Jump to main content
Jump to site search

All chapters
Previous chapter Next chapter

Chapter 10

Data Analysis for Data Independent Acquisition

Mass spectrometry-based proteomics using soft ionization techniques has been used successfully to identify large numbers of proteins from complex biological samples. However, reproducible quantification across a large number of samples is still highly challenging with commonly used “shotgun proteomics” which uses stochastic sampling of the peptide analytes (data dependent acquisition; DDA) to analyze samples. Recently, data independent acquisition (DIA) methods have been investigated for their potential for reproducible protein quantification, since they deterministically sample all peptide analytes in every single run. This increases reproducibility and sensitivity, reduces the number of missing values and removes stochasticity from the acquisition process. However, one of the major challenges for wider adoption of DIA has been data analysis. In this chapter we will introduce the five most well-known of these techniques, as well as their data analysis methods, classified either as targeted or untargeted; then, we will discuss briefly the meaning of the false discovery rate (FDR) in DIA experiments, to finally close the chapter with a review of the current challenges in this subject.

Publication details

Print publication date
23 Nov 2016
Copyright year
Print ISBN
ePub eISBN