Embrapa Agroenergia, Parque Estação Biológica (PqEB), Asa Norte, Brasília, DF, 70770-901, Brazil.
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, Leipzig, 04107, Germany.
BMC Bioinformatics. 2019 Mar 21;20(1):151. doi: 10.1186/s12859-019-2711-y.
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.
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.
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.
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/ .
长非编码 RNA（lncRNA）在调节基因表达方面发挥着重要作用，因此对于表型的决定至关重要。大多数测量 lncRNA 选择的尝试都集中在原始序列上。大多数小 RNA 至少部分 lncRNA 必须折叠成特定的结构才能发挥其生物学功能。因此，对 RNA 进行综合选择评估必须包括结构。可以在多个序列比对中检测到作用于非编码基因结构的选择压力。然而，这种类型的方法迄今为止都集中在负选择上。因此，需要一种识别正选择下的 ncRNA 的计算方法。
我们引入了 SSS 测试（Secondary Structure 上的选择测试）来识别正选择，从而识别适应性进化。具有生物学和合成对照的基准产生了负选择和正选择的一致信号，证明了测试的功能。对包含 15443 个家族的 lncRNA 集合的调查导致了 110 个候选物，这些候选物在人类中似乎受到正选择的影响。在与精神障碍相关的 26 个 lncRNA 中，我们鉴定了在人类谱系中具有正选择迹象的局部结构。
在全基因组范围内检测 RNA 二级结构上的正选择是可行的。在与认知障碍相关的 lncRNA 中检测到人类特有的正选择为进一步的实验测试提供了一组候选基因，并可能为人类认知能力的进化提供了一些见解。
SSS 测试及相关软件可在：https://github.com/waltercostamb/SSS-test 获得。本研究中使用的数据库可在：http://www.bioinf.uni-leipzig.de/Software/SSS-test/ 获得。
