TY - JOUR T1 - Exploratory analysis and error modeling of a sequencing technology JF - bioRxiv DO - 10.1101/043042 SP - 043042 AU - Michael Inouye AU - Kerrin S. Small AU - Yik Y. Teo AU - Heng Li AU - Nava Whiteford AU - Tom Skelly AU - Irina Abnizova AU - Daniel J. Turner AU - Panos Deloukas AU - Dominic P. Kwiatkowski AU - Clive G. Brown AU - Taane G. Clark Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/03/11/043042.abstract N2 - 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. ER -