The thesis addresses the non-trivial problem of computational analysis of high-throughput transcriptomics data. The focus is given to processing of RNAseq data in combination with CLIPseq in biological settings related to RNA demethylase activities (ALKBH5).

1. The student will analyse high-throughput data from different methods, such as RNAseq and CLIPseq. Particularly, the student will use the raw data from Illumina sequencer, map the reads to the human genome HG38, perform differential expression (DEseq) set analysis, alternative splicing (AS) quantification. Different tools for alternative splicing will be compared. 

2. Use the iCLIPseq dataset - map to the genome, peak calling analysis. 

3. Cross-correlate the DEseq and the CLIPseq results to identify how protein (in this case ALKBH5) binding to RNA correlates to transcript coverage.  Next, ALKBH5 binding (peaks) will be crosscorrelated with other publicly available datasets of CLIPseq data for other proteins of interest.

4. Ultimately, the student will develop a semi-automatized pipeline based on a comprehensive set of shell and R scripts, allowing to compute statistical analyses for the cross-comparison between the iCLIP and DEseq and AS datasets. The outcome will allow selection of specific thresholds and parameters to address concrete biological questions. The set of scripts will be initially prepared for the ALKBH5 datasets with a potential to be used for other proteins.

The pipeline implementation including the preprocessed data will be adequately documented to support the reproducibility of the results discussed in the thesis.