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Biology subjects

Shi, M.-W.

Publications and source records attributed to Shi, M.-W..

3 recordsLinked to original sources

Enhanced Functional Potential of Pseudogene-associated lncRNA Genes in Mammals

The functional significance of long non-coding RNAs (lncRNAs) remains a subject of debate, largely due to the complexity and cost associated with their validation experiments. However, emerging evidence suggests that pseudogenes, once viewed as genomic relics, may contribute to the origin of functional lncRNA genes. In this study spanning eight species, we systematically identified pseudogene-associated lncRNA genes using our PacBio long-read sequencing data and published RNA-seq data. Our investigation revealed that pseudogene-associated lncRNA genes exhibit heightened functional attributes compared to their non-pseudogene-associated counterparts. Notably, these pseudogene-associated lncRNAs show protein-binding proficiency, positioning them as potent regulators of gene expression. In particular, pseudogene-associated sense lncRNAs retain protein-binding capabilities inherited from parent genes of pseudogenes, thereby demonstrating greater protein-binding proficiency. Through detailed functional characterization, we elucidated the unique advantages and conserved roles of pseudogene-associated lncRNA genes, particularly in the context of gene expression regulation and DNA repair. Leveraging cross-species expression profiling, we demonstrated the prominent contribution of pseudogene-associated lncRNA genes to aging-related transcriptome changes across nine human tissues and eight mouse tissues. Overall, our findings demonstrate enhanced functional attributes of pseudogene-associated lncRNA genes and shed light on their conserved and close association with aging.

genomics↗

GenOrigin: A Comprehensive Protein-coding Gene Origination Database on the Evolutionary Timescale of Life

The origination of new genes contributes to the biological diversity of life. New genes may quickly build their own network in the genomes, exert important functions, and generate novel phenotypes. Dating gene age and inferring the origination mechanisms of new genes, like primate-specific gene, is the basis for the functional study of the genes. However, no comprehensive resource of gene age estimates across species is available. Here, we systematically dated the age of 9,102,113 protein-coding genes from 565 species in the Ensembl and Ensembl Genomes databases, including 82 bacteria, 57 protists, 134 fungi, 58 plants, 56 metazoa, and 178 vertebrates, using protein-family-based pipeline with Wagner parsimony algorithm. We also collected gene age estimate data from other studies and uniformed the gene age estimates to time ranges in million years for comparison across studies. All the data were cataloged into GenOrigin (http://genorigin.chenzxlab.cn/), a userfriendly new database of gene age estimates, where users can browse gene age estimates by species, age and gene ontology. In GenOrigin, the information such as gene age estimates, annotation, gene ontology, ortholog and paralog, as well as detailed gene presence/absence views for gene age inference based on the species tree with evolutionary timescale, was provided to researchers for exploring gene functions.

evolutionary biology↗

pyMeSHSim: an integrative python package to realize biomedical named entity recognition, normalization and comparison

SummaryComputing semantic similarity between two texts, like disease descriptions, has become important for many biomedical text mining applications. Here, we present PyMeSHSim, which is an integrative, lightweight and data-rich MeSH toolkit that recognizes biomedical named entities (bio-NEs) from texts, maps the bio-NEs to the controlled vocabulary MeSH and measures the semantic similarity between the MeSH terms.\n\nAvailabilityPackages source code and test datasets are available under the GPLv3 license at https://github.com/luozhhub/pyMeSHSim\n\nContactzhen-xia.chen@mail.hzau.edu.cn or zhy630@mail.hzau.edu.cn

bioinformatics↗