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Towards Automatic Detecting of Overlapping Genes - Clustered BLAST Analysis of Viral Genomes

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6023))

Abstract

Overlapping genes (encoded on the same DNA locus but in different frames) are thought to be rare and, therefore, were largely neglected in the past. In a test set of 800 viruses we found more than 350 potential overlapping open reading frames of >500 bp which generate BLAST hits, indicating a possible biological function. Interestingly, five overlaps with more than 2000 bp were found, the largest may even contain triple overlaps. In order to perform the vast amount of BLAST searches required to test all detected open reading frames, we compared two clustering strategies (BLASTCLUST and k-means) and queried the database with one representative only. Our results show that this approach achieves a significant speed-up while retaining a high quality of the results (>99% precision compared to single queries) for both clustering methods. Future wet lab experiments are needed to show whether the detected overlapping reading frames are biologically functional.

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Author information

Authors and Affiliations

  1. Chair of Microbial Ecology, Technische Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany

    Klaus Neuhaus & Siegfried Scherer

  2. Chair of Data Analysis and Visualization, Universität Konstanz, Universitätsstr. 10, 78457, Konstanz, Germany

    Daniela Oelke & Daniel A. Keim

  3. Chair of Data Management and Data Exploration, Rheinisch-Westfälische, Technische Hochschule Aachen, Informatik 9, 52056, Aachen, Germany

    David Fürst

Authors
  1. Klaus Neuhaus
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  2. Daniela Oelke
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  3. David Fürst
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  4. Siegfried Scherer
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  5. Daniel A. Keim
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Editor information

Editors and Affiliations

  1. Institute for High-Performance Computing and Networking (ICAR), Italian National Research Council (CNR), Via P. Bucci 41C, 87036, Rende, (CS), Italy

    Clara Pizzuti

  2. Department of Molecular Physiology and Biophysics, Vanderbilt University, Center for Human Genetics Research, 519 Light Hall, 37232, Nashville, TN, USA

    Marylyn D. Ritchie

  3. Department of Animal Production Epidemiology and Ecology, University of Torino, Molecular Biotechnology Center, Via Leonardo da Vinci 44, 10095, Grugliasco, (TO), Italy

    Mario Giacobini

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© 2010 Springer-Verlag Berlin Heidelberg

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Neuhaus, K., Oelke, D., Fürst, D., Scherer, S., Keim, D.A. (2010). Towards Automatic Detecting of Overlapping Genes - Clustered BLAST Analysis of Viral Genomes. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_20

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  • DOI: https://doi.org/10.1007/978-3-642-12211-8_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12210-1

  • Online ISBN: 978-3-642-12211-8

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