| Literature DB >> 31421119 |
Mia Yang Ang1, Teck Yew Low2, Pey Yee Lee1, Wan Fahmi Wan Mohamad Nazarie1, Victor Guryev3, Rahman Jamal1.
Abstract
One of the best-established area within multi-omics is proteogenomics, whereby the underpinning technologies are next-generation sequencing (NGS) and mass spectrometry (MS). Proteogenomics has contributed significantly to genome (re)-annotation, whereby novel coding sequences (CDS) are identified and confirmed. By incorporating in-silico translated genome variants in protein database, single amino acid variants (SAAV) and splice proteoforms can be identified and quantified at peptide level. The application of proteogenomics in cancer research potentially enables the identification of patient-specific proteoforms, as well as the association of the efficacy or resistance of cancer therapy to different mutations. Here, we discuss how NGS/TGS data are analyzed and incorporated into the proteogenomic framework. These sequence data mainly originate from whole genome sequencing (WGS), whole exome sequencing (WES) and RNA-Seq. We explain two major strategies for sequence analysis i.e., de novo assembly and reads mapping, followed by construction of customized protein databases using such data. Besides, we also elaborate on the procedures of spectrum to peptide sequence matching in proteogenomics, and the relationship between database size on the false discovery rate (FDR). Finally, we discuss the latest development in proteogenomics-assisted precision oncology and also challenges and opportunities in proteogenomics research.Entities:
Keywords: Genomic variant; Genomics; Mass spectrometry (MS); Next-generation sequencing (NGS); Proteogenomics; Proteomics
Mesh:
Year: 2019 PMID: 31421119 DOI: 10.1016/j.cca.2019.08.010
Source DB: PubMed Journal: Clin Chim Acta ISSN: 0009-8981 Impact factor: 3.786