Resource List
List all resources, or create a new resource.
GET /api/t/?topic=%22Proteomics%22
{ "count": 2091, "next": "?page=2", "previous": null, "list": [ { "name": "IDIA", "description": "An integrative signal extractor for data-independent acquisition proteomics.", "homepage": "https://github.com/Biocomputing-Research-Group/IDIA", "biotoolsID": "idia", "biotoolsCURIE": "biotools:idia", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3646", "term": "Peptide database search" }, { "uri": "http://edamontology.org/operation_3767", "term": "Protein identification" }, { "uri": "http://edamontology.org/operation_3695", "term": "Filtering" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0943", "term": "Mass spectrum" }, "format": [ { "uri": "http://edamontology.org/format_3654", "term": "mzXML" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0943", "term": "Mass spectrum" }, "format": [ { "uri": "http://edamontology.org/format_3651", "term": "MGF" }, { "uri": "http://edamontology.org/format_3244", "term": "mzML" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Java" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1109/BIBM55620.2022.9994873", "pmid": "37034305", "pmcid": "PMC10077956", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "IDIA: An Integrative Signal Extractor for Data-Independent Acquisition Proteomics", "abstract": "In proteomics, data-independent acquisition (DIA)has been shown to provide less biased and more reproducible results than data-dependent acquisition. Recently, many researchers have developed a series of methods to identify peptides and proteins by using spectrum libraries for DIA data. However, spectrum libraries are not always available for novel organisms or microbial communities. To detect peptides and proteins without a spectrum library, we developed IDIA, a library-free method using DIA data to generate pseudo-spectra that can be searched using conventional sequence database searching software. IDIA integrates two isotopic trace detection strategies and employs B-spline and Gaussian filters to help extract high-quality pseudo-spectra from the complex DIA data. The experimental results on human and yeast data demonstrated that our approach remarkably produced more peptide and protein identifications than the two state-of-the-art library-free methods, i.e., DIA-Umpire and Group-DIA. IDIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/IDIA.", "date": "2022-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Li J." }, { "name": "Pan C." }, { "name": "Guo X." } ], "journal": "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022" } } ], "credit": [ { "name": "Xuan Guo", "email": "xuan.guo@unt.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-25T20:53:22.903009Z", "lastUpdate": "2023-09-25T20:53:22.905395Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "mBONITA", "description": "Multi-omics boolean omics network invariant-time analysis.", "homepage": "https://github.com/Thakar-Lab/mBONITA", "biotoolsID": "mbonita", "biotoolsCURIE": "biotools:mbonita", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3928", "term": "Pathway analysis" }, { "uri": "http://edamontology.org/operation_3927", "term": "Network analysis" }, { "uri": "http://edamontology.org/operation_3501", "term": "Enrichment analysis" }, { "uri": "http://edamontology.org/operation_3436", "term": "Aggregation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "C", "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1021/ACS.JPROTEOME.2C00730", "pmid": "37000949", "pmcid": "PMC10167691", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Executable Network Models of Integrated Multiomics Data", "abstract": "Multiomics profiling provides a holistic picture of a condition being examined and captures the complexity of signaling events, beginning from the original cause (environmental or genetic), to downstream functional changes at multiple molecular layers. Pathway enrichment analysis has been used with multiomics data sets to characterize signaling mechanisms. However, technical and biological variability between these layered data limit an integrative computational analyses. We present a Boolean network-based method, multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), to integrate omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform topology-based pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular measurements by combining observed fold-changes and variance, with a measure of node (i.e., gene or protein) influence over signaling, and a measure of the strength of evidence for that gene across data sets. We used mBONITA to integrate multiomics data sets from RAMOS B cells treated with the immunosuppressant drug cyclosporine A under varying O2 tensions to identify pathways involved in hypoxia-mediated chemotaxis. We compare mBONITA’s performance with 6 other pathway analysis methods designed for multiomics data and show that mBONITA identifies a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.", "date": "2023-05-05T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Palshikar M.G." }, { "name": "Min X." }, { "name": "Crystal A." }, { "name": "Meng J." }, { "name": "Hilchey S.P." }, { "name": "Zand M.S." }, { "name": "Thakar J." } ], "journal": "Journal of Proteome Research" } } ], "credit": [ { "name": "Juilee Thakar", "email": "Juilee_Thakar@URMC.rochester.edu", "url": null, "orcidid": "https://orcid.org/0000-0003-4479-4183", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-25T18:35:03.752451Z", "lastUpdate": "2023-09-25T18:35:03.755242Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "DeepSTABp", "description": "An AI based web tool to predict the melting temperature (Tm) of proteins based on their amino acid sequence and various growth conditions.", "homepage": "https://csb-deepstabp.bio.rptu.de", "biotoolsID": "deepstabp", "biotoolsCURIE": "biotools:deepstabp", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3096", "term": "Editing" }, { "uri": "http://edamontology.org/operation_0331", "term": "Variant effect prediction" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" }, { "uri": "http://edamontology.org/format_1964", "term": "plain text format (unformatted)" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0897", "term": "Protein property" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0130", "term": "Protein folding, stability and design" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_0601", "term": "Protein modifications" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "JavaScript" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/CSBiology/deepStabP", "type": [ "Repository" ], "note": null }, { "url": "https://git.nfdi4plants.org/f_jung/deepstabp", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.3390/IJMS24087444", "pmid": "37108605", "pmcid": "PMC10138888", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability", "abstract": "Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can predict the thermal stability of a wide range of proteins, making it a powerful and efficient tool for large-scale prediction. The model captures the structural and biological properties that impact protein stability, and it allows for the identification of the structural features that contribute to protein stability. DeepSTABp is available to the public via a user-friendly web interface, making it accessible to researchers in various fields.", "date": "2023-04-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Jung F." }, { "name": "Frey K." }, { "name": "Zimmer D." }, { "name": "Muhlhaus T." } ], "journal": "International Journal of Molecular Sciences" } } ], "credit": [ { "name": "Timo Mühlhaus", "email": "timo.muehlhaus@rptu.de", "url": null, "orcidid": "https://orcid.org/0000-0003-3925-6778", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-25T17:54:30.731190Z", "lastUpdate": "2023-09-25T17:54:30.733846Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MineProt", "description": "AlphaFold-like systems are rapidly expanding the scale of proteome structuring, and MineProt provides an effective solution for custom curation of these novel high-throughput data. It enables researchers to build their own server in simple steps, run almost out-of-the-box scripts to annotate and curate their proteins, analyze their data via a user-friendly online interface, and utilize plugins to extend the functionality of server. It is expected to support researcher productivity and facilitate data sharing in the new era of structural proteomics.", "homepage": "http://mineprot-demo.bmeonline.cn", "biotoolsID": "mineprot", "biotoolsCURIE": "biotools:mineprot", "version": [ "1.0.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_2814", "term": "Protein structure analysis" } ], "operatingSystem": [ "Mac", "Windows", "Linux" ], "language": [ "PHP", "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/huiwenke/MineProt", "type": [ "Repository" ], "note": null }, { "url": "https://rdmkit.elixir-europe.org/structural_bioinformatics#solutions", "type": [ "Software catalogue" ], "note": "MineProt is included in the ELIXIR Research Data Management Kit as the recommended solution for building a dedicated web service for large-scale modelling efforts." } ], "download": [], "documentation": [ { "url": "https://github.com/huiwenke/MineProt/wiki", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/database/baad059", "pmid": "37562062", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "MineProt: a stand-alone server for structural proteome curation", "abstract": "AlphaFold-like systems are rapidly expanding the scale of proteome structuring, and MineProt provides an effective solution for custom curation of these novel high-throughput data. It enables researchers to build their own server in simple steps, run almost out-of-the-box scripts to annotate and curate their proteins, analyze their data via a user-friendly online interface, and utilize plugins to extend the functionality of server. It is expected to support researcher productivity and facilitate data sharing in the new era of structural proteomics. Database URL MineProt is open-source software available at https://github.com/huiwenke/MineProt.", "date": "2023-08-10T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Zhu Y." }, { "name": "Tong C." }, { "name": "Zhao Z." }, { "name": "Lu Z." } ], "journal": "Database : the journal of biological databases and curation" } } ], "credit": [ { "name": "Yunchi Zhu", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Chengda Tong", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Zuohan Zhao", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Zuhong Lu", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "community": null, "owner": "huiwenke", "additionDate": "2022-09-22T01:24:30.525461Z", "lastUpdate": "2023-09-24T07:55:22.045888Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "ACP-MLC", "description": "A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types.", "homepage": "https://github.com/Nicole-DH/ACP-MLC", "biotoolsID": "acp_mlc", "biotoolsCURIE": "biotools:acp_mlc", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3631", "term": "Peptide identification" }, { "uri": "http://edamontology.org/operation_3936", "term": "Feature selection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1772", "term": "Score" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [ "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://www.amazon.com/clouddrive/share/dW6uIgXPXfNBCLIcMcMJiKJ9rr5aXaJAo78VgGKVKRH", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/Nicole-DH/ACP-MLC/blob/master/Manual.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1016/J.COMPBIOMED.2023.106844", "pmid": "37058760", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types", "abstract": "Anticancer peptides (ACPs), a series of short bioactive peptides, are promising candidates in fighting against cancer due to their high activity, low toxicity, and not likely cause drug resistance. The accurate identification of ACPs and classification of their functional types is of great importance for investigating their mechanisms of action and developing peptide-based anticancer therapies. Here, we provided a computational tool, called ACP-MLC, to address binary classification and multi-label classification of ACPs for a given peptide sequence. Briefly, ACP-MLC is a two-level prediction engine, in which the 1st-level model predicts whether a query sequence is an ACP or not by random forest algorithm, and the 2nd-level model predicts which tissue types the sequence might target by the binary relevance algorithm. Development and evaluation by high-quality datasets, our ACP-MLC yielded an area under the receiver operating characteristic curve (AUC) of 0.888 on the independent test set for the 1st-level prediction, and obtained 0.157 hamming loss, 0.577 subset accuracy, 0.802 F1-scoremacro, and 0.826 F1-scoremicro on the independent test set for the 2nd-level prediction. A systematic comparison demonstrated that ACP-MLC outperformed existing binary classifiers and other multi-label learning classifiers for ACP prediction. Finally, we interpreted the important features of ACP-MLC by the SHAP method. User-friendly software and the datasets are available at https://github.com/Nicole-DH/ACP-MLC. We believe that the ACP-MLC would be a powerful tool in ACP discovery.", "date": "2023-05-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Deng H." }, { "name": "Ding M." }, { "name": "Wang Y." }, { "name": "Li W." }, { "name": "Liu G." }, { "name": "Tang Y." } ], "journal": "Computers in Biology and Medicine" } } ], "credit": [ { "name": "Yun Tang", "email": "ytang234@ecust.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T14:44:29.501966Z", "lastUpdate": "2023-09-22T14:44:29.504583Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "pyInfinityFlow", "description": "pyInfinityFlow is a Python package that enables imputation of hundreds of features from Flow Cytometry using XGBoost regression1. It is an adaptation of the original implementation in R2 with the goal of optimizing the workflow for large datasets by increasing the speed and memory efficiency of the analysis pipeline.", "homepage": "https://github.com/KyleFerchen/pyInfinityFlow", "biotoolsID": "pyinfinityflow", "biotoolsCURIE": "biotools:pyinfinityflow", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3557", "term": "Imputation" }, { "uri": "http://edamontology.org/operation_3659", "term": "Regression 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": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3934", "term": "Cytometry" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://pypi.org/project/pyInfinityFlow/", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "http://pyinfinityflow.readthedocs.io", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD287", "pmid": "37097893", "pmcid": "PMC10166583", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "pyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells", "abstract": "Motivation: While conventional flow cytometry is limited to dozens of markers, new experimental and computational strategies, such as Infinity Flow, allow for the generation and imputation of hundreds of cell surface protein markers in millions of cells. Here, we describe an end-to-end analysis workflow for Infinity Flow data in Python. Results: pyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established Python packages for single-cell genomics analysis. pyInfinityFlow accurately identifies both common and extremely rare cell populations which are challenging to define from single-cell genomics studies alone. We demonstrate that this workflow can nominate novel markers to design new flow cytometry gating strategies for predicted cell populations. pyInfinityFlow can be extended to diverse cell discovery analyses with flexibility to adapt to diverse Infinity Flow experimental designs. Availability and implementation: pyInfinityFlow is freely available in GitHub (https://github.com/KyleFerchen/pyInfinityFlow) and on PyPI (https://pypi.org/project/pyInfinityFlow/). Package documentation with tutorials on a test dataset is available by Read the Docs (pyinfinityflow.readthedocs.io). The scripts and data for reproducing the results are available at https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analy sis_scripts, along with the raw flow cytometry input data.", "date": "2023-05-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Ferchen K." }, { "name": "Salomonis N." }, { "name": "Grimes H.L." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "H Leighton Grimes", "email": "lee.grimes@cchmc.org", "url": null, "orcidid": "https://orcid.org/0000-0001-8162-6758", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T12:30:37.602955Z", "lastUpdate": "2023-09-22T12:30:37.605378Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "A2TEA", "description": "A2TEA is a software workflow facilitating identification of candidate genes for stress adaptation based on comparative genomics and transcriptomics. It combines differential gene expression with gene family expansion as an indicator for the evolution of adaptive traits.", "homepage": "https://github.com/tgstoecker/A2TEA.Workflow", "biotoolsID": "a2tea", "biotoolsCURIE": "biotools:a2tea", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression profiling" }, { "uri": "http://edamontology.org/operation_3501", "term": "Enrichment analysis" }, { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" }, { "uri": "http://edamontology.org/operation_3192", "term": "Sequence trimming" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_2977", "term": "Nucleic acid sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_1255", "term": "Sequence features" }, "format": [ { "uri": "http://edamontology.org/format_2306", "term": "GTF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3754", "term": "GO-term enrichment data" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0872", "term": "Phylogenetic tree" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0928", "term": "Gene expression profile" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Workflow", "Web application", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0780", "term": "Plant biology" }, { "uri": "http://edamontology.org/topic_0084", "term": "Phylogeny" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "R" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/tgstoecker/A2TEA.WebApp", "type": [ "Repository" ], "note": null }, { "url": "https://tgstoecker.shinyapps.io/A2TEA-WebApp", "type": [ "Other" ], "note": null }, { "url": "https://tgstoecker.github.io/A2TEA.WebApp", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.12688/F1000RESEARCH.126463.2", "pmid": "37224329", "pmcid": "PMC10186066", "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Tyll Stöcker", "email": "tyll.stoecker@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0001-7184-9472", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Heiko Schoof", "email": "schoof@uni-bonn.de", "url": null, "orcidid": "https://orcid.org/0000-0002-1527-3752", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T11:57:14.178569Z", "lastUpdate": "2023-09-22T11:57:14.182267Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "AutophagyNet", "description": "High-resolution data source for the analysis of autophagy and its regulation.", "homepage": "http://autophagynet.org", "biotoolsID": "autophagynet", "biotoolsCURIE": "biotools:autophagynet", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2426", "term": "Modelling and simulation" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" }, { "uri": "http://edamontology.org/operation_1781", "term": "Gene regulatory network analysis" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_0659", "term": "Functional, regulatory and non-coding RNA" }, { "uri": "http://edamontology.org/topic_0749", "term": "Transcription factors and regulatory sites" }, { "uri": "http://edamontology.org/topic_0602", "term": "Molecular interactions, pathways and networks" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0601", "term": "Protein modifications" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/korcsmarosgroup/AutophagyNetDB", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1080/15548627.2023.2247737", "pmid": "37589496", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation", "abstract": "Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user’s needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org. Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Csabai L." }, { "name": "Bohar B." }, { "name": "Turei D." }, { "name": "Prabhu S." }, { "name": "Foldvari-Nagy L." }, { "name": "Madgwick M." }, { "name": "Fazekas D." }, { "name": "Modos D." }, { "name": "Olbei M." }, { "name": "Halka T." }, { "name": "Poletti M." }, { "name": "Kornilova P." }, { "name": "Kadlecsik T." }, { "name": "Demeter A." }, { "name": "Szalay-Beko M." }, { "name": "Kapuy O." }, { "name": "Lenti K." }, { "name": "Vellai T." }, { "name": "Gul L." }, { "name": "Korcsmaros T." } ], "journal": "Autophagy" } } ], "credit": [ { "name": "Tamás Korcsmáros", "email": "t.korcsmaros@imperial.ac.uk", "url": null, "orcidid": "https://orcid.org/0000-0003-1717-996X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Orsolya Kapuy", "email": "kapuy.orsolya@semmelweis.hu", "url": null, "orcidid": "https://orcid.org/0000-0002-8484-4504", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T11:27:45.895976Z", "lastUpdate": "2023-09-22T11:27:45.899743Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PGNneo", "description": "Proteogenomics-based pipeline to predict neoantigens in noncoding regions.", "homepage": "https://github.com/tanxiaoxiu/PGNneo", "biotoolsID": "pgnneo", "biotoolsCURIE": "biotools:pgnneo", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3227", "term": "Variant calling" }, { "uri": "http://edamontology.org/operation_3631", "term": "Peptide identification" }, { "uri": "http://edamontology.org/operation_0252", "term": "Peptide immunogenicity prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3922", "term": "Proteogenomics" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "R" ], "license": "CC-BY-4.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://hub.docker.com/r/xiaoxiutan/pgnneo", "type": "Container file", "note": null, "version": null } ], "documentation": [], "publication": [ { "doi": "10.3390/CELLS12050782", "pmid": "36899918", "pmcid": "PMC10000440", "type": [], "version": null, "note": null, "metadata": { "title": "PGNneo: A Proteogenomics-Based Neoantigen Prediction Pipeline in Noncoding Regions", "abstract": "The development of a neoantigen-based personalized vaccine has promise in the hunt for cancer immunotherapy. The challenge in neoantigen vaccine design is the need to rapidly and accurately identify, in patients, those neoantigens with vaccine potential. Evidence shows that neoantigens can be derived from noncoding sequences, but there are few specific tools for identifying neoantigens in noncoding regions. In this work, we describe a proteogenomics-based pipeline, namely PGNneo, for use in discovering neoantigens derived from the noncoding region of the human genome with reliability. In PGNneo, four modules are included: (1) noncoding somatic variant calling and HLA typing; (2) peptide extraction and customized database construction; (3) variant peptide identification; (4) neoantigen prediction and selection. We have demonstrated the effectiveness of PGNneo and applied and validated our methodology in two real-world hepatocellular carcinoma (HCC) cohorts. TP53, WWP1, ATM, KMT2C, and NFE2L2, which are frequently mutating genes associated with HCC, were identified in two cohorts and corresponded to 107 neoantigens from non-coding regions. In addition, we applied PGNneo to a colorectal cancer (CRC) cohort, demonstrating that the tool can be extended and verified in other tumor types. In summary, PGNneo can specifically detect neoantigens generated by noncoding regions in tumors, providing additional immune targets for cancer types with a low tumor mutational burden (TMB) in coding regions. PGNneo, together with our previous tool, can identify coding and noncoding region-derived neoantigens and, thus, will contribute to a complete understanding of the tumor immune target landscape. PGNneo source code and documentation are available at Github. To facilitate the installation and use of PGNneo, we provide a Docker container and a GUI.", "date": "2023-03-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Tan X." }, { "name": "Xu L." }, { "name": "Jian X." }, { "name": "Ouyang J." }, { "name": "Hu B." }, { "name": "Yang X." }, { "name": "Wang T." }, { "name": "Xie L." } ], "journal": "Cells" } } ], "credit": [ { "name": "Tao Wang", "email": "neowangtao@sjtu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Lu Xie", "email": "xielu@sibpt.com", "url": null, "orcidid": "https://orcid.org/0000-0001-7541-2243", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Xiaoxiu Tan", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Linfeng Xu", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Xingxing Jian", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-19T08:22:32.515346Z", "lastUpdate": "2023-09-19T08:22:32.517920Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "FAS", "description": "Assessing the similarity between proteins using multi-layered feature architectures.", "homepage": "https://pypi.org/project/greedyFAS/", "biotoolsID": "fas", "biotoolsCURIE": "biotools:fas", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2474", "term": "Protein architecture comparison" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" }, { "uri": "http://edamontology.org/operation_3672", "term": "Gene functional annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1277", "term": "Protein features" }, "format": [ { "uri": "http://edamontology.org/format_2332", "term": "XML" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0089", "term": "Ontology and terminology" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0736", "term": "Protein folds and structural domains" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://pypi.org/project/greedyFAS/", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/BIONF/FAS", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD226", "pmid": "37084276", "pmcid": "PMC10185405", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "FAS: assessing the similarity between proteins using multi-layered feature architectures", "abstract": "Motivation: Protein sequence comparison is a fundamental element in the bioinformatics toolkit. When sequences are annotated with features such as functional domains, transmembrane domains, low complexity regions or secondary structure elements, the resulting feature architectures allow better informed comparisons. However, many existing schemes for scoring architecture similarities cannot cope with features arising from multiple annotation sources. Those that do fall short in the resolution of overlapping and redundant feature annotations. Results: Here, we introduce FAS, a scoring method that integrates features from multiple annotation sources in a directed acyclic architecture graph. Redundancies are resolved as part of the architecture comparison by finding the paths through the graphs that maximize the pair-wise architecture similarity. In a large-scale evaluation on more than 10 000 human-yeast ortholog pairs, architecture similarities assessed with FAS are consistently more plausible than those obtained using e-values to resolve overlaps or leaving overlaps unresolved. Three case studies demonstrate the utility of FAS on architecture comparison tasks: benchmarking of orthology assignment software, identification of functionally diverged orthologs, and diagnosing protein architecture changes stemming from faulty gene predictions. With the help of FAS, feature architecture comparisons can now be routinely integrated into these and many other applications. Availability and implementation: FAS is available as python package: https://pypi.org/project/greedyFAS/.", "date": "2023-05-01T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Dosch J." }, { "name": "Bergmann H." }, { "name": "Tran V." }, { "name": "Ebersberger I." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Ingo Ebersberger", "email": "ebersberger@bio.uni-frankfurt.de", "url": null, "orcidid": "https://orcid.org/0000-0001-8187-9253", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-18T10:18:12.761756Z", "lastUpdate": "2023-09-18T10:18:12.764098Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }