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456 results for “motifs”
ChromBPNet models and data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains ChromBPNet models and data used to train the models for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>.</p> <p>`data` contains bigwigs and regions (peaks + non-peaks) used for training each of the models. See `data/README.txt` for more details.</p> <p><strong>Models:</strong></p> <p><em>Loading the model:</em></p> <p>The models were trained using tf1.14. The models are provided in h5 format for tf1.14 (py3.7) and SavedModel format for tf2.X. tf2.X tested only for py3.8-11, tf2.8-13.</p> <p>To load the models in tf1.14:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model.h5")</code></pre> <p>In tf2:</p> <pre><code class="language-python">model = tf.keras.models.load_model("path/to/model_dir")</code></pre> <p>If all fails, you can load the architecture as provided in `model_arch.py` with default parameters (`bpnet_seq` for bias model and `chrombpnet` for chrombpnet model), and then load the weights using `model.load_weights` from the weights provided in the `weights` directory.</p> <p> </p> <p><em>Usage:</em></p> <p>The bias models take as input one-hot sequence of length 2000. It has 2 outputs, a vector of logits of length 2000, and 1 logcounts scalar:</p> <pre><code class="language-python"># seq_one_hot of length B x 2000 x 4 out_bias_logits, out_bias_logcounts = bias_model.predict(seq_one_hot) # out_bias_logits: B x 2000 # out_bias_logcounts: B x 1</code></pre> <p>The ChromBPNet model takes as input a one-hot sequence of length 2000, bias logits of length 2000 and bias log-counts scalar. It has the same output types as the bias model. To run the chrombpnet model to obtain predictions:</p> <pre><code class="language-python">pred_profile, pred_logcounts = chrombpnet_model.predict([seq_one_hot, out_bias_logits, out_bias_logcounts]) # pred_profile: B x 2000 # pred_logcounts: B x 1 </code></pre> <p>If you wish to obtain the "de-biased" predictions (see Methods), simply pass in zeros instead of the bias model predictions as:</p> <pre><code class="language-python">pred_profile_debiased, pred_logcounts_debiased = chrombpnet_model.predict([seq_one_hot, np.zeros((seq_one_hot.shape[0], 2000)), np.zeros((seq_one_hot.shape[0], 1))])</code></pre> <p>To obtain predicted per-base predicted counts (with or without bias):</p> <pre><code class="language-python">pred_per_base_counts = scipy.special.softmax(pred_profile, axis=-1) * (np.exp(pred_logcounts)-1) # pred_per_base_counts: B x 2000 </code></pre> <p>Note that in general predicted counts can't be compared across models as they are not corrected for sequencing depth.</p> <p> </p> <p><em>Note:</em></p> <p>All bias models used across folds are identical, except for the final intercept term in the counts output (see Methods), that is specific to each cell state, fold combination.</p> <p> </p> <p><em>Folds:</em></p> <p>The splits used for training the different folds are as below:</p> Fold Test Chromosomes Validation Chromosomes 0 chr1 chr8, chr10 1 chr2, chr19 chr1 2 chr3, chr20 chr2, chr19 3 chr6, chr13, chr22 chr3, chr20 4 chr5, chr16, chrY chr6, chr13, chr22 5 chr4, chr15, chr21 chr5, chr16, chrY 6 chr7, chr18, chr14 chr4, chr15, chr21 7 chr11, chr17, chrX chr7, chr18, chr14 8 chr9, chr12 chr11, chr17, chrX 9 chr8, chr10 chr9, chr12 <p>Remaining chromosomes were used as the training chromosome for each fold.</p>
Motif HOCOMOCO files preprocessed for DAStk
<p>Pre-scanned motif files HOCOMOCO_v11_p1e-6_grch38.tar.gz, HOCOMOCO_v11_p1e-6_hg19.tar.gz and HOCOMOCO_v11_p1e-6_mm10.tar.gz for the <code>GRCh38/hg38</code>, <code>hg19</code> and <code>mm10</code> reference genomes, respectively. These files are input files for DAStk (https://github.com/Dowell-Lab/DAStk) and have been generated from HOCOMOCO's v11 mononucleotide model, background-corrected for each reference genome, as described in the following paper:</p> <p><em>Kulakovskiy, I.V., Vorontsov, I.E., Yevshin, I.S., Sharipov, R.N., Fedorova, A.D., Rumynskiy, E.I., Medvedeva, Y.A., Magana-Mora, A., Bajic, V.B., Papatsenko, D.A., et al. (2018). <a href="https://academic.oup.com/nar/article/46/D1/D252/4616875">HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis</a>. Nucleic Acids Res 46, D252–D259.</em></p>
Data for: The conserved centrosomin motif, γTuNA, forms a dimer that directly activates microtubule nucleation by the γ-tubulin ring complex (γTuRC)
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Dataset for: A structural signature motif enlightens the origin and diversification of nuclear receptors
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Data from: Motifier: an IgOme profiler based on peptide-motifs using machine learning
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MicrosatNavigator: Exploring nonrandom distribution and lineage-specificity of microsatellite repeat motifs on vertebrate sex chromosomes across 186 whole genomes
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Photoenzymatic asymmetric incorporation of fluorinated motifs into olefins
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Viruses harness YxxØ motif to interact with host AP2M1 for replication: A vulnerable broad-spectrum antiviral target
<p>Targeting a universal host protein exploited by most viruses would be a game-changing strategy that offers broad-spectrum solution and rapid pandemic control including the current COVID-19. Here, we found a common YxxØ-motif of multiple viruses that exploits host AP2M1 for intracellular trafficking. A library chemical, <em>N</em>-(p-amylcinnamoyl)anthranilic acid (ACA), was identified to interrupt AP2M1-virus interaction and exhibit potent antiviral efficacy against a number of viruses in vitro and in vivo, including the influenza A viruses (IAVs), Zika virus (ZIKV), human immunodeficiency virus, and coronaviruses including MERS-CoV and SARS-CoV-2. YxxØ mutation, AP2M1 depletion, or disruption by ACA causes incorrect localization of viral proteins, which is exemplified by the failure of nuclear import of IAV nucleoprotein and diminished endoplasmic reticulum localization of ZIKV-NS3 and enterovirus-A71-2C proteins, thereby suppressing viral replication. Our study reveals an evolutionarily conserved mechanism of protein-protein interaction between host and virus that can serve as a broad-spectrum antiviral target.</p>
mtDNA from consensus motif paper
<p>Mitochondrial DNA (mtDNA) dataset from https://sites.google.com/site/consensusmotifs/ as backup for tutorial on consensus motifs</p>
Data from: DNA motifs are not general predictors of recombination in two Drosophila sister species.
Meiotic recombination is crucial for chromosomal segregation, and facilitates the spread of beneficial and removal of deleterious mutations. Recombination rates frequently vary along chromosomes and Drosophila melanogaster exhibits a remarkable pattern. Recombination rates gradually decrease towards centromeres and telomeres, with a dramatic impact on levels of variation in natural populations. Two close sister species, D. simulans and D. mauritiana do not only have higher recombination rates, but also exhibit a much more homogeneous recombination rate that only drops sharply very close to centromeres and telomeres. Because certain sequence motifs are associated with recombination rate variation in D. melanogaster, we tested whether the difference in recombination landscape between D. melanogaster and D. simulans can be explained by the genomic distribution of recombination-rate associated sequence motifs. We constructed the first high-resolution recombination map for D. simulans based on 189 haplotypes from a natural D. simulans population, and searched for short sequence motifs linked with higher than average recombination in both sister species. We identified five consensus motifs significantly associated with higher than average chromosome-wide recombination rates in at least one species and present in both. Testing fine resolution associations between motif density and recombination, we found strong and positive associations genome-wide over a range of scales in D. melanogaster, while the results were equivocal in D. simulans. Despite the strong association in D. melanogaster, we did not find a decreasing density of these short-repeat motifs towards centromeres and telomeres. We conclude that the density of recombination-associated repeat motifs cannot explain the large-scale recombination landscape in D. melanogaster, nor the differences to D. simulans. The strong association seen for the sequence motifs in D. melanogaster likely reflects their impact influencing local differences in recombination rates along the genome.
Easter Broomhouse Standing Stone - Motif
Frome Canmore -NT 68008 76615 Standing Stone (NR) OS 6" map (1957) Standing stone with cup-marks, Easter Broomhouse: Some 200 yds SSW of Easter Broomhouse is a red sandstone monolith 9ft high and 6ft in girth at its base, where it is roughly rectangular in plan, the east and west faces being 22 ins broad. On the west face there are three cup-marks. The deep grooves on the base of the stone were caused by a steam-plough cable. Source: Objaverse 1.0 / Sketchfab
HHJ4 Seal Motif Nose Pin, Hot Springs Village AK
Seal Motif, bone nose pin or cloak pin. Hot Springs Village, Port Moller, Alaska. Okada Excavations. HHJ. CAT # HHJ4-02. 600-800 CE The Hot Springs site is a massive village on the shore of Port Moller, on the Alaska Peninsula side of the southern Bering Sea. It was excavated by several different teams over the last 100 years. The main occupations are from 2000 BCE-1000 BCE, and from 100 CE to 800 CE. The Hot Springs artifacts are presented as a result of the research conducted under grants NSF 0137756, NSF 1204020, NSF 1139266, and NSF 1321411. H. Maschner, Principal Investigator. Original digitizing work done at the IVL at Id. St. Univ. Subsequent processing completed at Global Digital Heritage. Source: Objaverse 1.0 / Sketchfab
ChromBERT: Uncovering Chromatin State Motifs in the Human Genome using a BERT-based Approach
<ol> <li>Pretrain data and results for: <ul> <li>Promoter regions for all genes in 127 different cell lines in ROADMAP </li> <li>CRM (<em>cis</em>-regulatory module) regions longer than 2k bps in 127 different cell lines in ROADMAP</li> <li>Whole genome regions without continuous low signal state ("O") in 4-mer</li> </ul> </li> <li>Fine-tuning data and result for: <ul> <li>[Classification] Promoter regions of high-expressed genes (from RPKM>10 to RPKM>50) compared to not expressed genes (RPKM=0) or low-expressed genes (RPKM>0) with the directory names:<br> <ul> <li>not_n_rpkm0 : RPKM=0 vs. RPKM>0</li> <li>not_n_rpkm10 : RPKM=0 vs. RPKM>10</li> <li>not_n_rpkm20 : RPKM=0 vs. RPKM>20 </li> <li>not_n_rpkm30 : RPKM=0 vs. RPKM>30 </li> <li>not_n_rpkm50 : RPKM=0 vs. RPKM>50 </li> <li>rpkm0_n_rpkm10 : RPKM>0 vs. RPKM>10 </li> <li>rpkm0_n_rpkm20 : RPKM>0 vs. RPKM>20 </li> <li>rpkm0_n_rpkm30 : RPKM>0 vs. RPKM>30 </li> <li>rpkm0_n_rpkm50 : RPKM>0 vs. RPKM>50 </li> <li>rpkm10_n_rpkm20 : RPKM>10 vs. RPKM>20 </li> <li>rpkm10_n_rpkm30 : RPKM>10 vs. RPKM>30 </li> <li>rpkm10_n_rpkm50 : RPKM>10 vs. RPKM>50 </li> <li>rpkm20_n_rpkm30 : RPKM>20 vs. RPKM>30 </li> <li>rpkm20_n_rpkm50 : RPKM>20 vs. RPKM>50 </li> <li>rpkm30_n_rpkm50 : RPKM>30 vs. RPKM>50 </li> </ul> </li> <li>[Regression] Quantitative gene expression prediction for promoter regions</li> <li>CRM regions longer than 2k bps compared to non-CRM regions</li> </ul> </li> </ol>
Union of Upper and Lower Egypt motif
Motif depicting the unification of the Two Lands of Upper and Lower Egypt (Sema tawy). From the side of the throne of the Ramses II colossus at Luxor Temple, Egypt Created from 103 photographs (Canon EOS Rebel T71, 24MP) using Metashape 1.5.5. Internal dimensions are scaled to realworld meters. Photographed in January 2020 Source: Objaverse 1.0 / Sketchfab
Handmade Teapot (oriental motif)
Source: Objaverse 1.0 / Sketchfab
Isothermal Titration Calorimetry Data - Intramolecular autoinhibition regulates the selectivity of PRPF40A tandem WW domains for proline-rich motifs
<p>This is the repository of all ITC data related to the paper:</p> <p><strong><span>Intramolecular autoinhibition regulates the selectivity of PRPF40A tandem WW domains for proline-rich motifs</span></strong></p> <p><span>Santiago Martínez-Lumbreras<sup>1,2,*</sup>, Lena K. Träger<sup>2</sup>, Miriam M. Mulorz<sup>3</sup>, Marco Payr<sup>2</sup>, Varvara Dikaya<sup>2</sup>, Clara Hipp<sup>1,2</sup>, Julian König<sup>3</sup> and Michael Sattler<sup>1,2,*</sup></span><strong><span> </span></strong></p> <p><sup><span>1</span></sup><span> <span> </span>Helmholtz Munich, Molecular Targets and Therapeutics Center, Institute of Structural Biology, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany</span></p> <p><sup><span>2</span></sup><span><span> </span>Technical University of Munich, TUM School of Natural Sciences, Department of Bioscience, Bavarian NMR Center, Lichtenbergstrasse 4, 85747 Garching, Germany</span></p> <p><sup><span>3</span></sup><span> Institute of Molecular Biology (IMB) gGmbH, Ackermannweg 4, 55128 Mainz, </span><span>Germany</span></p> <p><span>* Correspondence should be addressed to: </span></p> <p><span>Santiago Martínez Lumbreras <a href="mailto:santiago.martinez@tum.de">santiago.martinez@tum.de</a> or</span></p> <p><span>Michael Sattler </span><span><a href="mailto:michael.sattler@helmholtz-munich.de"><span>michael.sattler@helmholtz-munich.de</span></a></span></p>
Single-molecule analysis of specificity and multivalency in binding of short linear substrate motifs to the APC/C
<p>Robust regulatory signals in the cell often depend on interactions between short linear motifs (SLiMs) and globular proteins. Many of these interactions are poorly characterized because the binding proteins cannot be produced in the amounts needed for traditional methods. To address this problem, we developed a single-molecule off-rate (SMOR) assay based on microscopy of fluorescent ligand binding to immobilized protein partners. We used it to characterize substrate binding to the Anaphase-Promoting Complex/Cyclosome (APC/C), a ubiquitin ligase that triggers chromosome segregation. We find that SLiMs in APC/C substrates (the D box and KEN box) display distinct affinities and specificities for the substrate-binding subunits of the APC/C, and we show that multiple SLiMs in a substrate generate a high-affinity multivalent interaction. The remarkably adaptable substrate-binding mechanisms of the APC/C have the potential to govern the order of substrate destruction in mitosis.</p>
Neural motif discovery manuscript reports
<p>This repository holds the supplementary data for the motif and motif-syntax/grammar discovery tool in the associated manuscript.</p> <p><strong>reports.tar.gz</strong><br> Contains the motif and motif-syntax/grammar analysis reports as HTML files for all analyzed transcription factors (TFs). See included README for more details.</p> <p><strong>models.tar.gz</strong><br> Contains the fine-tuned models. Each TF, this includes the fine-tuned single-task profile model for each task/experiment, and the fine-tuned multi-task profile model over all experiments.</p> <p><strong>motifs.tar.gz</strong><br> Contains the motifs discovered by TF-MoDISco on each profile model, for both the profile and counts heads. This includes the PFM, the CWM, and eCWM. This also includes the motifs that have been merged across the profile/counts heads (i.e. `cpmerged`).</p> <p><strong>motif_hits.tar.gz</strong><br> For each experiment, motif instances were called using FiNeMo on the best-performing model. Motif instances are either called for the profile head, count head, or merged across both heads. Each subdirectory includes: 1) the collapsed and filtered motif instances; 2) the set of peaks for which they come from; 3) a file matching motif instances to their peaks; 4) the co-occurrence of multiple motifs in peaks; and 5) the distance distribution of motif pairs which significantly co-occur. Unless stated otherwise, collapsing is done over all motif patterns.</p>
Identification and characterization of epicuticular proteins of nematodes sharing motifs with cuticular proteins of arthropods
Specific collagens and insoluble proteins called cuticlins are major constituents of the nematode cuticles. The epicuticle, which forms the outermost electron-dense layer of the cuticle, is composed of another category of insoluble proteins called epicuticlins. It is distinct from the insoluble cuticlins localized in the cortical layer and the fibrous ribbon underneath lateral alae. Our objective was to identify and characterize genes and their encoded proteins forming the epicuticle. The combination between previously obtained laboratory results and recently made available data through the whole-genome shotgun contigs (WGS) and the transcriptome Shotgun Assembly (TSA) sequencing projects of Ascaris suum allowed us to identify the first epicuticlin gene, Asu-epic-1, on the chromosome VI. This gene is formed of exon1 (55 bp) and exon2 (1067 bp), separated by an intron of 1593 bp. Exon 2 is formed of tandem repeats (TR) whose number varies in different cDNA and genomic clones of Asu-epic-1. These variations could be due to slippage of the polymerases during DNA replication and RNA transcription leading to insertions and deletions (Indels). The deduced protein, Asu-EPIC-1, consists of a signal peptide of 20 amino acids followed by 353 amino acids composed of seven TR of 49 or 51 amino acids each. Three highly conserved tyrosine motifs characterize each repeat. The GYR motif is the Pfam motif PF02756 present in several cuticular proteins of arthropods. Asu-EPIC-1 is an intrinsically disordered protein (IDP) containing seven predicted molecular recognition features (MoRFs). This type of protein undergoes a disorder-to-order transition upon binding protein partners. Three epicuticular sequences have been identified in A. suum, Ascaris lumbricoides, and Toxocara canis. Homologous epicuticular proteins were identified in over 50 other nematode species. The potential of this new category of proteins in forming the nematode cuticle through covalent interactions with other cuticular components, particularly with collagens, is discussed. Their localization in the outermost layer of the nematode body and their unique structure render them crucial candidates for biochemical and molecular interaction studies and targets for new biotechnological and biomedical applications.
Nether Glenny 32 - Spoked Wheel Motifs
A close-up rendering of the two unusual spoked wheel, or sun disc motifs I uncovered this week near Port of Menteith, Stirlingshire (26-27/12/21). Note : this is a preliminary model made in poor light with surface cleaning not completed. Scottish Rock Art Project (ScRAP) Ref : ScRAP 2650 Source: Objaverse 1.0 / Sketchfab
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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