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Bioinformatics Advance Access published online on October 25, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti701
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 4, 2005
Revised September 13, 2005
Accepted September 30, 2005

Article

Large scale prokaryotic gene prediction and comparison to genome annotation

Pernille Nielsen 1* and Anders Krogh 1

1 Bioinformatics Centre, Institute of Molecular Biology and Physiology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark

* To whom correspondence should be addressed.
Pernille Nielsen, E-mail: pern{at}binf.ku.dk


  Abstract

Motivation: Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to propagation of errors when questionable assignments are adapted from one genome to another. Genome comparison either on a large or small scale would be facilitated by using a single standard for annotation, which incorporates a transparency of why an open reading frame is considered to be a gene.

Results: 143 prokaryotic genomes were scored with an updated version of the prokaryotic genefinder EasyGene. Comparison of the GenBank and RefSeq annotations with the EasyGene predictions reveals that in some genomes up to about 60% of the genes may have been annotated with a wrong start codon, especially in the GC-rich genomes. The fractional difference between annotated and predicted confirms that too many short genes are annotated in numerous organisms. Furthermore, genes might be missing in the annotation of some of the genomes. We predict 41 of 143 genomes to be over-annotated by more than 5%, meaning that too many ORFs are annotated as genes. We also predict that 12 of 143 genomes are under-annotated. These results are based on the difference between the number of annotated genes not found by EasyGene and the number of predicted genes that are not annotated in GenBank.

We argue that the average performance of our standardised and fully automated method is slightly better than the annotation.

Availability: The EasyGene 1.2 predictions and statistics can be accessed at http://www.binf.ku.dk/cgi-bin/easygene/search.


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