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
languages