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Salma M, Alaterre E, Moreaux J, Soler E. Var∣Decrypt: a novel and user-friendly tool to explore and prioritize variants in whole-exome sequencing data. Epigenetics Chromatin 2023; 16:23. [PMID: 37312221 DOI: 10.1186/s13072-023-00497-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/23/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND High-throughput sequencing (HTS) offers unprecedented opportunities for the discovery of causative gene variants in multiple human disorders including cancers, and has revolutionized clinical diagnostics. However, despite more than a decade of use of HTS-based assays, extracting relevant functional information from whole-exome sequencing (WES) data remains challenging, especially for non-specialists lacking in-depth bioinformatic skills. RESULTS To address this limitation, we developed Var∣Decrypt, a web-based tool designed to greatly facilitate WES data browsing and analysis. Var∣Decrypt offers a wide range of gene and variant filtering possibilities, clustering and enrichment tools, providing an efficient way to derive patient-specific functional information and to prioritize gene variants for functional analyses. We applied Var∣Decrypt on WES datasets of 10 acute erythroid leukemia patients, a rare and aggressive form of leukemia, and recovered known disease oncogenes in addition to novel putative drivers. We additionally validated the performance of Var∣Decrypt using an independent dataset of ~ 90 multiple myeloma WES, recapitulating the identified deregulated genes and pathways, showing the general applicability and versatility of Var∣Decrypt for WES analysis. CONCLUSION Despite years of use of WES in human health for diagnosis and discovery of disease drivers, WES data analysis still remains a complex task requiring advanced bioinformatic skills. In that context, there is a need for user-friendly all-in-one dedicated tools for data analysis, to allow biologists and clinicians to extract relevant biological information from patient datasets. Here, we provide Var∣Decrypt (trial version accessible here: https://vardecrypt.com/app/vardecrypt ), a simple and intuitive Rshiny application created to fill this gap. Source code and detailed user tutorial are available at https://gitlab.com/mohammadsalma/vardecrypt .
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Affiliation(s)
- Mohammad Salma
- Institut de Génétique Moléculaire de Montpellier, Univ Montpellier, CNRS, Montpellier, France.
- Laboratory of Excellence GR-Ex, Université de Paris, Paris, France.
| | - Elina Alaterre
- Institute of Human Genetics, UMR 9002 CNRS-UM, Montpellier, France
| | - Jérôme Moreaux
- Department of Biological Hematology, CHU Montpellier, Montpellier, France
- Institute of Human Genetics, UMR 9002 CNRS-UM, Montpellier, France
- Institut Universitaire de France (IUF), Paris, France
| | - Eric Soler
- Institut de Génétique Moléculaire de Montpellier, Univ Montpellier, CNRS, Montpellier, France.
- Laboratory of Excellence GR-Ex, Université de Paris, Paris, France.
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Liu Y, Yeung WSB, Chiu PCN, Cao D. Computational approaches for predicting variant impact: An overview from resources, principles to applications. Front Genet 2022; 13:981005. [PMID: 36246661 PMCID: PMC9559863 DOI: 10.3389/fgene.2022.981005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.
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Affiliation(s)
- Ye Liu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - William S. B. Yeung
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Philip C. N. Chiu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
| | - Dandan Cao
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
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Cervantes-Gracia K, Chahwan R, Husi H. Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach. Front Genet 2022; 13:828786. [PMID: 35186042 PMCID: PMC8855827 DOI: 10.3389/fgene.2022.828786] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/12/2022] [Indexed: 12/24/2022] Open
Abstract
The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.
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Affiliation(s)
| | - Richard Chahwan
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
- *Correspondence: Richard Chahwan, ; Holger Husi,
| | - Holger Husi
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
- Division of Biomedical Sciences, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom
- *Correspondence: Richard Chahwan, ; Holger Husi,
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4
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Vezain M, Lecuyer M, Rubio M, Dupé V, Ratié L, David V, Pasquier L, Odent S, Coutant S, Tournier I, Trestard L, Adle-Biassette H, Vivien D, Frébourg T, Gonzalez BJ, Laquerrière A, Saugier-Veber P. A de novo variant in ADGRL2 suggests a novel mechanism underlying the previously undescribed association of extreme microcephaly with severely reduced sulcation and rhombencephalosynapsis. Acta Neuropathol Commun 2018; 6:109. [PMID: 30340542 PMCID: PMC6195752 DOI: 10.1186/s40478-018-0610-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 09/29/2018] [Indexed: 12/13/2022] Open
Abstract
Extreme microcephaly and rhombencephalosynapsis represent unusual pathological conditions, each of which occurs in isolation or in association with various other cerebral and or extracerebral anomalies. Unlike microcephaly for which several disease-causing genes have been identified with different modes of inheritance, the molecular bases of rhombencephalosynapsis remain unknown and rhombencephalosynapsis presents mainly as a sporadic condition consistent with de novo dominant variations. We report for the first time the association of extreme microcephaly with almost no sulcation and rhombencephalosynapsis in a fœtus for which comparative patient-parent exome sequencing strategy revealed a heterozygous de novo missense variant in the ADGRL2 gene. ADGRL2 encodes latrophilin 2, an adhesion G-protein-coupled receptor whose exogenous ligand is α-latrotoxin. Adgrl2 immunohistochemistry and in situ hybridization revealed expression in the telencephalon, mesencephalon and rhombencephalon of mouse and chicken embryos. In human brain embryos and fœtuses, Adgrl2 immunoreactivity was observed in the hemispheric and cerebellar germinal zones, the cortical plate, basal ganglia, pons and cerebellar cortex. Microfluorimetry experiments evaluating intracellular calcium release in response to α-latrotoxin binding showed significantly reduced cytosolic calcium release in the fœtus amniocytes vs amniocytes from age-matched control fœtuses and in HeLa cells transfected with mutant ADGRL2 cDNA vs wild-type construct. Embryonic lethality was also observed in constitutive Adgrl2−/− mice. In Adgrl2+/− mice, MRI studies revealed microcephaly and vermis hypoplasia. Cell adhesion and wound healing assays demonstrated that the variation increased cell adhesion properties and reduced cell motility. Furthermore, HeLa cells overexpressing mutant ADGRL2 displayed a highly developed cytoplasmic F-actin network related to cytoskeletal dynamic modulation. ADGRL2 is the first gene identified as being responsible for extreme microcephaly with rhombencephalosynapsis. Increased cell adhesion, reduced cell motility and cytoskeletal dynamic alterations induced by the variant therefore represent a new mechanism responsible for microcephaly.
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Mock A, Murphy S, Morris J, Marass F, Rosenfeld N, Massie C. CVE: an R package for interactive variant prioritisation in precision oncology. BMC Med Genomics 2017; 10:37. [PMID: 28545463 PMCID: PMC5445311 DOI: 10.1186/s12920-017-0261-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 04/20/2017] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND An increasing number of precision oncology programmes are being launched world-wide. To support this development, we present the Cancer Variant Explorer (CVE), an R package with an interactive Shiny web browser interface. RESULTS Leveraging Oncotator and the Drug Gene Interaction Database, CVE offers exploration of variants within single or multiple tumour exomes to identify drivers, resistance mechanisms and to assess druggability. We present example applications including the analysis of an individual patient and a cohort-wide study, and provide a first extension of CVE by adding a tumour-specific co-expression network. CONCLUSIONS The CVE package allows interactive variant prioritisation to expedite the analysis of cancer sequencing studies. Our framework also includes the prioritisation of druggable targets, allows exploratory analysis of tissue specific networks and is extendable for specific applications by virtue of its modular design. We encourage the use of CVE within translational research studies and molecular tumour boards. The CVE package is available via Bioconductor ( http://bioconductor.org/packages/CVE/ ).
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Affiliation(s)
- Andreas Mock
- Cancer Research UK Cambridge Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE UK
| | - Suzanne Murphy
- Cancer Research UK Cambridge Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE UK
| | - James Morris
- Cancer Research UK Cambridge Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE UK
| | - Francesco Marass
- Cancer Research UK Cambridge Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE UK
| | - Nitzan Rosenfeld
- Cancer Research UK Cambridge Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE UK
| | - Charlie Massie
- Cancer Research UK Cambridge Centre, Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE UK
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6
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Granata I, Sangiovanni M, Maiorano F, Miele M, Guarracino MR. Var2GO: a web-based tool for gene variants selection. BMC Bioinformatics 2016; 17:376. [PMID: 28185576 PMCID: PMC5123234 DOI: 10.1186/s12859-016-1197-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background One of the most challenging issue in the variant calling process is handling the resulting data, and filtering the genes retaining only the ones strictly related to the topic of interest. Several tools permit to gather annotations at different levels of complexity for the detected genes and to group them according to the pathways and/or processes they belong to. However, it might be a time consuming and frustrating task. This is partly due to the size of the file, that might contain many thousands of genes, and to the search of associated variants that requires a gene-by-gene investigation and annotation approach. As a consequence, the initial gene list is often reduced exploiting the knowledge of variants effect, novelty and genotype, with the potential risk of losing meaningful pieces of information. Results Here we present Var2GO, a new web-based tool to support the annotation and filtering of variants and genes coming from variant calling of high-throughput sequencing data. Var2GO permits to upload either the unprocessed Variant Calling Format file or a table containing the annotated variants. The raw data undergo a preliminary step of variants annotation, using the SnpEff tool, and are converted to a table format. The table is then uploaded into an on the fly generated database. Genes associated to the variants are automatically annotated with the corresponding Gene Ontology terms covering the three GO domains. Using the web interface it is then possible to filter and extract, from the whole list, genes having annotations in the domain of interest, by simply specifying filtering parameters and one or more keywords. The relevance of this tool is demonstrated on exome sequencing data. Conclusions Var2GO is a novel tool that implements a topic-based approach, expressly designed to help biologists in narrowing the search of relevant genes coming from variant calling analysis. Its main purpose is to support non-bioinformaticians in handling and processing raw variant calling data through an intuitive web interface. Furthermore, Var2GO offers a complete pipeline that, starting from the raw VCF file, allows to annotate both variants and associated genes and supports the extraction of relevant biological knowledge.
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Affiliation(s)
- Ilaria Granata
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy.
| | - Mara Sangiovanni
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy
| | - Francesco Maiorano
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy
| | - Marco Miele
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy
| | - Mario Rosario Guarracino
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131, Italy
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7
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Hintzsche J, Kim J, Yadav V, Amato C, Robinson SE, Seelenfreund E, Shellman Y, Wisell J, Applegate A, McCarter M, Box N, Tentler J, De S, Robinson WA, Tan AC. IMPACT: a whole-exome sequencing analysis pipeline for integrating molecular profiles with actionable therapeutics in clinical samples. J Am Med Inform Assoc 2016; 23:721-30. [PMID: 27026619 DOI: 10.1093/jamia/ocw022] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 02/01/2016] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Currently, there is a disconnect between finding a patient's relevant molecular profile and predicting actionable therapeutics. Here we develop and implement the Integrating Molecular Profiles with Actionable Therapeutics (IMPACT) analysis pipeline, linking variants detected from whole-exome sequencing (WES) to actionable therapeutics. METHODS AND MATERIALS The IMPACT pipeline contains 4 analytical modules: detecting somatic variants, calling copy number alterations, predicting drugs against deleterious variants, and analyzing tumor heterogeneity. We tested the IMPACT pipeline on whole-exome sequencing data in The Cancer Genome Atlas (TCGA) lung adenocarcinoma samples with known EGFR mutations. We also used IMPACT to analyze melanoma patient tumor samples before treatment, after BRAF-inhibitor treatment, and after BRAF- and MEK-inhibitor treatment. RESULTS IMPACT Food and Drug Administration (FDA) correctly identified known EGFR mutations in the TCGA lung adenocarcinoma samples. IMPACT linked these EGFR mutations to the appropriate FDA-approved EGFR inhibitors. For the melanoma patient samples, we identified NRAS p.Q61K as an acquired resistance mutation to BRAF-inhibitor treatment. We also identified CDKN2A deletion as a novel acquired resistance mutation to BRAFi/MEKi inhibition. The IMPACT analysis pipeline predicts these somatic variants to actionable therapeutics. We observed the clonal dynamic in the tumor samples after various treatments. We showed that IMPACT not only helped in successful prioritization of clinically relevant variants but also linked these variations to possible targeted therapies. CONCLUSION IMPACT provides a new bioinformatics strategy to delineate candidate somatic variants and actionable therapies. This approach can be applied to other patient tumor samples to discover effective drug targets for personalized medicine.IMPACT is publicly available at http://tanlab.ucdenver.edu/IMPACT.
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Affiliation(s)
- Jennifer Hintzsche
- Division of Medical Oncology, Department of Medicine, School of Medicine
| | - Jihye Kim
- Division of Medical Oncology, Department of Medicine, School of Medicine University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Vinod Yadav
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, School of Medicine
| | - Carol Amato
- Division of Medical Oncology, Department of Medicine, School of Medicine
| | - Steven E Robinson
- Division of Medical Oncology, Department of Medicine, School of Medicine
| | - Eric Seelenfreund
- Division of Medical Oncology, Department of Medicine, School of Medicine
| | - Yiqun Shellman
- Department of Dermatology, School of Medicine University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Joshua Wisell
- Department of Pathology, School of Medicine University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Allison Applegate
- Division of Medical Oncology, Department of Medicine, School of Medicine
| | - Martin McCarter
- Department of Surgery, School of Medicine University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Neil Box
- Department of Dermatology, School of Medicine University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - John Tentler
- Division of Medical Oncology, Department of Medicine, School of Medicine University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Subhajyoti De
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, School of Medicine Department of Biostatistics and Informatics, Colorado School of Public Health University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - William A Robinson
- Division of Medical Oncology, Department of Medicine, School of Medicine University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Aik Choon Tan
- Division of Medical Oncology, Department of Medicine, School of Medicine Department of Biostatistics and Informatics, Colorado School of Public Health University of Colorado Cancer Center All: University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
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8
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Vigeland MD, Gjøtterud KS, Selmer KK. FILTUS: a desktop GUI for fast and efficient detection of disease-causing variants, including a novel autozygosity detector. Bioinformatics 2016; 32:1592-4. [PMID: 26819469 PMCID: PMC4866527 DOI: 10.1093/bioinformatics/btw046] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 01/20/2016] [Indexed: 01/01/2023] Open
Abstract
Summary: FILTUS is a stand-alone tool for working with annotated variant files, e.g. when searching for variants causing Mendelian disease. Very flexible in terms of input file formats, FILTUS offers efficient filtering and a range of downstream utilities, including statistical analysis of gene sharing patterns, detection of de novo mutations in trios, quality control plots and autozygosity mapping. The autozygosity mapping is based on a hidden Markov model and enables accurate detection of autozygous regions directly from exome-scale variant files. Availability and implementation: FILTUS is written in Python and runs on Windows, Mac and Linux. Binaries and source code are freely available at http://folk.uio.no/magnusv/filtus.html and on GitHub: https://github.com/magnusdv/filtus. Automatic installation is available via PyPI (e.g. pip install filtus). Contact:magnusdv@medisin.uio.no Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Magnus D Vigeland
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo N-0424
| | | | - Kaja K Selmer
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo N-0424
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9
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Smedley D, Jacobsen JOB, Jäger M, Köhler S, Holtgrewe M, Schubach M, Siragusa E, Zemojtel T, Buske OJ, Washington NL, Bone WP, Haendel MA, Robinson PN. Next-generation diagnostics and disease-gene discovery with the Exomiser. Nat Protoc 2015; 10:2004-15. [PMID: 26562621 DOI: 10.1038/nprot.2015.124] [Citation(s) in RCA: 229] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Exomiser is an application that prioritizes genes and variants in next-generation sequencing (NGS) projects for novel disease-gene discovery or differential diagnostics of Mendelian disease. Exomiser comprises a suite of algorithms for prioritizing exome sequences using random-walk analysis of protein interaction networks, clinical relevance and cross-species phenotype comparisons, as well as a wide range of other computational filters for variant frequency, predicted pathogenicity and pedigree analysis. In this protocol, we provide a detailed explanation of how to install Exomiser and use it to prioritize exome sequences in a number of scenarios. Exomiser requires ∼3 GB of RAM and roughly 15-90 s of computing time on a standard desktop computer to analyze a variant call format (VCF) file. Exomiser is freely available for academic use from http://www.sanger.ac.uk/science/tools/exomiser.
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Affiliation(s)
- Damian Smedley
- Skarnes Faculty Group, Wellcome Trust Sanger Institute, Hinxton, UK
| | | | - Marten Jäger
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Brandenburg Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Köhler
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Manuel Holtgrewe
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute for Health, Berlin, Germany
| | - Max Schubach
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Enrico Siragusa
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute for Health, Berlin, Germany.,Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Tomasz Zemojtel
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.,Labor Berlin - Charité Vivantes, Humangenetik, Berlin, Germany
| | - Orion J Buske
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Nicole L Washington
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - William P Bone
- The National Institutes of Health (NIH) Undiagnosed Diseases Program, Common Fund, Office of the Director, NIH, Bethesda, Maryland, USA
| | - Melissa A Haendel
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health &Science University, Portland, Oregon, USA
| | - Peter N Robinson
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Brandenburg Center for Regenerative Therapies (BCRT), Charité-Universitätsmedizin Berlin, Berlin, Germany.,Max Planck Institute for Molecular Genetics, Berlin, Germany.,Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Berlin, Germany
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10
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Jekunen A. Clinicians' expectations for gene-driven cancer therapy. CLINICAL MEDICINE INSIGHTS-ONCOLOGY 2014; 8:159-64. [PMID: 25574148 PMCID: PMC4271717 DOI: 10.4137/cmo.s20737] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 11/19/2014] [Accepted: 11/21/2014] [Indexed: 12/15/2022]
Abstract
A new era of medicine is rapidly approaching, which will change not only pathological diagnosis but also medical decision-making. This paper raises the question of how well prepared doctors are to address the new issues that will soon confront them. The human genome has been completely sequenced and general understanding about cancer biology has increased enormously with understanding that unregulated gene function and complicated changes in signal pathways are related to uncontrolled cell growth. Thus, gene-driven therapy involving alterations to genes are recognized to present new therapy options. This advance will necessitate major changes to the decision-making aspect of physicians. This article focuses on defining the pertinent changes and addressing what they mean for practicing physicians.
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Affiliation(s)
- Antti Jekunen
- Vaasa Oncology Clinic, Turku University, Vaasa, Finland
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11
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Ameur A, Bunikis I, Enroth S, Gyllensten U. CanvasDB: a local database infrastructure for analysis of targeted- and whole genome re-sequencing projects. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau098. [PMID: 25281234 PMCID: PMC4184106 DOI: 10.1093/database/bau098] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
CanvasDB is an infrastructure for management and analysis of genetic variants from massively parallel sequencing (MPS) projects. The system stores SNP and indel calls in a local database, designed to handle very large datasets, to allow for rapid analysis using simple commands in R. Functional annotations are included in the system, making it suitable for direct identification of disease-causing mutations in human exome- (WES) or whole-genome sequencing (WGS) projects. The system has a built-in filtering function implemented to simultaneously take into account variant calls from all individual samples. This enables advanced comparative analysis of variant distribution between groups of samples, including detection of candidate causative mutations within family structures and genome-wide association by sequencing. In most cases, these analyses are executed within just a matter of seconds, even when there are several hundreds of samples and millions of variants in the database. We demonstrate the scalability of canvasDB by importing the individual variant calls from all 1092 individuals present in the 1000 Genomes Project into the system, over 4.4 billion SNPs and indels in total. Our results show that canvasDB makes it possible to perform advanced analyses of large-scale WGS projects on a local server. Database URL: https://github.com/UppsalaGenomeCenter/CanvasDB.
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Affiliation(s)
- Adam Ameur
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden
| | - Ignas Bunikis
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden
| | - Stefan Enroth
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden
| | - Ulf Gyllensten
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Sweden
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12
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Vandeweyer G, Van Laer L, Loeys B, Van den Bulcke T, Kooy RF. VariantDB: a flexible annotation and filtering portal for next generation sequencing data. Genome Med 2014; 6:74. [PMID: 25352915 PMCID: PMC4210545 DOI: 10.1186/s13073-014-0074-6] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 09/15/2014] [Indexed: 12/30/2022] Open
Abstract
Interpretation of the multitude of variants obtained from next generation sequencing (NGS) is labor intensive and complex. Web-based interfaces such as Galaxy streamline the generation of variant lists but lack flexibility in the downstream annotation and filtering that are necessary to identify causative variants in medical genomics. To this end, we built VariantDB, a web-based interactive annotation and filtering platform that automatically annotates variants with allele frequencies, functional impact, pathogenicity predictions and pathway information. VariantDB allows filtering by all annotations, under dominant, recessive or de novo inheritance models and is freely available at http://www.biomina.be/app/variantdb/.
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Affiliation(s)
- Geert Vandeweyer
- Department of Medical Genetics, University of Antwerp, 2650 Edegem, Antwerp Belgium ; Biomedical Informatics Research Center Antwerp, University and University Hospital of Antwerp, 2650 Edegem, Antwerp Belgium
| | - Lut Van Laer
- Department of Medical Genetics, University of Antwerp, 2650 Edegem, Antwerp Belgium ; Department of Medical Genetics, University Hospital of Antwerp, 2650 Edegem, Antwerp Belgium
| | - Bart Loeys
- Department of Medical Genetics, University of Antwerp, 2650 Edegem, Antwerp Belgium ; Department of Medical Genetics, University Hospital of Antwerp, 2650 Edegem, Antwerp Belgium
| | - Tim Van den Bulcke
- Biomedical Informatics Research Center Antwerp, University and University Hospital of Antwerp, 2650 Edegem, Antwerp Belgium
| | - R Frank Kooy
- Department of Medical Genetics, University of Antwerp, 2650 Edegem, Antwerp Belgium
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Smedley D, Köhler S, Czeschik JC, Amberger J, Bocchini C, Hamosh A, Veldboer J, Zemojtel T, Robinson PN. Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases. Bioinformatics 2014; 30:3215-22. [PMID: 25078397 PMCID: PMC4221119 DOI: 10.1093/bioinformatics/btu508] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Motivation: Whole-exome sequencing (WES) has opened up previously unheard of possibilities for identifying novel disease genes in Mendelian disorders, only about half of which have been elucidated to date. However, interpretation of WES data remains challenging. Results: Here, we analyze protein–protein association (PPA) networks to identify candidate genes in the vicinity of genes previously implicated in a disease. The analysis, using a random-walk with restart (RWR) method, is adapted to the setting of WES by developing a composite variant-gene relevance score based on the rarity, location and predicted pathogenicity of variants and the RWR evaluation of genes harboring the variants. Benchmarking using known disease variants from 88 disease-gene families reveals that the correct gene is ranked among the top 10 candidates in ≥50% of cases, a figure which we confirmed using a prospective study of disease genes identified in 2012 and PPA data produced before that date. We implement our method in a freely available Web server, ExomeWalker, that displays a ranked list of candidates together with information on PPAs, frequency and predicted pathogenicity of the variants to allow quick and effective searches for candidates that are likely to reward closer investigation. Availability and implementation: http://compbio.charite.de/ExomeWalker Contact: peter.robinson@charite.de
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Affiliation(s)
- Damian Smedley
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
| | - Sebastian Köhler
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
| | - Johanna Christina Czeschik
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
| | - Joanna Amberger
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
| | - Carol Bocchini
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
| | - Ada Hamosh
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
| | - Julian Veldboer
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
| | - Tomasz Zemojtel
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
| | - Peter N Robinson
- Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany Mouse Informatics Group, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Genome Informatics Department, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, 45122 Essen, Germany, McKusick-Nathans Institute of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA, Department of Mathematics and Computer Science, Institute for Bioinformatics, Freie Universität Berlin, Takustrasse 9, 14195 Berlin, Germany, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-701 Poznan, Poland, Berlin-Brandenburg Center for Regenerative Therapies, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin and Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany Mouse Informatics Group, The Wellcome Trust Sang
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Tournier I, Marlin R, Walton K, Charbonnier F, Coutant S, Théry JC, Charbonnier C, Spurrell C, Vezain M, Ippolito L, Bougeard G, Roman H, Tinat J, Sabourin JC, Stoppa-Lyonnet D, Caron O, Bressac-de Paillerets B, Vaur D, King MC, Harrison C, Frebourg T. Germline mutations of inhibins in early-onset ovarian epithelial tumors. Hum Mutat 2013; 35:294-7. [PMID: 24302632 PMCID: PMC4284000 DOI: 10.1002/humu.22489] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2013] [Accepted: 11/22/2013] [Indexed: 01/26/2023]
Abstract
To identify novel genetic bases of early-onset epithelial ovarian tumors, we used the trio exome sequencing strategy in a patient without familial history of cancer who presented metastatic serous ovarian adenocarcinomas at 21 years of age. We identified a single de novo mutation (c.1157A>G/p.Asn386Ser) within the INHBA gene encoding the βA-subunit of inhibins/activins, which play a key role in ovarian development. In vitro, this mutation alters the ratio of secreted activins and inhibins. In a second patient with early-onset serous borderline papillary cystadenoma, we identified an unreported germline mutation (c.179G>T/p.Arg60Leu) of the INHA gene encoding the α-subunit, the partner of the βA-subunit. This mutation also alters the secreted activin/inhibin ratio, by disrupting both inhibin A and inhibin B biosynthesis. In a cohort of 62 cases, we detected an additional unreported germline mutation of the INHBA gene (c.839G>A/p.Gly280Glu). Our results strongly suggest that inhibin mutations contribute to the genetic determinism of epithelial ovarian tumors.
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Affiliation(s)
- Isabelle Tournier
- Inserm U1079, Institute for Research and Innovation in Biomedicine, University of Rouen, Rouen, Cancéropôle Nord-Ouest, France
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15
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Riera C, Lois S, de la Cruz X. Prediction of pathological mutations in proteins: the challenge of integrating sequence conservation and structure stability principles. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2013. [DOI: 10.1002/wcms.1170] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Casandra Riera
- Laboratory of Translational Bioinformatics in Neuroscience; VHIR; Barcelona Spain
| | - Sergio Lois
- Laboratory of Translational Bioinformatics in Neuroscience; VHIR; Barcelona Spain
| | - Xavier de la Cruz
- Laboratory of Translational Bioinformatics in Neuroscience; VHIR; Barcelona Spain
- Institució Catalana per la Recerca i Estudis Avançats (ICREA); Barcelona Spain
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16
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Bromberg Y. Building a genome analysis pipeline to predict disease risk and prevent disease. J Mol Biol 2013; 425:3993-4005. [PMID: 23928561 DOI: 10.1016/j.jmb.2013.07.038] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Revised: 07/26/2013] [Accepted: 07/28/2013] [Indexed: 12/24/2022]
Abstract
Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.
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Affiliation(s)
- Y Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08873, USA.
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17
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Matullo G, Di Gaetano C, Guarrera S. Next generation sequencing and rare genetic variants: from human population studies to medical genetics. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2013; 54:518-532. [PMID: 23922201 DOI: 10.1002/em.21799] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Revised: 05/31/2013] [Accepted: 06/09/2013] [Indexed: 06/02/2023]
Abstract
The allelic frequency spectrum emerging from several Next Generation Sequencing (NGS) projects is revealing important details about evolutionary and demographic forces that shaped the human genome. Herein, we discuss some of the achievements of the use of low-frequency and rare variants from NGS studies. The majority of variants that affect protein-coding regions are recent and rare. Often, the novel rare variants are enriched for deleterious alleles and are population-specific, making them suitable for the study of disease susceptibility. To investigate this kind of variation and its effects in association studies, very large sample sizes will be necessary to achieve sufficient statistical power. Moreover, as these variants are typically population-specific, the replication of disease associations across populations could be very difficult due to population stratification. Therefore, the design of experiments focusing on the identification of rare variants and their effects should be carefully planned. Although several successes have already been achieved through NGS for genetic epidemiology, pharmacogenetic and clinical purposes, with improvements of the sequencing technology and decreased costs, further advances are expected in the near future.
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Affiliation(s)
- Giuseppe Matullo
- Dipartimento di Scienze Mediche, Università di Torino, Torino, Italy.
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18
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Olender T, Safran M, Edgar R, Stelzer G, Nativ N, Rosen N, Shtrichman R, Mazor Y, West MD, Keydar I, Rappaport N, Belinky F, Warshawsky D, Lancet D. An Overview of Synergistic Data Tools for Biological Scrutiny. Isr J Chem 2013. [DOI: 10.1002/ijch.201200094] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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19
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Wren JD, Dozmorov MG, Burian D, Kaundal R, Bridges S, Kupfer DM. Proceedings of the 2012 MidSouth computational biology and bioinformatics society (MCBIOS) conference. BMC Bioinformatics 2012; 13 Suppl 15:S1. [PMID: 23046182 PMCID: PMC3439718 DOI: 10.1186/1471-2105-13-s15-s1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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