Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
356
datasets available to search
ShareScore release 0.9.0
Dataset results
356 results for “In silico”
Identification of Y chromosome markers in the eastern three-lined skink (Bassiana duperreyi) using in silico whole genome subtraction
<p>Background: Homologous sex chromosomes can differentiate over time because recombination is suppressed in the region of the sex determining locus, leading to the accumulation of repeats, progressive loss of genes that lack differential influence on the sexes and sequence divergence on the hemizygous homolog. Divergence in the non-recombining regions leads to the accumulation of Y or W specific sequence useful for developing sex-linked markers. Here we use <i>in silico</i> whole-genome subtraction to identify putative sex-linked sequences in the scincid lizard <i>Bassiana duperreyi</i> which has heteromorphic XY sex chromosomes.</p> <p>Results: We generated 96.7 x 10<sup>9</sup> 150 bp paired-end genomic sequence reads from a XY male and 81.4 x 10<sup>9</sup> paired-end reads from an XX female for <i>in silico</i> whole genome subtraction to yield Y enriched contigs. We identified 7 reliable markers which were validated as Y chromosome specific by polymerase chain reaction (PCR) against a panel of 20 males and 20 females.</p> <p>Conclusions: The sex of <i>B. duperreyi</i> can be reversed by low temperatures (XX genotype reversed to a male phenotype). We have developed sex-specific markers to identify the underlying genotypic sex and its concordance or discordance with phenotypic sex in wild populations of <i>B. duperreyi</i>. Our pipeline can be applied to isolate Y or W chromosome-specific sequences of any organism and is not restricted to sequence residing within single-copy genes. This study greatly improves our knowledge of the Y chromosome in <i>B. duperreyi</i> and will enhance future studies of reptile sex determination and sex chromosome evolution.</p>
An in silico docking simulation of SARS Coronavirus2 and Ivermectin
<p><strong>ABSTRACT</strong></p> <p> COVID-19 is spreading and infecting in the world. And that is occurring a death very much. Of course, I want to cooperate for save peoples. And I was simulating a docking about proteins of SARS Coronavirus 2 in silico. That a papain-like protease, a karyopherin importin α,β and RNA Polymerase. One hypothesis [1] said Ivermectin can destabilises about a bind to an importin and the virus cargo proteins. And I got a significantly results from in silico simulation that at catalytic center of RNA Polymerase. Remdesivir is docking at here. [2] I dedicate this report to a current patients and a future patients.</p> <p><strong>1. Introduction</strong></p> <p> The virus is called the bacteriophage. You know that the virus infect the bacteria. Therefore, the bacteria need a counter plan. I think, Ivermectin(Avermectin) is that. One hypothesis, the Coronavirus2 is<br> replicating by using a main protease [4], papain-like protease [4] and this RNA Polymerase. And Ivermectin destablishes about bind to an importin and the virus cargo proteins [1]. I checked for these case. But I<br> seems that Importin is too big for Ivermectin. And netxt, I checked for RNA Polymerase. and I report this result simply.</p> <p><strong>2. A software for docking simulation</strong></p> <p> This case is using a software that “Autodock vina” [5]. This software is better performance than other docking simulation softwares. But a simulation accuracy isn’t a high quality more than a real phenomenon<br> yet. This software is using an affinity score [kcal/mol]. See also vina web-site [5].</p> <p><strong>3. A docking parameters and Dataset</strong></p> <p> This case is using following parameters for docking simulations.</p> <p>(1).exhaustiveness: 8<br> (2).num modes: 10<br> (3).energy range: 1</p> <p>and I was using a protein data PDB:7bzf for RNA Polymerase. And PDB:IVM for Ivermectin. I chosen a chain A from PDB:7bzf by using pymol [8]. And I converted PDB:IVM from .sdf to .pdb by using “PDB<br> format-PDBx/mmCIF conversion service” web-site [9].</p> <p><strong>4. Contents</strong></p> <p> Figure 2: Front view of RNA Polymerase and Ivermectin I tried to a docking simulation during a four monthes about RNA Polymerase (PDB:7bzf) of SARS Coronavirus2 and Ivermectin [6]. And<br> I got a significantly results. I chosen a few higher score to following.</p> <p>mode | affinity | dist from best mode<br> | (kcal/mol) | rmsd l.b.| rmsd u.b.<br> -----+------------+----------+----------<br> 1 -11.5 0.000 0.000<br> 2 -11.1 1.975 3.233<br> 3 -11.0 1.895 2.482</p> <p>mode | affinity | dist from best mode<br> | (kcal/mol) | rmsd l.b.| rmsd u.b.<br> -----+------------+----------+----------<br> 1 -11.4 0.000 0.000<br> 2 -11.0 1.572 2.041<br> 3 -10.8 4.406 14.971</p> <p>mode | affinity | dist from best mode<br> | (kcal/mol) | rmsd l.b.| rmsd u.b.<br> -----+------------+----------+----------<br> 1 -11.4 0.000 0.000<br> 2 -10.8 1.550 2.430<br> 3 -10.6 1.648 2.197</p> <p>These are a near points. You can see a number of rmsd (Root Mean Square Deviation). These are a catalytic center of RNA Polymerase. One paper said [2], this point can combine Remdesivir too. Figure 1,2,3 are a point of best of affinity score that -11.5[kcal/mol]. Of course, If you want to know detail of results then you can download an all eleven data about this simulation from my web-site.</p> <p><strong>5. Conclusion</strong></p> <p> I seems that is very significantly result. Because, One, here are a higher score point (Figure 4). Second, here are a catalytic center. Third, Remdesivir can combine at same point [2]. I consider about an accuracy<br> of Autodoc Vina and a conformation will change, probably. You know, this is a computer simulation absolutely. I want to wait a result of cryo-EM and a crystal structure complex.</p> <p><strong>Acknowledge</strong></p> <p> Thank you for the NIG supercomputer at ROIS National Institute of Genetics. Because I’m using this computer system everyday.</p> <p> </p>
Data from: Environmental metabarcodes for insects: in silico PCR reveals potential for taxonomic bias
Studies of insect assemblages are suited to the simultaneous DNA-based identification of multiple taxa known as metabarcoding. To obtain accurate estimates of diversity, metabarcoding markers ideally possess appropriate taxonomic coverage to avoid PCR-amplification bias, as well as sufficient sequence divergence to resolve species. We used in silico PCR to compare the taxonomic coverage and resolution of newly designed insect metabarcodes (targeting 16S) with that of existing markers (16S and COI) and then compared their efficiency in vitro. Existing metabarcoding primers amplified in silico less than 75% of insect species with complete mitochondrial genomes available, whereas new primers targeting 16S provided greater than 90% coverage. Furthermore, metabarcodes targeting COI appeared to introduce taxonomic PCR-amplification bias, typically amplifying a greater percentage of Lepidoptera and Diptera species, while failing to amplify certain orders in silico. To test whether bias predicted in silico was observed in vitro, we created an artificial DNA blend containing equal amounts of DNA from 14 species, representing 11 different insect orders and one arachnid. We PCR-amplified the blend using five primers sets, targeting either COI or 16S, with high-throughput amplicon sequencing yielding more than 6 million reads. In vitro results typically corresponded to in silico PCR predictions, with newly designed 16S primers detecting 11 insect taxa present, thus providing equivalent or better taxonomic coverage than COI metabarcodes. Our results demonstrate that in silico PCR is a useful tool for predicting taxonomic bias in mixed template PCR, and that researchers should be wary of potential bias when selecting metabarcoding markers.
Reproducible in-silico omics analyses - Supplementary Figure 1 (DEPRECATED)
<p>Supplementary Figure 1. Kallisto Native Pipeline. The Kallisto native pipeline is written in bash and calls Kallisto to perform indexing of the transcriptome, RNA-seq pseudo-mapping and quantification and for Sleuth to perform differential expression analysis.</p>
Reproducible in-silico omics analyses - Supplementary Figure 1
<p>Supplementary Figure 1. Kallisto Native Pipeline. The Kallisto native pipeline is written in BASH and calls Kallisto to perform indexing of the transcriptome, RNA-seq pseudo-mapping and quantification and for Sleuth to perform differential expression analysis.</p>
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p> <p>Please, refer to the readme.txt for the description.</p>
Targeting host-virus interactions: In silico analysis of the binding of human milk oligosaccharides to viral proteins involved in respiratory infections
<p><span>Respiratory viral infections, a major public health concern, necessitate the continuous development of novel antiviral strategies, particularly in the face of emerging and re-emerging pathogens. In this study, we </span><span>explored</span><span> the potential of human milk oligosaccharides (HMOs) as broad-spectrum antiviral agents against key respiratory viruses. </span><span>By examining the</span><span> structural mimicry of host cell receptors and </span><span>their </span><span>known biological functions, including antiviral activities, we assessed the </span><span>ability</span><span> of HMOs to bind and potentially inhibit viral proteins crucial for host</span><span> </span><span>cell entry. Our <em>in silico</em> analysis </span><span>focused</span><span> on viral proteins integral to host-virus interactions</span><span>, namely,</span><span> the hemagglutinin protein of influenza, fusion proteins of respiratory syncytial and human metapneumovirus, and the spike protein of SARS-CoV-2. Using molecular docking and simulation studies, we </span><span>demonstrated</span><span> that HMOs exhibit varying binding affinities to these viral proteins, suggesting their potential as viral entry inhibitors. </span><span>This</span><span> study </span><span>identified</span><span> several HMOs with promising binding profiles, highlighting their potential in antiviral drug development. This research provides a foundation for utilizing HMOs as a natural source for designing new therapeutics, offering a novel approach in the fight against respiratory viral infections.</span></p>
Scripts for "In-Silico-Assisted Derivatization of Triarylboranes for the Catalytic Reductive Functionalization of Aniline-Derived Amino Acids and Peptides with H2"
<p>Please carefully read "README" file enclosed.</p>
Fig. 4 in Characterization of undescribed melanoma inhibitors from Euphorbia mauritanica L. cultivated in Egypt targeting BRAF and MEK 1 kinases via in-silico study and ADME prediction
Fig. 4. Experimental and TDDFT-simulated electronic circular dichroism spctra of (A) euphomauritanol A (1), (B) euphomauritanol B (2), and euphomauritanophane A (3).
Fig. 6 in Characterization of undescribed melanoma inhibitors from Euphorbia mauritanica L. cultivated in Egypt targeting BRAF and MEK 1 kinases via in-silico study and ADME prediction
Fig. 6. Bioavailability radar chart from Swiss ADME online web tool for compounds (A) 1, (B) 2 and (C) 3. The pink area represents the range of the optimal property values for oral bioavailability and the red line is compounds (A) 1, (B) 2 and (C) 3 predicted properties. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Characterization of undescribed melanoma inhibitors from Euphorbia mauritanica L. cultivated in Egypt targeting BRAF and MEK 1 kinases via in-silico study and ADME prediction
Fig. 5. (A) 2D binding mode, (B) 3D binding mode of Euphomauritanol A (1) in the active site of BRaf Kinase V600E (PDB ID: 4XV2) (C) 2D binding mode, and (D) 3D binding mode of euphomauritanophane A (3) in the active site of MEK 1 kinase (PDB ID: 4LMN).
Supporting data for "The benefit of in silico predicted spectral libraries in data-independent acquisition data analysis workflows"
Open the record for dataset details and reuse information.
Validation of de novo designed water-soluble and transmembrane proteins by in silico folding and melting
<p>Here are all of the datasets generated and analysed during this study. </p> <p>Here is a breakdown of their content:</p> <ul> <li><strong>8_stranded_transmembrane_barrels.zip</strong> - raw data from Alphafold (3 and 48 recycles), ESMFold and raptor predictions of the 8 stranded TMBs. A file with all the sequences is also given</li> <li><strong>12_stranded_transmembrane_barrels.zip - </strong>raw data from the Alphafold and ESMfold predictions of the 12 stranded TMBs. A file with all the sequences is also given</li> <li><strong>water_soluble_barrels.zip</strong> - raw data from the Alphafold and ESMfold predictions of the water soluble beta barrels (designable and non-designable). A file with all the sequences is also given</li> <li><strong>all design models.zip</strong> - original design models for water-soluble (designable and non-designable), 8-stranded and 12-stranded TMBs</li> </ul> <p> </p> <ul> <li><strong>ESMfold_masking_exp.tar - </strong>this tar file contains all the ESMfold masking experiments performed to the water-soluble, 8 and 12-stranded transmembrane barrels. Inside there are zipped datasets for each masking experiment<br> </li> <li> <p><strong>ziped_raw_csv_files.zip - </strong>raw csv files with all the data necessary to analyse the figures </p> </li> <li> <p><strong>analysis_notebooks.zip </strong>- Jupyter notebooks used to analyse the output prediction data for all figures</p> </li> </ul> <p> </p> <p> </p>
Artificial intelligence-based parametrization of Michaelis–Menten maximal velocity: Toward in silico New Approach Methodologies (NAMs)
<p><span>The development of mechanistic systems biology models necessitates the utilization of numerous kinetic parameters once the enzymatic mode of action has been identified. Moreover, wet lab experimentation is associated with particularly high costs, does not adhere to the principle of reducing the number of animal tests, and is a time-consuming procedure. Alternatively, an artificial intelligence-based method is proposed that utilizes enzyme amino acid structures as input data. This method combines NLP techniques with molecular fingerprints of the catalyzed reaction to determine Michaelis–Menten maximal velocities (Vmax). The molecular fingerprints employed include RCDK standard fingerprints (1024 bits), MACCS keys (166 bits), PubChem fingerprints (881 bits), and E-States fingerprints (79 bits). These were integrated to produce reaction fingerprints. The data were sourced from SABIO RK, providing a concrete framework to support training procedures. After the data preprocessing stage, the dataset was randomly split into a training set (70%), a validation set (10%), and a test set (20%), ensuring unique amino acid sequences for each subset. The data points with structures similar to those used to train the model as well as uncommon reactions were employed to further test the model. The developed models were optimized during the training procedure to predict Vmax values efficiently and reliably. By utilizing a fully connected neural network, these models can be applied to all organisms. The amino acid proportions of enzymes were also tested, which revealed that the amino acid content was an unreliable predictor of the Vmax. During testing, the model demonstrated better performance on known structures than on unseen data. In the given use case, the model trained solely on enzyme representations achieved an R-squared of 0.45 on unseen data and 0.70 on known structures. When enzyme representations were integrated with RCDK fingerprints, the model achieved an R-squared of 0.46 for unseen data and 0.62 for known structures.</span></p>
In silico analysis dataset for HPDL missense variants
Open the record for dataset details and reuse information.
Designing Quinazoline Derivatives to Prevent Alzheimer's disease in silico and in vitro studies
<p>Input files, parameter files, topology files for molecular docking, MD, and perturbation simulations</p>
Supplemental information for- In-silico evaluation of potential inhibitors against all serotypes of Dengue Virus Envelope Glycoprotein
Open the record for dataset details and reuse information.
Salmonella In Silico Typing Resource (SISTR) commandline tool database version 1.1.3 used by SISTR release version 1.1.3 and up
<h3><strong>Context</strong></h3> <p>Salmonella In Silico Typing Resource (SISTR) commandline tool enables the identification of the Salmonella serovar and cgMLST types of Salmonella from whole genome sequencing (WGS) data tby using a large database (10,000+) of Salmonella genomes and cgMLST profiles based on the 330 alleles. This database is the central part of the SISTR tool and contains both metadata on 2660 serovars and the corresponding antigenic formula, 84463 genomes to serovar mappings, sequences of the O, H1 and H2 antigens, 139729 cgMLST sequences and 38240 profiles with pairwise distances, MASH sketch of the 15465 genomes used for species and serovar identification.</p> <p>For more information and citation please refer to the following publication and official repository at <a href="https://github.com/phac-nml/sistr_cmd/tree/master">https://github.com/phac-nml/sistr_cmd/tree/master</a> </p> <p>Note: This database was used by SISTR tool up to version 1.1.2 inclusive. From SISTR release 1.1.3 onwards the slightly modified version of this database will be used onwards with changes detailed in <a href="https://github.com/phac-nml/sistr_cmd/blob/master/CHANGELOG.md">https://github.com/phac-nml/sistr_cmd/blob/master/CHANGELOG.md</a></p> <h3><strong>Citation</strong></h3> <p><em>The Salmonella In Silico Typing Resource (SISTR): an open web-accessible tool for rapidly typing and subtyping draft Salmonella genome assemblies. Catherine Yoshida, Peter Kruczkiewicz, Chad R. Laing, Erika J. Lingohr, Victor P.J. Gannon, John H.E. Nash, Eduardo N. Taboada. PLoS ONE 11(1): e0147101. doi: 10.1371/journal.pone.0147101. <a title="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101" href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101</a></em></p> <p> </p> <h3><strong>Version changes from v2 to v3</strong></h3> <p>In<em> </em><code><strong>genomes-to-serovar.txt</strong></code><em> </em>following serovar changes were made to increase serovar prediction accuracy</p> <table> <tbody> <tr> <th>genome accession</th> <th>serovar previous</th> <th>serovar current</th> </tr> <tr> <td><strong>SRR3048937</strong></td> <td>Ibadan</td> <td>Mississippi</td> </tr> </tbody> </table>
Dataset 1 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution
<p><strong>Dataset</strong></p> <p>Molecular dynamics simulation trajectories of TTR peptide monomers and aggregation kinetics:</p> <ul> <li>Amber99sb_disp: TTR monomer in explicit solvent using amber99disp force field.</li> <li>multi-GO-monomer: TTR monomer simulation using the multi-GO force field.</li> <li>multi-eGO-monomer: TTR monomer simulation using the multi-eGO ensemble force field.</li> <li>multi-eGO-XXmM-Y: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration replicate Y.</li> <li>.ipynb files: analysis script employed for the aggregation kinetics simulations.</li> <li>TTR aggregation kinetics movies.</li> </ul>
In silico dataset of features for multidrug resistant efflux transporter protein familes
<p>This dataset constitutes in silico features as amino acids fractions, dipeptide count, and other basic features information for efflux transporter family proteins.</p>
ScienceDex guides
Understand access before you commit
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.