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456 results for “motifs”
Double-stranded RNA structural elements holding the key to translational regulation in cancer: the case of editing in RNA Binding Motif Protein 8A
<p>Raw data supporting the manuscript</p> <p>Abukar, A.;Wipplinger, M.;<br> Hariharan, A.; Sun, S.; Ronner, M.;<br> Sculco, M.; Okonska, A.;<br> Kresoja-Rakic, J.; Rehrauer, H.; Qi, W.;<br> et al. Double-Stranded RNA<br> Structural Elements Holding the Key<br> to Translational Regulation in Cancer:<br> The Case of Editing in RNA-Binding<br> Motif Protein 8A. Cells 2021, 10, 3543.<br> https://doi.org/10.3390/<br> cells10123543</p>
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet: transcription factor preferences
<p>The main output of our analysis, as a raw dataset. This data was used to create the plots depicting transcription factor preferences across our paper, including for our treemaps.</p>
The Reception of the Alea iacta est Motif | Katarzyna Marciniak | Locus Ludi Sept 2018
<p>Katarzyna Marciniak at the international conference “Play and Games in Antiquity. Definition–Transmission–Reception” organized in the Musée Suisse du Jeu in la Tour-de-Peilz by Prof. Véronique Dasen and her team within the project <a href="https://locusludi.unifr.ch/">“Locus Ludi” (ERC Advanced Grant)</a>.</p> <p>For more see <a href="http://omc.obta.al.uw.edu.pl/news/78">http://omc.obta.al.uw.edu.pl/news/78</a></p> <p>and the Locus Ludi YouTube channel: https://www.youtube.com/watch?v=pFlr_TWqSzI </p>
Vamos: VNTR annotation using efficient motif sets, 64-haplotype motifs
<p>This is the efficient motif set for 382,492 VNTR loci in 64 haplotype-resolved assemblies (10.1126/science.abf7117). The initial release is with compression parameter (delta) = 0.1</p>
Data from: Residues neighboring an SH3-binding motif participate in determining affinity and specificity in vivo
<p>In signaling networks, many protein-protein interactions are mediated by modular domains that bind short linear motifs. The motifs' sequences modulate many factors, among them affinity and specificity, or the ability to bind strongly and to bind the appropriate partners. Previous studies have proposed a trade-off between affinity and specificity, suggesting that motifs with high affinity are less capable of differentiating between domains with similar sequences and structures. Using Deep Mutational Scanning to create a mutant library of a well characterized binding motif, and protein complementation assays to measure protein-protein interactions, we tested this trade-off in vivo for the first time. We measured the binding strength and specificity of a library of mutants of a binding motif on the MAP kinase kinase Pbs2, which binds the SH3 domain of the osmosensor protein Sho1 in Saccharomyces cerevisiae. We find that many mutations in the region surrounding the binding motif modify binding strength, but that few mutations have a strong impact on specificity. Moreover, we find no systematic relationship between affinity and specificity as measured in vivo. Interestingly, all Pbs2 mutations which increase affinity or specificity are situated outside of the Pbs2 residues that interact with the canonical SH3-binding pocket, suggesting that other surfaces on Sho1 contribute to binding. We use predicted structures to propose a model of binding which involves residues neighboring the core Pbs2 motif binding outside of the canonical SH3-binding pocket, allowing affinity and specificity to be determined by a broader range of sequences than what has previously been considered.</p>
Fig. 4 in A Ladybird (Coleoptera: Coccinellidae) Motif In Crocheted Towel End Lace: The First Example Of Cultural Entomology In Latvia
Fig. 4. The emblem of the Entomological Society of Latvia with figure of Adalia bipunctata (Linnaeus, 1758) (http://leb.daba.lv/).
Fig. 2 in A Ladybird (Coleoptera: Coccinellidae) Motif In Crocheted Towel End Lace: The First Example Of Cultural Entomology In Latvia
Fig. 2. The entomomorphic ladybird-like element on the edge of towel lace. Krāslava district, Kombuļi civil parish, Skadiņi. Author: Albīna Plinta (70ies of the 20th century) (Photo by Arvīds Barševskis).
MHC Motif Atlas CSV
<p>CSV Format of MHC Motif Atlas data taken from <a href="http://mhcmotifatlas.org/home">http://mhcmotifatlas.org/home</a></p>
Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations
<p>Scripts and derived data for the indicated paper. Links to the <a href="https://zenodo.org/record/1204695">original data</a> and <a href="https://zenodo.org/record/3497585">library source code</a> used to generate this material. Inflate the ENU_mutation_classification.tar.gz archive, remove the ENU_mutation_classification/classifier directory (this will be replaced). Move all others archives into the ENU_mutation_classification directory and inflate them.</p>
Seismic Network and Motifs Vrancea
<p>A seismic network for earthquakes with magnitude larger than 4 in Vrancea, Romania seismic region. Here are also presented the network's 3 nodes motifs (triangles)</p>
Video no. 1 'Motif, phrase in working on music choreography' by Barbara Dutkiewicz
<p>The footage complements the publication by Barbara Dutkiewicz (2023) ‚<em>Choreography of music. Process of creation according to the principles of plastique animée on the example of music by Henryk Mikołaj Górecki 'Kleines Requiem für eine Polka op. 66’, choreography created by Barbara Dutkiewicz and Iga Eckert</em>’ (DOI: <a href="https://doi.org/10.5281/zenodo.7789711">https://doi.org/10.5281/zenodo.7789711</a>), written as a part of the intellectual output of the project EURHYTHMICS IN EDUCATION AND ARTISTIC PRACTICE (ERASMUS+). implemented in 2020-2023. Workshop with students of The Karol Szymanowski Academy of Music in Katowice, Poland and Universität für Musik und darstellende Kunst Wien, Austria took place during LTT, host Katowice 21-25.03.2022. Artistic direction: Associate Professor Barbara Dutkiewicz (PhD.hab) and Iga Eckert, MA.</p> <p>ISBN 978-83-963687-3-7</p> <p>The article is supplemented by photos and four videos:</p> <p>- Video no. 1 ’Motif, phrase in working on music choreography’ by Barbara Dutkiewicz <a href="https://doi.org/10.5281/zenodo.7922029">https://doi.org/10.5281/zenodo.7922029</a></p> <p>- Video no 2. ’Exercises with polymetric structure in working on music choreography’ by Iga Eckert and Barbara Dutkiewicz <a href="https://doi.org/10.5281/zenodo.7916295">https://doi.org/10.5281/zenodo.7916295</a> </p> <p>- Video no. 3 ’Movement stylization and multi planarity in working on music choreography’ by Barbara Dutkiewicz <a href="https://doi.org/10.5281/zenodo.7922104">https://doi.org/10.5281/zenodo.7922104</a></p> <p>- Video no. 4 'A short exercise on body awareness - movement technique in working on music choreography’ by Barbara Dutkiewicz <a href="https://doi.org/10.5281/zenodo.7916598">https://doi.org/10.5281/zenodo.7916598</a></p> <p>This article and videos are published on the digital platform "Atlas of Eurhythmics" <a href="https://www.kmh.se/in-english/atlas-of-eurhythmics/results-from-the-project.html">https://www.kmh.se/in-english/atlas-of-eurhythmics/results-from-the-project.html</a> <em> </em>at modul:<em> <strong>"Plastique animée - tradition and contemporary performing"</strong></em><strong>.</strong> </p>
Cortical topographic motifs emerge in a self-organized map of object space
<p>The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflects a smooth mapping of a unified representational space.</p>
Epitranscriptomic subtyping, visualization, and denoising by global motif visualization
<p>The Analysis of large clusters with density histogram based approximate clustering. </p>
Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains analysis products 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>. Please refer to the READMEs in the directories, which are summarized below.</p> <p>The record contains the following files:<br> <br> `clusters.tsv`: <strong> </strong>contains the cluster id, name and colour of clusters in the paper</p> <p><strong>scATAC.zip</strong></p> <p>Analysis products for the single-cell ATAC-seq data. Contains:</p> <p>- `cells.tsv`: list of barcodes that pass QC. Columns include:<br> - `barcode`<br> - `sample`: (time point)<br> - `umap1`<br> - `umap2`<br> - `cluster`<br> - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root<br> - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root<br> - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `features.tsv`: 50 dimensional representation of each cell <br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed` </p> <p><strong>scATAC_clusters.zip</strong></p> <p>Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data. </p> <p>- `clusters.tsv`: contains the cluster id, name and colour used in the paper<br> - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.<br> - `fragments`: contains per cluster fragment files </p> <p><strong>scATAC_scRNA_integration.zip</strong></p> <p>Analysis products from the integration of scATAC with scRNA. Contains:</p> <p>- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.<br> - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.<br> - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.</p> <p><strong>scRNA.zip</strong></p> <p>Analysis products for the single-cell RNA-seq data. Contains:</p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)<br> - `genes.txt`: list of all genes<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC) <br> - `sample`: sample name (D0, D2, .., D14, iPSC)<br> - `umap1`<br> - `umap2`<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `cluster`<br> - `percent.mt`: percent of mitochondrial transcripts in cell<br> - `percent.oskm`: percent of OSKM transcripts in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` <br> - `pca.tsv`: first 50 PC of each cell<br> - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`</p> <p><strong>multiome.zip</strong></p> <p><em>multiome/snATAC:</em></p> <p>These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2). </p> <p>- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:<br> - `barcode`<br> - `umap1`: These are the coordinates used for the figures involving multiome in the paper.<br> - `umap2`: ^^^ <br> - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq<br> - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels. <br> - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.<br> - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.<br> - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.</p> <p><em>multiome/snRNA:</em></p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.<br> - `genes.txt`: list of all genes (this is different from the list in scRNA analysis)<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively) <br> - `sample`: sample name (D1M, D2M)<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `percent.oskm`: percent of OSKM genes in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` </p>
MINI-AC motif mappings of Arabidopsis and maize
<p>MINI-AC motif mappings of Arabidopsis and maize.</p> <p>For each species, there are the genome-wide motif mappings and the locus-based motif mappings using a non-coding genomic space of 5kb upstream of the translation start site, introns, and 1kb downstream of the translation end site.</p> <p>In version 2 I added, for the locus-based on maize (<em>Zea mays</em>), the motif mappings using the non-coding genomic spaces of: (1) 1kb upstream of the translation start site, introns, and 1kb downstream of the translation end site and (2) 15kb upstream of the translation start site, introns, and 2.5kb downstream of the translation end site.</p> <p>In version 2.5 I added the bed files with the coordinates of the small (1kb upstream of the translation start site, the 1kb downstream of the translation end site, and the introns) and large (15kb upstream of the translation start site, the 2.5kb downstream of the translation end site, and the introns) non-coding genomic spaces.</p> <p>In version 3 I added for maize genome version 5 (B73 RefGen_v5), the bed files with the motif mappings using the non-coding genomic spaces of: (1) 1kb upstream of the translation start site, introns, and 1kb downstream of the translation end site and (2) 5kb upstream of the translation start site, introns, and 1kb downstream of the translation end site, and (3) 15kb upstream of the translation start site, introns, and 2.5kb downstream of the translation end site. Additionally, I added the bed files with the coordinates of the small (1kb upstream of the translation start site, the 1kb downstream of the translation end site, and the introns) and large (15kb upstream of the translation start site, the 2.5kb downstream of the translation end site, and the introns) non-coding genomic spaces.</p>
Zika virus prM protein contains cholesterol binding motifs required for virus entry and assembly - Molecular Dynamics Simulation Dataset
<p>The molecular dynamics (MD) simulation dataset. The contents:</p> <ul> <li><strong>5ire_BIOMT_expanded.pdb</strong>: The complete biological assembly of the cryo-EM structure of Zika Virus (PDB ID:5IRE) </li> <li><strong>5ire_Mprotein_BIOMT_expanded.pdb</strong>: The M proteins extracted from the complete biological assembly of the cryo-EM structure of Zika Virus (PDB ID:5IRE). The biological assembly shows the dimeric organization of M proteins.</li> <li><strong>0chol.zip, 10chol.zip, 20chol.zip, and 30chol.zip</strong> contain simulation input and output files for the simulated membrane compositions: 0:100, 10:90, 20:80, 30:70 (mol%:mol%) Cholesterol:POPC, respectively. <ul> <li>In each zip file, there are 5 directories: <strong>wt, R253L+F257A, R253L+F257S, K275L+Y278A, K275L+Y278S</strong> corresponding to each simulated M protein dimer variant: wild type, CARC 2-A, CARC 2-S, CARC 3-A, and CARC 3-S. In each directory, there are the following files: <ul> <li><strong>toppar</strong>: This directory contains all force field topologies and parameters</li> <li><strong>topol.top</strong>: GROMACS topology (top) file</li> <li><strong>index.ndx</strong>: GROMACS index (ndx) file</li> <li><strong>prod.mdp</strong>: GROMACS MD parameters (mdp) file</li> <li><strong>0, 1, 2, 3, 4, 5, 6, 7, 8, 9</strong>: These directories contain the simulation inputs and outputs for each simulation repeat. In each of these directories, there are the following files: <ul> <li><strong>t0.pdb</strong>: The pdb file of the starting coordinates</li> <li><strong>prod0.tpr</strong>: GROMACS binary run input (tpr) file </li> <li><strong>prod0.edr</strong>: GROMACS energy (edr) file</li> <li><strong>prod0.gro</strong>: GROMACS output coordinates and velocities after 1 microsecond of simulation</li> <li><strong>prod0.cpt</strong>: GROMACS checkpoint file after 1 microsecond of simulation</li> <li><strong>noW.pdb</strong>: The pdb file of the starting coordinates with all water molecules removed</li> <li><strong>noW.xtc</strong>: GROMACS compressed trajectory (xtc) file with all water molecules removed</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Supplementary data accompanying HOCOMOCO v12 collection of transcription factor binding motifs
<p><strong>*** SUMMARY ***</strong></p><p>This dataset contains supplementary data accompanying HOCOMOCO v12 collection</p><p>of DNA binding motifs for human and mouse transcription factors, https://hocomoco.autosome.org</p><p> </p><p>The contents include:</p><p>- the complete initial set of motifs discovered from ChIP-Seq and HT-SELEX data;</p><p>- the curated subset of motifs associated with distinct motif subtypes that were used in benchmarking;</p><p>- the benchmarking results and the resulting final motif collections, including motif logos;</p><p>- accompanying metadata.</p><p> </p><p>Please refer to the README and the HOCOMOCO website for further details.</p><p> </p>
Data from: Residues neighboring an SH3-binding motif participate in the interaction in vivo
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Cortical topographic motifs emerge in a self-organized map of object space
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Data and code from: Engineering bacteriophages through deep mining of metagenomic motifs
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