Data-dependent acquisition (DDA) quantification

In bottom-up proteomics, the DDA method is used to collect tandem MS spectra for peptide identification and is combined with conceptually different techniques for peptide quantify analytes.

Existing techniques can be loosely categorized in labeled and label-free approaches. Two major analytical techniques dominates currently DDA approaches, Isobariq methods and Label-free (LFQ) methods. DDA quantification methods shared multiple steps including: protein digestion, peptide fractionation and are mainly different in the way peptides from difference samples are multiplex in the (MS) run.

Label-free quantification (LFQ) is probably the most direct way of determining quantities of analytes from several biological samples as they detect and integrate chromatographic intensities of a peptide. To quantify across several MS runs corresponding peptide signals, so-called features, need to be linked between runs. While label-free quantification scales to a large number of experiments, it heavily relies on correct linking of corresponding peptides (read more details Label-free quantification with DDA (LFQ)).

Isobaric labeling , (TMT in particular) circumvent, to some extent, the problem of linking corresponding peptides as they multiplex more than one peptide from each experimental condition in a single MS run (read more details Isobaric quantification with DDA).

Label-free and Labeled methods share multiple steps of the DDA data analysis including: Peptide identification from fragment spectra, Modification localization. Both labeled and label-free quantification techniques yield relative quantities for an analyte and can be used to calculate fold changes between conditions. quantms is mainly focus on differential expression data analysis, but absolute expression is also possible to perform (read more Absolute expression using quantms)

• Label-free quantification with DDA (LFQ)
  • proteomicsLFQ metatool (OpenMS)
  • Relevant Parameters
  • References
• Isobaric quantification with DDA
  • Isobaric quantification with quantms
  • Isobaric Analyzer
  • Protein inference
  • Protein Quantification
  • References
• Protein inference
  • Bayesian inference
  • Aggregation inference
  • Additional inference parameters
  • Protein FDR
  • References