Proteome Discoverer 3.3 SP1: Processing DIA data with DIA-NN node

RECORD | Already taken place Tu, 9.6.2026
Learn how to analyze DIA proteomics data using Proteome Discoverer 3.3 SP1 and the new DIA-NN node. Discover InfinDIA workflows for fast processing and large-scale proteomics studies.
Thermo Fisher Scientific: Proteome Discoverer 3.3 SP1: Processing DIA data with DIA-NN node
Thermo Fisher Scientific: Proteome Discoverer 3.3 SP1: Processing DIA data with DIA-NN node

Join us for a free, hands-on Thermo Scientific™ Proteome Discoverer™ software online workshop. This session is designed to walk you through the latest best practices for Data Independent Acquisition (DIA) analysis, featuring the powerful new DIA-NN node.

Whether you are a novice or an intermediate user, this workshop provides the practical knowledge needed to leverage the novel InfinDIA mode for ultra-fast analysis against unlimited search spaces

What We’ll Cover

  • Proteome Discoverer 3.3 SP1 updates: A deep dive into the latest release, including news, installation, and licensing.
  • DIA-NN & InfinDIA: Guest speaker Vadim Demichev introduces DIA-NN 2.5 and the breakthrough InfinDIA mode for handling near-unlimited search spaces and selected applications.
  • Practical processing walkthrough: Michaela Scigelova leads a step-by-step guide using DIA-NN:
    • training materials, study, analysis
    • workflow parameter settings
    • DIA-NN node (discussion of all parameters, recommendet settings)
    • Results review
  • Tips for efficient data processing

Can’t attend the live broadcast? Register anyway! All registrants will receive a link to the session recording.

Presenter: Michaela Scigelova (Product Support, Thermo Fisher Scientific)

Michaela provides customer support to Thermo Scientific Proteome Discoverer software users.

Presenter: Vadim Demichev (MSTARS Group Leader, Charité - Universitätsmedizin Berlin)

Vadim Demichev is a founder of Aptila Biotech and a research group leader at the Charité – Universitätsmedizin Berlin, focusing on developing of LC-MS methods and data analysis approaches for quantitative proteomics.

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