Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. Here we show that the accessibility of translation initiation sites modelled using the mRNA base-unpairing across the Boltzmann's ensemble significantly outperforms alternative features. This approach accurately predicts the successes or failures of expression experiments, which utilised Escherichia coli cells to express 11,430 recombinant proteins from over 189 diverse species. On this basis, we develop TIsigner that uses simulated annealing to modify up to the first nine codons of mRNAs with synonymous substitutions. We show that accessibility captures the key propensity beyond the target region (initiation sites in this case), as a modest number of synonymous changes is sufficient to tune the recombinant protein expression levels. We build a stochastic simulation model and show that higher accessibility leads to higher protein production and slower cell growth, supporting the idea of protein cost, where cell growth is constrained by protein circuits during overexpression.

MOTIVATION: Recombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified. RESULTS: We have discovered that global structural flexibility, which can be modeled by normalized B-factors, accurately predicts the solubility of 12 216 recombinant proteins expressed in Escherichia coli. We have optimized these B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the 'Solubility-Weighted Index' (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed 'SoDoPE' (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximizing both protein expression and solubility. AVAILABILITY AND IMPLEMENTATION: The SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper_2020. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The aim of this study is to assess the performance of RNA-RNA interaction prediction tools for all domains of life. Results: Minimum free energy (MFE) and alignment methods constitute most of the current RNA interaction prediction algorithms. The MFE tools that include accessibility (i.e. RNAup, IntaRNA and RNAplex) to the final predicted binding energy have better true positive rates (TPRs) with a high positive predictive values (PPVs) in all datasets than other methods. They can also differentiate almost half of the native interactions from background. The algorithms that include effects of internal binding energies to their model and alignment methods seem to have high TPR but relatively low associated PPV compared to accessibility based methods. Availability and Implementation: We shared our wrapper scripts and datasets at Github (github.com/UCanCompBio/RNA_Interactions_Benchmark). All parameters are documented for personal use. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next generation sequencing technologies have provided us with a wealth of information on genetic variation, but predicting the functional significance of this variation is a difficult task. While many comparative genomics studies have focused on gene flux and large scale changes, relatively little attention has been paid to quantifying the effects of single nucleotide polymorphisms and indels on protein function, particularly in bacterial genomics. RESULTS: We present a hidden Markov model based approach we call delta-bitscore (DBS) for identifying orthologous proteins that have diverged at the amino acid sequence level in a way that is likely to impact biological function. We benchmark this approach with several widely used datasets and apply it to a proof-of-concept study of orthologous proteomes in an investigation of host adaptation in Salmonella enterica We highlight the value of the method in identifying functional divergence of genes, and suggest that this tool may be a better approach than the commonly used dN/dS metric for identifying functionally significant genetic changes occurring in recently diverged organisms. AVAILABILITY AND IMPLEMENTATION: A program implementing DBS for pairwise genome comparisons is freely available at: https://github.com/UCanCompBio/deltaBS CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.

The RNA world hypothesis, that RNA genomes and catalysts preceded DNA genomes and genetically-encoded protein catalysts, has been central to models for the early evolution of life on Earth. A key part of such models is continuity between the earliest stages in the evolution of life and the RNA repertoires of extant lineages. Some assessments seem consistent with a diverse RNA world, yet direct continuity between modern RNAs and an RNA world has not been demonstrated for the majority of RNA families, and, anecdotally, many RNA functions appear restricted in their distribution. Despite much discussion of the possible antiquity of RNA families, no systematic analyses of RNA family distribution have been performed. To chart the broad evolutionary history of known RNA families, we performed comparative genomic analysis of over 3 million RNA annotations spanning 1446 families from the Rfam 10 database. We report that 99% of known RNA families are restricted to a single domain of life, revealing discrete repertoires for each domain. For the 1% of RNA families/clans present in more than one domain, over half show evidence of horizontal gene transfer (HGT), and the rest show a vertical trace, indicating the presence of a complex protein synthesis machinery in the Last Universal Common Ancestor (LUCA) and consistent with the evolutionary history of the most ancient protein-coding genes. However, with limited interdomain transfer and few RNA families exhibiting demonstrable antiquity as predicted under RNA world continuity, our results indicate that the majority of modern cellular RNA repertoires have primarily evolved in a domain-specific manner.

Wikipedia is the world's most successful online encyclopedia, now containing over 3.3 million English language articles. It is probably the largest collection of knowledge ever assembled, and is certainly the most widely accessible. Wikipedia can be edited by anyone with Internet access that chooses to, but does it provide reliable information? A 2005 study by Nature found that a selection of Wikipedia articles on scientific subjects were comparable to a professionally edited encyclopedia [1], suggesting a community of volunteers can generate and sustain surprisingly accurate content. For better or worse, people are guided to Wikipedia when searching the Web for biomedical information [2]. So there is an increasing need for the scientific community to engage with Wikipedia to ensure that the information it contains is accurate and current. For scientists, contributing to Wikipedia is an excellent way of fulfilling public engagement responsibilities and sharing expertise. For example, some Wikipedian scientists have successfully integrated biological data with Wikipedia to promote community annotation [3], [4]. This, in turn, encourages wider access to the linked data via Wikipedia. Others have used the wiki model to develop their own specialist, collaborative databases [5]–[8]. Taking your first steps into Wikipedia can be daunting, but here we provide some tips that should make the editing process go smoothly.

MOTIVATION: As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. RESULT: We present a novel solution to the problem of simultaneous structure prediction and multiple alignment of RNA sequences. Using Markov chain Monte Carlo in a simulated annealing framework, the algorithm MASTR (Multiple Alignment of STructural RNAs) iteratively improves both sequence alignment and structure prediction for a set of RNA sequences. This is done by minimizing a combined cost function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency. AVAILABILITY: Source code available from http://mastr.binf.ku.dk/

MOTIVATION: The importance of non-coding RNAs is becoming increasingly evident, and often the function of these molecules depends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures. RESULTS: Mutual information is still used for secondary structure prediction, but the results of this study indicate which measures are useful. Incorporating more structural information by considering e.g. indels and stacking improves accuracy, suggesting that physically realistic measures yield improved predictions. This can be used to improve both current and future programs for secondary structure prediction. The best measure tested is the RNAalifold covariation measure modified to include stacking. AVAILABILITY: Scripts, data and supplementary material can be found at http://www.binf.ku.dk/Stinus_covariation

BACKGROUND: Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. RESULTS: We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes. Results can be downloaded and viewed from our web site. CONCLUSION: The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity.

BACKGROUND: In the last few decades there has been a great deal of discussion concerning whether or not noncoding RNA sequences (ncRNAs) fold in a more well-defined manner than random sequences. In this paper, we investigate several existing measures for how well an RNA sequence folds, and compare the behaviour of these measures over a large range of Rfam ncRNA families. Such measures can be useful in, for example, identifying novel ncRNAs, and indicating the presence of alternate RNA foldings. RESULTS: Our analysis shows that ncRNAs, but not mRNAs, in general have lower minimal free energy (MFE) than random sequences with the same dinucleotide frequency. Moreover, even when the MFE is significant, many ncRNAs appear to not have a unique fold, but rather several alternative folds, at least when folded in silico. Furthermore, we find that the six investigated measures are correlated to varying degrees. CONCLUSION: Due to the correlations between the different measures we find that it is sufficient to use only two of them in RNA folding studies, one to test if the sequence in question has lower energy than a random sequence with the same dinucleotide frequency (the Z-score) and the other to see if the sequence has a unique fold (the average base-pair distance, D).

BACKGROUND: An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene-finding and multiple-sequence-alignment algorithms. RESULTS: Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance. CONCLUSIONS: We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research.

Abstract Motivation: Noncoding RNA genes produce functional RNA molecules rather than coding for proteins. One such family is the H/ACA snoRNAs. Unlike the related C/D snoRNAs these have resisted automated detection to date. Results: We develop an algorithm to screen the yeast genome for novel H/ACA snoRNAs. To achieve this, we introduce some new methods for facilitating the search for noncoding RNAs in genomic sequences which are based on properties of predicted minimum free-energy (MFE) secondary structures. The algorithm has been implemented and can be generalized to enable screening of other eukaryote genomes. We find that use of primary sequence alone is insufficient for identifying novel H/ACA snoRNAs. Only the use of secondary structure filters reduces the number of candidates to a manageable size. From genomic context, we identify three strong H/ACA snoRNA candidates. These together with a further 47 candidates obtained by our analysis are being experimentally screened. Contact: [email protected] Supplementary Information: Tables 1–5 referred to in the text can be downloaded from http://RNA.massey.ac.nz/fisher/ * To whom correspondence should be addressed. † Both authors contributed equally to this work.