Among the 29 new miRNAs/new members of known miRNA families, 8 miRNAs were responsive to drought stress with both 4 miRNAs being up- and down-regulated, respectively.
In addition, among the 15 new members of known miRNA families, 3 miRNAs were responsive to drought stress with miR2111u, v being up-regulated and miR4414a being down-regulated.
In addition, we also predicted 29 new miRNAs/new members of known miRNA families, of which 8 miRNAs were responsive to drought stress by high-throughput sequencing and RT-qPCR.
Computational identification for pre-miRNA features depends on the knowledge of known miRNA genes, which may bring bias towards well-known miRNA genes (most are well conserved among species).
The pot-miRNAs identified in this analysis included many homologs of known miRNA families that varied in sequence and length to previously identified sequences (e.g. 71 miR156 homologs and 24 miR168 homologs).
Deep sequencing technology is superior to microarrays which determine limited known miRNAs and usually do not contain the full list of known miRNA antisense sequences.
In addition, the number of known miRNA targets upregulated between mutants did not correlate with the number of mRNAs upregulated between mutants.
The log-fold change distributions of known miRNA targets were also very similar between mutants with large transcriptome changes and mutants with small transcriptome changes (Figure S2).
Microarray assay can improve sampling depth and sensitivity, however, it is restricted to the detection and profiling of known miRNA sequences previously identified by experiment or homology searches, and even yields a high frequency of non-specific signals [50].
Differences and similarities of transcriptome changes between mutants were also not explained by known miRNA targets, as the number of known miRNA targets shared between two mutants was not correlated with the number of upregulated genes shared between two mutants.
Identification and characterization of known miRNA members.
