Analysis of Nontarget MS Data

01 - Organization - Analysis of Nontarget MS Data - 20.02.2024

General Information

Please see the respective section in the slides.

01 Organization
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The recordings of the lecture are available in the study materials. Please send me an email in case they are not accessible or you have any questions.

Agenda

Date
Teacher
ClassTopic
20.02.2024Helge Hecht
LectureIntroduction
27.02.2024
Thomas Contini
LectureInstrumental Analysis – Chromatography, Mass Spectrometry &
Acquisition methods
05.03.2024
Elliott Price
Lecture Introduction to -omics
12.03.
Helge Hecht
Lecture Introduction to untargeted mass spectrometry data & pre-processing
19.03.
Helge Hecht
Practical
Lecture
Getting started with Galaxy, exploring the data and pre-processing
Feature detection from instrumental data
26.03.

No class
02.04.No class
09.04
Thomas Contini Practical Feature detection from instrumental data
16.04.
Helge Hecht Lecture From features to spectra (deconvolution)
23.04.
Helge Hecht Practical From features to spectra (deconvolution)
30.04.
Helge Hecht Lecture Annotation – from spectra to compounds
07.05.
PracticalAnnotation – from spectra to compounds
14.05.

TBATBA
21.05.

TBATBA

Due to conflicts in the schedule, we will start the classes at 10:15 instead of 10 and finish around 11:50.

Introduction

In this course we are covering the theoretical concepts, methods and algorithms used for processing of untargeted mass spectrometry data and we are applying tools implementing those methods to process data using Galaxy.

During the course, we will go through the following larger modules and the individual steps. In the practicals, we apply those methods by using software packages implementing those algorithms using the Galaxy platform.

bi5020-Overview V2
Overview
Pre-processing
Conversion
Normalization
Denoising
Centroiding
Peak Detection
Peak
Detection
Alignment
Retention Time
Correction
Peak
Integration
Signal
Recovery
Deconvolution
Spectra
Reconstruction
Isotopic Pattern
Detection
Adduct 
Detection
Quantification
Normalization
Batch Correction
Annotation
Identification with Databases
Spectral Library Search
Retention Time / Index
Molecular Networking
In silico identification
Formula calculation
Retention prediction
Spectra prediction
Biotransformation