Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters
CLI: <tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
DIA Proteomics Analysis
"Analyze my DIA proteomics data" → Process data-independent acquisition MS data to identify and quantify proteins using library-free or library-based workflows.
CLI: diann for end-to-end DIA analysis with neural network scoring
CLI: EncyclopeDIA for chromatogram library-based quantification
DIA-NN Library-Free Analysis
Goal: Run DIA proteomics analysis without a pre-built spectral library, generating one from the data itself.
Approach: Use DIA-NN in library-free mode with FASTA-based in silico digestion and deep learning prediction.
Goal: Transfer peptide identifications between runs to reduce missing values.
Approach: Enable DIA-NN's two-pass reanalysis with the --reanalyse flag for automatic match-between-runs.
# DIA-NN MBR is automatic with --reanalyse flag
# First pass: identifies peptides per run
# Second pass: transfers IDs between runs
diann \
--f *.mzML \
--lib library.tsv \
--reanalyse \
--out report_mbr.tsv
DIA vs DDA Comparison
Feature
DIA
DDA
Acquisition
All precursors fragmented
Top-N precursors selected
Missing values
Lower (5-20%)
Higher (30-50%)
Dynamic range
Better for low-abundance
Better for high-abundance
Library required
Optional (library-free)
Not applicable
Quantification
More reproducible
More variable
Analysis tools
DIA-NN, Spectronaut
MaxQuant, MSFragger
Related Skills
data-import - Load raw MS data
spectral-libraries - Build and use spectral libraries