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            "name": "CoCoPyE",
            "description": "Feature-based prediction of genome quality indices",
            "homepage": "https://cocopye.uni-goettingen.de",
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                    "name": "Peter Meinicke",
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            "description": "Web-based interactive analysis platform for single-cell RNA sequencing data.",
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            "biotoolsCURIE": "biotools:shaoxia",
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0314",
                            "term": "Gene expression profiling"
                        },
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                            "uri": "http://edamontology.org/operation_3501",
                            "term": "Enrichment analysis"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_0099",
                    "term": "RNA"
                },
                {
                    "uri": "http://edamontology.org/topic_0769",
                    "term": "Workflows"
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                    "uri": "http://edamontology.org/topic_3316",
                    "term": "Computer science"
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                    "uri": "http://edamontology.org/topic_2229",
                    "term": "Cell biology"
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                "Python",
                "R",
                "Shell"
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                {
                    "url": "https://github.com/WiedenWei/shaoxia",
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                {
                    "doi": "10.1186/s12864-024-10322-1",
                    "pmid": "38658838",
                    "pmcid": "PMC11040744",
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                    "metadata": {
                        "title": "Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data",
                        "abstract": "Background: In recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis. Results: We proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise. Conclusion: Shaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud, and its source code is publicly available at https://github.com/WiedenWei/shaoxia.",
                        "date": "2024-12-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Wei W."
                            },
                            {
                                "name": "Xia X."
                            },
                            {
                                "name": "Li T."
                            },
                            {
                                "name": "Chen Q."
                            },
                            {
                                "name": "Feng X."
                            }
                        ],
                        "journal": "BMC Genomics"
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            "credit": [
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                    "name": "Xiaodong Feng",
                    "email": "xiaodongfeng@scu.edu.cn",
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            "name": "GenoMycAnalyzer",
            "description": "Web-based tool for species and drug resistance prediction for Mycobacterium genomes.",
            "homepage": "http://www.mycochase.org",
            "biotoolsID": "genomycanalyzer",
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                            "uri": "http://edamontology.org/operation_3196",
                            "term": "Genotyping"
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                        {
                            "uri": "http://edamontology.org/operation_3227",
                            "term": "Variant calling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3962",
                            "term": "Deletion detection"
                        }
                    ],
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                    "note": null,
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                "Web application"
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                {
                    "uri": "http://edamontology.org/topic_3673",
                    "term": "Whole genome sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_3305",
                    "term": "Public health and epidemiology"
                },
                {
                    "uri": "http://edamontology.org/topic_3324",
                    "term": "Infectious disease"
                },
                {
                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
                }
            ],
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                "Mac",
                "Linux",
                "Windows"
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                "Python"
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            "link": [
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                    "url": "https://github.com/IRCGP-Lab/GenoMycAnalyzer_Source",
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                        "Repository"
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                    "note": null
                }
            ],
            "download": [],
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            "publication": [
                {
                    "doi": "10.1186/s12864-024-10320-3",
                    "pmid": "38643090",
                    "pmcid": "PMC11031912",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes",
                        "abstract": "Background: Drug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options. Results: We developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8–96.7%) to 79.6% for pyrazinamide (95% CI 76.9–82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1–100.0%) to 74.9% for capreomycin (95% CI 69.3–80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8–99.3%) to 79.5% for ethionamide (95% CI 76.4–82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC. Conclusions: GenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org.",
                        "date": "2024-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Kim D."
                            },
                            {
                                "name": "Shin J.-I."
                            },
                            {
                                "name": "Yoo I.Y."
                            },
                            {
                                "name": "Jo S."
                            },
                            {
                                "name": "Chu J."
                            },
                            {
                                "name": "Cho W.Y."
                            },
                            {
                                "name": "Shin S.-H."
                            },
                            {
                                "name": "Chung Y.-J."
                            },
                            {
                                "name": "Park Y.-J."
                            },
                            {
                                "name": "Jung S.-H."
                            }
                        ],
                        "journal": "BMC Genomics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Seung-Hyun Jung",
                    "email": "hyun@catholic.ac.kr",
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        {
            "name": "rnaCrosslinkOO",
            "description": "Object-oriented R package for the analysis of RNA structural data generated by RNA crosslinking experiments.",
            "homepage": "https://CRAN.R-project.org/package=rnaCrosslinkOO",
            "biotoolsID": "rnacrosslinkoo",
            "biotoolsCURIE": "biotools:rnacrosslinkoo",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2441",
                            "term": "RNA structure prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0502",
                            "term": "RNA secondary structure alignment"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0278",
                            "term": "RNA secondary structure prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0474",
                            "term": "Protein structure prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0303",
                            "term": "Fold recognition"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
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            ],
            "toolType": [
                "Library"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0099",
                    "term": "RNA"
                },
                {
                    "uri": "http://edamontology.org/topic_0082",
                    "term": "Structure prediction"
                },
                {
                    "uri": "http://edamontology.org/topic_0097",
                    "term": "Nucleic acid structure analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0632",
                    "term": "Probes and primers"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                }
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                "R"
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                {
                    "doi": "10.1093/bioinformatics/btae193",
                    "pmid": "38597883",
                    "pmcid": "PMC11060868",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "rnaCrosslinkOO: an object-oriented R package for the analysis of RNA structural data generated by RNA crosslinking experiments",
                        "abstract": "Summary: RNA (ribonucleic acid) molecules have secondary and tertiary structures in vivo which play a crucial role in cellular processes such as the regulation of gene expression, RNA processing and localization. The ability to investigate these structures will enhance our understanding of their function and contribute to the diagnosis and treatment of diseases caused by RNA dysregulation. However, there are no mature pipelines or packages for processing and analyzing complex in vivo RNA structural data. Here, we present rnaCrosslinkOO (RNA Crosslink Object-Oriented), a novel software package for the comprehensive analysis of data derived from the COMRADES (Crosslinking of Matched RNA and Deep Sequencing) method. rnaCrosslinkOO offers a comprehensive pipeline from raw sequencing reads to the identification and comparison of RNA structural features. It includes read processing and alignment, clustering of duplexes, data exploration, folding and comparisons of RNA structures. rnaCrosslinkOO also enables comparisons between conditions, the identification of inter-RNA interactions, and the incorporation of reactivity data to improve structure prediction. Availability and implementation: rnaCrosslinkOO is freely available to noncommercial users and implemented in R, with the source code and documentation accessible at https://CRAN.R-project.org/package=rnaCrosslinkOO. The software is supported on Linux, macOS, and Windows platforms.",
                        "date": "2024-04-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Price J.L."
                            },
                            {
                                "name": "Ziv O."
                            },
                            {
                                "name": "Pinckert M.L."
                            },
                            {
                                "name": "Lim A."
                            },
                            {
                                "name": "Miska E.A."
                            }
                        ],
                        "journal": "Bioinformatics"
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            "credit": [
                {
                    "name": "Jonathan L Price",
                    "email": "jlp76@cam.ac.uk",
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            "name": "metabuli",
            "description": "Metabuli: specific and sensitive metagenomic classification via joint analysis of DNA and amino acid",
            "homepage": "https://metabuli.steineggerlab.com",
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                            "uri": "http://edamontology.org/operation_3460",
                            "term": "Taxonomic classification"
                        }
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2977",
                                "term": "Nucleic acid sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                        }
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                    "note": null,
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            "toolType": [
                "Command-line tool"
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                    "uri": "http://edamontology.org/topic_0637",
                    "term": "Taxonomy"
                }
            ],
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                "Linux",
                "Mac"
            ],
            "language": [
                "C++"
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                {
                    "doi": "10.1038/s41592-024-02273-y",
                    "pmid": null,
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                    "note": null,
                    "metadata": {
                        "title": "Metabuli: sensitive and specific metagenomic classification via joint analysis of amino acid and DNA",
                        "abstract": "Metagenomic taxonomic classifiers analyze either DNA or amino acid (AA) sequences. Metabuli (https://metabuli.steineggerlab.com), however, jointly analyzes both DNA and AA to leverage AA conservation for sensitive homology detection and DNA mutations for specific differentiation of closely related taxa. In the Critical Assessment of Metagenome Interpretation 2 plant-associated dataset, Metabuli covered 99% and 98% of classifications of state-of-the-art DNA- and AA-based classifiers, respectively.",
                        "date": "2024-06-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Kim J."
                            },
                            {
                                "name": "Steinegger M."
                            }
                        ],
                        "journal": "Nature Methods"
                    }
                },
                {
                    "doi": "10.1101/2023.05.31.543018",
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            "owner": "milot-mirdita",
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        {
            "name": "BREW3R.r",
            "description": "This R package provide functions that are used in the BREW3R workflow. This mainly contains a function that extend a gtf as GRanges using information from another gtf (also as GRanges). The process allows to extend gene annotation without increasing the overlap between gene ids.",
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                            "uri": "http://edamontology.org/operation_0362",
                            "term": "Genome annotation"
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                            "data": {
                                "uri": "http://edamontology.org/data_1270",
                                "term": "Feature table"
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                                    "uri": "http://edamontology.org/format_2306",
                                    "term": "GTF"
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                                    "uri": "http://edamontology.org/format_2306",
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                    "note": "The output is an improved gene annotation of the input.",
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                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
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            "credit": [
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                    "name": "Lucille Lopez-Delisle",
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                    "metadata": {
                        "title": "An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar",
                        "abstract": "How viruses evolve within hosts can dictate infection outcomes; however, reconstructing this process is challenging. We evaluate our multiplexed amplicon approach, PrimalSeq, to demonstrate how virus concentration, sequencing coverage, primer mismatches, and replicates influence the accuracy of measuring intrahost virus diversity. We develop an experimental protocol and computational tool, iVar, for using PrimalSeq to measure virus diversity using Illumina and compare the results to Oxford Nanopore sequencing. We demonstrate the utility of PrimalSeq by measuring Zika and West Nile virus diversity from varied sample types and show that the accumulation of genetic diversity is influenced by experimental and biological systems.",
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                        "authors": [
                            {
                                "name": "Grubaugh N.D."
                            },
                            {
                                "name": "Gangavarapu K."
                            },
                            {
                                "name": "Quick J."
                            },
                            {
                                "name": "Matteson N.L."
                            },
                            {
                                "name": "De Jesus J.G."
                            },
                            {
                                "name": "Main B.J."
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                            {
                                "name": "Tan A.L."
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                            {
                                "name": "Paul L.M."
                            },
                            {
                                "name": "Brackney D.E."
                            },
                            {
                                "name": "Grewal S."
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                            {
                                "name": "Gurfield N."
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                            {
                                "name": "Van Rompay K.K.A."
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                            {
                                "name": "Isern S."
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                            {
                                "name": "Michael S.F."
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                            {
                                "name": "Coffey L.L."
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                            {
                                "name": "Loman N.J."
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                            {
                                "name": "Andersen K.G."
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                        ],
                        "journal": "Genome Biology"
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                    "term": "Genetics"
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                    "uri": "http://edamontology.org/topic_3974",
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                        "title": "Detecting co-selection through excess linkage disequilibrium in bacterial genomes",
                        "abstract": "Population genomics has revolutionized our ability to study bacterial evolution by enabling data-driven discovery of the genetic architecture of trait variation. Genome-wide association studies (GWAS) have more recently become accompanied by genome-wide epistasis and co-selection (GWES) analysis, which offers a phenotype-free approach to generating hypotheses about selective processes that simultaneously impact multiple loci across the genome. However, existing GWES methods only consider associations between distant pairs of loci within the genome due to the strong impact of linkage-disequilibrium (LD) over short distances. Based on the general functional organisation of genomes it is nevertheless expected that majority of co-selection and epistasis will act within relatively short genomic proximity, on co-variation occurring within genes and their promoter regions, and within operons. Here, we introduce LDWeaver, which enables an exhaustive GWES across both short- and long-range LD, to disentangle likely neutral co-variation from selection. We demonstrate the ability of LDWeaver to efficiently generate hypotheses about co-selection using large genomic surveys of multiple major human bacterial pathogen species and validate several findings using functional annotation and phenotypic measurements. Our approach will facilitate the study of bacterial evolution in the light of rapidly expanding population genomic data.",
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                                "name": "Mallawaarachchi S."
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                                "name": "Tonkin-Hill G."
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                            {
                                "name": "Pontinen A.K."
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                            {
                                "name": "Calland J.K."
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                            {
                                "name": "Gladstone R.A."
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                            {
                                "name": "Arredondo-Alonso S."
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                            {
                                "name": "MacAlasdair N."
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                            {
                                "name": "Thorpe H.A."
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                            {
                                "name": "Top J."
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                            {
                                "name": "Sheppard S.K."
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                            {
                                "name": "Balding D."
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                            {
                                "name": "Croucher N.J."
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                            {
                                "name": "Corander J."
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                            "term": "Gene regulatory network analysis"
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                            "term": "Differential gene expression profiling"
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                            "uri": "http://edamontology.org/operation_3463",
                            "term": "Expression correlation analysis"
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                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
                },
                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
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                    "uri": "http://edamontology.org/topic_3518",
                    "term": "Microarray experiment"
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                    "term": "RNA-Seq"
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                        "title": "Inference of differential gene regulatory networks using boosted differential trees",
                        "abstract": "Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses.",
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                            {
                                "name": "Galindez G."
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                            {
                                "name": "List M."
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                            {
                                "name": "Baumbach J."
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                            {
                                "name": "Volker U."
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                            {
                                "name": "Mader U."
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                            {
                                "name": "Blumenthal D.B."
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                            {
                                "name": "Kacprowski T."
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                            "uri": "http://edamontology.org/operation_3929",
                            "term": "Metabolic pathway prediction"
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                            "term": "Phylogenetic reconstruction"
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                    "term": "Endocrinology and metabolism"
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                    "uri": "http://edamontology.org/topic_0194",
                    "term": "Phylogenomics"
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                {
                    "uri": "http://edamontology.org/topic_3299",
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                        "title": "Inferring and comparing metabolism across heterogeneous sets of annotated genomes using AuCoMe",
                        "abstract": "Comparative analysis of genome-scale metabolic networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe, a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three data sets, one bacterial, one fungal, and one algal, and showed that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared and divergent metabolic traits among evolutionarily distant algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.",
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                            {
                                "name": "Belcour A."
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                            {
                                "name": "Got J."
                            },
                            {
                                "name": "Aite M."
                            },
                            {
                                "name": "Delage L."
                            },
                            {
                                "name": "Collen J."
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                            {
                                "name": "Frioux C."
                            },
                            {
                                "name": "Leblanc C."
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                            {
                                "name": "Dittami S.M."
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                            {
                                "name": "Blanquart S."
                            },
                            {
                                "name": "Markov G.V."
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                            {
                                "name": "Siegel A."
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