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{ "count": 9049, "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": "VT3D", "description": "Visualization toolbox for 3D transcriptomic data.", "homepage": "https://github.com/BGI-Qingdao/VT3D", "biotoolsID": "vt3d", "biotoolsCURIE": "biotools:vt3d", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0092", "term": "Data visualisation" }, { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" } ], "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://github.com/BGI-Qingdao/VT3D_Browser", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "http://www.bgiocean.com/vt3d_example", "type": [ "Training material" ], "note": null } ], "publication": [ { "doi": "10.1016/J.JGG.2023.04.001", "pmid": "37054878", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "VT3D: a visualization toolbox for 3D transcriptomic data", "abstract": "Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way. 3D spatially resolved transcriptomic atlases constructed from multi-view and high-dimensional data have rapidly emerged as a powerful tool to unravel spatial gene expression patterns and cell type distribution in biological samples, revolutionizing the understanding of gene regulatory interactions and cell niches. However, limited accessible tools for data visualization impede the potential impact and application of this technology. Here we introduce VT3D, a visualization toolbox that allows users to explore 3D transcriptomic data, enabling gene expression projection to any 2D plane of interest, 2D virtual slice creation and visualization, and interactive 3D data browsing with surface model plots. In addition, it can either work on personal devices in standalone mode or be hosted as a web-based server. We apply VT3D to multiple datasets produced by the most popular techniques, including both sequencing-based approaches including Stereo-seq, spatial transcriptomics (ST), and Slide-seq, and imaging-based approaches including MERFISH and STARMap, and successfully build a 3D atlas database that allows interactive data browsing. We demonstrate that VT3D bridges the gap between researchers and spatially resolved transcriptomics, thus accelerating related studies such as embryogenesis and organogenesis processes. The source code of VT3D is available at https://github.com/BGI-Qingdao/VT3D, and the modeled atlas database is available at http://www.bgiocean.com/vt3d_example.", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Guo L." }, { "name": "Li Y." }, { "name": "Qi Y." }, { "name": "Huang Z." }, { "name": "Han K." }, { "name": "Liu X." }, { "name": "Liu X." }, { "name": "Xu M." }, { "name": "Fan G." } ], "journal": "Journal of Genetics and Genomics" } } ], "credit": [ { "name": "Mengyang Xu", "email": "xumengyang@genomics.cn", "url": null, "orcidid": "https://orcid.org/0000-0002-4487-7088", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Guangyi Fan", "email": "fanguangyi@genomics.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-25T18:46:03.722676Z", "lastUpdate": "2023-09-25T18:46:03.725417Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "high" }, { "name": "TB-ML", "description": "Framework for comparing machine learning approaches to predict drug resistance of Mycobacterium tuberculosis.", "homepage": "https://tb-ml.github.io/tb-ml-containers/", "biotoolsID": "tb_ml", "biotoolsCURIE": "biotools:tb_ml", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3482", "term": "Antimicrobial resistance prediction" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" }, { "uri": "http://edamontology.org/operation_3227", "term": "Variant calling" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_3673", "term": "Whole genome sequencing" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" } ], "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://github.com/jodyphelan/tb-ml", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOADV/VBAD040", "pmid": "37033466", "pmcid": "PMC10074023", "type": [], "version": null, "note": null, "metadata": { "title": "TB-ML - a framework for comparing machine learning approaches to predict drug resistance of Mycobacterium tuberculosis", "abstract": "Motivation: Machine learning (ML) has shown impressive performance in predicting antimicrobial resistance (AMR) from sequence data, including for Mycobacterium tuberculosis, the causative agent of tuberculosis. However, current ML development and publication practices make it difficult for researchers and clinicians to use, test or reproduce published models. Results: We packaged a number of published and unpublished ML models for predicting AMR of M.tuberculosis into Docker containers. Similarly, the pipelines required for pre-processing genomic data into the formats required by the models were also packaged into separate containers. By following a minimal container I/O standard, we ensured as much interoperability as possible. We also created a command-line application, TB-ML, which can be used to easily combine pre-processing and prediction containers into complete pipelines ready for predicting resistance from novel, raw data with a single command. As long as there is adherence to this minimal standard for the container interface, containers produced by researchers holding new models can likewise be included in these pipelines, making benchmark comparisons of different models simple and facilitating faster uptake in the clinic. Availability and implementation: TB-ML contains a simple Docker API written in Python and is available at https://github. com/jodyphelan/tb-ml. Example Docker containers for resistance prediction and corresponding data pre-processing as well as a tutorial on how to create new containers for TB-ML are available at https://tb-ml.github.io/tb-ml-containers/.", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Libiseller-Egger J." }, { "name": "Wang L." }, { "name": "Deelder W." }, { "name": "Campino S." }, { "name": "Clark T.G." }, { "name": "Phelan J.E." } ], "journal": "Bioinformatics Advances" } } ], "credit": [ { "name": "Jody E Phelan", "email": "jody.phelan@lshtm.ac.uk", "url": null, "orcidid": "https://orcid.org/0000-0001-8323-7019", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T14:27:28.879682Z", "lastUpdate": "2023-09-22T14:27:28.882224Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Insplico", "description": "Effective computational tool for studying splicing order of adjacent introns genome-wide with short and long RNA-seq reads.", "homepage": "http://gitlab.com/aghr/insplico", "biotoolsID": "insplico", "biotoolsCURIE": "biotools:insplico", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2499", "term": "Splicing analysis" }, { "uri": "http://edamontology.org/operation_0446", "term": "Exonic splicing enhancer prediction" }, { "uri": "http://edamontology.org/operation_1812", "term": "Parsing" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2012", "term": "Sequence coordinates" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_3495", "term": "RNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_2572", "term": "BAM" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3917", "term": "Count matrix" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3512", "term": "Gene transcripts" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_3320", "term": "RNA splicing" }, { "uri": "http://edamontology.org/topic_3500", "term": "Zoology" }, { "uri": "http://edamontology.org/topic_0780", "term": "Plant biology" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "Perl" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/liniguez/Insplico_simulations", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://gitlab.com/aghr/insplico/-/wikis/home", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/NAR/GKAD244", "pmid": "37026474", "pmcid": "PMC10250204", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Insplico: Effective computational tool for studying splicing order of adjacent introns genome-wide with short and long RNA-seq reads", "abstract": "Although splicing occurs largely co-Transcriptionally, the order by which introns are removed does not necessarily follow the order in which they are transcribed. Whereas several genomic features are known to influence whether or not an intron is spliced before its downstream neighbor, multiple questions related to adjacent introns' splicing order (AISO) remain unanswered. Here, we present Insplico, the first standalone software for quantifying AISO that works with both short and long read sequencing technologies. We first demonstrate its applicability and effectiveness using simulated reads and by recapitulating previously reported AISO patterns, which unveiled overlooked biases associated with long read sequencing. We next show that AISO around individual exons is remarkably constant across cell and tissue types and even upon major spliceosomal disruption, and it is evolutionarily conserved between human and mouse brains. We also establish a set of universal features associated with AISO patterns across various animal and plant species. Finally, we used Insplico to investigate AISO in the context of tissue-specific exons, particularly focusing on SRRM4-dependent microexons. We found that the majority of such microexons have non-canonical AISO, in which the downstream intron is spliced first, and we suggest two potential modes of SRRM4 regulation of microexons related to their AISO and various splicing-related features. Insplico is available on gitlab.com/aghr/insplico.", "date": "2023-06-09T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Gohr A." }, { "name": "Iniguez L.P." }, { "name": "Torres-Mendez A." }, { "name": "Bonnal S." }, { "name": "Irimia M." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Sophie Bonnal", "email": "sophie.bonnal@crg.eu", "url": null, "orcidid": "https://orcid.org/0000-0001-6096-3042", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Manuel Irimia", "email": "mirimia@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0002-2179-2567", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T14:18:29.863175Z", "lastUpdate": "2023-09-22T14:18:29.865797Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "GeoBind", "description": "Segmentation of nucleic acid binding interface on protein surface with geometric deep learning.", "homepage": "http://www.zpliulab.cn/GeoBind", "biotoolsID": "geobind", "biotoolsCURIE": "biotools:geobind", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3897", "term": "Ligand-binding site prediction" }, { "uri": "http://edamontology.org/operation_2464", "term": "Protein-protein binding site prediction" }, { "uri": "http://edamontology.org/operation_0420", "term": "Nucleic acids-binding site prediction" }, { "uri": "http://edamontology.org/operation_0415", "term": "Nucleic acid feature detection" }, { "uri": "http://edamontology.org/operation_2492", "term": "Protein interaction prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1460", "term": "Protein structure" }, "format": [ { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1566", "term": "Protein-ligand interaction report" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0166", "term": "Protein structural motifs and surfaces" }, { "uri": "http://edamontology.org/topic_3534", "term": "Protein binding sites" }, { "uri": "http://edamontology.org/topic_3511", "term": "Nucleic acid sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0082", "term": "Structure prediction" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/zpliulab/GeoBind", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/NAR/GKAD288", "pmid": "37070217", "pmcid": "PMC10250245", "type": [], "version": null, "note": null, "metadata": { "title": "GeoBind: Segmentation of nucleic acid binding interface on protein surface with geometric deep learning", "abstract": "Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-The-Art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances.", "date": "2023-06-09T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Li P." }, { "name": "Liu Z.-P." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Zhi-Ping Liu", "email": "zpliu@sdu.edu.cn", "url": null, "orcidid": "https://orcid.org/0000-0001-7742-9161", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T14:02:10.523470Z", "lastUpdate": "2023-09-22T14:02:10.526160Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "SASA-Net", "description": "Spatial-aware self-attention mechanism for building protein 3D structure directly from inter-residue distances.", "homepage": "https://github.com/gongtiansu/SASA-Net/", "biotoolsID": "sasa_net", "biotoolsCURIE": "biotools:sasa_net", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0476", "term": "Ab initio structure prediction" }, { "uri": "http://edamontology.org/operation_0477", "term": "Protein modelling" }, { "uri": "http://edamontology.org/operation_3907", "term": "Information extraction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_2814", "term": "Protein structure analysis" }, { "uri": "http://edamontology.org/topic_0082", "term": "Structure prediction" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1109/TCBB.2023.3240456", "pmid": "37022274", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "SASA-Net: A Spatial-aware Self-attention Mechanism for Building Protein 3D Structure Directly from Inter-residue Distances", "abstract": "Protein functions are tightly related to the fine details of their 3D structures. To understand protein structures, computational prediction approaches are highly needed. Recently, protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning techniques. Most of the distance-based <italic>ab initio</italic> prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising; however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential function. Here, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue's pose according to all other residues' features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. Source code of SASA-Net is available at <uri>https://github.com/gongtiansu/SASA-Net/</uri>", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Gong T." }, { "name": "Ju F." }, { "name": "Sun S." }, { "name": "Bu D." } ], "journal": "IEEE/ACM Transactions on Computational Biology and Bioinformatics" } } ], "credit": [ { "name": "Tiansu Gong", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-1407-5882", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T13:56:40.166472Z", "lastUpdate": "2023-09-22T13:56:40.168895Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "ChemGAPP", "description": "A tool for chemical genomics analysis and phenotypic profiling.", "homepage": "https://github.com/HannahMDoherty/ChemGAPP", "biotoolsID": "chemgapp", "biotoolsCURIE": "biotools:chemgapp", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" }, { "uri": "http://edamontology.org/operation_3799", "term": "Quantification" }, { "uri": "http://edamontology.org/operation_3920", "term": "DNA testing" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" }, { "uri": "http://edamontology.org/topic_3974", "term": "Epistasis" }, { "uri": "http://edamontology.org/topic_3343", "term": "Compound libraries and screening" }, { "uri": "http://edamontology.org/topic_0114", "term": "Gene structure" }, { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" } ], "operatingSystem": [ "Mac", "Windows" ], "language": [ "Python" ], "license": "CC-BY-NC-ND-4.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD171", "pmid": "37014365", "pmcid": "PMC10085634", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "ChemGAPP: a tool for chemical genomics analysis and phenotypic profiling", "abstract": "Motivation: High-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data. Results: ChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small for small-scale screens; and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the Escherichia coli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type.", "date": "2023-04-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Doherty H.M." }, { "name": "Kritikos G." }, { "name": "Galardini M." }, { "name": "Banzhaf M." }, { "name": "Moradigaravand D." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Hannah M Doherty", "email": "hxd476@student.bham.ac.uk", "url": null, "orcidid": "https://orcid.org/0000-0002-1216-0621", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Manuel Banzhaf", "email": "m.banzhaf@bham.ac.uk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Danesh Moradigaravand", "email": "danesh.moradigaravand@kaust.edu.sa", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T13:29:58.108538Z", "lastUpdate": "2023-09-22T13:29:58.111009Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "smalldisco", "description": "Pipeline for siRNA discovery and 3' tail identification.", "homepage": "https://doi.org/10.5281/zenodo.7799621", "biotoolsID": "smalldisco", "biotoolsCURIE": "biotools:smalldisco", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3192", "term": "Sequence trimming" }, { "uri": "http://edamontology.org/operation_2008", "term": "siRNA duplex prediction" }, { "uri": "http://edamontology.org/operation_3198", "term": "Read mapping" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3495", "term": "RNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_2572", "term": "BAM" } ] }, { "data": { "uri": "http://edamontology.org/data_1255", "term": "Sequence features" }, "format": [ { "uri": "http://edamontology.org/format_2305", "term": "GFF" }, { "uri": "http://edamontology.org/format_2306", "term": "GTF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3495", "term": "RNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_3002", "term": "Annotation track" }, "format": [ { "uri": "http://edamontology.org/format_3003", "term": "BED" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0659", "term": "Functional, regulatory and non-coding RNA" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_3512", "term": "Gene transcripts" }, { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/ianvcaldas/smalldisco", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/G3JOURNAL/JKAD092", "pmid": "37094819", "pmcid": "PMC10234390", "type": [], "version": null, "note": null, "metadata": { "title": "smalldisco, a pipeline for siRNA discovery and 3′ tail identification", "abstract": "Capturing and sequencing small RNAs is standard practice; however, identification of a group of these small RNAs—small interfering RNAs (siRNAs)—has been more difficult. We present smalldisco, a command-line tool for small interfering RNA discovery and annotation from small RNA-seq datasets. smalldisco can distinguish short reads that map antisense to an annotated genomic feature (e.g. exons or mRNAs), annotate these siRNAs, and quantify their abundance. smalldisco also uses the program Tailor to quantify 3′ nontemplated nucleotides of siRNAs or any small RNA species. smalldisco and supporting documentation are available for download from GitHub (https://github.com/ianvcaldas/smalldisco) and archived in Zenodo", "date": "2023-06-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Caldas I.V." }, { "name": "Kelley L.H." }, { "name": "Ahmed-Braimah Y.H." }, { "name": "Maine E.M." } ], "journal": "G3: Genes, Genomes, Genetics" } } ], "credit": [ { "name": "Yasir H Ahmed-Braimah", "email": "yahmed@syr.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Eleanor M Maine", "email": "emmaine@syr.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T13:25:25.656209Z", "lastUpdate": "2023-09-22T13:25:25.658793Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MicroMPN", "description": "MicroMPN: Software for automating most probable number (MPN) estimates from laboratory microplates", "homepage": "https://github.com/USDA-ARS-GBRU/micrompn", "biotoolsID": "micrompn", "biotoolsCURIE": "biotools:micrompn", "version": [ "v1.0.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0004", "term": "Operation" } ], "input": [], "output": [], "note": "Quantify cells in microplates using the most probable number (MPN) method.\n\nInput method: CSV file of microplate reader absorbance or fluorescence.", "cmd": "micrompn --wellmap micrompn/data/example1_mapfile.toml \\\n --data micrompn/data/example1_plate_data.csv \\\n --well_name plate_well \\\n --plate_name plate_unique \\\n --outfile test-output-cutoff6-trimmed.csv \\\n --trim_positive \\\n --cutoff 6" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3301", "term": "Microbiology" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "Python" ], "license": "CC0-1.0", "collectionID": [ "mpn", "microbiology" ], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://github.com/USDA-ARS-GBRU/micrompn/archive/refs/tags/v1.0.1.tar.gz", "type": "Source code", "note": null, "version": "v1.0.1" }, { "url": "https://ghcr.io/usda-ars-gbru/micrompn:main", "type": "Container file", "note": null, "version": "v1.0.1" } ], "documentation": [ { "url": "https://github.com/USDA-ARS-GBRU/micrompn/README.md", "type": [ "General" ], "note": null } ], "publication": [], "credit": [ { "name": "Adam Rivers", "email": "adam.rivers@usda.gov", "url": "https://www.ars.usda.gov/southeast-area/stoneville-ms/genomics-and-bioinformatics-research/", "orcidid": "https://orcid.org/0000-0002-3703-834X", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Developer" ], "note": null } ], "community": null, "owner": "arivers", "additionDate": "2023-09-20T13:42:10.344203Z", "lastUpdate": "2023-09-20T13:43:02.676701Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "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" } ] }