A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing

A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing

Abstract Background Next generation sequencing (NGS) has become a common technology for clinical genetic tests. The quality of NGS calls varies widely and is influenced by features like reference sequence characteristics, read depth, and mapping accuracy. With recent advances in NGS technology and s...

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Bibliographic Details
Main Authors: Jeroen van den Akker, Gilad Mishne, Anjali D. Zimmer, Alicia Y. Zhou
Format: Article
Language:English
Published: BMC 2018-04-01
Series:BMC Genomics
Subjects:
Next generation sequencing
Variant calling
Variant quality
Variant confidence
Secondary confirmation
Orthogonal confirmation
Online Access:http://link.springer.com/article/10.1186/s12864-018-4659-0

Internet

http://link.springer.com/article/10.1186/s12864-018-4659-0

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