Proteomics Short Course 2022 - Full Workshop

This introductory proteome analysis workshop is designed to expose participants to fundamental technology platforms and current methodologies in the field of mass spectrometry-based proteomics. The week will include a combination of equal parts hands-on laboratory work, lectures and interactive data analysis.

There are two options for attendance:

• Lecture, lab, and data analysis -- limited to 30 participants
• Lecture and data analysis only -- limited to 10 participants.  (Please be aware that there will be several hours of free time during Monday and Tuesday, due to lab activities.  See link below for last year's schedule.) 
  • Click here to register for lecture and analysis only

 

Survey Topics covered include:

• Fundamentals of mass spectrometry and proteomics

• Bottom-up quantitative and non-quantitative nano-electrospray proteomics tandem mass spectrometry (nESI-LC/MSMS)

• Sample preparation and processing

• Nano-scale HPLC & peptide-spectrum matching using open-source software

• Introduction to quantitative proteomics including reporter ion (TMT) and data independent analysis (DIA)

• Data analysis, using programs such as Fragpipe, Encyclopedia/Skyline  and Spectronaut. 

 

Lectures will be presented by the Proteomics Core staff as well as leading regional proteomics experts. Past lecturers included scientists from UC Davis, UCSF, Stanford, Thermo Scientific and the Buck Institute (lectures subject to change). This is an intensive, practical course that is meant to give the participant enough background to allow them to take the course material and build upon and apply it to their own individual projects.

Specific course topics may change due to sample and mass spectrometer availability.

Who should attend? … Prior course participants have included faculty, post-docs, grad students, staff, and industry researchers.

What are the prerequisites? … There are no prerequisites other than familiarity with general biological concepts and an enthusiasm for learning about proteomics. Laboratory experience working with proteins and prior experience with data analysis is usually helpful.

What do I need to bring? … This data analysis portion of the workshop will be held in a computer room on the UC Davis campus, which has desktop computers running Microsoft Windows OS.  You may bring your own laptop to use.  However, some of the proteomic-specific software only runs on Windows.

Can I bring my own samples? … We will provide samples for the wet lab portion of the workshop.  However, if you have your own samples you want to work with, please contact us to discuss their suitability.

Can I bring my own data? … We will be generating the data ourslves duing the first half of the course. There will be time, however, to discuss your own datasets and how you might work with them outside of the workshop.

How much does it cost? … For the full course (lecture/lab/data analysis), the registration fee is $1150 for current UC students, staff, and faculty, $1725 for other academic or non-profit participants and $2,875 for industry participants. For the lecture/data analysis-only option, the registration fee is $690 for current UC students, staff, and faculty, $1035 for other academic or non-profit participants and $1,725 for industry participants.

General registration is “first-come, first-served”, so sign up as soon as possible to ensure your place in the workshop. We accept credit cards, as well as UC Davis recharge accounts, for payment.  Registration fees include light breakfast, lunch, and snacks, but do not include dinner, lodging or parking fees.

 

Where is the workshop? … It will be held in Davis and will run from 8:00am to 5:00pm on the dates indicated. The specific location will be provided at a later date.

Pictures and schedule from previous years course

2019 Pictures

 

Sample Schedule:

https://docs.google.com/spreadsheets/d/1OifVDHugjSJ3rHJihSpldWyeAecPOd5ALTEbQ9F4R2w/edit?usp=sharing

Questions?

If you have any questions, please don’t hesitate to contact us at proteomics@ucdavis.edu.