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Dataset results
14 results for “Deep Panning”
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
Protein elution profiles accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p>Key to files</p> <p><strong>Experiment_Order.csv</strong></p> <ul> <li>Description: Meta details of each experiment.</li> <li>Format: experiment_name,ExperimentID_order,tissue,experiment_type,spec,ExperimentID</li> </ul> <p><strong>Fraction_Details.csv</strong></p> <ul> <li>Description: Meta details of each fraction</li> <li>Format:FractionID,frac_order,ExperimentID</li> </ul> <p><strong>plant_virNOG_orthology.csv.gz</strong></p> <ul> <li>Description: Conversion between orthogroup and protein IDs.</li> <li>Format:ID,ProteinID,spec</li> </ul> <p><strong>orthogroup_annotation.csv.gz</strong></p> <ul> <li>Description: Orthogroup annotations</li> <li>Format:ID,Annotation,arath_genenames,arath_Entries,arath_Entry_names,arath_Protein_names,disruptions,tair_disruptions,lloyd2012_LOFs,arath_functions,arath_misc,pathway,unipathway,BioCyc,Reactome,BRENDA,kegg_pws,ec,arath_masses,arath_protein_names,arath_GO,devstages,tissues,tair,araport,orysj_genenames,orysj_Entries,orysj_Entry_names,orysj_Protein_names,orysj_disruptions,orysj_functions,orysj_misc</li> </ul> <p><strong>panplant_tidy_elution_virNOG.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong> for all experiments. Includes parts per million in each fraction. </li> <li>Format: ExperimentID,FractionID,ID,Total_PeptideCount,spec,ExperimentID_order,FractionID_order,abundance_ppm</li> </ul> <p><strong>panplant_tidy_elution_protcount.csv.gz</strong></p> <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> for all experiments. </li> <li>Format: ExperimentID,FractionID,ProteinID,ProteinCount,spec,ExperimentID_order,FractionID_order</li> </ul> <p><strong>panplant_wide_elution_virNOG.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_virNOG_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups</strong>, includes annotation columns.</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong></li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>panplant_wide_elution_expnorm_annot.csv.gz</strong></p> <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong>, including columns with annotations</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> <p><strong>[experiment_name].virNOG.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) for all observed<strong> orthogroups</strong> in one experiment</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <ul> </ul> <p><strong>[experiment_name].protcount.wide.gz</strong></p> <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) counts for all observed <strong>proteins </strong>in one experiment</li> <li>Format: ProteinID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.virNOG.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>orthogroups </strong>from a particular species. Only present for species with more than one experiment. </li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <p><strong>[species]_specconcat.protcount.wide.gz</strong></p> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed <strong>proteins</strong> from a particular species. Only present for species with more than one experiment. </li> <li>Format: ProteinID,[Fractions]</li> </ul> <p> </p> <ul> </ul> <p>Species codes</p> <p>|Code | Species | Common name | Use |<br> |---|---|---|<br> | arath | Arabidopsis Thaliana | Arabidopsis | <br> | braol | Brassica oleracea | Broccoli |<br> | cansa | Cannabis sativa | hemp | <br> | cerri | Ceratopteris richardii | C-fern | <br> | chlre | Chlamydomonas reinhardtii | Chlamydomonas |<br> | chqui | Chenopodium quinoa | Quinoa | <br> | orysj | Oryza sativa var. japonica | Rice |<br> | selml | Selaginella moellendorffii | Selaginella | <br> | sollc | Solanum lycopersicum | Tomato | <br> | wheat | Triticum Aestivum | Wheat | <br> | soybn | Glycine max | Soybean | <br> | cocnu | Cocos nucifera | Coconut | </p> <p>| maize | MAIZE | maize | </p> <p> </p>
Fig. 2 in High diversity and pan-oceanic distribution of deep-sea polychaetes: Prionospio and Aurospio (Annelida: Spionidae) in the Atlantic and Pacific Ocean
Fig. 2 Phylogenetic tree of Prionospio and Aurospio species obtained in the study based on mitochondrial 16S gene fragments. Individual specimens can be found in Supplement 2. Posterior probabilities shown next to the nodes (values below 0.8 are not shown). Bootstrap values are
Supporting data for RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data.
<p>This is the data repository for <em>RCANE: A Deep Learning Algorithm for Whole-genome Pan-Cancer Somatic Copy Number Aberration Prediction using RNA-seq Data</em>. To use this dataset, please refer to <a href="https://github.com/HowardGech/RCANE" target="_blank" rel="noopener">https://github.com/HowardGech/RCANE</a>.</p>
High connectivity at abyssal depths: Genomic and proteomic insights into population structure of the pan-Atlantic deep-sea bivalve Ledella ultima (E. A. Smith, 1885)
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Supplemental tables for 'Pan-microalgal dark proteome mapping via interpretable deep learning and synthetic chimeras'
<p>Distinguishing genuine microbial proteins from contaminants remains a major bottleneck in genomics, particularly for environmental and non-model organisms where conventional homology-based tools are slow, resource-intensive, and leave large fractions of the "dark proteome" unclassified. LA<sup>4</sup>SR offers a scalable, interpretable framework that classifies algal and bacterial proteins directly from translated sequence data, achieving near-complete recall while accelerating inference by ~ 10,000-fold relative to BLASTP. By revealing that internal sequence features alone can drive robust classification, LA4SR bypasses the need for complete gene models or perfect annotations—opening new opportunities for analyzing complex microbial communities and metagenomes. Interpretability methods further link emergent amino acid signatures to evolutionary and ecological features, highlighting the potential of language models not only to accelerate genomics workflows but also to uncover new biological insights.</p> <p> </p> <p><strong>Table S1 | External spreadsheet. </strong>This spreadsheet contains LA<sup>4</sup>SR performance metrics, technical performance estimations, and BLAST results and runtimes of genomes comprising the algal training data.<strong> </strong></p> <p><strong>Table S2 | External spreadsheet. </strong>Captum attributions for 100 sequences each of algal and bacterial origin obtained using the LayerIntegratedGradients function.</p> <p><strong>Table S3 | External spreadsheet. </strong>Influential motifs found with the DeepMotifMinerPro software introduced in this work (see Data S3).</p> <p><strong>Table S4 | External spreadsheet.</strong> LA4SR and Diamond BLAST results for data from new assemblies from seen species (Fig. S7), contaminated assemblies from unseen genera (Fig. S7), and clean assemblies from unseen genera (Fig. 8). For the LA4SR results for genome assemblies from unseen genera, the genomes were published after the model was trained, and the genera shown were not included in the training dataset. Newly sequenced genomes uploaded to NCBI SRA accession SUB14799921.</p> <p> </p> <p> </p>
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Data from: Genome-wide investigation of the multiple origins hypothesis for deep-spawning kokanee salmon (Oncorhynchus nerka) across its pan-Pacific distribution
<p>Salmonids have emerged as important study systems for investigating molecular processes underlying parallel evolution given their tremendous life history variation. Kokanee, the resident form of anadromous sockeye salmon (<i>Oncorhynchus nerka</i>), have evolved multiple times across the species' pan-Pacific distribution, exhibiting multiple reproductive ecotypes including those that spawn in streams, on lake-shores, and at lake depths >50 meters. The latter has only been detected in five locations in Japan and British Columbia, Canada. Here, we investigated the multiple origins hypothesis for deep-spawning kokanee, using 9,721 SNPs distributed across the genome analyzed for the vast majority of known populations in Japan (Saiko Lake) and Canada (Anderson, Seton, East Barrière Lakes) relative to stream-spawning populations in both regions. We detected 397 outlier loci, none of which were robustly identified in paired-ecotype comparisons in Japan and Canada independently. Bayesian clustering and principal components analyses based on neutral loci revealed six distinct clusters, largely associated with geography or translocation history, rather than ecotype. Moreover, a high level of divergence between Canadian and Japanese populations, and between deep- and stream-spawning populations regionally, suggest the deep-spawning ecotype independently evolved on the two continents. On a finer level, Japanese kokanee populations exhibited low estimates of heterozygosity, significant levels of inbreeding, and reduced effective population sizes relative to Canadian populations, likely associated with transplantation history. Along with preliminary evidence for hybridization between deep-spawning and stream-spawning ecotypes in Saiko Lake, these findings should be considered within the context of on-going kokanee fisheries management in Japan.</p>
Data from: Phylogeography of a pan-Atlantic abyssal protobranch bivalve: implications for evolution in the Deep Atlantic
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Data from: Genome-wide investigation of the multiple origins hypothesis for deep-spawning kokanee salmon (Oncorhynchus nerka) across its pan-Pacific distribution
Open the record for dataset details and reuse information.
Data and analysis accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p>This repository contains files, instructions to replicate, and internal scripts for the manuscript "A pan-plant protein complex map reveals deep conservation and novel assemblies"</p> <p>See also <a href="http://plants.proteincomplexes.org/">http://plants.proteincomplexes.org</a></p>
Fig. 4 in High diversity and pan-oceanic distribution of deep-sea polychaetes: Prionospio and Aurospio (Annelida: Spionidae) in the Atlantic and Pacific Ocean
Fig. 4 Genotype networks of Prionospio and Aurospio species from the different localities of 18S gene fragments. Sampling localities are color coded. Different lineages are circled
Fig. 1 in High diversity and pan-oceanic distribution of deep-sea polychaetes: Prionospio and Aurospio (Annelida: Spionidae) in the Atlantic and Pacific Ocean
Fig. 1 Map of the worldwide sampling localities. The Clarion Clipperton Fracture Zone (CCZ) in the Pacific, the eastern Vema Fracture zone (eVFZ– stars), the Vema Transform Fault (VTF–rectangular), the western Vema Fracture Zone (wVFZ–hexagon), and the Puerto Rico Trench (PRT)
Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis
ClinicalTrials.gov study NCT06822413. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.