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Automated workflow for optimization of data independent acquisition mass spectrometry settings

Toresson, Saghar (2024) BINP51 20232
Degree Projects in Bioinformatics
Abstract
Over recent years, mass spectrometry has become essential for advancing proteomics. A typical approach in proteomics involves digesting proteins into peptides, analyzing these peptides using liquid chromatography and mass spectrometry (LC-MS), and identifying them via tandem mass spectrometry (MS/MS). In Data-Dependent Acquisition (DDA), quantification is often achieved by counting MS/MS spectra or by measuring the intensity of precursor ions at the MS1 level. On the other hand, Data-Independent Acquisition (DIA) typically quantifies peptides using the intensities of fragment ions collected at the MS/MS level.

This study introduces a computational pipeline, implemented using Snakemake, aimed at improving mass spectrometry workflows for... (More)
Over recent years, mass spectrometry has become essential for advancing proteomics. A typical approach in proteomics involves digesting proteins into peptides, analyzing these peptides using liquid chromatography and mass spectrometry (LC-MS), and identifying them via tandem mass spectrometry (MS/MS). In Data-Dependent Acquisition (DDA), quantification is often achieved by counting MS/MS spectra or by measuring the intensity of precursor ions at the MS1 level. On the other hand, Data-Independent Acquisition (DIA) typically quantifies peptides using the intensities of fragment ions collected at the MS/MS level.

This study introduces a computational pipeline, implemented using Snakemake, aimed at improving mass spectrometry workflows for proteomic analysis by optimizing Data- Independent Acquisition (DIA) techniques. The pipeline automates the entire process from raw data conversion, through feature extraction, to the generation of detailed analytical reports, supporting both DIA and DDA methods. By comparing (Less)
Popular Abstract
Optimal Settings for Maximal Proteome Coverage in Mass Spectrometry

In the field of biological sciences, the study of proteins, known as proteomics, is essential for advancing our understanding of health and disease. One of the key tools in this field is the mass spectrometer, an instrument that can precisely analyze the types and amounts of proteins in a sample. Proteins are vital to every cell and are involved in nearly all biological processes, from speeding up chemical reactions to fighting infections.

There are two techniques in mass spectrometer: Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA). DDA targets and analyzes the most abundant proteins in a sample, which makes it great for looking at well-known... (More)
Optimal Settings for Maximal Proteome Coverage in Mass Spectrometry

In the field of biological sciences, the study of proteins, known as proteomics, is essential for advancing our understanding of health and disease. One of the key tools in this field is the mass spectrometer, an instrument that can precisely analyze the types and amounts of proteins in a sample. Proteins are vital to every cell and are involved in nearly all biological processes, from speeding up chemical reactions to fighting infections.

There are two techniques in mass spectrometer: Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA). DDA targets and analyzes the most abundant proteins in a sample, which makes it great for looking at well-known proteins but can miss less common ones. DIA, on the other hand, captures information on all proteins present, not just the most abundant, providing a fuller picture of what's in the sample.

To manage and interpret the large amount of data, I developed a computational pipeline using a program called Snakemake in my project. This pipeline automates the entire process: from converting the raw data, extracting important features, to running quality control and identifying peptides, building blocks of proteins. This automation significantly speeds up the research process, allowing for reproducible data processing and fast overview of key parameters in the results. The workflow is adept at managing both Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) methods, allowing it to adapt the analysis based on the data type.

By adjusting how the mass spectrometer is set up, we can detect a wider range of proteins, including those that are less common. Using the developed pipeline we also could compare which settings worked better for our data. Our tests have shown that using narrower and overlapping windows during DIA analysis improves protein detection and this can be applied to similar datasets. This method can be used in future research projects to analyse the proteome more comprehensively and thus be able to get new knowledge about biological systems or cellular functions.

Reserchers in academic and industrial research focusing on proteomics can use this pipeline to enhance the precision and efficiency of their protein analyses.

Master’s Degree Project in Bioinformatics 45 credits 2024

Deaprtment of immunolotechnology, Lund University

Advisor: Fredrik Levander (Less)
Please use this url to cite or link to this publication:
author
Toresson, Saghar
supervisor
organization
course
BINP51 20232
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
9175565
date added to LUP
2024-09-27 12:06:01
date last changed
2024-09-27 12:06:01
@misc{9175565,
  abstract     = {{Over recent years, mass spectrometry has become essential for advancing proteomics. A typical approach in proteomics involves digesting proteins into peptides, analyzing these peptides using liquid chromatography and mass spectrometry (LC-MS), and identifying them via tandem mass spectrometry (MS/MS). In Data-Dependent Acquisition (DDA), quantification is often achieved by counting MS/MS spectra or by measuring the intensity of precursor ions at the MS1 level. On the other hand, Data-Independent Acquisition (DIA) typically quantifies peptides using the intensities of fragment ions collected at the MS/MS level. 

This study introduces a computational pipeline, implemented using Snakemake, aimed at improving mass spectrometry workflows for proteomic analysis by optimizing Data- Independent Acquisition (DIA) techniques. The pipeline automates the entire process from raw data conversion, through feature extraction, to the generation of detailed analytical reports, supporting both DIA and DDA methods. By comparing}},
  author       = {{Toresson, Saghar}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Automated workflow for optimization of data independent acquisition mass spectrometry settings}},
  year         = {{2024}},
}