Agent skill

bio-proteomics-dia-analysis

Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.

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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-proteomics-dia-analysis

SKILL.md

Version Compatibility

Reference examples tested with: numpy 1.26+, pandas 2.2+

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.

bash
# Library-free mode (generates library from data)
diann \
    --f sample1.mzML \
    --f sample2.mzML \
    --lib "" \
    --threads 8 \
    --verbose 1 \
    --out report.tsv \
    --qvalue 0.01 \
    --matrices \
    --out-lib generated_lib.tsv \
    --gen-spec-lib \
    --predictor \
    --fasta uniprot_human.fasta \
    --fasta-search \
    --min-fr-mz 200 \
    --max-fr-mz 1800 \
    --met-excision \
    --cut K*,R* \
    --missed-cleavages 1 \
    --min-pep-len 7 \
    --max-pep-len 30 \
    --min-pr-mz 300 \
    --max-pr-mz 1800 \
    --min-pr-charge 1 \
    --max-pr-charge 4 \
    --unimod4 \
    --var-mods 1 \
    --var-mod UniMod:35,15.994915,M \
    --reanalyse \
    --smart-profiling

DIA-NN with Spectral Library

Goal: Analyze DIA data using a pre-built or predicted spectral library for targeted extraction.

Approach: Supply an existing spectral library to DIA-NN for guided peptide detection and quantification.

bash
# Use pre-built or predicted library
diann \
    --f sample1.mzML \
    --f sample2.mzML \
    --lib spectral_library.tsv \
    --threads 8 \
    --verbose 1 \
    --out report.tsv \
    --qvalue 0.01 \
    --matrices \
    --reanalyse \
    --smart-profiling

DIA-NN Output Files

report.tsv                    # Main quantification report (long format)
report.stats.tsv              # Run statistics
report.pg_matrix.tsv          # Protein group quantities (wide format)
report.pr.matrix.tsv          # Precursor quantities (wide format)
report.gg_matrix.tsv          # Gene group quantities (wide format)
generated_lib.tsv             # Generated spectral library (if requested)

Load DIA-NN Results in R

Goal: Import DIA-NN quantification output into R for downstream statistical analysis.

Approach: Read the protein group matrix, convert to numeric matrix, and log2-transform raw intensities.

r
library(tidyverse)

# Load main report
report <- read_tsv('report.tsv')

# Load protein matrix (already wide format)
proteins <- read_tsv('report.pg_matrix.tsv')

# Filter and reshape for analysis
protein_matrix <- proteins %>%
    column_to_rownames('Protein.Group') %>%
    select(starts_with('sample')) %>%
    as.matrix()

# Log2 transform (DIA-NN outputs raw intensities)
log2_matrix <- log2(protein_matrix)
log2_matrix[is.infinite(log2_matrix)] <- NA

Load DIA-NN Results in Python

Goal: Import DIA-NN quantification output into Python for downstream analysis.

Approach: Read the protein group matrix with pandas and log2-transform, replacing zeros with NaN.

python
import pandas as pd
import numpy as np

# Load main report
report = pd.read_csv('report.tsv', sep='\t')

# Load protein matrix
proteins = pd.read_csv('report.pg_matrix.tsv', sep='\t')
proteins = proteins.set_index('Protein.Group')

# Log2 transform
log2_proteins = np.log2(proteins.replace(0, np.nan))

MSFragger-DIA Analysis

Goal: Perform DIA analysis using MSFragger as an alternative to DIA-NN.

Approach: Generate a predicted spectral library with EasyPQP from search results, then convert to the desired format.

bash
# MSFragger for DIA (alternative to DIA-NN)
# Requires FragPipe GUI or command-line workflow

# Generate predicted library with EasyPQP
easypqp library \
    --in psm_results.tsv \
    --out library.pqp \
    --psmtsv \
    --rt_reference irt.tsv

# Convert to DIA-NN format
easypqp convert \
    --in library.pqp \
    --out library.tsv \
    --format diann

Spectronaut Export Processing

Goal: Convert Spectronaut long-format report into a protein-level quantification matrix.

Approach: Pivot the Spectronaut output from long to wide format using protein group quantities.

r
# Load Spectronaut report
spectronaut <- read_tsv('spectronaut_report.tsv')

# Pivot to protein matrix
protein_matrix <- spectronaut %>%
    select(PG.ProteinGroups, R.FileName, PG.Quantity) %>%
    pivot_wider(names_from = R.FileName, values_from = PG.Quantity) %>%
    column_to_rownames('PG.ProteinGroups')

DIA Quality Metrics

Goal: Assess DIA data quality by summarizing identification counts and missing value rates per run.

Approach: Count unique precursors, proteins, and genes per run, then calculate missing value percentages from the protein matrix.

r
library(tidyverse)

report <- read_tsv('report.tsv')

# Identifications per run
ids_per_run <- report %>%
    group_by(Run) %>%
    summarise(
        precursors = n_distinct(Precursor.Id),
        proteins = n_distinct(Protein.Group),
        genes = n_distinct(Genes)
    )

# Missing value analysis
proteins <- read_tsv('report.pg_matrix.tsv')
protein_values <- proteins %>% select(-Protein.Group)
missing_pct <- colSums(protein_values == 0 | is.na(protein_values)) / nrow(protein_values) * 100

Match Between Runs

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.

bash
# 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
  • quantification - Normalization methods
  • differential-abundance - Statistical testing

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