In search of perfect reads

Soumitra Pal,Srinivas Aluru

doi:10.1186/1471-2105-16-s17-s7

Abstract

BackgroundContinued advances in next generation short-read sequencing technologies are increasing throughput and read lengths, while driving down error rates. Taking advantage of the high coverage sampling used in many applications, several error correction algorithms have been developed to improve data quality further. However, correcting errors in high coverage sequence data requires significant computing resources.MethodsWe propose a different approach to handle erroneous sequence data. Presently, error rates of high-throughput platforms such as the Illumina HiSeq are within 1%. 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 significantly impact the run-time complexity of applications. We present a simple and fast k-spectrum analysis based method to identify error-free reads. The filtration process to identify and weed out erroneous reads can be customized at several levels of stringency depending upon the downstream application need.ResultsOur 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 average coverage by about 7%, we found the remaining reads provide as uniform a coverage as the original dataset. We demonstrate the effectiveness of our approach on an example downstream application: we show that an error correction algorithm, Reptile, which rely on collectively analyzing the reads in a dataset to identify and correct erroneous bases, instead use reads predicted to be perfect by our method to correct the other reads, the overall accuracy improves further by up to 10%.ConclusionsThanks to the continuous technological improvements, the coverage and accuracy of reads from dominant sequencing platforms have now reached an extent where we can envision just filtering out reads with errors, thus making error correction less important. Our algorithm is a first attempt to propose and demonstrate this new paradigm. Moreover, our demonstration is applicable to any error correction algorithm as a downstream application, this in turn gives a new class of error correcting algorithms as a by product.

Highlights

Continued advances in generation short-read sequencing technologies are increasing throughput and read lengths, while driving down error rates
The focus of this work is applications of highthroughput sequencing in which a single genome is sampled at high coverage, such as resequencing and de novo sequencing
Several error correction algorithms for haploid genomes have been developed, using k-spectrum [1,2,3,4], suffix trees [5,6,7], or multiple sequence alignments [8,9] to identify overlapping reads

Summary

Introduction

Continued advances in generation short-read sequencing technologies are increasing throughput and read lengths, while driving down error rates. The focus of this work is applications of highthroughput sequencing in which a single genome is sampled at high coverage, such as resequencing and de novo sequencing. In these cases, the infrequent occurrence of errors in reads, and the apparent lack of affinity of errors to any fixed genomic location, provide a way to detect and correct erroneous bases in reads. If the reads covering a specific genomic position can be identified and properly positioned relative to their locations of genomic occurrence, this layout can be used to infer the true base by majority vote and correct the others. For a detailed survey of error correction methods, see [10,11]

Methods

Results

Conclusion