| swMATH ID: |
23024
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| Software Authors: |
Besemer, J.; Lomsadze, A.; Borodovsky, M.
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| Description: |
GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2 |
| Homepage: |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC55746/
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TRANSFAC;
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IQ-TREE;
Velvet;
MAKER;
ZCURVE
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| Cited in: |
4 Documents
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