bio/2021.markdown @ b9043aaa5f77

# Biology Notes

## 2021-04-26

Homework:

* Watch Ken Burns: the Gene.
* Go through <https://www.coursera.org/learn/introduction-genomics>
* Find practice exams from biology courses.
* Find a paper to go over.

Started Introduction to Genomic Tech class on Coursera.

In one of the videos he talked about the word gene.  What does it mean?

* A heritable unit of the genome?
* A sequence of the genome that codes for a protein?
* What about introns?  Are they part of the gene?
* What about the upstream region with enhancers/repressors/promoter?  Aren't
  those just as important as the content itself, since they manage whether the
  gene gets transcribed at all?
* What about parts that code for non-protein-making RNA (TODO what is this
  called again?)?

## 2021-04-27

More Coursera.

* Any given gene might have a bunch of exons that get spliced in many different
  ways.  Is there a way to tell just by the sequence which stretches are exons?
  I.e. is there something like the start/stop codons for exons/introns?

## 2021-04-28

More Coursera.  Learned about some sequencing variants I hadn't known before:

* CHiP-Seq is used to figure out where repressor/enhancer proteins bind to the DNA:
  * Start by "cross-linking" the protein to the DNA (basically "freezing" the proteins to the DNA so they can't detach).
  * Fragment the DNA.
  * Use antibodies (how?) to pull/separate out the fragments that have a protein fused to them.
  * Sequence just those fragments to find all the stretches of DNA that have proteins (presumably repressors/enhancers) bound to them.
* Bisulfite sequencing is used to figure out which C's are methylated:
  * Start with two identical DNA samples (unsure why we need this).
  * In one sample, do some chemical magic that converts all *non*-methylated C's into U's.
  * Sequence both samples.
  * Any remaining C's in the modified sample must have been methylated.

## 2021-04-29

Coursera CS section.  Mostly review for me, but a couple of interesting tidbits:

* I liked how he emphasized how you have to *understand* what the software you
  use is doing, and not just treat it as a black box.
* The example of RNA editing and misalignments was interesting.
* Need to look into "NCBI Genome Workbench" program.  How does it compare to IGV?
* The example RNAseq pipeline he used was bowtie → tophat → cufflinks → cuffdiff
  which was what we used in my class at RIT.

## 2021-05-01

Coursera statistics section:

* I like the term "ridiculogram".  I see these a lot.
* Batch effects are very common and very problematic.

## 2021-05-03

Lesson.

Gene definition: a region of the genome with some function.  Alternatives might
be called "genomic elements".

Introns/exons *might* use epigenetic information to determine splice sites.
Search for "regulation of RNA splicing" to find more.

Ligand: a molecule that can interact.

Homework:

* Might want to do DNA Algorithms class on Coursera.
* Do at least one of <https://ocw.mit.edu/courses/biology/7-012-introduction-to-biology-fall-2004/exams/>
* Look into supplementary material to find out if we were used in the journal club paper.

## 2021-05-09

Finally getting to the practice tests and the Coursera course.  This week has
been crazy.

Started with practice test 2.  Some parts were easy, some others I vaguely
recall but am going to need to use the book to refresh myself on.

## 2021-05-16

Trying the scavenger hunt Emily gave me.

There seem to be a LOT of results.  Maybe I should try limiting them by date?
Tried to figure that out, looks like there's a way to do it with an "Entrez
Query".  <https://www.ncbi.nlm.nih.gov/books/NBK3837/> is the handbook that
describes those queries.  I think it should be something like:

    2000/1/1:2020/5/5[Publication Date]

Not sure if there's a standard place to find the location, or if it's always
just randomly mixed into the text and you have to figure it out.

## 2021-05-19

Lesson.

Chatted about the COVID scavenger.

Question from last time: how does DNA replication terminate?  It goes until the
telomeres every time — it never partially replicates.

## 2021-05-23

Caught up on the coding side of the Coursera course.  Redid the gnuplot
interface in my Lisp utilities to match what the gnuplot book recommends, and
it's working pretty well.

## 2021-05-24

Lesson.

CD34+ cells: CD34 is a protein present only in young stem cells, used very often
as a marker to identify them.

Flow cytometry:

1. Pick 1 or more markers (proteins on the surface of a cell).
2. Buy antibodies that will bind to those markers, which have a fluorescent tag
   tag attached to them.
3. Draw blood.
4. Lyse the red blood cells with a special reagent.
5. Mix in the antibodies.  Antibodies bind to the cells of interest.
6. Use a microfludics thing to pipe cells one at a time through a channel.
7. Use lasers to excite the fluorescent tags.
8. Measure cell counts (and abundance of the marker on the individual cells!).

## 2021-06-01

Flow cytometry presentation.  FCS is a super interesting tool that I didn't
really know about before.  Interesting tidbits:

* About grill/refrigerator sized, $100k to $200k+ (depending on number of
  lasers, more = better).
* You can not only count cells, but also *sort* them on the fly.
* It analyzes basic non-wavelength stuff like the size of the cell (by shadow
  size) and density (by how much light is scattered at various depths in the
  cell).
* It also analyzes wavelength-specific stuff to figure out which
  receptors/antibodies got dyed.