Data Independent Acquisition (DIA) is a novel mode of mass spectrometry (MS) analysis that can generate MS/MS data for an unbiased selection of peptides, offering new opportunities to achieve more complete proteome coverage for downstream quantitative analysis. However, data extraction methods for DIA data are still in early development stage and few statistical approaches are available for this emerging platform of quantitative proteomics data.
Here I will describe a complete computational pipeline to extract DIA data and perform robust statistical analysis from the resulting MS2-level quantitative data. This workflow consists of two software packages called DIA-Umpire and MAP-DIA. DIA-Umpire performs spectral library-independent and dependent extraction of MS1 and MS2 level peak areas to generate the base material for quantitative analysis. This data will be further refined and analyzed by MAP-DIA, which performs essential data preprocessing, including novel retention time-based normalization method and a sequence of peptide/fragment selection steps. MAP-DIA also offers hierarchical model-based statistical significance analysis for multi-group comparisons under representative experimental designs (e.g. time course).
Using a comprehensive set of simulation datasets, it will be shown that MAP-DIA provides reliable classification of differentially expressed proteins with accurate control of the false discovery rates. I will also illustrate MAP-DIA using two recently published SWATH-MS datasets of 14-3-3 dynamic interaction network and prostate cancer glycoproteome, with detailed illustration of the data preprocessing steps and statistical analysis.
How to participate in this seminar:
1. Book your nearest ACE facility;
2. Notify the ACE contact person at the host institution (Darren Condon) to notify you will be participating.
No access to an ACE facility? Contact Maaike Wienk to arrange a temporary Visimeet licence for remote access (limited number of licences available – first come first serve)