RT Journal Article
SR Electronic
T1 Exploratory analysis and error modeling of a sequencing technology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 043042
DO 10.1101/043042
A1 Michael Inouye
A1 Kerrin S. Small
A1 Yik Y. Teo
A1 Heng Li
A1 Nava Whiteford
A1 Tom Skelly
A1 Irina Abnizova
A1 Daniel J. Turner
A1 Panos Deloukas
A1 Dominic P. Kwiatkowski
A1 Clive G. Brown
A1 Taane G. Clark
YR 2016
UL http://biorxiv.org/content/early/2016/03/11/043042.abstract
AB Next generation DNA sequencing methods have created an unprecedented leap in sequence data generation, thus novel computational tools and statistical models are required to optimize and assess the resulting data. In this report, we explore underlying causes of error for the Illumina Genome Analyzer (IGA) sequencing technology and attempt to quantify their effects using a human bacterial artificial chromosome sequenced to 60,000 fold coverage. Seven potential error predictors are considered: Phred score, read entropy, tile coordinates, local tile density, base position within read, nucleotide call, and lane. With these parameters, logistic regression and log-linear models are constructed and used to show that each of the potential predictors contributes to error (P<1×10−4). With this additional information, we apply the logistic model and achieve a 3% improvement in both the sensitivity and specificity to detect IGA errors. Further, we demonstrate that these modeling approaches can be used as a feedback loop to inform laboratory methods and identify specific machine or run bias.