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{ "count": 9266, "next": "?page=2", "previous": null, "list": [ { "name": "PECAT", "description": "De novo diploid genome assembly using long noisy reads.", "homepage": "https://github.com/lemene/PECAT", "biotoolsID": "pecat", "biotoolsCURIE": "biotools:pecat", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0525", "term": "Genome assembly" }, { "uri": "http://edamontology.org/operation_0524", "term": "De-novo assembly" }, { "uri": "http://edamontology.org/operation_3454", "term": "Phasing" }, { "uri": "http://edamontology.org/operation_3472", "term": "k-mer counting" }, { "uri": "http://edamontology.org/operation_0487", "term": "Haplotype mapping" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_2885", "term": "DNA polymorphism" }, { "uri": "http://edamontology.org/topic_0102", "term": "Mapping" } ], "operatingSystem": [], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1038/s41467-024-47349-7", "pmid": "38580638", "pmcid": "PMC10997618", "type": [], "version": null, "note": null, "metadata": { "title": "De novo diploid genome assembly using long noisy reads", "abstract": "The high sequencing error rate has impeded the application of long noisy reads for diploid genome assembly. Most existing assemblers failed to generate high-quality phased assemblies using long noisy reads. Here, we present PECAT, a Phased Error Correction and Assembly Tool, for reconstructing diploid genomes from long noisy reads. We design a haplotype-aware error correction method that can retain heterozygote alleles while correcting sequencing errors. We combine a corrected read SNP caller and a raw read SNP caller to further improve the identification of inconsistent overlaps in the string graph. We use a grouping method to assign reads to different haplotype groups. PECAT efficiently assembles diploid genomes using Nanopore R9, PacBio CLR or Nanopore R10 reads only. PECAT generates more contiguous haplotype-specific contigs compared to other assemblers. Especially, PECAT achieves nearly haplotype-resolved assembly on B. taurus (Bison×Simmental) using Nanopore R9 reads and phase block NG50 with 59.4/58.0 Mb for HG002 using Nanopore R10 reads.", "date": "2024-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Nie F." }, { "name": "Ni P." }, { "name": "Huang N." }, { "name": "Zhang J." }, { "name": "Wang Z." }, { "name": "Xiao C." }, { "name": "Luo F." }, { "name": "Wang J." } ], "journal": "Nature Communications" } } ], "credit": [ { "name": "Chuanle Xiao", "email": "xiaochuanle@126.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Feng Luo", "email": "luofeng@clemson.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Jianxin Wang", "email": "jxwang@mail.csu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T10:42:15.957876Z", "lastUpdate": "2024-04-19T10:42:15.960256Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "high" }, { "name": "Chemprop", "description": "Machine learning package for chemical property prediction.", "homepage": "http://github.com/chemprop/chemprop", "biotoolsID": "chemprop", "biotoolsCURIE": "biotools:chemprop", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3436", "term": "Aggregation" }, { "uri": "http://edamontology.org/operation_3799", "term": "Quantification" }, { "uri": "http://edamontology.org/operation_3216", "term": "Scaffolding" }, { "uri": "http://edamontology.org/operation_3359", "term": "Splitting" } ], "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_0593", "term": "NMR" }, { "uri": "http://edamontology.org/topic_3314", "term": "Chemistry" }, { "uri": "http://edamontology.org/topic_3407", "term": "Endocrinology and metabolism" }, { "uri": "http://edamontology.org/topic_3047", "term": "Molecular biology" } ], "operatingSystem": [ "Linux" ], "language": [ "Python", "Shell", "D" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "http://github.com/chemprop/chemprop_benchmark", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1021/ACS.JCIM.3C01250", "pmid": "38147829", "pmcid": "PMC10777403", "type": [], "version": null, "note": null, "metadata": { "title": "Chemprop: A Machine Learning Package for Chemical Property Prediction", "abstract": "Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.", "date": "2024-01-08T00:00:00Z", "citationCount": 7, "authors": [ { "name": "Heid E." }, { "name": "Greenman K.P." }, { "name": "Chung Y." }, { "name": "Li S.-C." }, { "name": "Graff D.E." }, { "name": "Vermeire F.H." }, { "name": "Wu H." }, { "name": "Green W.H." }, { "name": "McGill C.J." } ], "journal": "Journal of Chemical Information and Modeling" } } ], "credit": [ { "name": "Charles J. McGill", "email": "mcgillc2@vcu.edu", "url": null, "orcidid": "https://orcid.org/0000-0003-2704-7717", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T10:29:44.116723Z", "lastUpdate": "2024-04-19T10:29:44.118952Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "Clumppling", "description": "Cluster matching and permutation program with integer linear programming.", "homepage": "https://github.com/PopGenClustering/Clumppling", "biotoolsID": "clumppling", "biotoolsCURIE": "biotools:clumppling", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3432", "term": "Clustering" }, { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" }, { "uri": "http://edamontology.org/operation_0491", "term": "Pairwise sequence alignment" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0081", "term": "Structure analysis" }, { "uri": "http://edamontology.org/topic_3056", "term": "Population genetics" }, { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD751", "pmid": "38096585", "pmcid": "PMC10766593", "type": [], "version": null, "note": null, "metadata": { "title": "Clumppling: cluster matching and permutation program with integer linear programming", "abstract": "Motivation: In the mixed-membership unsupervised clustering analyses commonly used in population genetics, multiple replicate data analyses can differ in their clustering solutions. Combinatorial algorithms assist in aligning clustering outputs from multiple replicates so that clustering solutions can be interpreted and combined across replicates. Although several algorithms have been introduced, challenges exist in achieving optimal alignments and performing alignments in reasonable computation time. Results: We present Clumppling, a method for aligning replicate solutions in mixed-membership unsupervised clustering. The method uses integer linear programming for finding optimal alignments, embedding the cluster alignment problem in standard combinatorial optimization frameworks. In example analyses, we find that it achieves solutions with preferred values of a desired objective function relative to those achieved by Pong and that it proceeds with less computation time than Clumpak. It is also the first method to permit alignments across replicates with multiple arbitrary values of the number of clusters K.", "date": "2024-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Liu X." }, { "name": "Kopelman N.M." }, { "name": "Rosenberg N.A." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Xiran Liu", "email": "xiranliu@stanford.edu", "url": null, "orcidid": "https://orcid.org/0000-0002-3955-9026", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Noah A Rosenberg", "email": "noahr@stanford.edu", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T10:17:57.447656Z", "lastUpdate": "2024-04-19T10:17:57.449712Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "STCellbin", "description": "Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images.", "homepage": "https://github.com/STOmics/STCellbin", "biotoolsID": "stcellbin", "biotoolsCURIE": "biotools:stcellbin", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0314", "term": "Gene expression profiling" }, { "uri": "http://edamontology.org/operation_0315", "term": "Expression profile comparison" }, { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_3382", "term": "Imaging" }, { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" } ], "operatingSystem": [ "Linux", "Windows" ], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.46471/gigabyte.110", "pmid": "38434932", "pmcid": "PMC10905256", "type": [], "version": null, "note": null, "metadata": { "title": "Generating single-cell gene expression profiles for high-resolution spatial transcriptomics based on cell boundary images", "abstract": "In spatially resolved transcriptomics, Stereo-seq facilitates the analysis of large tissues at the single-cell level, offering subcellular resolution and centimeter-level field-of-view. Our previous work on StereoCell introduced a one-stop software using cell nuclei staining images and statistical methods to generate high-confidence single-cell spatial gene expression profiles for Stereo-seq data. With advancements allowing the acquisition of cell boundary information, such as cell membrane/wall staining images, we updated our software to a new version, STCellbin. Using cell nuclei staining images, STCellbin aligns cell membrane/wall staining images with spatial gene expression maps. Advanced cell segmentation ensures the detection of accurate cell boundaries, leading to more reliable single-cell spatial gene expression profiles. We verified that STCellbin can be applied to mouse liver (cell membranes) and Arabidopsis seed (cell walls) datasets, outperforming other methods. The improved capability of capturing single-cell gene expression profiles results in a deeper understanding of the contribution of single-cell phenotypes to tissue biology.", "date": "2024-02-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Zhang B." }, { "name": "Li M." }, { "name": "Kang Q." }, { "name": "Deng Z." }, { "name": "Qin H." }, { "name": "Su K." }, { "name": "Feng X." }, { "name": "Chen L." }, { "name": "Liu H." }, { "name": "Fang S." }, { "name": "Zhang Y." }, { "name": "Li Y." }, { "name": "Brix S." }, { "name": "Xu X." } ], "journal": "GigaByte" } } ], "credit": [ { "name": "Susanne Brix", "email": "sbrix@dtu.dk", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Xun Xu", "email": "xuxun@genomics.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T10:14:18.134821Z", "lastUpdate": "2024-04-19T10:14:18.136729Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "RESA", "description": "De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data.", "homepage": "https://github.com/ShenLab-Genomics/RESA", "biotoolsID": "resa", "biotoolsCURIE": "biotools:resa", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3227", "term": "Variant calling" }, { "uri": "http://edamontology.org/operation_3644", "term": "de Novo sequencing" }, { "uri": "http://edamontology.org/operation_3675", "term": "Variant filtering" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" }, { "uri": "http://edamontology.org/operation_3891", "term": "Essential dynamics" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0199", "term": "Genetic variation" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_0099", "term": "RNA" }, { "uri": "http://edamontology.org/topic_3676", "term": "Exome sequencing" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Perl", "Python", "Shell" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S13073-023-01269-1", "pmid": "38111063", "pmcid": "PMC10726641", "type": [], "version": null, "note": null, "metadata": { "title": "De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data", "abstract": "Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA – Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor mutational heterogeneity in a melanoma drug resistance dataset. By enabling high precision detection of expressed somatic mutations, RESA substantially enhances the reliability of mutational analysis in scRNA-seq. RESA is available at https://github.com/ShenLab-Genomics/RESA .", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Zhang T." }, { "name": "Jia H." }, { "name": "Song T." }, { "name": "Lv L." }, { "name": "Gulhan D.C." }, { "name": "Wang H." }, { "name": "Guo W." }, { "name": "Xi R." }, { "name": "Guo H." }, { "name": "Shen N." } ], "journal": "Genome Medicine" } } ], "credit": [ { "name": "Ning Shen", "email": "shenningzju@zju.edu.cn", "url": null, "orcidid": "https://orcid.org/0000-0003-4709-3374", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:38:36.936377Z", "lastUpdate": "2024-04-19T09:38:36.939049Z", "editPermission": { "type": "public", "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": "pyDarwin", "description": "A machine learning enhanced automated nonlinear mixed-effect model selection toolbox.", "homepage": "https://certara.github.io/pyDarwin/html/Options.html", "biotoolsID": "pydarwin", "biotoolsCURIE": "biotools:pydarwin", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2421", "term": "Database search" }, { "uri": "http://edamontology.org/operation_3436", "term": "Aggregation" } ], "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_0769", "term": "Workflows" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://certara.github.io/pyDarwin/html/Install.html", "type": [ "Installation instructions" ], "note": null } ], "publication": [ { "doi": "10.1002/CPT.3114", "pmid": "38037471", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "pyDarwin: A Machine Learning Enhanced Automated Nonlinear Mixed-Effect Model Selection Toolbox", "abstract": "pyDarwin is an open-source Python package for nonlinear mixed-effect model selection. pyDarwin combines machine-learning algorithms and NONMEM to perform a global search for the optimal model in a user-defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor-intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data.", "date": "2024-04-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Li X." }, { "name": "Sale M." }, { "name": "Nieforth K." }, { "name": "Bigos K.L." }, { "name": "Craig J." }, { "name": "Wang F." }, { "name": "Feng K." }, { "name": "Hu M." }, { "name": "Bies R." }, { "name": "Zhao L." } ], "journal": "Clinical Pharmacology and Therapeutics" } } ], "credit": [ { "name": "Robert Bies", "email": "robertbi@buffalo.edu", "url": null, "orcidid": "https://orcid.org/0000-0003-3818-2252", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T08:35:47.492408Z", "lastUpdate": "2024-04-19T08:35:47.494619Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "ATACAmp", "description": "Tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data.", "homepage": "https://github.com/chsmiss/ATAC-amp", "biotoolsID": "atacamp", "biotoolsCURIE": "biotools:atacamp", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3227", "term": "Variant calling" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" }, { "uri": "http://edamontology.org/operation_3228", "term": "Structural variation detection" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3673", "term": "Whole genome sequencing" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" }, { "uri": "http://edamontology.org/topic_0654", "term": "DNA" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3940", "term": "Chromosome conformation capture" } ], "operatingSystem": [], "language": [ "Python" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12864-023-09792-6", "pmid": "37950200", "pmcid": "PMC10638764", "type": [], "version": null, "note": null, "metadata": { "title": "ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data", "abstract": "Background: High oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference. Results: In the present study, an algorithm called “ATACAmp” that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells. Conclusions: ATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp . Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Cheng H." }, { "name": "Ma W." }, { "name": "Wang K." }, { "name": "Chu H." }, { "name": "Bao G." }, { "name": "Liao Y." }, { "name": "Yuan Y." }, { "name": "Gou Y." }, { "name": "Dong L." }, { "name": "Yang J." }, { "name": "Cai H." } ], "journal": "BMC Genomics" } } ], "credit": [ { "name": "Jian Yang", "email": "yangjian89@scu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Haoyang Cai", "email": "haoyang.cai@scu.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T08:35:02.486462Z", "lastUpdate": "2024-04-19T08:35:02.488474Z", "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" } ] }