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                    "note": "The Sequence Alignment/Map format and SAMtools.",
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                        "title": "The Sequence Alignment/Map format and SAMtools",
                        "abstract": "Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAM tools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. © 2009 The Author(s).",
                        "date": "2009-08-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Li H."
                            },
                            {
                                "name": "Handsaker B."
                            },
                            {
                                "name": "Wysoker A."
                            },
                            {
                                "name": "Fennell T."
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                            {
                                "name": "Ruan J."
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                            {
                                "name": "Homer N."
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                            {
                                "name": "Marth G."
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                            {
                                "name": "Abecasis G."
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                                "name": "Durbin R."
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                    "note": "Twelve years of SAMtools and BCFtools.",
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                        "title": "Twelve years of SAMtools and BCFtools",
                        "abstract": "© The Author(s) 2021. Published by Oxford University Press GigaScience.BACKGROUND: SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. FINDINGS: The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. CONCLUSION: Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.",
                        "date": "2021-02-16T00:00:00Z",
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                            {
                                "name": "Marshall J."
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                                "name": "Pollard M.O."
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                            {
                                "name": "Davies R.M."
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                            {
                                "name": "Li H."
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            "description": "A consensus classifier that combines six of the top performing tools for the prediction of the effects of mutation on protein function. The obtained results are provided together with annotations extracted from the Protein Mutant Database and the UniProt database.",
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                    "type": "Binaries",
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                        "title": "PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations",
                        "abstract": "Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. © 2014 Bendl et al.",
                        "date": "2014-01-01T00:00:00Z",
                        "citationCount": 320,
                        "authors": [
                            {
                                "name": "Bendl J."
                            },
                            {
                                "name": "Stourac J."
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                            {
                                "name": "Salanda O."
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                            {
                                "name": "Pavelka A."
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                            {
                                "name": "Wieben E.D."
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                            {
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                            {
                                "name": "Brezovsky J."
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                        "journal": "PLoS Computational Biology"
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                    "term": "Medical informatics"
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                        "title": "PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions",
                        "abstract": "© 2016 Bendl et al.An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To enable comprehensive evaluation of variants, the predictions are complemented with annotations from eight databases. The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.",
                        "date": "2016-05-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Bendl J."
                            },
                            {
                                "name": "Musil M."
                            },
                            {
                                "name": "Stourac J."
                            },
                            {
                                "name": "Zendulka J."
                            },
                            {
                                "name": "Damborsky J."
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                            {
                                "name": "Brezovsky J."
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        },
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            "name": "VarWatch",
            "description": "Stand-alone software tool for variant matching.\n\nVarWatch is a tool for professional human geneticists, genetic counselors and researchers working with genetic data from patients. High-throughput sequencing is uncovering an increasing number of genomic variants with a suspected link to an observed clinical phenotype. However, substantiating the clinical relevance of these variants-of-unknown-significance (VUS) typically requires one or more independent observations. VarWatch offers a non-commercial platform where such VUS can be registered to be continuously monitored for similar findings in external databases or the VarWatch register itself. Potential matches to other case descriptions will be forwarded to the variant owners to aid in the finding of a diagnosis for the affected patient(s).",
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            "biotoolsID": "varwatch",
            "biotoolsCURIE": "biotools:varwatch",
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                        },
                        {
                            "uri": "http://edamontology.org/operation_3196",
                            "term": "Genotyping"
                        },
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                    ],
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                    "term": "Genotype and phenotype"
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                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
                },
                {
                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3676",
                    "term": "Exome sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_3325",
                    "term": "Rare diseases"
                }
            ],
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                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "VarWatch—A stand-alone software tool for variant matching",
                        "abstract": "© 2019 Fredrich 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.Massively parallel DNA sequencing of clinical samples holds great promise for the gene-based diagnosis of human inherited diseases because it allows rapid detection of putatively causative mutations at genome-wide level. Without additional evidence complementing their initial bioinformatics evaluation, however, the clinical relevance of such candidate genetic variants often remains unclear. In consequence, dedicated ‘matching’ services have been established in recent years that aim at the discovery of other, comparable case reports to facilitate individual diagnoses. However, legal concerns have been raised about the global sharing of genetic data, particularly in Europe where the recently enacted General Data Protection Regulation EU-2016/679 classifies genetic data as highly sensitive. Hence, unrestricted sharing of genetic data from clinical cases on platforms outside the national jurisdiction increasingly may be perceived as problematic. To allow collaborative data producers, particularly large consortia of diagnostic laboratories, to acknowledge these concerns while still practicing efficient case matching internally, novel tools are required. To this end, we developed VarWatch, an easy-to-deploy and highly scalable case matching software that provides users with comprehensive programmatic tools and a user-friendly interface to fulfil said purpose.",
                        "date": "2019-04-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Fredrich B."
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                            {
                                "name": "Schmohl M."
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                                "name": "Junge O."
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                            {
                                "name": "Gundlach S."
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                            {
                                "name": "Ellinghaus D."
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                            {
                                "name": "Pfeufer A."
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                            {
                                "name": "Bettecken T."
                            },
                            {
                                "name": "Siddiqui R."
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                            {
                                "name": "Franke A."
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                                "name": "Wienker T.F."
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                                "name": "Hoeppner M.P."
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                            }
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            "name": "VariED",
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                    "term": "Gene expression"
                },
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                    "term": "Molecular interactions, pathways and networks"
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                    "metadata": {
                        "title": "VariED: The first integrated database of gene annotation and expression profiles for variants related to human diseases",
                        "abstract": "© 2019 The Author(s) 2019. Published by Oxford University Press.Integrated analysis of DNA variants and gene expression profiles may facilitate precise identification of gene regulatory networks involved in disease mechanisms. Despite the widespread availability of public resources, we lack databases that are capable of simultaneously providing gene expression profiles, variant annotations, functional prediction scores and pathogenic analyses. VariED is the first web-based querying system that integrates an annotation database and expression profiles for genetic variants. The database offers a user-friendly platform and locates gene/variant names in the literature by connecting to established online querying tools, biological annotation tools and records from free-text literature. VariED acts as a central hub for organized genome information consisting of gene annotation, variant allele frequency, functional prediction, clinical interpretation and gene expression profiles in three species: human, mouse and zebrafish. VariED also provides a novel scoring scheme to predict the functional impact of a DNA variant. With one single entry, all results regarding queried DNA variants can be downloaded. VariED can potentially serve as an efficient way to obtain comprehensive variant knowledge for clinicians and scientists around the world working on important drug discoveries and precision treatments.",
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                        "authors": [
                            {
                                "name": "Lee C.-Y."
                            },
                            {
                                "name": "Chattopadhyay A."
                            },
                            {
                                "name": "Chiang L.-M."
                            },
                            {
                                "name": "Juang J.-M.J."
                            },
                            {
                                "name": "Lai L.-C."
                            },
                            {
                                "name": "Tsai M.-H."
                            },
                            {
                                "name": "Lu T.-P."
                            },
                            {
                                "name": "Chuang E.Y."
                            }
                        ],
                        "journal": "Database"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Tzu-Pin Lu",
                    "email": "tplu@ntu.edu.tw",
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                },
                {
                    "name": "Eric Y Chuang",
                    "email": "chuangey@ntu.edu.tw",
                    "url": null,
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                            "term": "Quantification"
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                    "metadata": {
                        "title": "A Computational Framework for Genome-wide Characterization of the Human Disease Landscape",
                        "abstract": "© 2018 Elsevier Inc. A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, URSA HD (Unveiling RNA Sample Annotation for Human Diseases), that leverages machine learning and the hierarchy of anatomical relationships present among diseases to integrate thousands of clinical gene expression profiles and identify molecular characteristics specific to each of the hundreds of complex diseases. URSA HD can distinguish between closely related diseases more accurately than literature-validated genes or traditional differential-expression-based computational approaches and is applicable to any disease, including rare and understudied ones. We demonstrate the utility of URSA HD in classifying related nervous system cancers and experimentally verifying novel neuroblastoma-associated genes identified by URSA HD . We highlight the applications for potential targeted drug-repurposing and for quantitatively assessing the molecular response to clinical therapies. URSA HD is freely available for public use, including the use of underlying models, at ursahd.princeton.edu. Discovering unique properties among diseases is needed to develop targeted treatments, especially for related disorders. To address this, we developed a unified framework, URSA HD , which leverages physiological relationships between diseases and integrates thousands of clinical samples across >300 diseases to identify distinct characteristics that can be used to guide biomedical research. We demonstrate applications of URSA HD , including guiding hypothesis generation and experiments, drug repurposing, and quantitatively tracking drug response.",
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                            {
                                "name": "Lee Y.-S."
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                            {
                                "name": "Krishnan A."
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                            {
                                "name": "Oughtred R."
                            },
                            {
                                "name": "Rust J."
                            },
                            {
                                "name": "Chang C.S."
                            },
                            {
                                "name": "Ryu J."
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                            {
                                "name": "Kristensen V.N."
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                            {
                                "name": "Dolinski K."
                            },
                            {
                                "name": "Theesfeld C.L."
                            },
                            {
                                "name": "Troyanskaya O.G."
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                            "uri": "http://edamontology.org/operation_3227",
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                    "uri": "http://edamontology.org/topic_3676",
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                    "term": "Genotype and phenotype"
                },
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                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
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                    "term": "ChIP-on-chip"
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                    "metadata": {
                        "title": "Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts",
                        "abstract": "© 2020, The Author(s).Understanding the impact of rare variants is essential to understanding human health. We analyze rare (MAF < 0.1%) variants against 4264 phenotypes in 49,960 exome-sequenced individuals from the UK Biobank and 1934 phenotypes (1821 overlapping with UK Biobank) in 21,866 members of the Healthy Nevada Project (HNP) cohort who underwent Exome + sequencing at Helix. After using our rare-variant-tailored methodology to reduce test statistic inflation, we identify 64 statistically significant gene-based associations in our meta-analysis of the two cohorts and 37 for phenotypes available in only one cohort. Singletons make significant contributions to our results, and the vast majority of the associations could not have been identified with a genotyping chip. Our results are available for interactive browsing in a webapp (https://ukb.research.helix.com). This comprehensive analysis illustrates the biological value of large, deeply phenotyped cohorts of unselected populations coupled with NGS data.",
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                            {
                                "name": "Cirulli E.T."
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                            {
                                "name": "White S."
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                            {
                                "name": "Read R.W."
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                            {
                                "name": "Elhanan G."
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                            {
                                "name": "Metcalf W.J."
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                            {
                                "name": "Tanudjaja F."
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                            {
                                "name": "Fath D.M."
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                                "name": "Washington N.L."
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                        "journal": "Nature Communications"
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                    "metadata": {
                        "title": "Burden Testing of Rare Variants Identified through Exome Sequencing via Publicly Available Control Data",
                        "abstract": "© 2018 American Society of Human GeneticsThe genetic causes of many Mendelian disorders remain undefined. Factors such as lack of large multiplex families, locus heterogeneity, and incomplete penetrance hamper these efforts for many disorders. Previous work suggests that gene-based burden testing—where the aggregate burden of rare, protein-altering variants in each gene is compared between case and control subjects—might overcome some of these limitations. The increasing availability of large-scale public sequencing databases such as Genome Aggregation Database (gnomAD) can enable burden testing using these databases as controls, obviating the need for additional control sequencing for each study. However, there exist various challenges with using public databases as controls, including lack of individual-level data, differences in ancestry, and differences in sequencing platforms and data processing. To illustrate the approach of using public data as controls, we analyzed whole-exome sequencing data from 393 individuals with idiopathic hypogonadotropic hypogonadism (IHH), a rare disorder with significant locus heterogeneity and incomplete penetrance against control subjects from gnomAD (n = 123,136). We leveraged presumably benign synonymous variants to calibrate our approach. Through iterative analyses, we systematically addressed and overcame various sources of artifact that can arise when using public control data. In particular, we introduce an approach for highly adaptable variant quality filtering that leads to well-calibrated results. Our approach “re-discovered” genes previously implicated in IHH (FGFR1, TACR3, GNRHR). Furthermore, we identified a significant burden in TYRO3, a gene implicated in hypogonadotropic hypogonadism in mice. Finally, we developed a user-friendly software package TRAPD (Test Rare vAriants with Public Data) for performing gene-based burden testing against public databases.",
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                        "authors": [
                            {
                                "name": "Guo M.H."
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                    "term": "RNA splicing"
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                    "metadata": {
                        "title": "TGStools: A bioinformatics suit to facilitate transcriptome analysis of long reads from third generation sequencing platform",
                        "abstract": "© 2019 by the authors. Licensee MDPI, Basel, Switzerland.Recent analyses show that transcriptome sequencing can be utilized as a diagnostic tool for rare Mendelian diseases. The third generation sequencing de novo detects long reads of thousands of base pairs, thus greatly expanding the isoform discovery and identification of novel long noncoding RNAs. In this study, we developed TGStools, a bioinformatics suite to facilitate routine tasks such as characterizing full-length transcripts, detecting shifted types of alternative splicing, and long noncoding RNAs (lncRNAs) identification in transcriptome analysis. It also prioritizes the transcripts with a visualization framework that automatically integrates rich annotation with known genomic features. TGStools is a Python package freely available at Github.",
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                                "name": "Chen D."
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                            {
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                    "metadata": {
                        "title": "Data-driven characterization of molecular phenotypes across heterogeneous sample collections",
                        "abstract": "© 2019 The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.Existing large gene expression data repositories hold enormous potential to elucidate disease mechanisms, characterize changes in cellular pathways, and to stratify patients based on molecular profiles. To achieve this goal, integrative resources and tools are needed that allow comparison of results across datasets and data types. We propose an intuitive approach for data-driven stratifications of molecular profiles and benchmark our methodology using the dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE) with multi-study and multi-platform data on hematological malignancies. Our approach enables assessing the contribution of biological versus technical variation to sample clustering, direct incorporation of additional datasets to the same low dimensional representation, comparison of molecular disease subtypes identified from separate t-SNE representations, and characterization of the obtained clusters based on pathway databases and additional data. In this manner, we performed an integrative analysis across multi-omics acute myeloid leukemia studies. Our approach indicated new molecular subtypes with differential survival and drug responsiveness among samples lacking fusion genes, including a novel myelodysplastic syndrome-like cluster and a cluster characterized with CEBPA mutations and differential activity of the S-adenosylmethionine-dependent DNA methylation pathway. In summary, integration across multiple studies can help to identify novel molecular disease subtypes and generate insight into disease biology.",
                        "date": "2019-07-26T00:00:00Z",
                        "citationCount": 7,
                        "authors": [
                            {
                                "name": "Mehtonen J."
                            },
                            {
                                "name": "Polonen P."
                            },
                            {
                                "name": "Hayrynen S."
                            },
                            {
                                "name": "Dufva O."
                            },
                            {
                                "name": "Lin J."
                            },
                            {
                                "name": "Liuksiala T."
                            },
                            {
                                "name": "Granberg K."
                            },
                            {
                                "name": "Lohi O."
                            },
                            {
                                "name": "Hautamaki V."
                            },
                            {
                                "name": "Nykter M."
                            },
                            {
                                "name": "Heinaniemi M."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Matti Nykter",
                    "email": "matti.nykter@tuni.fi",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Merja Heinäniemi",
                    "email": "merja.heinaniemi@uef.fi",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-0071-6802",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                }
            ],
            "community": null,
            "owner": "Niclaskn",
            "additionDate": "2021-01-20T10:59:30Z",
            "lastUpdate": "2021-05-21T06:17:57Z",
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "validated": 0,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": "tool"
        }
    ]
}