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{ "count": 3916, "next": "?page=2", "previous": null, "list": [ { "name": "Hybkit", "description": "Python API and command-line toolkit for hybrid sequence data from chimeric RNA methods.", "homepage": "https://pypi.org/project/hybkit/", "biotoolsID": "hybkit", "biotoolsCURIE": "biotools:hybkit", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0463", "term": "miRNA target prediction" }, { "uri": "http://edamontology.org/operation_3792", "term": "miRNA expression analysis" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_1812", "term": "Parsing" } ], "input": [], "output": [], "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_3382", "term": "Imaging" }, { "uri": "http://edamontology.org/topic_3794", "term": "RNA immunoprecipitation" } ], "operatingSystem": [], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://github.com/RenneLab/hybkit", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "http://hybkit.readthedocs.io", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD721", "pmid": "38006335", "pmcid": "PMC10701094", "type": [], "version": null, "note": null, "metadata": { "title": "Hybkit: A Python API and command-line toolkit for hybrid sequence data from chimeric RNA methods", "abstract": "Summary: Experimental methods using microRNA/target ligation have recently provided significant insights into microRNA functioning through generation of chimeric (hybrid) RNA sequences. Here, we introduce Hybkit, a Python3 API, and command-line toolkit for analysis of hybrid sequence data in the \"hyb\"file format to enable customizable evaluation and annotation of hybrid characteristics. The Hybkit API includes a suite of python objects for developing custom analyses of hybrid data as well as miRNA-specific analysis methods, built-in plotting of analysis results, and incorporation of predicted miRNA/target interactions in Vienna format.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Stribling D." }, { "name": "Gay L.A." }, { "name": "Renne R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Daniel Stribling", "email": "ds@ufl.edu", "url": null, "orcidid": "https://orcid.org/0000-0002-0649-9506", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Rolf Renne", "email": "rrenne@ufl.edu", "url": null, "orcidid": "https://orcid.org/0000-0001-7391-8806", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-26T12:47:18.401889Z", "lastUpdate": "2024-04-26T12:47:18.404450Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "parazitCUB", "description": "An R package to streamline the process of investigating the adaptations of parasites' codon usage bias.", "homepage": "https://github.com/AliYoussef96/parazitCUB", "biotoolsID": "parazitcub", "biotoolsCURIE": "biotools:parazitcub", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2962", "term": "Codon usage bias calculation" }, { "uri": "http://edamontology.org/operation_0284", "term": "Codon usage table generation" }, { "uri": "http://edamontology.org/operation_0285", "term": "Codon usage table comparison" }, { "uri": "http://edamontology.org/operation_0335", "term": "Formatting" }, { "uri": "http://edamontology.org/operation_3891", "term": "Essential dynamics" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3302", "term": "Parasitology" }, { "uri": "http://edamontology.org/topic_3945", "term": "Molecular evolution" }, { "uri": "http://edamontology.org/topic_3500", "term": "Zoology" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.12688/f1000research.143223.1", "pmid": "38021405", "pmcid": "PMC10682597", "type": [], "version": null, "note": null, "metadata": { "title": "parazitCUB: An R package to streamline the process of investigating the adaptations of parasites' codon usage bias", "abstract": "Examining the intricate association between parasites and their hosts, particularly at the codon level, assumes paramount importance in comprehending evolutionary processes and forecasting the characteristics of novel parasites. While diverse metrics and statistical analyses are available to explore codon usage bias (CUB), there presently exists no dedicated tool for examining the co-adaptation of codon usage between parasites and hosts. Therefore, we introduce the parazitCUB R package to address this challenge in a scalable and efficient manner, as it is capable of handling extensive datasets and simultaneously analyzing of multiple parasites with optimized performance. parazitCUB enables the elucidation of parasite-host interactions and the evolutionary patterns of parasites through the implementation of various indices, cluster analysis, multivariate analysis, and data visualization techniques. The tool can be accessed at the following location: https://github.com/AliYoussef96/parazitCUB", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Anwar A.M." }, { "name": "Bayoumi S." }, { "name": "Elzalabany S." }, { "name": "Magdeldin S." }, { "name": "Ahmed A.E." } ], "journal": "F1000Research" } } ], "credit": [ { "name": "Amr E. Ahmed", "email": "Amreahmed@psas.bsu.edu.eg", "url": null, "orcidid": "https://orcid.org/0000-0003-0628-4422", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Ali Mostafa Anwar", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-26T11:53:20.839995Z", "lastUpdate": "2024-04-26T11:53:20.841976Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MCPtaggR", "description": "R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison.", "homepage": "https://github.com/tomoyukif/MCPtaggR", "biotoolsID": "mcptaggr", "biotoolsCURIE": "biotools:mcptaggr", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" }, { "uri": "http://edamontology.org/operation_3933", "term": "Demultiplexing" }, { "uri": "http://edamontology.org/operation_0484", "term": "SNP detection" }, { "uri": "http://edamontology.org/operation_0524", "term": "De-novo assembly" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" }, { "uri": "http://edamontology.org/topic_0780", "term": "Plant biology" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_2885", "term": "DNA polymorphism" }, { "uri": "http://edamontology.org/topic_3175", "term": "Structural variation" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://tomoyukif.github.io/MCPtaggR/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/DNARES/DSAD027", "pmid": "38134958", "pmcid": "PMC10799318", "type": [], "version": null, "note": null, "metadata": { "title": "MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison", "abstract": "Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.", "date": "2024-02-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Furuta T." }, { "name": "Yamamoto T." } ], "journal": "DNA Research" } } ], "credit": [ { "name": "Tomoyuki Furuta", "email": "f.tomoyuki@okayama-u.ac.jp", "url": null, "orcidid": "https://orcid.org/0000-0002-0869-6626", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T10:26:21.921850Z", "lastUpdate": "2024-04-19T10:26:21.924007Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "BRACNAC", "description": "A BRCA1 and BRCA2 copy number alteration caller from Next-Generation sequencing data.", "homepage": "https://github.com/aakechin/bracnac/", "biotoolsID": "bracnac", "biotoolsCURIE": "biotools:bracnac", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3961", "term": "Copy number variation detection" }, { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_3958", "term": "Copy number variation" }, { "uri": "http://edamontology.org/topic_3512", "term": "Gene transcripts" }, { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" }, { "uri": "http://edamontology.org/topic_0632", "term": "Probes and primers" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.3390/IJMS242316630", "pmid": "38068953", "pmcid": "PMC10706169", "type": [], "version": null, "note": null, "metadata": { "title": "BRACNAC: A BRCA1 and BRCA2 Copy Number Alteration Caller from Next-Generation Sequencing Data", "abstract": "Detecting copy number variations (CNVs) and alterations (CNAs) in the BRCA1 and BRCA2 genes is essential for testing patients for targeted therapy applicability. However, the available bioinformatics tools were initially designed for identifying CNVs/CNAs in whole-genome or -exome (WES) NGS data or targeted NGS data without adaptation to the BRCA1/2 genes. Most of these tools were tested on sample cohorts of limited size, with their use restricted to specific library preparation kits or sequencing platforms. We developed BRACNAC, a new tool for detecting CNVs and CNAs in the BRCA1 and BRCA2 genes in NGS data of different origin. The underlying mechanism of this tool involves various coverage normalization steps complemented by CNV probability evaluation. We estimated the sensitivity and specificity of our tool to be 100% and 94%, respectively, with an area under the curve (AUC) of 94%. The estimation was performed using the NGS data obtained from 213 ovarian and prostate cancer samples tested with in-house and commercially available library preparation kits and additionally using multiplex ligation-dependent probe amplification (MLPA) (12 CNV-positive samples). Using freely available WES and targeted NGS data from other research groups, we demonstrated that BRACNAC could also be used for these two types of data, with an AUC of up to 99.9%. In addition, we determined the limitations of the tool in terms of the minimum number of samples per NGS run (≥20 samples) and the minimum expected percentage of CNV-negative samples (≥80%). We expect that our findings will improve the efficacy of BRCA1/2 diagnostics. BRACNAC is freely available at the GitHub server.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Kechin A." }, { "name": "Boyarskikh U." }, { "name": "Borobova V." }, { "name": "Khrapov E." }, { "name": "Subbotin S." }, { "name": "Filipenko M." } ], "journal": "International Journal of Molecular Sciences" } } ], "credit": [ { "name": "Andrey Kechin", "email": "aa_kechin@niboch.nsc.ru", "url": null, "orcidid": "https://orcid.org/0000-0002-4822-0251", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:28:34.739649Z", "lastUpdate": "2024-04-19T09:28:34.741978Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "COMO", "description": "Pipeline for multi-omics data integration in metabolic modelling and drug discovery.", "homepage": "https://github.com/HelikarLab/COMO", "biotoolsID": "como", "biotoolsCURIE": "biotools:como", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3660", "term": "Metabolic network modelling" }, { "uri": "http://edamontology.org/operation_3929", "term": "Metabolic pathway prediction" }, { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression profiling" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3336", "term": "Drug discovery" }, { "uri": "http://edamontology.org/topic_3407", "term": "Endocrinology and metabolism" }, { "uri": "http://edamontology.org/topic_3375", "term": "Drug metabolism" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3379", "term": "Preclinical and clinical studies" } ], "operatingSystem": [], "language": [ "Python", "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIB/BBAD387", "pmid": "37930022", "pmcid": "PMC10627799", "type": [], "version": null, "note": null, "metadata": { "title": "COMO: a pipeline for multi-omics data integration in metabolic modeling and drug discovery", "abstract": "Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogeneous biological datasets, which can be challenging without efficient tools. We developed Constraint-based Optimization of Metabolic Objectives (COMO), a user-friendly pipeline that integrates multi-omics data processing, context-specific metabolic model development, simulations, drug databases and disease data to aid drug discovery. COMO can be installed as a Docker Image or with Conda and includes intuitive instructions within a Jupyter Lab environment. It provides a comprehensive solution for the integration of bulk and single-cell RNA-seq, microarrays and proteomics outputs to develop context-specific metabolic models. Using public databases, open-source solutions for model construction and a streamlined approach for predicting repurposable drugs, COMO enables researchers to investigate low-cost alternatives and novel disease treatments. As a case study, we used the pipeline to construct metabolic models of B cells, which simulate and analyze them to predict metabolic drug targets for rheumatoid arthritis and systemic lupus erythematosus, respectively. COMO can be used to construct models for any cell or tissue type and identify drugs for any human disease where metabolic inhibition is relevant. The pipeline has the potential to improve the health of the global community cost-effectively by providing high-confidence targets to pursue in preclinical and clinical studies. The source code of the COMO pipeline is available at https://github.com/HelikarLab/COMO. The Docker image can be pulled at https://github.com/HelikarLab/COMO/pkgs/container/como.", "date": "2023-11-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Bessell B." }, { "name": "Loecker J." }, { "name": "Zhao Z." }, { "name": "Aghamiri S.S." }, { "name": "Mohanty S." }, { "name": "Amin R." }, { "name": "Helikar T." }, { "name": "Puniya B.L." } ], "journal": "Briefings in Bioinformatics" } } ], "credit": [ { "name": "Tomáš Helikar", "email": "thelikar2@unl.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Bhanwar Lal Puniya", "email": "bpuniya2@unl.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:14:48.657400Z", "lastUpdate": "2024-04-19T09:14:48.659485Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "SOHPIE", "description": "Statistical approach via pseudo-value information and estimation for differential network analysis of microbiome data.", "homepage": "https://CRAN.R-project.org/package=SOHPIE", "biotoolsID": "sohpie", "biotoolsCURIE": "biotools:sohpie", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3659", "term": "Regression analysis" }, { "uri": "http://edamontology.org/operation_3927", "term": "Network analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3697", "term": "Microbial ecology" }, { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" }, { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD766", "pmid": "38134422", "pmcid": "PMC10807904", "type": [], "version": null, "note": null, "metadata": { "title": "SOHPIE: statistical approach via pseudo-value information and estimation for differential network analysis of microbiome data", "abstract": "Summary: The SOHPIE R package implements a novel functionality for “multivariable” differential co-abundance network (DN, hereafter) analyses of microbiome data. It incorporates a regression approach that adjusts for additional covariates for DN analyses. This distinguishes from previous prominent approaches in DN analyses such as MDiNE and NetCoMi which do not feature a covariate adjustment of finding taxa that are differentially connected (DC, hereafter) between individuals with different clinical and phenotypic characteristics.", "date": "2024-01-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Ahn S." }, { "name": "Datta S." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Seungjun Ahn", "email": "seungjun.ahn@mountsinai.org", "url": null, "orcidid": "https://orcid.org/0000-0002-4816-8924", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T08:27:27.983267Z", "lastUpdate": "2024-04-19T08:27:27.985435Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Parsley", "description": "A web app for parsing data from plate readers.", "homepage": "https://gbstan.shinyapps.io/parsleyapp/", "biotoolsID": "parsley", "biotoolsCURIE": "biotools:parsley", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_1812", "term": "Parsing" }, { "uri": "http://edamontology.org/operation_0335", "term": "Formatting" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3070", "term": "Biology" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "R" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/ec363/parsleyapp", "type": [ "Repository" ], "note": null }, { "url": "https://zenodo.org/records/10011752", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD733", "pmid": "38048610", "pmcid": "PMC10715767", "type": [], "version": null, "note": null, "metadata": { "title": "Parsley: a web app for parsing data from plate readers", "abstract": "As demand for the automation of biological assays has increased over recent years, the range of measurement types implemented by multiwell plate readers has broadened and the list of published software packages that caters to their analysis has grown. However, most plate readers export data in esoteric formats with little or no metadata, while most analytical software packages are built to work with tidy data accompanied by associated metadata. ‘Parser’ functions are therefore required to prepare raw data for analysis. Such functions are instrument- and data type-specific, and to date, no generic tool exists that can parse data from multiple data types or multiple plate readers, despite the potential for such a tool to speed up access to analysed data and remove an important barrier for less confident coders. We have developed the interactive web application, Parsley, to bridge this gap. Unlike conventional programmatic parser functions, Parsley makes few assumptions about exported data, instead employing user inputs to identify and extract data from data files. In doing so, it is designed to enable any user to parse plate reader data and can handle a wide variety of instruments (10þ) and data types (53þ). Parsley is freely available via a web interface, enabling access to its unique plate reader data parsing functionality, without the need to install software or write code. Availability and implementation: The Parsley web application can be accessed at: https://gbstan.shinyapps.io/parsleyapp/. The source code is available at: https://github.com/ec363/parsleyapp and is archived on Zenodo: https://zenodo.org/records/10011752.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Csibra E." }, { "name": "Stan G.-B." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Eszter Csibra", "email": "e.csibra@imperial.ac.uk", "url": null, "orcidid": "https://orcid.org/0000-0003-4759-5921", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Guy-Bart Stan", "email": "g.stan@imperial.ac.uk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T08:21:36.900782Z", "lastUpdate": "2024-04-19T08:21:36.903018Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PhosBoost", "description": "Improved phosphorylation prediction recall using gradient boosting and protein language models.", "homepage": "https://github.com/eporetsky/PhosBoost", "biotoolsID": "phosboost", "biotoolsCURIE": "biotools:phosboost", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0417", "term": "PTM site prediction" }, { "uri": "http://edamontology.org/operation_2476", "term": "Molecular dynamics" }, { "uri": "http://edamontology.org/operation_0267", "term": "Protein secondary structure prediction" }, { "uri": "http://edamontology.org/operation_3359", "term": "Splitting" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0601", "term": "Protein modifications" }, { "uri": "http://edamontology.org/topic_0128", "term": "Protein interactions" }, { "uri": "http://edamontology.org/topic_2269", "term": "Statistics and probability" }, { "uri": "http://edamontology.org/topic_0780", "term": "Plant biology" } ], "operatingSystem": [ "Linux" ], "language": [ "Python", "Shell" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1002/PLD3.554", "pmid": "38124705", "pmcid": "PMC10732782", "type": [], "version": null, "note": null, "metadata": { "title": "PhosBoost: Improved phosphorylation prediction recall using gradient boosting and protein language models", "abstract": "Protein phosphorylation is a dynamic and reversible post-translational modification that regulates a variety of essential biological processes. The regulatory role of phosphorylation in cellular signaling pathways, protein–protein interactions, and enzymatic activities has motivated extensive research efforts to understand its functional implications. Experimental protein phosphorylation data in plants remains limited to a few species, necessitating a scalable and accurate prediction method. Here, we present PhosBoost, a machine-learning approach that leverages protein language models and gradient-boosting trees to predict protein phosphorylation from experimentally derived data. Trained on data obtained from a comprehensive plant phosphorylation database, qPTMplants, we compared the performance of PhosBoost to existing protein phosphorylation prediction methods, PhosphoLingo and DeepPhos. For serine and threonine prediction, PhosBoost achieved higher recall than PhosphoLingo and DeepPhos (.78,.56, and.14, respectively) while maintaining a competitive area under the precision-recall curve (.54,.56, and.42, respectively). PhosphoLingo and DeepPhos failed to predict any tyrosine phosphorylation sites, while PhosBoost achieved a recall score of.6. Despite the precision-recall tradeoff, PhosBoost offers improved performance when recall is prioritized while consistently providing more confident probability scores. A sequence-based pairwise alignment step improved prediction results for all classifiers by effectively increasing the number of inferred positive phosphosites. We provide evidence to show that PhosBoost models are transferable across species and scalable for genome-wide protein phosphorylation predictions. PhosBoost is freely and publicly available on GitHub.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Poretsky E." }, { "name": "Andorf C.M." }, { "name": "Sen T.Z." } ], "journal": "Plant Direct" } } ], "credit": [ { "name": "Taner Z. Sen", "email": "taner.sen@usda.gov", "url": null, "orcidid": "https://orcid.org/0000-0002-5553-6190", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T08:21:06.741187Z", "lastUpdate": "2024-04-19T08:21:06.743436Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "itaxalogue", "description": "Toolkit to create comprehensive CO1 reference databases.", "homepage": "https://github.com/nwnoll/taxalogue", "biotoolsID": "itaxalogue", "biotoolsCURIE": "biotools:itaxalogue", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3200", "term": "DNA barcoding" }, { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" }, { "uri": "http://edamontology.org/operation_3695", "term": "Filtering" }, { "uri": "http://edamontology.org/operation_0224", "term": "Query and retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" }, { "uri": "http://edamontology.org/topic_0654", "term": "DNA" }, { "uri": "http://edamontology.org/topic_3071", "term": "Biological databases" }, { "uri": "http://edamontology.org/topic_3168", "term": "Sequencing" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Ruby" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.7717/PEERJ.16253", "pmid": "38077427", "pmcid": "PMC10702336", "type": [], "version": null, "note": null, "metadata": { "title": "taxalogue: a toolkit to create comprehensive CO1 reference databases", "abstract": "Background. Taxonomic identification through DNA barcodes gained considerable traction through the invention of next-generation sequencing and DNA metabarcoding. Metabarcoding allows for the simultaneous identification of thousands of organisms from bulk samples with high taxonomic resolution. However, reliable identifications can only be achieved with comprehensive and curated reference databases. Therefore, custom reference databases are often created to meet the needs of specific research questions. Due to taxonomic inconsistencies, formatting issues, and technical difficulties, building a custom reference database requires tremendous effort. Here, we present taxalogue, an easy-to-use software for creating comprehensive and customized reference databases that provide clean and taxonomically harmonized records. In combination with extensive geographical filtering options, taxalogue opens up new possibilities for generating and testing evolutionary hypotheses. Methods. taxalogue collects DNA sequences from several online sources and combines them into a reference database. Taxonomic incongruencies between the different data sources can be harmonized according to available taxonomies. Dereplication and various filtering options are available regarding sequence quality or metadata information. taxalogue is implemented in the open-source Ruby programming language, and the source code is available at https://github.com/nwnoll/taxalogue. We benchmark four reference databases by sequence identity against eight queries from different localities and trapping devices. Subsamples from each reference database were used to compare how well another one is covered. Results. taxalogue produces reference databases with the best coverage at high identities for most tested queries, enabling more accurate, reliable predictions with higher certainty than the other benchmarked reference databases. Additionally, the performance of taxalogue is more consistent while providing good coverage for a variety of habitats, regions, and sampling methods. taxalogue simplifies the creation of reference databases and makes the process reproducible and transparent. Multiple available output formats for commonly used downstream applications facilitate the easy adoption of taxalogue in many different software pipelines. The resulting reference databases improve the taxonomic classification accuracy through high coverage of the query sequences at high identities.", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Noll N.W." }, { "name": "Scherber C." }, { "name": "Schaffler L." } ], "journal": "PeerJ" } } ], "credit": [ { "name": "Niklas W. Noll", "email": "N.Noll@leibniz-lib.de", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T08:15:18.576654Z", "lastUpdate": "2024-04-19T08:15:18.578562Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Patchwork", "description": "Alignment-based retrieval and concatenation of phylogenetic markers from genomic data.", "homepage": "https://github.com/fethalen/Patchwork", "biotoolsID": "patchwork_alig", "biotoolsCURIE": "biotools:patchwork_alig", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3192", "term": "Sequence trimming" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" }, { "uri": "http://edamontology.org/operation_0232", "term": "Sequence merging" }, { "uri": "http://edamontology.org/operation_0323", "term": "Phylogenetic inference" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0194", "term": "Phylogenomics" }, { "uri": "http://edamontology.org/topic_3673", "term": "Whole genome sequencing" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [ "Mac", "Windows", "Linux" ], "language": [ "Julia" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/fethalen/patchwork", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/GBE/EVAD227", "pmid": "38085033", "pmcid": "PMC10735302", "type": [], "version": null, "note": null, "metadata": { "title": "Patchwork: Alignment-Based Retrieval and Concatenation of Phylogenetic Markers from Genomic Data", "abstract": "Low-coverage whole-genome sequencing (also known as “genome skimming”) is becoming an increasingly affordable approach to large-scale phylogenetic analyses. While already routinely used to recover organellar genomes, genome skimming is rather rarely utilized for recovering single-copy nuclear markers. One reason might be that only few tools exist to work with this data type within a phylogenomic context, especially to deal with fragmented genome assemblies. We here present a new software tool called Patchwork for mining phylogenetic markers from highly fragmented short-read assemblies as well as directly from sequence reads. Patchwork is an alignment-based tool that utilizes the sequence aligner DIAMOND and is written in the programming language Julia. Homologous regions are obtained via a sequence similarity search, followed by a “hit stitching” phase, in which adjacent or overlapping regions are merged into a single unit. The novel sliding window algorithm trims away any noncoding regions from the resulting sequence. We demonstrate the utility of Patchwork by recovering near-universal single-copy orthologs within a benchmarking study, and we additionally assess the performance of Patchwork in comparison with other programs. We find that Patchwork allows for accurate retrieval of (putatively) single-copy genes from genome skimming data sets at different sequencing depths with high computational speed, outperforming existing software targeting similar tasks. Patchwork is released under the GNU General Public License version 3. Installation instructions, additional documentation, and the source code itself are all available via GitHub at https://github.com/fethalen/ Patchwork.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Thalen F." }, { "name": "Kohne C.G." }, { "name": "Bleidorn C." } ], "journal": "Genome Biology and Evolution" } } ], "credit": [ { "name": "Christoph Bleidorn", "email": "christoph.bleidorn@biologie.uni-goettingen.de", "url": null, "orcidid": "https://orcid.org/0000-0001-7295-6616", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-18T13:18:34.582989Z", "lastUpdate": "2024-04-18T13:18:34.585411Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }