In search of perfect reads

Continued advances in next generation short-read sequencing technologies are increasing throughput and read lengths, while driving down the error rates, for example within 1% for Illumina HiSeq reads. Moreover, the errors are not uniformly distributed in all reads, and a large percentage of reads are indeed error-free. Ability to predict such perfect reads can have significant impact on run-time complexity of applications. In this paper, we present a simple and fast k-spectrum analysis based method to identify error-free reads. Our experiments show that if around 80% of the reads in a dataset are perfect, then our method retains almost 99.9% of them with more than 90% precision rate. Though filtering out reads identified as erroneous by our method reduces the coverage by about 7% on an average, coverage pattern across genome remains similar. The filtration process can be customized at several levels of stringency depending upon the downstream application need.