Computational identification of microRNAs and their targets in Gossypium hirsutum expressed sequence tags

NlicroRNAs (miRNAs) are a class of non-coding RNAs that regulate gene post-transcriptional expression in animals and plants. Comparatively genomic computational methods have been developed to predict new miRNAs in worms, humans, and Arabidopsis. Here we present an EST (Expressed Sequence Tags) - and GSS (Genomic Survey Sequences)-based combined approach for the detection of novel miRNAs in Gossypium hirsutum. This was initiated by using previously known miRNA sequences from Arabidopsis, rice and other plant species and an algorithm called miRNAassist to blast the databases of G. hirsutum EST and GSS. A total of 37 potential miRNAs were detected following a range of filtering criteria. Using these potential miRNAs sequences, we further blasted the publicly available mRNA database and detected 96 potential targets in G. hirsutum. According to the mRNA information provided by the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/), most of the miRNA targeted genes were predicted to encode transcription factors that regulate cell growth and development, signaling, and metabolism. So far, little is known about experimental or computational identification of miRNA in G. hirsutum species. These new miRNAs and their targets in G. hirsutum have been run through miRNAassist to yield data that may help us better understanding of the possible role of miRNAs in regulating the growth and development of G. hirsutum. (C) 2007 Elsevier B.V. All rights reserved.