1
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Liu Z, Xie Z, Li M. Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data. Genome Biol 2024; 25:188. [PMID: 39010145 PMCID: PMC11247875 DOI: 10.1186/s13059-024-03324-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 06/26/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Structural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection. RESULTS This study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines' detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking . CONCLUSIONS This study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.
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Affiliation(s)
- Zhi Liu
- Program in Bioinformatics, Zhongshan School of Medicine, The Fifth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control (Sun Yat-Sen University), Ministry of Education, Guangzhou, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Miaoxin Li
- Program in Bioinformatics, Zhongshan School of Medicine, The Fifth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
- Key Laboratory of Tropical Disease Control (Sun Yat-Sen University), Ministry of Education, Guangzhou, China.
- Center for Precision Medicine, Sun Yat-Sen University, Guangzhou, China.
- Department of Psychiatry, The University of Hong Kong, Hong Kong, SAR, China.
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China.
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2
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Chukwu W, Lee S, Crane A, Zhang S, Webster S, Mittra I, Imielinski M, Beroukhim R, Dubois F, Dalin S. Comparison of germline and somatic structural variants in cancers reveal systematic differences in variant generating and selection processes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.09.561462. [PMID: 38106141 PMCID: PMC10723258 DOI: 10.1101/2023.10.09.561462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Although several recent studies have characterized structural variants (SVs) in germline and cancer genomes, the features of SVs in these different contexts have not been directly compared. We examined similarities and differences between 2 million germline and 115 thousand tumor SVs from a cohort of 963 patients from The Cancer Genome Atlas (TCGA). We found significant differences in features related to their genomic sequences and localization that suggest differences between SV-generating processes and selective pressures. For example, we found that transposon-mediated processes shape germline much more than somatic SVs, while somatic SVs more frequently show features characteristic of chromoanagenesis. These differences were extensive enough to enable us to develop a classifier - "the great GaTSV" - that accurately distinguishes between germline and cancer SVs in tumor samples that lack a matched normal sample.
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Affiliation(s)
- Wolu Chukwu
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Siyun Lee
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Alexander Crane
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Shu Zhang
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Sophie Webster
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Ipsa Mittra
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Marcin Imielinski
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; New York Genome Center, New York, NY, USA; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA; Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA; Department of Pathology and Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA
| | - Rameen Beroukhim
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Frank Dubois
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin; Humboldt-Universität zu Berlin, Institute of Pathology
| | - Simona Dalin
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Departments of Cancer Biology and Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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3
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Xu Z, Li Q, Marchionni L, Wang K. PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants. Nat Commun 2023; 14:7805. [PMID: 38016949 PMCID: PMC10684511 DOI: 10.1038/s41467-023-43651-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 11/15/2023] [Indexed: 11/30/2023] Open
Abstract
Structural variants (SVs) represent a major source of genetic variation associated with phenotypic diversity and disease susceptibility. While long-read sequencing can discover over 20,000 SVs per human genome, interpreting their functional consequences remains challenging. Existing methods for identifying disease-related SVs focus on deletion/duplication only and cannot prioritize individual genes affected by SVs, especially for noncoding SVs. Here, we introduce PhenoSV, a phenotype-aware machine-learning model that interprets all major types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse genomic features and employs a transformer-based architecture to predict their impacts under a multiple-instance learning framework. With phenotype information, PhenoSV further utilizes gene-phenotype associations to prioritize phenotype-related SVs. Evaluation on extensive human SV datasets covering all SV types demonstrates PhenoSV's superior performance over competing methods. Applications in diseases suggest that PhenoSV can determine disease-related genes from SVs. A web server and a command-line tool for PhenoSV are available at https://phenosv.wglab.org .
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Affiliation(s)
- Zhuoran Xu
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Quan Li
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON, M5G2C1, Canada
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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4
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Ahmad RM, Ali BR, Al-Jasmi F, Sinnott RO, Al Dhaheri N, Mohamad MS. A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer. Brief Bioinform 2023; 25:bbad479. [PMID: 38149678 PMCID: PMC10782903 DOI: 10.1093/bib/bbad479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/22/2023] [Accepted: 12/04/2023] [Indexed: 12/28/2023] Open
Abstract
Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.
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Affiliation(s)
- Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
| | - Bassam R Ali
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
| | - Fatma Al-Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Richard O Sinnott
- School of Computing and Information System, Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Noura Al Dhaheri
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Tawam road, Al Maqam district, Al Ain, Abu Dhabi, United Arab Emirates
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5
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Liu X, Xu W, Leng F, Zhang P, Guo R, Zhang Y, Hao C, Ni X, Li W. NeuroCNVscore: a tissue-specific framework to prioritise the pathogenicity of CNVs in neurodevelopmental disorders. BMJ Paediatr Open 2023; 7:e001966. [PMID: 37407247 DOI: 10.1136/bmjpo-2023-001966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/15/2023] [Indexed: 07/07/2023] Open
Abstract
BACKGROUND Neurodevelopmental disorders (NDDs) are associated with altered development of the brain especially in childhood. Copy number variants (CNVs) play a crucial role in the genetic aetiology of NDDs by disturbing gene expression directly at linear sequence or remotely at three-dimensional genome level in a tissue-specific manner. Despite the substantial increase in NDD studies employing whole-genome sequencing, there is no specific tool for prioritising the pathogenicity of CNVs in the context of NDDs. METHODS Using an XGBoost classifier, we integrated 189 features that represent genomic sequences, gene information and functional/genomic segments for evaluating genome-wide CNVs in a neuro/brain-specific manner, to develop a new tool, neuroCNVscore. We used Human Phenotype Ontology to construct an independent NDD-related set. RESULTS Our neuroCNVscore framework (https://github.com/lxsbch/neuroCNVscore) achieved high predictive performance (precision recall=0.82; area under curve=0.85) and outperformed an existing reference method SVScore. Notably, the predicted pathogenic CNVs showed enrichment in known genes associated with autism. CONCLUSIONS NeuroCNVscore prioritises functional, deleterious and pathogenic CNVs in NDDs at whole genome-wide level, which is important for genetic studies and clinical genomic screening of NDDs as well as for providing novel biological insights into NDDs.
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Affiliation(s)
- Xuanshi Liu
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- Genetics and Birth Defects Control Centre, National Centre for Children's Health, Beijing, China
| | - Wenjian Xu
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- Genetics and Birth Defects Control Centre, National Centre for Children's Health, Beijing, China
| | - Fei Leng
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- Genetics and Birth Defects Control Centre, National Centre for Children's Health, Beijing, China
| | - Peng Zhang
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- Genetics and Birth Defects Control Centre, National Centre for Children's Health, Beijing, China
| | - Ruolan Guo
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- Genetics and Birth Defects Control Centre, National Centre for Children's Health, Beijing, China
| | - Yue Zhang
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- Genetics and Birth Defects Control Centre, National Centre for Children's Health, Beijing, China
| | - Chanjuan Hao
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- Genetics and Birth Defects Control Centre, National Centre for Children's Health, Beijing, China
| | - Xin Ni
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- National Centre for Children's Health, Beijing, China
| | - Wei Li
- Beijing Children's Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing, China
- MOE Key Laboratory of Major Diseaseas in Children, Beijing, China
- Genetics and Birth Defects Control Centre, National Centre for Children's Health, Beijing, China
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6
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Kumar S, Gerstein M. Unified views on variant impact across many diseases. Trends Genet 2023; 39:442-450. [PMID: 36858880 PMCID: PMC10192142 DOI: 10.1016/j.tig.2023.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 03/03/2023]
Abstract
Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.
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Affiliation(s)
- Sushant Kumar
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA.
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7
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Nguyen TV, Vander Jagt CJ, Wang J, Daetwyler HD, Xiang R, Goddard ME, Nguyen LT, Ross EM, Hayes BJ, Chamberlain AJ, MacLeod IM. In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants. Genet Sel Evol 2023; 55:9. [PMID: 36721111 PMCID: PMC9887926 DOI: 10.1186/s12711-023-00783-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/23/2023] [Indexed: 02/02/2023] Open
Abstract
Studies have demonstrated that structural variants (SV) play a substantial role in the evolution of species and have an impact on Mendelian traits in the genome. However, unlike small variants (< 50 bp), it has been challenging to accurately identify and genotype SV at the population scale using short-read sequencing. Long-read sequencing technologies are becoming competitively priced and can address several of the disadvantages of short-read sequencing for the discovery and genotyping of SV. In livestock species, analysis of SV at the population scale still faces challenges due to the lack of resources, high costs, technological barriers, and computational limitations. In this review, we summarize recent progress in the characterization of SV in the major livestock species, the obstacles that still need to be overcome, as well as the future directions in this growing field. It seems timely that research communities pool resources to build global population-scale long-read sequencing consortiums for the major livestock species for which the application of genomic tools has become cost-effective.
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Affiliation(s)
- Tuan V. Nguyen
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Christy J. Vander Jagt
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Jianghui Wang
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
| | - Hans D. Daetwyler
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia ,grid.1018.80000 0001 2342 0938School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
| | - Ruidong Xiang
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia ,grid.1008.90000 0001 2179 088XFaculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville, VIC 3052 Australia
| | - Michael E. Goddard
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia ,grid.1008.90000 0001 2179 088XFaculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville, VIC 3052 Australia
| | - Loan T. Nguyen
- grid.1003.20000 0000 9320 7537Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD 4072 Australia
| | - Elizabeth M. Ross
- grid.1003.20000 0000 9320 7537Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD 4072 Australia
| | - Ben J. Hayes
- grid.1003.20000 0000 9320 7537Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, QLD 4072 Australia
| | - Amanda J. Chamberlain
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia ,grid.1018.80000 0001 2342 0938School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083 Australia
| | - Iona M. MacLeod
- grid.452283.a0000 0004 0407 2669Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083 Australia
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8
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Nicholas TJ, Cormier MJ, Quinlan AR. Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate. BMC Bioinformatics 2022; 23:490. [PMID: 36384437 PMCID: PMC9670370 DOI: 10.1186/s12859-022-05008-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/25/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Identification of deleterious genetic variants using DNA sequencing data relies on increasingly detailed filtering strategies to isolate the small subset of variants that are more likely to underlie a disease phenotype. Datasets reflecting population allele frequencies of different types of variants serve as powerful filtering tools, especially in the context of rare disease analysis. While such population-scale allele frequency datasets now exist for structural variants (SVs), it remains a challenge to match SV calls between multiple datasets, thereby complicating estimates of a putative SV's population allele frequency. RESULTS We introduce SVAFotate, a software tool that enables the annotation of SVs with variant allele frequency and related information from existing SV datasets. As a result, VCF files annotated by SVAFotate offer a variety of metrics to aid in the stratification of SVs as common or rare in the broader human population. CONCLUSIONS Here we demonstrate the use of SVAFotate in the classification of SVs with regards to their population frequency and illustrate how SVAFotate's annotations can be used to filter and prioritize SVs. Lastly, we detail how best to utilize these SV annotations in the analysis of genetic variation in studies of rare disease.
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Affiliation(s)
- Thomas J. Nicholas
- grid.223827.e0000 0001 2193 0096Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112 USA
| | - Michael J. Cormier
- grid.223827.e0000 0001 2193 0096Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112 USA
| | - Aaron R. Quinlan
- grid.223827.e0000 0001 2193 0096Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112 USA ,grid.223827.e0000 0001 2193 0096Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112 USA
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9
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Kuksa PP, Greenfest-Allen E, Cifello J, Ionita M, Wang H, Nicaretta H, Cheng PL, Lee WP, Wang LS, Leung YY. Scalable approaches for functional analyses of whole-genome sequencing non-coding variants. Hum Mol Genet 2022; 31:R62-R72. [PMID: 35943817 PMCID: PMC9585666 DOI: 10.1093/hmg/ddac191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
Non-coding genetic variants outside of protein-coding genome regions play an important role in genetic and epigenetic regulation. It has become increasingly important to understand their roles, as non-coding variants often make up the majority of top findings of genome-wide association studies (GWAS). In addition, the growing popularity of disease-specific whole-genome sequencing (WGS) efforts expands the library of and offers unique opportunities for investigating both common and rare non-coding variants, which are typically not detected in more limited GWAS approaches. However, the sheer size and breadth of WGS data introduce additional challenges to predicting functional impacts in terms of data analysis and interpretation. This review focuses on the recent approaches developed for efficient, at-scale annotation and prioritization of non-coding variants uncovered in WGS analyses. In particular, we review the latest scalable annotation tools, databases and functional genomic resources for interpreting the variant findings from WGS based on both experimental data and in silico predictive annotations. We also review machine learning-based predictive models for variant scoring and prioritization. We conclude with a discussion of future research directions which will enhance the data and tools necessary for the effective functional analyses of variants identified by WGS to improve our understanding of disease etiology.
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Affiliation(s)
- Pavel P Kuksa
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emily Greenfest-Allen
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jeffrey Cifello
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matei Ionita
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hui Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Heather Nicaretta
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Po-Liang Cheng
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wan-Ping Lee
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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10
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Spielmann M, Kircher M. Computational and experimental methods for classifying variants of unknown clinical significance. Cold Spring Harb Mol Case Stud 2022; 8:mcs.a006196. [PMID: 35483875 PMCID: PMC9059783 DOI: 10.1101/mcs.a006196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.
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Affiliation(s)
- Malte Spielmann
- Institute of Human Genetics, University of Lübeck, 23562 Lübeck, Germany;,Institute of Human Genetics, Christian-Albrechts-Universität, 24105 Kiel, Germany;,Human Molecular Genomics Group, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany;,DZHK (German Centre for Cardiovascular Research), partner site Hamburg/Lübeck/Kiel, 23562 Lübeck, Germany
| | - Martin Kircher
- Institute of Human Genetics, University of Lübeck, 23562 Lübeck, Germany;,Berlin Institute of Health at Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany;,DZHK (German Centre for Cardiovascular Research), partner site Berlin, 10115 Berlin, Germany
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11
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Hertzberg J, Mundlos S, Vingron M, Gallone G. TADA-a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs. Genome Biol 2022; 23:67. [PMID: 35232478 PMCID: PMC8886976 DOI: 10.1186/s13059-022-02631-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 02/11/2022] [Indexed: 01/08/2023] Open
Abstract
Few methods have been developed to investigate copy number variants (CNVs) based on their predicted pathogenicity. We introduce TADA, a method to prioritise pathogenic CNVs through assisted manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigourous enrichment analysis. We demonstrate that our classifiers are able to accurately predict pathogenic CNVs, outperforming current alternative methods, and produce a well-calibrated pathogenicity score. Our results suggest that functional annotation-based prioritisation of pathogenic CNVs is a promising approach to support clinical diagnostics and to further the understanding of mechanisms controlling the disease impact of larger genomic alterations.
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Affiliation(s)
- Jakob Hertzberg
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, Berlin, 14195 Germany
- Charité Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117 Germany
| | - Stefan Mundlos
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, Berlin, 14195 Germany
- Charité Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117 Germany
| | - Martin Vingron
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, Berlin, 14195 Germany
| | - Giuseppe Gallone
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, Berlin, 14195 Germany
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12
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A framework to score the effects of structural variants in health and disease. Genome Res 2022; 32:766-777. [PMID: 35197310 PMCID: PMC8997355 DOI: 10.1101/gr.275995.121] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 02/22/2022] [Indexed: 11/25/2022]
Abstract
While technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing. Here, we describe CADD-SV, a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Previously, supervised learning approaches were limited due to a small number and biased set of annotated pathogenic or benign SVs. We overcome this problem by using a surrogate training-objective, the Combined Annotation Dependent Depletion (CADD) of functional variants. We use human and chimpanzee derived SVs as proxy-neutral and contrast them with matched simulated variants as proxy-deleterious, an approach that has proven powerful for short sequence variants. Our tool computes summary statistics over diverse variant annotations and uses random forest models to prioritize deleterious structural variants. The resulting CADD-SV scores correlate with known pathogenic and rare population variants. We further show that we can prioritize somatic cancer variants as well as noncoding variants known to affect gene expression. We provide a website and offline-scoring tool for easy application of CADD-SV.
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13
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Valls-Margarit J, Galván-Femenía I, Matías-Sánchez D, Blay N, Puiggròs M, Carreras A, Salvoro C, Cortés B, Amela R, Farre X, Lerga-Jaso J, Puig M, Sánchez-Herrero J, Moreno V, Perucho M, Sumoy L, Armengol L, Delaneau O, Cáceres M, de Cid R, Torrents D. GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing. Nucleic Acids Res 2022; 50:2464-2479. [PMID: 35176773 PMCID: PMC8934637 DOI: 10.1093/nar/gkac076] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/24/2021] [Accepted: 02/09/2022] [Indexed: 11/17/2022] Open
Abstract
The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization, and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression Models (LRMs), we present a catalogue of 35 431 441 variants, including 89 178 SVs (≥50 bp), 30 325 064 SNVs and 5 017 199 indels, across 785 Illumina high coverage (30x) whole-genomes from the Iberian GCAT Cohort, containing a median of 3.52M SNVs, 606 336 indels and 6393 SVs per individual. The haplotype panel is able to impute up to 14 360 728 SNVs/indels and 23 179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with Mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies.
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Affiliation(s)
| | | | | | - Natalia Blay
- Genomes for Life-GCAT lab Group, Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona 08916, Spain
| | - Montserrat Puiggròs
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Anna Carreras
- Genomes for Life-GCAT lab Group, Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona 08916, Spain
| | - Cecilia Salvoro
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Beatriz Cortés
- Genomes for Life-GCAT lab Group, Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona 08916, Spain
| | - Ramon Amela
- Life Sciences Department, Barcelona Supercomputing Center (BSC), Barcelona 08034, Spain
| | - Xavier Farre
- Genomes for Life-GCAT lab Group, Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona 08916, Spain
| | - Jon Lerga-Jaso
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Marta Puig
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Jose Francisco Sánchez-Herrero
- High Content Genomics and Bioinformatics Unit, Institute for Health Science Research Germans Trias i Pujol (IGTP), 08916 Badalona, Spain
| | - Victor Moreno
- Catalan Institute of Oncology, Hospitalet del Llobregat, 08908, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet del Llobregat, 08908, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain
- Universitat de Barcelona (UB), Barcelona 08007, Spain
| | - Manuel Perucho
- Sanford Burnham Prebys Medical Discovery Institute (SBP), La Jolla, CA 92037, USA
- Cancer Genetics and Epigenetics, Program of Predictive and Personalized Medicine of Cancer (PMPPC), Health Science Research Institute Germans Trias i Pujol (IGTP), Badalona 08916, Spain
| | - Lauro Sumoy
- High Content Genomics and Bioinformatics Unit, Institute for Health Science Research Germans Trias i Pujol (IGTP), 08916 Badalona, Spain
| | - Lluís Armengol
- Quantitative Genomic Medicine Laboratories (qGenomics), Esplugues del Llobregat, 08950, Spain
| | - Olivier Delaneau
- Department of Computational Biology, University of Lausanne, Génopode, 1015 Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), University of Lausanne, Quartier Sorge – Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Mario Cáceres
- Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- ICREA, Barcelona 08010, Spain
| | - Rafael de Cid
- Correspondence may also be addressed to Rafael de Cid. Tel: +34 930330542;
| | - David Torrents
- To whom correspondence should be addressed. Tel: +34 934134074;
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14
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Yang Y, Wang X, Zhou D, Wei DQ, Peng S. SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants. Brief Bioinform 2022; 23:6531897. [PMID: 35180781 DOI: 10.1093/bib/bbac014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 11/12/2022] Open
Abstract
Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to predict the pathogenicity of deletions, insertions and duplications structural variations that occur in exons. We constructed three types of annotation features for each structural variation event in the ClinVar database. First, we treated complex structural variations as multiple consecutive single nucleotide polymorphisms events, and annotated them with correlation scores based on single nucleic acid substitutions, such as the impact on protein function. Second, we determined which genes the variation occurred in, and constructed gene-based annotation features for each structural variation. Third, we also calculated related features based on the transcriptome, such as histone signal, the overlap ratio of variation and genomic element definitions, etc. Finally, we employed a gradient boosting decision tree machine learning method, and used the deletions, insertions and duplications in the ClinVar database to train a structural variation pathogenicity prediction model SVPath. These structural variations are clearly indicated as pathogenic or benign. Experimental results show that our SVPath has achieved excellent predictive performance and outperforms existing state-of-the-art tools. SVPath is very promising in evaluating the clinical pathogenicity of structural variants. SVPath can be used in clinical research to predict the clinical significance of unknown pathogenicity and new structural variation, so as to explore the relationship between diseases and structural variations in a computational way.
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Affiliation(s)
- Yaning Yang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Deshan Zhou
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.,School of Computer Science, National University of Defense Technology, Changsha, China.,Peng Cheng Lab, Shenzhen, China
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15
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Sharo AG, Hu Z, Sunyaev SR, Brenner SE. StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants. Am J Hum Genet 2022; 109:195-209. [PMID: 35032432 PMCID: PMC8874149 DOI: 10.1016/j.ajhg.2021.12.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022] Open
Abstract
Whole-genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole-genome sequencing. Structural variants (SVs; genomic variants larger than 50 base pairs) of uncertain significance are the genetic cause of a portion of these unresolved cases. As sequencing methods using long or linked reads become more accessible and SV detection algorithms improve, clinicians and researchers are gaining access to thousands of reliable SVs of unknown disease relevance. Methods to predict the pathogenicity of these SVs are required to realize the full diagnostic potential of long-read sequencing. To address this emerging need, we developed StrVCTVRE to distinguish pathogenic SVs from benign SVs that overlap exons. In a random forest classifier, we integrated features that capture gene importance, coding region, conservation, expression, and exon structure. We found that features such as expression and conservation are important but are absent from SV classification guidelines. We leveraged multiple resources to construct a size-matched training set of rare, putatively benign and pathogenic SVs. StrVCTVRE performs accurately across a wide SV size range on independent test sets, which will allow clinicians and researchers to eliminate about half of SVs from consideration while retaining a 90% sensitivity. We anticipate clinicians and researchers will use StrVCTVRE to prioritize SVs in probands where no SV is immediately compelling, empowering deeper investigation into novel SVs to resolve cases and understand new mechanisms of disease. StrVCTVRE runs rapidly and is publicly available.
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Affiliation(s)
- Andrew G Sharo
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
| | - Zhiqiang Hu
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Shamil R Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Steven E Brenner
- Biophysics Graduate Group, University of California, Berkeley, Berkeley, CA 94720, USA; Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, CA 94720, USA.
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16
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Gažiová M, Sládeček T, Pös O, Števko M, Krampl W, Pös Z, Hekel R, Hlavačka M, Kucharík M, Radvánszky J, Budiš J, Szemes T. Automated prediction of the clinical impact of structural copy number variations. Sci Rep 2022; 12:555. [PMID: 35017614 PMCID: PMC8752772 DOI: 10.1038/s41598-021-04505-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 11/01/2021] [Indexed: 11/09/2022] Open
Abstract
Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of these structural variants is a challenging problem due to highly variable numbers of gene, regulatory, or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. We designed and validated a prediction method (ISV; Interpretation of Structural Variants) that is based on boosted trees which takes into account annotations of CNVs from several publicly available databases. The presented approach achieved more than 98% prediction accuracy on both copy number loss and copy number gain variants while also allowing CNVs being assigned "uncertain" significance in predictions. We believe that ISV's prediction capability and explainability have a great potential to guide users to more precise interpretations and classifications of CNVs.
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Affiliation(s)
- M Gažiová
- Geneton Ltd, 84104, Bratislava, Slovakia
- Department of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University, 84248, Bratislava, Slovakia
| | - T Sládeček
- Geneton Ltd, 84104, Bratislava, Slovakia
| | - O Pös
- Geneton Ltd, 84104, Bratislava, Slovakia
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 84215, Bratislava, Slovakia
- Comenius University Science Park, 84104, Bratislava, Slovakia
| | - M Števko
- Geneton Ltd, 84104, Bratislava, Slovakia
| | - W Krampl
- Geneton Ltd, 84104, Bratislava, Slovakia
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 84215, Bratislava, Slovakia
- Comenius University Science Park, 84104, Bratislava, Slovakia
| | - Z Pös
- Geneton Ltd, 84104, Bratislava, Slovakia
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 84215, Bratislava, Slovakia
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, 84505, Bratislava, Slovakia
| | - R Hekel
- Geneton Ltd, 84104, Bratislava, Slovakia
- Comenius University Science Park, 84104, Bratislava, Slovakia
- Slovak Center of Scientific and Technical Information, 81104, Bratislava, Slovakia
| | - M Hlavačka
- Geneton Ltd, 84104, Bratislava, Slovakia
| | - M Kucharík
- Geneton Ltd, 84104, Bratislava, Slovakia
- Comenius University Science Park, 84104, Bratislava, Slovakia
| | - J Radvánszky
- Geneton Ltd, 84104, Bratislava, Slovakia
- Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, 84505, Bratislava, Slovakia
- Comenius University Science Park, 84104, Bratislava, Slovakia
| | - J Budiš
- Geneton Ltd, 84104, Bratislava, Slovakia.
- Comenius University Science Park, 84104, Bratislava, Slovakia.
- Slovak Center of Scientific and Technical Information, 81104, Bratislava, Slovakia.
| | - T Szemes
- Geneton Ltd, 84104, Bratislava, Slovakia
- Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 84215, Bratislava, Slovakia
- Comenius University Science Park, 84104, Bratislava, Slovakia
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17
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Fino J, Marques B, Dong Z, David D. SVInterpreter: A Comprehensive Topologically Associated Domain-Based Clinical Outcome Prediction Tool for Balanced and Unbalanced Structural Variants. Front Genet 2021; 12:757170. [PMID: 34925449 PMCID: PMC8671832 DOI: 10.3389/fgene.2021.757170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/12/2021] [Indexed: 11/13/2022] Open
Abstract
With the advent of genomic sequencing, a number of balanced and unbalanced structural variants (SVs) can be detected per individual. Mainly due to incompleteness and the scattered nature of the available annotation data of the human genome, manual interpretation of the SV's clinical significance is laborious and cumbersome. Since bioinformatic tools developed for this task are limited, a comprehensive tool to assist clinical outcome prediction of SVs is warranted. Herein, we present SVInterpreter, a free Web application, which analyzes both balanced and unbalanced SVs using topologically associated domains (TADs) as genome units. Among others, gene-associated data (as function and dosage sensitivity), phenotype similarity scores, and copy number variants (CNVs) scoring metrics are retrieved for an informed SV interpretation. For evaluation, we retrospectively applied SVInterpreter to 97 balanced (translocations and inversions) and 125 unbalanced (deletions, duplications, and insertions) previously published SVs, and 145 SVs identified from 20 clinical samples. Our results showed the ability of SVInterpreter to support the evaluation of SVs by (1) confirming more than half of the predictions of the original studies, (2) decreasing 40% of the variants of uncertain significance, and (3) indicating several potential position effect events. To our knowledge, SVInterpreter is the most comprehensive TAD-based tool to identify the possible disease-causing candidate genes and to assist prediction of the clinical outcome of SVs. SVInterpreter is available at http://dgrctools-insa.min-saude.pt/cgi-bin/SVInterpreter.py.
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Affiliation(s)
- Joana Fino
- Department of Human Genetics, National Health Institute Doutor Ricardo Jorge, Lisbon, Portugal
| | - Bárbara Marques
- Department of Human Genetics, National Health Institute Doutor Ricardo Jorge, Lisbon, Portugal
| | - Zirui Dong
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
- Hong Kong Hub of Pediatric Excellence, The Chinese University of Hong Kong, Hong Kong, China
| | - Dezső David
- Department of Human Genetics, National Health Institute Doutor Ricardo Jorge, Lisbon, Portugal
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18
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Zhou Y, Lauschke VM. Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability. Clin Pharmacol Ther 2021; 110:626-636. [PMID: 33998671 DOI: 10.1002/cpt.2289] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/07/2021] [Indexed: 12/19/2022]
Abstract
Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.
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Affiliation(s)
- Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
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19
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Zhang W, Chen X, Wong KC. Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome. J Gastroenterol Hepatol 2021; 36:823-831. [PMID: 33880763 DOI: 10.1111/jgh.15500] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 12/15/2022]
Abstract
The maturing development in artificial intelligence (AI) and genomics has propelled the advances in intestinal diseases including intestinal cancer, inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS). On the other hand, colorectal cancer is the second most deadly and the third most common type of cancer in the world according to GLOBOCAN 2020 data. The mechanisms behind IBD and IBS are still speculative. The conventional methods to identify colorectal cancer, IBD, and IBS are based on endoscopy or colonoscopy to identify lesions. However, it is invasive, demanding, and time-consuming for early-stage intestinal diseases. To address those problems, new strategies based on blood and/or human microbiome in gut, colon, or even feces were developed; those methods took advantage of high-throughput sequencing and machine learning approaches. In this review, we summarize the recent research and methods to diagnose intestinal diseases with machine learning technologies based on cell-free DNA and microbiome data generated by amplicon sequencing or whole-genome sequencing. Those methods play an important role in not only intestinal disease diagnosis but also therapy development in the near future.
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Affiliation(s)
- Weitong Zhang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Xingjian Chen
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.,Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
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20
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Mc Cartney AM, Mahmoud M, Jochum M, Agustinho DP, Zorman B, Al Khleifat A, Dabbaghie F, K Kesharwani R, Smolka M, Dawood M, Albin D, Aliyev E, Almabrazi H, Arslan A, Balaji A, Behera S, Billingsley K, L Cameron D, Daw J, T. Dawson E, De Coster W, Du H, Dunn C, Esteban R, Jolly A, Kalra D, Liao C, Liu Y, Lu TY, M Havrilla J, M Khayat M, Marin M, Monlong J, Price S, Rafael Gener A, Ren J, Sagayaradj S, Sapoval N, Sinner C, C. Soto D, Soylev A, Subramaniyan A, Syed N, Tadimeti N, Tater P, Vats P, Vaughn J, Walker K, Wang G, Zeng Q, Zhang S, Zhao T, Kille B, Biederstedt E, Chaisson M, English A, Kronenberg Z, J. Treangen T, Hefferon T, Chin CS, Busby B, J Sedlazeck F. An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates. F1000Res 2021; 10:246. [PMID: 34621504 PMCID: PMC8479851 DOI: 10.12688/f1000research.51477.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 11/20/2022] Open
Abstract
In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.
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Affiliation(s)
| | | | | | | | | | | | - Fawaz Dabbaghie
- Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | | | | | | | | | | | | | - Ahmed Arslan
- Stanford University School of Medicine, California, USA
| | | | | | | | - Daniel L Cameron
- Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | - Joyjit Daw
- NVIDIA Corporation, Santa Clara, California, USA
| | | | | | - Haowei Du
- Baylor College of Medicine, Houston, USA
| | | | | | | | | | | | | | | | | | | | | | - Jean Monlong
- UC Santa Cruz Genomics Institute, Santa Cruz, USA
| | | | | | | | | | | | | | | | - Arda Soylev
- Konya Food and Agriculture University, Konya, Turkey
| | | | | | | | | | - Pankaj Vats
- NVIDIA Corporation, Santa Clara, California, USA
| | | | | | | | - Qiandong Zeng
- Laboratory Corporation of America Holdings, Westborough, USA
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Mc Cartney AM, Mahmoud M, Jochum M, Agustinho DP, Zorman B, Al Khleifat A, Dabbaghie F, K Kesharwani R, Smolka M, Dawood M, Albin D, Aliyev E, Almabrazi H, Arslan A, Balaji A, Behera S, Billingsley K, L Cameron D, Daw J, T. Dawson E, De Coster W, Du H, Dunn C, Esteban R, Jolly A, Kalra D, Liao C, Liu Y, Lu TY, M Havrilla J, M Khayat M, Marin M, Monlong J, Price S, Rafael Gener A, Ren J, Sagayaradj S, Sapoval N, Sinner C, C. Soto D, Soylev A, Subramaniyan A, Syed N, Tadimeti N, Tater P, Vats P, Vaughn J, Walker K, Wang G, Zeng Q, Zhang S, Zhao T, Kille B, Biederstedt E, Chaisson M, English A, Kronenberg Z, J. Treangen T, Hefferon T, Chin CS, Busby B, J Sedlazeck F. An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates. F1000Res 2021; 10:246. [PMID: 34621504 PMCID: PMC8479851 DOI: 10.12688/f1000research.51477.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/04/2021] [Indexed: 11/08/2023] Open
Abstract
In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.
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Affiliation(s)
| | | | | | | | | | | | - Fawaz Dabbaghie
- Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | | | | | | | | | | | | | - Ahmed Arslan
- Stanford University School of Medicine, California, USA
| | | | | | | | - Daniel L Cameron
- Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | - Joyjit Daw
- NVIDIA Corporation, Santa Clara, California, USA
| | | | | | - Haowei Du
- Baylor College of Medicine, Houston, USA
| | | | | | | | | | | | | | | | | | | | | | - Jean Monlong
- UC Santa Cruz Genomics Institute, Santa Cruz, USA
| | | | | | | | | | | | | | | | - Arda Soylev
- Konya Food and Agriculture University, Konya, Turkey
| | | | | | | | | | - Pankaj Vats
- NVIDIA Corporation, Santa Clara, California, USA
| | | | | | | | - Qiandong Zeng
- Laboratory Corporation of America Holdings, Westborough, USA
| | | | | | | | | | | | | | | | | | | | | | | | | |
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