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import bio.tools data on Sun Aug 27 01:23:36 UTC 2023
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108 changes: 108 additions & 0 deletions data/aftm/aftm.biotools.json
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{
"accessibility": "Open access",
"additionDate": "2023-08-24T13:27:07.687905Z",
"biotoolsCURIE": "biotools:aftm",
"biotoolsID": "aftm",
"confidence_flag": "tool",
"cost": "Free of charge",
"credit": [
{
"email": "[email protected]",
"name": "Qian Cong",
"typeEntity": "Person",
"typeRole": [
"Primary contact"
]
},
{
"name": "Jimin Pei",
"orcidid": "https://orcid.org/0000-0002-3505-9665",
"typeEntity": "Person"
}
],
"description": "The AFTM database reports the transmebrane segments identified by AlphaFold structural models of human proteins and compares them to those in the UniProt database and the Human Transmembrane Proteome (HTP) database.",
"editPermission": {
"type": "private"
},
"function": [
{
"operation": [
{
"term": "Nucleic acids-binding site prediction",
"uri": "http://edamontology.org/operation_0420"
},
{
"term": "PTM site prediction",
"uri": "http://edamontology.org/operation_0417"
},
{
"term": "Protein modelling",
"uri": "http://edamontology.org/operation_0477"
},
{
"term": "Transmembrane protein prediction",
"uri": "http://edamontology.org/operation_0269"
},
{
"term": "Transmembrane protein visualisation",
"uri": "http://edamontology.org/operation_2241"
}
]
}
],
"homepage": "http://conglab.swmed.edu/AFTM",
"lastUpdate": "2023-08-24T13:27:07.690510Z",
"name": "AFTM",
"operatingSystem": [
"Linux",
"Mac",
"Windows"
],
"owner": "Pub2Tools",
"publication": [
{
"doi": "10.1093/DATABASE/BAAD008",
"metadata": {
"abstract": "Transmembrane proteins (TMPs), with diverse cellular functions, are difficult targets for structural determination. Predictions of TMPs and the locations of transmembrane segments using computational methods could be unreliable due to the potential for false positives and false negatives and show inconsistencies across different programs. Recent advances in protein structure prediction methods have made it possible to identify TMPs and their membrane-spanning regions using high-quality structural models. We developed the AlphaFold Transmembrane proteins (AFTM) database of candidate human TMPs by identifying transmembrane regions in AlphaFold structural models of human proteins and their domains using the positioning of proteins in membranes, version 3 program, followed by automatic corrections inspired by manual analysis of the results. We compared our results to annotations from the UniProt database and the Human Transmembrane Proteome (HTP) database. While AFTM did not identify transmembrane regions in some single-pass TMPs, it identified more transmembrane regions for multipass TMPs than UniProt and HTP. AFTM also showed more consistent results with experimental structures, as benchmarked against the Protein Data Bank Transmembrane proteins (PDBTM) database. In addition, some proteins previously annotated as TMPs were suggested to be non-TMPs by AFTM. We report the results of AFTM together with those of UniProt, HTP, TmAlphaFold, PDBTM and Membranome in the online AFTM database compiled as a comprehensive resource of candidate human TMPs with structural models. Database URL http://conglab.swmed.edu/AFTM",
"authors": [
{
"name": "Cong Q."
},
{
"name": "Pei J."
}
],
"date": "2023-01-01T00:00:00Z",
"journal": "Database",
"title": "AFTM: a database of transmembrane regions in the human proteome predicted by AlphaFold"
},
"pmcid": "PMC10013729",
"pmid": "36917599"
}
],
"toolType": [
"Database portal"
],
"topic": [
{
"term": "Human biology",
"uri": "http://edamontology.org/topic_2815"
},
{
"term": "Membrane and lipoproteins",
"uri": "http://edamontology.org/topic_0820"
},
{
"term": "Protein folds and structural domains",
"uri": "http://edamontology.org/topic_0736"
},
{
"term": "Proteomics",
"uri": "http://edamontology.org/topic_0121"
},
{
"term": "Sequence analysis",
"uri": "http://edamontology.org/topic_0080"
}
]
}
156 changes: 156 additions & 0 deletions data/cenfind/cenfind.biotools.json
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{
"accessibility": "Open access",
"additionDate": "2023-08-24T10:11:15.264912Z",
"biotoolsCURIE": "biotools:cenfind",
"biotoolsID": "cenfind",
"confidence_flag": "tool",
"cost": "Free of charge",
"credit": [
{
"email": "[email protected]",
"name": "Pierre Gönczy",
"typeEntity": "Person",
"typeRole": [
"Primary contact"
]
},
{
"name": "Adrien Journé",
"typeEntity": "Person"
},
{
"name": "Georgios Hatzopoulos",
"typeEntity": "Person"
},
{
"name": "Léo Bürgy",
"typeEntity": "Person"
},
{
"name": "Martin Weigert",
"typeEntity": "Person"
},
{
"name": "Matthias Minder",
"typeEntity": "Person"
},
{
"name": "Sahand Jamal Rahi",
"typeEntity": "Person"
}
],
"description": "Deep-learning pipeline for efficient centriole detection in microscopy datasets.",
"editPermission": {
"type": "private"
},
"function": [
{
"operation": [
{
"term": "Image analysis",
"uri": "http://edamontology.org/operation_3443"
},
{
"term": "Image annotation",
"uri": "http://edamontology.org/operation_3553"
},
{
"term": "Microscope image visualisation",
"uri": "http://edamontology.org/operation_3552"
},
{
"term": "Splitting",
"uri": "http://edamontology.org/operation_3359"
}
]
}
],
"homepage": "https://figshare.com/articles/dataset/Cenfind_datasets/21581358",
"language": [
"Python"
],
"lastUpdate": "2023-08-24T10:11:15.267355Z",
"license": "MIT",
"link": [
{
"type": [
"Other"
],
"url": "https://figshare.com/articles/software/Cenfind_model_weights/21724421"
},
{
"type": [
"Repository"
],
"url": "https://github.com/UPGON/cenfind"
}
],
"name": "CenFind",
"operatingSystem": [
"Linux",
"Mac",
"Windows"
],
"owner": "Pub2Tools",
"publication": [
{
"doi": "10.1186/S12859-023-05214-2",
"metadata": {
"abstract": "Background: High-throughput and selective detection of organelles in immunofluorescence images is an important but demanding task in cell biology. The centriole organelle is critical for fundamental cellular processes, and its accurate detection is key for analysing centriole function in health and disease. Centriole detection in human tissue culture cells has been achieved typically by manual determination of organelle number per cell. However, manual cell scoring of centrioles has a low throughput and is not reproducible. Published semi-automated methods tally the centrosome surrounding centrioles and not centrioles themselves. Furthermore, such methods rely on hard-coded parameters or require a multichannel input for cross-correlation. Therefore, there is a need for developing an efficient and versatile pipeline for the automatic detection of centrioles in single channel immunofluorescence datasets. Results: We developed a deep-learning pipeline termed CenFind that automatically scores cells for centriole numbers in immunofluorescence images of human cells. CenFind relies on the multi-scale convolution neural network SpotNet, which allows the accurate detection of sparse and minute foci in high resolution images. We built a dataset using different experimental settings and used it to train the model and evaluate existing detection methods. The resulting average F1-score achieved by CenFind is > 90% across the test set, demonstrating the robustness of the pipeline. Moreover, using the StarDist-based nucleus detector, we link the centrioles and procentrioles detected with CenFind to the cell containing them, overall enabling automatic scoring of centriole numbers per cell. Conclusions: Efficient, accurate, channel-intrinsic and reproducible detection of centrioles is an important unmet need in the field. Existing methods are either not discriminative enough or focus on a fixed multi-channel input. To fill this methodological gap, we developed CenFind, a command line interface pipeline that automates cell scoring of centrioles, thereby enabling channel-intrinsic, accurate and reproducible detection across experimental modalities. Moreover, the modular nature of CenFind enables its integration in other pipelines. Overall, we anticipate CenFind to prove critical for accelerating discoveries in the field.",
"authors": [
{
"name": "Burgy L."
},
{
"name": "Gonczy P."
},
{
"name": "Hatzopoulos G."
},
{
"name": "Journe A."
},
{
"name": "Minder M."
},
{
"name": "Rahi S.J."
},
{
"name": "Weigert M."
}
],
"date": "2023-12-01T00:00:00Z",
"journal": "BMC Bioinformatics",
"title": "CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets"
},
"pmcid": "PMC10045196",
"pmid": "36977999"
}
],
"toolType": [
"Command-line tool"
],
"topic": [
{
"term": "Cell biology",
"uri": "http://edamontology.org/topic_2229"
},
{
"term": "Imaging",
"uri": "http://edamontology.org/topic_3382"
},
{
"term": "Machine learning",
"uri": "http://edamontology.org/topic_3474"
},
{
"term": "Statistics and probability",
"uri": "http://edamontology.org/topic_2269"
},
{
"term": "Workflows",
"uri": "http://edamontology.org/topic_0769"
}
]
}
117 changes: 117 additions & 0 deletions data/cfago/cfago.biotools.json
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{
"accessibility": "Open access",
"additionDate": "2023-08-24T13:59:16.245106Z",
"biotoolsCURIE": "biotools:cfago",
"biotoolsID": "cfago",
"confidence_flag": "tool",
"cost": "Free of charge",
"credit": [
{
"email": "[email protected]",
"name": "Bin Liu",
"orcidid": "https://orcid.org/0000-0003-3685-9469",
"typeEntity": "Person"
},
{
"email": "[email protected]",
"name": "Junjie Chen",
"orcidid": "https://orcid.org/0000-0002-0483-303X",
"typeEntity": "Person"
},
{
"name": "Mingyue Guo",
"typeEntity": "Person"
},
{
"name": "Xiaopeng Jin",
"typeEntity": "Person"
},
{
"name": "Zhourun Wu",
"typeEntity": "Person"
}
],
"description": "Cross-fusion of network and attributes based on attention mechanism for protein function prediction.",
"editPermission": {
"type": "private"
},
"function": [
{
"operation": [
{
"term": "Network analysis",
"uri": "http://edamontology.org/operation_3927"
},
{
"term": "Protein feature detection",
"uri": "http://edamontology.org/operation_3092"
},
{
"term": "Protein function prediction",
"uri": "http://edamontology.org/operation_1777"
}
]
}
],
"homepage": "http://bliulab.net/CFAGO/",
"lastUpdate": "2023-08-24T13:59:16.247807Z",
"name": "CFAGO",
"operatingSystem": [
"Linux",
"Mac",
"Windows"
],
"owner": "Pub2Tools",
"publication": [
{
"doi": "10.1093/BIOINFORMATICS/BTAD123",
"metadata": {
"abstract": "Motivation: Protein function annotation is fundamental to understanding biological mechanisms. The abundant genome-scale protein-protein interaction (PPI) networks, together with other protein biological attributes, provide rich information for annotating protein functions. As PPI networks and biological attributes describe protein functions from different perspectives, it is highly challenging to cross-fuse them for protein function prediction. Recently, several methods combine the PPI networks and protein attributes via the graph neural networks (GNNs). However, GNNs may inherit or even magnify the bias caused by noisy edges in PPI networks. Besides, GNNs with stacking of many layers may cause the over-smoothing problem of node representations. Results: We develop a novel protein function prediction method, CFAGO, to integrate single-species PPI networks and protein biological attributes via a multi-head attention mechanism. CFAGO is first pre-trained with an encoder-decoder architecture to capture the universal protein representation of the two sources. It is then fine-tuned to learn more effective protein representations for protein function prediction. Benchmark experiments on human and mouse datasets show CFAGO outperforms state-of-the-art single-species network-based methods by at least 7.59%, 6.90%, 11.68% in terms of m-AUPR, M-AUPR, and Fmax, respectively, demonstrating cross-fusion by multi-head attention mechanism can greatly improve the protein function prediction. We further evaluate the quality of captured protein representations in terms of Davies Bouldin Score, whose results show that cross-fused protein representations by multi-head attention mechanism are at least 2.7% better than that of original and concatenated representations. We believe CFAGO is an effective tool for protein function prediction.",
"authors": [
{
"name": "Chen J."
},
{
"name": "Guo M."
},
{
"name": "Jin X."
},
{
"name": "Liu B."
},
{
"name": "Wu Z."
}
],
"citationCount": 3,
"date": "2023-03-01T00:00:00Z",
"journal": "Bioinformatics",
"title": "CFAGO: cross-fusion of network and attributes based on attention mechanism for protein function prediction"
},
"pmcid": "PMC10032634",
"pmid": "36883697"
}
],
"toolType": [
"Web application"
],
"topic": [
{
"term": "Function analysis",
"uri": "http://edamontology.org/topic_1775"
},
{
"term": "Machine learning",
"uri": "http://edamontology.org/topic_3474"
},
{
"term": "Protein interactions",
"uri": "http://edamontology.org/topic_0128"
},
{
"term": "Zoology",
"uri": "http://edamontology.org/topic_3500"
}
]
}
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