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492 results for “sequence modeling”
Raman spectra of the Adenoma-Carcinoma-Sequence in a mice model
<p>In the following, a short desciption for each csv files:</p> <ol> <li>Meta data: includes information about mice ID, scans collected from each mouse, location of extracted scans, activity of P53 gene, mouce gender, tissue type.</li> <li>MSpectra: contains mean spectra of tissue types with respect to each extracted scan.</li> <li>TissueLabels: describes different divisions of tissue types;e.g. normal vs abnormal, normal vs HB vs Karzinom, normal vs HB vs adenoma vs carcinoma</li> <li>Wavenumbers: includes Raman spectra wavenumbers. </li> </ol>
Datasets of sequences, alignments and structural models generated for the structural prediction of complexes mediated by intrinsically disordered regions.
<p>This repository contains input and ouput files used and generated for the scanning of intrinsically disordered region and the prediction of their binding sites to receptor proteins using the <a href="https://github.com/i2bc/SCAN_IDR">SCAN_IDR</a> pipeline with AlphaFold2-Multimer.</p><p>It contains two archives: </p><ol><li><a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> dedicated to the analysis of a dataset of 42 protein complexes non redundant with the dataset used for AlphaFold2 training,</li><li><a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> dedicated to the analysis of 923 complexes from the ELM database.</li></ol><p>These data can be used to rerun specific sections of the pipeline and scripts provided in: <a href="https://github.com/i2bc/SCAN_IDR">https://github.com/i2bc/SCAN_IDR</a></p><h4><strong>Dataset of 42 non redundant complexes</strong></h4><p>The first archive <a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> contains 3 compressed directories and a README file detailing their contents :</p><ul><li>the initial raw sequence and alignment data for every chain -> DIRECTORY <strong>fasta_msa/</strong></li><li>the input and output data of every Alphafold run for every complex -> DIRECTORY <strong>af2_runs/</strong></li><li>the native reference structures -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p>The protein-peptide complex cases have been assigned a distinct index number, from 1 to 42, consistent across the several directories of the archive. Their corresponding directories are labelled as <i><index>_<pdbcode></i>.</p><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.2</i></p><h4><strong>Dataset of 923 complexes selected from the ELM database</strong></h4><p>The second archive <a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> contains input and ouput files used and generated for the analysis of 923 Eukaryotic Linear Motifs (ELM) database entries.</p><p>Each ELM entry is indexed with specific integer id and is composed of a receptor and a ligand protein. </p><p>The archive contains a Table associating ELM indexes with the ELM entry information, 5 directories and a README file detailing their contents:</p><ul><li>the table describing ELM entries -> FILE <strong>Table_923ELM_uid_delimitations_info_for_archive.txt</strong></li><li>the initial raw sequence and multiple sequence alignment (MSA) data for every chain -> DIRECTORY <strong>fasta_msa/</strong></li><li>the concatenated MSA model for every ELM complex and protocol used -> DIRECTORY <strong>af2_elm_coali_inputs/</strong></li><li>the best model of every AF2 protocol for every complex according to the AF2 -> DIRECTORY <strong>af2_elm_models/</strong></li><li>the best model cut in the ligand part to select only the ELM motifs as used for the evaluation of the models -> DIRECTORY <strong>elm_cut_models/</strong></li><li>the reference structures used for the evaluation of the models -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.3</i></p>
Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"
<p>Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," <em>Journal of Geophysical Research: Solid Earth</em><em>.</em></p> <p>The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt) </p> <p><strong>BP4-QD Benchmark Simulations:</strong><br>1000 m: jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4<br>500 m: jiang.3, lambert.3, barbot.5, barbot.7, ozawa</p> <p><strong>BP5-QD Benchmark Simulations:</strong><br>2000 m: jiang.6, lambert.8, liu.4, cattania.5, dli.7, barbot.3, dliu.10, li.3<br>1000 m: jiang.2, lambert.7, liu.5, cattania.3, ozawa, dli.5, barbot, dliu.6, li.2<br>500 m: jiang.4, lambert.9, liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8<br>250 m: lambert.10, liu.7</p> <p><strong>BP5-QD with Off-Fault Data:</strong><br>1000 m: lambert.7, dli.5, barbot, dliu.6, li.2<br>500 m: lambert.9, dli.6, barbot.2, dliu.8</p> <p>Tables 2–4 in our paper summarizes details of numerical codes and selected simulations.</p> <p>The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.</p>
Data supporting "Transformer Model Generated Bacteriophage Genomes are Compositionally Distinct from Natural Sequences"
<p>Sequence and composition data supporting doi: <a href="https://doi.org/10.1101/2024.03.19.585716" target="_blank" rel="noopener">10.1101/2024.03.19.585716</a>. Uncompressed file size is ~5.8GB.</p> <p>Data in zip files is organized by sequence provenance (generRNA, natural, or transformer (megaDNA)). Common file types between folders include:</p> <ul> <li>Multi-record fasta file: Sequence data for all sequences of a given provenance. For generRNA sequences, these are found within the `seq` column of file "MFE_distribution_Fig4a.csv"</li> <li>Composition files: Individual sequence level compositional metrics for sliding 120 bp windows. Only structural metrics were used in this study.</li> <li>Genomad: Results from the genomad pipeline (https://portal.nersc.gov/genomad/)</li> <li>Stats: Aggregate statistics for all sequences of a given provenance.</li> </ul> <p>The natural folder also has a metadata file detailing the taxonomy for all natural sequences.<br><br>Figure datasets are the cleaned (sometimes aggregated) datasets that underly specific figures in the manuscript. The figure designations are based on the order in: https://www.biorxiv.org/content/10.1101/2024.03.19.585716v1.</p>
Figure 2. D in Neotypification of Drawida hattamimizu Hatai, 1930 (Annelida, Oligochaeta, Megadrili, Moniligastridae) as a model linking mtDNA (COI) sequences to an earthworm type, with a response to the 'Can of Worms' theory of cryptic species
Figure 2. D. hattamimizu unscaled habitus (from Watanabe, 2005, fig. 1 after Hatai's 1931 original).
Supplementary material for "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences"
<p>This material supplements the following conference publication:</p> <p>Winkler, Rosenthal, Strecker (2024). "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences". Modellierung 2024.</p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Complex models of sequence evolution improve fit, but not gene tree discordance, for tetrapod mitogenomes
<p>Variation in gene tree estimates is widely observed in empirical phylogenomic data and is often assumed to be the result of biological processes. However, a recent study using tetrapod mitochondrial genomes to control for biological sources of variation due to their haploid, uniparentally inherited, and non-recombining nature found that levels of discordance among mitochondrial gene trees were comparable to those found in studies that assume only biological sources of variation. Additionally, they found that several of the models of sequence evolution chosen to infer gene trees were doing an inadequate job of fitting the sequence data. These results indicated that significant amounts of gene tree discordance in empirical data may be due to poor fit of sequence evolution models and that more complex and biologically realistic models may be needed. To test how the fit of sequence evolution models relates to gene tree discordance, we analyzed the same mitochondrial datasets as the previous study using two additional, more complex models of sequence evolution that each model a different biologically realistic aspect of the evolutionary process: a covarion model to incorporate heterotachy, and a model partitioned model to incorporate variable evolutionary patterns by codon position. Our results show that both additional models fit the data better than the models used in the previous study, with the covarion being consistently and strongly preferred as tree size increases. However, even these more preferred models still inferred highly discordant mitochondrial gene trees, thus deepening the mystery around what we label the "Mito-Phylo Paradox" and leading us to ask whether the observed variation could be biological after all.</p>
Automatic message sequence chart creation from simulation run of the parametric colored Petri net model of the Chandy-Lamport algorithm with four processes
<p><span>The video shows the creation of the message sequence chart from a simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with four constituting processes. The picture shows the resulting message sequence chart. </span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 4 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The model's number of constituting processes is parametric and was set to four. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Picture of Message Sequence Chart of Parametric Model With 4 Processes_SuppInfo.png:</span></strong><span> This picture shows the automatically generated message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm that is visible in the above video clip. The number of constituting processes was set to four. </span></p>
A volumetric model of rabbit heart and torso including ECG data and ventricular activation sequence
<p>Data generated and analyzed of our work titled "A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution". Please see the respective publication for more context.</p> <p> </p> <ul> <li>BSPM_filtered.dat <ul> <li>Contains the filtered ECG Data</li> </ul> </li> <li>BSPM_original.bdf <ul> <li>Contains the originally recorded signal.<br> Information on the file format itself can be found here: <a href="https://www.biosemi.com/faq/file_format.htm">https://www.biosemi.com/faq/file_format.htm</a><br> Links to various toolboxes to open the file can be found here: <a href="https://www.biosemi.com/download.htm">https://www.biosemi.com/download.htm</a></li> </ul> </li> <li>CT_DataDCM.zip <ul> <li>Contains the recorded CT images of heart and torso in DCM file format</li> </ul> </li> <li>ECG_NodeIndices.txt <ul> <li>Contains the the node IDs of the torso mesh corresponding to the electrode positions of the ECG Vest</li> </ul> </li> <li>Endocardial_Surface_Papillary.stl <ul> <li>Segmented endocardial surface including papillary muscles</li> </ul> </li> <li>Mat_LeadField.dat <ul> <li>Contains the calculated lead field matrix to be used in combination with the provided Mesh_Ven.vtu. Make sure to keep the node order</li> <li> <pre><code class="language-python"># Python example of usage # Define a read_vm_vec function which reads your calculated data beforehand import numpy as np t_begin = 0 t_end = 400 LF_mat = np.loadtxt('Mat_LeadField.dat') times = np.linspace(t_begin, t_end, t_end-t_begin) result = np.zeros((len(times), 31)) for i,t in enumerate(times): vm_vec = read_vm_vec(t) result[i, :] = LF_mat.dot(vm_vec)[0:31] result = np.insert(result, 0, times, axis=1) np.savetxt('BSPM.dat', result)</code></pre> <p> </p> </li> </ul> </li> <li>Mesh_PurkinjeTree.vtp <ul> <li>The resulting optimized Purkinje Node tree. We recommend using <a href="https://www.paraview.org/">ParaView</a> for visualization</li> </ul> </li> <li>Mesh_StimPoints.vtp <ul> <li>The resulting points of stimulation.</li> </ul> </li> <li>Stim_IndexTime.dat <ul> <li>Contains the stimulation pattern in terms the node index of Mesh_Ven.vtu and the respective stimulation time</li> </ul> </li> <li>Mesh_Ven.vtu <ul> <li>Contains the ventricular mesh as well as the repective lead field matrix values for each surface node.</li> <li>Material:<br> Right Ventricle 30<br> Left Ventricle 31</li> </ul> </li> <li>Mesh_Torso.vtu <ul> <li>Contains the whole torso mesh.</li> <li>Material:<br> Fat 2<br> Bones 3<br> Blood 9<br> Cartilage 14<br> Liver 20<br> Lungs 17<br> Right Ventricle 30<br> Left Ventricle 31<br> Right Atrium 32<br> Left Atrium 33<br> Aorta 60<br> Pulmonary artery 61<br> Left Vena Jugularis 62<br> Right Vena Jugularis 62<br> Post Vena Cava 62</li> </ul> </li> </ul> <p> </p>
Structural modelling results to accompany the paper "Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing: an update"
<p>This repository contains the results of modelling missense mutants in KRAS, NRAS and BRAF observed in our study in the corresponding protein structures. Modelling was performed using FoldX.</p>
Fig. 3 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 3. Prevalence of pathogens associated with D. nuttalli. If there was only one study included in a certain pathogen, the positive rate would be calculated by the positive number of ticks divided by the total number of detected ticks, and without the 95% confidence interval. If there were more studies, the positive rate and 95% confidence interval would be calculated by meta-analysis.
Fig. 2 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 2. Study design and data sources of the meta-analysis. A comprehensive meta-analysis was performed to evaluate D. nuttalli's potential threats based on detected pathogens and geographical distribution positions. The database of D. nuttalli was constructed from four sources, including field surveys, literature review, a reference book, and an online biodiversity database (Global Biodiversity Information Facility, GBIF, https://www.gbif.org).
Fig. 1 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 1. Relative pathogen abundance of four D. nuttalli samples and the phylogenomic analysis of four Rickettsia genomes. (A) Pathogen abundance at the family level. (B) Pathogen abundance at the genus level. (C) The phylogenetic tree of four Rickettsia assemblies. The phylogenetic tree of four Rickettsia assemblies (Rickettsia conorii subsp. raoultii str XinjiangF1, Rickettsia conorii subsp. raoultii str XinjiangF2, Rickettsia conorii subsp. raoultii str XinjiangF3, and Rickettsia conorii subsp. raoultii str XinjiangM1) was built with 28 other publicly available established or proposed Rickettsiales species. The tree was inferred by IQ-TREE based on 277 single-copy orthologs identified by OrthoFinder. Anaplasma phagocytophilum and Ehrlichia ruminantium were two outgroup species.
Fig. 5 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 5. Global potential distribution of D. nuttalli. The red area indicates greater possibilities of suitability for D. nuttalli, while the blue area is less likely to be suitable for D. nuttalli.
Fig. 4 in Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling
Fig. 4. Geographical distribution of D. nuttalli. D. nuttalli lived mainly between 23◦–53◦ latitude and 76◦–133◦ longitude in the Northern Hemisphere. Triangles represent the locations in prefecture-level regions, while circles represent the distribution locations in county-level regions. The green circles represent points from GBIF, the yellow circles are points from literature, the purple circles represent the points from the field survey and the blue points are points from a reference book. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Deep learning model for characterizing protein-RNA interactions from sequence at single-base resolution
<p> </p> <p><a href="https://zenodo.org/api/records/14021440/draft/files/encode_eclip.h5/content" target="_blank" rel="noopener noreferrer">encode_eclip.h5</a> - This file contains the training, validation, and test data for the Reformer model.</p> <p><a href="https://zenodo.org/api/records/14021440/draft/files/encode_eclip_bc.h5/content" target="_blank" rel="noopener noreferrer">encode_eclip_bc.h5</a> - This file contains the training, validation, and test data for the Reformer-BC model.</p> <p><a href="https://zenodo.org/api/records/14027315/draft/files/Reformer-code.zip/content" target="_blank" rel="noopener">Reformer-code.zip</a> - This file contains the training code of Reformer.</p>
Code and data associated with Christiansen et al. 2021 "Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing"
<p>All code and data input and output files (except reference genome and raw sequencing data) needed to reproduce the results of Christiansen et al. 2021 as released on <a href="https://github.com/notothen/radpilot">https://github.com/notothen/radpilot</a> alongside journal publication. See published paper:</p> <p>Christiansen, H., Heindler, F.M., Hellemans, B. <em>et al.</em> Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing. <em>BMC Genomics</em> <strong>22, </strong>625 (2021). <a href="https://doi.org/10.1186/s12864-021-07917-3">https://doi.org/10.1186/s12864-021-07917-3</a></p>
Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
<p>This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript:</p> <p><strong>Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).</strong></p> <p><br> This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.</p>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.