Statistical Analysis of RNA-Seq Data

Korbinian Strimmer and Steve Hoffmann
University of Leipzig, Summer Term 2012

Synopsis:

In summer term 2012 the seminar "Current Topics in Biostatistics" will be concerned with statistical techniques for the analysis of RNA-Seq data. These data sets are now frequently encountered in transcriptome analysis and replace data from earlier technologies, such as microarrays.

A "Schein" will be awarded on the basis of a presentation and active discussion and possibly the writing of an short essay (depending on the student's requirements).

Literature:

1. J. Shendure and H. Ji. 2008. Next-generation DNA sequencing. Nature Biotechnology 26:1135-1145.
2. M. L. Metzger. 2010. Sequencing technologies - the next generation. Nature Reviews Genetics. 11:31-46.
3. Z. Wang et al. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. 10:57-63.
4. A. C. Frazee et al. 2011. ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count data sets. BMC Bioinformatics 12:449.
5. P. L. Auer and R. W. Doerge. 2010. Statistical design and analysis of RNA sequencing data. Genetics 185:405-416.
6. H. Jiang and W. H. Wong. 2009. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics 25:1025-1032.
7. J. Salzman et al. 2011. Statistical modeling of RNA-Seq data. Statistical Science 26:62-83.
8. B. Li et al. 2010. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26: 493-500.
9. B. Li and C. N. Dewey. 2011. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323.
10. M. D. Robinson and A. Oshlack. 2010. A scaling normalization method for differential expression analysis of RNA-Seq data. Genome Biology 11:R25.
11. J. H. Bullard et al. 2010. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11:94.
12. S. Anders and W. Huber. 2010. Differential expression for sequence count data. Genome Biology 11:R106.
13. L. Pachter. 2011. Models for transcript quantification from RNA-Seq. ArXiv 1104.3889.
14. O. Muralidharan et al. 2012. Detecting mutations in mixed sample sequencing data using empirical Bayes. Annals of Applied Statistics, in press.
15. D. Witten. 2011. Classification and clustering of sequencing data using a Poisson model. Annals of Applied Statistics 5:2493-2518.

All papers are available as PDF from the instructors.

Schedule:

The first meeting is on 11 April 2012 where the papers will be distributed and talks assigned.

Sessions:

Last modified:
13 February 2012