PT  - JOURNAL ARTICLE
AU  - Oliver M. Crook
AU  - Aikaterini Geladaki
AU  - Daniel J.H. Nightingale
AU  - Owen Vennard
AU  - Kathryn S. Lilley
AU  - Laurent Gatto
AU  - Paul D.W. Kirk
TI  - A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection
AID  - 10.1101/2020.05.05.078345
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.05.05.078345
4099  - http://biorxiv.org/content/early/2020/08/12/2020.05.05.078345.short
4100  - http://biorxiv.org/content/early/2020/08/12/2020.05.05.078345.full
AB  - The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein’s sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to hyper LOPIT datasets validates its utility by recovering enrichment with chromatin-associated proteins without annotation and uncovers sub-nuclear compartmentalisation which was not identified in the original analysis. Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.Competing Interest StatementThe authors have declared no competing interest.