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Reading Patterns Changing
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作者 YU LINTAO 《Beijing Review》 2011年第21期40-41,共2页
Modern life is changing the way people read April 23 was the 16th World Book and Copyright Day,also known as the World Book Day.Reading-related problems have once again attracted people’s attention.Today,living a lif... Modern life is changing the way people read April 23 was the 16th World Book and Copyright Day,also known as the World Book Day.Reading-related problems have once again attracted people’s attention.Today,living a life with an increasingly rapid pace,most people 展开更多
关键词 reading patterns Changing World CPC
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DeepRetention:A Deep Learning Approach for Intron Retention Detection
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作者 Zhenpeng Wu Jiantao Zheng +2 位作者 Jiashu Liu Cuixiang Lin Hong-Dong Li 《Big Data Mining and Analytics》 EI CSCD 2023年第2期115-126,共12页
As the least understood mode of alternative splicing,Intron Retention(IR)is emerging as an interesting area and has attracted more and more attention in the field of gene regulation and disease studies.Existing method... As the least understood mode of alternative splicing,Intron Retention(IR)is emerging as an interesting area and has attracted more and more attention in the field of gene regulation and disease studies.Existing methods detect IR exclusively based on one or a few predefined metrics describing local or summarized characteristics of retained introns.These metrics are not able to describe the pattern of sequencing depth of intronic reads,which is an intuitive and informative characteristic of retained introns.We hypothesize that incorporating the distribution pattern of intronic reads will improve the accuracy of IR detection.Here we present DeepRetention,a novel approach for IR detection by modeling the pattern of sequencing depth of introns.Due to the lack of a gold standard dataset of IR,we first compare DeepRetention with two state-of-the-art methods,i.e.iREAD and IRFinder,on simulated RNA-seq datasets with retained introns.The results show that DeepRetention outperforms these two methods.Next,DeepRetention performs well when it is applied to third-generation long-read RNA-seq data,while IRFinder and iREAD are not applicable to detecting IR from the third-generation sequencing data.Further,we show that IRs predicted by DeepRetention are biologically meaningful on an RNA-seq dataset from Alzheimer’s Disease(AD)samples.The differential IRs are found to be significantly associated with AD based on statistical evaluation of an AD-specific functional gene network.The parent genes of differential IRs are enriched in AD-related functions.In summary,DeepRetention detects IR from a new angle of view,providing a valuable tool for IR analysis. 展开更多
关键词 Alternative Splicing(AS) Intron Retention(IR) intronic reads distribution pattern RNA-SEQ
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