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GET /api/t/?topic=%22Proteomics%22
{ "count": 2218, "next": "?page=2", "previous": null, "list": [ { "name": "DegronMD", "description": "DegronMD: protein-targeted degradation, mutation and drug response to degrons.", "homepage": "https://bioinfo.uth.edu/degronmd", "biotoolsID": "degronmd", "biotoolsCURIE": "biotools:degronmd", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_4009", "term": "Small molecule design" }, { "uri": "http://edamontology.org/operation_0417", "term": "PTM site prediction" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_0140", "term": "Protein targeting and localisation" }, { "uri": "http://edamontology.org/topic_0199", "term": "Genetic variation" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/MOLBEV/MSAD253", "pmid": "37992195", "pmcid": "PMC10701100", "type": [], "version": null, "note": null, "metadata": { "title": "DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons", "abstract": "Protein-Targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as degrons. The human genome encodes over 600 E3 ligases; however, only a small number of targeted degron instances have been identified so far. In this study, we introduced DegronMD, an open knowledgebase designed for the investigation of degrons, their associated dysfunctional events, and drug responses. We revealed that degrons are evolutionarily conserved and tend to occur near the sites of protein translational modifications, particularly in the regions of disordered structure and higher solvent accessibility. Through pattern recognition and machine learning techniques, we constructed the degrome landscape across the human proteome, yielding over 18,000 new degrons for targeted protein degradation. Furthermore, dysfunction of degrons disrupts the degradation process and leads to the abnormal accumulation of proteins; this process is associated with various types of human cancers. Based on the estimated phenotypic changes induced by somatic mutations, we systematically quantified and assessed the impact of mutations on degron function in pan-cancers; these results helped to build a global mutational map on human degrome, including 89,318 actionable mutations that may induce the dysfunction of degrons and disrupt protein degradation pathways. Multiomics integrative analysis unveiled over 400 drug resistance events associated with the mutations in functional degrons. DegronMD, accessible at https://bioinfo.uth.edu/degronmd, is a useful resource to explore the biological mechanisms, infer protein degradation, and assist with drug discovery and design on degrons.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Xu H." }, { "name": "Hu R." }, { "name": "Zhao Z." } ], "journal": "Molecular Biology and Evolution" } } ], "credit": [ { "name": "Zhongming Zhao", "email": "zhongming.zhao@uth.tmc.edu", "url": null, "orcidid": "https://orcid.org/0000-0002-3477-0914", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Haodong Xu", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Ruifeng Hu", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-30T12:31:41.312350Z", "lastUpdate": "2024-04-30T12:31:41.315403Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "G-Aligner", "description": "Graph-based feature alignment method for untargeted LC-MS-based metabolomics.", "homepage": "https://github.com/CSi-Studio/G-Aligner", "biotoolsID": "g-aligner", "biotoolsCURIE": "biotools:g-aligner", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3937", "term": "Feature extraction" }, { "uri": "http://edamontology.org/operation_0447", "term": "Sequence alignment validation" }, { "uri": "http://edamontology.org/operation_3633", "term": "Retention time prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" }, { "uri": "http://edamontology.org/topic_0081", "term": "Structure analysis" } ], "operatingSystem": [], "language": [ "Python", "Java" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05525-4", "pmid": "37964228", "pmcid": "PMC10644574", "type": [], "version": null, "note": null, "metadata": { "title": "G-Aligner: a graph-based feature alignment method for untargeted LC–MS-based metabolomics", "abstract": "Background: Liquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching. Results: To comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC–MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034 Conclusions: In this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC–MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Wang R." }, { "name": "Lu M." }, { "name": "An S." }, { "name": "Wang J." }, { "name": "Yu C." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Changbin Yu", "email": "yu_lab@sdfmu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Ruimin Wang", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-26T12:51:51.787252Z", "lastUpdate": "2024-04-26T12:51:51.789259Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "RefMetaPlant", "description": "Reference metabolome database for plants across five major phyla.", "homepage": "https://www.biosino.org/RefMetaDB/", "biotoolsID": "refmetaplant", "biotoolsCURIE": "biotools:refmetaplant", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3803", "term": "Natural product identification" }, { "uri": "http://edamontology.org/operation_3801", "term": "Spectral library search" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_3172", "term": "Metabolomics" }, { "uri": "http://edamontology.org/topic_0780", "term": "Plant biology" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_3810", "term": "Agricultural science" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/NAR/GKAD980", "pmid": "37953341", "pmcid": "PMC10767953", "type": [], "version": null, "note": null, "metadata": { "title": "RefMetaPlant: a reference metabolome database for plants across five major phyla", "abstract": "Plants are unique with tremendous chemical diversity and metabolic complexity, which is highlighted by estimates that green plants collectively produce metabolites numbering in the millions. Plant metabolites play crucial roles in all aspects of plant biology, like growth, development, stress responses, etc. However, the lack of a reference metabolome for plants, and paucity of high-quality standard compound spectral libraries and related analytical tools, have hindered the discovery and functional study of phytochemicals in plants. Here, by leveraging an advanced LC–MS platform, we generated untargeted mass spectral data from >150 plant species collected across the five major phyla. Using a self-developed computation protocol, we constructed reference metabolome for 153 plant species. A ‘Reference Metabolome Database for Plants’ (RefMetaPlant) was built to encompass the reference metabolome, integrated standard compound mass spectral libraries for annotation, and related query and analytical tools like ‘LC–MS/MS Query’, ‘RefMetaBlast’ and ‘CompoundLibBlast’ for searches and profiling of plant metabolome and metabolite identification. Analogous to a reference genome in genomic research, RefMetaPlant provides a powerful platform to support plant genome-scale metabolite analysis to promote knowledge/data sharing and collaboration in the field of metabolomics. RefMetaPlant is freely available at https://www.biosino.org/RefMetaDB/.", "date": "2024-01-05T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Shi H." }, { "name": "Wu X." }, { "name": "Zhu Y." }, { "name": "Jiang T." }, { "name": "Wang Z." }, { "name": "Li X." }, { "name": "Liu J." }, { "name": "Zhang Y." }, { "name": "Chen F." }, { "name": "Gao J." }, { "name": "Xu X." }, { "name": "Zhang G." }, { "name": "Xiao N." }, { "name": "Feng X." }, { "name": "Zhang P." }, { "name": "Wu Y." }, { "name": "Li A." }, { "name": "Chen P." }, { "name": "Li X." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Ping Chen", "email": "pchen@cemps.ac.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Xuan Li", "email": "lixuan@sippe.ac.cn", "url": null, "orcidid": "https://orcid.org/0000-0002-7435-9652", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:48:49.564191Z", "lastUpdate": "2024-04-19T09:48:49.566259Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MarFERReT", "description": "Open-source, version-controlled reference library of marine microbial eukaryote functional genes.", "homepage": "https://github.com/armbrustlab/marferret", "biotoolsID": "marferret", "biotoolsCURIE": "biotools:marferret", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" }, { "uri": "http://edamontology.org/operation_3672", "term": "Gene functional annotation" }, { "uri": "http://edamontology.org/operation_0362", "term": "Genome annotation" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3941", "term": "Metatranscriptomics" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" }, { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "Shell", "R" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1038/S41597-023-02842-4", "pmid": "38129449", "pmcid": "PMC10739892", "type": [], "version": null, "note": null, "metadata": { "title": "MarFERReT, an open-source, version-controlled reference library of marine microbial eukaryote functional genes", "abstract": "Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of about half of eukaryotic environmental transcripts. Here, we introduce Marine Functional EukaRyotic Reference Taxa (MarFERReT), a marine microbial eukaryotic sequence library designed for use with taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 publicly accessible marine eukaryote genomes and transcriptomes and assessed their sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in MarFERReT. Version 1.1 of MarFERReT contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. The MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Groussman R.D." }, { "name": "Blaskowski S." }, { "name": "Coesel S.N." }, { "name": "Armbrust E.V." } ], "journal": "Scientific Data" } } ], "credit": [ { "name": "R. D. Groussman", "email": "rgrous83@uw.edu", "url": null, "orcidid": "https://orcid.org/0000-0001-7874-7217", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "E. V. Armbrust", "email": "armbrust@uw.edu", "url": null, "orcidid": "https://orcid.org/0000-0001-7865-5101", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:47:27.591034Z", "lastUpdate": "2024-04-19T09:47:27.593586Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "NPS-MS", "description": "Deep learning-enabled MS/MS spectrum prediction facilitates automated identification of novel psychoactive substances.", "homepage": "https://nps-ms.ca/", "biotoolsID": "nps_ms", "biotoolsCURIE": "biotools:nps_ms", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3860", "term": "Spectrum calculation" }, { "uri": "http://edamontology.org/operation_3644", "term": "de Novo sequencing" }, { "uri": "http://edamontology.org/operation_3646", "term": "Peptide database search" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_2840", "term": "Toxicology" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3314", "term": "Chemistry" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1021/ACS.ANALCHEM.3C02413", "pmid": "38048435", "pmcid": "PMC10733899", "type": [], "version": null, "note": null, "metadata": { "title": "Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances", "abstract": "The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.ca/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds.", "date": "2023-12-19T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Wang F." }, { "name": "Pasin D." }, { "name": "Skinnider M.A." }, { "name": "Liigand J." }, { "name": "Kleis J.-N." }, { "name": "Brown D." }, { "name": "Oler E." }, { "name": "Sajed T." }, { "name": "Gautam V." }, { "name": "Harrison S." }, { "name": "Greiner R." }, { "name": "Foster L.J." }, { "name": "Dalsgaard P.W." }, { "name": "Wishart D.S." } ], "journal": "Analytical Chemistry" } } ], "credit": [ { "name": "David S. Wishart", "email": "dwishart@ualberta.ca", "url": null, "orcidid": "https://orcid.org/0000-0002-3207-2434", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:45:45.211711Z", "lastUpdate": "2024-04-19T09:45:45.213961Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "high" }, { "name": "PEPMatch", "description": "A tool to identify short peptide sequence matches in large sets of proteins.", "homepage": "https://nextgen-tools.iedb.org", "biotoolsID": "pepmatch", "biotoolsCURIE": "biotools:pepmatch", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0252", "term": "Peptide immunogenicity prediction" }, { "uri": "http://edamontology.org/operation_0416", "term": "Epitope mapping" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0804", "term": "Immunology" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/IEDB/PEPMatch", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/IEDB/PEPMatch/tree/master/benchmarking", "type": "Source code", "note": null, "version": null } ], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05606-4", "pmid": "38110863", "pmcid": "PMC10726511", "type": [], "version": null, "note": null, "metadata": { "title": "PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins", "abstract": "Background: Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. Results: We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch’s code and benchmark datasets are publicly available. Conclusions: PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .", "date": "2023-12-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Marrama D." }, { "name": "Chronister W.D." }, { "name": "Westernberg L." }, { "name": "Vita R." }, { "name": "Kosaloglu-Yalcin Z." }, { "name": "Sette A." }, { "name": "Nielsen M." }, { "name": "Greenbaum J.A." }, { "name": "Peters B." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Bjoern Peters", "email": "bpeters@lji.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:11:25.967125Z", "lastUpdate": "2024-04-19T09:11:25.970036Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "stabJGL", "description": "A stability approach to sparsity and similarity selection in multiple-network reconstruction.", "homepage": "https://github.com/Camiling/stabJGL", "biotoolsID": "stabjgl", "biotoolsCURIE": "biotools:stabjgl", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3927", "term": "Network analysis" }, { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" }, { "uri": "http://edamontology.org/operation_3463", "term": "Expression correlation analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" }, { "uri": "http://edamontology.org/topic_3678", "term": "Experimental design and studies" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/Camiling/stabJGL_simulations", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/Camiling/stabJGL_analysis", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOADV/VBAD185", "pmid": "38152341", "pmcid": "PMC10751232", "type": [], "version": null, "note": null, "metadata": { "title": "StabJGL: a stability approach to sparsity and similarity selection in multiple-network reconstruction", "abstract": "Motivation: In recent years, network models have gained prominence for their ability to capture complex associations. In statistical omics, networks can be used to model and study the functional relationships between genes, proteins, and other types of omics data. If a Gaussian graphical model is assumed, a gene association network can be determined from the non-zero entries of the inverse covariance matrix of the data. Due to the high-dimensional nature of such problems, integrative methods that leverage similarities between multiple graphical structures have become increasingly popular. The joint graphical lasso is a powerful tool for this purpose, however, the current AIC-based selection criterion used to tune the network sparsities and similarities leads to poor performance in high-dimensional settings. Results: We propose stabJGL, which equips the joint graphical lasso with a stable and well-performing penalty parameter selection approach that combines the notion of model stability with likelihood-based similarity selection. The resulting method makes the powerful joint graphical lasso available for use in omics settings, and outperforms the standard joint graphical lasso, as well as state-of-the-art joint methods, in terms of all performance measures we consider. Applying stabJGL to proteomic data from a pan-cancer study, we demonstrate the potential for novel discoveries the method brings.", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Lingjaerde C." }, { "name": "Richardson S." } ], "journal": "Bioinformatics Advances" } } ], "credit": [ { "name": "Camilla Lingjærde", "email": "camilla.lingjaerde@mrc-bsu.cam.ac.uk", "url": null, "orcidid": "https://orcid.org/0000-0003-2701-5686", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T07:51:14.003906Z", "lastUpdate": "2024-04-19T07:51:14.005771Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Ursa", "description": "A comprehensive multiomics toolbox for high-throughput single-cell analysis.", "homepage": "https://github.com/singlecellomics/ursa", "biotoolsID": "ursa", "biotoolsCURIE": "biotools:ursa", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2436", "term": "Gene-set enrichment analysis" }, { "uri": "http://edamontology.org/operation_3935", "term": "Dimensionality reduction" }, { "uri": "http://edamontology.org/operation_3891", "term": "Essential dynamics" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Suite" ], "topic": [ { "uri": "http://edamontology.org/topic_3967", "term": "Immunomics" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_3934", "term": "Cytometry" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "R" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/MOLBEV/MSAD267", "pmid": "38091963", "pmcid": "PMC10752348", "type": [], "version": null, "note": null, "metadata": { "title": "Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis", "abstract": "The burgeoning amount of single-cell data has been accompanied by revolutionary changes to computational methods to map, quantify, and analyze the outputs of these cutting-edge technologies. Many are still unable to reap the benefits of these advancements due to the lack of bioinformatics expertise. To address this issue, we present Ursa, an automated single-cell multiomics R package containing 6 automated single-cell omics and spatial transcriptomics workflows. Ursa allows scientists to carry out post-quantification single or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics, and immunomics at the single-cell level. It serves as a 1-stop analytic solution by providing users with outcomes to quality control assessments, multidimensional analyses such as dimension reduction and clustering, and extended analyses such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims bridge the gap between those with bioinformatics expertise and those without by providing an easy-To-use bioinformatics package for scientists in hoping to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Pan L." }, { "name": "Mou T." }, { "name": "Huang Y." }, { "name": "Hong W." }, { "name": "Yu M." }, { "name": "Li X." } ], "journal": "Molecular Biology and Evolution" } } ], "credit": [ { "name": "Weifeng Hong", "email": "hongweifeng413@163.com", "url": null, "orcidid": "https://orcid.org/0000-0001-6640-2537", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Min Yu", "email": "yumin@gdph.org.cn", "url": null, "orcidid": "https://orcid.org/0000-0003-1875-740X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Xuexin Li", "email": "xuexin.li@ki.se", "url": null, "orcidid": "https://orcid.org/0000-0001-5824-9720", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-18T13:34:15.434348Z", "lastUpdate": "2024-04-18T13:34:15.437534Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "iProPhos", "description": "Web-based interactive platform for integrated proteome and phosphoproteome analysis.", "homepage": "http://longlab-zju.cn/iProPhos", "biotoolsID": "iprophos", "biotoolsCURIE": "biotools:iprophos", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3501", "term": "Enrichment analysis" }, { "uri": "http://edamontology.org/operation_3463", "term": "Expression correlation analysis" }, { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression profiling" }, { "uri": "http://edamontology.org/operation_3766", "term": "Weighted correlation network analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0128", "term": "Protein interactions" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/longLabzj/iProPhos/", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1016/J.MCPRO.2023.100693", "pmid": "38097182", "pmcid": "PMC10828474", "type": [], "version": null, "note": null, "metadata": { "title": "iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis", "abstract": "Large-scale omics studies have generated a wealth of mass spectrometry–based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature–related, and correlation analyses, including protein–protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein–protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.", "date": "2024-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Zou J." }, { "name": "Qin Z." }, { "name": "Li R." }, { "name": "Yan X." }, { "name": "Huang H." }, { "name": "Yang B." }, { "name": "Zhou F." }, { "name": "Zhang L." } ], "journal": "Molecular and Cellular Proteomics" } } ], "credit": [ { "name": "Ran Li", "email": "ranli1993@zju.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Fangfang Zhou", "email": "zhoufangfang@suda.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Long Zhang", "email": "L_Zhang@zju.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-18T12:58:03.200346Z", "lastUpdate": "2024-04-18T12:58:03.202996Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "POOE", "description": "Predicting oomycete effectors based on a pre-trained large protein language model.", "homepage": "http://zzdlab.com/pooe/index.php", "biotoolsID": "pooe", "biotoolsCURIE": "biotools:pooe", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0477", "term": "Protein modelling" }, { "uri": "http://edamontology.org/operation_0267", "term": "Protein secondary structure prediction" }, { "uri": "http://edamontology.org/operation_3461", "term": "Virulence prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0780", "term": "Plant biology" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0218", "term": "Natural language processing" }, { "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": [ { "url": "https://github.com/zzdlabzm/POOE", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1128/MSYSTEMS.01004-23", "pmid": "38078741", "pmcid": "PMC10804963", "type": [], "version": null, "note": null, "metadata": { "title": "POOE: predicting oomycete effectors based on a pre-trained large protein language model", "abstract": "Oomycetes are fungus-like eukaryotic microorganisms which can cause catastrophic diseases in many plants. Successful infection of oomycetes depends highly on their effector proteins that are secreted into plant cells to subvert plant immunity. Thus, systematic identification of effectors from the oomycete proteomes remains an initial but crucial step in understanding plant–pathogen relationships. However, the number of experimentally identified oomycete effectors is still limited. Currently, only a few bioinformatics predictors exist to detect potential effectors, and their prediction performance needs to be improved. Here, we used the sequence embeddings from a pre-trained large protein language model (ProtTrans) as input and developed a support vector machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance with an area under the precision-recall curve of 0.804 (area under the receiver operating characteristic curve = 0.893, accuracy = 0.874, precision = 0.777, recall = 0.684, and specificity = 0.936) in the fivefold cross-validation, considerably outperforming various combinations of popular machine learning algorithms and other commonly used sequence encoding schemes. A similar prediction performance was also observed in the independent test. Compared with the existing oomycete effector prediction methods, POOE provided very competitive and promising performance, suggesting that ProtTrans effectively captures rich protein semantic information and dramatically improves the prediction task. We anticipate that POOE can accelerate the identification of oomycete effectors and provide new hints to systematically understand the functional roles of effectors in plant–pathogen interactions. The web server of POOE is freely accessible at http://zzdlab.com/pooe/index.php. The corresponding source codes and data sets are also available at https://github.com/zzdlabzm/POOE. IMPORTANCE In this work, we use the sequence representations from a pre-trained large protein language model (ProtTrans) as input and develop a Support Vector Machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance in the independent test set, considerably outperforming existing oomycete effector prediction methods. We expect that this new bioinformatics tool will accelerate the identification of oomycete effectors and further guide the experimental efforts to interrogate the functional roles of effectors in plant-pathogen interaction.", "date": "2024-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Zhao M." }, { "name": "Lei C." }, { "name": "Zhou K." }, { "name": "Huang Y." }, { "name": "Fu C." }, { "name": "Yang S." }, { "name": "Zhang Z." } ], "journal": "mSystems" } } ], "credit": [ { "name": "Shiping Yang", "email": "shipingyang@cau.edu.cn", "url": null, "orcidid": "https://orcid.org/0009-0002-0464-4279", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Ziding Zhang", "email": "zidingzhang@cau.edu.cn", "url": null, "orcidid": "https://orcid.org/0000-0002-9296-571X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-18T12:51:11.920346Z", "lastUpdate": "2024-04-18T12:51:11.923773Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }