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Crowgey EL, Stabley DL, Chen C, Huang H, Robbins KM, Polson SW, Sol-Church K, Wu CH. An integrated approach for analyzing clinical genomic variant data from next-generation sequencing. J Biomol Tech 2015; 26:19-28. [PMID: 25649353 DOI: 10.7171/jbt.15-2601-002] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Next-generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis for data interpretation. We have developed an integrated approach for end-to-end clinical NGS data analysis from variant detection to functional profiling. Robust bioinformatics pipelines were implemented for genome alignment, single nucleotide polymorphism (SNP), small insertion/deletion (InDel), and copy number variation (CNV) detection of whole exome sequencing (WES) data from the Illumina platform. Quality-control metrics were analyzed at each step of the pipeline by use of a validated training dataset to ensure data integrity for clinical applications. We annotate the variants with data regarding the disease population and variant impact. Custom algorithms were developed to filter variants based on criteria, such as quality of variant, inheritance pattern, and impact of variant on protein function. The developed clinical variant pipeline links the identified rare variants to Integrated Genome Viewer for visualization in a genomic context and to the Protein Information Resource's iProXpress for rich protein and disease information. With the application of our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for downstream variant filtering that empowers clinicians and researchers to interpret more effectively the relevance of genomic alterations within a rare genetic disease.
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Affiliation(s)
- Erin L Crowgey
- 1 Center for Bioinformatics and Computational Biology, and 2 Biomolecular Core Laboratory, Nemours Alfred I. duPont Hospital for Children Wilmington, Delaware 19803, USA; and 3 Department of Biological Sciences, University of Delaware, Newark, Delaware 19711, USA
| | - Deborah L Stabley
- 1 Center for Bioinformatics and Computational Biology, and 2 Biomolecular Core Laboratory, Nemours Alfred I. duPont Hospital for Children Wilmington, Delaware 19803, USA; and 3 Department of Biological Sciences, University of Delaware, Newark, Delaware 19711, USA
| | - Chuming Chen
- 1 Center for Bioinformatics and Computational Biology, and 2 Biomolecular Core Laboratory, Nemours Alfred I. duPont Hospital for Children Wilmington, Delaware 19803, USA; and 3 Department of Biological Sciences, University of Delaware, Newark, Delaware 19711, USA
| | - Hongzhan Huang
- 1 Center for Bioinformatics and Computational Biology, and 2 Biomolecular Core Laboratory, Nemours Alfred I. duPont Hospital for Children Wilmington, Delaware 19803, USA; and 3 Department of Biological Sciences, University of Delaware, Newark, Delaware 19711, USA
| | - Katherine M Robbins
- 1 Center for Bioinformatics and Computational Biology, and 2 Biomolecular Core Laboratory, Nemours Alfred I. duPont Hospital for Children Wilmington, Delaware 19803, USA; and 3 Department of Biological Sciences, University of Delaware, Newark, Delaware 19711, USA
| | - Shawn W Polson
- 1 Center for Bioinformatics and Computational Biology, and 2 Biomolecular Core Laboratory, Nemours Alfred I. duPont Hospital for Children Wilmington, Delaware 19803, USA; and 3 Department of Biological Sciences, University of Delaware, Newark, Delaware 19711, USA
| | - Katia Sol-Church
- 1 Center for Bioinformatics and Computational Biology, and 2 Biomolecular Core Laboratory, Nemours Alfred I. duPont Hospital for Children Wilmington, Delaware 19803, USA; and 3 Department of Biological Sciences, University of Delaware, Newark, Delaware 19711, USA
| | - Cathy H Wu
- 1 Center for Bioinformatics and Computational Biology, and 2 Biomolecular Core Laboratory, Nemours Alfred I. duPont Hospital for Children Wilmington, Delaware 19803, USA; and 3 Department of Biological Sciences, University of Delaware, Newark, Delaware 19711, USA
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Abstract
Annotation of prokaryotic sequences can be separated into structural and functional annotation. Structural annotation is dependent on algorithmic interrogation of experimental evidence to discover the physical characteristics of a gene. This is done in an effort to construct accurate gene models, so understanding function or evolution of genes among organisms is not impeded. Functional annotation is dependent on sequence similarity to other known genes or proteins in an effort to assess the function of the gene. Combining structural and functional annotation across genomes in a comparative manner promotes higher levels of accurate annotation as well as an advanced understanding of genome evolution. As the availability of bacterial sequences increases and annotation methods improve, the value of comparative annotation will increase.
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Affiliation(s)
- Nicholas Beckloff
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, USA
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