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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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            "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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            "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,
                        "authors": [
                            {
                                "name": "Yan K."
                            },
                            {
                                "name": "Feng J."
                            },
                            {
                                "name": "Huang J."
                            },
                            {
                                "name": "Wu H."
                            }
                        ],
                        "journal": "Briefings in Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Ke Yan",
                    "email": "kyan@bliulab.net",
                    "url": null,
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                {
                    "name": "Hao Wu",
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        },
        {
            "name": "BacSeq",
            "description": "Automated pipeline for whole-genome sequence analysis of bacterial genomes.",
            "homepage": "https://github.com/mecobpsu/bacseq",
            "biotoolsID": "bacseq",
            "biotoolsCURIE": "biotools:bacseq",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0310",
                            "term": "Sequence assembly"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3192",
                            "term": "Sequence trimming"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3840",
                            "term": "Multilocus sequence typing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0324",
                            "term": "Phylogenetic analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Workflow"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3673",
                    "term": "Whole genome sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_0196",
                    "term": "Sequence assembly"
                },
                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_0769",
                    "term": "Workflows"
                },
                {
                    "uri": "http://edamontology.org/topic_3301",
                    "term": "Microbiology"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Java",
                "Shell"
            ],
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            "publication": [
                {
                    "doi": "10.3390/MICROORGANISMS11071769",
                    "pmid": "37512941",
                    "pmcid": "PMC10385524",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "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.",
                        "date": "2023-07-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Chukamnerd A."
                            },
                            {
                                "name": "Jeenkeawpiam K."
                            },
                            {
                                "name": "Chusri S."
                            },
                            {
                                "name": "Pomwised R."
                            },
                            {
                                "name": "Singkhamanan K."
                            },
                            {
                                "name": "Surachat K."
                            }
                        ],
                        "journal": "Microorganisms"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Kamonnut Singkhamanan",
                    "email": "skamonnu@medicine.psu.ac.th",
                    "url": null,
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                },
                {
                    "name": "Komwit Surachat",
                    "email": "komwit.s@psu.ac.th",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-7793-7561",
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        },
        {
            "name": "AGHmatrix",
            "description": "AGHmatrix software is an R-package to build relationship matrices using pedigree (A matrix) and/or molecular markers (G matrix) with the possibility to build a combined matrix of Pedigree corrected by Molecular (H matrix). The package works with diploid and autopolyploid data.",
            "homepage": "https://cran.r-project.org/web/packages/AGHmatrix/index.html",
            "biotoolsID": "aghmatrix",
            "biotoolsCURIE": "biotools:aghmatrix",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3196",
                            "term": "Genotyping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3891",
                            "term": "Essential dynamics"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Library"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3974",
                    "term": "Epistasis"
                },
                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
                },
                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_3517",
                    "term": "GWAS study"
                }
            ],
            "operatingSystem": [],
            "language": [
                "R"
            ],
            "license": "GPL-3.0",
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
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            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/rramadeu/AGHmatrix",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/BIOINFORMATICS/BTAD445",
                    "pmid": "37471595",
                    "pmcid": "PMC10371492",
                    "type": [],
                    "version": null,
                    "note": null,
                    "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.",
                        "date": "2023-07-01T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Amadeu R.R."
                            },
                            {
                                "name": "Garcia A.A.F."
                            },
                            {
                                "name": "Munoz P.R."
                            },
                            {
                                "name": "Ferrao L.F.V."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Luís Felipe V Ferrão",
                    "email": "lferrao@ufl.edu",
                    "url": null,
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                },
                {
                    "name": "Rodrigo R Amadeu",
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            "owner": "Pub2Tools",
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        },
        {
            "name": "FASTdRNA",
            "description": "FastdRNA: a workflow for analysis of ONT direct RNA seq dataset.",
            "homepage": "https://github.com/Tomcxf/FASTdRNA",
            "biotoolsID": "fastdrna",
            "biotoolsCURIE": "biotools:fastdrna",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3185",
                            "term": "Base-calling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0264",
                            "term": "Alternative splicing prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0428",
                            "term": "PolyA signal detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3431",
                            "term": "Deposition"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Workflow"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_0769",
                    "term": "Workflows"
                },
                {
                    "uri": "http://edamontology.org/topic_0099",
                    "term": "RNA"
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                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
                }
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            "operatingSystem": [],
            "language": [
                "Python",
                "R"
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            "license": "MIT",
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            "publication": [
                {
                    "doi": "10.1093/BIOADV/VBAD099",
                    "pmid": "37521311",
                    "pmcid": "PMC10375421",
                    "type": [],
                    "version": null,
                    "note": null,
                    "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",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Chen X."
                            },
                            {
                                "name": "Liu Y."
                            },
                            {
                                "name": "Lv K."
                            },
                            {
                                "name": "Wang M."
                            },
                            {
                                "name": "Liu X."
                            },
                            {
                                "name": "Li B."
                            }
                        ],
                        "journal": "Bioinformatics Advances"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Bosheng Li",
                    "email": "bosheng.li@pku-iaas.edu.cn",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-1816-7007",
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                },
                {
                    "name": "Xiaofeng Chen",
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            "owner": "Pub2Tools",
            "additionDate": "2024-03-27T21:24:55.776589Z",
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        },
        {
            "name": "LZerD",
            "description": "Web server for protein-protein docking prediction using the LZerD algorithm.",
            "homepage": "https://lzerd.kiharalab.org",
            "biotoolsID": "lzerd_web",
            "biotoolsCURIE": "biotools:lzerd_web",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3899",
                            "term": "Protein-protein docking"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0474",
                            "term": "Protein structure prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0570",
                            "term": "Structure visualisation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2275",
                    "term": "Molecular modelling"
                },
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                }
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            "publication": [
                {
                    "doi": "10.1007/978-1-0716-3327-4_28",
                    "pmid": "37450159",
                    "pmcid": "PMC10561630",
                    "type": [],
                    "version": null,
                    "note": null,
                    "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",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Harini K."
                            },
                            {
                                "name": "Christoffer C."
                            },
                            {
                                "name": "Gromiha M.M."
                            },
                            {
                                "name": "Kihara D."
                            }
                        ],
                        "journal": "Methods in Molecular Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Daisuke Kihara",
                    "email": "dkihara@purdue.edu",
                    "url": null,
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                },
                {
                    "name": "Kannan Harini",
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            "owner": "Pub2Tools",
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        },
        {
            "name": "ICAnnoLncRNA",
            "description": "Snakemake Pipeline for a long non-coding-RNA search and annotation in transcriptomic sequences.",
            "homepage": "https://github.com/artempronozin95/ICAnnoLncRNA-identification-classification-and-annotation-of-LncRNA",
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3258",
                            "term": "Transcriptome assembly"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0314",
                            "term": "Gene expression profiling"
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                        "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.",
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                        "title": "UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data",
                        "abstract": "Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).",
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                            {
                                "name": "Abe K."
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                        "title": "Hostile: accurate decontamination of microbial host sequences",
                        "abstract": "Motivation: Microbial sequences generated from clinical samples are often contaminated with human host sequences that must be removed for ethical and legal reasons. Care must be taken to excise host sequences without inadvertently removing target microbial sequences to the detriment of downstream analyses such as variant calling and de novo assembly. Results: To facilitate accurate host decontamination of both short and long sequencing reads, we developed Hostile, a tool capable of accurate host read removal using a laptop. We demonstrate that our approach removes at least 99.6% of real human reads and retains at least 99.989% of simulated bacterial reads. Using Hostile with a masked reference genome further increases bacterial read retention (≥99.997%) with negligible (≤0.001%) reduction in human read removal performance. Compared with an existing tool, Hostile removes 21%-23% more human short reads and 21-43 times fewer bacterial reads, typically in less time.",
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                    "term": "Proteomics experiment"
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                    "metadata": {
                        "title": "MsPHep: An online application for low molecular weight heparin rapid characterization based on liquid chromatography-tandem mass spectrometry",
                        "abstract": "Low-molecular-weight heparins (LMWHs) are important anticoagulants widely used in clinic. Since they are comprised of complex and heterogenous glycan chains, liquid chromatography-tandem mass spectrometry (LC-MS) is commonly used for structural analysis and quality control of LMWHs to ensure their safety and efficacy. Yet, the structural complexity arising from the parent heparin macromolecules, as well as the different depolymerization methods used for preparing LMWHs, makes processing and assigning the LC-MS data of LWMHs very tedious and challenging. We therefore developed, and here report, an open-source and easy-to-use web application, MsPHep, to facilitate the LMWH analysis based on LC-MS data. MsPHep is compatible with various LMWHs and chromatographic separation methods. With the HepQual function, MsPHep is capable of annotating both the LMWH compound and its isotopic distribution from mass spectra. Moreover, the HepQuant function enables automatic quantification of LMWH compositions without prior knowledge or any database generation. To demonstrate the reliability and system stability of MsPHep, we tested various types of LMWHs that were analyzed with different chromatographic methods coupled to MS. The results show that MsPHep has its own advantages compared to another public tool GlycReSoft for LMWH analysis, and it is available online under an open-source license at https://ngrc-glycan.shinyapps.io/MsPHep.",
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                                "name": "Chi L."
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            "description": "AMEND (Active Module identification using Experimental data and Network Diffusion) is an algorithm designed to find a subset of connected nodes in a molecular interaction network that have large experimental values. It makes use of random walk with restart (RWR) to create node weights, and a heuristic approach for solving the Maximum-weight Connected Subgraph problem using these weights. The resulting subnetwork is then scored based on average experimental values and connectivity, and it is used as input into RWR for the next iteration. This process is performed iteratively until an optimal subnetwork (i.e., module) is found.",
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                            "term": "Network analysis"
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                            "uri": "http://edamontology.org/operation_3223",
                            "term": "Differential gene expression profiling"
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                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
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                    "term": "Protein interactions"
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                    "term": "Transcriptomics"
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                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
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                    "metadata": {
                        "title": "AMEND: active module identification using experimental data and network diffusion",
                        "abstract": "Background: Molecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein–protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions. Results: To address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets. Conclusion: The AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND .",
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