Chapter 19
Qualitative and Quantitative Shotgun Proteomics Data
Analysis from Data-Dependent Acquisition Mass
Spectrometry
Jesse G. Meyer
Abstract
Shotgun proteomics is the inferential analysis of proteoforms using peptide proxies produced by enzymecatalyzed hydrolysis of entire proteomes. Such peptides are usually identified by nanoflow liquid chromatography coupled to tandem mass spectrometry analysis (nLC-MS/MS). Traditionally, MS/MS analysis is
performed in data-dependent acquisition (DDA) mode, which usually produces a pattern of fragment
masses unique to a single peptide’s fragmentation. Here, I describe a statistically rigorous qualitative and
quantitative computational analysis for shotgun proteomics DDA analysis using free open-source software
tools. MS/MS data are used to identify peptides, and the area of peptide mass/charge over chromatographic elution is used to quantify peptides. All peptides that uniquely map to a protein sequence
predicted from the genome are combined into a single protein quantity, which can then be compared across
experimental conditions. Statistically significant protein changes can be summarized using gene ontology or
pathway term enrichment analysis.
Key words Proteomics, Mass spectrometry, Data-dependent acquisition, Quantification, Peptides,
Gene ontology analysis, Pathway analysis
1 Introduction
Shotgun (or bottom-up) proteomics using mass spectrometry has
become the most widely used method for unbiased protein quantification from biological samples. Major progress over the last
30 years has enabled over 10,000 proteins to be identified and
quantified from a single analysis [1]. The field has developed
many free tools for proteomic data analysis. Although the availability of competing tools is healthy, too many options can make it
difficult for a novice to know where to start. There are many
potential pitfalls that a new proteomics practitioner may not be
aware of during the analysis, so standardized and rigorous analysis
pipelines are needed. This chapter describes one such data analysis
pipeline for qualitative and quantitative proteomic data collected by
Mo ´ nica Carrera and Jesu ´ s Mateos (eds.), Shotgun Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2259,
https://doi.org/10.1007/978-1-0716-1178-4_19, © Springer Science+Business Media, LLC, part of Springer Nature 2021
297
Qualitative and Quantitative Shotgun Proteomics Data
Analysis from Data-Dependent Acquisition Mass
Spectrometry
Jesse G. Meyer
Abstract
Shotgun proteomics is the inferential analysis of proteoforms using peptide proxies produced by enzymecatalyzed hydrolysis of entire proteomes. Such peptides are usually identified by nanoflow liquid chromatography coupled to tandem mass spectrometry analysis (nLC-MS/MS). Traditionally, MS/MS analysis is
performed in data-dependent acquisition (DDA) mode, which usually produces a pattern of fragment
masses unique to a single peptide’s fragmentation. Here, I describe a statistically rigorous qualitative and
quantitative computational analysis for shotgun proteomics DDA analysis using free open-source software
tools. MS/MS data are used to identify peptides, and the area of peptide mass/charge over chromatographic elution is used to quantify peptides. All peptides that uniquely map to a protein sequence
predicted from the genome are combined into a single protein quantity, which can then be compared across
experimental conditions. Statistically significant protein changes can be summarized using gene ontology or
pathway term enrichment analysis.
Key words Proteomics, Mass spectrometry, Data-dependent acquisition, Quantification, Peptides,
Gene ontology analysis, Pathway analysis
1 Introduction
Shotgun (or bottom-up) proteomics using mass spectrometry has
become the most widely used method for unbiased protein quantification from biological samples. Major progress over the last
30 years has enabled over 10,000 proteins to be identified and
quantified from a single analysis [1]. The field has developed
many free tools for proteomic data analysis. Although the availability of competing tools is healthy, too many options can make it
difficult for a novice to know where to start. There are many
potential pitfalls that a new proteomics practitioner may not be
aware of during the analysis, so standardized and rigorous analysis
pipelines are needed. This chapter describes one such data analysis
pipeline for qualitative and quantitative proteomic data collected by
Mo ´ nica Carrera and Jesu ´ s Mateos (eds.), Shotgun Proteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2259,
https://doi.org/10.1007/978-1-0716-1178-4_19, © Springer Science+Business Media, LLC, part of Springer Nature 2021
297