Removing duplicate reads using graphics processing units. Issue 12 (October 2016)

• Record Type: Journal Article
• Authors: Manconi, Andrea; Moscatelli, Marco; Armano, Giuliano; Gnocchi, Matteo; Orro, Alessandro; Milanesi, Luciano
• Is Part Of: BMC bioinformatics. Volume 17:Issue 12(2016)
• Page Start: 59
• Page End: 71
• Publication Date: 2016-10
• Subjects: Next generation sequencing -- Duplicate reads -- Graphics processing units -- CUDA; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1192-5
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background During library construction polymerase chain reaction is used to enrich the DNA before sequencing. Typically, this process generates duplicate read sequences. Removal of these artifacts is mandatory, as they can affect the correct interpretation of data in several analyses. Ideally, duplicate reads should be characterized by identical nucleotide sequences. However, due to sequencing errors, duplicates may also be nearly-identical. Removing nearly-identical duplicates can result in a notable computational effort. To deal with this challenge, we recently proposed a GPU method aimed at removing identical and nearly-identical duplicates generated with an Illumina platform. The method implements an approach based on prefix-suffix comparison. Read sequences with identical prefix are considered potential duplicates. Then, their suffixes are compared to identify and remove those that are actually duplicated. Although the method can be efficiently used to remove duplicates, there are some limitations that need to be overcome. In particular, it cannot to detect potential duplicates in the event that prefixes are longer than 27 bases, and it does not provide support for paired-end read libraries. Moreover, large clusters of potential duplicates are split into smaller with the aim to guarantees a reasonable computing time. This heuristic may affect the accuracy of the analysis. Results In this work we propose GPU-DupRemoval, a new implementation of our method able to (i ) cluster reads without constraints on the maximum length of the prefixes, (ii ) support both single- and paired-end read libraries, and (iii ) analyze large clusters of potential duplicates. Conclusions Due to the massive parallelization obtained by exploiting graphics cards, GPU-DupRemoval removes duplicate reads faster than other cutting-edge solutions, while outperforming most of them in terms of amount of duplicates reads.