Statistical modeling of isoform splicing dynamics from RNA-seq time series data

Bioinformatics
Yuanhua Huang, Guido Sanguinetti

Abstract

Isoformquantification is an important goal of RNA-seq experiments, yet it remains problematic for genes with low expression or several isoforms. These difficulties may in principle be ameliorated by exploiting correlated experimental designs, such as time series or dosage response experiments. Time series RNA-seq experiments, in particular, are becoming increasingly popular, yet there are no methods that explicitly leverage the experimental design to improve isoform quantification. Here we present DICEseq, the first isoform quantification method tailored to correlated RNAseq experiments. DICEseq explicitly models the correlations between different RNA-seq experiments to aid the quantification of isoforms across experiments. Numerical experiments on simulated data sets show that DICEseq yields more accurate results than state-of-the-art methods, an advantage that can become considerable at low coverage levels. On real data sets, our results show that DICEseq provides substantially more reproducible and robust quantifications, increasing the correlation of estimates from replicate data sets by up to 10% on genes with low or moderate expression levels (bottom third of all genes). Furthermore, DICEseq permits to quantify the trade-...Continue Reading

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Citations

Jun 29, 2017·Genome Biology·Yuanhua Huang, Guido Sanguinetti

Related Concepts

Genes
RNA
Protein Isoforms
Research Study
Sequence Determinations, RNA
Analysis
Protein Expression
Nucleic Acid Sequencing
Statistical Technique

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