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GET /api/t/?publication=10.12688/f1000research.12974.1
http://research.libd.org/BiocMAP/", "biotoolsID": "biocmap", "biotoolsCURIE": "biotools:biocmap", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3186", "term": "Bisulfite mapping" }, { "uri": "http://edamontology.org/operation_3198", "term": "Read mapping" }, { "uri": "http://edamontology.org/operation_3919", "term": "Methylation calling" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3809", "term": "DMR identification" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3674", "term": "Methylated DNA immunoprecipitation" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_3295", "term": "Epigenetics" }, { "uri": "http://edamontology.org/topic_0654", "term": "DNA" }, { "uri": "http://edamontology.org/topic_3419", "term": "Psychiatry" } ], "operatingSystem": [ "Linux" ], "language": [ "R", "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/LieberInstitute/BiocMAP", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05461-3", "pmid": "37704947", "pmcid": "PMC10498615", "type": [], "version": null, "note": null, "metadata": { "title": "BiocMAP: a Bioconductor-friendly, GPU-accelerated pipeline for bisulfite-sequencing data", "abstract": "Background: Bisulfite sequencing is a powerful tool for profiling genomic methylation, an epigenetic modification critical in the understanding of cancer, psychiatric disorders, and many other conditions. Raw data generated by whole genome bisulfite sequencing (WGBS) requires several computational steps before it is ready for statistical analysis, and particular care is required to process data in a timely and memory-efficient manner. Alignment to a reference genome is one of the most computationally demanding steps in a WGBS workflow, taking several hours or even days with commonly used WGBS-specific alignment software. This naturally motivates the creation of computational workflows that can utilize GPU-based alignment software to greatly speed up the bottleneck step. In addition, WGBS produces raw data that is large and often unwieldy; a lack of memory-efficient representation of data by existing pipelines renders WGBS impractical or impossible to many researchers. Results: We present BiocMAP, a Bioconductor-friendly methylation analysis pipeline consisting of two modules, to address the above concerns. The first module performs computationally-intensive read alignment using Arioc, a GPU-accelerated short-read aligner. Since GPUs are not always available on the same computing environments where traditional CPU-based analyses are convenient, the second module may be run in a GPU-free environment. This module extracts and merges DNA methylation proportions—the fractions of methylated cytosines across all cells in a sample at a given genomic site. Bioconductor-based output objects in R utilize an on-disk data representation to drastically reduce required main memory and make WGBS projects computationally feasible to more researchers. Conclusions: BiocMAP is implemented using Nextflow and available at http://research.libd.org/BiocMAP/ . To enable reproducible analysis across a variety of typical computing environments, BiocMAP can be containerized with Docker or Singularity, and executed locally or with the SLURM or SGE scheduling engines. By providing Bioconductor objects, BiocMAP’s output can be integrated with powerful analytical open source software for analyzing methylation data.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Eagles N.J." }, { "name": "Wilton R." }, { "name": "Jaffe A.E." }, { "name": "Collado-Torres L." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Leonardo Collado-Torres", "email": "lcolladotor@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0003-2140-308X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T17:11:02.458783Z", "lastUpdate": "2024-03-18T17:11:02.461690Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MAVEN", "description": "Compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny.", "homepage": "https://laylagerami.github.io/MAVEN", "biotoolsID": "maven", "biotoolsCURIE": "biotools:maven", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3928", "term": "Pathway analysis" }, { "uri": "http://edamontology.org/operation_2489", "term": "Subcellular localisation prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_2258", "term": "Cheminformatics" }, { "uri": "http://edamontology.org/topic_0749", "term": "Transcription factors and regulatory sites" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/laylagerami/MAVEN", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05416-8", "pmid": "37715141", "pmcid": "PMC10502988", "type": [], "version": null, "note": null, "metadata": { "title": "MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny", "abstract": "Background: Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. Results: To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein–protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. Conclusions: MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN .", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Hosseini-Gerami L." }, { "name": "Hernansaiz Ballesteros R." }, { "name": "Liu A." }, { "name": "Broughton H." }, { "name": "Collier D.A." }, { "name": "Bender A." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Layla Hosseini-Gerami", "email": "laylagerami@hotmail.com", "url": null, "orcidid": "https://orcid.org/0000-0003-0948-2387", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Andreas Bender", "email": "ab454@cam.ac.uk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T16:40:51.834877Z", "lastUpdate": "2024-03-18T16:40:51.837421Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PC3T", "description": "A signature-driven predictor of chemical compounds for cellular transition.", "homepage": "http://pc3t.idrug.net.cn/", "biotoolsID": "pc3t", "biotoolsCURIE": "biotools:pc3t", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3891", "term": "Essential dynamics" }, { "uri": "http://edamontology.org/operation_0314", "term": "Gene expression profiling" }, { "uri": "http://edamontology.org/operation_0310", "term": "Sequence assembly" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3342", "term": "Translational medicine" }, { "uri": "http://edamontology.org/topic_0749", "term": "Transcription factors and regulatory sites" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0153", "term": "Lipids" }, { "uri": "http://edamontology.org/topic_0108", "term": "Protein expression" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1038/S42003-023-05225-Y", "pmid": "37758874", "pmcid": "PMC10533498", "type": [], "version": null, "note": null, "metadata": { "title": "PC3T: a signature-driven predictor of chemical compounds for cellular transition", "abstract": "Cellular transitions hold great promise in translational medicine research. However, therapeutic applications are limited by the low efficiency and safety concerns of using transcription factors. Small molecules provide a temporal and highly tunable approach to overcome these issues. Here, we present PC3T, a computational framework to enrich molecules that induce desired cellular transitions, and PC3T was able to consistently enrich small molecules that had been experimentally validated in both bulk and single-cell datasets. We then predicted small molecule reprogramming of fibroblasts into hepatic progenitor-like cells (HPLCs). The converted cells exhibited epithelial cell-like morphology and HPLC-like gene expression pattern. Hepatic functions were also observed, such as glycogen storage and lipid accumulation. Finally, we collected and manually curated a cell state transition resource containing 224 time-course gene expression datasets and 153 cell types. Our framework, together with the data resource, is freely available at http://pc3t.idrug.net.cn/ . We believe that PC3T is a powerful tool to promote chemical-induced cell state transitions.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Han L." }, { "name": "Song B." }, { "name": "Zhang P." }, { "name": "Zhong Z." }, { "name": "Zhang Y." }, { "name": "Bo X." }, { "name": "Wang H." }, { "name": "Zhang Y." }, { "name": "Cui X." }, { "name": "Zhou W." } ], "journal": "Communications Biology" } } ], "credit": [ { "name": "Yong Zhang", "email": "yzhang@tongji.edu.cn", "url": null, "orcidid": "https://orcid.org/0000-0001-6316-2734", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Xiuliang Cui", "email": "wafyai@163.com", "url": null, "orcidid": "https://orcid.org/0000-0002-0165-5020", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Wenxia Zhou", "email": "zhouwx@bmi.ac.cn", "url": null, "orcidid": "https://orcid.org/0000-0002-6175-4821", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T15:49:34.098555Z", "lastUpdate": "2024-03-18T15:49:34.101167Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "CRUSTY", "description": "A versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data.", "homepage": "https://crusty.humanitas.it/", "biotoolsID": "crusty", "biotoolsCURIE": "biotools:crusty", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" }, { "uri": "http://edamontology.org/operation_3935", "term": "Dimensionality reduction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3934", "term": "Cytometry" }, { "uri": "http://edamontology.org/topic_0804", "term": "Immunology" }, { "uri": "http://edamontology.org/topic_3360", "term": "Biomarkers" }, { "uri": "http://edamontology.org/topic_3473", "term": "Data mining" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1038/S41467-023-40790-0", "pmid": "37666818", "pmcid": "PMC10477295", "type": [], "version": null, "note": null, "metadata": { "title": "CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data", "abstract": "Flow cytometry (FCM) can investigate dozens of parameters from millions of cells and hundreds of specimens in a short time and at a reasonable cost, but the amount of data that is generated is considerable. Computational approaches are useful to identify novel subpopulations and molecular biomarkers, but generally require deep expertize in bioinformatics and the use of different platforms. To overcome these limitations, we introduce CRUSTY, an interactive, user-friendly webtool incorporating the most popular algorithms for FCM data analysis, and capable of visualizing graphical and tabular results and automatically generating publication-quality figures within minutes. CRUSTY also hosts an interactive interface for the exploration of results in real time. Thus, CRUSTY enables a large number of users to mine complex datasets and reduce the time required for data exploration and interpretation. CRUSTY is accessible at https://crusty.humanitas.it/ .", "date": "2023-12-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Puccio S." }, { "name": "Grillo G." }, { "name": "Alvisi G." }, { "name": "Scirgolea C." }, { "name": "Galletti G." }, { "name": "Mazza E.M.C." }, { "name": "Consiglio A." }, { "name": "De Simone G." }, { "name": "Licciulli F." }, { "name": "Lugli E." } ], "journal": "Nature Communications" } } ], "credit": [ { "name": "Simone Puccio", "email": "simone.puccio@humanitasresearch.it", "url": null, "orcidid": "https://orcid.org/0000-0003-4007-4365", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Enrico Lugli", "email": "enrico.lugli@humanitasresearch.it", "url": null, "orcidid": "https://orcid.org/0000-0002-1964-7678", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T15:19:54.941419Z", "lastUpdate": "2024-03-18T15:19:54.944071Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "TIPred", "description": "A novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides.", "homepage": "http://pmlabstack.pythonanywhere.com/TIPred", "biotoolsID": "tipred", "biotoolsCURIE": "biotools:tipred", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3938", "term": "Virtual screening" }, { "uri": "http://edamontology.org/operation_3936", "term": "Feature selection" }, { "uri": "http://edamontology.org/operation_0267", "term": "Protein secondary structure prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0821", "term": "Enzymes" }, { "uri": "http://edamontology.org/topic_3314", "term": "Chemistry" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05463-1", "pmid": "37735626", "pmcid": "PMC10512532", "type": [], "version": null, "note": null, "metadata": { "title": "TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides", "abstract": "Background: Tyrosinase is an enzyme involved in melanin production in the skin. Several hyperpigmentation disorders involve the overproduction of melanin and instability of tyrosinase activity resulting in darker, discolored patches on the skin. Therefore, discovering tyrosinase inhibitory peptides (TIPs) is of great significance for basic research and clinical treatments. However, the identification of TIPs using experimental methods is generally cost-ineffective and time-consuming. Results: Herein, a stacked ensemble learning approach, called TIPred, is proposed for the accurate and quick identification of TIPs by using sequence information. TIPred explored a comprehensive set of various baseline models derived from well-known machine learning (ML) algorithms and heterogeneous feature encoding schemes from multiple perspectives, such as chemical structure properties, physicochemical properties, and composition information. Subsequently, 130 baseline models were trained and optimized to create new probabilistic features. Finally, the feature selection approach was utilized to determine the optimal feature vector for developing TIPred. Both tenfold cross-validation and independent test methods were employed to assess the predictive capability of TIPred by using the stacking strategy. Experimental results showed that TIPred significantly outperformed the state-of-the-art method in terms of the independent test, with an accuracy of 0.923, MCC of 0.757 and an AUC of 0.977. Conclusions: The proposed TIPred approach could be a valuable tool for rapidly discovering novel TIPs and effectively identifying potential TIP candidates for follow-up experimental validation. Moreover, an online webserver of TIPred is publicly available at http://pmlabstack.pythonanywhere.com/TIPred .", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Charoenkwan P." }, { "name": "Kongsompong S." }, { "name": "Schaduangrat N." }, { "name": "Chumnanpuen P." }, { "name": "Shoombuatong W." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Pramote Chumnanpuen", "email": "pramote.c@ku.th", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Watshara Shoombuatong", "email": "watshara.sho@mahidol.ac.th", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T15:15:44.560903Z", "lastUpdate": "2024-03-18T15:15:44.563354Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "DGDTA", "description": "Dynamic graph attention network for predicting drug-target binding affinity.", "homepage": "https://github.com/luojunwei/DGDTA", "biotoolsID": "dgdta", "biotoolsCURIE": "biotools:dgdta", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3927", "term": "Network analysis" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" }, { "uri": "http://edamontology.org/operation_3938", "term": "Virtual screening" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3336", "term": "Drug discovery" }, { "uri": "http://edamontology.org/topic_3373", "term": "Drug development" }, { "uri": "http://edamontology.org/topic_3374", "term": "Biotherapeutics" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05497-5", "pmid": "37777712", "pmcid": "PMC10543834", "type": [], "version": null, "note": null, "metadata": { "title": "DGDTA: dynamic graph attention network for predicting drug–target binding affinity", "abstract": "Background: Obtaining accurate drug–target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive. Results: Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA . Conclusions: The experimental results show that DGDTA can predict DTA more accurately than some other methods.", "date": "2023-12-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Zhai H." }, { "name": "Hou H." }, { "name": "Luo J." }, { "name": "Liu X." }, { "name": "Wu Z." }, { "name": "Wang J." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Junwei Luo", "email": "luojunwei@hpu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Haixia Zhai", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T15:14:35.788960Z", "lastUpdate": "2024-03-18T15:14:35.791224Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "A3DyDB", "description": "Exploring structural aggregation propensities in the yeast proteome.", "homepage": "http://biocomp.chem.uw.edu.pl/A3D2/yeast", "biotoolsID": "a3dydb", "biotoolsCURIE": "biotools:a3dydb", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3436", "term": "Aggregation" }, { "uri": "http://edamontology.org/operation_0409", "term": "Protein solubility prediction" }, { "uri": "http://edamontology.org/operation_4008", "term": "Protein design" }, { "uri": "http://edamontology.org/operation_0408", "term": "Protein globularity prediction" }, { "uri": "http://edamontology.org/operation_0303", "term": "Fold recognition" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0130", "term": "Protein folding, stability and design" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_0736", "term": "Protein folds and structural domains" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12934-023-02182-3", "pmid": "37716955", "pmcid": "PMC10504709", "type": [], "version": null, "note": null, "metadata": { "title": "A3DyDB: exploring structural aggregation propensities in the yeast proteome", "abstract": "Background: The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is a well-established model system for studying protein aggregation due to the conservation of essential cellular structures and pathways found across eukaryotes. However, limited structural knowledge of its proteome has prevented a deeper understanding of yeast functionalities, interactions, and aggregation. Results: In this study, we introduce the A3D yeast database (A3DyDB), which offers an extensive catalog of aggregation propensity predictions for the S. cerevisiae proteome. We used Aggrescan 3D (A3D) and the newly released protein models from AlphaFold2 (AF2) to compute the structure-based aggregation predictions for 6039 yeast proteins. The A3D algorithm exploits the information from 3D protein structures to calculate their intrinsic aggregation propensities. To facilitate simple and intuitive data analysis, A3DyDB provides a user-friendly interface for querying, browsing, and visualizing information on aggregation predictions from yeast protein structures. The A3DyDB also allows for the evaluation of the influence of natural or engineered mutations on protein stability and solubility. The A3DyDB is freely available at http://biocomp.chem.uw.edu.pl/A3D2/yeast . Conclusion: The A3DyDB addresses a gap in yeast resources by facilitating the exploration of correlations between structural aggregation propensity and diverse protein properties at the proteome level. We anticipate that this comprehensive database will become a standard tool in the modeling of protein aggregation and its implications in budding yeast.", "date": "2023-12-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Garcia-Pardo J." }, { "name": "Badaczewska-Dawid A.E." }, { "name": "Pintado-Grima C." }, { "name": "Iglesias V." }, { "name": "Kuriata A." }, { "name": "Kmiecik S." }, { "name": "Ventura S." } ], "journal": "Microbial Cell Factories" } } ], "credit": [ { "name": "Sebastian Kmiecik", "email": "sekmi@chem.uw.edu.pl", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Salvador Ventura", "email": "Salvador.Ventura@uab.cat", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T15:00:04.734438Z", "lastUpdate": "2024-03-18T15:00:04.737044Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "cgMSI", "description": "Pathogen detection within species from nanopore metagenomic sequencing data.", "homepage": "https://github.com/ZHU-XU-xmu/cgMSI", "biotoolsID": "cgmsi", "biotoolsCURIE": "biotools:cgmsi", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0310", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/operation_3198", "term": "Read mapping" }, { "uri": "http://edamontology.org/operation_0346", "term": "Sequence similarity search" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3837", "term": "Metagenomic sequencing" }, { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" }, { "uri": "http://edamontology.org/topic_3324", "term": "Infectious disease" } ], "operatingSystem": [], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05512-9", "pmid": "37821827", "pmcid": "PMC10568937", "type": [], "version": null, "note": null, "metadata": { "title": "cgMSI: pathogen detection within species from nanopore metagenomic sequencing data", "abstract": "Background: Metagenomic sequencing is an unbiased approach that can potentially detect all the known and unidentified strains in pathogen detection. Recently, nanopore sequencing has been emerging as a highly potential tool for rapid pathogen detection due to its fast turnaround time. However, identifying pathogen within species is nontrivial for nanopore sequencing data due to the high sequencing error rate. Results: We developed the core gene alleles metagenome strain identification (cgMSI) tool, which uses a two-stage maximum a posteriori probability estimation method to detect pathogens at strain level from nanopore metagenomic sequencing data at low computational cost. The cgMSI tool can accurately identify strains and estimate relative abundance at 1× coverage. Conclusions: We developed cgMSI for nanopore metagenomic pathogen detection within species. cgMSI is available at https://github.com/ZHU-XU-xmu/cgMSI .", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Zhu X." }, { "name": "Zhao L." }, { "name": "Huang L." }, { "name": "Yang W." }, { "name": "Wang L." }, { "name": "Yu R." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Liansheng Wang", "email": "lswang@xmu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Rongshan Yu", "email": "rsyu@xmu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T14:52:36.386356Z", "lastUpdate": "2024-03-18T14:52:36.389165Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "KidneyGPS", "description": "User-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies.", "homepage": "https://kidneygps.ur.de/gps/", "biotoolsID": "kidneygps", "biotoolsCURIE": "biotools:kidneygps", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3226", "term": "Variant prioritisation" }, { "uri": "http://edamontology.org/operation_3791", "term": "Collapsing methods" }, { "uri": "http://edamontology.org/operation_3227", "term": "Variant calling" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3517", "term": "GWAS study" }, { "uri": "http://edamontology.org/topic_3337", "term": "Biobank" }, { "uri": "http://edamontology.org/topic_3422", "term": "Urology and nephrology" }, { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" }, { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "CC-BY-4.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/kjstanzick/KidneyGPS", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05472-0", "pmid": "37735349", "pmcid": "PMC10512588", "type": [], "version": null, "note": null, "metadata": { "title": "KidneyGPS: a user-friendly web application to help prioritize kidney function genes and variants based on evidence from genome-wide association studies", "abstract": "Background: Genome-wide association studies (GWAS) have identified hundreds of genetic loci associated with kidney function. By combining these findings with post-GWAS information (e.g., statistical fine-mapping to identify independent association signals and to narrow down signals to causal variants; or different sources of annotation data), new hypotheses regarding physiology and disease aetiology can be obtained. These hypotheses need to be tested in laboratory experiments, for example, to identify new therapeutic targets. For this purpose, the evidence obtained from GWAS and post-GWAS analyses must be processed and presented in a way that they are easily accessible to kidney researchers without specific GWAS expertise. Main: Here we present KidneyGPS, a user-friendly web-application that combines genetic variant association for estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics consortium with annotation of (i) genetic variants with functional or regulatory effects (“SNP-to-gene” mapping), (ii) genes with kidney phenotypes in mice or human (“gene-to-phenotype”), and (iii) drugability of genes (to support re-purposing). KidneyGPS adopts a comprehensive approach summarizing evidence for all 5906 genes in the 424 GWAS loci for eGFR identified previously and the 35,885 variants in the 99% credible sets of 594 independent signals. KidneyGPS enables user-friendly access to the abundance of information by search functions for genes, variants, and regions. KidneyGPS also provides a function (“GPS tab”) to generate lists of genes with specific characteristics thus enabling customizable Gene Prioritisation (GPS). These specific characteristics can be as broad as any gene in the 424 loci with a known kidney phenotype in mice or human; or they can be highly focussed on genes mapping to genetic variants or signals with particularly with high statistical support. KidneyGPS is implemented with RShiny in a modularized fashion to facilitate update of input data (https://kidneygps.ur.de/gps/). Conclusion: With the focus on kidney function related evidence, KidneyGPS fills a gap between large general platforms for accessing GWAS and post-GWAS results and the specific needs of the kidney research community. This makes KidneyGPS an important platform for kidney researchers to help translate in silico research results into in vitro or in vivo research.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Stanzick K.J." }, { "name": "Stark K.J." }, { "name": "Gorski M." }, { "name": "Schodel J." }, { "name": "Kruger R." }, { "name": "Kronenberg F." }, { "name": "Warth R." }, { "name": "Heid I.M." }, { "name": "Winkler T.W." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Iris M. Heid", "email": "iris.heid@ukr.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Thomas W. Winkler", "email": "thomas.winkler@ukr.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-18T14:41:54.313503Z", "lastUpdate": "2024-03-18T14:41:54.316006Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "revoluzer", "description": "Various tools for genome rearrangement analysis. CREx, TreeREx, etc", "homepage": "https://gitlab.com/Bernt/revoluzer/", "biotoolsID": "revoluzer", "biotoolsCURIE": "biotools:revoluzer", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3228", "term": "Structural variation detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3479", "term": "Gene order" }, "format": [ { "uri": "http://edamontology.org/format_2200", "term": "FASTA-like (text)" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2855", "term": "Distance matrix" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3945", "term": "Molecular evolution" }, { "uri": "http://edamontology.org/topic_0084", "term": "Phylogeny" } ], "operatingSystem": [ "Linux" ], "language": [ "C++" ], "license": "GPL-3.0", "collectionID": [ "revoluzer" ], "maturity": null, "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://gitlab.com/Bernt/revoluzer/", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://gitlab.com/Bernt/revoluzer/-/tags", "type": "Downloads page", "note": null, "version": null } ], "documentation": [], "publication": [ { "doi": "10.1093/bioinformatics/btm468", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "CREx: Inferring genomic rearrangements based on common intervals", "abstract": "Summary: We present the web-based program CREx for heuristically determining pairwise rearrangement events in unichromosomal genomes. CREx considers transpositions, reverse transpositions, reversals and tandem-duplication-random-loss (TDRL) events. It supports the user in finding parsimonious rearrangement scenarios given a phylogenetic hypothesis. CREx is based on common intervals, which reflect genes that appear consecutively in several of the input gene orders. © The Author 2007. Published by Oxford University Press. All rights reserved.", "date": "2007-01-01T00:00:00Z", "citationCount": 249, "authors": [ { "name": "Bernt M." }, { "name": "Merkle D." }, { "name": "Ramsch K." }, { "name": "Fritzsch G." }, { "name": "Perseke M." }, { "name": "Bernhard D." }, { "name": "Schlegel M." }, { "name": "Stadler P.F." }, { "name": "Middendorf M." } ], "journal": "Bioinformatics" } }, { "doi": "10.1007/978-3-540-87989-3_11", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "An algorithm for inferring mitogenome rearrangements in a phylogenetic tree", "abstract": "Given the mitochondrial gene orders and the phylogenetic relationship of a set of unichromosomal taxa, we study the problem of finding a plausible and parsimonious assignment of genomic rearrangement events to the edges of the given phylogenetic tree. An algorithm called algorithm TreeREx (tree rearrangement explorer) is proposed for solving this problem heuristically. TreeREx is based on an extended version of algorithm CREx (common interval rearrangement explorer, [4]) that heuristically computes pairwise rearrangement scenarios for gene order data. As phylogenetic events in such scenarios reversals, transpositions, reverse transpositions, and tandem duplication random loss (TDRL) operations are considered. CREx can detect such events as patterns in the signed strong interval tree, a data structure representing gene groups that appear consecutively in a set of two gene orders. TreeREx then tries to assign events to the edges of the phylogenetic tree, such that the pairwise scenarios are reflected on the paths of the tree. It is shown that TreeREx can automatically infer the events and the ancestral gene orders for realistic biological examples of mitochondrial gene orders. In an analysis of gene order data for teleosts, algorithm TreeREx is able to identify a yet undocumented TDRL towards species Bregmaceros nectabanus. © 2008 Springer-Verlag.", "date": "2008-12-01T00:00:00Z", "citationCount": 52, "authors": [ { "name": "Bernt M." }, { "name": "Merkle D." }, { "name": "Middendorf M." } ], "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)" } } ], "credit": [], "community": null, "owner": "m.bernt", "additionDate": "2024-03-15T16:00:13.669021Z", "lastUpdate": "2024-03-15T16:00:29.489049Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null } ] }{ "count": 5827, "next": "?page=2", "previous": null, "list": [ { "name": "BiocMAP", "description": "Bioconductor-friendly, GPU-accelerated pipeline for bisulfite-sequencing data.", "homepage": "