Abstract
This book chapter discusses proteogenomics data integration and provides an overview into the different omics layer involved in defining the proteome of a living organism. Various aspects of genome variability affecting either the sequence or abundance level of proteins are discussed in this book chapter, such as the effect of single-nucleotide variants or larger genomic structural variants on the proteome. Next, various sequencing technologies are introduced and discussed from a proteogenomics data integration perspective such as those providing short- and long-read sequencing and listing their respective advantages and shortcomings for accurate protein variant prediction using genomic/transcriptomics sequencing data. Finally, the various bioinformatics tools used to process and analyze DNA/RNA sequencing data are discussed with the ultimate goal of obtaining accurately predicted sample-specific protein sequences that can be used as a drop-in replacement in existing approaches for peptide and protein identification using popular database search engines such as MSFragger, SearchGUI/PeptideShaker.
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We thank Prof. Dr. Rainer Bischoff for his input, feedback, and discussions related to the content of this book chapter.
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Hagemeijer, Y.P., Guryev, V., Horvatovich, P. (2022). Accurate Prediction of Protein Sequences for Proteogenomics Data Integration. In: Corrales, F.J., Paradela, A., Marcilla, M. (eds) Clinical Proteomics. Methods in Molecular Biology, vol 2420. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1936-0_18
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