Webinar – Multiplexing with isotopic labels for DDA and DIA in MaxQuant

Tuesday, June 11, 2024
4:00-5:00pm CEST (UTC+2)
45 minute presentation, 15 min. Q&A

MaxQuant has traditionally been a platform for DDA proteomics data analysis with or without labels. Recently, the MaxDIA workflow was introduced, which performs scalable analysis of DIA datasets. In particular, DIA data multiplexed by chemical or isotopic labels such as SILAC can be analyzed with large depth and quantitative accuracy. We provide an introduction to the computational workflow, covering the peptide-centric identification workflow as well as the MaxLFQ algorithm adapted to multiplexed DIA data.

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Speaker: Dr. Jürgen Cox

Group leader
Max-Planck-Institute for Biochemistry


More information about Dr. Jürgen Cox and his work

Research Group Computational Systems Biochemistry

Modern high-throughput processes produce huge amounts of data, which in this raw form hardly provide any information about biological processes. However, such amounts of data cannot be analysed by hand. Only computer-based methods are reliable enough for the automated identification and quantification of proteins and other biomolecules. Jürgen Cox and his research group “Computational Systems Biochemistry” are developing tailor-made software for these tasks. Cox has developed MaxQuant, a worldwide platform for computer-based proteomics. Researchers all over the world can use this software for highly precise analyses of their data – online and free of charge.

Research Overview

We specilized in developing algorithms and software for the analysis of mass spectral data produced in modern proteomics experiments.

Mass spectrometry-based Proteomics

We develop algorithms and software for the analysis of vast amounts of mass spectral data that are produced in modern proteomics experiments. Learn more.

Machine learning applications in computational biology

We apply conventional and deep machine learning to data analysis problems in proteomics and other high throughput data fields, utilize the flexibility and predictive strength of deep learning models as well as finding alternatives from conventional machine learning models. Learn more.

Posttranslational Modifications

One of our main interests lies in the interpretation of quantitative profiles of many proteins simultaneously. The Perseus software is designed to achieve this by integrating computational methods from bioinformatics, statistics and machine learning. Learn more.

Curriculum Vitae Dr. Jürgen Cox

Academic education  

Education

  • 2001: PhD in Physics Massachusetts Institute of Technology
  • 1997: Diploma in Physics RWTH Aachen University, Germany
  • 2002: PhD in Chemistry, University of Halle-Wittenberg, Mentor: Prof. Dr. T. Braun

Professional experience

  • Since 2014: Research Group Leader at Max Planck Intitute of Biochemistry, Martinsried, Germany
  • Since 2018: Researcher at Department of Biological and Medical Psychology, University of Bergen, Norway

Learn more about Dr. Cox’s CV and publications.