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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Using population data for assessing next-generation sequencing performance.

Darren T Houniet¹, Thahira J Rahman¹, Saeed Al Turki¹

¹Oxford Gene Technology, Begbroke Science Park, Oxford, Oxfordshire, OX5 1PF, Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway NE1 3BZ, Newcastle upon Tyne and The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK.

Bioinformatics (Oxford, England)

|September 20, 2014

Summary

This summary is machine-generated.

We developed a new maximum likelihood framework to assess whole-exome sequencing (WES) performance. Our method accurately estimates sensitivity and specificity, crucial for diagnosing Mendelian disorders using WES data.

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Area of Science:

Genomics
Bioinformatics

Background:

Whole-exome sequencing (WES) is vital for identifying rare variants in Mendelian disorders.
Numerous sequencing platforms and analysis methods necessitate performance evaluation.
Traditional assessment methods are limited; novel approaches are required.

Purpose of the Study:

To develop and validate a maximum likelihood framework for assessing WES performance.
To estimate the sensitivity and specificity of WES data.
To evaluate the impact of population frequency misspecification on performance estimates.

Main Methods:

Developed a maximum likelihood framework for WES data analysis.
Utilized Perl and R for implementation.
Applied the framework to WES data from 19 individuals.

Main Results:

Estimated sensitivity and specificity comparable to microarray-based assessments.
Demonstrated that population frequency misspecification affects estimates but not procedure rankings.
Validated the robustness of the developed framework.

Conclusions:

The maximum likelihood framework provides a reliable method for evaluating WES performance.
This approach enhances the accuracy of variant detection for Mendelian disorders.
The findings support the rigorous assessment of sequencing methodologies.