miRNA annotations were made according to miRbase version 16. Raw data and annotated sequences of the small RNA libraries are uploaded in the GEO database (accession number GSE30286). To quantify and compare miRNA expression across data sets, we used edgeR package developed by Robinson and Smyth (Robinson et al., 2010). Briefly, we used “calcNormFactors” function which calculated the sample whose seventy-fifth percentile (of

library-scale-scaled counts) is closest to the mean of seventy-fifth percentiles as the reference to get the effective library size for normalization (TMM [trimmed mean of M values] normalization). To detect pairwise differential expression of miRNAs in different cell/tissue types, we used “exact test” which is based on negative binomial models and the qCML method (Robinson and Smyth, 2008 and Robinson and Smyth, 2007). The results of the “exact test” was accessed by the function “topTags” to get the p EPZ-6438 solubility dmso value, fold change and the false discovery rate (FDR) for error control (Benjamini and Hochberg, 1995). The same data sets were randomly

shuffled 10,000 times and then processed under the same procedure. Panobinostat According to this result, p value for the actual data set was set to 0.001 as the cutoff to report differentia expression of miRNAs (Robinson and Oshlack, 2010 and Robinson et al., 2010). To generate the heatmap of miRNA expression across data set, we used the mean centered expression of each miRNA and miRNA∗. For hierarchical clustering, the average linkage of Pearson Correlation was employed (Eisen et al., 1998). To classify reads from 5′ and 3′ arms, we grouped reads from each library according to alignment with miRNA precursors. For each miRNA, we summarized the reads in libraries prepared from the same cell type or tissue type. The fold enrichment was calculated as the log2 ratio of 5′ and 3′ arm reads after

adding pseudocounts of one. Only miRNAs with unique precursor and five or more reads on either arm in at least ten libraries were considered and reported. Each sequencing library was filtered for sequences that uniquely aligned to the genome with one mismatch >2 nt from the 3′ end of these miRNA or miRNA∗. The 12 possible mismatch types were then quantified at each position covered by the filtered reads. The individual editing fraction in each library was calculated as the number of reads containing a certain mismatch at a particular position divided by the number of filtered reads covering that position. To screen inferred A-to-I editing sites, A-to-G mismatches were filtered for editing fraction >5% at a particular position and reads number >10 for each library, respectively, and then combined together to calculate the editing fraction in all libraries. None of the inferred A-to-I editing sites was found to correspond to known SNPs by checking in the Perlegen SNP database and dbSNP.