A rapid method for detection of putative RNAi target genes in genomic data.
RNAi, inhibition of gene expression by double stranded RNA molecules, has rapidly become a powerful laboratory technique to study gene function. The effectiveness of the procedure raised the question of whether this laboratory technique may actually mimic a natural cellular control mechanism that works on similar principles. Indeed recent evidence is accumulating to suggest that RNAi is a natural control mechanism that might even serve as a primitive immune response against RNA viruses and retroposons. Three different interference scenarios seem to be utilized by various RNAi mechanisms. One of the mechanisms involves degradation of mRNA molecules. Here we suggest a method to systematically scan entire genomes simultaneously for RNAi elements and the presence of cellular genes that are degraded by these RNAi elements via exact short base-pair matching. The method is based on scanning the genomes using a suffix tree data structure that was specifically modified to identify sets of combinations of repeated and inverted repeated sequences of 20 bp or more. Initial scan suggest that a large number, about 7% of C.elegans and 3% of C.briggsae genes, have the potential to be subject to natural RNAi control. Two methods are proposed to further analyze these genes to select the cases that are more likely to be actual cases of RNAi control. One method involves looking for ESTs that can provide direct evidence that RNAi control element are indeed expressed. The other method looks for synteny between C.elegans and C.briggsae assuming that genes that might be under RNAi control in both organisms are more likely to be biological significant. Taken together, supportive evidence was found for about 70 genes to be under RNAi control. Among these genes are: transposase, hormone receptors, homeobox proteins, defensin, actins, and several types of collagens. While our method is not capable of detecting all cases of natural RNAi control, it points to a large number of potential cases that can be further verified by experimental work.
Horesh
Yair
Y
Department of Computer Science and Faculty of Life Sciences Bar-Ilan University, Ramat-Gan, Israel.
Amir
Amihood
A
Michaeli
Shulamit
S
Unger
Ron
R
D000818:Animals; D001483:Base Sequence; D002107:Caenorhabditis / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods*; D030541:Databases, Genetic; D018390:Gene Targeting / Q000379:methods*; D008969:Molecular Sequence Data; D034622:RNA Interference; D034741:RNA, Small Interfering / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*
D034741:RNA, Small Interfering