Towards automatic detecting of overlapping genes : clustered BLAST analysis of viral genomes

Neuhaus, Klaus; Oelke, Daniela; Fürst, David; Scherer, Siegfried; Keim, Daniel A.

Towards automatic detecting of overlapping genes : clustered BLAST analysis of viral genomes

Dateien

Mansmannpdf.pdfGröße: 4.71 MBDownloads: 514

Datum

2010

Autor:innen

Neuhaus, Klaus

Oelke, Daniela

Fürst, David

Scherer, Siegfried

Keim, Daniel A.

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

PIZZUTI, Clara, ed., Marylyn D. RITCHIE, ed., Mario GIACOBINI, ed.. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 228-239. Lecture Notes in Computer Science. 6023. ISBN 978-3-642-12210-1. Available under: doi: 10.1007/978-3-642-12211-8_20

Zusammenfassung

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.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

overlapping genes, clustering, BLAST analysis

Zitieren

ISO 690

NEUHAUS, Klaus, Daniela OELKE, David FÜRST, Siegfried SCHERER, Daniel A. KEIM, 2010. Towards automatic detecting of overlapping genes : clustered BLAST analysis of viral genomes. In: PIZZUTI, Clara, ed., Marylyn D. RITCHIE, ed., Mario GIACOBINI, ed.. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 228-239. Lecture Notes in Computer Science. 6023. ISBN 978-3-642-12210-1. Available under: doi: 10.1007/978-3-642-12211-8_20

BibTex

@inproceedings{Neuhaus2010Towar-12732,
  year={2010},
  doi={10.1007/978-3-642-12211-8_20},
  title={Towards automatic detecting of overlapping genes : clustered BLAST analysis of viral genomes},
  number={6023},
  isbn={978-3-642-12210-1},
  publisher={Springer Berlin Heidelberg},
  address={Berlin, Heidelberg},
  series={Lecture Notes in Computer Science},
  booktitle={Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics},
  pages={228--239},
  editor={Pizzuti, Clara and Ritchie, Marylyn D. and Giacobini, Mario},
  author={Neuhaus, Klaus and Oelke, Daniela and Fürst, David and Scherer, Siegfried and Keim, Daniel A.}
}

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