alexa Automating the Computational Analysis of Exome Sequencing Data: A Prototype Methodology to Overcome Bottlenecks Observed with Operator Driven Clinical Interpretation for Known Pathogenic Mutations| Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
Open Access

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  • Research Article   
  • J Comput Sci Syst Biol 2019, Vol 12(2): 299
  • DOI: 10.4172/0974-7230.1000299

Automating the Computational Analysis of Exome Sequencing Data: A Prototype Methodology to Overcome Bottlenecks Observed with Operator Driven Clinical Interpretation for Known Pathogenic Mutations

Shahid Mian1,2*, Wafaa Al-Turaif1, Abdullah Al-Nawfal1, Mohammed Mudhish1, Eissa Faqeih3 and Manar Samman2
1Division of Bioinformatics, Molecular Pathology, Pathology and Clinical Laboratory Medicine, King Fahad Medical City, Riyadh 11525, Saudi Arabia
2Molecular Pathology, Pathology and Clinical Laboratory Medicine, King Fahad Medical City, Riyadh 11525, Saudi Arabia
3Clinical Research Department, Children’s Hospital, King Fahad Medical City, Riyadh 11525, Saudi Arabia
*Corresponding Author : Shahid Mian, Division of Bioinformatics, Molecular Pathology, Pathology and Clinical Laboratory Medicine, King Fahad Medical City, Riyadh 11525, Saudi Arabia, Tel: 00966539417360, Email: [email protected]

Received Date: Apr 08, 2019 / Accepted Date: Apr 22, 2019 / Published Date: Apr 29, 2019

Abstract

Objective: Clinical exome sequencing produces between 90,000-100,00 variants per individual. Bottlenecks are manifested due to manual (operator based) interpretation of data. Given an increasing demand for genomic screening, automated computational methodologies are urgently required to meet both throughput and interpretation. Objective: determine if algorithms can be developed to identify and report specific pathogenic variants.
Methods: Clinical exome sequencing was performed on 961 individuals presented for diagnostic analysis to King Fahad Medical City (KFMC). Variant Call Format (VCF 4.2) files from each patient were used for algorithm development. Perl (v5.28.1) was used as the construct language. 137 known pathogenic variants were used as a search test bed. A 10-step procedural workflow was implemented to automate the process of searching for targets. Where a positive identification was elicited, variants were annotated, merged with clinical data and output as a pdf report. Negative findings were output as a pdf report with clinical data only.
Results: 961 VCF files were screened for 137 pathogenic variants of interest to KFMC. Target variants were compared against each variant within a patient’s VCF using logic operators. A total processing time including report production for 961 individuals was completed in 11.38 hours. 177 patients (18.4%) were positive for at least one variant and 15 patients had two variants (1.6%). All positive cases were verified manually in the originating VCF. The 137-target list of variants were “spiked” into a negative control patient VCF to act as a positive control (sensitivity). All variants were detected by the algorithm. 10 negative finding patients were chosen at random and manually checked for the absence (specificity) of the 137 variants. No variants were detected.
Conclusion: Automated searching and production of reports for specific pathogenic variants using computational searching is feasible for diagnostic laboratories undertaking clinical exome sequencing.

Keywords: Diagnostics; Bioinformatics; Algorithm; Exome; Screening; Clinical; Automation

Citation: Mian S, Al-Turaif W, Al-Nawfal A, Mudhish M, Faqeih E, et al. (2019) Automating the Computational Analysis of Exome Sequencing Data: A Prototype Methodology to Overcome Bottlenecks Observed with Operator Driven Clinical Interpretation for Known Pathogenic Mutations. J Comput Sci Syst Biol 12:47-52. Doi: 10.4172/0974-7230.1000299

Copyright: © 2019 Mian S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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