Francesca Barry is enrolled in the University of Ottawa’s M.Sc. Program in Biochemistry with a Specialization in Bioinformatics under the supervision of Dr. Mathieu Lavallée-Adam and Dr. Alain Stintzi. She obtained an Honours B.Sc. in Biochemistry from the University of Ottawa in 2017.

Research Interests

Most studies aiming at understanding bacterial microbiomes focus on the characterization of the genomes and the gene expression profiles of their bacteria. While the metatranscriptome provides information about the genes that are expressed by these bacteria, most regulation takes place before RNA transcripts are translated into active proteins. A mass spectrometry-based proteomics analysis of microbiomes can, however, provide information about the proteins that are translated from these transcripts and their post-translational modifications, which may result in their activation or repression. Nevertheless, the identification of proteins from mass spectra obtained using mass spectrometry in complex samples, such as those originating from microbiomes, remains a difficult problem. Protein relationships, such as protein-protein interactions (PPIs) or RNA co-expression can provide additional information to help identify proteins from mass spectra. Unfortunately, current protein identification algorithms do not make use of this information and discard the majority of unidentified spectra. The goal of her research project is to design algorithms that will match unidentified mass spectra from microbiome samples to proteins using protein relationship information, such as PPIs and RNA co-expression. The inclusion of protein relationship information to a machine-learning procedure will generate more sensitive protein identifications in bacterial microbiomes, thereby providing a greater coverage of their metaproteome. Due to this increased sensitivity, these algorithms will allow for the identification of low abundance proteins that may originate from bacteria that are less populous. The resulting methods will improve our understanding of microbiomes, and of the proteins that play a role in their regulation and that are interfacing with their environment.

 Program Goals

  • Design novel computational methods for the improved identification of proteins in complex biological samples using mass spectrometry
  • Become more familiar with the different -omics technologies that are used in current microbiome studies
  • Develop communication skills to present scientific findings to different audiences
  • Acquire the skills required for competing in science and computational job markets