Chapter 5 Data and Instrumentation

5.1 Instrumentation

We work with highly sensitive, but also very expensive instrumentation. Below find guidelines for the use of instrumentation in the lab.

Instrumentation and equipment need to be maintained so that every member of the lab has access to complete their research projects. When you have finished using an instrument, you should leave it in a condition so that the next person who needs to use it does not need to fix/clean it. This essential to keeping the lab functioning efficiently.

5.1.1 Mass spectrometers

By far the best part of our armamentarium to understand whole proteome dynamics, the mass spectrometers (MSs) are fantastic resources within the lab. Over the course of your work in the lab there will be opportunities to learn more about using, running and fixing the the MSs. I highly encourage you to learn as much as you can as well through the literature to understand what the instruments are capable of and not capable of.

If you run into issues in the operation of the instruments (including but not limited to hardware malfunctions or failures and software faults or errors), alert me immediately! These problems cannot be ignored and are more likely than not to cause considerable harm to the instrument if left unattended. Unaddressed issues are also likely to result in expensive instrument repairs (tens of thousands of dollars).

If you notice significant degradation of instrument performance during or after your runs, alert both Devin and the next user so that samples are not lost!

5.1.2 HPLCs

Separation or enrichment of peptides, proteins, and small molecules using high performance liquid chromatography (HPLC) is an integral part of our workflows. Normal HPLCs are often quite robust, while nano-HPLCs can be prone to failure due to their high operational pressures. As with the mass spectrometers any issues with the hardware or software need to be addressed immediately.

5.2 Data, Data, Data

5.2.1 Scripts and Code

All scripts and code used for lab projects (including programs, websites, tools, data analysis work) should be deposited or version controlled in lab storage/servers. This includes depositing repositories in the lab GitHub.

5.2.2 Raw Data

Raw data will be backed up in at least two places to ensure that should it need to be accessed it can be. This is especially important for published data as someone may ask for these files years later. Unpublished data should be similarly managed to ensure that we do not need to revisit the same basic work again. If you are unsure or where to store your data contact Devin!

5.2.3 Data sharing

Data for publication will be shared through PRIDE/ProteomeXchange or a similar repository. Sharing these data with the general community is essential for future development of MS methods.

5.2.4 Lab Storage

Data can be stored in the following places:

  • Lab server
    • Store: Raw data, search results, basic analyses.
  • Lab partition of departmental storage
    • Store: Raw data, data analysis, programs, general utilities, presentations, lab meeting information, protocols, collaboration data.
  • Lab GitHub
    • Store: All code/scripts. Yup, all of it.
  • Google Drive (in shared folders)
    • Store: Personal project information, presentations, figures, pre-print manuscripts, proofs, collaboration data.