Abstract
The development of novel high-throughput DNA sequencing methods has provided a powerful method for both mapping and quantifying transcriptomes. This method, termed RNA-seq (RNA sequencing), has advantages over microarray-based approaches in terms of wide dynamic range of expressions, less reliance on existing knowledge about genome sequence, and low background noise. After aligning the reads to the reference genomes, the first step of RNA-seq analysis is to infer relative transcript abundances. This can be done at the whole transcript level, at the isoform-specific relative abundance level assuming a known set of isoforms, and at the level where transcripts are identified and their abundances are quantified. We review these methods briefly and add some recent developments in dealing with non-uniform read distribution within a transcript. We focus on methods for simultaneous transcript discovery and quantification.
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This research is supported by NIH grants CA127334 and GM097505.
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Li, H. (2014). Isoform Expression Analysis Based on RNA-seq Data. In: Datta, S., Nettleton, D. (eds) Statistical Analysis of Next Generation Sequencing Data. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-07212-8_12
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DOI: https://doi.org/10.1007/978-3-319-07212-8_12
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