Major Methods for Estimating Phylogenetic Trees

Today, I worked through Chapter 5 in my book, which gives a good overview of the major methods for estimating phylogenetic trees.

There are two primary approaches to tree estimation: algorithmic and tree-searching. The algorithmic approach uses an algorithm to estimate a tree from the data. The tree-searching method estimates many trees, then uses some criterion to decide which is the best tree.

The algorithmic approach has two advantages. It is fast, and it yields only a single tree from any given data set. The Neighbor Joining method is the most common algorithmic method, and I'll be learning how to use it next week.

All the other currently used approaches are tree-searching methods. They are generally slower, and some will produce several equally good trees. Methods such as Parsimony, Maximum Likelihood, and Bayesian analysis search for the tree that best meets the criteria by evaluating individual trees. Maximum Likelihood looks for the tree that, under some model of evolution, maximizes the likelihood of observing the data. Bayesian Inference is a recent variant of Maximum Likelihood. Instead of seeking the tree that maximizes the likelihood of observing the data, it seeks those trees with the greatest likelihoods given the data, and produces a set of trees with roughly equal likelihoods. Parsimony is the simplest method, and it looks for the tree or trees with the minimum number of changes.

 It's almost impossible to evaluate each possible tree because of the sheer number of possibilities (even with only 10 taxa, there are more than 34 million rooted trees). Therefore, something called a "branch-addition algorithm" is used to find each of the possible trees, which I won't go into detail here.

It's important to realize that since we don't know what happened in the past, we can never be entirely sure how accurate the tree is. In addition, there is no "right" tree-- we can only hope to find the tree that most closely approximates what happened in the past.

Aligning Sequences

Once you acquire sequences (which I covered in my previous blog post), you must then align the sequences before constructing the phylogenetic tree. In MEGA, Two alignment methods are provided: ClustalW and MUSCLE. Either can be used, but in general MUSCLE is preferable. In the Alignment menu in MEGA, I chose the MUSCLE button, and then I clicked on the "Align Codons" button. There were two choices: Align DNA and Align Codons, and since my sequence was a DNA coding sequence I chose Align Codons, which ensure that the sequences are aligned by codons, a much more realistic approach than direct alignment of the DNA sequences because that avoids introducing gaps into positions that would result in frame shifts in the real sequences. Once I started the alignment process in the program, it took about two minutes. Next, I exported my file in the correct format so I would be able to use it to estimate my phylogenetic tree later. 

Here is my final aligned file:

Once I completed the alignment, I noticed that gaps were introduced into the sequences. Those gaps represent historical insertions or deletions, and their purpose is to bring homologous sites into alignment in the same column. Justas a phylogenetic tree is an “estimate” of relationships among sequences, an alignment is just an estimate of the positions of historical insertions and deletions. 

Acquiring Sequences

My mentor was in San Diego this week, so I worked through Chapter 4: Acquiring Sequences of the book Phylogenetic Trees Made Easy on my own.

The most time-consuming part of estimating a phylogenetic tree is acquiring the sequences that will be the tips of the tree. Today, I learned how to find related sequences, and what criteria I should consider as I decide which sequences to include. The BLAST search in the MEGA program is the primary tool for identifying sequences that are homologous to your sequence of interest, the query sequence, and I used it today to acquire sequences related to the E. Coli bacteria.

Here is a screenshot of all the DNA sequences I acquired in BLAST:

It took me some time to figure out how to correctly acquire sequences. When you enter the query sequence, BLAST pulls up all the related DNA sequences, but it's up to you to determine which results will actually be useful. Then, you have to click on each sequence of interest and go through a process in which you add the sequence to your list in the correct format. Since the book was written for an earlier version of MEGA than the one I have, it took a lot of trial and error to correctly acquire all the sequences.

Phylogenetic Trees

This week, I started learning phylogenetic analysis, which biologists use to understand evolutionary and molecular relationships. Dr. Miller gave me a book called "Phylogenetic Trees Made Easy," and I completed the first tutorial in it, which outlined the steps for creating a simple phylogenetic tree.

To make the tree, I first had to download a program called MEGA 5, which analyzes and aligns sequences from related organisms. Specifically, I looked at a sequence from the bacterium Thermotoga petrophila. I used a "search engine" called BLAST to help me find sequences that produced similar alignments to the original sequence. I chose the 6 most closely related sequences, and then used the program to produce a DNA alignment:

The final step, which was to construct a phylogenetic tree based on the alignment, was actually pretty simple. All I did was click on a button labelled "Neighbor Joining Tree," and the program created a phylogenetic tree for me! Getting the alignments was the hard part, because it's so easy to make simple mistakes, which can mess up the whole alignment. It took Dr. Miller and me several tries to get it right. 

Phylogenetic tree!

Internship Revealed!

My STEM internship for 2014-2015 is (drumroll, please)... evolutionary biology with Dr. Mark Miller! Dr. Miller is currently working on a research project called Next Generation Tools for Biology at the San Diego Supercomputer Center (he commutes to Albany each week). He develops software tools and infrastructure for biomedical applications. This internship is perfect for me, because I can build on the programming skills I learned last year but also explore the field of evolutionary biology.

I don't know yet what research problem we'll be working on-- right now, I'll just be learning the basics of creating phylogenetic trees with him. We'll start on a research project next semester. Last year, Peggy and Dr. Miller worked on a project with the San Diego Zoo to trace the origins of a disease affecting a species of birds there. It was really cool and I look forward to what I'll be doing!

New year, new STEM internship!

I'm excited to continue doing a STEM internship this year! Unfortunately, I won't get to continue working on my particle physics research with Dr. Bellis, because he isn't a mentor this year, but I'm looking forward to a new experience.

I'm interested in a STEM internship because I love questions that don't have answers. Science research is all about exploring the unknown, and that's something that has always fascinated me. I had a wonderfully challenging experience last year, and I definitely learned a lot about having to work through and find solutions to unexpected obstacles that come up. This year, I want to both build on these skills and develop new ones. I'm going to work towards improving my experimental technique, and I want to get more practice doing basic lab work. I also want to improve my reasoning skills, which is important not only in scientific research but pretty much any field a person can go into.

It's going to be a great year! I can't wait to find out which mentor I get placed with.

Looking back

I am so grateful that I got the chance to participate in the STEM program this year and work with Dr. Bellis. Before this year, I had never even taken a physics class before. It was definitely challenging to start my internship without a lot of background knowledge, especially when I had to do independent work at school. I’d also always thought of scientific research as being in the lab, but my work with Dr. Bellis taught me otherwise. I spent a lot of time on my computer, reading published papers and writing code. It was frustrating at times because I couldn’t really see where all of my work was taking me, but by the end all the pieces started to come together and I really understood what I was studying. I also learned to not let minor obstacles deter me from reaching my research goal. Overall, this year has been a very rewarding experience. I’m looking forward to continuing to do scientific research.

From my poster:

“Particle physicists study the smallest possible things in the universe, and ask the biggest questions. There might be no immediate practical applications to discovering the hybrid meson, but it will take us one step closer to understanding what we’re made of. My internship has given me both practical research skills and a deeper understanding of the unknown. I am no longer scared of the biggest questions. Instead, they push me to keep learning.”