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            "name": "Scipion PKPD",
            "description": "Scipion PKPD is a plug-in tool for Scipion which provides an Open-Source Platform for Biopharmaceutics, Pharmacokinetics and Pharmacodynamics Data Analysis.",
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                    "term": "Drug metabolism"
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                    "term": "Biophysics"
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                    "url": "https://pypi.org/search/?q=scipion",
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                    "url": "https://sonarcloud.io/dashboard?id=cossorzano_scipion-pkpd",
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                {
                    "doi": "10.1007/S11095-021-03065-1",
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                {
                    "name": "C O S Sorzano",
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                    "name": "M. A. Pérez de la Cruz-Moreno",
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                    "term": "RNA-Seq"
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                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
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                    "term": "Exome sequencing"
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                    "doi": "10.1186/S12864-021-07790-0",
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                    "metadata": {
                        "title": "A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data",
                        "abstract": "BACKGROUND: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work.The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data. RESULTS: In this study, we transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a scaling-free minimum enclosing ball (SFMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed SFMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the SFMEB method could be easily extended to different species without normalization. CONCLUSIONS: Simulation studies demonstrate that the SFMEB method works well in a wide range of settings, especially when the data are heterogeneous or biological replicates. Analysis of the real data also supports the conclusion that the SFMEB method outperforms other existing competitors. The R package of the proposed method is available at https://bioconductor.org/packages/MEB .",
                        "date": "2021-06-26T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Zhou Y."
                            },
                            {
                                "name": "Yang B."
                            },
                            {
                                "name": "Wang J."
                            },
                            {
                                "name": "Zhu J."
                            },
                            {
                                "name": "Tian G."
                            }
                        ],
                        "journal": "BMC genomics"
                    }
                }
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                {
                    "name": "Jiadi Zhu",
                    "email": "2160090406@email.szu.edu.cn",
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                    "name": "Guoliang Tian",
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                                "uri": "http://edamontology.org/data_0863",
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                    "term": "Sequence analysis"
                }
            ],
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                    "type": [
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                    "url": "https://mafft.cbrc.jp/alignment/software/",
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            "publication": [
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                    "doi": "10.1007/978-1-62703-646-7_8",
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                    "metadata": {
                        "title": "MAFFT: Iterative refinement and additional methods",
                        "abstract": "This chapter outlines several methods implemented in the MAFFT package. MAFFT is a popular multiple sequence alignment (MSA) program with various options for the progressive method, the iterative refinement method and other methods. We first outline basic usage of MAFFT and then describe recent practical extensions, such as dot plot and adjustment of direction in DNA alignment. We also refer to MUSCLE, another high-performance MSA program. © 2014 Springer Science+Business Media, LLC.",
                        "date": "2014-01-01T00:00:00Z",
                        "citationCount": 232,
                        "authors": [
                            {
                                "name": "Katoh K."
                            },
                            {
                                "name": "Standley D.M."
                            }
                        ],
                        "journal": "Methods in Molecular Biology"
                    }
                },
                {
                    "doi": "10.7490/f1000research.1114334.1",
                    "pmid": null,
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                    "type": [
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                    "pmid": null,
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Parallelization of MAFFT for large-scale multiple sequence alignments",
                        "abstract": "© The Author(s) 2018. Published by Oxford University Press.Summary: We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences.",
                        "date": "2018-07-15T00:00:00Z",
                        "citationCount": 238,
                        "authors": [
                            {
                                "name": "Nakamura T."
                            },
                            {
                                "name": "Yamada K.D."
                            },
                            {
                                "name": "Tomii K."
                            },
                            {
                                "name": "Katoh K."
                            }
                        ],
                        "journal": "Bioinformatics"
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                    "doi": "10.1093/bib/bbx108",
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                    "version": null,
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                    "metadata": {
                        "title": "MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization",
                        "abstract": "© 2017 The Author. Published by Oxford University Press.This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.",
                        "date": "2018-03-27T00:00:00Z",
                        "citationCount": 1615,
                        "authors": [
                            {
                                "name": "Katoh K."
                            },
                            {
                                "name": "Rozewicki J."
                            },
                            {
                                "name": "Yamada K.D."
                            }
                        ],
                        "journal": "Briefings in Bioinformatics"
                    }
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                            "term": "Transmembrane protein prediction"
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                    "term": "Membrane and lipoproteins"
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                    "term": "Chemistry"
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                    "term": "Transcription factors and regulatory sites"
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                    "term": "Small molecules"
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                    "metadata": {
                        "title": "Fluorescence Anisotropy-Based Assay for Characterization of Ligand Binding Dynamics to GPCRs: The Case of Cy3B-Labeled Ligands Binding to MC4 Receptors in Budded Baculoviruses",
                        "abstract": "© 2021, Springer Science+Business Media, LLC, part of Springer Nature.During the past decade, fluorescence methods have become valuable tools for characterizing ligand binding to G protein-coupled receptors (GPCRs). However, only a few of the assays enable studying wild-type receptors and monitor the ligand binding in real time. One of the approaches that is inherently suitable for this purpose is the fluorescence anisotropy (FA) assay. In the FA assay, the change of ligand’s rotational freedom connected with its binding to the receptor can be monitored with a conventional fluorescence plate reader equipped with suitable optical filters. To achieve the high receptor concentration required for the assay and the low autofluorescence levels essential for reliable results, budded baculoviruses that display GPCRs on their surfaces can be used. The monitoring process generates a substantial amount of kinetic data, which is usually stored as a proprietary file format limiting the flexibility of data analysis. To solve this problem, we propose the use of the data curation software Aparecium (http://gpcr.ut.ee/aparecium.html ), which integrates experimental data with metadata in a Minimum Information for Data Analysis in Systems Biology (MIDAS) format. Aparecium enables data export to different software packages for fitting to suitable kinetic or equilibrium models. A combination of the FA assay with the novel data analysis strategy is suitable for screening new active compounds, but also for modeling complex systems of ligand binding to GPCRs. We present the proposed approach using different fluorescent probes and assay types to characterize ligand binding to melanocortin 4 (MC4) receptor.",
                        "date": "2021-01-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Veiksina S."
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                            {
                                "name": "Tahk M.-J."
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                            {
                                "name": "Laasfeld T."
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                                "name": "Kopanchuk S."
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                        "title": "SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks",
                        "abstract": "© 2021, The Author(s).Background: Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results: In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads that takes raw reads as the input and detects structural variants of size larger than 50 bp. Our pipeline utilizes state-of-the-art long-read aligners, namely NGMLR and Minimap2, and structural variation callers, videlicet Sniffle and SVIM. We found that by using a neural network, we can extract features from Minimap2 output to detect a subset of reads that provide useful information for structural variation detection. By only mapping this subset with NGMLR, which is far slower than Minimap2 but better serves downstream structural variation detection, we can increase the sensitivity in an efficient way. As a result of using multiple tools intelligently, SVNN achieves up to 20 percentage points of sensitivity improvement in comparison with state-of-the-art methods and is three times faster than a naive combination of state-of-the-art tools to achieve almost the same accuracy. Conclusion: Since prohibitive costs of using high-coverage data have impeded long-read applications, with SVNN, we provide the users with a much faster structural variation detection platform for PacBio reads with high precision and sensitivity in low-coverage scenarios.",
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                    "term": "Sequence sites, features and motifs"
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                    "metadata": {
                        "title": "Genexpi: A toolset for identifying regulons and validating gene regulatory networks using time-course expression data",
                        "abstract": "© 2018 The Author(s).Background: Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks. Results: We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package. Conclusions: Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.",
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                            "term": "k-mer counting"
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                            "term": "Sequence trimming"
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                            "term": "Aggregation"
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                            "term": "De-novo assembly"
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                    "term": "Genetic variation"
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                    "term": "Mapping"
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                    "metadata": {
                        "title": "2-kupl: mapping-free variant detection from DNA-seq data of matched samples",
                        "abstract": "© 2021, The Author(s).Background: The detection of genome variants, including point mutations, indels and structural variants, is a fundamental and challenging computational problem. We address here the problem of variant detection between two deep-sequencing (DNA-seq) samples, such as two human samples from an individual patient, or two samples from distinct bacterial strains. The preferred strategy in such a case is to align each sample to a common reference genome, collect all variants and compare these variants between samples. Such mapping-based protocols have several limitations. DNA sequences with large indels, aggregated mutations and structural variants are hard to map to the reference. Furthermore, DNA sequences cannot be mapped reliably to genomic low complexity regions and repeats. Results: We introduce 2-kupl, a k-mer based, mapping-free protocol to detect variants between two DNA-seq samples. On simulated and actual data, 2-kupl achieves higher accuracy than other mapping-free protocols. Applying 2-kupl to prostate cancer whole exome sequencing data, we identify a number of candidate variants in hard-to-map regions and propose potential novel recurrent variants in this disease. Conclusions: We developed a mapping-free protocol for variant calling between matched DNA-seq samples. Our protocol is suitable for variant detection in unmappable genome regions or in the absence of a reference genome.",
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                {
                    "uri": "http://edamontology.org/topic_0780",
                    "term": "Plant biology"
                },
                {
                    "uri": "http://edamontology.org/topic_3320",
                    "term": "RNA splicing"
                },
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
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                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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                {
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                    "term": "Agricultural science"
                },
                {
                    "uri": "http://edamontology.org/topic_0749",
                    "term": "Transcription factors and regulatory sites"
                }
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                "Mac",
                "Linux",
                "Windows"
            ],
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            "license": "Not licensed",
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            "cost": "Free of charge",
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            "link": [
                {
                    "url": "http://tpia.teaplant.org/",
                    "type": [
                        "Repository"
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                    "url": "http://teacon.wchoda.com/",
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                        "Repository"
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                {
                    "url": "http://www.teaas.cn/static/html/teaIndex.html",
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            "publication": [
                {
                    "doi": "10.1186/S12870-021-03065-8",
                    "pmid": "34154536",
                    "pmcid": "PMC8215737",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "TeaAS: a comprehensive database for alternative splicing in tea plants (Camellia sinensis)",
                        "abstract": "© 2021, The Author(s).Alternative splicing (AS) increases the diversity of transcripts and proteins through the selection of different splice sites and plays an important role in the growth, development and stress tolerance of plants. With the release of the reference genome of the tea plant (Camellia sinensis) and the development of transcriptome sequencing, researchers have reported the existence of AS in tea plants. However, there is a lack of a platform, centered on different RNA-seq datasets, that provides comprehensive information on AS. To facilitate access to information on AS and reveal the molecular function of AS in tea plants, we established the first comprehensive AS database for tea plants (TeaAS, http://www.teaas.cn/index.php). In this study, 3.96 Tb reads from 66 different RNA-seq datasets were collected to identify AS events. TeaAS supports four methods of retrieval of AS information based on gene ID, gene name, annotation (non-redundant/Kyoto encyclopedia of genes and genomes/gene ontology annotation or chromosomal location) and RNA-seq data. It integrates data pertaining to genome annotation, type of AS event, transcript sequence, and isoforms expression levels from 66 RNA-seq datasets. The AS events resulting from different environmental conditions and that occurring in varied tissue types, and the expression levels of specific transcripts can be clearly identified through this online database. Moreover, it also provides two useful tools, Basic Local Alignment Search Tool and Generic Genome Browser, for sequence alignment and visualization of gene structure. The features of the TeaAS database make it a comprehensive AS bioinformatics platform for researchers, as well as a reference for studying AS events in woody crops. It could also be helpful for revealing the novel biological functions of AS in gene regulation in tea plants.",
                        "date": "2021-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Mi X."
                            },
                            {
                                "name": "Yue Y."
                            },
                            {
                                "name": "Tang M."
                            },
                            {
                                "name": "An Y."
                            },
                            {
                                "name": "Xie H."
                            },
                            {
                                "name": "Qiao D."
                            },
                            {
                                "name": "Ma Z."
                            },
                            {
                                "name": "Liu S."
                            },
                            {
                                "name": "Wei C."
                            }
                        ],
                        "journal": "BMC Plant Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Chaoling Wei",
                    "email": "weichl@ahau.edu.cn",
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            "owner": "Jennifer",
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}