In-depth evaluation of software tools for data-independent acquisition based label-free quantification

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Label-free quantification (LFQ) is increasingly popular for analyzing proteome data.
• Data-independent acquisition (DIA) workflows, especially with ion mobility, offer enhanced proteome coverage.
• Several software tools are available for LFQ analysis, necessitating performance evaluation.

Purpose of the Study:

• To evaluate and compare the performance of three LFQ software packages: Progenesis, synapter, and ISOQuant.
• To assess the accuracy and reproducibility of these tools using ion mobility enhanced DIA data.
• To analyze the impact of different algorithms on quantification results.

Main Methods:

• Generation of hybrid proteome samples with defined quantitative compositions from mouse, yeast, and E. coli.
• Application of Progenesis, synapter, and ISOQuant to analyze the generated samples.
• Benchmarking LFQ performance based on protein quantification, dynamic range, and technical reproducibility.

Main Results:

• Progenesis and ISOQuant showed high technical reproducibility with median coefficients of variation below 5% for MS(E) data.
• ISOQuant and synapter provided superior accuracy in LFQ compared to Progenesis.
• The study analyzed the influence of various algorithms on quantification outcomes.

Conclusions:

• ISOQuant and synapter are recommended for accurate LFQ analysis with ion mobility enhanced DIA data.
• The choice of software and algorithms significantly impacts proteomic quantification results.
• The study provides a valuable benchmark for selecting appropriate LFQ tools for complex biological samples.