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{ "count": 398, "next": "?page=2", "previous": null, "list": [ { "name": "SSA", "description": "SSA (Signal Search Analysis) is a software package for the analysis of nucleic acid sequence motifs that are postionally correlated with a functional site (e.g a transcription or translation initiation site).", "homepage": "https://epd.expasy.org/ssa/", "biotoolsID": "ssa", "biotoolsCURIE": "biotools:ssa", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2403", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/operation_0238", "term": "Sequence motif discovery" }, { "uri": "http://edamontology.org/operation_0240", "term": "Sequence motif comparison" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Suite" ], "topic": [ { "uri": "http://edamontology.org/topic_0160", "term": "Sequence sites, features and motifs" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "Perl", "Fortran", "C" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://epd.expasy.org/ssa/doc/ssa_tutorial.php", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": null, "pmid": "12824379", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Signal search analysis server", "abstract": "Signal search analysis is a general method to discover and characterize sequence motifs that are positionally correlated with a functional site (e.g. a transcription or translation start site). The method has played an instrumental role in the analysis of eukaryotic promoter elements. The signal search analysis server provides access to four different computer programs as well as to a large number of precompiled functional site collections. The programs offered allow: (i) the identification of non-random sequence regions under evolutionary constraint; (ii) the detection of consensus sequence-based motifs that are over- or under-represented at a particular distance from a functional site; (iii) the analysis of the positional distribution of a consensus sequence- or weight matrix-based sequence motif around a functional site; and (iv) the optimization of a weight matrix description of a locally over-represented sequence motif. These programs can be accessed at: http://www.isrec.isb-sib.ch/ssa/.", "date": "2003-07-01T00:00:00Z", "citationCount": 20, "authors": [ { "name": "Ambrosini G." }, { "name": "Praz V." }, { "name": "Jagannathan V." }, { "name": "Bucher P." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "SIB Swiss Institute of Bioinformatics", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [], "note": null }, { "name": "Prof. Philipp Bucher", "email": "epd@sib.swiss", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "sduvaud", "additionDate": "2017-01-13T13:16:40Z", "lastUpdate": "2023-09-05T10:29:08.667997Z", "editPermission": { "type": "group", "authors": [ "metanetx" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "LDhat", "description": "LDhat is a package written in the C and C++ languages for the analysis of recombination rates from population genetic data.", "homepage": "https://github.com/auton1/LDhat", "biotoolsID": "ldhat", "biotoolsCURIE": "biotools:ldhat", "version": [ "2.2a" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0451", "term": "Recombination detection" }, { "uri": "http://edamontology.org/operation_2403", "term": "Sequence analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [], "topic": [], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/auton1/LDhat", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/auton1/LDhat.git", "type": "Source code", "note": "git clone https://github.com/auton1/LDhat.git", "version": "2.2a" } ], "documentation": [], "publication": [], "credit": [], "community": null, "owner": "pauffret", "additionDate": "2023-06-08T11:39:49.279911Z", "lastUpdate": "2023-06-08T11:46:30.355648Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "RD-Connect Genome-Phenome Analysis Platform (GPAP)", "description": "An online tool for diagnosis and gene discovery in rare disease research. The platform features allow identifying disease-causing mutations in rare disease patients and linking them with detailed clinical information.", "homepage": "https://platform.rd-connect.eu/", "biotoolsID": "rd-connect_platform", "biotoolsCURIE": "biotools:rd-connect_platform", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2403", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/operation_3197", "term": "Genetic variation analysis" }, { "uri": "http://edamontology.org/operation_0361", "term": "Sequence annotation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" }, { "uri": "http://edamontology.org/topic_3325", "term": "Rare diseases" }, { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" }, { "uri": "http://edamontology.org/topic_3473", "term": "Data mining" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": null, "collectionID": [ "RD-connect", "Rare Disease", "ELIXIR-ES", "INB", "RIS3CAT VEIS" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Restricted access", "elixirPlatform": [], "elixirNode": [ "Spain" ], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1002/humu.24353", "pmid": "35178824", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases", "abstract": "© 2022 The Authors. Human Mutation published by Wiley Periodicals LLC.Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.", "date": "2022-06-01T00:00:00Z", "citationCount": 2, "authors": [ { "name": "Laurie S." }, { "name": "Piscia D." }, { "name": "Matalonga L." }, { "name": "Corvo A." }, { "name": "Fernandez-Callejo M." }, { "name": "Garcia-Linares C." }, { "name": "Hernandez-Ferrer C." }, { "name": "Luengo C." }, { "name": "Martinez I." }, { "name": "Papakonstantinou A." }, { "name": "Pico-Amador D." }, { "name": "Protasio J." }, { "name": "Thompson R." }, { "name": "Tonda R." }, { "name": "Bayes M." }, { "name": "Bullich G." }, { "name": "Camps-Puchadas J." }, { "name": "Paramonov I." }, { "name": "Trotta J.-R." }, { "name": "Alonso A." }, { "name": "Attimonelli M." }, { "name": "Beroud C." }, { "name": "Bros-Facer V." }, { "name": "Buske O.J." }, { "name": "Canada-Pallares A." }, { "name": "Fernandez J.M." }, { "name": "Hansson M.G." }, { "name": "Horvath R." }, { "name": "Jacobsen J.O.B." }, { "name": "Kaliyaperumal R." }, { "name": "Lair-Preterre S." }, { "name": "Licata L." }, { "name": "Lopes P." }, { "name": "Lopez-Martin E." }, { "name": "Mascalzoni D." }, { "name": "Monaco L." }, { "name": "Perez-Jurado L.A." }, { "name": "Posada de la Paz M." }, { "name": "Rambla J." }, { "name": "Rath A." }, { "name": "Riess O." }, { "name": "Robinson P.N." }, { "name": "Salgado D." }, { "name": "Smedley D." }, { "name": "Spalding D." }, { "name": "'t Hoen P.A.C." }, { "name": "Topf A." }, { "name": "Zaharieva I." }, { "name": "Graessner H." }, { "name": "Gut I.G." }, { "name": "Lochmuller H." }, { "name": "Beltran S." } ], "journal": "Human Mutation" } }, { "doi": "10.1007/s11606-014-2908-8", "pmid": "25029978", "pmcid": "PMC4124112", "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "RD-connect: An integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research", "abstract": "Research into rare diseases is typically fragmented by data type and disease. Individual efforts often have poor interoperability and do not systematically connect data across clinical phenotype, genomic data, biomaterial availability, and research/trial data sets. Such data must be linked at both an individual-patient and whole-cohort level to enable researchers to gain a complete view of their disease and patient population of interest. Data access and authorization procedures are required to allow researchers in multiple institutions to securely compare results and gain new insights. Funded by the European Union's Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project initiated in November 2012 that links genomic data with registries, biobanks, and clinical bioinformatics tools to produce a central research resource for rare diseases. © 2014 Society of General Internal Medicine.", "date": "2014-01-01T00:00:00Z", "citationCount": 133, "authors": [ { "name": "Thompson R." }, { "name": "Johnston L." }, { "name": "Taruscio D." }, { "name": "Monaco L." }, { "name": "Beroud C." }, { "name": "Gut I.G." }, { "name": "Hansson M.G." }, { "name": "T Hoen P.-B.A." }, { "name": "Patrinos G.P." }, { "name": "Dawkins H." }, { "name": "Ensini M." }, { "name": "Zatloukal K." }, { "name": "Koubi D." }, { "name": "Heslop E." }, { "name": "Paschall J.E." }, { "name": "Posada M." }, { "name": "Robinson P.N." }, { "name": "Bushby K." }, { "name": "Lochmuller H." } ], "journal": "Journal of General 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COVIDep: a web-based platform for real-time reporting of vaccine target recommendations for SARS-CoV-2. Nature Protocols (2020).", "metadata": { "title": "COVIDep: a web-based platform for real-time reporting of vaccine target recommendations for SARS-CoV-2", "abstract": "", "date": "2020-07-01T00:00:00Z", "citationCount": 16, "authors": [ { "name": "Ahmed S.F." }, { "name": "Quadeer A.A." }, { "name": "McKay M.R." } ], "journal": "Nature Protocols" } } ], "credit": [ { "name": null, "email": "covidep@ust.hk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null } ], "community": null, "owner": "Wting", "additionDate": "2020-06-18T04:24:54Z", "lastUpdate": "2022-09-21T13:43:27.087189Z", "editPermission": { "type": "group", "authors": [ "Wting" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "LGEpred", "description": "Allows user to analsis the expresion data (Microarray Data) where it calculate correlation coefficient between amino acid residue and gene expression level.", "homepage": "http://webs.iiitd.edu.in/raghava/lgepred/", "biotoolsID": "lgepred", "biotoolsCURIE": "biotools:lgepred", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2403", "term": "Sequence analysis" } ], "input": [], "output": [], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_2423", "term": "Prediction and recognition" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3518", "term": "Microarray experiment" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3321", "term": "Molecular genetics" } ], "operatingSystem": [ "Linux" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://www.mybiosoftware.com/lgepred-correlation-analysis-prediction-genes-expression-amino-acid-sequence-proteins.html", "type": [ "Software catalogue" ], "note": null } ], "download": [], "documentation": [ { "url": "http://crdd.osdd.net/raghava//lgepred/help1.html", "type": [ "General" ], "note": null } ], "publication": [ { "doi": null, "pmid": "15773999", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein", "abstract": "Background: A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed genes, clustering of genes and regulatory network. On other hand there are only few studies on relation between expression level and composition of nucleotide/protein sequence, using expression data. There is a need to understand why particular genes/proteins express more in particular conditions. In this study, we analyze 3468 genes of Saccharomyces cerevisiae obtained from Holstege et al., (1998) to understand the relationship between expression level and amino acid composition. Results: We compute the correlation between expression of a gene and amino acid composition of its protein. It was observed that some residues (like Ala, Gly, Arg and Val) have significant positive correlation (r > 0.20) and some other residues (Like Asp, Leu, Asn and Ser) have negative correlation (r < -0.15) with the expression of genes. A significant negative correlation (r = -0.18) was also found between length and gene expression. These observations indicate the relationship between percent composition and gene expression level. Thus, attempts have been made to develop a Support Vector Machine (SVM) based method for predicting the expression level of genes from its protein sequence. In this method the SVM is trained with proteins whose gene expression data is known in a given condition. Then trained SVM is used to predict the gene expression of other proteins of the same organism in the same condition. A correlation coefficient r = 0.70 was obtained between predicted and experimentally determined expression of genes, which improves from r = 0.70 to 0.72 when dipeptide composition was used instead of residue composition. The method was evaluated using 5-fold cross validation test. We also demonstrate that amino acid composition information along with gene expression data can be used for improving the function classification of proteins. Conclusion: There is a correlation between gene expression and amino acid composition that can be used to predict the expression level of genes up to a certain extent. A web server based on the above strategy has been developed for calculating the correlation between amino acid composition and gene expression and prediction of expression level http://kiwi.postech.ac.kr/raghava/lgepred/. This server will allow users to study the evolution from expression data. © 2005 Raghava and Han; licensee BioMed Central Ltd.", "date": "2005-03-17T00:00:00Z", "citationCount": 42, "authors": [ { "name": "Raghava G.P.S." }, { "name": "Han J.H." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Gajendra P.S. Raghava", "email": "raghava@imtech.res.in", "url": "http://imtech.res.in/raghava", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "raghavagps", "additionDate": "2017-08-03T18:48:02Z", "lastUpdate": "2022-09-20T11:12:57.682166Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null } ] }