Find research datasets worth reusing

Figure 10. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 10. Spheniopsis brasiliensis. A transverse section through the heart. AM, Amoebocyte; AU, auricle; PE, pericardium; PEG, pericardial gland; R, rectum; SM, suspensory membrane; V, ventricle.

Figure 3. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 3. Spheniopsis brasiliensis. A ventral view of the septum, foot and mouth. BG, Byssal groove; F, foot; F(T), 'toe' of foot; M, mouth; SE, septum; SEM, margin of septal membrane; SEP(1),(2),(3),(4), septal pores.

Figure 1 in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 1. Spheniopsis brasiliensis. SEM views of the siphonal apparatus. (A) Posterior view of the exhalant and inhalant siphons, with three and four siphonal papillae, respectively. (B) Higher magnification view of a single siphonal papilla with a terminal array of sensory cilia. CI, Cilia; ES, exhalant siphon; IS, Inhalant siphon; SP, sensory papilla; SPB, base of sensory papillae.

Figure 9. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 9. Spheniopsis brasiliensis. A transverse section through the pedal ganglia and the statocysts. PEGA, Pedal ganglia; STAT, statocyst; STL, statolith.

Figure 5 in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 5. Spheniopsis brasiliensis. Transverse sections through the (A) oesophagous; (B) crystalline style sac; (C) mid gut; (D) hind gut; and (E) rectum, all drawn to the same scale. CC, Collagen coat; CS, crystalline style.

Figure 8. Spheniopsis brasiliensis. A transverse section through a in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 8. Spheniopsis brasiliensis. A transverse section through a single digestive tubule. AM, Amoebocyte; CRC, crypt cell; DC, digestive cell.

Figure 4. Spheniopsis brasiliensis. A in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 4. Spheniopsis brasiliensis. A transverse section through the stomach in the region of the conjoined style sac and mid gut. CS, Crystalline style; CSMG, conjoined style sac and mid gut; CSS, crystalline style sac; FIPI, fragments of ingested prey; GS, gastric shield; MG, mid gut; SC, secretory cells.

Figure 7 in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 7. Spheniopsis brasiliensis. Histological sections through the visceral mass and ingested prey items. (A) A transverse section through the stomach with ingested prey items inside it. (B, C) The remains of captured and ingested ostracods. (D) The skeletal remains of an unknown prey item. CSS, Crystalline style sac; GS, gastric shield; IPI, ingested prey item; ST, stomach.

Figure 12. Spheniopsis brasiliensis. A section through a in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait

Figure 12. Spheniopsis brasiliensis. A section through a portion of a gonadial follicle. C, Cuticle; DN, dividing nucleus; DO, developing oocyte; EO, encapsulated oocyte; GE, germinal epithelium; N, nucleus; RT, regressing testes; STA, stalk; SPZ, spermatozoan; Y, yolk.

opencc-by-4.0Sep 2020View details →

Dataset for identification of peptidomimetics and FDA approved drugs binding to novel allosteric pocket of the IRE1 RNase domain.

Input files, protein-peptide and ligand docking datasets, and simulation trajectories, compressed in "rar" format. All calculations performed using the Schrödinger 2020-2 / 2020-3 software (modules Glide, Phase, Desmond).  SI includes folders: 1- "Peptide" folder contains the best peptide "Docking" complexes and "Pharmacophore" models  2-"Quercitrin" folder contains the molecular "Docking", "MMGBSA" calculations, and "MD" simulation 3-"Pemetrexed" folder contains the molecular "Docking", "MMGBSA" calculations, and "MD" simulation

opencc-by-4.0Nov 2020View details →

Novel Coronavirus (COVID-19) Cases in The Netherlands

On 27 February 2020, the first case of COVID-19 disease was confirmed in The Netherlands by RIVM (National Institute for Public Health and the Environment). In the weeks after, thousands of people were diagnosed with the infectious disease. Data on COVID-19 case counts are important for research and applications on various topics like epidemiology and statistics. This dataset contains reported case counts derived from official sources like RIVM (National Institute for Public Health and the Environment), LCPS (National Coordination Center for Patient Distribution), and NICE (National Intensive Care Evaluation). Data from these sources are collected, standardized, and published in various formats on a daily basis. The README document in this repository provides an overview of the available datasets, their file location(s), and codebooks. Copies of the original data are stored in the folder named 'raw_data'. Scripts to process the raw data into standardized files can be found in the folder workflows.

Data for the article: "Molecular Modelling Reveals Eight Novel Druggable Binding Sites in SARS-CoV-2's Spike Protein" by Ilke Ugur and Antoine Marion

This upload contains data related to the article published as a preprint on ChemRxiv with DOI https://doi.org/10.26434/chemrxiv.13292768 "Molecular Modelling Reveals Eight Novel Druggable Binding Sites in SARS-CoV-2's Spike Protein" by Ilke Ugur and Antoine Marion (2020) Department of Chemistry, Middle East Technical University, Ankara, Turkey. For further information, please contact: ilkeugur@metu.edu.tr ; amarion@metu.edu.tr The manuscript is currently under peer-review. Content: Library of molecules derived from DrugBank v 5.1.5: - DrugBank_2020_5.1.5/        # All necessary files for the docking and refinement of the library of molecules. -- DB_5.1.5_pH7.4_pdbqt/      ## PDBQT readily usable for docking with AutoDock Vina. -- DB_5.1.5_pH7.4_mol2amber/  ## mol2 files containing assigned GAFF atom types and Gasteiger atomic charges. -- DB_5.1.5_pH7.4_frcmod/     ## frcmod files containing missing molecular mechanics parameters -- dbID_name.dat              ## DrugBank ID to generic name dictionary Note: The files were prepared automatically via a series of operations handling openbabel and antechamber.       The protonation state of ionizable groups as well as Gasteiger atomic charges were assigned by openbabel for a pH of 7.4       mol2 and frcmod files can be used readily via the tleap module of AmberTools to produce topology files. Receptor structures: - receptors/                  # PDB files for the four structures of the spike protein considered in this work -- CS00ns.pdb                 ## Closed state after the remodelling of missing loops (PDB ID 6vxx) -- OS00ns.pdb                 ## Open   state after the remodelling of missing loops (PDB ID 6vyb) -- CS25ns.pdb                 ## Closed state after 25 ns of molecular dynamics in explicit water -- OS25ns.pdb                 ## Open   state after 25 ns of molecular dynamics in explicit water Note: All structures are aligned to CS00ns.pdb and can be converted to pdbqt for docking with AutoDock Vina Docking grid centers: - dockingCenters/             # XYZ files containing the coordinates of each docking grid center considered in this work Note: The coordinates are given in the same frame as that of the four structures of the receptor. Binding sites: - bindingSites/               # XYZ files with the coordinates of the representative atomic centres                               # of each binding site identified in this work (A-H). Note: These files can be used to get a clearer picture of the binding sites within the structures       of the spike protein shared in the receptors directory. Final modelling results: - allData.txt                 # data for all molecules in the set (approved and investigational) - appData.txt                 # data for approved molecules only - data.xlsx                   # data for all molecules in the set (approved and investigational)                               # as a formatted excel spreadsheet Note: The columns are delimited with semi-colons ";".       The files contain the results for the best pose of all approved molecules for which       molecular mechanics-based geometry optimization succeeded, regardless of their score.       For other molecules, the result of their best pose is reported only for those complexes       having MM interaction energy lower or equal to -22.00 kcal/mol. Visualization: - bs.pse                      # pymol session representing the binding sites within the                               # closed state structure of the spike protein (CS00ns) - pt.pse                      # pymol session representing the docking grid centres within                               # closed statestructure of the spike protein (CS00ns) Note: the PSE files should be compatible with version 7.0 of pymol and later

Simulated NGS read datasets for novel human virus prediction

This repository contains simulated Illumina read datasets for novel human virus prediction and associated metadata extracted from the Virus Host Database (<a href="https://www.genome.jp/virushostdb/">https://www.genome.jp/virushostdb/</a>). The reads are 250bp long and were simulated with Mason (<a href="https://www.seqan.de/apps/mason/">https://www.seqan.de/apps/mason/</a>) from genomes downloaded from NCBI. The training-validation-test split was done on whole viral sequences to ensure "novelty" of validation and test viruses. The training sets contain 10 million reads per class, validation sets - 1.25 million reads per class, and test sets - 1.25 million paired reads per class. The negative class sets contain reads simulated from chordate-infecting ("cho"), metazoan-infecting ("met"), eukariote-infecting ("euk") and all-nonhuman viruses. The positive class contains human-infecting viruses. The stratified dataset ("strat") contains an equal number of reads from "cho", "met but not cho", "euk but not met" and "all but not euk".  Species-level datasets ("humspec", "allspec" and "chospec", with the corresponding fasta and *_species.rds files) are constructed analogously, but ensuring that all viruses of a given species were assigned to either training, val or test set. This is a stricter setting modelling a "novel viral species" scenario while reflecting within-species phenotype diversity. blast_hits.gz contains blast hits of human virome reads form Moustafa et al., 2017 (https://doi.org/10.1371/journal.ppat.1006292) blasted against our training database (see paper for details). In the second column you can find the matched label and the accession number of the matched reference. blast_labels_complete.gz contains extracted labels for all virome reads, including those without any matches. Note: one of the read headers (>3c8ac47039d32b11c8fe23f588e444e9) from Moustafa et al. is slightly corrupted with null characters. You can remove them with sed 's/\x0//g' or equivalent.

Novel Foraging Behaviors of Scolopendra dehaani (Chilopoda: Scolopendridae) in Nakhon Ratchasima, Thailand

Data from three observations of Scolopendra dehaani predation on vertebrate prey, and two supplemental observations further highlighting diurnal foraging and use of trees. Observation data from Nakhon Ratchasima province, Thailand. Included: photographic evidence of predation observations, diurnal foraging, and arboreality with a .csv file with corresponding event data.   Data_S.dehaani_observations.csv file column headings: folderID: The Zenodo folder ID containing the photographic or video evidence of events. obvdate: Date of observation (mm/dd/yyyy) obvtime: Time of observation (24hr; ICT) Easting_utm: UTM easting (Datum WGS84) Northing_utm: UTM northing (Datum WGS84) ups_zone: UTM Zone (47N or 48N) gps_accuracy: Accuracy of GPS location (m) notes: Comments and details on events/observations

Supporting data for Novel functional sequences uncovered through a bovine multi-assembly graph

Description of the datasets Data are organized as a folder and compressed with tar.gz. You need to unzip the folder using the command tar -xzvf data.tar.gz. Unzipping will output a folder named data_tidy, which is organized as follow: <ul> <li>graph.gfa : Graph in GFA format constructed from 6 cattle assemblies</li> <li>nonref.fa : Non-reference sequences extracted from the graph</li> <li>nonref.fa.masked: Hard masked repetitive regions version of nonref.fa</li> <li>nonref_woflanking.fa: Nonref.fa without flanking sequences</li> <li>nonref_woflanking.fa.masked: Masked version of nonref_woflanking.fa</li> <li>augustus_predict.gtf: Annotated gene models of Augustus from non-ref sequences</li> <li>augustus_prot.fa: Protein fasta of the predicted gene models from Augustus</li> <li>breeds_assembled.gtf: Annotation of the StringTie assembled across-breed transcriptome</li> <li>breeds_expressed.tsv: Expression data of breeds_assembled.gtf</li> <li>de_assembled.gtf: Annotation of the StringTie assembled differentially-expressed transcriptome on non-ref sequences</li> <li>de_expression.tsv: Differential expression results from de_assembled.gtf</li> <li>variant_nonref.tsv: Variants called from non-ref sequences (-1, 0, 1, 2 indicates no call, hom ref, het, and hom alt respectively)</li> </ul>

A novel phosphoproteomic landscape evoked in response to type I interferon in the brain and in glial cells

Type I interferons (IFN-I) are key responders to central nervous system infection and injury. They mediate their effects primarily via transcriptional regulation of several hundred interferon-regulated genes. Using a mouse model for IFN-I-induced neurodegeneration, we identified widespread protein phosphorylation as a new mechanism by which IFN-I mediate their effects. Protein phosphorylation aligned with the clinical hallmarks and pathological outcome, including impaired development, motor dysfunction and seizures. In vitro experiments revealed extensive and rapid IFN-I-induced protein phosphorylation in microglia and astrocytes, the brain’s primary IFN-I-responding cells. Response to acute IFN-I stimulation was independent of gene expression and mediated by a small number of kinase families. The changes in the phosphoproteome affected a diverse range of cellular processes and functional analysis suggested that this response induced an immediate reactive state and prepared cells for subsequent transcriptional responses. Our studies reveal a hitherto unappreciated role for changes in the protein phosphorylation landscape in cellular responses to IFN-I and thus provide insights for novel diagnostic and therapeutic strategies for neurological diseases caused by IFN-I.

A novel phosphoproteomic landscape evoked in response to type I interferon in the brain and in glial cells

Type I interferons (IFN-I) are key responders to central nervous system infection and injury. They mediate their effects primarily via transcriptional regulation of several hundred interferon-regulated genes. Using a mouse model for IFN-I-induced neurodegeneration, we identified widespread protein phosphorylation as a new mechanism by which IFN-I mediate their effects. Protein phosphorylation aligned with the clinical hallmarks and pathological outcome, including impaired development, motor dysfunction and seizures. In vitro experiments revealed extensive and rapid IFN-I-induced protein phosphorylation in microglia and astrocytes, the brain’s primary IFN-I-responding cells. Response to acute IFN-I stimulation was independent of gene expression and mediated by a small number of kinase families. The changes in the phosphoproteome affected a diverse range of cellular processes and functional analysis suggested that this response induced an immediate reactive state and prepared cells for subsequent transcriptional responses. Our studies reveal a hitherto unappreciated role for changes in the protein phosphorylation landscape in cellular responses to IFN-I and thus provide insights for novel diagnostic and therapeutic strategies for neurological diseases caused by IFN-I.

opencc-by-4.0Feb 2020View details →

Protein elution profiles accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"

Key to files Experiment_Order.csv <ul> <li>Description: Meta details of each experiment.</li> <li>Format: experiment_name,ExperimentID_order,tissue,experiment_type,spec,ExperimentID</li> </ul> Fraction_Details.csv <ul> <li>Description: Meta details of each fraction</li> <li>Format:FractionID,frac_order,ExperimentID</li> </ul> plant_virNOG_orthology.csv.gz <ul> <li>Description: Conversion between orthogroup and protein IDs.</li> <li>Format:ID,ProteinID,spec</li> </ul> orthogroup_annotation.csv.gz <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> panplant_tidy_elution_virNOG.csv.gz <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed orthogroups 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> panplant_tidy_elution_protcount.csv.gz <ul> <li>Description: Tidy (long format) table of counts of peptide spectral matches (PSMs) for all observed proteins for all experiments. </li> <li>Format: ExperimentID,FractionID,ProteinID,ProteinCount,spec,ExperimentID_order,FractionID_order</li> </ul> panplant_wide_elution_virNOG.csv.gz <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed orthogroups</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> panplant_wide_elution_virNOG_annot.csv.gz <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed orthogroups, includes annotation columns.</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> panplant_wide_elution_expnorm.csv.gz <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed orthogroups</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> panplant_wide_elution_expnorm_annot.csv.gz <ul> </ul> <ul> <li>Description: Table of concatenated elution profiles reporting per-fractionation experiment-normalized peptide spectral matches (PSMs) for all observed orthogroups, including columns with annotations</li> <li>Format: OrthogroupID,[Annotations],[Fractions]</li> </ul> [experiment_name].virNOG.wide.gz <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) for all observed orthogroups in one experiment</li> <li>Format: OrthogroupID,[Fractions]</li> </ul> <ul> </ul> [experiment_name].protcount.wide.gz <ul> <li>Description: Elution profile of raw counts of peptide spectral matches (PSMs) counts for all observed proteins in one experiment</li> <li>Format: ProteinID,[Fractions]</li> </ul> [species]_specconcat.virNOG.wide.gz <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed orthogroups from a particular species. Only present for species with more than one experiment. </li> <li>Format: OrthogroupID,[Fractions]</li> </ul> [species]_specconcat.protcount.wide.gz <ul> <li>Description: Table of concatenated elution profiles of raw counts of peptide spectral matches (PSMs) for all observed proteins from a particular species. Only present for species with more than one experiment. </li> <li>Format: ProteinID,[Fractions]</li> </ul>   <ul> </ul> Species codes |Code | Species | Common name   | Use | |---|---|---| | arath  | Arabidopsis Thaliana  | Arabidopsis  |  | braol  | Brassica oleracea  | Broccoli  | | cansa  | Cannabis sativa  | hemp  |  | cerri | Ceratopteris richardii | C-fern |  | chlre  | Chlamydomonas reinhardtii  | Chlamydomonas   | | chqui  | Chenopodium quinoa  | Quinoa  |  | orysj  |  Oryza sativa var. japonica | Rice  | | selml | Selaginella moellendorffii | Selaginella  |  | sollc  | Solanum  lycopersicum | Tomato  |  | wheat  | Triticum  Aestivum | Wheat  |  | soybn | Glycine max | Soybean |  | cocnu | Cocos nucifera | Coconut |  | maize | MAIZE | maize |

Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.

This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive  of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms  We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references).  This repository contains 3 different files: TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions TableS2_physical_priors.xlsx - contains three tabs: ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) +  ChIP-seq datasets (GSE28350, GSE81521) from  (Hochbaum et. al, 2011, Li et. al, 2016). eY1HATAC- contains  3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016). motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1 TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources) --- Description of methodology to obtain regulatory interactions in TableS2: Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations. For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands. An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs. For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair. For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).