The prediction and training steps are repeated, each time detecting a larger set of true coding and non-coding sequences that are used to further improve the model employed in statistical pattern recognition. When the new sequence breakdown coincides with the previous one, the researchers record their final set of predicted genes.
To test the algorithm, the researchers selected 16 fungal species from the phyla Ascomycota, Basidiomycota and Zygomycota and compiled sets of genome sequences containing previously validated genes. The species spanned large evolutional distances and exhibited significant variability in genome size, gene number and average number of introns per gene. The results showed that by including branch site information in the model, the researchers could more accurately predict exon-intron structures of fungal genes.
"The enhanced program predicted fungal genes with higher accuracy than either the original self-training algorithm or known algorithms with supervised training," noted Borodovsky. "And because we didn't need any additional training information for our program, the sequencing teams could immediately proceed with gene annotation right after the genomic sequence was in hand without spending time and effort to extract a set of validated genes necessary for estimating parameters of traditional algorithms."
Researchers at the U.S. Department of Energy Joint Genome Institute and the Broad Institute of the Massachusetts Institute of Technology and Harvard University have already realized the advantages of the new algorithm. They have already used the new program to annotate about 20 novel fungal genomes. In addition, hundreds of fungal genome sequencing projects currently in progress should benefit from the new method as well, according to Borodovsky.
With the fungal software compl
|Contact: Abby Vogel|
Georgia Institute of Technology Research News