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DeVoe E, Oliver GR, Zenka R, Blackburn PR, Cousin MA, Boczek NJ, Kocher JPA, Urrutia R, Klee EW, Zimmermann MT. P 2T 2: Protein Panoramic annoTation Tool for the interpretation of protein coding genetic variants. JAMIA Open 2021; 4:ooab065. [PMID: 34377961 PMCID: PMC8346652 DOI: 10.1093/jamiaopen/ooab065] [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: 04/16/2021] [Revised: 07/06/2021] [Accepted: 07/17/2021] [Indexed: 11/29/2022] Open
Abstract
MOTIVATION Genomic data are prevalent, leading to frequent encounters with uninterpreted variants or mutations with unknown mechanisms of effect. Researchers must manually aggregate data from multiple sources and across related proteins, mentally translating effects between the genome and proteome, to attempt to understand mechanisms. MATERIALS AND METHODS P2T2 presents diverse data and annotation types in a unified protein-centric view, facilitating the interpretation of coding variants and hypothesis generation. Information from primary sequence, domain, motif, and structural levels are presented and also organized into the first Paralog Annotation Analysis across the human proteome. RESULTS Our tool assists research efforts to interpret genomic variation by aggregating diverse, relevant, and proteome-wide information into a unified interactive web-based interface. Additionally, we provide a REST API enabling automated data queries, or repurposing data for other studies. CONCLUSION The unified protein-centric interface presented in P2T2 will help researchers interpret novel variants identified through next-generation sequencing. Code and server link available at github.com/GenomicInterpretation/p2t2.
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Affiliation(s)
- Elias DeVoe
- Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA
| | - Gavin R Oliver
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Roman Zenka
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Patrick R Blackburn
- Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA
- Center for Individualized Medicine, Mayo Clinic, Jacksonville, Florida, USA
| | - Margot A Cousin
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Nicole J Boczek
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jean-Pierre A Kocher
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Raul Urrutia
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA
- Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, 53226, USA
| | - Eric W Klee
- Department of Health Science Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael T Zimmermann
- Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA
- Genomic Sciences and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA
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2
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Zhang Z, Li H, Jiang S, Li R, Li W, Chen H, Bo X. A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data. Brief Bioinform 2020; 20:1524-1541. [PMID: 29617727 PMCID: PMC6781580 DOI: 10.1093/bib/bby023] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 02/22/2018] [Indexed: 12/28/2022] Open
Abstract
The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive ‘atlas’ of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.
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Affiliation(s)
- Zhuo Zhang
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Hao Li
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Shuai Jiang
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Ruijiang Li
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Wanying Li
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Hebing Chen
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing 100850, China
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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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Peng HH, Chang NC, Chen KT, Lu JJ, Chang PY, Chang SC, Wu-Chou YH, Chou YT, Phang W, Cheng PJ. Nonsynonymous variants in MYH9 and ABCA4 are the most frequent risk loci associated with nonsyndromic orofacial cleft in Taiwanese population. BMC MEDICAL GENETICS 2016; 17:59. [PMID: 27527345 PMCID: PMC4986225 DOI: 10.1186/s12881-016-0322-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 06/10/2016] [Indexed: 11/10/2022]
Abstract
BACKGROUND Nonsyndromic orofacial cleft is a common birth defect with a complex etiology, including multiple genetic and environmental risk factors. Recent whole genome analyses suggested associations between nonsyndromic orofacial cleft and up to 18 genetic risk loci (ABCA4, BMP4, CRISPLD2, GSTT1, FGF8, FGFR2, FOXE1, IRF6, MAFB, MSX1, MTHFR, MYH9, PDGFC, PVRL1, SUMO1, TGFA, TGFB3, and VAX1), each of which confers a different relative risk in different populations. We evaluate the nonsynonymous variants in these 18 genetic risk loci in nonsyndromic orofacial clefts and normal controls to clarify the specific variants in Taiwanese population. METHODS We evaluated these 18 genetic risk loci in 103 cases of nonsyndromic orofacial clefts and 100 normal controls using a next-generation sequencing (NGS) customized panel and manipulated a whole-exon targeted-sequencing study based on the NGS system of an Ion Torrent Personal Genome Machine (IT-PGM). IT-PGM data processing, including alignment with the human genome build 19 reference genome (hg19), base calling, trimming of barcoded adapter sequences, and filtering of poor signal reads, was performed using the IT platform-specific pipeline software Torrent Suite, version 4.2, with the plug-in "variant caller" program. Further advanced annotation was facilitated by uploading the exported VCF file from Variant Caller to the commercial software package Ion Reporter; the free online annotation software Vanno and Mutation Taster. Benign or tolerated amino acid changes were excluded after analysis using sorting intolerant from tolerant and polymorphism phenotyping. Sanger sequencing was used to validate the significant variants identified by NGS. Furthermore, each variant was confirmed in asymptomatic controls using the Sequenom MassARRAY (San Diego, CA, USA). RESULTS We identified totally 22 types of nonsynonymous variants specific in nonsyndromic orofacial clefts, including 19 single nucleotide variants, 2 deletions, and 1 duplication in 10 studied genes(ABCA4, MYH9, MTHFR, CRISPLD2, FGF8, PVRL1, FOXE1, VAX1, FGFR2, and IRF6). Nonsynonymous variants in MYH9 and ABCA4, which were detected in 6 and 5 individuals, respectively, were identified to be the most frequent risk loci in nonsyndromic orofacial clefts in the Taiwanese population. CONCLUSIONS Nonsynonymous variants in MYH9 and ABCA4 were identified to be the most frequent risk loci in nonsyndromic orofacial clefts in the Taiwanese population. These findings in our study have provided additional information regarding specific variants associated with nonsyndromic orofacial clefts in different population and demonstrate the power of our customized NGS panel, which is clinically useful for the simultaneous detection of multiple genes associated with nonsyndromic orofacial clefts.
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Affiliation(s)
- Hsiu-Huei Peng
- Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan.,Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Nai-Chung Chang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Kuo-Ting Chen
- Chang Gung University College of Medicine, Taoyuan, Taiwan.,Department of Plastic and Reconstructive surgery, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Jang-Jih Lu
- Chang Gung University College of Medicine, Taoyuan, Taiwan.,Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Pi-Yueh Chang
- Chang Gung University College of Medicine, Taoyuan, Taiwan.,Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Shih-Cheng Chang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Yah-Huei Wu-Chou
- Chang Gung University College of Medicine, Taoyuan, Taiwan.,Human Molecular Genetics Laboratory, Department of Medical Research, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Yi-Ting Chou
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Wanni Phang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Po-Jen Cheng
- Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan. .,Chang Gung University College of Medicine, Taoyuan, Taiwan.
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Pavlopoulos GA, Malliarakis D, Papanikolaou N, Theodosiou T, Enright AJ, Iliopoulos I. Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future. Gigascience 2015; 4:38. [PMID: 26309733 PMCID: PMC4548842 DOI: 10.1186/s13742-015-0077-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Accepted: 08/03/2015] [Indexed: 01/31/2023] Open
Abstract
"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.
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Affiliation(s)
- Georgios A Pavlopoulos
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete, Medical School, 70013 Heraklion, Crete Greece
| | | | - Nikolas Papanikolaou
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete, Medical School, 70013 Heraklion, Crete Greece
| | - Theodosis Theodosiou
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete, Medical School, 70013 Heraklion, Crete Greece
| | - Anton J Enright
- EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD UK
| | - Ioannis Iliopoulos
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete, Medical School, 70013 Heraklion, Crete Greece
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