Deconvolution of isotopic pattern in 2D-FTICR Mass Spectrometry of peptides and proteins

TL;DR

This paper presents a method for deconvolving isotopic patterns in 2D-FTICR MS data of peptides and proteins, enabling efficient analysis of large datasets with a new Python tool and optimization algorithm.

Contribution

It introduces a novel Python-based approach and algorithm for deconvolving complex 2D-FTICR MS data, improving data accessibility and analysis efficiency.

Findings

Developed a Python program for converting raw data into HDF5 format.

Implemented a Primal-Dual Splitting algorithm for deconvolution.

Demonstrated efficient processing of large 2D-FTICR MS datasets.

Abstract

Mass Spectrometry (MS) is a largely used analytical technique in biology with applications such as the determination of molecule masses or the elucidation of structural data. Fourier Transform Ion Cyclotron Resonance MS is one implementation of the technique allowing high resolution and mass accuracy and based on trapping ions in circular orbits thanks to powerful Electromagnetic fields. This method has been extended to two-dimensional analysis, this gives signals containing a lot more information, for a reasonable amount of time and samples. However, the data provided by such experiments cannot be stored in a classical manner and require some tools and architecture to store and compress data so that they remain accessible and usable without necessitating too much computer memory. The developed python program permits to turn these huge raw data into exploitable FTICR MS HDF5 formatted datasets which can be relatively rapidly and efficiently deconvolved and analysed using a Primal-Dual Splitting algorithm.