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Current Applications
The goal of mutational profiling is to completely sequence the genome of both the mutant and the parent, creating a complete catalog of all differences between the two samples. This has numerous benefits over traditional forward genetic methods of identifying causative mutations. Second generation sequencing is extremely fast, and it can be completed in under one week for almost any organism. This allows detection of mutations far faster than performing labor intensive back crossing and linkage analysis experiments.
One of the most common applications of RUFUS is in trio analysis. Often times, researchers are looking for a causative mutation to explain disease. Any individual has roughly 3 million SNP's in their genome. Traditional variant calling methods will also produce many false discoveries due to sequencing error or mapping error. Trying to find the causative mutation from this large list is a daunting task. Many of the SNP's in an infected individual will also be seen in the individuals parents. If a variation event is seen in an unaffected parent and the infected individual, it is likely that the variation is non-causative. RUFUS is able to detect novel alleles that are only seen in the effected individual. There will be far less variants in the novel alleles, compared to variants found by comparing the affected individual to a reference. This dramatically reduces the search space of the causative mutation, making causative mutational discoveries far more tractable than reference based variant calling methods.
Possibly the greatest advantage RUFUS has over mapping based methods is that it is not limited a reference genome, allowing RUFUS to identify variants in completely novel DNA sequences, as well as highly repetitive regions. RUFUS has successfully identified many variants that mapping methods could not detect. These variants tend to occur in low complexity regions, or regions that are not represented in a genomic reference.
The most significant limitation of mapping-based mutation detection methods is the requirement of a reference genome. This restricts genomic studies to well-studied model organisms with previously assembled reference genomes. Model organisms are generally chosen, not for their biological significance, but for their ease of use in the lab. This makes it impossible to directly study many important biological processes that are unique to other species, as they lack the basic genetic tools that are required to perform such analyses. RUFUS provides researchers with a way to study these non-model organisms and biological processes.
RUFUS is able to produce highly accurate SNV calls, with far lower false discovery rate than traditional mapping methods. Mapping methods introduce a high false positive discovery rate due to the errors and biases associated with the use of a human reference genome. When the human reference is eliminated, so are many of the false discoveries associated with mapping errors.