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{ "count": 5836, "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": "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": "HALD", "description": "Human aging and longevity knowledge graph for precision gerontology and geroscience analyses.", "homepage": "https://bis.zju.edu.cn/hald", "biotoolsID": "hald", "biotoolsCURIE": "biotools:hald", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0306", "term": "Text mining" }, { "uri": "http://edamontology.org/operation_3778", "term": "Text annotation" }, { "uri": "http://edamontology.org/operation_0305", "term": "Literature search" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_3399", "term": "Geriatric medicine" }, { "uri": "http://edamontology.org/topic_0218", "term": "Natural language processing" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3360", "term": "Biomarkers" }, { "uri": "http://edamontology.org/topic_0152", "term": "Carbohydrates" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/zexuwu/hald", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1038/S41597-023-02781-0", "pmid": "38040715", "pmcid": "PMC10692171", "type": [], "version": null, "note": null, "metadata": { "title": "HALD, a human aging and longevity knowledge graph for precision gerontology and geroscience analyses", "abstract": "Human aging is a natural and inevitable biological process that leads to an increased risk of aging-related diseases. Developing anti-aging therapies for aging-related diseases requires a comprehensive understanding of the mechanisms and effects of aging and longevity from a multi-modal and multi-faceted perspective. However, most of the relevant knowledge is scattered in the biomedical literature, the volume of which reached 36 million in PubMed. Here, we presented HALD, a text mining-based human aging and longevity dataset of the biomedical knowledge graph from all published literature related to human aging and longevity in PubMed. HALD integrated multiple state-of-the-art natural language processing (NLP) techniques to improve the accuracy and coverage of the knowledge graph for precision gerontology and geroscience analyses. Up to September 2023, HALD had contained 12,227 entities in 10 types (gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease), 115,522 relations, 1,855 aging biomarkers, and 525 longevity biomarkers from 339,918 biomedical articles in PubMed. HALD is available at https://bis.zju.edu.cn/hald .", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Wu Z." }, { "name": "Feng C." }, { "name": "Hu Y." }, { "name": "Zhou Y." }, { "name": "Li S." }, { "name": "Zhang S." }, { "name": "Hu Y." }, { "name": "Chen Y." }, { "name": "Chao H." }, { "name": "Ni Q." }, { "name": "Chen M." } ], "journal": "Scientific Data" } } ], "credit": [ { "name": "Ming Chen", "email": "mchen@zju.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T10:07:48.352340Z", "lastUpdate": "2024-04-19T10:07:48.354939Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "DeepHLAPred", "description": "Deep learning-based method for non-classical HLA binder prediction.", "homepage": "http://www.biolscience.cn/DeepHLApred/", "biotoolsID": "deephlapred", "biotoolsCURIE": "biotools:deephlapred", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0252", "term": "Peptide immunogenicity prediction" }, { "uri": "http://edamontology.org/operation_0440", "term": "Promoter prediction" }, { "uri": "http://edamontology.org/operation_3435", "term": "Standardisation and normalisation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_2830", "term": "Immunoproteins and antigens" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/tangxingyu0/DeepHLApred", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12864-023-09796-2", "pmid": "37993812", "pmcid": "PMC10666343", "type": [], "version": null, "note": null, "metadata": { "title": "DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction", "abstract": "Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learning-based tool called DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, integer numerical mapping and accumulated amino acid frequency as initial representation of non-classical HLA binder sequence. The deep learning module was used to further refine high-level representations. The deep learning module comprised two parallel convolutional neural networks, each followed by maximum pooling layer, dropout layer, and bi-directional long short-term memory network. The experimental results showed that the DeepHLAPred reached the state-of-the-art performanceson the cross-validation test and the independent test. The extensive test demonstrated the rationality of the DeepHLAPred. We further analyzed sequence pattern of non-classical HLA class I binders by information entropy. The information entropy of non-classical HLA binder sequence implied sequence pattern to a certain extent. In addition, we have developed a user-friendly webserver for convenient use, which is available at http://www.biolscience.cn/DeepHLApred/ . The tool and the analysis is helpful to detect non-classical HLA Class I binder. The source code and data is available at https://github.com/tangxingyu0/DeepHLApred .", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Huang G." }, { "name": "Tang X." }, { "name": "Zheng P." } ], "journal": "BMC Genomics" } } ], "credit": [ { "name": "Guohua Huang", "email": "guohuahhn@163.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Xingyu Tang", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Peijie Zheng", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T10:04:52.539998Z", "lastUpdate": "2024-04-19T10:04:52.542062Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MarFERReT", "description": "Open-source, version-controlled reference library of marine microbial eukaryote functional genes.", "homepage": "https://github.com/armbrustlab/marferret", "biotoolsID": "marferret", "biotoolsCURIE": "biotools:marferret", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" }, { "uri": "http://edamontology.org/operation_3672", "term": "Gene functional annotation" }, { "uri": "http://edamontology.org/operation_0362", "term": "Genome annotation" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_3941", "term": "Metatranscriptomics" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0637", "term": "Taxonomy" }, { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "Shell", "R" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1038/S41597-023-02842-4", "pmid": "38129449", "pmcid": "PMC10739892", "type": [], "version": null, "note": null, "metadata": { "title": "MarFERReT, an open-source, version-controlled reference library of marine microbial eukaryote functional genes", "abstract": "Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of about half of eukaryotic environmental transcripts. Here, we introduce Marine Functional EukaRyotic Reference Taxa (MarFERReT), a marine microbial eukaryotic sequence library designed for use with taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 publicly accessible marine eukaryote genomes and transcriptomes and assessed their sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in MarFERReT. Version 1.1 of MarFERReT contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. The MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Groussman R.D." }, { "name": "Blaskowski S." }, { "name": "Coesel S.N." }, { "name": "Armbrust E.V." } ], "journal": "Scientific Data" } } ], "credit": [ { "name": "R. D. Groussman", "email": "rgrous83@uw.edu", "url": null, "orcidid": "https://orcid.org/0000-0001-7874-7217", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "E. V. Armbrust", "email": "armbrust@uw.edu", "url": null, "orcidid": "https://orcid.org/0000-0001-7865-5101", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:47:27.591034Z", "lastUpdate": "2024-04-19T09:47:27.593586Z", "editPermission": { "type": "private", "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": "PEPMatch", "description": "A tool to identify short peptide sequence matches in large sets of proteins.", "homepage": "https://nextgen-tools.iedb.org", "biotoolsID": "pepmatch", "biotoolsCURIE": "biotools:pepmatch", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0252", "term": "Peptide immunogenicity prediction" }, { "uri": "http://edamontology.org/operation_0416", "term": "Epitope mapping" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0804", "term": "Immunology" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/IEDB/PEPMatch", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/IEDB/PEPMatch/tree/master/benchmarking", "type": "Source code", "note": null, "version": null } ], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05606-4", "pmid": "38110863", "pmcid": "PMC10726511", "type": [], "version": null, "note": null, "metadata": { "title": "PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins", "abstract": "Background: Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. Results: We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch’s code and benchmark datasets are publicly available. Conclusions: PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .", "date": "2023-12-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Marrama D." }, { "name": "Chronister W.D." }, { "name": "Westernberg L." }, { "name": "Vita R." }, { "name": "Kosaloglu-Yalcin Z." }, { "name": "Sette A." }, { "name": "Nielsen M." }, { "name": "Greenbaum J.A." }, { "name": "Peters B." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Bjoern Peters", "email": "bpeters@lji.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T09:11:25.967125Z", "lastUpdate": "2024-04-19T09:11:25.970036Z", "editPermission": { "type": "private", "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": "SingleScan", "description": "A comprehensive resource for single-cell sequencing data processing and mining.", "homepage": "http://cailab.labshare.cn/SingleScan", "biotoolsID": "singlescan", "biotoolsCURIE": "biotools:singlescan", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" }, { "uri": "http://edamontology.org/operation_0314", "term": "Gene expression profiling" }, { "uri": "http://edamontology.org/operation_3935", "term": "Dimensionality reduction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_3068", "term": "Literature and language" }, { "uri": "http://edamontology.org/topic_3169", "term": "ChIP-seq" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/victorwang123/SingleScan", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05590-9", "pmid": "38062357", "pmcid": "PMC10704760", "type": [], "version": null, "note": null, "metadata": { "title": "SingleScan: a comprehensive resource for single-cell sequencing data processing and mining", "abstract": "Single-cell sequencing has shed light on previously inaccessible biological questions from different fields of research, including organism development, immune function, and disease progression. The number of single-cell-based studies increased dramatically over the past decade. Several new methods and tools have been continuously developed, making it extremely tricky to navigate this research landscape and develop an up-to-date workflow to analyze single-cell sequencing data, particularly for researchers seeking to enter this field without computational experience. Moreover, choosing appropriate tools and optimal parameters to meet the demands of researchers represents a major challenge in processing single-cell sequencing data. However, a specific resource for easy access to detailed information on single-cell sequencing methods and data processing pipelines is still lacking. In the present study, an online resource called SingleScan was developed to curate all up-to-date single-cell transcriptome/genome analyzing tools and pipelines. All the available tools were categorized according to their main tasks, and several typical workflows for single-cell data analysis were summarized. In addition, spatial transcriptomics, which is a breakthrough molecular analysis method that enables researchers to measure all gene activity in tissue samples and map the site of activity, was included along with a portion of single-cell and spatial analysis solutions. For each processing step, the available tools and specific parameters used in published articles are provided and how these parameters affect the results is shown in the resource. All information used in the resource was manually extracted from related literature. An interactive website was designed for data retrieval, visualization, and download. By analyzing the included tools and literature, users can gain insights into the trends of single-cell studies and easily grasp the specific usage of a specific tool. SingleScan will facilitate the analysis of single-cell sequencing data and promote the development of new tools to meet the growing and diverse needs of the research community. The SingleScan database is publicly accessible via the website at http://cailab.labshare.cn/SingleScan .", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Wang K." }, { "name": "Zhang X." }, { "name": "Cheng H." }, { "name": "Ma W." }, { "name": "Bao G." }, { "name": "Dong L." }, { "name": "Gou Y." }, { "name": "Yang J." }, { "name": "Cai H." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Jian Yang", "email": "stardustcx@163.com", "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": "2024-04-19T08:16:51.295534Z", "lastUpdate": "2024-04-19T08:16:51.298059Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PAPerFly", "description": "Partial assembly-based peak finder for ab initio binding site reconstruction.", "homepage": "https://doi.org/10.5281/zenodo.7116424", "biotoolsID": "paperfly", "biotoolsCURIE": "biotools:paperfly", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3222", "term": "Peak calling" }, { "uri": "http://edamontology.org/operation_0524", "term": "De-novo assembly" }, { "uri": "http://edamontology.org/operation_3472", "term": "k-mer counting" }, { "uri": "http://edamontology.org/operation_0525", "term": "Genome assembly" }, { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3169", "term": "ChIP-seq" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" }, { "uri": "http://edamontology.org/topic_0749", "term": "Transcription factors and regulatory sites" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "C#" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/Caeph/paperfly/", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05613-5", "pmid": "38114921", "pmcid": "PMC10731698", "type": [], "version": null, "note": null, "metadata": { "title": "PAPerFly: Partial Assembly-based Peak Finder for ab initio binding site reconstruction", "abstract": "Background: The specific recognition of a DNA locus by a given transcription factor is a widely studied issue. It is generally agreed that the recognition can be influenced not only by the binding motif but by the larger context of the binding site. In this work, we present a novel heuristic algorithm that can reconstruct the unique binding sites captured in a sequencing experiment without using the reference genome. Results: We present PAPerFly, the Partial Assembly-based Peak Finder, a tool for the binding site and binding context reconstruction from the sequencing data without any prior knowledge. This tool operates without the need to know the reference genome of the respective organism. We employ algorithmic approaches that are used during genome assembly. The proposed algorithm constructs a de Bruijn graph from the sequencing data. Based on this graph, sequences and their enrichment are reconstructed using a novel heuristic algorithm. The reconstructed sequences are aligned and the peaks in the sequence enrichment are identified. Our approach was tested by processing several ChIP-seq experiments available in the ENCODE database and comparing the results of Paperfly and standard methods. Conclusions: We show that PAPerFly, an algorithm tailored for experiment analysis without the reference genome, yields better results than an aggregation of ChIP-seq agnostic tools. Our tool is freely available at https://github.com/Caeph/paperfly/ or on Zenodo (https://doi.org/10.5281/zenodo.7116424).", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Faltejskova K." }, { "name": "Vondrasek J." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Kateřina Faltejsková", "email": "katerina.faltejskova@uochb.cas.cz", "url": null, "orcidid": "https://orcid.org/0000-0001-7390-8077", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Jiří Vondrášek", "email": "jiri.vondrasek@uochb.cas.cz", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-04-19T08:10:49.324635Z", "lastUpdate": "2024-04-19T08:10:49.326869Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }