ABSTRACT Proteogenomic searches allow the identification of protein variants by extending canonical protein sequence databases with the predicted product of genomic variation. In this study, we investigate how including germline sequence variants influences the scoring and yield of proteogenomic searches. Then, we show how the peptides distribute relatively to their predicted retention time and fragmentation. By combining these features, we anticipate proteogenomic pipelines to enable the calling of genetic variants in proteomics data, which in turn will be helpful for the analysis of their consequences and pathogenicity. How including germline sequence variation influences the search space in proteomics Dafni Skiadopoulou1, Jakub Vašíček1, Ksenia Kuznetsova2, Pål R. Njølstad1,3, Stefan Johansson1,4, Lukas Käll5, Marc Vaudel1 ACKNOWLEDGEMENTS This project is supported by the Research Council of Norway (project #301178). 1Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway 2Research and Clinical Center of Physical-Chemical Medicine, Moscow, Russia 3Children and Youth Clinic, Haukeland University Hospital, NO-5020 Bergen, Norway 4Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway 5Science of Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology – KTH, Stockholm, Sweden Fig. 1: Proposed proteogenomics pipeline. Fig. 2: Posterior error probability density (left) and number of peptide-spectrum matches at a given q-value (right) for searches using the UniProt database and the extended protein sequence database. REFERENCES [1]Umer, H.M., Zhu, Y., Pfeuffer, J., Sachsenberg, T., Lehtiö, J., Branca, R. and Perez-Riverol, Y., 2021. Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides. bioRxiv. [2]Bouwmeester, R.; Gabriels, R.; Hulstaert, N.; Martens, L.; Degroeve, S. DeepLC Can Predict Retention Times for Peptides That Carry As-yet Unseen Modifications. Nat. Methods 2021, 18, 1363–1369. [3]Sven Degroeve, Lennart Martens, MS2PIP: a tool for MS/MS peak intensity prediction, Bioinformatics, Volume 29, Issue 24, 15 December 2013 [4]Käll, L., Canterbury, J. D., Weston, J., Noble, W. S. & Mac Coss, M. J. Semisupervised learning for peptide identification from shotgun proteomics datasets. Nature Methods 4, 923–925 (2007). [5] Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019). RESULTS ❖Including variants in the protein database increases sequence redundancy and reduces the yield of peptide identification (Fig. 2). ❖The retention time and fragmentation features show outlying peptides that are assigned a high confidence score by Percolator but show a large difference between the measured and the predicted values (Fig. 3 and 4). ❖Decoys distribute differently from targets in the space defined by agreement with retention time and fragmentation with prediction (Fig. 5). Variant peptides confidently identified by Percolator show a high prevalence of outliers in both retention time and fragmentation (Fig. 5). Fig. 4: Predicted vs. observed retention time of the peptide-spectrum matches obtained by searching in the UniProt database (left) and the extended protein sequence database (right). Fig. 3: Density of the retention time (left) and fragmentation (right) for all peptide-spectrum matches using the UniProt database and the extended protein sequence database. METHODS Protein sequence database created with py-pgatk[1]: ●canonical proteome ●variants with minor allele frequency > 1% (Ensembl, no population filter) ●variants acquired from exome sequencing of the studied tissue samples ●common sample contaminants Peptide features agreement with predictors: ●Retention time using DeepLC[2]: distance evaluated using a linear model ●Fragmentation using MS2PIP[3]: spectral log-distance Identificat