DIA Proteomics Analysis

DIA-NN Library-Free Analysis

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

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

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

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

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

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

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

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