RNAs In Human Genome Research Articles

Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells.

Gene transcription and protein translation are two key steps of the 'central dogma.' It is still a major challenge to quantitatively deconvolute factors contributing to the coding ability of transcripts in mammals. Here, we propose ribosome calculator (RiboCalc) for quantitatively modeling the coding ability of RNAs in human genome. In addition to effectively predicting the experimentally confirmed coding abundance via sequence and transcription features with high accuracy, RiboCalc provides interpretable parameters with biological information. Large-scale analysis further revealed a number of transcripts with a variety of coding ability for distinct types of cells (i.e. context-dependent coding transcripts), suggesting that, contrary to conventional wisdom, a transcript's coding ability should be modeled as a continuous spectrum with a context-dependent nature.

Accurate inference of the full base-pairing structure of RNA by deep mutational scanning and covariation-induced deviation of activity.

Despite the large number of noncoding RNAs in human genome and their roles in many diseases include cancer, we know very little about them due to lack of structural clues. The centerpiece of the structural clues is the full RNA base-pairing structure of secondary and tertiary contacts that can be precisely obtained only from costly and time-consuming 3D structure determination. Here, we performed deep mutational scanning of self-cleaving CPEB3 ribozyme by error-prone PCR and showed that a library of <5 × 104 single-to-triple mutants is sufficient to infer 25 of 26 base pairs including non-nested, nonhelical, and noncanonical base pairs with both sensitivity and precision at 96%. Such accurate inference was further confirmed by a twister ribozyme at 100% precision with only noncanonical base pairs as false negatives. The performance was resulted from analyzing covariation-induced deviation of activity by utilizing both functional and nonfunctional variants for unsupervised classification, followed by Monte Carlo (MC) simulated annealing with mutation-derived scores. Highly accurate inference can also be obtained by combining MC with evolution/direct coupling analysis, R-scape or epistasis analysis. The results highlight the usefulness of deep mutational scanning for high-accuracy structural inference of self-cleaving ribozymes with implications for other structured RNAs that permit high-throughput functional selections.

The mirror RNA expression pattern in human tissues.

It has been realized in recent years that non-coding RNAs are playing important roles in genome functions and human diseases. Here we developed a new technology and observed a new pattern of gene expression. We observed that over 72% of RNAs in human genome are expressed in forward-reverse pairs, just like mirror images of each other between forward expression and reverse expression; the overview showed that it cannot be simply described as transcript overlapping, so we designated it as mirror expression. Furthermore, we found that the mirror expression is gene-specific and tissue-specific, and less common in the proximal promoter regions. The size of the shadows varies between different genes, different tissues and different classes. The shadow expression is most significant in the Alu element, it was also observed among L1, Simple Repeats and LTR elements, but rare in other repeats such as low-complexity, LINE/L2, DNA and MIRs. Although there is no evidence yet about the relationship of this mirror pattern and double-strand RNA (dsRNA), this new striking pattern provides a new clue and a new direction to unveil the role of RNAs in the genome functions and diseases.