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Project properties |
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| Title | Regeneration genes |
| Group | Molecular Biology, Laboratory of |
| Project type | thesis |
| Credits | 18-39 |
| Supervisor(s) | Dr. Ir. R Heidstra and Dr H Nijveen (BIF) |
| Examiner(s) | Ben Scheres |
| Contact info | renze.heidstra@wur.nl |
| Begin date | 2018/10/01 |
| End date | 2024/10/01 |
| Description | Introduction of transgenes in plants has been a force in molecular biology and biotechnology for decades. Agrobacterium tumefaciens is generally used as a vehicle to introduce genetic material in the plant cell. Unfortunately, not all plants (particularly agronomically important crops) have the ability to regenerate a complete plant from a single (transgenic) cell.
Therefore, identification and knowledge on the molecular factors involved in the regeneration process is required [1]. To gain more information on the regeneration process, an RNA-sequencing experiment was conducted using Arabidopsis thaliana regenerating tissue at multiple time-points up to the fully regenerated shoot. In this project the goal is to uncover candidates whose function is currently not associated with the regeneration process, as well as characterize the differential expression of genes suspected to be involved in regeneration through time. This requires the analysis of the RNAseq dataset individually, comparing expression data from different time-points, and to existing datasets. Genes/transcripts can be clustered based on co-expression [2,3] measures to find genes that show expression patterns similar to regeneration related genes. Clustered genes can then be analysed for enriched Gene Ontology annotations or common transcriptional regulators [4] to learn more about the underlying regulatory mechanisms. 1. Radhakrishnan D, Kareem A, Durgaprasad K, Sreeraj E, Sugimoto K, Prasad K: Shoot regeneration: a journey from acquisition of competence to completion. Current Opinion in Plant Biology 2018, 41:23-31. 2. Langfelder P, Horvath S: WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559. 3. Serin EAR, Nijveen H, Hilhorst HWM, Ligterink W: Learning from Co-expression Networks: Possibilities and Challenges. Frontiers in Plant Science 2016, 7:444. 4. Kulkarni SR, Vaneechoutte D, Van de Velde J, Vandepoele K: TF2Network: predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information. Nucleic Acids Research 2018, 46:e31-e31. |
| Used skills | Genomics, programming, statistics, machine learning |
| Requirements | BIF30806, MAT22306 or ABG30806 |