1
|
Ross ME, Mason CE, Finnell RH. Genomic approaches to the assessment of human spina bifida risk. Birth Defects Res 2018; 109:120-128. [PMID: 27883265 DOI: 10.1002/bdra.23592] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 10/02/2016] [Accepted: 10/10/2016] [Indexed: 12/30/2022]
Abstract
Structural birth defects are a leading cause of mortality and morbidity in children world-wide, affecting as much as 6% of all live births. Among these conditions, neural tube defects (NTDs), including spina bifida and anencephaly, arise from a combination of complex gene and environment interactions that are as yet poorly understood within human populations. Rapid advances in massively parallel DNA sequencing and bioinformatics allow for analyses of the entire genome beyond the 2% of the genomic sequence covering protein coding regions. Efforts to collect and analyze these large datasets hold promise for illuminating gene network variations and eventually epigenetic events that increase individual risk for failure to close the neural tube. In this review, we discuss current challenges for DNA genome sequence analysis of NTD affected populations, and compare experience in the field with other complex genetic disorders for which large datasets are accumulating. The ultimate goal of this research is to find strategies for optimizing conditions that promote healthy birth outcomes for individual couples. Birth Defects Research 109:120-128, 2017. © 2016 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- M Elizabeth Ross
- Center for Neurogenetics, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York
| | - Christopher E Mason
- Center for Neurogenetics, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, New York.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | - Richard H Finnell
- Dell Pediatric Research Institute, Department of Nutritional Sciences, The University of Texas at Austin, Austin, Texas
| |
Collapse
|
2
|
Feng BJ. PERCH: A Unified Framework for Disease Gene Prioritization. Hum Mutat 2017; 38:243-251. [PMID: 27995669 PMCID: PMC5299048 DOI: 10.1002/humu.23158] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 12/12/2016] [Indexed: 12/30/2022]
Abstract
To interpret genetic variants discovered from next-generation sequencing, integration of heterogeneous information is vital for success. This article describes a framework named PERCH (Polymorphism Evaluation, Ranking, and Classification for a Heritable trait), available at http://BJFengLab.org/. It can prioritize disease genes by quantitatively unifying a new deleteriousness measure called BayesDel, an improved assessment of the biological relevance of genes to the disease, a modified linkage analysis, a novel rare-variant association test, and a converted variant call quality score. It supports data that contain various combinations of extended pedigrees, trios, and case-controls, and allows for a reduced penetrance, an elevated phenocopy rate, liability classes, and covariates. BayesDel is more accurate than PolyPhen2, SIFT, FATHMM, LRT, Mutation Taster, Mutation Assessor, PhyloP, GERP++, SiPhy, CADD, MetaLR, and MetaSVM. The overall approach is faster and more powerful than the existing quantitative method pVAAST, as shown by the simulations of challenging situations in finding the missing heritability of a complex disease. This framework can also classify variants of unknown significance (variants of uncertain significance) by quantitatively integrating allele frequencies, deleteriousness, association, and co-segregation. PERCH is a versatile tool for gene prioritization in gene discovery research and variant classification in clinical genetic testing.
Collapse
Affiliation(s)
- Bing-Jian Feng
- Department of Dermatology, University of Utah, Salt Lake City, UT 84132, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132, USA
| |
Collapse
|
3
|
D'Souza M, Sulakhe D, Wang S, Xie B, Hashemifar S, Taylor A, Dubchak I, Conrad Gilliam T, Maltsev N. Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks. Methods Mol Biol 2017; 1613:85-99. [PMID: 28849559 DOI: 10.1007/978-1-4939-7027-8_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.
Collapse
Affiliation(s)
- Mark D'Souza
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA.
- Argonne National Laboratory, Building 221, Room: A142, 9700 South Cass Avenue, Argonne, IL, 60439, USA.
| | - Dinanath Sulakhe
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, 60637, USA
| | - Sheng Wang
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, 60637, USA
| | - Bing Xie
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Department of Computer Science, Illinois Institute of Technology, Chicago, IL, 60616, USA
| | - Somaye Hashemifar
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, 60637, USA
| | - Andrew Taylor
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
| | - Inna Dubchak
- Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America, Department of Energy Joint Genome Institute, Walnut Creek, CA, USA
| | - T Conrad Gilliam
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, 60637, USA
| | - Natalia Maltsev
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, 60637, USA
- Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, 60637, USA
| |
Collapse
|
4
|
Sulakhe D, Xie B, Taylor A, D'Souza M, Balasubramanian S, Hashemifar S, White S, Dave UJ, Agam G, Xu J, Wang S, Gilliam TC, Maltsev N. Lynx: a knowledge base and an analytical workbench for integrative medicine. Nucleic Acids Res 2016; 44:D882-7. [PMID: 26590263 PMCID: PMC4702889 DOI: 10.1093/nar/gkv1257] [Citation(s) in RCA: 8] [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: 09/15/2015] [Revised: 10/29/2015] [Accepted: 10/30/2015] [Indexed: 01/29/2023] Open
Abstract
Lynx (http://lynx.ci.uchicago.edu) is a web-based database and a knowledge extraction engine. It supports annotation and analysis of high-throughput experimental data and generation of weighted hypotheses regarding genes and molecular mechanisms contributing to human phenotypes or conditions of interest. Since the last release, the Lynx knowledge base (LynxKB) has been periodically updated with the latest versions of the existing databases and supplemented with additional information from public databases. These additions have enriched the data annotations provided by Lynx and improved the performance of Lynx analytical tools. Moreover, the Lynx analytical workbench has been supplemented with new tools for reconstruction of co-expression networks and feature-and-network-based prioritization of genetic factors and molecular mechanisms. These developments facilitate the extraction of meaningful knowledge from experimental data and LynxKB. The Service Oriented Architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.
Collapse
Affiliation(s)
- Dinanath Sulakhe
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL 60637, USA
| | - Bingqing Xie
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA Department of Computer Science, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Andrew Taylor
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA
| | - Mark D'Souza
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA
| | - Sandhya Balasubramanian
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA
| | - Somaye Hashemifar
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL 60637, USA
| | - Steven White
- Department of Medicine, University of Chicago, 5841 S. Maryland Avenue, Chicago, IL 60637, USA
| | - Utpal J Dave
- Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL 60637, USA
| | - Gady Agam
- Department of Computer Science, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL 60637, USA
| | - Sheng Wang
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL 60637, USA
| | - T Conrad Gilliam
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL 60637, USA
| | - Natalia Maltsev
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL 60637, USA Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL 60637, USA
| |
Collapse
|
5
|
AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model. BIOMED RESEARCH INTERNATIONAL 2015; 2015:678764. [PMID: 26339631 PMCID: PMC4538422 DOI: 10.1155/2015/678764] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2014] [Accepted: 03/11/2015] [Indexed: 12/14/2022]
Abstract
Motivation. The solvent accessibility of protein residues is one of the driving forces of protein folding, while the contact number of protein residues limits the possibilities of protein conformations. The de novo prediction of these properties from protein sequence is important for the study of protein structure and function. Although these two properties are certainly related with each other, it is challenging to exploit this dependency for the prediction. Method. We present a method AcconPred for predicting solvent accessibility and contact number simultaneously, which is based on a shared weight multitask learning framework under the CNF (conditional neural fields) model. The multitask learning framework on a collection of related tasks provides more accurate prediction than the framework trained only on a single task. The CNF method not only models the complex relationship between the input features and the predicted labels, but also exploits the interdependency among adjacent labels. Results. Trained on 5729 monomeric soluble globular protein datasets, AcconPred could reach 0.68 three-state accuracy for solvent accessibility and 0.75 correlation for contact number. Tested on the 105 CASP11 domain datasets for solvent accessibility, AcconPred could reach 0.64 accuracy, which outperforms existing methods.
Collapse
|
6
|
Correction: an integrative computational approach for prioritization of genomic variants. PLoS One 2015; 10:e0124700. [PMID: 25853668 PMCID: PMC4390378 DOI: 10.1371/journal.pone.0124700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|