الفهرس 
Strategies to identify miRNAsOnly 14 pages are availabe for public view
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Abstract
Mona Gaber Abd-El-Aziz Mahmoud Shalaan. Bioinformatics and Computational Biological Approaches to Identify Insect microRNAs. Faculty of Science, Ain Shams University, 2010.
MicroRNAs (miRNAs) are a large family of 21-22 nucleotides (nts) long non- protein-coding small RNAs, which are involved in regulation of mRNA translation and expression in the cell. Mature miRNAs are processed from a longer sequence known as pre-miRNAs, which can form stem-loop hairpin secondary structure. Mature miRNAs unite with multi-protein complexes known as RNA-induced silencing complex (RISC), which binds to specific sequences in target mRNAs. This binding triggers the translational repression or degradation of many mRNAs.
Due to the very short sequence of miRNAs and their repeats in genomes, bioinformatics and comparative genomics have been the method of choice to identify them, with subsequent validation by biochemical and molecular cloning. The integration of theoretical in silico and experimental miRNAs identification approaches helps to overcome the problems of misidentification (false positives) and missed identification (false negatives) of these tiny genes. These also enabled researchers to distinguish them from other small RNAs scattered in the genome.
A large number of miRNAs has been identified in many insect species including the fruit fly Drosophila, the mosquito Anopheles gambiae, the red flour beetle Tribolium castaneum and the silk worm moth Bombyx mori. Identification of miRNAs critical for insect development will help develop new tools to control serious disease vectors and pests. In this study we extended the process of miRNAs identification to the southern house mosquito Culex pipiens quinquefasciatus, a serious vector of filariasis and arboviral diseases in the world and Egypt. The genome of this mosquito is in the final stages of completion and raw sequence files were accessed from www.vectorbase.org.
Identification of miRNAs from the genome of Cx. p. quinquefasciatus (Cpq) was carried out using two different predictions methods. The first method depended on the identification of Cpq-miRNAs directly from the whole genome. A region of nearly 280000 nts from the whole genome was finished. To facilitate the prediction process, this region was divided into 28 microcontigs (micons) each of 10,000 nts in length. Three different overlapping windows between micons were tested: 50-nts, 250-nts and 500-nts. The 50-nts window was the best one; it produced predictions more than the other overlapping windows. from five of these 28 micons, 51 pre-mirs were predicted including 9 pre-mirs (17.9%) that are considered as novel mirs.
The second identification method was by homology search using identified miRNAs to search for homologous pre-mirs in Cx. quinquefasciatus genome. By this method the following miRNAs were identified as a test set, including Cpq-mir-1, Cpq-let-7, Cpq-mir-263b, Cpq-mir-276, Cpq-mir-307, Cpq-mir-315, Cpq-mir-7, Cpq-mir9c, Cpq-bantam and Cpq-mir-87. These mirs conformed to the general miRNAs criteria and with >90% homology at the mature sequences in insect mirs.
Prediction of secondary structure stem-loop formation of all pre-mirs identified by the 2 methods was carried out by using MFold programme. The following mirs were identified by both methods: mir-315, mir-7, mir-9c, bantam and mir-87.
To identify all Cpq-miRNAs, it was necessary to build a customized database that contains the whole genome sequence of the test mosquito Cx. p. quinquefasciatus and a reference genome, such as that of Drosophila or An. gambiae. We were able to finish programming of the primary functions and processes such as gene splicing and translation. However, it was difficult to finish more complex functions needed for miRNAs large-scale prediction (within the time-frame of the MSc thesis). This task is a future objective, in addition to the experimental characterization of identified Culex miRNAs.
Identification of Culex miRNAs opens the field for the functional analysis of these genes to understand their role in mosquito development. The results of such research will pinpoint novel targets for mosquito control.
Key words: Bioinformatics, computational biology, Culex mosquitoes, microRNAs