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        {
            "name": "CDS-DB",
            "description": "Database for patient-derived gene expression signatures induced by cancer treatment.",
            "homepage": "http://cdsdb.ncpsb.org.cn/",
            "biotoolsID": "cds-db",
            "biotoolsCURIE": "biotools:cds-db",
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2436",
                            "term": "Gene-set enrichment analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3223",
                            "term": "Differential gene expression profiling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3463",
                            "term": "Expression correlation analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Database portal"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2640",
                    "term": "Oncology"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_3336",
                    "term": "Drug discovery"
                },
                {
                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
                }
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            "publication": [
                {
                    "doi": "10.1093/NAR/GKAD888",
                    "pmid": "37889038",
                    "pmcid": "PMC10767794",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment",
                        "abstract": "Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.",
                        "date": "2024-01-05T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Liu Z."
                            },
                            {
                                "name": "Chen R."
                            },
                            {
                                "name": "Yang L."
                            },
                            {
                                "name": "Jiang J."
                            },
                            {
                                "name": "Ma S."
                            },
                            {
                                "name": "Chen L."
                            },
                            {
                                "name": "He M."
                            },
                            {
                                "name": "Mao Y."
                            },
                            {
                                "name": "Guo C."
                            },
                            {
                                "name": "Kong X."
                            },
                            {
                                "name": "Zhang X."
                            },
                            {
                                "name": "Qi Y."
                            },
                            {
                                "name": "Liu F."
                            },
                            {
                                "name": "He F."
                            },
                            {
                                "name": "Li D."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Zhongyang Liu",
                    "email": "liuzy1984@163.com",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-3371-2392",
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                },
                {
                    "name": "Fuchu He",
                    "email": "hefc@nic.bmi.ac.cn",
                    "url": null,
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                },
                {
                    "name": "Dong Li",
                    "email": "lidong.bprc@foxmail.com",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-8680-0468",
                    "gridid": null,
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            "owner": "Pub2Tools",
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        {
            "name": "FLARE",
            "description": "FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I.",
            "homepage": "https://github.com/YeoLab/FLARE",
            "biotoolsID": "flare_rna",
            "biotoolsCURIE": "biotools:flare_rna",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3096",
                            "term": "Editing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3432",
                            "term": "Clustering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3902",
                            "term": "RNA binding site prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Workflow"
            ],
            "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"
                },
                {
                    "uri": "http://edamontology.org/topic_3534",
                    "term": "Protein binding sites"
                },
                {
                    "uri": "http://edamontology.org/topic_3794",
                    "term": "RNA immunoprecipitation"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Python"
            ],
            "license": null,
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            "cost": "Free of charge",
            "accessibility": "Open access",
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            "publication": [
                {
                    "doi": "10.1186/S12859-023-05452-4",
                    "pmid": "37784060",
                    "pmcid": "PMC10544219",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "FLARE: a fast and flexible workflow for identifying RNA editing foci",
                        "abstract": "Background: Fusion of RNA-binding proteins (RBPs) to RNA base-editing enzymes (such as APOBEC1 or ADAR) has emerged as a powerful tool for the discovery of RBP binding sites. However, current methods that analyze sequencing data from RNA-base editing experiments are vulnerable to false positives due to off-target editing, genetic variation and sequencing errors. Results: We present FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I. We applied FLARE to C-to-U editing data from a RBFOX2-APOBEC1 STAMP experiment, to show that our approach attains high specificity for detecting RBFOX2 binding sites. We also applied FLARE to detect regions of exogenously introduced as well as endogenous A-to-I editing. Conclusions: FLARE is a fast and flexible workflow that identifies significantly edited regions from RNA-seq data. The FLARE codebase is available at https://github.com/YeoLab/FLARE .",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Kofman E."
                            },
                            {
                                "name": "Yee B."
                            },
                            {
                                "name": "Medina-Munoz H.C."
                            },
                            {
                                "name": "Yeo G.W."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Gene W. Yeo",
                    "email": "geneyeo@ucsd.edu",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0799-6037",
                    "gridid": null,
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                    "typeRole": [],
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                },
                {
                    "name": "Eric Kofman",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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                    "fundrefid": null,
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                }
            ],
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            "owner": "Pub2Tools",
            "additionDate": "2024-03-28T17:04:09.437629Z",
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        },
        {
            "name": "OPUS-Fold3",
            "description": "Gradient-based protein all-atom folding and docking framework on TensorFlow.",
            "homepage": "http://github.com/OPUS-MaLab/opus_fold3",
            "biotoolsID": "opus-fold3",
            "biotoolsCURIE": "biotools:opus-fold3",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3899",
                            "term": "Protein-protein docking"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0480",
                            "term": "Side chain modelling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0479",
                            "term": "Backbone modelling"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Workflow"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2275",
                    "term": "Molecular modelling"
                },
                {
                    "uri": "http://edamontology.org/topic_0130",
                    "term": "Protein folding, stability and design"
                },
                {
                    "uri": "http://edamontology.org/topic_0736",
                    "term": "Protein folds and structural domains"
                },
                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Python"
            ],
            "license": null,
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/BIB/BBAD365",
                    "pmid": "37833840",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow",
                        "abstract": "For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.",
                        "date": "2023-11-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Xu G."
                            },
                            {
                                "name": "Luo Z."
                            },
                            {
                                "name": "Zhou R."
                            },
                            {
                                "name": "Wang Q."
                            },
                            {
                                "name": "Ma J."
                            }
                        ],
                        "journal": "Briefings in Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Gang Xu",
                    "email": null,
                    "url": null,
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                },
                {
                    "name": "Jianpeng Ma",
                    "email": "jpma@fudan.edu.cn",
                    "url": null,
                    "orcidid": null,
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            "owner": "Pub2Tools",
            "additionDate": "2024-03-28T16:59:10.763046Z",
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        {
            "name": "KSFinder",
            "description": "Knowledge graph model for link prediction of novel phosphorylated substrates of kinases.\n\nKSFinder changes based on major revision comments from PeerJ.",
            "homepage": "https://github.com/manju-anandakrishnan/ksfinder/",
            "biotoolsID": "ksfinder",
            "biotoolsCURIE": "biotools:ksfinder",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0417",
                            "term": "PTM site prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0306",
                            "term": "Text mining"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Subcellular localisation prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_0218",
                    "term": "Natural language processing"
                },
                {
                    "uri": "http://edamontology.org/topic_0601",
                    "term": "Protein modifications"
                },
                {
                    "uri": "http://edamontology.org/topic_3295",
                    "term": "Epigenetics"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "Python"
            ],
            "license": null,
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            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
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            "documentation": [],
            "publication": [
                {
                    "doi": "10.7717/PEERJ.16164",
                    "pmid": "37818330",
                    "pmcid": "PMC10561642",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "KSFinder-a knowledge graph model for link prediction of novel phosphorylated substrates of kinases",
                        "abstract": "Background. Aberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder. Methods. KSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low- dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder's generalization capability on four different datasets and compare its performance with other state- of-the-art prediction models. We employ KSFinder to predict substrates of 68 \"dark\"kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates. Results. KSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥ 0:7 (nine at > 0:9, six at 0.8-0.9, and two at 0.7-0.8). The evaluation of 93,593 negative predictions (probability ≤0:3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis. Conclusions. KSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Anandakrishnan M."
                            },
                            {
                                "name": "Ross K.E."
                            },
                            {
                                "name": "Chen C."
                            },
                            {
                                "name": "Shanker V."
                            },
                            {
                                "name": "Cowart J."
                            },
                            {
                                "name": "Wu C.H."
                            }
                        ],
                        "journal": "PeerJ"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Manju Anandakrishnan",
                    "email": "manjua@udel.edu",
                    "url": null,
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                    "gridid": null,
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                },
                {
                    "name": "Cathy H. Wu",
                    "email": null,
                    "url": null,
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                }
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            "owner": "Pub2Tools",
            "additionDate": "2024-03-28T16:51:14.572282Z",
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        },
        {
            "name": "iDRPro-SC",
            "description": "Identifying DNA-binding proteins and RNA-binding proteins based on subfunction classifiers.",
            "homepage": "http://bliulab.net/iDRPro-SC",
            "biotoolsID": "idrpro-sc",
            "biotoolsCURIE": "biotools:idrpro-sc",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3901",
                            "term": "RNA-binding protein prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3900",
                            "term": "DNA-binding protein prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0420",
                            "term": "Nucleic acids-binding site prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
                },
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_3125",
                    "term": "DNA binding sites"
                },
                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
                }
            ],
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                "Mac",
                "Linux",
                "Windows"
            ],
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            "license": null,
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            "cost": "Free of charge",
            "accessibility": "Open access",
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            "publication": [
                {
                    "doi": "10.1093/BIB/BBAD251",
                    "pmid": "37405873",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "iDRPro-SC: identifying DNA-binding proteins and RNA-binding proteins based on subfunction classifiers",
                        "abstract": "Nucleic acid-binding proteins are proteins that interact with DNA and RNA to regulate gene expression and transcriptional control. The pathogenesis of many human diseases is related to abnormal gene expression. Therefore, recognizing nucleic acid-binding proteins accurately and efficiently has important implications for disease research. To address this question, some scientists have proposed the method of using sequence information to identify nucleic acid-binding proteins. However, different types of nucleic acid-binding proteins have different subfunctions, and these methods ignore their internal differences, so the performance of the predictor can be further improved. In this study, we proposed a new method, called iDRPro-SC, to predict the type of nucleic acid-binding proteins based on the sequence information. iDRPro-SC considers the internal differences of nucleic acid-binding proteins and combines their subfunctions to build a complete dataset. Additionally, we used an ensemble learning to characterize and predict nucleic acid-binding proteins. The results of the test dataset showed that iDRPro-SC achieved the best prediction performance and was superior to the other existing nucleic acid-binding protein prediction methods. We have established a web server that can be accessed online: http://bliulab.net/iDRPro-SC.",
                        "date": "2023-07-01T00:00:00Z",
                        "citationCount": 2,
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                                "name": "Feng J."
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                                "name": "Huang J."
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                                "name": "Wu H."
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                        "title": "BacSeq: A User-Friendly Automated Pipeline for Whole-Genome Sequence Analysis of Bacterial Genomes",
                        "abstract": "Whole-genome sequencing (WGS) of bacterial pathogens is widely conducted in microbiological, medical, and clinical research to explore genetic insights that could impact clinical treatment and molecular epidemiology. However, analyzing WGS data of bacteria can pose challenges for microbiologists, clinicians, and researchers, as it requires the application of several bioinformatics pipelines to extract genetic information from raw data. In this paper, we present BacSeq, an automated bioinformatic pipeline for the analysis of next-generation sequencing data of bacterial genomes. BacSeq enables the assembly, annotation, and identification of crucial genes responsible for multidrug resistance, virulence factors, and plasmids. Additionally, the pipeline integrates comparative analysis among isolates, offering phylogenetic tree analysis and identification of single-nucleotide polymorphisms (SNPs). To facilitate easy analysis in a single step and support the processing of multiple isolates, BacSeq provides a graphical user interface (GUI) based on the JAVA platform. It is designed to cater to users without extensive bioinformatics skills.",
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                            {
                                "name": "Surachat K."
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                        "journal": "Microorganisms"
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                    "metadata": {
                        "title": "AGHmatrix: Genetic relationship matrices in R",
                        "abstract": "Motivation: The resemble between relatives computed from pedigree and genomic data is an important resource for geneticists and ecologists, who are interested in understanding how genes influence phenotypic variation, fitness adaptation, and population dynamics. Results: The AGHmatrix software is an R package focused on the construction of pedigree (A matrix) and/or molecular markers (G matrix), with the possibility of building a combined matrix of pedigree corrected by molecular markers (H matrix). Designed to estimate the relationships for any ploidy level, the software also includes auxiliary functions related to filtering molecular markers, and checks pedigree errors in large data sets. After computing the relationship matrices, results from the AGHmatrix can be used in different contexts, including on prediction of (genomic) estimated breeding values and genome-wide association studies.",
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                    "metadata": {
                        "title": "FASTdRNA: A workflow for the analysis of ONT direct RNA sequencing",
                        "abstract": "Motivation: Direct RNA-seq (dRNA-seq) using Oxford Nanopore Technology (ONT) has revolutionized transcript mapping by offering enhanced precision due to its long-read length. Unlike traditional techniques, dRNA-seq eliminates the need for PCR amplification, reducing the impact of GC bias, and preserving valuable base physical information, such as RNA modification and poly(A) length estimation. However, the rapid advancement of ONT devices has set higher standards for analytical software, resulting in potential challenges of software incompatibility and reduced efficiency. Results: We present a novel workflow, called FASTdRNA, to manipulate dRNA-seq data efficiently. This workflow comprises two modules: A data preprocessing module and a data analysis module. The preprocessing data module, dRNAmain, encompasses basecalling, mapping, and transcript counting, which are essential for subsequent analyses. The data analysis module consists of a range of downstream analyses that facilitate the estimation of poly(A) length, prediction of RNA modifications, and assessment of alternative splicing events across different conditions with duplication. The FASTdRNA workflow is designed for the Snakemake framework and can be efficiently executed locally or in the cloud. Comparative experiments have demonstrated its superior performance compared to previous methods. This innovative workflow enhances the research capabilities of dRNA-seq data analysis pipelines by optimizing existing processes and expanding the scope of analysis.",
                        "date": "2023-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Chen X."
                            },
                            {
                                "name": "Liu Y."
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                            {
                                "name": "Lv K."
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                            {
                                "name": "Wang M."
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                            {
                                "name": "Liu X."
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                            {
                                "name": "Li B."
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                        "journal": "Bioinformatics Advances"
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                    "name": "Bosheng Li",
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        {
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                            "uri": "http://edamontology.org/operation_3899",
                            "term": "Protein-protein docking"
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                            "uri": "http://edamontology.org/operation_0474",
                            "term": "Protein structure prediction"
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                            "uri": "http://edamontology.org/operation_0570",
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                    "term": "Molecular modelling"
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                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
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                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
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                    "metadata": {
                        "title": "Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server",
                        "abstract": "Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https://lzerd.kiharalab.org. This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.",
                        "date": "2023-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Harini K."
                            },
                            {
                                "name": "Christoffer C."
                            },
                            {
                                "name": "Gromiha M.M."
                            },
                            {
                                "name": "Kihara D."
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                        "journal": "Methods in Molecular Biology"
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            "name": "ICAnnoLncRNA",
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                            "uri": "http://edamontology.org/operation_0314",
                            "term": "Gene expression profiling"
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                            "uri": "http://edamontology.org/operation_3695",
                            "term": "Filtering"
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                            "uri": "http://edamontology.org/operation_0524",
                            "term": "De-novo assembly"
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                    "term": "Functional, regulatory and non-coding RNA"
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                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
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                {
                    "doi": "10.3390/GENES14071331",
                    "pmid": "37510236",
                    "pmcid": "PMC10379598",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "ICAnnoLncRNA: A Snakemake Pipeline for a Long Non-Coding-RNA Search and Annotation in Transcriptomic Sequences",
                        "abstract": "Long non-coding RNAs (lncRNAs) are RNA molecules longer than 200 nucleotides that do not encode proteins. Experimental studies have shown the diversity and importance of lncRNA functions in plants. To expand knowledge about lncRNAs in other species, computational pipelines that allow for standardised data-processing steps in a mode that does not require user control up until the final result were actively developed recently. These advancements enable wider functionality for lncRNA data identification and analysis. In the present work, we propose the ICAnnoLncRNA pipeline for the automatic identification, classification and annotation of plant lncRNAs in assembled transcriptomic sequences. It uses the LncFinder software for the identification of lncRNAs and allows the adjustment of recognition parameters using genomic data for which lncRNA annotation is available. The pipeline allows the prediction of lncRNA candidates, alignment of lncRNA sequences to the reference genome, filtering of erroneous/noise transcripts and probable transposable elements, lncRNA classification by genome location, comparison with sequences from external databases and analysis of lncRNA structural features and expression. We used transcriptomic sequences from 15 maize libraries assembled by Trinity and Hisat2/StringTie to demonstrate the application of the ICAnnoLncRNA pipeline.",
                        "date": "2023-07-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Pronozin A.Y."
                            },
                            {
                                "name": "Afonnikov D.A."
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                        "journal": "Genes"
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                },
                {
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