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26 results for “Database Search”
Data for "A learned score function improves the power of mass spectrometry database search"
<div> <h1>DATA for "A learned score function improves the power of mass spectrometry database search"</h1> <br> <div>These data files are associated with the following publication:</div> <br> <div> <ul> <li>Varun Ananth, Justin Sanders, Melih Yilmaz, Sewoong Oh and William Stafford Noble. "<a title="biorXiv Preprint Link" href="https://www.biorxiv.org/content/10.1101/2024.01.26.577425v2" target="_blank" rel="noopener">A learned score function improves the power of mass spectrometry database search</a>". Bioinformatics (Proceedings of the ISMB). 2024.</li> </ul> </div> <br> <div>For the benchmarking data, we used a dataset that is publicly available on ProteomeXchange (PXD028735). The paper that introduced this dataset is:</div> <br> <div> <ul> <li>Van Puyvelde, B., Daled, S., Willems, S., Gabriels, R., Gonzalez de Peredo, A., Chaoui, K., Mouton-Barbosa, E., Bouyssié, D., Boonen, K., Hughes, C. J., Gethings, L. A., Perez-Riverol, Y., Bloomfield, N., Tate, S., Schiltz, O., Martens, L., Deforce, D., & Dhaenens, M. (2022). A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01216-6</li> </ul> </div> <br> <div>More specifically, the following `.raw` files were downloaded:</div> <br> <ul> <li><code>LFQ_Orbitrap_DDA_Ecoli_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Human_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Yeast_01.raw</code></li> </ul> <br> <div>Those files can be accessed via FTP <a title="Link to ProteomeXchange: PXD028735" href="https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/" target="_blank" rel="noopener">here</a>.</div> <br> <div>We upload here the annotated <code>.mgf</code> files created from these <code>.raw</code> files, as described in our paper.</div> <br> <div>The human, yeast, and E. coli .fasta files used in all database searches were downloaded from UniProt on 11/6/23, 4:30 PM.</div> <br> <div> <ul> <li>Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., … Zhang, J. (2022). UniProt: the Universal Protein Knowledgebase in 2023. In Nucleic Acids Research (Vol. 51, Issue D1, pp. D523–D531). Oxford University Press (OUP). https://doi.org/10.1093/nar/gkac1052</li> </ul> </div> <br> <div>We include these files here, with only minor modifications to replace `U` amino acids with `X` so that all amino acids fall into Casanovo-DB's vocabulary.</div> </div>
Database Search Results for Resource Management in Converged Optical and MillimeterWave Radio Networks Review
<p><strong>Paper Selection Procedure</strong></p> <p>In order to conduct the survey titled "Resource Management in Converged Optical and MillimeterWave Radio Networks: A Review", the authors reviewed works published in the literature with a focus on those that cover most of the identified optimization requirements for converged optical fronthaul and mmWave wireless access networks. The research method is based on the research steps given in "The PRISMA 2020 statement"[1]. The selection procedure is also illustrated in "Database Search Flow Chart.png" figure.</p> <p>The first step was the selection of the papers. We completed this step by making database searches in the ACM, Elsevier (Science Direct), IEEE, IET, MDPI, Optical Society (OSA), Springer, Taylor & Francis, and Wiley online library databases with keywords ``resource allocation AND converged mmWave fiber wireless (FiWi)'', ``resource management AND converged mmWave fiber wireless (FiWi)'', and ``resource allocation AND converged fiber wireless (FiWi)''. The searches in all databases were completed in May 2021. The resulting collection was screened, to exclude non-scientific texts, book chapters, out of context papers, and survey papers. The remaining 189 papers found in our database search are provided in the excel file titled "FiWi Resource Allocation Database Search.xlsx".</p> <p>Among these papers, our selection criteria was created to present the works that are most relevant to the target network architecture, providing novel implementation solutions to the requirements of the optimization objective. The criteria selected for our eligibility step can be summarized as follows:</p> <ul> <li>The study provided a sound research approach and published after a scholarly review process;</li> <li>The study had a resource management optimization objective for mmWave networks;</li> <li>The study explained the system model and proposed a well-defined optimization algorithm;</li> <li>The effects of the algorithm on a performance metric was reported and the different aspects of the performance metric was analyzed with different evaluation criteria.</li> </ul> <p>This review is limited to the focus scope on converged optical and mmWave radio network solutions and by the databases taken into consideration. The prioritization of the works that address a well-defined optimization algorithm led to the omission of relevant papers. We did not include works that do not clearly define a resource management objective, i.e., a study that focuses on the the hardware implementation aspects of optical and mmWave radio networks with no resource management perspective. We manually excluded all studies that do not match these criteria with a simple scoring system, in which a point is deducted from an eligible paper for each missing criterion. The initial screening process and the data collection steps were carried out by the first author and the final inclusion decision was made by all the reviewers for the studies with the highest scores. After this screening process, we identified 37 papers that focused on at least one of the resource management objectives of throughput maximization, delay minimization, energy-efficiency, and virtualized resource allocation. The papers that have joint objectives are classified under their main optimization focus of that paper. The list of the selected papers are provided in "FiWi Resource Allocation Papers Selected for Review.xlsx" file. Our target in this review is to understand the recent optimization techniques used in resource allocation for converged optical fronthaul and radio mmWave access network implementations, therefore we focused our search to the works completed in the last five years (between 2016 and 2021), and approximately 95% of the selected papers fit under this category.</p> <p><strong>Overview of the data collected from selected papers</strong></p> <p>In this section, we provide answers to the three following questions with the data collected from the eligible studies:</p> <ul> <li>Question 1: Which algorithms are used more often in performance optimization in converged mmWave networks?</li> <li>Question 2: Which performance metrics are determined to show that the optimization method achieves the objective?</li> <li>Question 3: Which criteria are used to evaluate the solution method?</li> </ul> <p>Regarding the first question, the figure titled "Distribution of Optimization Algorithms in Selected Papers" shows the distribution of the optimization algorithms used by the selected papers. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 (Distribution of Evaluation Criteria) and the evaluation criteria to test the performances of the selected papers are grouped in Table 2 (Distribution of Main Performance Metrics Depending on Optimization Objectives), which shows how many times each criterion is used together with how many of the resource management objectives use these criterion.</p> <p><strong>References: </strong></p> <p>[1] Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;Brennan, S.E.; Chou, R.; Glanville, J.; Grimshaw, J.M.; Hróbjartsson, A.; Lalu, M.M.; Li, T.; Loder, E.W.; Mayo-Wilson, E.;McDonald, S.; McGuinness, L.A.; Stewart, L.A.; Thomas, J.; Tricco, A.C.; Welch, V.A.; Whiting, P.; Moher, D. The PRISMA 2020statement: an updated guideline for reporting systematic reviews.Systematic Reviews2021,10. doi:10.1186/s13643-021-01626-4.</p>
Search results for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)
<p>This datasets is the result of the search for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)</p>
Supplementary Text A1. Detailed search terms and steps in all databases
<p>Supplement to the article "Efficacy and safety of PD-1 inhibitors in recurrent or metastatic nasopharyngeal carcinoma patients after failure of platinum-containing regimens: a systematic review and meta-analysis".</p> <p><strong>Supplementary Text </strong><strong>A</strong><strong>1</strong><strong>.</strong><strong> Detailed search terms and steps in all databases</strong></p>
MetaPep: A core peptide database for faster human gut metaproteomics database searches
<p>Metaproteomics has increasingly been applied to study functional changes in the human gut microbiome. And peptide identification is an important step in metaproteomics research. However, the large search space in metaproteomics studies causes significant challenges for peptide identification. Here, we constructed MetaPep, a core peptide database (including both collections of peptide sequences and tandem MS spectra) greatly accelerating the peptide identifications. Raw files from fifteen metaproteomics projects were re-analyzed and the identified peptide-spectrum matches (PSMs) were used to construct the MetaPep database. The constructed MetaPep database achieved rapid and accurate identification of peptides for human gut metaproteomics.</p>
Part-by-part interface-based search and automatic reassembly of CAD models for database expansion and model reuse
<p>This dataset contains the generated cad assembly from three differents strategy from the article "Part-by-part interface-based search and automatic reassembly of CAD models for database expansion and model reuse".<br> In ReplacePart strategy, each assembly is available in .FCStd format which has the kinematic constraints in the A2+ workbench. A .STEP format of the assembly as well as a PNG screen of the assembly is also available. For the two other strategies, juste STEP files are availables.</p>
Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database
<p>NA</p>
Supplemental Data for "Identifying novel variants of small molecules through database search of mass spectra"
<p>Supplemental Dataset 1 contains GNPS dataset information for the large scale search of GNPS vs Pubchem.</p> <p>Supplemental Dataset 2a contains the top scoring exact mode hit for each spectrum against PubChem and COCONUT. Files are split into "*chunk*" files of up to 10 million records each.</p> <p>Supplemental Dataset 2b contains the top scoring variable mode hit for each spectrum against COCONUT.</p> <p>Supplemental Dataset 3 contains mass spectra provided by Waters Corporation to analyze impurities of Imatinib.</p>
Search for huntingtin interactors in online databases – 2018/08/08
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Rationale</strong> - To identify different huntingtin interaction partners. </p> <p><strong>Overview</strong> - Different online databases which detail protein interaction partners were searched for huntingtin protein interaction partners. Data detailing huntingtin interaction partners from 9 different databases was extracted and simplified – worksheets 1-15. The information from each database was collated – worksheet 16. Huntingtin protein interaction partners were ranked according to the number of databases they were found in as well as the number of different experiments detailing the interaction with huntingtin – worksheet 17. </p>
Comparative analysis of huntingtin interaction partner database searches with Dr. Maiuri's work - 2018/08/31
<p><strong>Project </strong>- Huntingtin structure-function open lab notebook. </p> <p><strong>Rationale</strong> - To compare the ROS-specific huntingtin interaction partners identified by Dr. Tamara Maiuri with those detailed in existing databases. </p> <p><strong>Overview</strong> - Previously, different online databases which detail protein interaction partners were searched for huntingtin protein interaction partners. Following completion of this initial analysis, Dr. Tamara Maiuri posted in her open notebook a detailed list of high and medium confidence ROS-specific huntingtin interacting proteins: <a href="https://zenodo.org/record/1319540">https://zenodo.org/record/1319540</a>. A comparison of these interactors with those identified in the previously mined databases is briefly detailed. </p>
Data mining antibody sequences for database searching in bottom-up proteomics
<p>Mass spectrometry (MS)-based proteomics is a powerful method for identifying and quantifying antibodies. Among the various MS approaches, bottom-up proteomics is especially effective for analyzing thousands of antibodies in complex mixtures. In this method, proteins are enzymatically digested into smaller peptides, typically using the protease trypsin, which are then analyzed via mass spectrometry. These peptides are matched to sequences in standard databases like UniProt or NCBI-RefSeq for identification.</p> <p>However, a major limitation of this approach is the absence of comprehensive disease-specific antibody databases. Current databases, such as UniProt, include only a fraction of the antibody sequences present in the human body. For instance, as of January 2024, UniProt contains just 38,800 immunoglobulin sequences, far short of the billions of antibodies the human immune system can produce. As a result, relying on such limited databases can lead to under-detection of antibodies, particularly those associated with specific diseases. Expanding antibody databases with disease-specific sequences is crucial for improving the accuracy of MS-based proteomics in identifying antibodies relevant to human health.</p> <p>Recently, through next-generation sequencing of antibody gene repertoires, it has become possible to obtain billions of antibody sequences (in amino acid format) by annotating, translating, and numbering antibody gene sequences. These large numbers of sequences are now available in public databases such as the <a href="https://opig.stats.ox.ac.uk/webapps/oas/" rel="nofollow">Observed Antibody Space</a>. We hypothesize that using these theoretical antibody sequences as new databases for bottom-up proteomics could address the current lack of antibody coverage in standard databases.</p> <p>We developed a workflow to create disease-specific antibody peptide databases for bottom-up proteomics. The workflow details are available on <a href="https://github.com/trinhxt/SDU_Immunoinformatics">GitHub</a>. The database and metadata files generated by this workflow are stored in this Zenodo dataset, and they are used in DAT-DB — a web application that allows researchers to obtain FASTA files of disease-specific antibody peptides for direct use in bottom-up proteomics (see <a href="https://trinhxt.shinyapps.io/DAT-DB/">Demo version</a>).</p> <p>Each database file in this dataset is in <em>.duckdb</em> format and contains tables with 10 columns: Sequence, Filename, Patient, BSource, BType, Isotype, N_patient, N_antibody, Length_aa, and CDR3. The "<strong>Sequence</strong>" column contains tryptic peptides. "<strong>Filename</strong>" is the file where the data was collected. "<strong>Patient</strong>" refers to the patient number as listed in <em>metadata2.csv</em>. "<strong>BSource</strong>" refers to the B-cells' source, and "<strong>BType</strong>" refers to the type of B-cells. "<strong>Isotype</strong>" specifies the antibody isotype (IgA, IgD, IgE, IgG, IgM, or Bulk). "<strong>N_patient</strong>" indicates the number of patients having this peptide, and "<strong>N_antibody</strong>" specifies the number of antibodies containing this peptide. "<strong>Length_aa</strong>" indicates the number of amino acids in the peptide, while "<strong>CDR3</strong>" shows whether the peptide is found in the CDR3 region.</p> <p>The file <em>metadata1.csv</em> contains information about each database file, while <em>metadata2.csv</em> provides details about the sources of the collected antibodies.</p>
Data from: Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database
Open the record for dataset details and reuse information.
The clinical impact of high-profile animal-based research reported in the UK national press: a detailed discussion of articles from 1995, and full search results from the Nexis database
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objectives</b>: We evaluated animal-based biomedical 'breakthroughs' reported in the UK national press in 1995 (25 years prior to the conclusion of this study). Based on evidence of over-speculative reporting of biomedical research in other areas (e.g. press releases and scientific papers), we specifically examined animal research in the media, asking, "In a given year, what proportion of animal research 'breakthroughs' published in the UK national press had translated, more than 20 years later, to approved interventions?"</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods</b>: We searched the Nexis media database (LexisNexis.com) for animal-based biomedical reports in the UK national press. The only restrictions were that the intervention should be specific, such as a named drug, gene, biomedical pathway, to facilitate follow-up, and that there should be claims of some clinical promise. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Main Outcome Measures</b>: Were any interventions approved for human use? If so, when and by which agency? If not, why, and how far did development proceed? Were any other, directly related interventions approved? Did any of the reports over-state human relevance?</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results</b>: Over-speculation and exaggeration of human relevance was evident in all the articles examined. Of 27 unique published 'breakthroughs', only one had clearly resulted in human benefit. Twenty were classified as failures, three were inconclusive, and three were partially successful.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions</b>: The results of animal-based pre-clinical research studies are commonly over-stated in media reports, to prematurely imply often-imminent 'breakthroughs' relevant to human medicine.</span></span></span></span></span></span></span></span></span></span></span></p>
Supplementary Table 1: Search terms used for each database.
Open the record for dataset details and reuse information.
Data for "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach"
<p>Data for "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach"</p> <p>Contains files with the SMILES retrieved from the database (fragnet_query_outputs.zip) and sdf files containing the full lists of scored compounds for Fragalysis target test cases (scored_output_sdfs.zip). The compound sets used in the bioisosteric and perfect comparisons are saved in a separate directory.</p>
Core competencies of a scholarly journal editor. Database and citation search report and PRISMA_flow_diagram
<p><strong>Report on Database and Citation search and PRISMA_flow_diagram</strong></p>
Data for "The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges"
<p>The input and output data to the filtering pipeline described in "The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges"; code available from https://github.com/oxpig/fragment_network_merges. </p>
The clinical impact of high-profile animal-based research reported in the UK national press: a detailed discussion of articles from 1995, and full search results from the Nexis database
Open the record for dataset details and reuse information.
Data for "A learned score function improves the power of mass spectrometry database search"
<div>These data files are associated with the following publication:</div> <div> <ul> <li>Varun Ananth, Justin Sanders, Melih Yilmaz, Bo Wen, Sewoong Oh and William Stafford Noble. "<a title="biorXiv Preprint Link" href="https://www.biorxiv.org/content/10.1101/2024.01.26.577425v2" target="_blank" rel="noopener">A learned score function improves the power of mass spectrometry database search</a>". Bioinformatics (Proceedings of the ISMB). 2024.</li> </ul> </div> <div>For the benchmarking data, we used a dataset that is publicly available on ProteomeXchange (PXD028735). The paper that introduced this dataset is:</div> <div> <ul> <li>Van Puyvelde, B., Daled, S., Willems, S., Gabriels, R., Gonzalez de Peredo, A., Chaoui, K., Mouton-Barbosa, E., Bouyssié, D., Boonen, K., Hughes, C. J., Gethings, L. A., Perez-Riverol, Y., Bloomfield, N., Tate, S., Schiltz, O., Martens, L., Deforce, D., & Dhaenens, M. (2022). A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. In Scientific Data (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41597-022-01216-6</li> </ul> </div> <div>More specifically, the following `.raw` files were downloaded:</div> <ul> <li><code>LFQ_Orbitrap_DDA_Ecoli_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Human_01.raw</code></li> <li><code>LFQ_Orbitrap_DDA_Yeast_01.raw</code></li> </ul> <div>Those files can be accessed via FTP <a title="Link to ProteomeXchange: PXD028735" href="https://ftp.pride.ebi.ac.uk/pride/data/archive/2022/02/PXD028735/" target="_blank" rel="noopener">here</a>.</div> <div>We upload here the annotated <code>.mgf</code> files created from these <code>.raw</code> files, as described in our paper.</div> <div>The human, yeast, and E. coli .fasta files used in all database searches were downloaded from UniProt on 11/6/23, 4:30 PM.</div> <div> <ul> <li>Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., Bye-A-Jee, H., Cukura, A., Denny, P., Dogan, T., Ebenezer, T., Fan, J., Garmiri, P., da Costa Gonzales, L. J., Hatton-Ellis, E., Hussein, A., … Zhang, J. (2022). UniProt: the Universal Protein Knowledgebase in 2023. In Nucleic Acids Research (Vol. 51, Issue D1, pp. D523–D531). Oxford University Press (OUP). https://doi.org/10.1093/nar/gkac1052</li> </ul> </div> <div>We include these files here, with only minor modifications to replace <code>U</code> amino acids with <code>X</code> so that all amino acids fall into Casanovo-DB's vocabulary.</div>
Code, benchmarks and experiment data for the SoCS 2022 paper "Additive Pattern Databases for Decoupled Search"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper.</p> <p>sievers-et-al-socs2022-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.0 for reproducing all experiments of the paper, under experiments/decoupled-abstractions. The scripts 2022-04-* contain configurations for running the experiments and the script paper-tables-*.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.)</p> <p>sievers-et-al-socs2022-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2018 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks).</p> <p>sievers-et-al-socs2022-autoscale-benchmarks.zip contains the Autoscale 21.11 benchmarks (from https://github.com/AI-Planning/autoscale-benchmarks).</p> <p>sievers-et-al-socs2022-lab.tar.gz contains a copy of Lab 7.0 (https://github.com/aibasel/lab).</p> <p>sievers-et-al-socs2022-raw-data.zip and sievers-et-al-socs2022-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-socs2022-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" (sievers-et-al-socs2022-processed-data.zip) ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</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.