Xin‐Lei Lian

It has been a long debate whether the 98% 'non-coding' fraction of human genome can encode functional proteins besides short peptides. With full-length translating mRNA sequencing and ribosome profiling, we found that up to 3330 long non-coding RNAs (lncRNAs) were bound to ribosomes with active translation elongation. With shotgun proteomics, 308 lncRNA-encoded new proteins were detected. A total of 207 unique peptides of these new proteins were verified by multiple reaction monitoring (MRM) and/or parallel reaction monitoring (PRM); and 10 new proteins were verified by immunoblotting. We found that these new proteins deviated from the canonical proteins with various physical and chemical properties, and emerged mostly in primates during evolution. We further deduced the protein functions by the assays of translation efficiency, RNA folding and intracellular localizations. As the new protein UBAP1-AST6 is localized in the nucleoli and is preferentially expressed by lung cancer cell lines, we biologically verified that it has a function associated with cell proliferation. In sum, we experimentally evidenced a hidden human functional proteome encoded by purported lncRNAs, suggesting a resource for annotating new human proteins.

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.

In the process of translation, ribosomes first assemble on mRNAs (translation initiation) and then translate along the mRNA (elongation) to synthesize proteins. Elongation pausing is deemed highly relevant to co-translational folding of nascent peptides and the functionality of protein products, which positioned the evaluation of elongation speed as one of the central questions in the field of translational control. By integrating three types of RNA-seq methods, we experimentally and computationally resolved elongation speed, with our proposed elongation velocity index (EVI), a relative measure at individual gene level and under physiological condition in human cells. We successfully distinguished slow-translating genes from the background translatome. We demonstrated that low-EVI genes encoded more stable proteins. We further identified cell-specific slow-translating codons, which might serve as a causal factor of elongation deceleration. As an example for the biological relevance, we showed that the relatively slow-translating genes tended to be associated with the maintenance of malignant phenotypes per pathway analyses. In conclusion, EVI opens a new view to understand why human cells tend to avoid simultaneously speeding up translation initiation and decelerating elongation, and the possible cancer relevance of translating low-EVI genes to gain better protein quality.