Sequence evaluation of microRNA properties.
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This user friendly tool evaluates a sequence in regards to its possible microRNA-like properties. The properties used to evaluate each sequence are the following: microRNA precursor-like structure(1), sequence similarity with a known microRNA(2) and sequence conservation(3).
1: Stretches of 80 nt from the input sequence will be folded and evaluated by an SVM classifier (triplet-SVM [1]). Sequences longer than 80 nt will be evaluated by a sliding window of 80 nt with a 20 nt step. If the "advanced" option is used, sequences will also be evaluated with a structural clustering technique[4].
2: Input sequences will be blasted against the mirBase[2] dataset on all organisms and significant similarities will be reported.
3: Input sequences will be located on their corresponding genome and the conservation score of this genomic location will be downloaded from the Ensembl database. Conservation scores are pre-calculated with the GERP algorithm [3] on an alignment of 10 mammalian species.
4: Check whether you are in a microRNA enriched cluster. microRNAs are often found in clusters and this criteria can be used to find novel microRNAs [5].
Because mirEval connects to numerous on-line resources, we do not distribute the source code to avoid overloading these resources.
Criteria 3 and 4 will only be evaluated if your sequence comes from one of the 10 mammalian species that are selectable below. If your sequence does not come from one of these 10 species, select "other species"
Paste sequence:
Select organism
Other species
Bos taurus
Canis familiaris
Gallus gallus
Homo sapiens
Macaca mulatta
Monodelphis domestica
Mus musculus
Ornithorhynchus anatinus
Pan troglodytes
Rattus norvegicus
Select Analysis type
(no conservation analysis for "other species")
Structure:
Advanced structure (longer):
Conservation:
miRBase:
Clusters:
William Ritchie
1. Chenghai Xue, Fei Li, Tao He, Guo-Ping Liu, Yanda Li, Xuegong Zhang: Classification of Real and Pseudo MicroRNA Precursors Using Local Struct ure-Sequence Features and Support Vector Machine. BMC Bioinformatics, 6: 310, 2005.
2. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ: miRBase microRNA sequences, targets and gene nomenclature. NAR Database Issue, D140, 2006.
3. Cooper GM, Stone EA, Asimenos G, NISC Comparative Sequencing Program, Green ED, Batzoglou S, Sidow A: Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 2005 Jul;15(7):901-913.
4. Ritchie W, Legendre M, Gautheret D. RNA stem-loops: to be or not to be cleaved by RNAse III. RNA. 2007 Apr;13(4):457-62.
5. Sewer A, Paul N, Landgraf P, Aravin A, Pfeffer S, Brownstein MJ, Tuschl T, van Nimwegen E, Zavolan M. Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics. 2005 Nov 7;6:267.