Empirical estimation of sequencing error rates using smoothing splines.

Xuan Zhu,Bo Peng,Sanjay Shete,Jian Wang

doi:10.1186/s12859-016-1052-3

Abstract

BackgroundNext-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling. However, compared to conventional sequencing, the error rates for next-generation sequencing are often higher, which impacts the downstream genomic analysis. Recently, Wang et al. (BMC Bioinformatics 13:185, 2012) proposed a shadow regression approach to estimate the error rates for next-generation sequencing data based on the assumption of a linear relationship between the number of reads sequenced and the number of reads containing errors (denoted as shadows). However, this linear read-shadow relationship may not be appropriate for all types of sequence data. Therefore, it is necessary to estimate the error rates in a more reliable way without assuming linearity. We proposed an empirical error rate estimation approach that employs cubic and robust smoothing splines to model the relationship between the number of reads sequenced and the number of shadows.ResultsWe performed simulation studies using a frequency-based approach to generate the read and shadow counts directly, which can mimic the real sequence counts data structure. Using simulation, we investigated the performance of the proposed approach and compared it to that of shadow linear regression. The proposed approach provided more accurate error rate estimations than the shadow linear regression approach for all the scenarios tested. We also applied the proposed approach to assess the error rates for the sequence data from the MicroArray Quality Control project, a mutation screening study, the Encyclopedia of DNA Elements project, and bacteriophage PhiX DNA samples.ConclusionsThe proposed empirical error rate estimation approach does not assume a linear relationship between the error-free read and shadow counts and provides more accurate estimations of error rates for next-generation, short-read sequencing data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1052-3) contains supplementary material, which is available to authorized users.

Highlights

Next-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling
Simulation results As shown in Additional files 3 and 4, using the frequency-based simulation approach can better capture the relationship between error-free read and shadow counts, we used this simulation approach to perform further simulations based on next-generation sequencing data from the MicroArray Quality Control (MAQC), mutation screening, Encyclopedia of DNA Elements (ENCODE), and PhiX DNA sample data sets
We compared the performance of our proposed EER approach using the cubic or robust smoothing spline method (EER_CS or EER_RS, respectively) with that of the shadow regression error rate (SRER) approach

Summary

Introduction

Wang et al (BMC Bioinformatics 13:185, 2012) proposed a shadow regression approach to estimate the error rates for next-generation sequencing data based on the assumption of a linear relationship between the number of reads sequenced and the number of reads containing errors (denoted as shadows). This linear read-shadow relationship may not be appropriate for all types of sequence data. These assessments include measuring intrinsic quality metrics (FastQC) [16], sequence coverage, Zhu et al BMC Bioinformatics (2016) 17:177 sequence error rates, and paired-end, fragment-size distributions [15, 17]

Methods

Results

Conclusion