  Isoform quantification is an important goal of RNA-seq experiments, yet it
remains prob- lematic 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 in- creasingly 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 RNA-seq experiments. DICEseq
explicitly models the corre- lations 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
substan- tially 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 (bot- tom third of all genes). Furthermore,
DICEseq permits to quantify the trade-off between temporal sampling of RNA and
depth of sequencing, frequently an important choice when planning experiments.
Our results have strong implications for the design of RNA-seq ex- periments,
and offer a novel tool for improved analysis of such data sets. Python code is
freely available at http://diceseq.sf.net.
