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      Mycoplasma contamination in the 1000 Genomes Project

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          Abstract

          Background

          In silco Biology is increasingly important and is often based on public data. While the problem of contamination is well recognised in microbiology labs the corresponding problem of database corruption has received less attention.

          Results

          Mapping 50 billion next generation DNA sequences from The Thousand Genome Project against published genomes reveals many that match one or more Mycoplasma but are not included in the reference human genome GRCh37.p5. Many of these are of low quality but NCBI BLAST searches confirm some high quality, high entropy sequences match Mycoplasma but no human sequences.

          Conclusions

          It appears at least 7% of 1000G samples are contaminated.

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          Most cited references4

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          ContEst: estimating cross-contamination of human samples in next-generation sequencing data.

          Aaron McKenna, Kristian Cibulskis, Eric D. Banks … (2011)
          Here, we present ContEst, a tool for estimating the level of cross-individual contamination in next-generation sequencing data. We demonstrate the accuracy of ContEst across a range of contamination levels, sources and read depths using sequencing data mixed in silico at known concentrations. We applied our tool to published cancer sequencing datasets and report their estimated contamination levels. ContEst is a GATK module, and distributed under a BSD style license at http://www.broadinstitute.org/cancer/cga/contest kcibul@broadinstitute.org; gadgetz@broadinstitute.org Supplementary data is available at Bioinformatics online.
          Article 0 comments Cited 115 times – based on 0 reviews     Review now
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            Mycoplasma contamination of cell cultures: Incidence, sources, effects, detection, elimination, prevention.

            Hans G. Drexler, Cord C. Uphoff (2002)
            The contamination of cell cultures by mycoplasmas remains a major problem in cell culture. Mycoplasmas can produce a virtually unlimited variety of effects in the cultures they infect. These organisms are resistant to most antibiotics commonly employed in cell cultures. Here we provide a concise overview of the current knowledge on: (1) the incidence and sources of mycoplasma contamination in cell cultures, the mycoplasma species most commonly detected in cell cultures, and the effects of mycoplasmas on the function and activities of infected cell cultures; (2) the various techniques available for the detection of mycoplasmas with particular emphasis on the most reliable detection methods; (3) the various methods available for the elimination of mycoplasmas highlighting antibiotic treatment; and (4) the recommended procedures and working protocols for the detection, elimination and prevention of mycoplasma contamination. The availability of accurate, sensitive and reliable detection methods and the application of robust and successful elimination methods provide powerful means for overcoming the problem of mycoplasma contamination in cell cultures.
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              Mycoplasma infection significantly alters microarray gene expression profiles.

              D Pepper, T Ward, Samar Kassem … (2003)
              Article 0 comments Cited 10 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                William B Langdon
                Journal
                Journal ID (nlm-ta): BioData Min
                Journal ID (iso-abbrev): BioData Min
                Title: BioData Mining
                Publisher: BioMed Central
                ISSN (Electronic): 1756-0381
                Publication date Collection: 2014
                Publication date (Electronic): 29 April 2014
                Volume: 7
                Page: 3
                Affiliations
                [1 ]Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK
                Article
                Publisher ID: 1756-0381-7-3
                DOI: 10.1186/1756-0381-7-3
                PMC ID: 4022254
                SO-VID: a5f915e4-8def-47d3-98e1-a1de94cd30a4
                Copyright © Copyright © 2014 Langdon; licensee BioMed Central Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

                History
                Date received : 23 May 2013
                Date accepted : 19 February 2014
                Categories
                Subject: Research

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: molecular biology,microbiology,genetics,metagenomic,data mining,next-generation dna sequencing,data cleansing,high throughput,solexa,454,solid
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: molecular biology, microbiology, genetics, metagenomic, data mining, next-generation dna sequencing, data cleansing, high throughput, solexa, 454, solid

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