Pin-pointing a certain gene amongst 13,000 genes has never been more precise, following the development of new software by a Curtin University PhD student.
Alison Testa from Curtin’s Centre for Crop and Disease Management has developed the gene-prediction software CodingQuarry to help researchers find genes with greater accuracy in fungal genomes.
For CCDM researchers it will make finding disease-causing genes a lot easier and on the path to reducing the economic impact of major crop diseases for the Australian agricultural industry.
Ms Testa said while genomic research for humans and animals has the technology in place to make correct predictions, there was no such thing for fungal genomes and much of the genetic research related to fungi was often set back due to unreliable gene predictions.
“For example, a couple of years ago the pathogen responsible for an important barley disease, net-form net blotch (Pyrenophora teres f. teres), was thought to have around 12,000 genes in its genome,” Ms Testa said.
“With predictions made by CodingQuarry, we have been able to identify around 1000 extra genes, with corrections made to several thousand of the known genes.
“This puts our barley disease team in a great position to unravel how this pathogen causes disease.”
Ms Testa said after benchmarking CodingQuarry against other methods, the software was able to predict the location of genes with more than 90 per cent accuracy.
“This means researchers can quickly achieve high-quality, reliable gene predictions and use these to get to the bottom of their research questions,” Ms Testa said.
The software was developed by combining two techniques, hidden-Markov-model prediction and alignment of RNA-seq transcriptome sequences, commonly used separately in gene annotation.
CCDM Bioinformatics group leader and Ms Testa’s PhD co-supervisor Dr James Hane said CodingQuarry is fast becoming an important tool for fungal researchers, especially as gene annotation is often the first step in analysing the gene content of an organism after assembly of its genome.
“CodingQuarry is going to allow us to dig even deeper into fungal genomes to find hidden genes, particularly those involved in crop disease, and really get a grip on how fungal pathogens operate – the more we know about the genes and genome biology of fungal pathogens, the better we are positioned to manage crop diseases,” Dr Hane said.
CodingQuarry is available to download from https://sourceforge.net/projects/codingquarry/. The research paper, CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts, has been published in BMC Genomics http://www.biomedcentral.com/1471-2164/16/170.