Abstract
In this paper, we describe the basis of EuGéne, a gene finder for eukaryotic organisms applied to Arabidopsis thaliana. The specificity of EuGéne, compared to existing gene finding software, is that EuGéne has been designed to combine the output of several information sources, including output of other software or user information. To achieve this, a weighted directed acyclic graph (DAG) is built in such a way that a shortest feasible path in this graph represents the most likely gene structure of the underlying DNA sequence.
The usual simple Bellman linear time shortest path algorithm for DAG has been replaced by a shortest path with constraints algorithm. The constraints express minimum length of introns or intergenic regions. The specificity of the constraints leads to an algorithm which is still linear both in time and space. p] EuGéne effectiveness has been assessed on Araset, a recent dataset of Arabidopsis thaliana sequences used to evaluate several existing gene finding software. It appears that, despite its simplicity, EuGéne gives results which compare very favourably to existing software. We try to analyse the reasons of these results.
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Schiex, T., Moisan, A., Rouzé, P. (2001). Eugène: An Eukaryotic Gene Finder That Combines Several Sources of Evidence. In: Gascuel, O., Sagot, MF. (eds) Computational Biology. JOBIM 2000. Lecture Notes in Computer Science, vol 2066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45727-5_10
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DOI: https://doi.org/10.1007/3-540-45727-5_10
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