Lunch Talk 28th of February 2020


CMI. G004

Event type
Lunch Talk

Prof. Thomas Abeel - TUDelft: Genomics insight into travel destinations for multi-drug resistance tuberculosis

Joris Van Houtven - UHasselt: PRiSM: pattern-based, assumption-free identification for mass spectrometry-based proteomics

About the Lunch Talks

The Biomina Lunch Talks are an initiative of a number of young researchers in the biomina network and is sponsored by the Flemish Government. We aim to stimulate the interaction between researchers from different disciplines who encounter bioinformatics and computational biology, and consequently we focus on a broad and multidisciplinary public. With this informal medium we would like to provide a platform where knowledge and experience can be presented and exchanged, across partners from both academia and industry. In this manner we have had the pleasure to welcome speakers from various institutes such as the University of Antwerp, the Institute of Tropical Medicine, Janssen Pharmaceutica, the Antwerp University Hospital and Open Analytics. Last, but not least, these sessions can provide a great opportunity for young researchers to acquaint themselves with new ideas and methods in the field of bioinformatics and medical informatics.


Prof. Thomas Abeel, TUDelft

Title: Genomics insight into travel destinations for multi-drug resistance tuberculosis

Abstract: Tuberculosis kills 1.5 million people per year. Globally it is one of the top 10 causes of mortality and the number one infectious disease in terms of absolute mortality. M. tuberculosis, the bacteria causing the disease has been studied through genomics for over two decades. Data integration of DNA sequencing data across five continents have now given us unprecedented insights in the evolution and mobility of strains. It has allowed us to unravel mutation patterns in drug resistance acquisition, as well as geographic transmissions.

Joris Van Houtven, UHasselt

Title: PRiSM: pattern-based, assumption-free identification for mass spectrometry-based proteomics

Abstract: A typical mass spectrometry (MS)-based proteomics analysis of a biological sample results in gigabytes of data - fragment spectra - revealing information the sample's protein content. Despite ever increasing computational power, today's database search methods are struggling to convert more than about a third of the fragment spectra into meaningful information. That is because currently available methods are all constrained by specific assumptions to restrict the search space and thus computational resources. The community needs a reliable way to identify proteins without using assumptions, so that blind spots can be avoided.

PRiSM (protein-spectrum matching) provides precisely such a framework as well as an algorithm we call comprehensive protein convolution (CPC). In this framework the 'mass pattern' plays the key role in spectral identification, instead of the actual mass values. They are pattern-matched (counting the number of matching peaks) against a database of theoretical spectra containing pseudo-ions (generated from a FASTA file). The second essential ingredient is to not perform intermediate peptide identifications, but to match the spectral peaks directly to proteins. Statistical analysis of empirical score distributions will reveal the correct protein identification. This framework enables identification regardless of spectral purity, precursor mass, acquisition type, fragmentation mechanism, modifications or sample preparation.