Empirical estimation of sequencing error rates using smoothing splines.
BACKGROUND
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. 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.


RESULTS
We 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.


CONCLUSIONS
The 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.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Xuan
X
Zhu
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Jian
J
Wang
Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Bo
B
Peng
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. sshete@mdanderson.org.|Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. sshete@mdanderson.org.
Sanjay
S
Shete
D010584:Bacteriophage phi X 174 / Q000235:genetics; D003198:Computer Simulation; D004279:DNA, Viral / Q000235:genetics; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D020014:K562 Cells; D016014:Linear Models; D020411:Oligonucleotide Array Sequence Analysis; D017422:Sequence Analysis, DNA / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
D004279:DNA, Viral
Empirical error rate; Frequency-based simulation; Next-generation sequencing; Short reads; Smoothing spline