Abstract
BACKGROUND: Long non-coding RNAs (lncRNAs) play an important role in regulating gene expression and are thus important for determining phenotypes. Most attempts to measure selection in lncRNAs have focused on the primary sequence. The majority of small RNAs and at least some parts of lncRNAs must fold into specific structures to perform their biological function. Comprehensive assessments of selection acting on RNAs therefore must also encompass structure. Selection pressures acting on the structure of non-coding genes can be detected within multiple sequence alignments. Approaches of this type, however, have so far focused on negative selection. Thus, a computational method for identifying ncRNAs under positive selection is needed. RESULTS: We introduce the SSS-test (test for Selection on Secondary Structure) to identify positive selection and thus adaptive evolution. Benchmarks with biological as well as synthetic controls yield coherent signals for both negative and positive selection, demonstrating the functionality of the test. A survey of a lncRNA collection comprising 15,443 families resulted in 110 candidates that appear to be under positive selection in human. In 26 lncRNAs that have been associated with psychiatric disorders we identified local structures that have signs of positive selection in the human lineage. CONCLUSIONS: It is feasible to assay positive selection acting on RNA secondary structures on a genome-wide scale. The detection of human-specific positive selection in lncRNAs associated with cognitive disorder provides a set of candidate genes for further experimental testing and may provide insights into the evolution of cognitive abilities in humans. AVAILABILITY: The SSS-test and related software is available at: https://github.com/waltercostamb/SSS-test . The databases used in this work are available at: http://www.bioinf.uni-leipzig.de/Software/SSS-test/ .