Two separate labs based in British Columbia have released freely available bioinformatics tools, one for predicting orthologs, or genes that have diverged after a speciation event, and the other for clustering data from SNP genotyping experiments.
The first seeks to replace the reciprocal-best-Blast-hits approach in ortholog prediction, while the second hopes to give array primer extension-based sequencing methods a kick in the pants.
In the case of orthologs, Fiona Brinkman's lab at Simon Fraser University in Burnaby, BC, has developed a new tool called Ortholuge, which she described in an e-mail to BioArray News this week as "robust ortholog prediction, with a focus on identifying orthologs that have diverged as expected (i.e. have not undergone unusual divergence in one species) and are more likely to have retained the same function."
The tool is designed to be used with another tool developed by the Brinkman lab called ProbeLynx, which maps microarray probe sequences to genes, assesses probe specificity, and integrates gene annotation information as well as ortholog links.
Brinkman explained that "ortholog prediction for large genome-scale datasets is typically performed using a reciprocal-best-Blast-hits approach." This method suffers from a number of problems, however, such as a tendency to incorrectly predict a paralog as an ortholog in the case of incomplete genome sequences or when gene loss is involved.
"This offers improved identification of orthologs, since the most popular RBH-based methods appear to err in identifying orthologs roughly 5 percent to 10 percent of the time according to our analyses," she added.
In response, Brinkman's lab developed Ortholuge, which evaluates previously predicted orthologs and identifies which orthologs most closely reflect species divergence and may more likely have similar function.
"Through simulations of incomplete genome data/gene loss, we show that genes falsely predicted as orthologs by an RBH-based method can be identified," she said.
Brinkman said her lab is also offering "higher quality datasets of orthologs," which she calls "supporting-species-divergence orthologs," as well as the Ortholuge software on the lab's website. A paper detailing the tool has been submitted for publication, she said.
Less than 10 miles away in Vancouver, Scott Tebbutt's lab at the James Hogg iCAPTURE Centre for Cardiovascular and Pulmonary Research recently published a tool that enables labor-free clustering of SNP genotyping data garnered from array primer extension-based mini-sequencing.
Tebbutt claims that his lab's multi-dimensional automated clustering genotyping tool, or MACGT, works by importing spot intensity output files from microarray experiments across multiple samples, and automatically clustering the multi-dimensionality data sets for each sample.
"We use a method called array primer extension for genotyping," Tebbutt told BioArray News this week. "We are interested in exploring different chemistries and how robust a system we can develop," he said.
Tebbutt said that while few commercial entities sell array primer extension, or APEX, probes and arrays — which are mostly sold through Estonian array shop Asper Biotech — he is connected with researchers that use APEX globally, and that there is a need for a clustering analysis tool like MACGT because "there is a major problem in trying to call genotypes because [the method] gives you a huge amount of data."
"Say you have 400 patients and 400 samples, and you are genotyping 100 SNPs. It becomes extremely time-consuming to look at the data manually. So you have to automate, and MACGT does in seconds what it would take a researcher weeks and weeks to do," he said. He said current APEX users use their own in-house software, and that he hoped his tool could give the technology a leg up in the industry.
MACGT is freely available from the iCapture center website (http://www.mrl.ubc.ca/who/MACGT.shtml). Users can also read about the tool in the March 7 online edition of Bioinformatics.
— Justin Petrone ([email protected])