Online Program Home
My Program

Abstract Details

Activity Number: 554
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Scientific and Public Affairs Advisory Committee
Abstract #318534
Title: Empirical Estimation of Sequencing Error Rates Using Smoothing Splines
Author(s): Xuan Zhu* and Jian Wang and Bo Peng and Sanjay Shete
Companies: MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
Keywords: Empirical error rate ; next-generation sequencing ; smoothing spline ; frequency-based simulation ; short reads
Abstract:

Next-generation sequencing has been used to addressed a diverse range of biological problems through. However, the error rates for next-generation sequencing are often higher, which impacts the downstream genomic analysis, in comparison with conventional sequencing. Recently, a shadow regression approach was proposed to estimate the error rates under the assumption of a linear relationship between the number of error-free sequenced reads and the number of reads containing errors (denoted as shadows). However, this linear read-shadow relationship assumption may not be appropriate for all types of sequencing data. Therefore, it is essential to propose a more reliable approach to estimate the sequencing error rates without assuming linearity. In this study, we proposed an empirical error rate estimation approach, which is free of linearity, and provides more accurate error rate estimations for next-generation sequencing data.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association