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      A parallel and sensitive software tool for methylation analysis on multicore platforms

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          Abstract

          Motivation: DNA methylation analysis suffers from very long processing time, as the advent of Next-Generation Sequencers has shifted the bottleneck of genomic studies from the sequencers that obtain the DNA samples to the software that performs the analysis of these samples. The existing software for methylation analysis does not seem to scale efficiently neither with the size of the dataset nor with the length of the reads to be analyzed. As it is expected that the sequencers will provide longer and longer reads in the near future, efficient and scalable methylation software should be developed.

          Results: We present a new software tool, called HPG-Methyl, which efficiently maps bisulphite sequencing reads on DNA, analyzing DNA methylation. The strategy used by this software consists of leveraging the speed of the Burrows–Wheeler Transform to map a large number of DNA fragments (reads) rapidly, as well as the accuracy of the Smith–Waterman algorithm, which is exclusively employed to deal with the most ambiguous and shortest reads. Experimental results on platforms with Intel multicore processors show that HPG-Methyl significantly outperforms in both execution time and sensitivity state-of-the-art software such as Bismark, BS-Seeker or BSMAP, particularly for long bisulphite reads.

          Availability and implementation: Software in the form of C libraries and functions, together with instructions to compile and execute this software. Available by sftp to anonymous@clariano.uv.es (password ‘anonymous’).

          Contact: juan.orduna@ 123456uv.es or jdopazo@ 123456cipf.es

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          Most cited references6

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          Identification of common molecular subsequences.

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            Principles and challenges of genomewide DNA methylation analysis.

            Methylation of cytosine bases in DNA provides a layer of epigenetic control in many eukaryotes that has important implications for normal biology and disease. Therefore, profiling DNA methylation across the genome is vital to understanding the influence of epigenetics. There has been a revolution in DNA methylation analysis technology over the past decade: analyses that previously were restricted to specific loci can now be performed on a genome-scale and entire methylomes can be characterized at single-base-pair resolution. However, there is such a diversity of DNA methylation profiling techniques that it can be challenging to select one. This Review discusses the different approaches and their relative merits and introduces considerations for data analysis.
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              Tools for mapping high-throughput sequencing data.

              A ubiquitous and fundamental step in high-throughput sequencing analysis is the alignment (mapping) of the generated reads to a reference sequence. To accomplish this task, numerous software tools have been proposed. Determining the mappers that are most suitable for a specific application is not trivial. This survey focuses on classifying mappers through a wide number of characteristics. The goal is to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem.
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                01 October 2015
                10 June 2015
                10 June 2015
                : 31
                : 19
                : 3130-3138
                Affiliations
                1Department of Computational Genomics, Centro de Investigación Príncipe Felipe,
                2Departamento de Informática, Universidad de Valencia and
                3DISCA, Universidad Politécnica de Valencia, Valencia, Spain
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Alfonso Valencia

                Article
                btv357
                10.1093/bioinformatics/btv357
                4679392
                26069264
                275f7e2d-ab9d-42b5-ae30-2cd8576de663
                © The Author 2015. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 8 September 2014
                : 8 May 2015
                : 5 June 2015
                Page count
                Pages: 9
                Categories
                Original Papers
                Sequence Analysis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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