Predicting RNA secondary structure by a neural network: what features may be learned?

Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data may be learned by such models. Here, we describe PredPair, a deep learning neural network trained to predict base pairs in RNA structure from sequence alone, without any incorporated prior knowledge, such as the stacking energies or possible spatial structures. PredPair learned the Watson-Crick and wobble base-pairing rules and created an internal representation of the stacking energies and helices. Application to independent experimental (DMS-Seq) data on nucleotide accessibility in mRNA showed that the nucleotides predicted as paired indeed tend to be involved in the RNA structure. The performance of the constructed model was comparable with the state-of-the-art method based on the thermodynamic approach, but with a higher false positives rate. On the other hand, it successfully predicted pseudoknots. t-SNE clusters of embeddings of RNA sequences created by PredPair tend to contain embeddings from particular Rfam families, supporting the predictions of PredPair being in line with biological classification.

A hierarchy in clusters of cephalopod mRNA editing sites.

RNA editing in the form of substituting adenine with inosine (A-to-I editing) is the most frequent type of RNA editing in many metazoan species. In most species, A-to-I editing sites tend to form clusters and editing at clustered sites depends on editing of the adjacent sites. Although functionally important in some specific cases, A-to-I editing usually is rare. The exception occurs in soft-bodied coleoid cephalopods, where tens of thousands of potentially important A-to-I editing sites have been identified, making coleoids an ideal model for studying of properties and evolution of A-to-I editing sites. Here, we apply several diverse techniques to demonstrate a strong tendency of coleoid RNA editing sites to cluster along the transcript. We show that clustering of editing sites and correlated editing substantially contribute to the transcriptome diversity that arises due to extensive RNA editing. Moreover, we identify three distinct types of editing site clusters, varying in size, and describe RNA structural features and mechanisms likely underlying formation of these clusters. In particular, these observations may explain sequence conservation at large distances around editing sites and the observed dependency of editing on mutations in the vicinity of editing sites.

Translation at first sight: the influence of leading codons.

First triplets of mRNA coding region affect the yield of translation. We have applied the flowseq method to analyze >30 000 variants of the codons 2-11 of the fluorescent protein reporter to identify factors affecting the protein synthesis. While the negative influence of mRNA secondary structure on translation has been confirmed, a positive role of rare codons at the beginning of a coding sequence for gene expression has not been observed. The identity of triplets proximal to the start codon contributes more to the protein yield then more distant ones. Additional in-frame start codons enhance translation, while Shine-Dalgarno-like motifs downstream the initiation codon are inhibitory. The metabolic cost of amino acids affects the yield of protein in the poor medium. The most efficient translation was observed for variants with features resembling those of native Escherichia coli genes.