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Open Access Highly Accessed Methodology article

BayMiR: inferring evidence for endogenous miRNA-induced gene repression from mRNA expression profiles

Hossein Radfar1, Willy Wong1 and Quaid Morris1234*

Author Affiliations

1 Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada

2 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

3 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

4 Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada

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BMC Genomics 2013, 14:592  doi:10.1186/1471-2164-14-592

Published: 30 August 2013

Additional files

Additional file 1:

Figure S1. Cumulative distribution of scores for the validated targets. Validated targets are assigned higher BayMiR scores and gene variation scores compared to the other putative targets. Shown are the cumulative distributions of BayMiR (left plot) and gene variation scores (right plot) scores for validated targets (blue) and all putative targets (red).

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Additional file 2:

Figure S2. Comparing BayMiR and Cometa. BayMiR high scoring targets are more down-regulated in miRNA over-expression assays than Cometa high scoring targets. The cumulative distribution of log-fold change for high-scoring mRNAs; blue, red, and black represent graphs associated with BayMiR, gene variation, and Cometa.

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Additional file 3:

Table S1. Excel file. Enriched GO-BP terms

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Additional file 4:

Table S2. Excel file. Enriched KEGG terms

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Additional file 5:

Table S3. Validated KEGG pathways. List of miRNAs with proposed functions found in our enriched KEGG list; the third column gives the Pubmed IDs of the references.

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Additional file 6:

Figure S3. KEGG “Pathways in cancer”: 68 targets of 10 miRNAs are involved in the pathway (red boxes). 38 genes targeted by the other miRNAs are colored in yellow; and 62 genes involved in the pathway were excluded from the BayMiR target list since their expression variabilities across arrays were very low (white boxes). The miRNA family IDs: miR-17/17-5p/20ab/20b-5p/93/106ab/427/518a-3p/519d,miR-548ah/3609,miR-4729,miR-203,miR-548p,miR-3647-3p,miR-300/381/539-3p,miR-142-5p,miR-545,miR-125a-5p/125b-5p/351/670/4319’.

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Additional file 7:

Table S4. Excel file: miRNA expression data retrieved from the mimiRNA repository.

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Additional file 8:

Figure S7. Blue: the position contribution scores of miRNA-mRNA pairs whose BayMiR scores  > medianBayMiRscores. Red: the position contribution scores of miRNA-mRNA pairs whose BayMiR scores  < medianBayMiRscores.

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Additional file 9:

Figure S4. The 3' UTR of mRNAs harbor many conserved seed matches. Shown is the cumulative distribution of number of seed matches in the 3'UTR of 14,816 mRNA transcripts with at least one miRNA seed match.

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Additional file 10:

Figure S5. Example of combinatorial regulation masking inverse correlation. Shown in green is the expression level of a target gene and in red the expression levels of three targeting miRNAs. The negative correlation of each individual miRNAs with the target is insignificant, but when considered together they explain perfectly the down-regulation impact of miRNAs.

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Additional file 11:

Figure S6. Gene expression variability increases as the number of target sites increases in the 3’ UTR of genes. (top) miRNA targets have high expression variation. (bottom) Red and blue demonstrate the cumulative distributions of genes whose variance is larger than median and 75th percentile, respectively. Dark: cumulative distribution of variances corresponding to all genes.

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