N sensitivity (fewer false negatives) and specificity (more false positives). AN sensitivity (fewer false negatives)

N sensitivity (fewer false negatives) and specificity (more false positives). A
N sensitivity (fewer false negatives) and specificity (more false positives). A cut-off score 1 provides a typical starting point for miRNA identification;Table 2 Selected gene target predictions for novel P. alecto miRNA pal-can-276 (miR-541)Minimum free energy (Best hit) -28.73 -28.62 -25.01 -23.6 -23.07 -22.97 -21.93 -21.91 -21.56 -21.08 -20.74 -20.45 -20.09 3′ UTR length 1592 881 578 570 2124 1595 2398 1906 754 1404 1437 2358 679 Hit positions 1189 725 246,390 153 127 155 2193 1405 651 1334 443 832DNA-damage-inducible transcript 4 CXXC finger 5 ubiquitin specific peptidase 19 polymerase (DNA directed), mu tumor necrosis factor receptor superfamily, member PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/27385778 1B leucine rich repeat containing 32 leucine rich repeat (in FLII) interacting protein 2 cytokine receptor-like factor 1 interleukin 28 receptor, alpha (interferon, lambda receptor) ATG9 autophagy related 9 homolog A (S. cerevisiae) apoptosis-inducing factor, mitochondrion-associated, 2 zinc finger and AT hook domain containingCowled et al. BMC Genomics 2014, 15:682 http://www.biomedcentral.com/1471-2164/15/Page 9 ofhowever many highly-conserved miRNAs which are very likely to be real do not meet this stringent cut-off. If the cut-off score is lowered, however, the number of highly improbable candidates rapidly increases. Relying on a single, stringent cut-off point comes at a significant cost of missing many genuine miRNAs. We concluded that within our data, miRDeep2 score and read depth were the factors that best enabled miRNA identification. We chose a model that accepted all hits to known miRNAs (regardless of score), novel hits with scores 1 and read depth 2 (typical hits), and novel hits with scores -5 and read depth 5 (outliers). Following the initial screening, a further 27 candidates were culled because they overlapped higher-scoring candidates, leaving a total of 399 candidates under consideration. This model, in our opinion, reflected a better balance between sensitivity and specificity than a single parameter cut-off model. In PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/26240184 support of our decision, we found that 48 out of the 96 miRNAs that scored less than 1 returned good BLAST hits to known vertebrate miRNAs in miRBase. The remaining 48 could not be specifically identified, but included 8 in clusters and 13 that had BLAST hits to mature miRNAs in other bats. Our further effort to prioritise novel miRNA candidates based on the combined evidence provides additional guidance to assist with selecting candidates for further study. One miRNA (pal-can-276) featured noticeably in the analysis. Representing a homolog of miR-541, this P. alecto miRNA LT-253 molecular weight contained three unique changes in the mature sequence relative to other vertebrates, including two changes within the critical seed region. Amongst its predicted gene targets was ZFAT, a zinc-finger protein with roles in cell survival and apoptosis (particularly in immunerelated cells) [26], and the TNF-receptor TNFRSF1B. In the rat, miR-541 is described as a brain-specific miRNA involved in neuron proliferation and neurite outgrowth via suppression of synapsin I [27], while the corresponding star sequence (which is also unique to P. alecto including one difference in the seed region), has been reported to be downregulated in a region of the brain (the spinal dorsal horn) in rats with experimentally-induced neuropathic pain [28]. It is well established that miRNAs play important roles in apoptosis [29-31], and one possibility is that viruses such as HeV block apoptosis via.