Human MicroRNA Specific Expression Analysis Method And The Development Of MicroRNA Target Tool

MicroRNAs(miRNAs) are ~22 nt endogenous small non-coding regulatory RNAs, which play key regulatory roles in various developmental, physiological and pathological processes by the negative regulation of gene expression. A comprehensive analysis of large scale small RNA sequencing data will be very helpful to explore tissue or disease specific miRNA markers and characterize miRNA regulatory functions.Advances in high-throughput next-generation sequencing technology have resulted in the popular use of small RNA sequencing(smRNA-seq) as a method to detect mi RNA expressions. We obtained comprehensive datasets of human smRNA-seq data by downloading more than 700 human smRNA-seq data sets from NCBI SRA database. After quality control and data filtering, we left 410 human smRNA-seq datasets, which samples are from 24 tissue/disease/cell lines. Through our smRNA-seq pipeline, we analyzed all known miRNA expression in different dataset and concisely showed all the mi RNA expression data in an online database, Human MicroRNA Expression Database(HMED), which is freely available at http://bioinfo.life.hust.edu.cn/smallRNA/.A tissue specific miRNA was defined with specifically highly expression in a special tissue. Based on the whole expression profile, we combined Shannon entropy and the Z score methods to identify specific miRNAs. Shannon entropy is used to measure the concentration ratio of the expression levels in different samples and Z score is used for outlier detection. We identified 41 miRNAs specifically expressed in one tissue/disease/ cell line and 17 miRNAs selectively expressed in 2 tissues/diseases/cell lines. Such as hsa-miR-124-3p, hsa-miR-129-5p, hsa-mi R-9-5p, hsa-miR-338-5p, hsa-miR-598 and hsa-miR-219-2-3p were specifically expression in brain. We noticed that brain, testis, and the HEK293 T cell line had relatively more specific miRNAs. To identify disease and control differentially expressed miRNAs, we used the edgeR tool for those data sets with both disease and control samples. We identified 51,41,8,2 differentially expressed miRNAs between breast cancer, lung cancer, HCC, psoriasis and their corresponding control samples, respectively.EasymiR is a java tool developed for miRNA target prediction and functional analysis. EasymiR integrates mi RNA target prediction by combining the predicted target genes from TargetScan, miRanda, RNAhybrid and PicTar as well as the experimentally supported targets from Tar Base, miRTarBase, miRecords and miR2 disease. EasymiR integrates function analysis by combining GO function annotation database as well as KEGG, Biocarta, Reactome pathway databases and provide gene enrichment tool and miRNA-targets mapping tool.In conclusion, our comprehensive analysis of huge smRNA-seq data sets provided a whole and tissue-specific mi RNA profiles, including tissue or disease specific miRNAs and disease/control differentially expressed miRNAs by combining Shannon entropy and Z score method. Besides, we also developed EasymiR, a tool to integrate miRNA targets to perform gene function enrichment analysis. We hope our study would help both basic research and biomarker applications.