Literature DB >> 16770443

An approach for the identification of microRNA with an application to Anopheles gambiae.

Raghunath Chatterjee1, Keya Chaudhuri.   

Abstract

MicroRNAs (miRNAs) are an abundant class of 20-27 nt long noncoding RNAs, involved in post-transcriptional regulation of genes in eukaryotes. These miRNAs are usually highly conserved between the genomes of related organisms and their pre-miRNA transcript, about 60-120 nt long, forms extended stem-loop structure. Keeping these facts in mind miRsearch is developed which relies on searching the homologues of all known miRNAs of one organism in the genome of a related organism allowing few mismatches depending on the phylogenetic distance between them, followed by assessing for the capability of formation of stem-loop structure. The precursor sequences so obtained were then screened through the RNA folding program MFOLD selecting the cut-off values on the basis of known Drosophila melanogaster pre-miRNAs. With this approach, about 91 probable candidate miRNAs along with pre-miRNAs were identified in Anopheles gambiae using known D. melanogaster miRNAs. Out of these, 41 probable miRNAs have 100% similarity with already known D. melanogaster miRNAs and others were found to be at least 85% similar to the miRNAs of various other organisms.

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Year:  2006        PMID: 16770443

Source DB:  PubMed          Journal:  Acta Biochim Pol        ISSN: 0001-527X            Impact factor:   2.149


  16 in total

1.  Identification and developmental profiling of conserved and novel microRNAs in Manduca sexta.

Authors:  Xiufeng Zhang; Yun Zheng; Guru Jagadeeswaran; Ren Ren; Ramanjulu Sunkar; Haobo Jiang
Journal:  Insect Biochem Mol Biol       Date:  2012-03-01       Impact factor: 4.714

Review 2.  MicroRNAs of parasites: current status and future perspectives.

Authors:  Quan Liu; Wenbin Tuo; Hongwei Gao; Xing-Quan Zhu
Journal:  Parasitol Res       Date:  2010-06-08       Impact factor: 2.289

Review 3.  RNA stem-loops: to be or not to be cleaved by RNAse III.

Authors:  William Ritchie; Matthieu Legendre; Daniel Gautheret
Journal:  RNA       Date:  2007-02-13       Impact factor: 4.942

4.  MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features.

Authors:  Jiandong Ding; Shuigeng Zhou; Jihong Guan
Journal:  BMC Bioinformatics       Date:  2010-12-14       Impact factor: 3.169

5.  Identification of Schistosoma mansoni microRNAs.

Authors:  Mariana C Simões; Jonathan Lee; Appolinaire Djikeng; Gustavo C Cerqueira; Adhemar Zerlotini; Rosiane A da Silva-Pereira; Andrew R Dalby; Philip LoVerde; Najib M El-Sayed; Guilherme Oliveira
Journal:  BMC Genomics       Date:  2011-01-19       Impact factor: 3.969

6.  Transcriptome-wide analysis of microRNA expression in the malaria mosquito Anopheles gambiae.

Authors:  Inna Biryukova; Tao Ye; Elena Levashina
Journal:  BMC Genomics       Date:  2014-07-04       Impact factor: 3.969

7.  Direct sequencing and expression analysis of a large number of miRNAs in Aedes aegypti and a multi-species survey of novel mosquito miRNAs.

Authors:  Song Li; Edward A Mead; Shaohui Liang; Zhijian Tu
Journal:  BMC Genomics       Date:  2009-12-04       Impact factor: 3.969

Review 8.  Current tools for the identification of miRNA genes and their targets.

Authors:  N D Mendes; A T Freitas; M-F Sagot
Journal:  Nucleic Acids Res       Date:  2009-03-18       Impact factor: 16.971

9.  Computational prediction and experimental validation of Ciona intestinalis microRNA genes.

Authors:  Trina M Norden-Krichmar; Janette Holtz; Amy E Pasquinelli; Terry Gaasterland
Journal:  BMC Genomics       Date:  2007-11-29       Impact factor: 3.969

10.  Cloning, characterization, and expression of microRNAs from the Asian malaria mosquito, Anopheles stephensi.

Authors:  Edward Andrew Mead; Zhijian Tu
Journal:  BMC Genomics       Date:  2008-05-23       Impact factor: 3.969

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