As the fundamental unit of a gene and its transcripts, nucleotides have enormous impacts on the gene function and evolution, and thus on phenotypes and diseases. In order to identify the key nucleotides of one specific gene, it is quite crucial to quantitatively measure the importance of each base on the gene. However, there are still no sequence-based methods of doing that. Here, we proposed Base Importance Calculator (BIC), an algorithm to calculate the importance score of each single base based on sequence information of human mRNAs and long noncoding RNAs (lncRNAs). We then confirmed its power by applying BIC to three different tasks. Firstly, we revealed that BIC can effectively evaluate the pathogenicity of both genes and single bases through single nucleotide variations. Moreover, the BIC score in The Cancer Genome Atlas somatic mutations is able to predict the prognosis of some cancers. Finally, we show that BIC can also precisely predict the transmissibility of SARS-CoV-2. The above results indicate that BIC is a useful tool for evaluating the single base importance of human mRNAs and lncRNAs.

Animal models have a certain degree of similarity with human in genes and physiological processes, which leads them to be valuable tools for studying human diseases and for assisting drug development. However, translational researches adopting animal models are largely restricted by the species heterogeneity, which is also a major reason for the failure of drug research. Currently, computational method for exploring the functional differences between orthologous genes is still insufficient. For this purpose, here, we presented an algorithm, functional divergence score (FDS), by comprehensively evaluating the functional differences between the microRNAs regulating the paired orthologous genes. Given that mouse is one of the most popular model animals, currently, FDS was designed to dissect the functional divergence of orthologous genes between human and mouse. The results showed that gene FDS value is significantly associated with gene evolutionary characteristics and can discover expression divergence of human-mouse orthologous genes. Moreover, FDS performed well in distinguishing the targets of approved drugs and the failed ones. These results suggest that FDS is a valuable tool to evaluate the functional divergence of paired human and mouse orthologous genes. In addition, for each orthologous gene pair, FDS can provide detailed differences in functions and phenotypes. Our study provided a useful tool for quantifying the functional difference between human and mouse, and the presented framework is easily to be extended to the orthologous genes between human and other species. An online server of FDS is available at http://www.cuilab.cn/fds/.

Recently, it was confirmed that ACE2 is the receptor of SARS-CoV-2, the pathogen causing the recent outbreak of severe pneumonia around the world. It is confused that ACE2 is widely expressed across a variety of organs and is expressed moderately but not highly in lung, which, however, is the major infected organ. Therefore, we hypothesized that there could be some other genes playing key roles in the entry of SARS-CoV-2 into human cells. Here we found that AGTR2 (angiotensin II receptor type 2), a G-protein coupled receptor, has interaction with ACE2 and is highly expressed in lung with a high tissue specificity. More importantly, simulation of 3D structure based protein-protein interaction reveals that AGTR2 shows a higher binding affinity with the Spike protein of SARS-CoV-2 than ACE2 (energy: -8.2 vs. -5.1 [kcal/mol]). A number of compounds, biologics and traditional Chinese medicine that could decrease the expression level of AGTR2 were predicted. Finally, we suggest that AGTR2 could be a putative novel gene for the entry of SARS-CoV-2 into human cells, which could provide different insight for the research of SARS-CoV-2 proteins with their receptors.

BACKGROUND: Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. RESULTS: In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. CONCLUSIONS: We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at https://www.cuilab.cn/smorfunction .

MOTIVATION: MicroRNAs (miRNAs) are one class of small noncoding RNA molecules, which regulate gene expression at the post-transcriptional level and play important roles in health and disease. To dissect the critical miRNAs in miRNAome, it is needed to predict the essentiality of miRNAs, however, bioinformatics methods for this purpose are limited. RESULTS: Here we propose miES, a novel algorithm, for the prioritization of miRNA essentiality. miES implements a machine learning strategy based on learning from positive and unlabeled samples. miES uses sequence features of known essential miRNAs and performs miRNAome-wide searching for new essential miRNAs. miES achieves an AUC of 0.9 for 5-fold cross validation. Moreover, experiments further show that the miES score is significantly correlated with some established biological metrics for miRNA importance, such as miRNA conservation, miRNA disease spectrum width (DSW) and expression level. AVAILABILITY AND IMPLEMENTATION: The R source code is available at the download page of the web server, http://www.cuilab.cn/mies. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.