Thiebaut et al. BMC Genomics 2012, 13:290 http://www.biomedcentral.com/1471-2164/13/290 days. After this period, whole plants were harvested and examined for bacterial colonization by plate counting (data not shown), and small RNA libraries of control and inoculated plants were constructed. RNA extraction and sequencing small RNA library construction Total RNA was isolated from fresh root, leaves and whole plants materials using Trizol (Invitrogen, CA, USA) as described by the manufacturer. The amount of RNA was measured using Thermo Scientific NanoDrop™ 2000c Spectrophotometer and then quality was verified by elec- trophoresis on a 1% agarose gel. Total RNA (~10 μg) from ten samples (4-water deficit assay, 4-saline stress assay and 2-pathogen assay) was sent to Fasteris Life Sciences SA (Plan-les-Ouates, Switzerland) for construction of small RNA libraries and subsequent sequencing of 20–30 nt single-end reads. Quality of the sequences was evalu- ated by measuring the quality of the reads according to the percentage of bases having a base quality greater or equal than 30 (Q30). On average, 80% of the channel had Q30 of quality. After this, 3’ Illumina adapters (CTGTAGGCACCATCAAT) and “N” bases were trimmed of the reads and reads in between a 20–24 nt range were separated for further analysis. Prediction of new miRNA candidates Small RNA reads trimmed were filtered out if they had an exact full-length match to known plant tRNA or rRNA sequences and low-complexity sequences. Using the UEA sRNA toolkit-Plant version filter pipeline (http://srna-tools.cmp.uea.ac.uk/) [65], and three differ- ent databases - all RNAs from Rfam (excepted miRNAs), all Arabidopsis tRNAs from The Genomic tRNA Data- base and all plant t/rRNA sequences from EMBL release - reads with low-complexity (less than 3 different bases) and both sense and antisense, matches with different types of RNAs (e.g. sno/t/rRNAs) were removed. The remained reads were then submitted to the UEA sRNA toolkit-Plant version miRCat pipeline to predict miRNAs from high-throughput small RNA sequencing data. miR- Cat was running with minimum sRNA abundance of 5 reads, minimum size of 20 nt, maximum size of 24 nt and maximum number of 16 genome hits. Sequences of sRNA were then mapped to a Sorghum bicolor genome (JGI, v1.0) to find clusters of sRNA that matched the fol- lowing criteria: i) clusters must contain no more than four non-overlapping sRNAs; ii) each sRNA in a cluster must be no more than 200 nt from its neighbor; iii) at least 90 % of sRNAs in a cluster must be in the same orientation. The most abundant sRNA read within a cluster is chosen as the likely miRNA candidate. The flanking sequence surrounding this sRNA is extracted from the genome using a 75 nt of window length. Each Page 11 of 14 sequence window is then folded using RNAfold. miRCat trims and analyses the resulting secondary structure to verify whether it forms a characteristic miRNA hairpin and executes the following additional checks: i) no more than 3 consecutive mismatches between miRNA and miRNA*; ii) at least 17 of the 25 nucleotides centered around the miRNA must be involved in base-pairing; iii) the hairpin must be at least 75nt in length; iv) at least 50 % of bases in the hairpin should be paired. The most stable valid hairpin from each of the sequence windows is then chosen as the precursor miRNA candidate. The precursor miRNA candidate is then tested using rand- fold (using a cutoff of 0.1). Additional minimal folding free energy index (MFEI) was calculated manually according to Zhang et al (2006) [32]. The miRNAs can- didates were searched against miRBase database release 17 (http://www.mirbase.org/ftp.shtml) and PMRD: Plant microRNA database (http://bioinformatics.cau.edu.cn/ PMRD/) using standalone BLAST [66] blastn with de- fault parameters. Only reads with e-value >10-3 at miR- Base were considered with new sugarcane miRNAs candidates. The folding structures of the precursors of the new miRNA with miRNA* identified were carried out with UEA sRNA toolkit-RNA hairpin folding and annotation tool, that uses the Vienna Package to obtain the secondary structure of a precursor sequence and highlighting up miRNA/miRNA* sequences on hairpin structure. These set were considered as high confidence miRNAs sequences and these sequence were deposited in the miRBase database. The analysis of the conserved novel sugarcane miR- NAs in Arabidopsis, rice and maize was carried out fol- lowing the miRCat pipeline using new miRNAs mapped to the Arabidopsis (Arabidopsis thaliana, TAIR v.9), rice (Oryza sativa, TIGR/MSU v5.0) and maize (Zea mays, AGP v1.0) genomes, respectively. Identification of potential miRNA targets The high degree of homology between the mature se- quence of the miRNA and the cleavage site in their tar- gets allows in silico identification of putative miRNA targets in the databases. To identify the putative new miRNA targets we used the standalone-based UEA sRNA toolkit-Plant target prediction pipeline. The stan- dalone version of the plant target prediction tool per- mits one to choose transcripts databases for searching targets. In this investigation we used three different transcripts databases: S. bicolor gene models [31], S. bicolor ESTs – DFCI gene index release 9 and Sac- charum officinarum ESTs – DFCI gene Index release 3. The rules used for the plant target prediction are based on criteria previously suggested by other researchers [45,46]. miRNA/target duplexes must obey the following rules: i) no more than four mismatches