Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches.

MOTIVATION: In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated. RESULTS: In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches. AVAILABILITY: Full source code and benchmark datasets are available at: https://github.com/sinc-lab/lncRNA-folding. CONTACT: lbugnon@sinc.unl.edu.ar.

Key plant species and detritivores drive diversity effects on instream leaf litter decomposition more than functional diversity: A microcosm study.

Anthropogenic impacts on freshwater ecosystems cause critical losses of biodiversity that can in turn impair key processes such as decomposition and nutrient cycling. Forest streams are mainly subsidized by terrestrial organic detritus, so their functioning and conservation status can be altered by changes in forest biodiversity and composition, particularly if these changes involve the replacement of functional groups or the loss of key species. We examined this issue using a microcosm experiment where we manipulated plant functional diversity (FD) (monocultures and low-FD and high-FD mixtures, resulting from different combinations of deciduous and evergreen Quercus species) and the presence of a key species (Alnus glutinosa), all in presence and absence of detritivores, and assessed effects on litter decomposition, nutrient cycling, and fungal and detritivore biomass. We found (i) positive diversity effects on detritivore-mediated decomposition, litter nutrient losses and detritivore biomass exclusively when A. glutinosa was present; and (ii) negative effects on the same processes when microbially mediated and on fungal biomass. Most positive trends could be explained by the higher litter palatability and litter trait variability obtained with the inclusion of alder leaves in the mixture. Our results support the hypothesis of a consistent slowing down of the decomposition process as a result of plant biodiversity loss, and hence effects on stream ecosystem functioning, especially when a key (N-fixing) species is lost; and underscore the importance of detritivores as drivers of plant diversity effects in the studied ecosystem processes.