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QuickIsoSeq for Isoform Quantification in Large-Scale RNA Sequencing

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RNA Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2284))


RNA-sequencing (RNA-seq) is a powerful technology for transcriptome profiling. While most RNA-seq projects focus on gene-level quantification and analysis, there is growing evidence that most mammalian genes are alternatively spliced to generate different isoforms that can be subsequently translated to protein molecules with diverse or even opposing biological functions. Quantifying the expression levels of these isoforms is key to understanding the genes biological functions in healthy tissues and the progression of diseases. Among open source tools developed for isoform quantification, Salmon, Kallisto, and RSEM are recommended based upon previous systematic evaluation of these tools using both experimental and simulated RNA-seq datasets. However, isoform quantification in practical RNA-seq data analysis needs to deal with many QC issues, such as the abundance of rRNAs in mRNA-seq, the efficiency of globin RNA depletion in whole blood samples, and potential sample swapping. To overcome these practical challenges, QuickIsoSeq was developed for large-scale RNA-seq isoform quantification along with QC. In this chapter, we describe the pipeline and detailed the steps required to deploy and use it to analyze RNA-seq datasets in practice. The QuickIsoSeq package can be downloaded from

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Change history

  • 03 August 2021

    In the original version of this book, chapter 8 was published with incomplete list of authors. This has now been rectified in this revised version of the book.


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The authors would like to thank Robert Stanton for his critical reading of the draft manuscript.

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Correspondence to Shanrong Zhao .

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Gamini, R. et al. (2021). QuickIsoSeq for Isoform Quantification in Large-Scale RNA Sequencing. In: Picardi, E. (eds) RNA Bioinformatics. Methods in Molecular Biology, vol 2284. Humana, New York, NY.

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1306-1

  • Online ISBN: 978-1-0716-1307-8

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