Sign in
BioinformaticsJournal of Proteome Research
Peer-reviewed journal article

DIA-NN EasyFilter workflow for the fast and user-friendly critical assessment and visualization of DIA-NN proteomics analysis outcome

Mabuse Gontse MoagiiD, Thatiana Ferraz Ferreira, Endre KristófiD, Adzani Gaisani ArdaiD, Rini AriantiiD, Peter L. HorvatovichiD, Éva CsősziD

Abstract

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics, particularly data-independent acquisition (DIA), has become widely adopted across in One Health approaches for biological and clinical research for quantitative protein characterization. Among the many computational tools available, DIA-NN has demonstrated superior performance; however, the primary output of the current versions is provided as a compact, compressed PARQUET file that can be difficult to interrogate without programming expertise. To address this limitation, we developed DIA-NN EasyFilter (DEF), a fast, user-friendly, KNIME-based workflow for comprehensive protein filtering, and visualization. DEF integrates chromatographic peak-based filtering, curated contaminant libraries, and quantity-quality assessment, along with interactive modules for qualitative and quantitative data exploration. The workflow is optimized for efficient execution within the KNIME local desktop environment and is designed to support end-users in improving accuracy and interpretability without requiring coding skills. We provide detailed description on how to run DEF and demonstrate the utility and robustness of DEF using published large-scale proteomics datasets, showing high comparability across studies regardless of instrument platform or dataset size. Table of Contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/710308v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@ce9f1dorg.highwire.dtl.DTLVardef@13042faorg.highwire.dtl.DTLVardef@17d3907org.highwire.dtl.DTLVardef@2b3aee_HPS_FORMAT_FIGEXP M_FIG C_FIG

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.