miR2GO |
Table of ContentsIntroductionmiRmut2GO miRpair2GO |
miR2GO is an integrative web-platform for comparative analysis of microRNA function. It includes two functions: miRmut2GO and miRpair2GO. miRmut2GO is used to compare target gene sets for the reference (wild-type) and derived (mutated) alleles of miRNAs with genetic and /or somatic mutations, while miRpair2GO is used to compare target gene sets for different miRNAs.
Select prediction method:
Users can select one of the following miRNA target prediction methods
TargetScan: predict miRNA targets using TargetScan.
miRanda: predict miRNA targets using miRanda.
Or users can combine the output of these two prediction methods by using:
Union of the predicted target sets from TargetScan and miRanda, or
Intersection of the predicted target sets from TargetScan and miRanda.
Specify p-value threshold for functional enrichment analysis The p-value threshold is used to determine the significantly enriched functional categories for each target gene set.
Select Gene Ontology hierarchical filtering level
Hierarchical filtering is used to group the similar GO terms in the hierarchy of the GO graph. For each group of GO terms, one GO term is selected as the representative term for the group. Three hierarchical filtering options are:
none: No hierarchical filtering, thus all the enriched GO terms are presented independently.
moderate: Moderate filtering allows a single parent for each group in the GO hierarchy where all the enriched descendent terms of the parent term are included in the group and then the term with the lowest enrichment p-value is considered as representative term for the group.
strong: Strong filtering allows multiple parents for the same group. For strong filtering option, the groups in the GO term hierarchies are defined by including the GO terms with common descendent in the same group. Thus the total number of representative terms are expected to be lower by using strong filtering.
Paste miRNA sequences
The first option is to enter miRNA ID (a unique identifier for the miRNA, but it does not have to be a miRBase ID) and miRNA sequence in the textbox. IDs and sequences are should be in csv (comma separated) or fasta formats. miRNA sequence is the ~22 nucleotide of mature miRNA sequence. The input sequence specifies the SNP (or mutation) as [reference allele/derived allele] at the SNP (or mutation) site. Users can enter multiple input entries for multiple miRNAs.
The second option is to enter either the miRNA IDs (must be the miRBase ID) or dbSNP IDs as input. Users can enter multiple input rows for multiple miRNAs.
*Note: One miRNA could have multiple SNPs in its sequence and for such cases the webserver will give output for each SNP in the miRNA sequence. For a dbSNP id the webserver will only give the result for that SNP. This is visible from our miRNA id example.
Enriched functional categories for miRNA target predictions:
Example:
miRNA ID | Sequence | Reference targets |
Derived targets |
Reference targets functional enrichment |
Derived targets functional enrichment |
Common targets functional enrichment |
hsa-miR-593-5p | AGG[C/G]ACCAGCCAGGCAUUGCUCAGC | download | download | display download |
display download |
display download |
Functional similarity scores and gene ontology graphs:
Example:
miRNA ID | Sequence | Biological process similarity score |
Molecular function similarity score |
Cellular component similarity score |
Gene Ontology figure |
hsa-miR-593-5p | AGG[C/G]ACCAGCCAGGCAUUGCUCAGC | 0.551 | 0.434 | 0.561 |
Biological Process Molecular Function Cellular Component |
Select prediction method:
Users can select one of the following miRNA target prediction methods
TargetScan: predict miRNA targets using TargetScan.
miRanda: predict miRNA targets using miRanda.
Or users can combine the output of these two prediction methods
Union of the predicted target sets from TargetScan and miRanda.
Intersection of the predicted target sets from TargetScan and miRanda.
Specify p-value threshold for functional enrichment analysis The p-value threshold is used to determine the significantly enriched functional categories for each target gene set.
Select Gene Ontology hierarchical filtering level
Hierarchical filtering is used to group the similar GO terms in the hierarchy of the GO graph. For each group of GO terms, one GO term is selected as the representative term for the group. Three hierarchical filtering options are:
none: No hierarchical filtering, thus all the enriched GO terms are presented independently.
moderate: Moderate filtering allows a single parent for each group in the GO hierarchy where all the enriched descendent terms of the parent term are included in the group and then the term with the lowest enrichment p-value is considered as representative term for the group.
strong: Strong filtering allows multiple parents for the same group. For strong filtering option, the groups in the GO term hierarchies are defined by including the GO terms with common descendent in the same group. Thus the total number of representative terms are expected to be lower by using strong filtering.
Paste miRNA pairs
Paste miRNA id pairs: Users can enter multiple input rows for multiple miRNA pairs.
Enriched functional categories for miRNA target predictions:
Example:
miRNA ID pair miRNA I,miRNA II |
miRNA I targets |
miRNA II targets |
miRNA I targets functional enrichment |
miRNA II targets functional enrichment |
Common targets functional enrichment |
hsa-miR-1,hsa-miR-9-5p | download | download | display download |
display download |
display download |
Functional similarity scores and gene ontology graphs:
Example:
miRNA ID pair miRNA I,miRNA II |
Biological process similarity score |
Molecular function similarity score |
Cellular component similarity score |
Gene Ontology figure |
hsa-miR-1,hsa-miR-9-5p | 0.419 | 0.74 | 0.816 |
Biological Process Molecular Function Cellular Component |