An often-asked question when meeting new scientists
is, wet lab or dry?
This is the wet lab.
I am a wet lab person.
I work down here with wet things,
with liquids, with pipettes.
I do the things with the liquids.
Dry lab refers to computational biology,
doing a lot of analysis, and coding,
and writing tools, and all kinds of cool stuff.
When I came into grad school, I was 100% wet lab
and had zero interest in looking at dry lab stuff.
But I had to.
As part of my program, I had to learn how to program
and learn how to code.
And honestly, it made sense.
I had to learn how to analyze my own data that I
was generating in the wet lab.
And I've really come to admire bioinformatics and dry lab
work.
I think that it can be really beautiful, clean, wonderful,
biology, and science.
And I think it's super cool.
But I am still a wet lab biologist at heart.
Here in the lab is where I feel comfortable.
This is where I do my work.
But I think it's becoming a lot harder
to just be a wet lab biologist.
Now, this is definitely skewed by my field.
I'm in genetics.
I think this is definitely a trend in my field.
But what I see at the moment is that if I
want to be able to analyze my own data,
I really need to build those computational skills.
From what I see around me in my field and in my department,
I think you can definitely be just a dry lab scientist
and never come down here and touch a pipette.
But I think it's becoming harder and harder
to be just a wetlab biologist.
Especially for me, I do a lot of sequencing.
And if I want to be able to analyze my sequencing data,
I have to be able to understand and run
the code that does that myself.
I don't want to just hand it off to somebody else
and say, I did an experiment, analyze it, and find out
what it says.
I want to and need to be able to do that myself.
So I think that being just a wet lab biologist--
I might be one of the last generations of scientists
or, at least, geneticists, who can kind of claim
to be just a wet lab scientist, because that's becoming
less and less true for me.
And I think that for people coming into the field,
it's becoming far less true.
You really need to be able to do both, or at least,
dabble in both as a wet lab person.
So what this means is that I'm learning to code.
I had to take a crazily scary intense coding
course my first year here that was dealing with genetics data.
And for the people who already knew how to analyze this stuff,
I think it was super easy.
But for me, it was taking me 30 to 40 hours a week just
to do the problem sets, because I'd never
done any kind of coding before.
And before the class, I'd worked my way through the code academy
Python thing.
And then I was learning Python in the course.
And now, I really like it.
I don't spend very much time on it.
Really, I am focused down here with the experiments
and the pipettes.
But when I do get the chance to work in coding
and in doing this sort of analysis,
I think it's really interesting.
And it's very satisfying when you
want to build or write a script or a tool that does something.
Once you have finally get it to the point where
it does that something, that is such a satisfying feeling.
And I like that feedback from coding.
But I think you can probably tell
that I am like not a dry lab person by the fact
that I keep calling it coding.
I feel like that's the wrong term.
I feel like I should be doing your software
development or tool writing or scripting or something.
But I don't know what I'm doing.
I'm just writing things in Python.
And I get a lot of emails and comments and messages
from people who are programmers and developers, who
are watching my videos.
And they want to help out.
And so they're offering to build me tools
that I need if they don't exist and offering to help.
And while I love that enthusiasm and excitement, that's my job.
If there is a tool that I need that doesn't exist,
it's my job to write it.
That is now part of my title as scientist
is to be creating the tools that I need.
And so I'm doing that right now in a project.
I have a kind of weird data set.
It's a little unusual.
And I need to do a type of analysis to it.
And I looked around.
And there is not a tool that does the analysis
I want on the data that I have.
So I'm writing it.
Am I great at coding and scripting this tool?
No, it is slow.
And it is certainly not the most efficient code
I could be writing.
And currently, it only does like 90% of the things
that I want it to do.
But I'm doing it.
And it's starting to work.
And I'm making progress.
And I'm learning.
And that's what I'm here to do in grad school is
to learn new things, even if there
are things that when I came into grad school,
I threw up my hands and said, nope, nope,
do you not want to touch that.
I am a slow coder who only knows Python and asks
a ridiculous amount of questions.
But I'm doing it.
And I'm getting it done.
And I'm kind of a little bit proud of that--
go forth and do wet and dry science.
Cerebellum-- this little gray thing here is your hippocampus.
Oh, OK.
And then this is like the brain stem, so like pons medulla,
corpus callosum.