Research Project 9
Analysis of tRNA dynamics using native RNA nanopore sequencing
About the Project
Centre for Genomic Regulation
Eva Maria Novoa
Universitat Pompeu Fabra
Transfer RNAs (tRNAs) are abundant small non–coding RNAs that play a pivotal role in decoding
genetic information, determining which transcripts are highly and poorly translated at a given
moment. Dysregulation of tRNA abundances and their RNA modifications is a well–known feature
in cancer cells, which leads to enhanced expression of specific oncogenic transcripts and proteins.
Our laboratory has recently developed a novel approach to studying tRNA populations using native
RNA Nanopore sequencing technologies (Nano–tRNAseq), providing tRNA abundance, length, and
tRNA modification information from the same individual molecules. Nano–tRNAseq generates
powerful and information–rich data, which can in turn be used to predict the biological status (e.g.
health, disease, stress) of the cells. The Doctoral candidate will develop novel bioinformatic
algorithms and tools to integrate tRNA modification and abundance information, as well as
alignment–free classification methods of Nanopore current intensity signals. Once these tools have
been built, the PhD candidate will train novel machine–learning algorithms to classify and stratify
samples based on different characteristics (disease, exposure to stress, cancer, metastasis, etc). The
Doctoral candidate will also be responsible for preparing and sequencing Nanopore native RNA
libraries, which will be used to train the candidate’s machine learning models, together with other
existing datasets previously generated in the lab. The ideal candidate must hold or be in the process
of gaining a Master’s degree in Bioinformatics, Biotechnology, or Biochemistry as well as have
experience with wet and dry lab techniques as well as knowledge of R or Python programming
- Oxford Nanopore Technologies (Aino Järvelin)
- talian Institute of Technology (Francesco Nicassio, Tommasso Leonardi)
About the Main Supervisor and Host Group
Eva Maria Novoa
Centre for Genomic Regulation,
A current major challenge in biology is to understand how gene expression is regulated with surgical precision in a tissue-dependent, spatial and temporal dimension. Historically, genome-wide studies of gene expression have typically measured mRNA abundance rather than protein synthesis, in large part because such data are much easier to obtain. However, the correlation between mRNA levels and protein abundance is as low as r=0.35-0.40, suggesting that transcriptional regulation alone is not sufficient to unveil the complex orchestration of gene expression. In the last few decades, the scientific community has started to acknowledge the pivotal role that post-transcriptional regulatory mechanisms play in gene expression, however, we are still far from understanding how gene expression is finely tuned and regulated across tissues and conditions, suggesting that we are missing variables in the equation.
In our lab, we are employing a combination of experimental (RNASeq, polysome profiling, mouse/cell knockouts, Oxford Nanopore direct RNA sequencing) and computational techniques (NGS data analysis, algorithm development, machine learning), to unveil the secrets of three post-transcriptional regulatory layers: the epitranscriptome, RNA structure and ribosome specialization.