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      Quality control and preprocessing of metagenomic datasets

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      Author(s): 1 , 2 , * , 1 , 3 , *
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Summary: Here, we present PRINSEQ for easy and rapid quality control and data preprocessing of genomic and metagenomic datasets. Summary statistics of FASTA (and QUAL) or FASTQ files are generated in tabular and graphical form and sequences can be filtered, reformatted and trimmed by a variety of options to improve downstream analysis.

          Availability and Implementation: This open-source application was implemented in Perl and can be used as a stand alone version or accessed online through a user-friendly web interface. The source code, user help and additional information are available at http://prinseq.sourceforge.net/.

          Contact: rschmied@ 123456sciences.sdsu.edu ; redwards@ 123456cs.sdsu.edu

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          Manipulation of FASTQ data with Galaxy

          Daniel Blankenberg, Assaf Gordon, Gregory Von Kuster … (2010)
          Summary: Here, we describe a tool suite that functions on all of the commonly known FASTQ format variants and provides a pipeline for manipulating next generation sequencing data taken from a sequencing machine all the way through the quality filtering steps. Availability and Implementation: This open-source toolset was implemented in Python and has been integrated into the online data analysis platform Galaxy (public web access: http://usegalaxy.org; download: http://getgalaxy.org). Two short movies that highlight the functionality of tools described in this manuscript as well as results from testing components of this tool suite against a set of previously published files are available at http://usegalaxy.org/u/dan/p/fastq Contact: james.taylor@emory.edu; anton@bx.psu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            Systematic artifacts in metagenomes from complex microbial communities.

            Vicente Gomez-Alvarez, Tracy K. Teal, Thomas M. Schmidt (2009)
            Metagenomics is providing an unprecedented view of the taxonomic diversity, metabolic potential and ecological role of microbial communities in biomes as diverse as the mammalian gastrointestinal tract, the marine water column and soils. However, we have found a systematic error in metagenomes generated by 454-based pyrosequencing that leads to an overestimation of gene and taxon abundance; between 11% and 35% of sequences in a typical metagenome are artificial replicates. Here we document the error in several published and original datasets and offer a web-based solution (http://microbiomes.msu.edu/replicates) for identifying and removing these artifacts.
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              TagCleaner: Identification and removal of tag sequences from genomic and metagenomic datasets

              Robert Schmieder, Yan Wei Lim, Forest Rohwer … (2010)
              Background Sequencing metagenomes that were pre-amplified with primer-based methods requires the removal of the additional tag sequences from the datasets. The sequenced reads can contain deletions or insertions due to sequencing limitations, and the primer sequence may contain ambiguous bases. Furthermore, the tag sequence may be unavailable or incorrectly reported. Because of the potential for downstream inaccuracies introduced by unwanted sequence contaminations, it is important to use reliable tools for pre-processing sequence data. Results TagCleaner is a web application developed to automatically identify and remove known or unknown tag sequences allowing insertions and deletions in the dataset. TagCleaner is designed to filter the trimmed reads for duplicates, short reads, and reads with high rates of ambiguous sequences. An additional screening for and splitting of fragment-to-fragment concatenations that gave rise to artificial concatenated sequences can increase the quality of the dataset. Users may modify the different filter parameters according to their own preferences. Conclusions TagCleaner is a publicly available web application that is able to automatically detect and efficiently remove tag sequences from metagenomic datasets. It is easily configurable and provides a user-friendly interface. The interactive web interface facilitates export functionality for subsequent data processing, and is available at http://edwards.sdsu.edu/tagcleaner.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 March 2011
                Publication date (Electronic): 28 January 2011
                Publication date PMC-release: 28 January 2011
                Volume: 27
                Issue: 6
                Pages: 863-864
                Affiliations
                1Department of Computer Science, 2Computational Science Research Center, San Diego State University, San Diego, CA 92182 and 3Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Alex Bateman

                Article
                Publisher ID: btr026
                DOI: 10.1093/bioinformatics/btr026
                PMC ID: 3051327
                PubMed ID: 21278185
                SO-VID: 244a01be-c7c0-40cf-979c-5c5dad3d1669
                Copyright © © The Author(s) 2011. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 8 November 2010
                Date revision received : 11 January 2011
                Date accepted : 12 January 2011
                Categories
                Subject: Applications Note
                Subject: Sequence Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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