Abstract
In the field of RNA-Seq transcriptomics, detecting differences in expression levels between two data-sets remains a challenging question. Most current methods consider only point estimates of the expression levels, and thus neglect the uncertainty of these estimates. Further, testing for differential expression is often done on predefined regions. Here, we propose Pardiff, a method that reconstructs the profile of differential expression at a base-pair resolution and incorporate uncertainty via the use of a Bayesian framework. This method is built on our approach, Parseq, to infer the transcriptional landscape from RNA-seq data.
A program, named Pardiff, implements this strategy and will be made available at: http://www.lgm.upmc.fr/parseq/.
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Keywords
- Fold Change
- Positive Predictive Value
- Posterior Distribution
- Fold Change Threshold
- Transcriptional Landscape
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Mirauta, B., Nicolas, P., Richard, H. (2013). Pardiff: Inference of Differential Expression at Base-Pair Level from RNA-Seq Experiments. In: Petrosino, A., Maddalena, L., Pala, P. (eds) New Trends in Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41190-8_45
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DOI: https://doi.org/10.1007/978-3-642-41190-8_45
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