +<tt>K562.proCapNet.neg.bw</tt>. Contribution scores, which exist for GRCh38 only, +are under <tt>contrib/</tt>, for example <tt>K562.proCapNet-contrib.bw</tt>. +</p> + <p> The data can be explored interactively in table format with the <a href="../cgi-bin/hgTables">Table Browser</a> or the <a href="../cgi-bin/hgIntegrator">Data Integrator</a> and exported from there to spreadsheet or tab-sep tables. From scripts, the data can be accessed through our <a href="https://api.genome.ucsc.edu" target="_blank">API</a>. The API returns one bigWig at a time, so name a single strand of one cell line rather than the container, for example track=<i>proCapNet_K562_pred_pos</i>. </p> <p> -The Files column of the table on this page links each cell line's bigWigs -directly, so a single file can be fetched without working out its path. -</p> - -<p> -For automated download and analysis, the genome annotation is stored in bigWig -files that can be downloaded from -<a href="http://hgdownload.soe.ucsc.edu/gbdb/$db/proCapNet/" target="_blank">our -download server</a>. Predictions are under <tt>pred/</tt> and are named for the -cell line, the model and the strand, for example -<tt>K562.proCapNet.pos.bw</tt> and <tt>K562.proCapNet.neg.bw</tt>. Contribution -scores, which exist for GRCh38 only, are under <tt>contrib/</tt>, for example -<tt>K562.proCapNet-contrib.bw</tt>. Individual regions or the whole genome annotation -can be obtained using our tool <tt>bigWigToBedGraph</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 are on the +Individual regions or the whole genome annotation can be obtained using our tool +<tt>bigWigToBedGraph</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 are on the <a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads" -target="_blank">utilities download page</a>. -The tool can also be used to obtain features within a given range, e.g. +target="_blank">utilities download page</a>. The tool can also be used to obtain +features within a given range, e.g. <tt>bigWigToBedGraph http://hgdownload.soe.ucsc.edu/gbdb/$db/proCapNet/pred/K562.proCapNet.pos.bw -chrom=chr21 -start=0 -end=100000000 stdout</tt> </p> <p> The ProCapNet models are on the <a href="https://www.encodeproject.org" target="_blank">ENCODE portal</a> as BPNet-model annotations, one per cell line, linked from the Experiment column of the table on this page. Each annotation also holds the trained model, sequence contribution scores and predicted signal over a selected set of regions. The genome-wide predictions shown here are not part of that ENCODE release. </p> <h2>Credits</h2> <p> ProCapNet was developed by Kelly Cochran in the Kundaje lab at Stanford University. The genome-wide predictions and contribution scores were generated by Kelly Cochran in collaboration with the GENCODE consortium. Thanks to Kelly Cochran and Anshul Kundaje for making the data available. </p> <h2>References</h2> <p> Cochran K, Yin M, Mantripragada A, Schreiber J, Marinov GK, Shah SR, Yu H, Lis JT, Kundaje A. <a href="https://www.ncbi.nlm.nih.gov/pubmed/38853896" target="_blank"> Dissecting the cis-regulatory syntax of transcription initiation with deep learning</a>. <em>bioRxiv</em>. 2024 Nov 21;. DOI: <a href="https://doi.org/10.1101/2024.05.28.596138" target="_blank">10.1101/2024.05.28.596138</a>; PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/38853896" target="_blank">38853896</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11160661/" target="_blank">PMC11160661</a> </p> <p> Avsec Ž, Weilert M, Shrikumar A, Krueger S, Alexandari A, Dalal K, Fropf R, McAnany C, Gagneur J, Kundaje A <em>et al</em>. <a href="https://www.ncbi.nlm.nih.gov/pubmed/33603233" target="_blank"> Base-resolution models of transcription-factor binding reveal soft motif syntax</a>. <em>Nat Genet</em>. 2021 Mar;53(3):354-366. DOI: <a href="https://doi.org/10.1038/s41588-021-00782-6" target="_blank">10.1038/s41588-021-00782-6</a>; PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/33603233" target="_blank">33603233</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812996/" target="_blank">PMC8812996</a> </p> <p> Kwak H, Fuda NJ, Core LJ, Lis JT. <a href="https://www.ncbi.nlm.nih.gov/pubmed/23430654" target="_blank"> Precise maps of RNA polymerase reveal how promoters direct initiation and pausing</a>. <em>Science</em>. 2013 Feb 22;339(6122):950-3. DOI: <a href="https://doi.org/10.1126/science.1229386" target="_blank">10.1126/science.1229386</a>; PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/23430654" target="_blank">23430654</a>; PMC: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974810/" target="_blank">PMC3974810</a> </p>