Statistical bioinformatic analyses of RNA-seq and ChIP-seq data

Statistical bioinformatic analyses of RNA-seq and ChIP-seq data

Project details

A number of projects are available for students to develop new computational and statistical methods for analysing genomic data, especially data from RNA-seq, ChIP-seq and related technologies. 

Projects might include the analysis of single cell RNA-seq, or detection of splice variants, or the analysis of higher order expression signatures representing biological pathways or cell fates, or integrative analyses combining information from a number of different technologies. Projects may also include the analysis of data from emerging technologies. Projects will involve collaborations with biomedical researchers in other laboratories working on breast cancer and other diseases.

These projects would suit students with training in mathematics, statistics, computer science, genetics or related fields.

About our research group

Our group has a history of developing new statistical techniques for the analysis of microarray, RNA-seq and ChIP-seq data. Our research has made particular use, for example, of empirical Bayes and generalised linear model techniques and has given particular attention to the analysis of complex experiments involving multiple treatment factors. The group has developed a number of well-known software packages including limma, edgeR, goseq, csaw and diffHic.


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