Transcript quantification with RNA-Seq data
© Bohnert et al; licensee BioMed Central Ltd. 2009
Published: 19 October 2009
Novel high-throughput sequencing technologies open exciting new approaches to transcriptome profiling. Sequencing transcript populations of interest, e.g. from different tissues or variable stress conditions, with RNA sequencing (RNA-Seq)  generates millions of short reads. Accurately aligned to a reference genome, they provide digital counts and thus facilitate transcript quantification. As the observed read counts only provide the summation of all expressed sequences at one locus, the inference of the underlying transcript abundances is crucial for further quantitative analyses.
To approach this problem, we have developed a new technique, called rQuant, based on quadratic programming. Given a gene annotation and position-wise exon/intron read coverage from read alignments, we determine the abundances for each annotated transcript by minimising a suitable loss function. It penalises the deviation of the observed from the expected read coverage given the transcript weights. The observed read coverage is typically non-uniformly distributed over the transcript due to several biases in the generation of the sequencing libraries and the sequencing. This leads to distortions of the transcript abundances, if not corrected properly. We therefore extended our approach to jointly optimise transcript profiles, modeling the coverage deviations depending on the position in the transcript. Our method can be applied without knowledge of the underlying transcript abundances and equally benefits from loci with and without alternative transcripts.
Correlation of underlying expression level and inferred abundances for different approaches
Within genes (mean)
Position-wise inference with transcript profiles
Segment-wise inference with transcript profiles
Position-wise inference without transcript profiles
Segment-wise inference without transcript profiles
Our preliminary results show that modeling the transcript profiles can significantly improve the accuracy of transcript abundance estimates from RNA-Seq data. However, the described and other recent approaches [3, 4] for transcript quantification with RNA-Seq rely on annotated gene structures. As most genome annotations are incomplete, they cannot reveal and quantify novel and also (novel) alternative transcripts. Nevertheless, rQuant can be extended to quantify de novo transcripts by combining it with a gene finding system such as mGene .
Revealing and quantifying novel alternative transcripts with the powerful tool of RNA-Seq will be a fundamental step towards a deeper understanding of RNA transcript regulation.
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