Accurate Prediction of Protein Sequences for Proteogenomics Data Integration.
DNA/RNA next-generation sequencing
Genomics
Mass spectrometry
Proteogenomics
Proteomics
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
14
12
2021
pubmed:
15
12
2021
medline:
29
1
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 34905178
doi: 10.1007/978-1-0716-1936-0_18
doi:
Substances chimiques
Proteome
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
233-260Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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