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IFDlong: a model-based isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data.

Authors: Wang, Wenjia; Liu, Jia-Jun; Li, Yuzhen; Ko, Sungjin; Feng, Ning; Zhang, Manling; Lin, Qingqi; +6 Authors

IFDlong: a model-based isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data.

Abstract

Long-read transcriptome sequencing (long-RNA-seq) revolutionizes transcriptome research by enabling full-length transcript analysis for comprehensive exploration of isoform diversity. We developed IFDlong, a probabilistic framework and software suite for detecting isoform and fusion transcripts from bulk or single-cell long-RNA-seq data. IFDlong annotates each long read, identifies novel isoforms, quantifies expression via an expectation-maximization algorithm, and profiles fusion transcripts. In large-scale simulation and real data analyses, IFDlong outperforms existing tools and demonstrated high accuracy and robustness across multiple in-house and public datasets, including healthy tissues, cell lines, and different diseases.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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