These are some of our workhorse day to day programs in the lab. We largely favor the free / open source versions of many types of software even if there is a similar proprietary program. These programs can be easily downloaded for free from the Internet wherever you are, and will run identically on Mac / Windows / Linux. Their successive versions are also usually completely backwards compatible. All means that if you need to explain to someone else how to do something very specific using one of these programs, or learn how to do it yourself, you'll only have to do it once.

We strongly suggest using these free versions for the best compatibility with everyone else in the lab. That said, they usually work pretty interchangeably with similar proprietary software.

LibreOffice - Libreoffice is a great general purpose office suite very similar to Microsoft Office. You will most often use the word processor (LibreOffice Write), the spreadsheet software (LibreOffice Calc), and the slide presentation software (LibreOffice Impress).

GiMP - GiMP is an excellent image editing program very similar to Adobe Photoshop. You might use it in lab for adding captions and labels to images such as gel pics and spotting assays, among many other things.

Inkscape - Inkscape is a vector graphics editing tool that is very useful for drawing figures for posters, talks, and publications. It is very similar to Adobe Illustrator.

Zotero - Zotero is a bibliographic management tool that helps you organize your references and add them to manuscripts easily. It serves the same purpose as e.g. EndNote.

UGENE - UGENE is at heart a sequence manipulation tool, that we use a lot for DNA sequence analysis, but it can do a lot more types of bioinformatic analysis as well.

R - We use R for a lot of our simple statistical analysis and graph creation, and as you get more proficient with it it can be used for much, much more. If you are beginning to use R from scratch, this online Introduction to R for Data Science course is a nice place to get started, or you can browse other courses here: https://www.edx.org/learn/r-programming.

These are the general tools that all lab members are pretty likely to need. More computationally-focused lab members will use a lot of other software as well (e.g. Python). If you are going down this road and are a beginner, the best place to start is probably hands-on training with a mentor in the lab.