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                            "term": "Sequence trimming"
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                    "term": "Transcription factors and regulatory sites"
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                    "term": "Cell biology"
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                    "name": "Leyi Wei",
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                            "term": "Gene-set enrichment analysis"
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                    "term": "Molecular interactions, pathways and networks"
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                    "uri": "http://edamontology.org/topic_0204",
                    "term": "Gene regulation"
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                    "uri": "http://edamontology.org/topic_2640",
                    "term": "Oncology"
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                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
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                    "doi": "10.3389/FGENE.2021.647653",
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                        "title": "DysPIA: A Novel Dysregulated Pathway Identification Analysis Method",
                        "abstract": "© Copyright © 2021 Wang, Xie, Li, Wang, Li, Feng and Li.Differential co-expression-based pathway analysis is still limited and not widely used. In most current methods, the pathways were considered as gene sets, but the gene regulation relationships were not considered, and the computational speed was slow. In this article, we proposed a novel Dysregulated Pathway Identification Analysis (DysPIA) method to overcome these shortcomings. We adopted the idea of Correlation by Individual Level Product into analysis and performed a fast enrichment analysis. We constructed a combined gene-pair background which was much more sufficient than the background used in Edge Set Enrichment Analysis. In simulation study, DysPIA was able to identify the causal pathways with high AUC (0.9584 to 0.9896). In p53 mutation data, DysPIA obtained better performance than other methods. It obtained more potential dysregulated pathways that could be literature verified, and it ran much faster (∼1,700–8,000 times faster than other methods when 10,000 permutations). DysPIA was also applied to breast cancer relapse dataset and breast cancer subtype dataset. The results show that DysPIA is effective and has a great biological significance. R packages “DysPIA” and “DysPIAData” are constructed and freely available on R CRAN (https://cran.r-project.org/web/packages/DysPIA/index.html and https://cran.r-project.org/web/packages/DysPIAData/index.html), and on GitHub (https://github.com/lemonwang2020).",
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                            {
                                "name": "Wang L."
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                            {
                                "name": "Xie W."
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                            {
                                "name": "Li K."
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                            {
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                            {
                                "name": "Li X."
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                            {
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                            {
                                "name": "Li J."
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                        "journal": "Frontiers in Genetics"
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            "description": "Predicting isoform function using deep multi-instance learning.",
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                            "term": "Alternative splicing prediction"
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                            "term": "Expression correlation analysis"
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                            "term": "Network analysis"
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                    "term": "RNA splicing"
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                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
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                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
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                    "term": "Machine learning"
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            "credit": [
                {
                    "name": "Guoxian Yu",
                    "email": "guomaozu@bucea.edu.cn",
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                    "name": "Maozu Guo",
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            "name": "POMAShiny",
            "description": "POMAShiny is an user-friendly web-based workflow for metabolomics and proteomics data analysis.",
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            "biotoolsID": "pomashiny",
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                            "term": "Essential dynamics"
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                            "term": "Principal component visualisation"
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                    "uri": "http://edamontology.org/topic_3172",
                    "term": "Metabolomics"
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                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
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                    "term": "Workflows"
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                    "term": "Sequence analysis"
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                    "term": "Biomarkers"
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                    "term": "Machine learning"
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                    "doi": "10.1371/JOURNAL.PCBI.1009148",
                    "pmid": "34197462",
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                    "metadata": {
                        "title": "POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis",
                        "abstract": "© 2021 Public Library of Science. All rights reserved.Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github. com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively.",
                        "date": "2021-07-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Castellano-Escuder P."
                            },
                            {
                                "name": "Gonzalez-Domnguez R."
                            },
                            {
                                "name": "Carmona-Pontaque F."
                            },
                            {
                                "name": "Andres-Lacueva C."
                            },
                            {
                                "name": "Sanchez-Pla A."
                            }
                        ],
                        "journal": "PLoS Computational Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Pol Castellano-Escuder",
                    "email": "pcastellano@ub.edu",
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                {
                    "name": "Alex Sánchez-Pla",
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                {
                    "name": "Raúl González-Domínguez",
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                {
                    "name": "Cristina Andrés-Lacueva",
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                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-8494-4978",
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                {
                    "name": "Francesc Carmona-Pontaque",
                    "email": null,
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        {
            "name": "PPI-MASS",
            "description": "An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data.",
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                            "uri": "http://edamontology.org/operation_2492",
                            "term": "Protein interaction prediction"
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                            "uri": "http://edamontology.org/operation_3899",
                            "term": "Protein-protein docking"
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                            "uri": "http://edamontology.org/operation_2464",
                            "term": "Protein-protein binding site prediction"
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                            "term": "Molecular dynamics"
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                    ],
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            ],
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                "Web application"
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                    "uri": "http://edamontology.org/topic_0128",
                    "term": "Protein interactions"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3520",
                    "term": "Proteomics experiment"
                },
                {
                    "uri": "http://edamontology.org/topic_3957",
                    "term": "Protein interaction experiment"
                },
                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
                }
            ],
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                "Mac",
                "Linux",
                "Windows"
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            "documentation": [
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                    "url": "https://minicad.appsbio.utalca.cl/ppi-mass/user_guide/",
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            ],
            "publication": [
                {
                    "doi": "10.3389/FMOLB.2021.701477",
                    "pmid": "34277709",
                    "pmcid": "PMC8281810",
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                    "version": null,
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                    "metadata": {
                        "title": "PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data",
                        "abstract": "© Copyright © 2021 González-Avendaño, Zúñiga-Almonacid, Silva, Lavanderos, Robinson, Rosales-Rojas, Durán-Verdugo, González, Cáceres, Cerda and Vergara-Jaque.Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.",
                        "date": "2021-07-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Gonzalez-Avendano M."
                            },
                            {
                                "name": "Zuniga-Almonacid S."
                            },
                            {
                                "name": "Silva I."
                            },
                            {
                                "name": "Lavanderos B."
                            },
                            {
                                "name": "Robinson F."
                            },
                            {
                                "name": "Rosales-Rojas R."
                            },
                            {
                                "name": "Duran-Verdugo F."
                            },
                            {
                                "name": "Gonzalez W."
                            },
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                                "name": "Caceres M."
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                        "abstract": "© 2021 The AuthorsMetaproteomics is becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled with liquid chromatography. In this paper, we proposed a deep-learning-based algorithm, named DeepFilter, for improving peptide identifications from a collection of tandem mass spectra. The key advantage of the DeepFilter is that it does not need ad hoc training or fine-tuning as in existing filtering tools. DeepFilter is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/DeepFilter. Significance: The identification of peptides and proteins from MS data involves the computational procedure of searching MS/MS spectra against a predefined protein sequence database and assigning top-scored peptides to spectra. Existing computational tools are still far from being able to extract all the information out of MS/MS data sets acquired from metaproteome samples. Systematical experiment results demonstrate that the DeepFilter identified up to 12% and 9% more peptide-spectrum-matches and proteins, respectively, compared with existing filtering algorithms, including Percolator, Q-ranker, PeptideProphet, and iProphet, on marine and soil microbial metaproteome samples with false discovery rate at 1%. The taxonomic analysis shows that DeepFilter found up to 7%, 10%, and 14% more species from marine, soil, and human gut samples compared with existing filtering algorithms. Therefore, DeepFilter was believed to generalize properly to new, previously unseen peptide-spectrum-matches and can be readily applied in peptide identification from metaproteomics data.",
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                        "abstract": "Copyright: © 2021 Sladek et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.",
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