sRNA-Seq of porcine macrophage: IFN-W1
Total RNA was extracted from ~2.5107 cells of each treatment using a column-based RNA/DNA/protein purification kit (Norgen Biotek, Ontario, Canada). RNA integrity and concentration were evaluated with a NanoDrop 8000 spectrometer (NanoDrop, Wilmington, DE) and an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) to ensure RNA samples with A260/A280>1.8 and RNA integrity number (RIN) >7.0 qualified for construction of sequencing libraries. Total RNA was fractionated into mRNA and sRNA fractions.  A TruSeq Small RNA Sample Preparation Kit (Illumina, San Diego, CA, USA) was used for the library preparation following the manufacturers protocol. The libraries were sequenced using the Illumina HiSeq 2000 platform (Illumina, San Diego, CA, USA) for paired-end 50 bp at Novogene (Sacramento, CA). Finally, 22-28 Mb sequence was generated for each sample. The bioinformatics procedure for sRNA-Seq was illustrated as in Figure S1 and programs used were listed in Table S1. In brief, clean reads were obtained by filtering out a small portion (<1%) of adaptor contamination and low-quality reads, and went through the length filtering to enrich sRNA population (Table S2 and S3). The sRNA reads were then mapped to swine reference genome through NCBI genome portal (http://www.ncbi.nlm.nih.gov/RefSeq/) (Figure S2), and classified based on molecular/structural patterns of each type of sRNA species (Figure S3). Further analyses focusing on miRNA were performed regarding such as hairpin structures, family clustering, differential expression and target gene prediction. Differential expression of miRNA were cross-sample statistically analyzed and normalized by transcripts per million reads (TPM), which does not need to consider the length of sRNA because the sRNA fragment obtained by sequencing covered mostly a complete sRNA tag. The P-value corresponded to a differential gene expression test where FDR (False Discovery Rate) was used to determine the threshold of the P-value in multiple tests. The functional classification of miRNA target genes was carried out through Gene Ontology and KEGG pathway analyses using the DAVID web tool [3,27-29].