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Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology. Brief Bioinform 2021; 22:6330938. [PMID: 34329375 DOI: 10.1093/bib/bbab259] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 06/14/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022] Open
Abstract
Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.
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Affiliation(s)
- Amarinder Singh Thind
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Isha Monga
- Columbia University, New York City, NY, USA
| | | | - Pallawi Kumari
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | - Kiran Dindhoria
- Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India
| | | | - Marie Ranson
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
| | - Bruce Ashford
- University of Wollongong, Wollongong, Australia.,Illawarra Health and Medical Research Institute, Wollongong, Australia
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2
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Umar M, Megarbane A, Shan J, Syed N, Chouery E, Aliyev E, Jithesh P, Temanni R, Mansour I, Chouchane L, Ismail Chouchane A. Genome sequencing unveils mutational landscape of the familial Mediterranean fever: Potential implications of IL33/ST2 signalling. J Cell Mol Med 2020; 24:11294-11306. [PMID: 32853466 PMCID: PMC7576248 DOI: 10.1111/jcmm.15701] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 07/09/2020] [Accepted: 07/14/2020] [Indexed: 12/30/2022] Open
Abstract
Familial Mediterranean fever (FMF) is the most common auto-inflammatory disease. It is transmitted as autosomal recessive trait with mutations in MEditerranean FeVer (MEFV) gene. Despite a typical clinical expression, many patients have either a single or no mutation in MEFV. The current work is aimed to revisit the genetic landscape of FMF disease using high-coverage whole genome sequencing. In atypical patients (carrying a single or no mutation in MEFV), we revealed many rare variants in genes associated with auto-inflammatory disorders, and more interestingly, we discovered a novel variant ( a 2.1-Kb deletion) in exon 11 of IL1RL1 gene, present only in patients. To validate and screen this patient-specific variant, a tandem of allele-specific PCR and quantitative real-time PCR was performed in 184 FMF patients and 218 healthy controls and we demonstrated that the novel deletion was absent in controls and was present in more than 19% of patients. This study sheds more light on the mutational landscape of FMF. Our discovery of a disease-specific variant in IL1RL1 gene may constitute a novel genetic marker for FMF. This finding suggesting a potential role of the IL33/ST2 signalling in the disease pathogenicity highlights a new paradigm in FMF pathophysiology.
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Affiliation(s)
- Meenakshi Umar
- Laboratory of Inflammation Research, Immunology Department, Sidra Medicine, Doha, Qatar
| | - Andre Megarbane
- Institut Jérôme Lejeune, Paris, France.,Centre Medical et Psychopedagogique, Beirut, Lebanon
| | - Jingxuan Shan
- Laboratory of Genetic Medicine and Immunology, Weill Cornell Medicine-Qatar, Qatar.,Department of Genetic Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Najeeb Syed
- Biomedical Informatics Division, Sidra Medicine, Doha, Qatar
| | - Eliane Chouery
- Medical School, Lebanese American University, Beirut, Lebanon
| | - Elbay Aliyev
- Human Genetics Department, Sidra Medicine, Doha, Qatar
| | - Puthen Jithesh
- Biomedical Informatics Division, Sidra Medicine, Doha, Qatar
| | - Ramzi Temanni
- Biomedical Informatics Division, Sidra Medicine, Doha, Qatar
| | - Issam Mansour
- American University of Science and Technology (AUST), Beirut, Lebanon
| | - Lotfi Chouchane
- Laboratory of Genetic Medicine and Immunology, Weill Cornell Medicine-Qatar, Qatar.,Department of Genetic Medicine, Weill Cornell Medicine, New York, NY, USA.,Department of Microbiology & Immunology, Weill Cornell Medicine, New York, NY, USA
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3
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Galano-Frutos JJ, García-Cebollada H, Sancho J. Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when. Brief Bioinform 2019; 22:3-19. [PMID: 31813950 DOI: 10.1093/bib/bbz146] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/22/2019] [Accepted: 10/25/2019] [Indexed: 12/18/2022] Open
Abstract
The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical-chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80-85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore's law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.
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Affiliation(s)
- Juan J Galano-Frutos
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
| | | | - Javier Sancho
- Protein Folding and Molecular Design (ProtMol)' group at BIFI, University of Zaragoza
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4
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Geoffroy V, Herenger Y, Kress A, Stoetzel C, Piton A, Dollfus H, Muller J. AnnotSV: an integrated tool for structural variations annotation. Bioinformatics 2019; 34:3572-3574. [PMID: 29669011 DOI: 10.1093/bioinformatics/bty304] [Citation(s) in RCA: 198] [Impact Index Per Article: 39.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 04/13/2018] [Indexed: 01/27/2023] Open
Abstract
Summary Structural Variations (SV) are a major source of variability in the human genome that shaped its actual structure during evolution. Moreover, many human diseases are caused by SV, highlighting the need to accurately detect those genomic events but also to annotate them and assist their biological interpretation. Therefore, we developed AnnotSV that compiles functionally, regulatory and clinically relevant information and aims at providing annotations useful to (i) interpret SV potential pathogenicity and (ii) filter out SV potential false positive. In particular, AnnotSV reports heterozygous and homozygous counts of single nucleotide variations (SNVs) and small insertions/deletions called within each SV for the analyzed patients, this genomic information being extremely useful to support or question the existence of an SV. We also report the computed allelic frequency relative to overlapping variants from DGV (MacDonald et al., 2014), that is especially powerful to filter out common SV. To delineate the strength of AnnotSV, we annotated the 4751 SV from one sample of the 1000 Genomes Project, integrating the sample information of four million of SNV/indel, in less than 60 s. Availability and implementation AnnotSV is implemented in Tcl and runs in command line on all platforms. The source code is available under the GNU GPL license. Source code, README and Supplementary data are available at http://lbgi.fr/AnnotSV/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Véronique Geoffroy
- Laboratoire de Génétique Médicale, UMR_S INSERM U1112, IGMA, Faculté de Médecine FMTS, Université de Strasbourg, Strasbourg, France
| | - Yvan Herenger
- Service de Génétique Médicale, CHU de Tours, Tours, France
| | - Arnaud Kress
- ICUBE UMR 7357, Complex Systems and Translational Bioinformatics (CSTB), Université de Strasbourg-CNRS-FMTS, Strasbourg, France
| | - Corinne Stoetzel
- Laboratoire de Génétique Médicale, UMR_S INSERM U1112, IGMA, Faculté de Médecine FMTS, Université de Strasbourg, Strasbourg, France
| | - Amélie Piton
- Laboratoires de Diagnostic Génétique, Institut de Génétique Médicale d'Alsace (IGMA), Hôpitaux Universitaires de Strasbourg, Strasbourg Cedex, France.,Institut de Génétique et de Biologie Moleculaire et Cellulaire, INSERM U964, CNRS UMR7104, Université de Strasbourg, Illkirch, France
| | - Hélène Dollfus
- Laboratoire de Génétique Médicale, UMR_S INSERM U1112, IGMA, Faculté de Médecine FMTS, Université de Strasbourg, Strasbourg, France.,Centre de référence pour les Affections Rares en Génétique Ophtalmologique (CARGO), Filière SENSGENE, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Jean Muller
- Laboratoire de Génétique Médicale, UMR_S INSERM U1112, IGMA, Faculté de Médecine FMTS, Université de Strasbourg, Strasbourg, France.,Laboratoires de Diagnostic Génétique, Institut de Génétique Médicale d'Alsace (IGMA), Hôpitaux Universitaires de Strasbourg, Strasbourg Cedex, France
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Klimiankou M, Uenalan M, Kandabarau S, Nustede R, Steiert I, Mellor-Heineke S, Zeidler C, Skokowa J, Welte K. Ultra-Sensitive CSF3R Deep Sequencing in Patients With Severe Congenital Neutropenia. Front Immunol 2019; 10:116. [PMID: 30891028 PMCID: PMC6413711 DOI: 10.3389/fimmu.2019.00116] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Accepted: 01/15/2019] [Indexed: 11/16/2022] Open
Abstract
High frequency of acquired CSF3R (colony stimulating factor 3 receptor, granulocyte) mutations has been described in patients with severe congenital neutropenia (CN) at pre-leukemia stage and overt acute myeloid leukemia (AML) or myelodysplastic syndrome (MDS). Here, we report the establishment of an ultra-sensitive deep sequencing of a CSF3R segment encoding the intracellular “critical region” of the G-CSFR known to be mutated in CN-MDS/AML patients. Using this method, we achieved a mutant allele frequency (MAF) detection rate of 0.01%. We detected CSF3R mutations in CN patients with different genetic backgrounds, but not in patients with other types of bone marrow failure syndromes chronically treated with G-CSF (e.g., Shwachman-Diamond Syndrome). Comparison of CSF3R deep sequencing results of DNA and cDNA from the bone marrow and peripheral blood cells revealed the highest sensitivity of cDNA from the peripheral blood polymorphonuclear neutrophils. This approach enables the identification of low-frequency CSF3R mutant clones, increases sensitivity, and earlier detection of CSF3R mutations acquired during the course of leukemogenic evolution of pre-leukemia HSCs of CN patients. We suggest application of sequencing of the entire CSF3R gene at diagnosis to identify patients with inherited lost-of-function CSF3R mutations and annual ultra-deep sequencing of the critical region of CSF3R to monitor acquisition of CSF3R mutations.
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Affiliation(s)
- Maksim Klimiankou
- Department of Hematology, Oncology, Immunology, Rheumatology and Pulmonology, University Hospital Tübingen, Tübingen, Germany
| | - Murat Uenalan
- Department of Molecular Hematopoiesis, Hannover Medical School, Hannover, Germany
| | - Siarhei Kandabarau
- Department of Hematology, Oncology, Immunology, Rheumatology and Pulmonology, University Hospital Tübingen, Tübingen, Germany
| | - Rainer Nustede
- Department of Surgery, Children's Hospital, Hannover Medical School, Hannover, Germany
| | - Ingeborg Steiert
- Department of Hematology, Oncology, Immunology, Rheumatology and Pulmonology, University Hospital Tübingen, Tübingen, Germany
| | - Sabine Mellor-Heineke
- Department of Hematology, Oncology and Bone Marrow Transplantation, Hannover Medical School, Hannover, Germany
| | - Cornelia Zeidler
- Department of Hematology, Oncology and Bone Marrow Transplantation, Hannover Medical School, Hannover, Germany
| | - Julia Skokowa
- Department of Hematology, Oncology, Immunology, Rheumatology and Pulmonology, University Hospital Tübingen, Tübingen, Germany
| | - Karl Welte
- Department of Pediatric Hematology, Oncology and Bone Marrow Transplantation, University Hospital Tübingen, Tübingen, Germany
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Huang PJ, Lee CC, Chiu LY, Huang KY, Yeh YM, Yang CY, Chiu CH, Tang P. VAReporter: variant reporter for cancer research of massive parallel sequencing. BMC Genomics 2018; 19:86. [PMID: 29764369 PMCID: PMC5954270 DOI: 10.1186/s12864-018-4468-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Background High throughput sequencing technologies have been an increasingly critical aspect of precision medicine owing to a better identification of disease targets, which contributes to improved health care cost and clinical outcomes. In particular, disease-oriented targeted enrichment sequencing is becoming a widely-accepted application for diagnostic purposes, which can interrogate known diagnostic variants as well as identify novel biomarkers from panels of entire human coding exome or disease-associated genes. Results We introduce a workflow named VAReporter to facilitate the management of variant assessment in disease-targeted sequencing, the identification of pathogenic variants, the interpretation of biological effects and the prioritization of clinically actionable targets. State-of-art algorithms that account for mutation phenotypes are used to rank the importance of mutated genes through visual analytic strategies. We established an extensive annotation source by integrating a wide variety of biomedical databases and followed the American College of Medical Genetics and Genomics (ACMG) guidelines for interpretation and reporting of sequence variations. Conclusions In summary, VAReporter is the first web server designed to provide a “one-stop” resource for individual’s diagnosis and large-scale cohort studies, and is freely available at http://rnd.cgu.edu.tw/vareporter.
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Affiliation(s)
- Po-Jung Huang
- Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.,Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.,Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Chi-Ching Lee
- Department and Graduate Institute of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Ling-Ya Chiu
- Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Kuo-Yang Huang
- Graduate Institute of Pathology and Parasitology, National Defense Medical Center, Taipei, Taiwan
| | - Yuan-Ming Yeh
- Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Chia-Yu Yang
- Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan
| | - Cheng-Hsun Chiu
- Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan
| | - Petrus Tang
- Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan. .,Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan. .,Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.
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7
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GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases. BMC Bioinformatics 2018; 19:25. [PMID: 29374474 PMCID: PMC5787240 DOI: 10.1186/s12859-018-2027-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 01/15/2018] [Indexed: 01/26/2023] Open
Abstract
Background GenIO is a novel web-server, designed to assist clinical genomics researchers and medical doctors in the diagnostic process of rare genetic diseases. The tool identifies the most probable variants causing a rare disease, using the genomic and clinical information provided by a medical practitioner. Variants identified in a whole-genome, whole-exome or target sequencing studies are annotated, classified and filtered by clinical significance. Candidate genes associated with the patient’s symptoms, suspected disease and complementary findings are identified to obtain a small manageable number of the most probable recessive and dominant candidate gene variants associated with the rare disease case. Additionally, following the American College of Medical Genetics and Genomics and the Association of Molecular Pathology (ACMG-AMP) guidelines and recommendations, all potentially pathogenic variants that might be contributing to disease and secondary findings are identified. Results A retrospective study was performed on 40 patients with a diagnostic rate of 40%. All the known genes that were previously considered as disease causing were correctly identified in the final inherit model output lists. In previously undiagnosed cases, we had no additional yield. Conclusion This unique, intuitive and user-friendly tool to assists medical doctors in the clinical genomics diagnostic process is openly available at https://bioinformatics.ibioba-mpsp-conicet.gov.ar/GenIO/. Electronic supplementary material The online version of this article (10.1186/s12859-018-2027-3) contains supplementary material, which is available to authorized users.
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8
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Ruiz-Martínez J, Azcona LJ, Bergareche A, Martí-Massó JF, Paisán-Ruiz C. Whole-exome sequencing associates novel CSMD1 gene mutations with familial Parkinson disease. NEUROLOGY-GENETICS 2017; 3:e177. [PMID: 28808687 PMCID: PMC5540655 DOI: 10.1212/nxg.0000000000000177] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 06/16/2017] [Indexed: 12/26/2022]
Abstract
OBJECTIVE Despite the enormous advancements made in deciphering the genetic architecture of Parkinson disease (PD), the majority of PD is idiopathic, with single gene mutations explaining only a small proportion of the cases. METHODS In this study, we clinically evaluated 2 unrelated Spanish families diagnosed with PD, in which known PD genes were previously excluded, and performed whole-exome sequencing analyses in affected individuals for disease gene identification. RESULTS Patients were diagnosed with typical PD without relevant distinctive symptoms. Two different novel mutations were identified in the CSMD1 gene. The CSMD1 gene, which encodes a complement control protein that is known to participate in the complement activation and inflammation in the developing CNS, was previously shown to be associated with the risk of PD in a genome-wide association study. CONCLUSIONS We conclude that the CSMD1 mutations identified in this study might be responsible for the PD phenotype observed in our examined patients. This, along with previous reported studies, may suggest the complement pathway as an important therapeutic target for PD and other neurodegenerative diseases.
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Affiliation(s)
- Javier Ruiz-Martínez
- Department of Neurology (J.R.-M., A.B., J.F.M.-M.), University Hospital Donostia, San Sebastián, Spain; Neurosciences Area, Biodonostia Research Institute (J.R.-M., A.B., J.F.M.-M.), San Sebastián, Spain; Center for Biomedical Research in Neurodegenerative Diseases Network (CIBERNED) (J.R.-M., A.B., J.F.M.-M.), Madrid, Spain; Department of Neuroscience (L.J.A.), Departments of Neurology, Psychiatry, and Genetics and Genomic Sciences (C.P.-R.), Friedman Brain and Mindich Child Health and Development Institutes (C.P.-R.), Icahn School of Medicine at Mount Sinai, New York, NY; and Department of Neuroscience (J.F.M.-M.), University of the Basque Country-UPV-EHU, San Sebastián, Spain
| | - Luis J Azcona
- Department of Neurology (J.R.-M., A.B., J.F.M.-M.), University Hospital Donostia, San Sebastián, Spain; Neurosciences Area, Biodonostia Research Institute (J.R.-M., A.B., J.F.M.-M.), San Sebastián, Spain; Center for Biomedical Research in Neurodegenerative Diseases Network (CIBERNED) (J.R.-M., A.B., J.F.M.-M.), Madrid, Spain; Department of Neuroscience (L.J.A.), Departments of Neurology, Psychiatry, and Genetics and Genomic Sciences (C.P.-R.), Friedman Brain and Mindich Child Health and Development Institutes (C.P.-R.), Icahn School of Medicine at Mount Sinai, New York, NY; and Department of Neuroscience (J.F.M.-M.), University of the Basque Country-UPV-EHU, San Sebastián, Spain
| | - Alberto Bergareche
- Department of Neurology (J.R.-M., A.B., J.F.M.-M.), University Hospital Donostia, San Sebastián, Spain; Neurosciences Area, Biodonostia Research Institute (J.R.-M., A.B., J.F.M.-M.), San Sebastián, Spain; Center for Biomedical Research in Neurodegenerative Diseases Network (CIBERNED) (J.R.-M., A.B., J.F.M.-M.), Madrid, Spain; Department of Neuroscience (L.J.A.), Departments of Neurology, Psychiatry, and Genetics and Genomic Sciences (C.P.-R.), Friedman Brain and Mindich Child Health and Development Institutes (C.P.-R.), Icahn School of Medicine at Mount Sinai, New York, NY; and Department of Neuroscience (J.F.M.-M.), University of the Basque Country-UPV-EHU, San Sebastián, Spain
| | - Jose F Martí-Massó
- Department of Neurology (J.R.-M., A.B., J.F.M.-M.), University Hospital Donostia, San Sebastián, Spain; Neurosciences Area, Biodonostia Research Institute (J.R.-M., A.B., J.F.M.-M.), San Sebastián, Spain; Center for Biomedical Research in Neurodegenerative Diseases Network (CIBERNED) (J.R.-M., A.B., J.F.M.-M.), Madrid, Spain; Department of Neuroscience (L.J.A.), Departments of Neurology, Psychiatry, and Genetics and Genomic Sciences (C.P.-R.), Friedman Brain and Mindich Child Health and Development Institutes (C.P.-R.), Icahn School of Medicine at Mount Sinai, New York, NY; and Department of Neuroscience (J.F.M.-M.), University of the Basque Country-UPV-EHU, San Sebastián, Spain
| | - Coro Paisán-Ruiz
- Department of Neurology (J.R.-M., A.B., J.F.M.-M.), University Hospital Donostia, San Sebastián, Spain; Neurosciences Area, Biodonostia Research Institute (J.R.-M., A.B., J.F.M.-M.), San Sebastián, Spain; Center for Biomedical Research in Neurodegenerative Diseases Network (CIBERNED) (J.R.-M., A.B., J.F.M.-M.), Madrid, Spain; Department of Neuroscience (L.J.A.), Departments of Neurology, Psychiatry, and Genetics and Genomic Sciences (C.P.-R.), Friedman Brain and Mindich Child Health and Development Institutes (C.P.-R.), Icahn School of Medicine at Mount Sinai, New York, NY; and Department of Neuroscience (J.F.M.-M.), University of the Basque Country-UPV-EHU, San Sebastián, Spain
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9
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Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI, Roy S, Tsimberidou AM, Vnencak-Jones CL, Wolff DJ, Younes A, Nikiforova MN. Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn 2017; 19:4-23. [PMID: 27993330 DOI: 10.1016/j.jmoldx.2016.10.002] [Citation(s) in RCA: 1164] [Impact Index Per Article: 166.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 10/03/2016] [Accepted: 10/13/2016] [Indexed: 01/01/2023] Open
Abstract
Widespread clinical laboratory implementation of next-generation sequencing-based cancer testing has highlighted the importance and potential benefits of standardizing the interpretation and reporting of molecular results among laboratories. A multidisciplinary working group tasked to assess the current status of next-generation sequencing-based cancer testing and establish standardized consensus classification, annotation, interpretation, and reporting conventions for somatic sequence variants was convened by the Association for Molecular Pathology with liaison representation from the American College of Medical Genetics and Genomics, American Society of Clinical Oncology, and College of American Pathologists. On the basis of the results of professional surveys, literature review, and the Working Group's subject matter expert consensus, a four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Cancer genomics is a rapidly evolving field; therefore, the clinical significance of any variant in therapy, diagnosis, or prognosis should be reevaluated on an ongoing basis. Reporting of genomic variants should follow standard nomenclature, with testing method and limitations clearly described. Clinical recommendations should be concise and correlate with histological and clinical findings.
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Affiliation(s)
- Marilyn M Li
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Pathology and Laboratory Medicine, Division of Genomic Diagnostics, the Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
| | - Michael Datto
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Duke University School of Medicine, Durham, North Carolina
| | - Eric J Duncavage
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Shashikant Kulkarni
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Baylor Genetics, Houston, Texas
| | - Neal I Lindeman
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Somak Roy
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Apostolia M Tsimberidou
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Cindy L Vnencak-Jones
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Daynna J Wolff
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, South Carolina
| | - Anas Younes
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marina N Nikiforova
- Interpretation of Sequence Variants in Somatic Conditions Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Bethesda, Maryland; University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
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10
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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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12
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Li J, Batcha AMN, Grüning B, Mansmann UR. An NGS Workflow Blueprint for DNA Sequencing Data and Its Application in Individualized Molecular Oncology. Cancer Inform 2016; 14:87-107. [PMID: 27081306 PMCID: PMC4827795 DOI: 10.4137/cin.s30793] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Revised: 03/02/2016] [Accepted: 03/17/2016] [Indexed: 12/23/2022] Open
Abstract
Next-generation sequencing (NGS) technologies that have advanced rapidly in the past few years possess the potential to classify diseases, decipher the molecular code of related cell processes, identify targets for decision-making on targeted therapy or prevention strategies, and predict clinical treatment response. Thus, NGS is on its way to revolutionize oncology. With the help of NGS, we can draw a finer map for the genetic basis of diseases and can improve our understanding of diagnostic and prognostic applications and therapeutic methods. Despite these advantages and its potential, NGS is facing several critical challenges, including reduction of sequencing cost, enhancement of sequencing quality, improvement of technical simplicity and reliability, and development of semiautomated and integrated analysis workflow. In order to address these challenges, we conducted a literature research and summarized a four-stage NGS workflow for providing a systematic review on NGS-based analysis, explaining the strength and weakness of diverse NGS-based software tools, and elucidating its potential connection to individualized medicine. By presenting this four-stage NGS workflow, we try to provide a minimal structural layout required for NGS data storage and reproducibility.
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Affiliation(s)
- Jian Li
- Institute for Medical Informatics, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany.; German Cancer Consortium (DKTK), Heidelberg, Germany.; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Aarif Mohamed Nazeer Batcha
- Institute for Medical Informatics, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany.; German Cancer Consortium (DKTK), Heidelberg, Germany.; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany.; Center for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany
| | - Ulrich R Mansmann
- Institute for Medical Informatics, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany.; German Cancer Consortium (DKTK), Heidelberg, Germany
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Bergareche A, Bednarz M, Sánchez E, Krebs CE, Ruiz-Martinez J, De La Riva P, Makarov V, Gorostidi A, Jurkat-Rott K, Marti-Masso JF, Paisán-Ruiz C. SCN4A pore mutation pathogenetically contributes to autosomal dominant essential tremor and may increase susceptibility to epilepsy. Hum Mol Genet 2015; 24:7111-20. [PMID: 26427606 DOI: 10.1093/hmg/ddv410] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Accepted: 09/25/2015] [Indexed: 12/19/2022] Open
Abstract
Essential tremor (ET) is the most prevalent movement disorder, affecting millions of people in the USA. Although a positive family history is one of the most important risk factors for ET, the genetic causes of ET remain unknown. In an attempt to identify genetic causes for ET, we performed whole-exome sequencing analyses in a large Spanish family with ET, in which two patients also developed epilepsy. To further assess pathogenicity, site-directed mutagenesis, mouse and human brain expression analyses, and patch clamp techniques were performed. A disease-segregating mutation (p.Gly1537Ser) in the SCN4A gene was identified. Posterior functional analyses demonstrated that more rapid kinetics at near-threshold potentials altered ion selectivity and facilitated the conductance of both potassium and ammonium ions, which could contribute to tremor and increase susceptibility to epilepsy, respectively. In this report, for the first time, we associated the genetic variability of SCN4A with the development of essential tremor, which adds ET to the growing list of neurological channelopathies.
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Affiliation(s)
- Alberto Bergareche
- Movement Disorders Unit, Department of Neurology Hospital Universitario Donostia San Sebastián Guipuzcoa Spain, Biodonostia Research Institute, Area of Neurosciences, Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED), Carlos III Health Institute, Madrid, Spain
| | - Marcin Bednarz
- Division of Neurophysiology, Ulm University, Albert-Einstein-Allee 11, 89070 Ulm, Germany
| | | | | | - Javier Ruiz-Martinez
- Movement Disorders Unit, Department of Neurology Hospital Universitario Donostia San Sebastián Guipuzcoa Spain, Biodonostia Research Institute, Area of Neurosciences, Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED), Carlos III Health Institute, Madrid, Spain
| | - Patricia De La Riva
- Movement Disorders Unit, Department of Neurology Hospital Universitario Donostia San Sebastián Guipuzcoa Spain, Biodonostia Research Institute, Area of Neurosciences, Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED), Carlos III Health Institute, Madrid, Spain
| | - Vladimir Makarov
- Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Ana Gorostidi
- Movement Disorders Unit, Department of Neurology Hospital Universitario Donostia San Sebastián Guipuzcoa Spain, Biodonostia Research Institute, Area of Neurosciences, Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED), Carlos III Health Institute, Madrid, Spain
| | - Karin Jurkat-Rott
- Division of Neurophysiology, Ulm University, Albert-Einstein-Allee 11, 89070 Ulm, Germany
| | - Jose Felix Marti-Masso
- Movement Disorders Unit, Department of Neurology Hospital Universitario Donostia San Sebastián Guipuzcoa Spain, Biodonostia Research Institute, Area of Neurosciences, Department of Neurosciences University of the Basque Country, EHU-UPV San Sebastián Gipuzkoa Spain, Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED), Carlos III Health Institute, Madrid, Spain
| | - Coro Paisán-Ruiz
- Department of Neurology, Department of Psychiatry, Department of Genetics and Genomic Sciences, Friedman Brain Institute, Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA and
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Yang H, Wang K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat Protoc 2015; 10:1556-66. [PMID: 26379229 DOI: 10.1038/nprot.2015.105] [Citation(s) in RCA: 607] [Impact Index Per Article: 67.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Recent developments in sequencing techniques have enabled rapid and high-throughput generation of sequence data, democratizing the ability to compile information on large amounts of genetic variations in individual laboratories. However, there is a growing gap between the generation of raw sequencing data and the extraction of meaningful biological information. Here, we describe a protocol to use the ANNOVAR (ANNOtate VARiation) software to facilitate fast and easy variant annotations, including gene-based, region-based and filter-based annotations on a variant call format (VCF) file generated from human genomes. We further describe a protocol for gene-based annotation of a newly sequenced nonhuman species. Finally, we describe how to use a user-friendly and easily accessible web server called wANNOVAR to prioritize candidate genes for a Mendelian disease. The variant annotation protocols take 5-30 min of computer time, depending on the size of the variant file, and 5-10 min of hands-on time. In summary, through the command-line tool and the web server, these protocols provide a convenient means to analyze genetic variants generated in humans and other species.
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Affiliation(s)
- Hui Yang
- Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California, USA.,Neuroscience Graduate Program, University of Southern California, Los Angeles, California, USA
| | - Kai Wang
- Zilkha Neurogenetic Institute, University of Southern California, Los Angeles, California, USA.,Department of Psychiatry, University of Southern California, Los Angeles, California, USA.,Department of Preventive Medicine, Division of Bioinformatics, University of Southern California, Los Angeles, California, USA
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15
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Podicheti R, Mockaitis K. FEATnotator: A tool for integrated annotation of sequence features and variation, facilitating interpretation in genomics experiments. Methods 2015; 79-80:11-7. [PMID: 25934264 DOI: 10.1016/j.ymeth.2015.04.028] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Revised: 03/25/2015] [Accepted: 04/22/2015] [Indexed: 11/16/2022] Open
Abstract
As approaches are sought for more efficient and democratized uses of non-model and expanded model genomics references, ease of integration of genomic feature datasets is especially desirable in multidisciplinary research communities. Valuable conclusions are often missed or slowed when researchers refer experimental results to a single reference sequence that lacks integrated pan-genomic and multi-experiment data in accessible formats. Association of genomic positional information, such as results from an expansive variety of next-generation sequencing experiments, with annotated reference features such as genes or predicted protein binding sites, provides the context essential for conclusions and ongoing research. When the experimental system includes polymorphic genomic inputs, rapid calculation of gene structural and protein translational effects of sequence variation from the reference can be invaluable. Here we present FEATnotator, a lightweight, fast and easy to use open source software program that integrates and reports overlap and proximity in genomic information from any user-defined datasets including those from next generation sequencing applications. We illustrate use of the tool by summarizing whole genome sequence variation of a widely used natural isolate of Arabidopsis thaliana in the context of gene models of the reference accession. Previous discovery of a protein coding deletion influencing root development is replicated rapidly. Appropriate even in investigations of a single gene or genic regions such as QTL, comprehensive reports provided by FEATnotator better prepare researchers for interpretation of their experimental results. The tool is available for download at http://featnotator.sourceforge.net.
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Affiliation(s)
- Ram Podicheti
- Center for Genomics and Bioinformatics, Indiana University, 1001 E. Third Street, Bloomington, IN 47405, USA; School of Informatics and Computing, Indiana University, 919 E. Tenth Street, Bloomington, IN 47408, USA.
| | - Keithanne Mockaitis
- Pervasive Technology Institute, Indiana University, 2709 E. Tenth Street, Bloomington, IN 47408, USA; Department of Biology, Indiana University, 915 E. Third Street, Bloomington, IN 47405, USA.
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16
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Pham PH, Shipman WJ, Erikson GA, Schork NJ, Torkamani A. Scripps Genome ADVISER: Annotation and Distributed Variant Interpretation SERver. PLoS One 2015; 10:e0116815. [PMID: 25706643 PMCID: PMC4338027 DOI: 10.1371/journal.pone.0116815] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Accepted: 12/01/2014] [Indexed: 12/31/2022] Open
Abstract
Interpretation of human genomes is a major challenge. We present the Scripps Genome ADVISER (SG-ADVISER) suite, which aims to fill the gap between data generation and genome interpretation by performing holistic, in-depth, annotations and functional predictions on all variant types and effects. The SG-ADVISER suite includes a de-identification tool, a variant annotation web-server, and a user interface for inheritance and annotation-based filtration. SG-ADVISER allows users with no bioinformatics expertise to manipulate large volumes of variant data with ease--without the need to download large reference databases, install software, or use a command line interface. SG-ADVISER is freely available at genomics.scripps.edu/ADVISER.
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Affiliation(s)
- Phillip H. Pham
- Cypher Genomics, Inc., La Jolla, CA 92037, United States of America
| | - William J. Shipman
- Scripps Health, La Jolla, CA 92037, United States of America
- The Scripps Translational Science Institute, La Jolla, CA 92037, United States of America
| | - Galina A. Erikson
- Scripps Health, La Jolla, CA 92037, United States of America
- The Scripps Translational Science Institute, La Jolla, CA 92037, United States of America
| | - Nicholas J. Schork
- Scripps Health, La Jolla, CA 92037, United States of America
- The Scripps Translational Science Institute, La Jolla, CA 92037, United States of America
- The Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA 92037, United States of America
- Cypher Genomics, Inc., La Jolla, CA 92037, United States of America
| | - Ali Torkamani
- Scripps Health, La Jolla, CA 92037, United States of America
- The Scripps Translational Science Institute, La Jolla, CA 92037, United States of America
- The Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, United States of America
- Cypher Genomics, Inc., La Jolla, CA 92037, United States of America
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Kumar P, Al-Shafai M, Al Muftah WA, Chalhoub N, Elsaid MF, Aleem AA, Suhre K. Evaluation of SNP calling using single and multiple-sample calling algorithms by validation against array base genotyping and Mendelian inheritance. BMC Res Notes 2014; 7:747. [PMID: 25339461 PMCID: PMC4216909 DOI: 10.1186/1756-0500-7-747] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Accepted: 10/03/2014] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND With diminishing costs of next generation sequencing (NGS), whole genome analysis becomes a standard tool for identifying genetic causes of inherited diseases. Commercial NGS service providers in general not only provide raw genomic reads, but further deliver SNP calls to their clients. However, the question for the user arises whether to use the SNP data as is, or process the raw sequencing data further through more sophisticated SNP calling pipelines with more advanced algorithms. RESULTS Here we report a detailed comparison of SNPs called using the popular GATK multiple-sample calling protocol to SNPs delivered as part of a 40x whole genome sequencing project by Illumina Inc of 171 human genomes of Arab descent (108 unrelated Qatari genomes, 19 trios, and 2 families with rare diseases) and compare them to variants provided by the Illumina CASAVA pipeline. GATK multi-sample calling identifies more variants than the CASAVA pipeline. The additional variants from GATK are robust for Mendelian consistencies but weak in terms of statistical parameters such as TsTv ratio. However, these additional variants do not make a difference in detecting the causative variants in the studied phenotype. CONCLUSION Both pipelines, GATK multi-sample calling and Illumina CASAVA single sample calling, have highly similar performance in SNP calling at the level of putatively causative variants.
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Affiliation(s)
| | | | | | | | | | | | - Karsten Suhre
- Weill Cornell Medical College in Qatar, Education City, Doha, Qatar.
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18
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Li MJ, Wang J. Current trend of annotating single nucleotide variation in humans--A case study on SNVrap. Methods 2014; 79-80:32-40. [PMID: 25308971 DOI: 10.1016/j.ymeth.2014.10.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Revised: 09/25/2014] [Accepted: 10/02/2014] [Indexed: 12/16/2022] Open
Abstract
As high throughput methods, such as whole genome genotyping arrays, whole exome sequencing (WES) and whole genome sequencing (WGS), have detected huge amounts of genetic variants associated with human diseases, function annotation of these variants is an indispensable step in understanding disease etiology. Large-scale functional genomics projects, such as The ENCODE Project and Roadmap Epigenomics Project, provide genome-wide profiling of functional elements across different human cell types and tissues. With the urgent demands for identification of disease-causal variants, comprehensive and easy-to-use annotation tool is highly in demand. Here we review and discuss current progress and trend of the variant annotation field. Furthermore, we introduce a comprehensive web portal for annotating human genetic variants. We use gene-based features and the latest functional genomics datasets to annotate single nucleotide variation (SNVs) in human, at whole genome scale. We further apply several function prediction algorithms to annotate SNVs that might affect different biological processes, including transcriptional gene regulation, alternative splicing, post-transcriptional regulation, translation and post-translational modifications. The SNVrap web portal is freely available at http://jjwanglab.org/snvrap.
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Affiliation(s)
- Mulin Jun Li
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, China
| | - Junwen Wang
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, China.
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19
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Rosse SA, Auer PL, Carlson CS. Functional annotation of putative regulatory elements at cancer susceptibility Loci. Cancer Inform 2014; 13:5-17. [PMID: 25288875 PMCID: PMC4179605 DOI: 10.4137/cin.s13789] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2014] [Revised: 06/16/2014] [Accepted: 06/17/2014] [Indexed: 01/07/2023] Open
Abstract
Most cancer-associated genetic variants identified from genome-wide association studies (GWAS) do not obviously change protein structure, leading to the hypothesis that the associations are attributable to regulatory polymorphisms. Translating genetic associations into mechanistic insights can be facilitated by knowledge of the causal regulatory variant (or variants) responsible for the statistical signal. Experimental validation of candidate functional variants is onerous, making bioinformatic approaches necessary to prioritize candidates for laboratory analysis. Thus, a systematic approach for recognizing functional (and, therefore, likely causal) variants in noncoding regions is an important step toward interpreting cancer risk loci. This review provides a detailed introduction to current regulatory variant annotations, followed by an overview of how to leverage these resources to prioritize candidate functional polymorphisms in regulatory regions.
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Affiliation(s)
- Stephanie A Rosse
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Paul L Auer
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. ; School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Christopher S Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. ; Department of Epidemiology, University of Washington, Seattle, WA, USA
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Adusumalli S, Mohd Omar MF, Soong R, Benoukraf T. Methodological aspects of whole-genome bisulfite sequencing analysis. Brief Bioinform 2014; 16:369-79. [DOI: 10.1093/bib/bbu016] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Accepted: 04/17/2014] [Indexed: 12/17/2022] Open
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Jäger M, Wang K, Bauer S, Smedley D, Krawitz P, Robinson PN. Jannovar: a java library for exome annotation. Hum Mutat 2014; 35:548-55. [PMID: 24677618 DOI: 10.1002/humu.22531] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Accepted: 02/11/2014] [Indexed: 01/03/2023]
Abstract
Transcript-based annotation and pedigree analysis are two basic steps in the computational analysis of whole-exome sequencing experiments in genetic diagnostics and disease-gene discovery projects. Here, we present Jannovar, a stand-alone Java application as well as a Java library designed to be used in larger software frameworks for exome and genome analysis. Jannovar uses an interval tree to identify all transcripts affected by a given variant, and provides Human Genome Variation Society-compliant annotations both for variants affecting coding sequences and splice junctions as well as untranslated regions and noncoding RNA transcripts. Jannovar can also perform family-based pedigree analysis with Variant Call Format (VCF) files with data from members of a family segregating a Mendelian disorder. Using a desktop computer, Jannovar requires a few seconds to annotate a typical VCF file with exome data. Jannovar is freely available under the BSD2 license. Source code as well as the Java application and library file can be downloaded from http://compbio.charite.de (with tutorial) and https://github.com/charite/jannovar.
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Affiliation(s)
- 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
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Computational approaches to identify functional genetic variants in cancer genomes. Nat Methods 2013; 10:723-9. [PMID: 23900255 DOI: 10.1038/nmeth.2562] [Citation(s) in RCA: 127] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2013] [Accepted: 06/07/2013] [Indexed: 12/13/2022]
Abstract
The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.
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Roy S, Durso MB, Wald A, Nikiforov YE, Nikiforova MN. SeqReporter: automating next-generation sequencing result interpretation and reporting workflow in a clinical laboratory. J Mol Diagn 2013; 16:11-22. [PMID: 24220144 DOI: 10.1016/j.jmoldx.2013.08.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Revised: 08/20/2013] [Accepted: 08/27/2013] [Indexed: 01/15/2023] Open
Abstract
A wide repertoire of bioinformatics applications exist for next-generation sequencing data analysis; however, certain requirements of the clinical molecular laboratory limit their use: i) comprehensive report generation, ii) compatibility with existing laboratory information systems and computer operating system, iii) knowledgebase development, iv) quality management, and v) data security. SeqReporter is a web-based application developed using ASP.NET framework version 4.0. The client-side was designed using HTML5, CSS3, and Javascript. The server-side processing (VB.NET) relied on interaction with a customized SQL server 2008 R2 database. Overall, 104 cases (1062 variant calls) were analyzed by SeqReporter. Each variant call was classified into one of five report levels: i) known clinical significance, ii) uncertain clinical significance, iii) pending pathologists' review, iv) synonymous and deep intronic, and v) platform and panel-specific sequence errors. SeqReporter correctly annotated and classified 99.9% (859 of 860) of sequence variants, including 68.7% synonymous single-nucleotide variants, 28.3% nonsynonymous single-nucleotide variants, 1.7% insertions, and 1.3% deletions. One variant of potential clinical significance was re-classified after pathologist review. Laboratory information system-compatible clinical reports were generated automatically. SeqReporter also facilitated quality management activities. SeqReporter is an example of a customized and well-designed informatics solution to optimize and automate the downstream analysis of clinical next-generation sequencing data. We propose it as a model that may envisage the development of a comprehensive clinical informatics solution.
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Affiliation(s)
- Somak Roy
- Division of Molecular and Genomic Pathology, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
| | - Mary Beth Durso
- Division of Molecular and Genomic Pathology, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Abigail Wald
- Division of Molecular and Genomic Pathology, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Yuri E Nikiforov
- Division of Molecular and Genomic Pathology, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Marina N Nikiforova
- Division of Molecular and Genomic Pathology, Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
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Peterson TA, Doughty E, Kann MG. Towards precision medicine: advances in computational approaches for the analysis of human variants. J Mol Biol 2013; 425:4047-63. [PMID: 23962656 PMCID: PMC3807015 DOI: 10.1016/j.jmb.2013.08.008] [Citation(s) in RCA: 106] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 08/07/2013] [Accepted: 08/08/2013] [Indexed: 12/26/2022]
Abstract
Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.
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Affiliation(s)
- Thomas A Peterson
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Emily Doughty
- Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA
| | - Maricel G Kann
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
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Pavlopoulos GA, Oulas A, Iacucci E, Sifrim A, Moreau Y, Schneider R, Aerts J, Iliopoulos I. Unraveling genomic variation from next generation sequencing data. BioData Min 2013; 6:13. [PMID: 23885890 PMCID: PMC3726446 DOI: 10.1186/1756-0381-6-13] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2013] [Accepted: 07/18/2013] [Indexed: 12/29/2022] Open
Abstract
Elucidating the content of a DNA sequence is critical to deeper understand and decode the genetic information for any biological system. As next generation sequencing (NGS) techniques have become cheaper and more advanced in throughput over time, great innovations and breakthrough conclusions have been generated in various biological areas. Few of these areas, which get shaped by the new technological advances, involve evolution of species, microbial mapping, population genetics, genome-wide association studies (GWAs), comparative genomics, variant analysis, gene expression, gene regulation, epigenetics and personalized medicine. While NGS techniques stand as key players in modern biological research, the analysis and the interpretation of the vast amount of data that gets produced is a not an easy or a trivial task and still remains a great challenge in the field of bioinformatics. Therefore, efficient tools to cope with information overload, tackle the high complexity and provide meaningful visualizations to make the knowledge extraction easier are essential. In this article, we briefly refer to the sequencing methodologies and the available equipment to serve these analyses and we describe the data formats of the files which get produced by them. We conclude with a thorough review of tools developed to efficiently store, analyze and visualize such data with emphasis in structural variation analysis and comparative genomics. We finally comment on their functionality, strengths and weaknesses and we discuss how future applications could further develop in this field.
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Affiliation(s)
- Georgios A Pavlopoulos
- Division of Basic Sciences, University of Crete Medical School, Heraklion 71110, Greece.
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27
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Krebs CE, Karkheiran S, Powell JC, Cao M, Makarov V, Darvish H, Di Paolo G, Walker RH, Shahidi GA, Buxbaum JD, De Camilli P, Yue Z, Paisán-Ruiz C. The Sac1 domain of SYNJ1 identified mutated in a family with early-onset progressive Parkinsonism with generalized seizures. Hum Mutat 2013; 34:1200-7. [PMID: 23804563 DOI: 10.1002/humu.22372] [Citation(s) in RCA: 251] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Accepted: 06/18/2013] [Indexed: 02/06/2023]
Abstract
This study aimed to elucidate the genetic causes underlying early-onset Parkinsonism (EOP) in a consanguineous Iranian family. To attain this, homozygosity mapping and whole-exome sequencing were performed. As a result, a homozygous mutation (c.773G>A; p.Arg258Gln) lying within the NH2 -terminal Sac1-like inositol phosphatase domain of polyphosphoinositide phosphatase synaptojanin 1 (SYNJ1), which has been implicated in the regulation of endocytic traffic at synapses, was identified as the disease-segregating mutation. This mutation impaired the phosphatase activity of SYNJ1 against its Sac1 domain substrates in vitro. We concluded that the SYNJ1 mutation identified here is responsible for the EOP phenotype seen in our patients probably due to deficiencies in its phosphatase activity and consequent impairment of its synaptic functions. Our finding not only opens new avenues of investigation in the synaptic dysfunction mechanisms associated with Parkinsonism, but also suggests phosphoinositide metabolism as a novel therapeutic target for Parkinsonism.
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Affiliation(s)
- Catharine E Krebs
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
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28
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Pabinger S, Dander A, Fischer M, Snajder R, Sperk M, Efremova M, Krabichler B, Speicher MR, Zschocke J, Trajanoski Z. A survey of tools for variant analysis of next-generation genome sequencing data. Brief Bioinform 2013; 15:256-78. [PMID: 23341494 PMCID: PMC3956068 DOI: 10.1093/bib/bbs086] [Citation(s) in RCA: 335] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.
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Affiliation(s)
- Stephan Pabinger
- Division for Bioinformatics, Innsbruck Medical University, Innrain 80, 6020 Innsbruck, Austria. Tel.: +43-512-9003-71401; Fax: +43-512-9003-73100;
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29
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Identification of COL6A2 mutations in progressive myoclonus epilepsy syndrome. Hum Genet 2012; 132:275-83. [DOI: 10.1007/s00439-012-1248-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2012] [Accepted: 10/30/2012] [Indexed: 02/02/2023]
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30
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Sifrim A, Van Houdt JKJ, Tranchevent LC, Nowakowska B, Sakai R, Pavlopoulos GA, Devriendt K, Vermeesch JR, Moreau Y, Aerts J. Annotate-it: a Swiss-knife approach to annotation, analysis and interpretation of single nucleotide variation in human disease. Genome Med 2012; 4:73. [PMID: 23013645 PMCID: PMC3580443 DOI: 10.1186/gm374] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 09/14/2012] [Accepted: 09/26/2012] [Indexed: 12/18/2022] Open
Abstract
The increasing size and complexity of exome/genome sequencing data requires new tools for clinical geneticists to discover disease-causing variants. Bottlenecks in identifying the causative variation include poor cross-sample querying, constantly changing functional annotation and not considering existing knowledge concerning the phenotype. We describe a methodology that facilitates exploration of patient sequencing data towards identification of causal variants under different genetic hypotheses. Annotate-it facilitates handling, analysis and interpretation of high-throughput single nucleotide variant data. We demonstrate our strategy using three case studies. Annotate-it is freely available and test data are accessible to all users at http://www.annotate-it.org.
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Affiliation(s)
- Alejandro Sifrim
- KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
- IBBT Future Health Department, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
| | - Jeroen KJ Van Houdt
- KU Leuven, Centre for Human Genetics, University Hospital Gasthuisberg, Herestraat 49, 3000 Leuven, Belgium
| | - Leon-Charles Tranchevent
- KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
- IBBT Future Health Department, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
| | - Beata Nowakowska
- KU Leuven, Centre for Human Genetics, University Hospital Gasthuisberg, Herestraat 49, 3000 Leuven, Belgium
| | - Ryo Sakai
- KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
- IBBT Future Health Department, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
| | - Georgios A Pavlopoulos
- KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
- IBBT Future Health Department, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
| | - Koen Devriendt
- KU Leuven, Centre for Human Genetics, University Hospital Gasthuisberg, Herestraat 49, 3000 Leuven, Belgium
| | - Joris R Vermeesch
- KU Leuven, Centre for Human Genetics, University Hospital Gasthuisberg, Herestraat 49, 3000 Leuven, Belgium
| | - Yves Moreau
- KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
- IBBT Future Health Department, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
| | - Jan Aerts
- KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
- IBBT Future Health Department, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium
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31
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Cheng YC, Hsiao FC, Yeh EC, Lin WJ, Tang CYL, Tseng HC, Wu HT, Liu CK, Chen CC, Chen YT, Yao A. VarioWatch: providing large-scale and comprehensive annotations on human genomic variants in the next generation sequencing era. Nucleic Acids Res 2012; 40:W76-81. [PMID: 22618869 PMCID: PMC3394242 DOI: 10.1093/nar/gks397] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
VarioWatch (http://genepipe.ncgm.sinica.edu.tw/variowatch/) has been vastly improved since its former publication GenoWatch in the 2008 Web Server Issue. It is now at least 10 000-times faster in annotating a variant. Drastic speed increase, through complete re-design of its working mechanism, makes VarioWatch capable of annotating millions of human genomic variants generated from next generation sequencing in minutes, if not seconds. While using MegaQuery of VarioWatch to quickly annotate variants, users can apply various filters to retrieve a subgroup of variants according to the risk levels, interested regions, etc. that satisfy users’ requirements. In addition to performance leap, many new features have also been added, such as annotation on novel variants, functional analyses on splice sites and in/dels, detailed variant information in tabulated form, plus a risk level decision tree regarding the analyzed variant. Up to 1000 target variants can be visualized with our carefully designed Genome View, Gene View, Transcript View and Variation View. Two commonly used reference versions, NCBI build 36.3 and NCBI build 37.2, are supported. VarioWatch is unique in its ability to annotate comprehensively and efficiently millions of variants online, immediately delivering the results in real time, plus visualizes up to 1000 annotated variants.
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Affiliation(s)
- Yu-Chang Cheng
- National Center for Genome Medicine and Institute of Biomedical Sciences, Academia Sinica, Taiwan 11529, R.O.C
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Exome sequencing identifies GCDH (glutaryl-CoA dehydrogenase) mutations as a cause of a progressive form of early-onset generalized dystonia. Hum Genet 2011; 131:435-42. [DOI: 10.1007/s00439-011-1086-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2011] [Accepted: 08/28/2011] [Indexed: 01/20/2023]
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