Ribosome profiling is a high-throughput sequencing technique that captures the positions of translating ribosomes on RNAs. Recent advancements in ribosome profiling include achieving highly phased ribosome footprints for plant translatomes and more recently for bacterial translatomes. This substantially increases the specificity of detecting open reading frames (ORFs) that can be translated, such as small ORFs located upstream and downstream of the annotated ORFs. However, most genomes (e.g. bacterial genomes) lack the annotations for the transcription start and termination sites. This hinders the systematic discovery of novel ORFs in the 'untranslated' regions in ribosome profiling data. Here, we develop a new computational pipeline called RIBOSS to discover noncanonical ORFs and assess their translational potential against annotated ORFs. The RIBOSS Python modules are versatile, and we use them to analyse both prokaryotic and eukaryotic data. We present a resulting list of noncanonical ORFs with high translational potential in Homo sapiens, Arabidopsis thaliana, and Salmonella enterica. We further illustrate RIBOSS utility when studying organisms with incomplete transcriptome annotations. We leverage long-read and short-read data for reference-guided transcriptome assembly and highly phased ribosome profiling data for detecting novel translational events in the assembled transcriptome for S. enterica. In sum, RIBOSS is the first integrated computational pipeline for noncanonical ORF detection and translational potential assessment that incorporates long- and short-read sequencing technologies to investigate translation. RIBOSS is freely available at https://github.com/lcscs12345/riboss.

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.

A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is more challenging. In this study, we manually curated 1235 SVs, which can ultimately be used to evaluate SV callers or train machine learning models. We developed a crowdsourcing app-SVCurator-to help GIAB curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy. SVCurator displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. 'Expert' curators were 93% concordant with each other, and 37 of the 61 curators had at least 78% concordance with a set of 'expert' curators. The curators were least concordant for complex SVs and SVs that had inaccurate breakpoints or size predictions. After filtering events with low concordance among curators, we produced high confidence labels for 935 events. The SVCurator crowdsourced labels were 94.5% concordant with the heuristic-based draft benchmark SV callset from GIAB. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies.