Identification of putative miR direct targets by sequencing data
Overview
Introduction

This pipeline infers putative direct gene targets of miRs based on miRseq and mRNAseq expression profiles across multiple samples. This pipeline has the following steps:

1. The CLR approach[8] is applied to infer putative miR:gene regulatory connections.

2. Filtering miR:gene pairs based on Pearson correlation (<= -0.3).

3. Filtering miR:gene pairs based on predicted interactions in three sequence prediction databases (Miranda, Pictar, Targetscan)

Summary

The CLR algorithm was applied on 754 miRs and 18131 mRNAs across 80 samples. After 2 filtering steps, the number of 43 miR:gene pairs were detected.

Results
Significant miR:gene pairs

Figure 1. Get High-res Image All miR hubs with their strong anti-correlated genes and predicted interactions in three sequence prediction databases.

Table 1. Get Full Table List of miR:gene pairs with corr < -0.3 and predicted interactions in three sequence prediction databases.

Mir | Gene | Corr | p | q | Prediction.DBs | Miranda | Pictar | Targetscan | Total
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
hsa-miR-1-3p | RIT2 | -0.57 | 3.1e-15 | 9.7e-15 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-137 | JADE2 | -0.62 | 6e-15 | 1.8e-14 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-149-5p | CLC | -0.75 | 1.4e-08 | 2.1e-08 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-155-5p | BRPF1 | -0.6 | 1.9e-06 | 2.1e-06 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-155-5p | WWC1 | -0.57 | 1.1e-09 | 1.9e-09 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-16-5p | DHDDS | -0.56 | 1.1e-09 | 1.9e-09 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-16-5p | UBXN10 | -0.47 | 6e-07 | 7.2e-07 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-193b-3p | CELSR2 | -0.46 | 1.1e-07 | 1.4e-07 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-195-5p | PRPF38A | -0.54 | 3.1e-12 | 6.8e-12 | miranda,pictar,targetscan | 1 | 1 | 1 | 3
hsa-miR-195-5p | LUZP1 | -0.52 | 1.7e-06 | 1.9e-06 | miranda,pictar,targetscan | 1 | 1 | 1 | 3

miR connections

Table 2. Get Full Table All miR hubs with their associated genes in the putative direct target network.

Mir | Number.of.Genes | Genes
--- | --- | ---
hsa-miR-200b-3p | 6 | AMOTL2, EIF2B5, KIAA1045, MPRIP, MTUS1,RAB7A
hsa-miR-195-5p | 5 | CSDE1, GRAMD3, LUZP1, UBXN10,PRPF38A
hsa-miR-29c-3p | 5 | COL4A1, COL4A2, COL5A3, SPARC,EIF4E2
hsa-miR-211-5p | 4 | AKAP1, CORO1C, RAB10,KHDRBS3
hsa-miR-155-5p | 2 | BRPF1,WWC1
hsa-miR-200c-3p | 2 | MTUS1,ZNF532
hsa-miR-22-3p | 2 | BOLL,CENPV
hsa-miR-299-3p | 2 | RAB6A,FBXO33
hsa-miR-221-3p | 2 | CNR1,RBM24
hsa-miR-16-5p | 2 | UBXN10,DHDDS

Gene connections

Table 3. Get Full Table All gene hubs with their associated miRs in the putative direct target network.

Gene | Number.of.Mirs | Mirs
--- | --- | ---
UBXN10 | 2 | hsa-miR-195-5p,hsa-miR-16-5p
MTUS1 | 2 | hsa-miR-200c-3p,hsa-miR-200b-3p
RBM24 | 2 | hsa-miR-222-3p,hsa-miR-221-3p
RIT2 | 1 | hsa-miR-1-3p
CORO1C | 1 | hsa-miR-211-5p
KHDRBS3 | 1 | hsa-miR-211-5p
COL5A3 | 1 | hsa-miR-29c-3p
EIF4A2 | 1 | hsa-miR-212-3p
KIAA1045 | 1 | hsa-miR-200b-3p
TRPV3 | 1 | hsa-miR-203a-3p

Methods & Data
Input

- mature miR RPM value (reads per million reads aligned to miRBase precursor) with log2 transformation = TCGA-UVM-TP.miR_mature_RPM_log2.txt

- mRNA protein coding genes FPKM value with log2 transformation = TCGA-UVM-TP.mRNA_FPKM_log2_protein.coding.txt

- miR:gene predicted interactions file = human_interactions.predicted.v2.mirbase21.txt

- Miranda = microrna.org Aug 2010 release, Microcosm version 5

- Pictar = version 1

- Targetscan = release 5.2

CLR method

The CLR (Context Likelihood of Relatedness) algorithm builds upon the relevance network strategies, by applying a background correction step. After computing the mutual information between regulators and their potential target genes, CLR calculates the statistical likelihood of each mutual information value within its network context. The algorithm compares the mutual information between a miR/gene pair to the background distribution of mutual information scores for all possible miR/gene pairs that include either the miR or its target. After this background correction, the most probable interactions are those whose mutual information scores stand significantly above the background distribution of mutual information scores[8].

Pearson correlation

Pairwise Pearson correlations coefficients between all miR:gene pairs are first computed. All genes that have correlation values less than the user-defined threshold (-0.3) with a particular miR and have been predicted as targets of that miR in three sequence based prediction databases (Miranda[1][2], Pictar[3][4], and TargetScan[5][6][7]) are identified as putative direct targets of that miR. We infer a direct target miR:gene network which comprises all such putative direct associations.

- threshold = -0.3

References
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[7] Friedman RC, Farh KK, Burge CB, Bartel DP, Most mammalian mRNAs are conserved targets of microRNAs, Genome Res 19(1):92-105 (2009)
[8] Genovese G, Ergun A, Shukla SA, Campos B, Hanna J, Ghosh P, Quayle SN, Rai K, Colla S, Ying H, Wu CJ, Sarkar S, Xiao Y, Zhang J, Zhang H, Kwong L, Dunn K, Wiedemeyer WR, Brennan C, Zheng H, Rimm DL, Collins JJ, Chin L, microRNA regulatory network inference identifies miR-34a as a novel regulator of TGF-β signaling in glioblastoma, Cancer Discov 2(8):736-49 (2012)
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