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            "name": "xTea",
            "description": "xTea (comprehensive transposable element analyzer) is designed to identify TE insertions from paired-end Illumina reads, barcode linked-reads, long reads (PacBio or Nanopore), or hybrid data from different sequencing platforms and takes whole-exome sequencing (WES) or whole-genome sequencing (WGS) data as input.",
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                            "term": "Genotyping"
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                            "uri": "http://edamontology.org/operation_2478",
                            "term": "Nucleic acid sequence analysis"
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                "Command-line tool"
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                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
                },
                {
                    "uri": "http://edamontology.org/topic_3673",
                    "term": "Whole genome sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_0196",
                    "term": "Sequence assembly"
                },
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                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
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                    "term": "Informatics"
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            "publication": [
                {
                    "doi": "10.1038/S41467-021-24041-8",
                    "pmid": "34158502",
                    "pmcid": "PMC8219666",
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                    "version": null,
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                    "metadata": {
                        "title": "Comprehensive identification of transposable element insertions using multiple sequencing technologies",
                        "abstract": "© 2021, The Author(s).Transposable elements (TEs) help shape the structure and function of the human genome. When inserted into some locations, TEs may disrupt gene regulation and cause diseases. Here, we present xTea (x-Transposable element analyzer), a tool for identifying TE insertions in whole-genome sequencing data. Whereas existing methods are mostly designed for short-read data, xTea can be applied to both short-read and long-read data. Our analysis shows that xTea outperforms other short read-based methods for both germline and somatic TE insertion discovery. With long-read data, we created a catalogue of polymorphic insertions with full assembly and annotation of insertional sequences for various types of retroelements, including pseudogenes and endogenous retroviruses. Notably, we find that individual genomes have an average of nine groups of full-length L1s in centromeres, suggesting that centromeres and other highly repetitive regions such as telomeres are a significant yet unexplored source of active L1s. xTea is available at https://github.com/parklab/xTea.",
                        "date": "2021-12-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Chu C."
                            },
                            {
                                "name": "Borges-Monroy R."
                            },
                            {
                                "name": "Viswanadham V.V."
                            },
                            {
                                "name": "Lee S."
                            },
                            {
                                "name": "Li H."
                            },
                            {
                                "name": "Lee E.A."
                            },
                            {
                                "name": "Park P.J."
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                        ],
                        "journal": "Nature Communications"
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                    "name": "Eunjung Alice Lee",
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                    "name": "Peter J. Park",
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            "name": "Proteome-pI",
            "description": "The database of pre-computed isoelectric points for proteomes from different model organisms (5029 species). Goals of the database include making statistical comparisons of the various prediction methods (18 algorithms implemented) as well as facilitating biological investigation of protein isoelectric point space.",
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                            "term": "Protein sequence analysis"
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                                "term": "Sequence set (protein)"
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                    ],
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                            "data": {
                                "uri": "http://edamontology.org/data_1528",
                                "term": "Protein isoelectric point"
                            },
                            "format": []
                        }
                    ],
                    "note": "Query full proteome to obtain proteins within given range of isoelectric point and molecular weight",
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            ],
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                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
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                    "uri": "http://edamontology.org/topic_3520",
                    "term": "Proteomics experiment"
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                    "term": "Protein properties"
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                        "title": "Proteome-pI: Proteome isoelectric point database",
                        "abstract": "© The Author(s) 2016.Proteome-pI is an online database containing information about predicted isoelectric points for 5029 proteomes calculated using 18 methods. The isoelectric point, the pH at which a particular molecule carries no net electrical charge, is an important parameter for many analytical biochemistry and proteomics techniques, especially for 2D gel electrophoresis (2D-PAGE), capillary isoelectric focusing, liquid chromatography-mass spectrometry and X-ray protein crystallography. The database, available at http://isoelectricpointdb.org allows the retrieval of virtual 2D-PAGE plots and the development of customised fractions of proteome based on isoelectric point and molecular weight. Moreover, Proteome-pI facilitates statistical comparisons of the various prediction methods as well as biological investigation of protein isoelectric point space in all kingdoms of life. For instance, using Proteome-pI data, it is clear that Eukaryotes, which evolved tight control of homeostasis, encode proteins with pI values near the cell pH. In contrast, Archaea living frequently in extreme environments can possess proteins with a wide range of isoelectric points. The database includes various statistics and tools for interactive browsing, searching and sorting. Apart from data for individual proteomes, datasets corresponding to major protein databases such as UniProtKB/TrEMBL and the NCBI non-redundant (nr) database have also been precalculated and made available in CSV format.",
                        "date": "2017-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Kozlowski L.P."
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                        "journal": "Nucleic Acids Research"
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            "name": "Proteome-pI 2.0",
            "description": "The database of pre-computed isoelectric points and molecular weights for proteins and digest peptides from model organism's proteomes. Contains data for over 61 million protein sequences from 20,115 proteomes with isoelectric point predicted using 21 algorithms. Additionally, 5.38 Billion dissociation constant (pKa) predictions for proteins are available. Furthermore, it contains also proteomes digested in silico with the five most frequently used proteases (Trypsin, Chymotrypsin, Trypsin+LysC, LysN, ArgC) - in total, 9.58 Billion peptides.",
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                    "url": "http://isoelectricpointdb2.org/download.html",
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                    "note": "Instructions for downloading individual proteomes, the full content of the database, and additional datasets (e.g. isoelectric point for all UniProt proteins)",
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            "description": "Software for analyzing next generation sequencing data. The software can handle a number of different input types from mapped reads to imputed genotype probabilities. Most methods take genotype uncertainty into account instead of basing the analysis on called genotypes. This is especially useful for low and medium depth data.",
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                            "term": "Genotyping"
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                    "term": "Population genetics"
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                        "title": "ANGSD: Analysis of Next Generation Sequencing Data",
                        "abstract": "© 2014 Korneliussen et al.Background: High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously. Results: We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods. Conclusions: The open source c/c++ program ANGSD is available at . The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.",
                        "date": "2014-11-25T00:00:00Z",
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                            {
                                "name": "Albrechtsen A."
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                            {
                                "name": "Nielsen R."
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            "description": "eCOMPASS (evaluative comparison of multiple protein alignments by statistical score) addresses this problem using a statistical measure of relative alignment quality based on direct coupling analysis (DCA): to maintain protein structural integrity over evolutionary time, substitutions at one residue position typically result in compensating substitutions at other positions. eCOMPASS computes the statistical significance of the congruence between high scoring directly coupled pairs and 3D contacts in corresponding structures, which depends upon properly aligned homologous residues.",
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                        "title": "rboAnalyzer: A Software to Improve Characterization of Non-coding RNAs From Sequence Database Search Output",
                        "abstract": "© Copyright © 2020 Schwarz, Vohradský, Modrák and Pánek.Searching for similar sequences in a database via BLAST or a similar tool is one of the most common bioinformatics tasks applied in general, and to non-coding RNAs in particular. However, the results of the search might be difficult to interpret due to the presence of partial matches to the database subject sequences. Here, we present rboAnalyzer – a tool that helps with interpreting sequence search result by (1) extending partial matches into plausible full-length subject sequences, (2) predicting homology of RNAs represented by full-length subject sequences to the query RNA, (3) pooling information across homologous RNAs found in the search results and public databases such as Rfam to predict more reliable secondary structures for all matches, and (4) contextualizing the matches by providing the prediction results and other relevant information in a rich graphical output. Using predicted full-length matches improves secondary structure prediction and makes rboAnalyzer robust with regards to identification of homology. The output of the tool should help the user to reliably characterize non-coding RNAs in BLAST output. The usefulness of the rboAnalyzer and its ability to correctly extend partial matches to full-length is demonstrated on known homologous RNAs. To allow the user to use custom databases and search options, rboAnalyzer accepts any search results as a text file in the BLAST format. The main output is an interactive HTML page displaying the computed characteristics and other context of the matches. The output can also be exported in an appropriate sequence and/or secondary structure formats.",
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                    "term": "Sequence composition, complexity and repeats"
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                        "title": "GPCRsclass: A web tool for the classification of amine type of G-protein-coupled receptors",
                        "abstract": "The receptors of amine subfamily are specifically major drug targets for therapy of nervous disorders and psychiatric diseases. The recognition of novel amine type of receptors and their cognate ligands is of paramount interest for pharmaceutical companies. In the past, Chou and co-workers have shown that different types of amine receptors are correlated with their amino acid composition and are predictable on its basis with considerable accuracy [Elrod and Chou (2002) Protein Eng., 15, 713-715]. This motivated us to develop a better method for the recognition of novel amine receptors and for their further classification. The method was developed on the basis of amino acid composition and dipeptide composition of proteins using support vector machine. The method was trained and tested on 167 proteins of amine subfamily of G-protein-coupled receptors (GPCRs). The method discriminated amine subfamily of GPCRs from globular proteins with Matthew's correlation coefficient of 0.98 and 0.99 using amino acid composition and dipeptide composition, respectively. In classifying different types of amine receptors using amino acid composition and dipeptide composition, the method achieved an accuracy of 89.8 and 96.4%, respectively. The performance of the method was evaluated using 5-fold cross-validation. The dipeptide composition based method predicted 67.6% of protein sequences with an accuracy of 100% with a reliability index ≥5. A web server GPCRsclass has been developed for predicting amine-binding receptors from its amino acid sequence [http://www.imtech.res.in/raghava/gpcrsclass/ and http://bioinformatics.uams.edu/raghava/gpersclass/ (mirror site)]. © 2005 Oxford University Press.",
                        "date": "2005-07-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Bhasin M."
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                            {
                                "name": "Raghava G.P.S."
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