When: February - December 2021

Where: online 

Registration: here

Payment: here
email: Jeannette Martins This email address is being protected from spambots. You need JavaScript enabled to view it.
phone: +1-530-754-5357




UC Davis offers 1-week and 2-week intensive courses in Metabolomics, but many researchers cannot contribute so much time! Therefore, we are offering 'Bits and Bites' of some of our most popular parts of our intensive courses, see below. We hope to see you there! 

This 6-part short course series will feature in-depth topics in untargeted metabolomics such as pathway-based interpretation, Bayesian statistics, and a deeper look into MS-DIAL version 4.0. Each short course can be taken individually or you can select multiple Bites. Participants will gain a deeper insight into current software, methods, and pitfalls. Each session starts promptly at 9 a.m. (Pacific Time) and will take approx. 4 hours. The courses will be conducted in a highly interactive manner, with the use of freely available software and databases.  The tuition is $150 per Bite. We do offer a discounted price when you take 5 Bites for $700 or 6 Bites for $800.

Bits & Bites # 01:
Network inference, visualization, and module detection in metabolomics data with Dr. Jan Krumsiek, Cornell University
Date: February 04, 2021
Instructor: Dr. Jan Krumsiek, Cornell University
Required software:
R, version 3.6.3 or higher
GeneNet package
igraph package
RCy3 package
Modentify package + dependencies: https://github.com/krumsieklab/MoDentify
Participant prerequisites: Basic knowledge of data analysis in R is required for this course since we will directly start by reading a metabolomics data matrix and applying statistical models to it.Short description of the course: In this short course, we will implement typical steps of network analysis in metabolomics data. We will start from a metabolomics data matrix and walk through every step of the process, all the way to network visualizations and the identification of regulated network modules. For this, we will use Gaussian Graphical Models, a correlation-based statistical model which has been widely used in metabolomics datasets. The networks will be visualized using the igraph package and the RCy3 Cytoscape interface. A simple version of a module identification algorithm will be implemented from scratch. At the end of the course, the participants will be able to reproduce the major steps of network analysis contained in several publications from the Krumsiek lab.
Bits & Bites # 02:
Introduction to Bayesian statistics in metabolomics with Dr. Christopher Brydges, UC Davis
Date: April 1, 2021
Instructor: Dr. Christopher Brydges, UC Davis
Required software: JASP (can be downloaded for free from https://jasp-stats.org). The current version is 0.14, but it may well be updated between now and April.
Participant prerequisites: Basic knowledge of statistics (e.g., know what a t-test and a correlation are). No coding experience is needed, and there is no coding taught in this session. 
Short description of the course: Bayesian statistics are a useful method for estimating effect sizes and testing the strength of evidence in favor of one hypothesis over another - things that p-values and traditional statistics can't do. However, they are under-utilized in metabolomics research.  This short course will provide a brief refresher on traditional statistics, teach the basic principles behind Bayesian statistics, learn how to conduct basic Bayesian analyses in JASP (free, open-source software available from https://jasp-stats.org/) and learn how to report the results in the style of a journal article.
Bits & Bites # 03: 
Using MSDIAL to generate accurate comprehensive LC-MS/MS metabolomics datasets with Jake Folz, UC Davis
Date: June 03, 2021

Instructor: Jacob Folz, UC Davis
Required software: MS-DIAL vs. 4.0 for Windows.
Participant prerequisites: Basic understanding of LC-MS and understanding of how MS/MS spectra are used in metabolite identification. 
Short description of the course: This short course will focus on how to perform fine-tuned curation of processed LC-MS/MS data generated through MS-DIAL including compound identification, data quality analysis, and unknown feature reduction. Data from rat blood plasma analyzed using LC-MS/MS with MS/MS data collected in a data-dependent manner will be used to generate an example dataset, but the methods and techniques are applicable to many different sample types.
Bits & Bites # 04:
Identification of unknown compounds in untargeted metabolomics using freely available software with Dr. Arpana Vaniya, UC Davis
Date: August 05, 2021

Instructor: Dr. Arpana Vaniya, UC Davis
Required software: MS-FINDER for Windows (http://prime.psc.riken.jp/compms/msfinder/main.html) & SIRIUS+CSI:FingerID for Windows (https://bio.informatik.uni-jena.de/software/sirius/). CFM-ID will be a web-based tool. Versions of tools to be used will be announced closer to the course date.
Participant prerequisites: Basic knowledge of computer skills. No coding experience is needed. 
Short description of the course: Compound identification is known as the bottleneck in metabolomics. However, there are many approaches one may consider while tackling this challenge (i.e. mass spectral library search, in silico fragmentation tools, or database searching). This short course will provide an overview of the current status of compound ID in metabolomics, participants will learn how to use some current tools for compound ID (i.e. CFM-ID, MS-FINDER, and SIRIUS+CSI:FingerID), and apply those skills to some unknown challenges.
Bits & Bites #05:
Advanced Statistics in Metabolomics with Dr. Christopher Brydges, UC Davis
Date: October 07, 2021
Instructor: Dr. Christopher Brydges, UC Davis
Required software: jamovi (free, open-source statistical software available from https://www.jamovi.org/)
Participant prerequisites: Basic knowledge of statistics (e.g., know what a t-test and a correlation are). No coding experience is needed, and there is no coding taught in this session.
Short description of the course: Investigating associations between metabolites and phenotypes is becoming increasingly important in the field.  This short course will teach researchers the basics of conducting and interpreting correlation and linear regression, before covering more advanced topics such as partial correlation, multiple regression, and mediation.
Bits & Bites # 06: 
Think big: from study design to metabolomics data interpretation with Dr. Oliver Fiehn, UC Davis
Date: December 02, 2021
Instructor: Dr. Oliver Fiehn, UC Davis
Required software: none, but we will visit several databases and utilize online resources.
Participant prerequisites: Basic knowledge of biology, metabolism, statistics, chemistry.
Short description of the course: In this short course, we will discuss pitfalls in study designs that may severely hamper metabolomic studies, shortly reviewing power analyses, bias in studies, biological and chemical controls. We will very briefly review the types of metabolomic assays that give investigators data and problems associated with the choice of assays. Most of the time will be allotted to data interpretation, i.e. what to do once you have received metabolomics data, once data have been curated and once statistics have been completed: how can you then further interpret the data, how to generate new hypotheses, how to link biological data and other -omics data, and how to utilize databases that are available for free online.