演讲摘要：Recent studies reveal that circular RNAs (circRNAs) are a novel class of abundant, stable and ubiquitous noncoding RNA molecules in animals, and some of them function as microRNA sponges. A comprehensive detection of circRNAs from high throughput RNA transcriptome data is an initial and crucial step to study the biogenesis and function of circular RNAs. We proposed a novel chiastic clipping signal based algorithm to unbiasedly and accurately detect circRNAs from transcriptome data by employing multiple filtration strategies (Genome Biology, 2015; Briefings in Bioinformatics, 2018). We further described a new feature, reverse overlap, for circRNA detection, which outperforms back-splice junction-based methods in identifying low-abundance circRNAs. By combining both features, we developed a novel approach for the effective reconstruction of full-length circRNAs and isoform-level quantification from the transcriptome (Genome Medicine, 2019). To comprehensively understand the diversity of circRNAs and prioritize their significance, we presented a large-scale study of circRNA repertoires from multiple tissues of human, macaque and mouse. We delineated genome-wide expression patterns and evolutionary conservation of circRNAs, and unveiled that they are highly tissue-specific and exhibited distinct expression patterns compared with linear transcripts. Using these full-length circRNAs, we further identified thousands of evolutionarily conserved circRNAs that were valuable for functional screening and prioritization (Cell Reports, 2019). We further proposed a novel algorithm, CIRIquant, for accurate circRNA quantification and differential expression analysis. By constructing pseudo-circular reference for re-alignment of RNA-seq reads and employing sophisticated statistical models to correct RNase R treatment biases, CIRIquant can provide more accurate expression values for circRNAs with significantly reduced false discovery rate (Nature Communications, 2020). We built CIRI-vis, a Java command-line tool for quantifying and visualizing circRNAs by integrating the alignments and junctions of circular transcripts. CIRI-vis can be applied to visualize the internal structure and isoform abundance of circRNAs and perform circRNA transcriptome comparison across multiple samples (Bioinformatics, 2020). We have developed a number of bioinformatics tools for exploring the landscape of circRNAs, which would greatly expand our knowledge of circRNAs on a genome-wide scale.
讲者简介：博士，中国科学院北京生命科学研究院研究员。先后获得中科院百人计划（2011）、基金委优青基金（2017）、北京市杰出青年基金（2018）和国家杰出青年基金（2020）。2006年在中国科学院海洋研究所获博士学位，在此期间获“中国科学院院长特别奖”和“国家海洋科学技术奖一等奖”。2006年7月至2010年12月在美国宾州州立大学比较基因组学和生物信息学研究中心，从事计算生物学和基因组学研究工作。2011年被中国科学院北京生命科学研究院聘为“百人计划”研究员。现任中科院北京生科院科研部副主任、技术平台部主任、中国生物工程学会计算生物学与生物信息学专委会副主任。在Briefings in Bioinformatics、Genomics, Proteomics & Bioinformatics、BMC Evolutionary Biology、Medicine in Microecology和Hereditas等国际学术刊物担任副主编或编委。主要致力于建立高效的算法模型和实验技术，探索人体微生物与非编码RNA的结构组成与变化规律，以期解析它们与人类健康和疾病的关系。在Cell、Gut、Nature Biotech 等刊物上发表通讯作者论文40余篇，平均影响因子超过14；论文总引用次数超过8000次（H-index 42），其中十余篇入选ESI高被引论文；连续3年荣获“中国科学院优秀导师奖”（2017，2018，2019）；培养的研究生已有6人次获得“中科院院长奖”和“中科院优秀博士学位论文”。
第六届中国计算机学会生物信息学会议
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