Where we stand, where we are moving: Surveying computational techniques for identifying miRNA genes and uncovering their regulatory role

详细信息    查看全文

• 作者：Dimitrios Kleftogiannis^a ; Aigli Korfiati^b ; Konstantinos Theofilatos^b ; ^{theofilk@ceid.upatras.gr} ; Spiros Likothanassis^b ; Athanasios Tsakalidis^b ; Seferina Mavroudi^b ; ^c
• 关键词：microRNA genes ; miRNA identification ; miRNA gene prediction ; Gene regulatory networks ; miRNA transcription mechanism ; Machine learning
• 刊名：Journal of Biomedical Informatics
• 出版年：2013
• 出版时间：June, 2013
• 年：2013
• 卷：46
• 期：3
• 页码：563-573
• 全文大小：276 K

文摘

Traditional biology was forced to restate some of its principles when the microRNA (miRNA) genes and their regulatory role were firstly discovered. Typically, miRNAs are small non-coding RNA molecules which have the ability to bind to the 3¡äuntraslated region (UTR) of their mRNA target genes for cleavage or translational repression. Existing experimental techniques for their identification and the prediction of the target genes share some important limitations such as low coverage, time consuming experiments and high cost reagents. Hence, many computational methods have been proposed for these tasks to overcome these limitations. Recently, many researchers emphasized on the development of computational approaches to predict the participation of miRNA genes in regulatory networks and to analyze their transcription mechanisms. All these approaches have certain advantages and disadvantages which are going to be described in the present survey. Our work is differentiated from existing review papers by updating the methodologies list and emphasizing on the computational issues that arise from the miRNA data analysis. Furthermore, in the present survey, the various miRNA data analysis steps are treated as an integrated procedure whose aims and scope is to uncover the regulatory role and mechanisms of the miRNA genes. This integrated view of the miRNA data analysis steps may be extremely useful for all researchers even if they work on just a single step.