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      Trimmomatic: a flexible trimmer for Illumina sequence data

      research-article
      1 , 2 , 1 , 2 , 3 , *
      Bioinformatics
      Oxford University Press

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          Abstract

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data.

          Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.

          Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic

          Contact: usadel@ 123456bio1.rwth-aachen.de

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references4

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          AdapterRemoval: easy cleaning of next-generation sequencing reads

          Background With the advent of next-generation sequencing there is an increased demand for tools to pre-process and handle the vast amounts of data generated. One recurring problem is adapter contamination in the reads, i.e. the partial or complete sequencing of adapter sequences. These adapter sequences have to be removed as they can hinder correct mapping of the reads and influence SNP calling and other downstream analyses. Findings We present a tool called AdapterRemoval which is able to pre-process both single and paired-end data. The program locates and removes adapter residues from the reads, it is able to combine paired reads if they overlap, and it can optionally trim low-quality nucleotides. Furthermore, it can look for adapter sequence in both the 5’ and 3’ ends of the reads. This is a flexible tool that can be tuned to accommodate different experimental settings and sequencing platforms producing FASTQ files. AdapterRemoval is shown to be good at trimming adapters from both single-end and paired-end data. Conclusions AdapterRemoval is a comprehensive tool for analyzing next-generation sequencing data. It exhibits good performance both in terms of sensitivity and specificity. AdapterRemoval has already been used in various large projects and it is possible to extend it further to accommodate application-specific biases in the data.
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            The Newick utilities: high-throughput phylogenetic tree processing in the Unix shell

            Summary: We present a suite of Unix shell programs for processing any number of phylogenetic trees of any size. They perform frequently-used tree operations without requiring user interaction. They also allow tree drawing as scalable vector graphics (SVG), suitable for high-quality presentations and further editing, and as ASCII graphics for command-line inspection. As an example we include an implementation of bootscanning, a procedure for finding recombination breakpoints in viral genomes. Availability: C source code, Python bindings and executables for various platforms are available from http://cegg.unige.ch/newick_utils. The distribution includes a manual and example data. The package is distributed under the BSD License. Contact: thomas.junier@unige.ch
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              A survey of sequence alignment algorithms for next-generation sequencing.

              Rapidly evolving sequencing technologies produce data on an unparalleled scale. A central challenge to the analysis of this data is sequence alignment, whereby sequence reads must be compared to a reference. A wide variety of alignment algorithms and software have been subsequently developed over the past two years. In this article, we will systematically review the current development of these algorithms and introduce their practical applications on different types of experimental data. We come to the conclusion that short-read alignment is no longer the bottleneck of data analyses. We also consider future development of alignment algorithms with respect to emerging long sequence reads and the prospect of cloud computing.
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                01 August 2014
                01 April 2014
                01 April 2014
                : 30
                : 15
                : 2114-2120
                Affiliations
                1Department Metabolic Networks, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Golm, 2Institut für Biologie I, RWTH Aachen, Worringer Weg 3, 52074 Aachen and 3Institute of Bio- and Geosciences: Plant Sciences, Forschungszentrum Jülich, Leo-Brandt-Straße, 52425 Jülich, Germany
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Inanc Birol

                Article
                btu170
                10.1093/bioinformatics/btu170
                4103590
                24695404
                344a5c2a-a4e4-40f4-82ee-ea52efe6c4b6
                © The Author 2014. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 July 2013
                : 9 March 2014
                : 25 March 2014
                Page count
                Pages: 7
                Categories
                Original Papers
                Genome Analysis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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