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biotools-import on Sun Jan 8 01:07:30 UTC 2023 #18

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58 changes: 58 additions & 0 deletions data/apis-wings-eu/apis-wings-eu.biotools.json
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{
"additionDate": "2022-12-30T06:46:43.806950Z",
"biotoolsCURIE": "biotools:apis-wings-eu",
"biotoolsID": "apis-wings-eu",
"description": "Collection of wing images for conservation of honey bees (Apis mellifera) biodiversity in Europe",
"editPermission": {
"type": "private"
},
"function": [
{
"operation": [
{
"term": "Statistical calculation",
"uri": "http://edamontology.org/operation_2238"
}
]
}
],
"homepage": "https://zenodo.org/record/7244070",
"language": [
"R"
],
"lastUpdate": "2022-12-30T06:50:52.737447Z",
"license": "CC0-1.0",
"link": [
{
"type": [
"Repository"
],
"url": "https://zenodo.org/record/7244070"
}
],
"name": "Apis-wings-EU",
"owner": "tofilski",
"publication": [
{
"doi": "10.5281/zenodo.7244070",
"version": "2"
}
],
"toolType": [
"Script"
],
"topic": [
{
"term": "Biological databases",
"uri": "http://edamontology.org/topic_3071"
},
{
"term": "Imaging",
"uri": "http://edamontology.org/topic_3382"
},
{
"term": "Workflows",
"uri": "http://edamontology.org/topic_0769"
}
]
}
67 changes: 67 additions & 0 deletions data/best_bam-error-stats-tool/best_bam-error-stats-tool.biotools.json
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{
"accessibility": "Open access",
"additionDate": "2023-01-06T14:15:37.670188Z",
"biotoolsCURIE": "biotools:best_Bam-Error-Stats-Tool",
"biotoolsID": "best_Bam-Error-Stats-Tool",
"cost": "Free of charge",
"credit": [
{
"name": "Daniel Liu, Daniel E. Cook"
}
],
"description": "Bam Error Stats Tool (best): analysis of error types in aligned reads.\nbest is used to assess the quality of reads after aligning them to a reference assembly.",
"documentation": [
{
"type": [
"User manual"
],
"url": "https://github.com/google/best/blob/main/Usage.md"
}
],
"download": [
{
"note": "Github page",
"type": "Source code",
"url": "https://github.com/google/best",
"version": "0.1.0"
}
],
"editPermission": {
"type": "private"
},
"function": [
{
"operation": [
{
"term": "Sequence alignment validation",
"uri": "http://edamontology.org/operation_0447"
}
]
}
],
"homepage": "https://github.com/google/best",
"language": [
"Other"
],
"lastUpdate": "2023-01-06T14:15:37.673685Z",
"license": "MIT",
"maturity": "Emerging",
"name": "best",
"owner": "pauffret",
"toolType": [
"Command-line tool"
],
"topic": [
{
"term": "Bioinformatics",
"uri": "http://edamontology.org/topic_0091"
},
{
"term": "Sequence analysis",
"uri": "http://edamontology.org/topic_0080"
}
],
"version": [
"0.1.0"
]
}
131 changes: 131 additions & 0 deletions data/e-snpsgo/e-snpsgo.biotools.json
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{
"accessibility": "Open access",
"additionDate": "2023-01-05T08:42:10.922859Z",
"biotoolsCURIE": "biotools:E-SNPsGO",
"biotoolsID": "E-SNPsGO",
"description": "E-SNPs&GO is a machine-learning method the pathogenicity of human variations. E-SNPs&GO classify input variations into pathogenic or benign.",
"editPermission": {
"authors": [
"ELIXIR-ITA-BOLOGNA"
],
"type": "group"
},
"elixirCommunity": [
"Rare Diseases"
],
"elixirNode": [
"Italy"
],
"elixirPlatform": [
"Tools"
],
"function": [
{
"input": [
{
"data": {
"term": "Protein sequence",
"uri": "http://edamontology.org/data_2976"
},
"format": [
{
"term": "FASTA",
"uri": "http://edamontology.org/format_1929"
}
]
},
{
"data": {
"term": "Sequence variations",
"uri": "http://edamontology.org/data_3498"
},
"format": [
{
"term": "Textual format",
"uri": "http://edamontology.org/format_2330"
}
]
}
],
"operation": [
{
"term": "Variant effect prediction",
"uri": "http://edamontology.org/operation_0331"
}
],
"output": [
{
"data": {
"term": "Score",
"uri": "http://edamontology.org/data_1772"
},
"format": [
{
"term": "HTML",
"uri": "http://edamontology.org/format_2331"
},
{
"term": "JSON",
"uri": "http://edamontology.org/format_3464"
},
{
"term": "TSV",
"uri": "http://edamontology.org/format_3475"
}
]
}
]
}
],
"homepage": "https://esnpsandgo.biocomp.unibo.it/",
"language": [
"Other"
],
"lastUpdate": "2023-01-05T09:18:20.176934Z",
"name": "E-SNPs and GO",
"operatingSystem": [
"Linux",
"Mac",
"Windows"
],
"owner": "PierLuigiMartelli",
"publication": [
{
"doi": "10.1093/bioinformatics/btac678",
"metadata": {
"abstract": "© The Author(s) 2022. Published by Oxford University Press.MOTIVATION: The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations from neutral ones is one of the crucial challenges in precision medicine. Computational tools based on artificial intelligence provide models for protein sequence encoding, bypassing database searches for evolutionary information. We leverage the new encoding schemes for an efficient annotation of protein variants. RESULTS: E-SNPs&GO is a novel method that, given an input protein sequence and a single amino acid variation, can predict whether the variation is related to diseases or not. The proposed method adopts an input encoding completely based on protein language models and embedding techniques, specifically devised to encode protein sequences and GO functional annotations. We trained our model on a newly generated dataset of 101 146 human protein single amino acid variants in 13 661 proteins, derived from public resources. When tested on a blind set comprising 10 266 variants, our method well compares to recent approaches released in literature for the same task, reaching a Matthews Correlation Coefficient score of 0.72. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets. AVAILABILITY AND IMPLEMENTATION: The method is available as a webserver at https://esnpsandgo.biocomp.unibo.it. Datasets and predictions are available at https://esnpsandgo.biocomp.unibo.it/datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",
"authors": [
{
"name": "Casadio R."
},
{
"name": "Manfredi M."
},
{
"name": "Martelli P.L."
},
{
"name": "Savojardo C."
}
],
"date": "2022-11-30T00:00:00Z",
"journal": "Bioinformatics (Oxford, England)",
"title": "E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants"
},
"pmcid": "PMC9710551",
"pmid": "36227117",
"type": [
"Primary"
]
}
],
"toolType": [
"Web application"
],
"topic": [
{
"term": "Protein variants",
"uri": "http://edamontology.org/topic_3120"
}
]
}
136 changes: 136 additions & 0 deletions data/genecloudomics/genecloudomics.biotools.json
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{
"accessibility": "Open access",
"additionDate": "2022-12-31T15:51:53.047663Z",
"biotoolsCURIE": "biotools:genecloudomics",
"biotoolsID": "genecloudomics",
"confidence_flag": "tool",
"cost": "Free of charge",
"credit": [
{
"email": "Kumar_Selvarajoo@bii.a-star.edu.sg",
"name": "Kumar Selvarajoo",
"typeEntity": "Person"
},
{
"email": "mohamed_helmy@bii.a-star.edu.sg",
"name": "Mohamed Helmy",
"typeEntity": "Person"
},
{
"name": "Rahul Agrawal"
},
{
"name": "Thuy Tien Bui"
}
],
"description": "A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis.",
"documentation": [
{
"type": [
"User manual"
],
"url": "https://github.com/buithuytien/GeneCloudOmics/blob/master/GeneCloudOmics_Help_1.0.pdf"
}
],
"editPermission": {
"type": "private"
},
"function": [
{
"operation": [
{
"term": "Essential dynamics",
"uri": "http://edamontology.org/operation_3891"
},
{
"term": "Expression correlation analysis",
"uri": "http://edamontology.org/operation_3463"
},
{
"term": "Expression data visualisation",
"uri": "http://edamontology.org/operation_0571"
},
{
"term": "Gene expression profiling",
"uri": "http://edamontology.org/operation_0314"
},
{
"term": "Gene-set enrichment analysis",
"uri": "http://edamontology.org/operation_2436"
}
]
}
],
"homepage": "http://combio-sifbi.org/GeneCloudOmics/",
"language": [
"Python",
"R"
],
"lastUpdate": "2022-12-31T15:51:53.050195Z",
"license": "Not licensed",
"link": [
{
"type": [
"Repository"
],
"url": "https://github.com/cbio-astar-tools/GeneCloudOmics"
}
],
"name": "GeneCloudOmics",
"operatingSystem": [
"Linux",
"Mac",
"Windows"
],
"owner": "Jennifer",
"publication": [
{
"doi": "10.1007/978-1-0716-2617-7_12",
"metadata": {
"abstract": "© 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.Research in synthetic biology and metabolic engineering require a deep understanding on the function and regulation of complex pathway genes. This can be achieved through gene expression profiling which quantifies the transcriptome-wide expression under any condition, such as a cell development stage, mutant, disease, or treatment with a drug. The expression profiling is usually done using high-throughput techniques such as RNA sequencing (RNA-Seq) or microarray. Although both methods are based on different technical approaches, they provide quantitative measures of the expression levels of thousands of genes. The expression levels of the genes are compared under different conditions to identify the differentially expressed genes (DEGs), the genes with different expression levels under different conditions. DEGs, usually involving thousands in number, are then investigated using bioinformatics and data analytic tools to infer and compare their functional roles between conditions. Dealing with such large datasets, therefore, requires intensive data processing and analyses to ensure its quality and produce results that are statistically sound. Thus, there is a need for deep statistical and bioinformatics knowledge to deal with high-throughput gene expression data. This represents a barrier for wet biologists with limited computational, programming, and data analytic skills that prevent them from getting the full potential of the data. In this chapter, we present a step-by-step protocol to perform transcriptome analysis using GeneCloudOmics, a cloud-based web server that provides an end-to-end platform for high-throughput gene expression analysis.",
"authors": [
{
"name": "Helmy M."
},
{
"name": "Selvarajoo K."
}
],
"date": "2023-01-01T00:00:00Z",
"journal": "Methods in Molecular Biology",
"title": "Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology"
},
"pmid": "36227547"
},
{
"doi": "10.3389/FBINF.2021.693836",
"pmcid": "PMC9581002",
"pmid": "36303746"
}
],
"toolType": [
"Web application"
],
"topic": [
{
"term": "Microarray experiment",
"uri": "http://edamontology.org/topic_3518"
},
{
"term": "RNA-Seq",
"uri": "http://edamontology.org/topic_3170"
},
{
"term": "Statistics and probability",
"uri": "http://edamontology.org/topic_2269"
},
{
"term": "Synthetic biology",
"uri": "http://edamontology.org/topic_3895"
},
{
"term": "Transcriptomics",
"uri": "http://edamontology.org/topic_3308"
}
]
}
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