38ccb38771eddfec3230ab3494fb12e11e7cb4ac jnavarr5 Fri Sep 25 17:52:32 2026 -0700 Putting references in alphabetical order. Adding years to citations. Not using the word 'here' and using the actual location/noun. refs #35528 diff --git src/hg/makeDb/trackDb/human/proCapNet.html src/hg/makeDb/trackDb/human/proCapNet.html index 6ea55a46079..6f90f9f7db8 100644 --- src/hg/makeDb/trackDb/human/proCapNet.html +++ src/hg/makeDb/trackDb/human/proCapNet.html @@ -1,233 +1,228 @@ <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.</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> <h3>Model</h3> <p> ProCapNet adapts the BPNet architecture. It reads 2,114 bp of one-hot encoded sequence and outputs both a base-resolution profile over the central 1,000 bp, covering both strands as a single softmax so the model can learn strand asymmetry, and a scalar giving the log total number of initiation events in that window. Training used ENCODE PRO-cap read alignments in which the first read of each pair was discarded and only the single 5'-most base of the second read was kept, merged across replicates and kept separate by strand. Each model was trained on all PRO-cap peaks in its cell line plus randomly sampled DNase-hypersensitive sites from the same cell line at a 7:1 peak to background ratio, with 7-fold cross-validation split by chromosome. Bases that are not uniquely mappable by 36-mer reads were given zero loss weight during training. -Full details are in Cochran <em>et al</em>. +Full details are in Cochran <em>et al.</em>, 2024. </p> <h3>Predicted PRO-cap</h3> <p> Genome-wide predictions were generated by the Kundaje lab by applying the model to every 2,114 bp window at a stride of 250 bp, so each base is the average of four overlapping predictions, 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. </p> <h3>Sequence contribution scores</h3> <p> Scores were computed with DeepSHAP, which estimates each base's contribution by contrasting the model's output on the real sequence against its output on a set of reference sequences, here 25 dinucleotide shuffles of the sequence being scored. Because DeepSHAP needs a single scalar to explain, the base-resolution profile output was summarized by mean-normalizing the pre-softmax logits and taking their dot product with the post-softmax profile, which weights each base's logit by its predicted probability of being used and sums over the 1,000 bp output window and both strands. This is the profile or TSS-positioning task; ProCapNet can also produce scores for its read-count task, which are not -shown here. Each scored sequence was run through all seven cross-validation +included in this track. Each scored sequence was run through all seven cross-validation models and in both orientations, and the scores averaged. </p> <h3>Source</h3> <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 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/$db/proCapNet/" target="_blank">our download server</a>. Predictions are under <tt>pred/</tt> and are named for the cell line, the model and the strand, for example <tt>K562.proCapNet.pos.bw</tt> and <tt>K562.proCapNet.neg.bw</tt>. Contribution scores, which exist for GRCh38 only, are under <tt>contrib/</tt>, for example <tt>K562.proCapNet-contrib.bw</tt>. Individual regions or the whole genome annotation can be obtained using our tool <tt>bigWigToBedGraph</tt>, which can be compiled from the source code or downloaded as a precompiled binary for your system. -Instructions for downloading source code and binaries can be found -<a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads" target="_blank">here</a>. +Instructions for downloading source code and binaries are on the +<a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads" +target="_blank">utilities download page</a>. The tool can also be used to obtain features within a given range, e.g. <tt>bigWigToBedGraph http://hgdownload.soe.ucsc.edu/gbdb/$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> +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> 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> -