Learn at your own pace with recordings from our 9-part Bits & Bites 2025 series, now available for purchase. Choose the topics that interest you and customize your learning experience.
For more information:
Email: Jeannette Martins [email protected]
Phone: +1-530-754-5357
Bits & Bites 2025
Bits & Bites # 01: Using Advanced Lipidomics for Better Understanding of Biology, Dr. Tong Shen, UC Davis
- Level: Introductory
- Short description of the course: This comprehensive course dives into systems-level lipid analysis, equipping participants with the knowledge and skills needed for advanced lipidomics studies. You will learn the fundamentals of lipid biochemistry and metabolism and cutting-edge techniques for analyzing lipids using mass spectrometry. We will guide you through key considerations for lipidomics research, including how to tailor experimental approaches to address specific biological questions. The course covers every stage of the experimental pipeline, from experimental design and sample extraction to detection methodologies, quality control, lipid structural elucidation, and data analysis. You will also gain insights into state-of-the-art technologies and emerging perspectives in the field. Designed for both practicality and depth, this course ensures you can achieve reliability and accuracy in your lipidomics research while advancing your scientific expertise.
Bits & Bites # 02: From Sample to Signal: Learn Best Practices in LC-MS for Metabolomics, Dr. Uri Keshet, UC Davis
- Level: Introductory
- Short description of the course: This course offers a comprehensive introduction to liquid chromatography-mass spectrometry (LC-MS) for metabolomics, covering both targeted and untargeted approaches. Participants will gain insights into LC-MS instrumentation, troubleshooting, and best practices for sample preparation, method development, and data analysis. Key topics include principles of sample preparation, quality management, method validation, selecting columns and solvents, optimizing gradients and flows, and employing DDA acquisition mode. Designed for researchers new to LC-MS or seeking to enhance their expertise, this course provides practical knowledge to improve the use of this powerful technique in metabolomics research.
Bits & Bites # 03: Skyline: Getting Started with Targeted Metabolomics Data Processing,
Dr. Christopher Ashwood, Protea Glycosciences
- Required Software: Skyline (https://skyline.ms/skyline64.url)
- Participant prerequisites: Basic understanding of LC-MS and method design (targeted and untargeted methods)
- Level: Intermediate
- Short description of the course: Skyline is a freely available, vendor-neutral software that facilitates targeted qualitative and quantitative analysis of both protein and small-molecule mass spectrometry data. Using hands-on tutorials, we will provide best practices on analyzing metabolomics LC-MS data with Skyline including iterative and sensitive targeted method development, small molecule target confirmation at both the MS1 and MS/MS levels, and analyte quantification using internal calibrants and external calibration curves.
Bits & Bites # 04: Quality Control and Quality Assurance (mQACC) from sample preparation to reporting, Dr. Oliver Fiehn, UC Davis
- Level: Introductory
- Short description of the course: For several years, an independent consortium of more than 100 metabolomic scientists has discussed and published best practice documents in metabolomics, from sample preparation and data acquisition (LC-MS, GC-MS, and NMR) to data processing and reporting, including for the use of reference materials and quality control samples. In this course, we will review the major conclusions of all mQACC reports and discuss strategies to utilize this information for data and manuscript submissions that require detailed descriptions of methods used in metabolomics studies. We will discuss if best-practice documents are clear enough for implementing strategies into standardized lab workflows, or where the community reports do not clearly distinguish quality differences between different strategies.
Bits & Bites # 05: Bayesian Statistics for Metabolomics, Dr. Christopher Brydges
- Required Software: JASP (version will be announced before the course)
- 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
- Level: Introductory
- Short description: 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 analysis in JASP, and learn how to report the results in the style of a journal article.
Bits & Bites # 06: Using MetaboAnalyst for Metabolomics Statistics and Data Visualizations, Dr. Jeff Xia, McGill University
- Participant prerequisites: Basic knowledge of computer skills. No programming experience is necessary.
- Level: Introductory
- Short description of the course: In this short course, we will focus on mastering MetaboAnalyst 5.0, the robust platform for statistical analysis in metabolomics. Learn data input, preprocessing, and key analyses like PCA, PLS-DA, and OPLS-DA. Explore functional analysis techniques, and biomarker identification, and tackle complex metadata for robust statistical insights in metabolomics data.
Bits & Bites # 07: GNPS2 for Metabolomics Analysis, Annotation Propagation, and Visualization,
Dr. Mingxun Wang, UC Riverside
- Participant prerequisites: GNPS Account, no programming experience necessary
- Level: Intermediate
- Short description of the course: In this short course, we will get familiar with GNPS (Global Natural Products Social Molecular Networking) a web-based mass spectrometry ecosystem, and learn how to look at your data using classical molecular networking. Explore GNPS Tools for MassIVE data navigation, including classical molecular networking, data selection, mastering molecular network workflows, interactive LC/MS visualization, and compound identification. Uncover insights into intricate mass spectrometry data efficiently.
Bits & Bites # 08: What is your metabolomics data telling you? Data interpretation for biomedical scientists and clinicians, Dr. Oliver Fiehn, UC Davis
- Level: Introductory
- Short description of the course: What do you do after you have completed statistics? How do you map and visualize your data? This short course will focus on helping you interpret data, generate hypotheses, integrate biological and metabolomics data, and make the most of freely available online databases. We will provide practical tips on curation, data mapping, and visualization.
Bits & Bites # 09: Compound ID in nontargeted analysis by integrating MS, MS/MS, retention time, and biological likelihood, Dr. Oliver Fiehn, UC Davis
- Level: Introductory
- Short description of the course: Metabolomics data can only be interpreted if metabolites are correctly identified. Confidence in metabolomic data reports requires a transparent strategy for combining different types of information. Accurate mass and MS/MS is often not sufficient, because many types of isomeric compounds exist. UC Davis offers Mass.Wiki to match user data against a standardized repository that includes retention time predictions and matches against biological specimens in UC Davis data, GNPS, MetabolomicsWorkbench, and MetaboLight repositories. After introductory remarks, participants will work in compound annotations, including for currently unidentified compounds that we explore in 'Fuzzy Searches'.