PMC:1892782 / 26351-27920 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/1892782","sourcedb":"PMC","sourceid":"1892782","source_url":"https://www.ncbi.nlm.nih.gov/pmc/1892782","text":"The distribution of decision values of the SVM is shown in Fig. 3. The majority of alignments were classified correctly. Most of them have large absolute decision values stating that they belong to the corresponding class with high probability. If RNAstrand is applied to shuffled alignments the decision values are more concentrated around 0, but most of them are still classified correctly. To explain this observation we checked which combination of descriptors performs best on shuffled alignments. We trained a SVM model for each possible descriptor combination and calculated the true and false positive rates at different decision levels by using plotroc.py of the libsvm 2.8 package [18]. The corresponding ROC curves are given in Fig. 4 and indicate that except of Δmeanmfe all descriptors classify shuffled alignments randomly. Individual shuffled sequences, presumably by virtue of their base composition (see Additional file 1), still contain information on the reading direction of the structured RNA which is captured by Δmeanmfe. This observation implies that RNAstrand must not be used for alignments that do not contain structured RNAs. In other words, RNAstrand cannot be used to infer an ncRNA on the grounds that it returned a preferred reading direction for a non-structured input alignment. We could have also removed Δmeanmfe from the set of descriptors, because of this bias. However, due to its high sensitivity (Fig. 1) it seems preferable to keep it as descriptor, in particular since RNAstrand is designed to operate on structured RNAs only.","tracks":[]}