count: 9266
list:
- accessibility: null
  additionDate: '2024-04-19T10:42:15.957876Z'
  biotoolsCURIE: biotools:pecat
  biotoolsID: pecat
  collectionID: []
  community: null
  confidence_flag: high
  cost: null
  credit:
  - email: xiaochuanle@126.com
    fundrefid: null
    gridid: null
    name: Chuanle Xiao
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: luofeng@clemson.edu
    fundrefid: null
    gridid: null
    name: Feng Luo
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: jxwang@mail.csu.edu.cn
    fundrefid: null
    gridid: null
    name: Jianxin Wang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: De novo diploid genome assembly using long noisy reads.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
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  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Genome assembly
      uri: http://edamontology.org/operation_0525
    - term: De-novo assembly
      uri: http://edamontology.org/operation_0524
    - term: Phasing
      uri: http://edamontology.org/operation_3454
    - term: k-mer counting
      uri: http://edamontology.org/operation_3472
    - term: Haplotype mapping
      uri: http://edamontology.org/operation_0487
    output: []
  homepage: https://github.com/lemene/PECAT
  homepage_status: 0
  language: []
  lastUpdate: '2024-04-19T10:42:15.960256Z'
  license: null
  link: []
  maturity: null
  name: PECAT
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1038/s41467-024-47349-7
    metadata:
      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.
      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.
      citationCount: 0
      date: '2024-12-01T00:00:00Z'
      journal: Nature Communications
      title: De novo diploid genome assembly using long noisy reads
    note: null
    pmcid: PMC10997618
    pmid: '38580638'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Sequence assembly
    uri: http://edamontology.org/topic_0196
  - term: DNA polymorphism
    uri: http://edamontology.org/topic_2885
  - term: Mapping
    uri: http://edamontology.org/topic_0102
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:29:44.116723Z'
  biotoolsCURIE: biotools:chemprop
  biotoolsID: chemprop
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: mcgillc2@vcu.edu
    fundrefid: null
    gridid: null
    name: Charles J. McGill
    note: null
    orcidid: https://orcid.org/0000-0003-2704-7717
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Machine learning package for chemical property prediction.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    - term: Quantification
      uri: http://edamontology.org/operation_3799
    - term: Scaffolding
      uri: http://edamontology.org/operation_3216
    - term: Splitting
      uri: http://edamontology.org/operation_3359
    output: []
  homepage: http://github.com/chemprop/chemprop
  homepage_status: 0
  language:
  - Python
  - Shell
  - D
  lastUpdate: '2024-04-19T10:29:44.118952Z'
  license: MIT
  link:
  - note: null
    type:
    - Repository
    url: http://github.com/chemprop/chemprop_benchmark
  maturity: null
  name: Chemprop
  operatingSystem:
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1021/ACS.JCIM.3C01250
    metadata:
      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.
      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.
      citationCount: 7
      date: '2024-01-08T00:00:00Z'
      journal: Journal of Chemical Information and Modeling
      title: 'Chemprop: A Machine Learning Package for Chemical Property Prediction'
    note: null
    pmcid: PMC10777403
    pmid: '38147829'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: NMR
    uri: http://edamontology.org/topic_0593
  - term: Chemistry
    uri: http://edamontology.org/topic_3314
  - term: Endocrinology and metabolism
    uri: http://edamontology.org/topic_3407
  - term: Molecular biology
    uri: http://edamontology.org/topic_3047
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:17:57.447656Z'
  biotoolsCURIE: biotools:clumppling
  biotoolsID: clumppling
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: xiranliu@stanford.edu
    fundrefid: null
    gridid: null
    name: Xiran Liu
    note: null
    orcidid: https://orcid.org/0000-0002-3955-9026
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: noahr@stanford.edu
    fundrefid: null
    gridid: null
    name: Noah A Rosenberg
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Cluster matching and permutation program with integer linear programming.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Clustering
      uri: http://edamontology.org/operation_3432
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Pairwise sequence alignment
      uri: http://edamontology.org/operation_0491
    output: []
  homepage: https://github.com/PopGenClustering/Clumppling
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T10:17:57.449712Z'
  license: null
  link: []
  maturity: null
  name: Clumppling
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1093/BIOINFORMATICS/BTAD751
    metadata:
      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.'
      authors:
      - name: Liu X.
      - name: Kopelman N.M.
      - name: Rosenberg N.A.
      citationCount: 0
      date: '2024-01-01T00:00:00Z'
      journal: Bioinformatics
      title: 'Clumppling: cluster matching and permutation program with integer linear
        programming'
    note: null
    pmcid: PMC10766593
    pmid: '38096585'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Structure analysis
    uri: http://edamontology.org/topic_0081
  - term: Population genetics
    uri: http://edamontology.org/topic_3056
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T10:14:18.134821Z'
  biotoolsCURIE: biotools:stcellbin
  biotoolsID: stcellbin
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: sbrix@dtu.dk
    fundrefid: null
    gridid: null
    name: Susanne Brix
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: xuxun@genomics.cn
    fundrefid: null
    gridid: null
    name: Xun Xu
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Generating single-cell gene expression profiles for high-resolution
    spatial transcriptomics based on cell boundary images.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Gene expression profiling
      uri: http://edamontology.org/operation_0314
    - term: Expression profile comparison
      uri: http://edamontology.org/operation_0315
    - term: Deposition
      uri: http://edamontology.org/operation_3431
    output: []
  homepage: https://github.com/STOmics/STCellbin
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T10:14:18.136729Z'
  license: MIT
  link: []
  maturity: null
  name: STCellbin
  operatingSystem:
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.46471/gigabyte.110
    metadata:
      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.
      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.
      citationCount: 0
      date: '2024-02-01T00:00:00Z'
      journal: GigaByte
      title: Generating single-cell gene expression profiles for high-resolution spatial
        transcriptomics based on cell boundary images
    note: null
    pmcid: PMC10905256
    pmid: '38434932'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Transcriptomics
    uri: http://edamontology.org/topic_3308
  - term: Cell biology
    uri: http://edamontology.org/topic_2229
  - term: Imaging
    uri: http://edamontology.org/topic_3382
  - term: Genotype and phenotype
    uri: http://edamontology.org/topic_0625
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T09:38:36.936377Z'
  biotoolsCURIE: biotools:resa
  biotoolsID: resa
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: shenningzju@zju.edu.cn
    fundrefid: null
    gridid: null
    name: Ning Shen
    note: null
    orcidid: https://orcid.org/0000-0003-4709-3374
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: De novo identification of expressed cancer somatic mutations from single-cell
    RNA sequencing data.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    - term: de Novo sequencing
      uri: http://edamontology.org/operation_3644
    - term: Variant filtering
      uri: http://edamontology.org/operation_3675
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    - term: Essential dynamics
      uri: http://edamontology.org/operation_3891
    output: []
  homepage: https://github.com/ShenLab-Genomics/RESA
  homepage_status: 0
  language:
  - Perl
  - Python
  - Shell
  lastUpdate: '2024-04-19T09:38:36.939049Z'
  license: MIT
  link: []
  maturity: null
  name: RESA
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/S13073-023-01269-1
    metadata:
      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
        .
      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.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: Genome Medicine
      title: De novo identification of expressed cancer somatic mutations from single-cell
        RNA sequencing data
    note: null
    pmcid: PMC10726641
    pmid: '38111063'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Genetic variation
    uri: http://edamontology.org/topic_0199
  - term: RNA-Seq
    uri: http://edamontology.org/topic_3170
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: RNA
    uri: http://edamontology.org/topic_0099
  - term: Exome sequencing
    uri: http://edamontology.org/topic_3676
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T09:28:34.739649Z'
  biotoolsCURIE: biotools:bracnac
  biotoolsID: bracnac
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: aa_kechin@niboch.nsc.ru
    fundrefid: null
    gridid: null
    name: Andrey Kechin
    note: null
    orcidid: https://orcid.org/0000-0002-4822-0251
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: A BRCA1 and BRCA2 copy number alteration caller from Next-Generation
    sequencing data.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Copy number variation detection
      uri: http://edamontology.org/operation_3961
    - term: Standardisation and normalisation
      uri: http://edamontology.org/operation_3435
    - term: Genotyping
      uri: http://edamontology.org/operation_3196
    output: []
  homepage: https://github.com/aakechin/bracnac/
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T09:28:34.741978Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: BRACNAC
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.3390/IJMS242316630
    metadata:
      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.
      authors:
      - name: Kechin A.
      - name: Boyarskikh U.
      - name: Borobova V.
      - name: Khrapov E.
      - name: Subbotin S.
      - name: Filipenko M.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: International Journal of Molecular Sciences
      title: 'BRACNAC: A BRCA1 and BRCA2 Copy Number Alteration Caller from Next-Generation
        Sequencing Data'
    note: null
    pmcid: PMC10706169
    pmid: '38068953'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Copy number variation
    uri: http://edamontology.org/topic_3958
  - term: Gene transcripts
    uri: http://edamontology.org/topic_3512
  - term: Genomics
    uri: http://edamontology.org/topic_0622
  - term: Probes and primers
    uri: http://edamontology.org/topic_0632
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T08:35:47.492408Z'
  biotoolsCURIE: biotools:pydarwin
  biotoolsID: pydarwin
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: robertbi@buffalo.edu
    fundrefid: null
    gridid: null
    name: Robert Bies
    note: null
    orcidid: https://orcid.org/0000-0003-3818-2252
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: A machine learning enhanced automated nonlinear mixed-effect model
    selection toolbox.
  documentation:
  - note: null
    type:
    - Installation instructions
    url: https://certara.github.io/pyDarwin/html/Install.html
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Aggregation
      uri: http://edamontology.org/operation_3436
    output: []
  homepage: https://certara.github.io/pyDarwin/html/Options.html
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T08:35:47.494619Z'
  license: null
  link: []
  maturity: null
  name: pyDarwin
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1002/CPT.3114
    metadata:
      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.
      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.
      citationCount: 1
      date: '2024-04-01T00:00:00Z'
      journal: Clinical Pharmacology and Therapeutics
      title: 'pyDarwin: A Machine Learning Enhanced Automated Nonlinear Mixed-Effect
        Model Selection Toolbox'
    note: null
    pmcid: null
    pmid: '38037471'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Machine learning
    uri: http://edamontology.org/topic_3474
  - term: Workflows
    uri: http://edamontology.org/topic_0769
  validated: 0
  version: []
- accessibility: Open access
  additionDate: '2024-04-19T08:35:02.486462Z'
  biotoolsCURIE: biotools:atacamp
  biotoolsID: atacamp
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: yangjian89@scu.edu.cn
    fundrefid: null
    gridid: null
    name: Jian Yang
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  - email: haoyang.cai@scu.edu.cn
    fundrefid: null
    gridid: null
    name: Haoyang Cai
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: Variant calling
      uri: http://edamontology.org/operation_3227
    - term: Database search
      uri: http://edamontology.org/operation_2421
    - term: Structural variation detection
      uri: http://edamontology.org/operation_3228
    output: []
  homepage: https://github.com/chsmiss/ATAC-amp
  homepage_status: 0
  language:
  - Python
  lastUpdate: '2024-04-19T08:35:02.488474Z'
  license: MIT
  link: []
  maturity: null
  name: ATACAmp
  operatingSystem: []
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1186/S12864-023-09792-6
    metadata:
      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.'
      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.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: BMC Genomics
      title: 'ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq
        data'
    note: null
    pmcid: PMC10638764
    pmid: '37950200'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Whole genome sequencing
    uri: http://edamontology.org/topic_3673
  - term: Oncology
    uri: http://edamontology.org/topic_2640
  - term: DNA
    uri: http://edamontology.org/topic_0654
  - term: Gene expression
    uri: http://edamontology.org/topic_0203
  - term: Chromosome conformation capture
    uri: http://edamontology.org/topic_3940
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T08:21:06.741187Z'
  biotoolsCURIE: biotools:phosboost
  biotoolsID: phosboost
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: taner.sen@usda.gov
    fundrefid: null
    gridid: null
    name: Taner Z. Sen
    note: null
    orcidid: https://orcid.org/0000-0002-5553-6190
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Improved phosphorylation prediction recall using gradient boosting
    and protein language models.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: public
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: PTM site prediction
      uri: http://edamontology.org/operation_0417
    - term: Molecular dynamics
      uri: http://edamontology.org/operation_2476
    - term: Protein secondary structure prediction
      uri: http://edamontology.org/operation_0267
    - term: Splitting
      uri: http://edamontology.org/operation_3359
    output: []
  homepage: https://github.com/eporetsky/PhosBoost
  homepage_status: 0
  language:
  - Python
  - Shell
  lastUpdate: '2024-04-19T08:21:06.743436Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: PhosBoost
  operatingSystem:
  - Linux
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.1002/PLD3.554
    metadata:
      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.
      authors:
      - name: Poretsky E.
      - name: Andorf C.M.
      - name: Sen T.Z.
      citationCount: 0
      date: '2023-12-01T00:00:00Z'
      journal: Plant Direct
      title: 'PhosBoost: Improved phosphorylation prediction recall using gradient
        boosting and protein language models'
    note: null
    pmcid: PMC10732782
    pmid: '38124705'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Protein modifications
    uri: http://edamontology.org/topic_0601
  - term: Protein interactions
    uri: http://edamontology.org/topic_0128
  - term: Statistics and probability
    uri: http://edamontology.org/topic_2269
  - term: Plant biology
    uri: http://edamontology.org/topic_0780
  validated: 0
  version: []
- accessibility: null
  additionDate: '2024-04-19T08:15:18.576654Z'
  biotoolsCURIE: biotools:itaxalogue
  biotoolsID: itaxalogue
  collectionID: []
  community: null
  confidence_flag: tool
  cost: Free of charge
  credit:
  - email: N.Noll@leibniz-lib.de
    fundrefid: null
    gridid: null
    name: Niklas W. Noll
    note: null
    orcidid: null
    rorid: null
    typeEntity: Person
    typeRole: []
    url: null
  description: Toolkit to create comprehensive CO1 reference databases.
  documentation: []
  download: []
  editPermission:
    authors: []
    type: private
  elixirCommunity: []
  elixirNode: []
  elixirPlatform: []
  elixir_badge: 0
  function:
  - cmd: null
    input: []
    note: null
    operation:
    - term: DNA barcoding
      uri: http://edamontology.org/operation_3200
    - term: Taxonomic classification
      uri: http://edamontology.org/operation_3460
    - term: Filtering
      uri: http://edamontology.org/operation_3695
    - term: Query and retrieval
      uri: http://edamontology.org/operation_0224
    output: []
  homepage: https://github.com/nwnoll/taxalogue
  homepage_status: 0
  language:
  - Ruby
  lastUpdate: '2024-04-19T08:15:18.578562Z'
  license: GPL-3.0
  link: []
  maturity: null
  name: itaxalogue
  operatingSystem:
  - Mac
  - Linux
  - Windows
  otherID: []
  owner: Pub2Tools
  publication:
  - doi: 10.7717/PEERJ.16253
    metadata:
      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.
      authors:
      - name: Noll N.W.
      - name: Scherber C.
      - name: Schaffler L.
      citationCount: 0
      date: '2023-01-01T00:00:00Z'
      journal: PeerJ
      title: 'taxalogue: a toolkit to create comprehensive CO1 reference databases'
    note: null
    pmcid: PMC10702336
    pmid: '38077427'
    type: []
    version: null
  relation: []
  toolType:
  - Command-line tool
  topic:
  - term: Taxonomy
    uri: http://edamontology.org/topic_0637
  - term: DNA
    uri: http://edamontology.org/topic_0654
  - term: Biological databases
    uri: http://edamontology.org/topic_3071
  - term: Sequencing
    uri: http://edamontology.org/topic_3168
  - term: Model organisms
    uri: http://edamontology.org/topic_0621
  validated: 0
  version: []
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