Applications of GeneMark in Multispecies Environments

James D. McIninch, William S. Hayes, and Mark Borodovsky

This paper is supposed to bridge the gap between practical experience in using GeneMark for a rapidly widening repertoire of genomes, and the available publications that determine and compare the gene prediction accuracy of the GeneMark method for different genomes. Here we tbcus on the genome-specific variability of prediction error rates and their sources. DNA sequence inhomogeneity is present both in training and control sets of coding and non-coding regions. Coding region inhomogeneity, caused by differences in sequence composition between "native" and horizontally transferred genes or between genes expressed at different levels, contributes to the false negative error rate. Inhomogeneity of non-coding region may frequently be caused by the presence of unnoticed genes and contributes to the false positive error rate. We have documented such unnoticed genes in GenBank sequences for several species. Some of protein products of these genes have been characterized by similarity search methods. For others, which we call "pioneer genes", no significant similarity has been found at a protein sequence level although the confidence of GeneMark prediction is high. For instance, to date a majority of those pioneer gene predictions made for E. coil now show strong similarity to more recently characterized proteins that have been added to protein sequence database. Another practical question is related to genomic sequence inhomogeneity at interspecies level: if GeneMark has not been trained for a particular species, is it possible to apply models derived for phylogenetically close genomes? The answer is, yes. The results of cross-species gene prediction experimentshow that cross-species prediction can often be reasonably accurate.


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