The Tree of Life – II (advanced)

1. Locate an "old" evolutionary tree—that is one that was created without the help of molecular biology. (A pre-1995 text book is one good place to find a non-molecular biology evolutionary tree.) Generate a hypothesis for whether or not you think the tree built by protein sequence comparison will match those relationships outlined in this "old" tree. Why did you generate this hypothesis? Back up your reasoning!
2. Identify a few genes/proteins of interest. You may choose a gene/protein you heard about through a major media source like the news or a magazine. One other good resource is a database called "Online Mendelian Inheritance in Man" (OMIM) which catalogs all known human genetic diseases and which genes may be involved in causing or creating susceptibility to that disease (if that information is known). You can also try to use "PubMed" which is a searchable database of scientific journal articles on as many topics as you can think of, but these articles are usually pretty dense and tough to read—but you can learn a lot from reading the abstracts available for every paper. Remember to choose a few as not all proteins are found in all species, nor is sequence data available for all of the species that have that protein. Also, think about which "type" of protein you are choosing. See variations discussion for more thoughts on choosing a "type" of protein.
3. Go to the NCBI website and search the "protein" category for the name of the protein you have chosen. Notice that sometimes it may be listed under an abbreviation or slightly different spelling, so just keep trying variations until you get it. (If you can not find it using the protein search, you can try searching for the gene's name in the "nucleotide" search category. That listing will often show the protein sequence as well.) Get the protein's amino acid sequence data from NCBI/Genbank. Get help from your advisor making sure you've got the "whole protein" as you will probably get more than one "hit" when you do your search. And, be sure to document the accession number of each protein you select. (See definition of accession number.)

Tips for Searching Genbank

• For making a better query, try asking for both the name of the protein and the organism from which you would like that sequence. (For example, try entering "myoglobin homo sapiens" to look for the human version of the myoglobin protein sequence.)
• When sorting through the results, many entry titles are followed by two Latin words in parentheses such as (Homo sapiens). These words represent the scientific name of the animal from which the sequence came.
  • Some animals are used very often in genetic research and are called "model organisms." You are very likely to run across sequences from these organisms. See below for a table of some of the commonly used model organisms and their scientific names.

The genomes of many other animals are being sequenced (mainly) for sequence comparison studies such as the one you are performing. Here are a few of these types of organisms you might run across. If the name you find is not on this list, just do a web search and see if you can figure out to which animal the name refers!

• These queries are very literal and just look for your search words anywhere in the title or description of the sequence. So if you enter "myoglobin," you may get a hit to a protein described as a "myoglobin binding protein" or "similar to myoglobin," neither of which is what you are looking for. You can get around this by using the "Advanced" search option: Click on the word "Advanced" below the text box where you enter your gene or protein name. In the new window, go to the pull down menu that says "All Fields" and change it to "Title." This way, your search term has to appear in the title of the database entry. You still may get multiple hits, so just keep scrolling through the results until you find the myoglobin protein itself.
• Some sequence entries represent fragments or pieces of a protein that have been important for one experiment or another. For this project, you need the whole sequence, so try to avoid using sequences that have the words partial, chain or subunit in them. If all of the entries seem to be incomplete, you may need to choose a different protein.
  • Note: If you are doing a project that involves searching for DNA/mRNA sequences, some entries will be marked as either "partial cds" or "complete cds," where cds stands for "coding sequence." You should choose "complete cds" if such an entry can be found for your gene—remember this point applies only to DNA/mRNA searches.
• Finally, what is the best way to refer to the "right" sequence when you find it? The first string of letters/numbers in the output of any search on NCBI is called the accession number. This may look something like NP_00555 or AAH14547. This is just a unique identifier for that sequence file in that database. So you could tell anyone else in the world you are using the sequence from accession number NP_00555 and they would be able to access the exact same record. If you want to get back to that file, you can just enter that accession number for your query and the proper file should come up every time.
• One other thing you might run in to is cDNA. What is a cDNA? A cDNA is just the sequence of a DNA string that would be COMPLEMENTARY (thus the "c") to an mRNA of interest. (RNA molecules are inherently unstable, so scientists often make a DNA copy of an RNA to make experiments easier.) So a cDNA sequence represents an mRNA found in a cell.

Tips for Formatting Sequences

• Are you looking at DNA or protein sequence? If the string of letters in your sequence is made up of a, t, g and c, then you are looking at a DNA or RNA sequence. If the string looks like "random" letters from the alphabet (like MQPLLG), then you are looking at a protein sequence. Each amino acid has been assigned a single letter code to represent it. (You can find this code online or in a textbook.)
• When copying your sequence of interest to save it or enter it into another program, there are a couple of ways you can do it. Whichever way you choose, it is probably easiest to "store" and manipulate the sequence in a word processing program. You can then save it as a "text" file so that it doesn't have any weird word processing formatting. (You may also be able to upload a text file into a DNA analysis program.)
• The first way to save the sequence is just using the string of sequence alone. From the accession number file, you can just copy and paste the letters onto a word processing document. Then just take out any of the numbers that were copied over. You do not need to worry about spaces and returns because the programs you will be using are trained to ignore them and only read the "letters" in the file. Then, you can just copy and paste this saved text into the program you are using.
• Most programs that analyze DNA/mRNA/protein sequences require them to be in a certain format. Thus, when you are saving the sequences that you plan to use in your later analysis, you should save them in the conventional format. The most common format is called "FASTA" format. FASTA files are structured with the ">" symbol followed by the name of the sequence (like myoglobin), then a return and the text of the sequence. Most of the programs you use will recognize a FASTA file and know that what comes after the > and before the first return is the "name" of the sequence. Here is an example of a FASTA format sequence:

  >Myoglobin protein
  mglsdgewql vlnvwgkvea dipghgqevl irlfkghpet lekfdkfkhl
  ksedemkase dlkkhgatvl talggilkkk ghheaeikpl aqshatkhki
  pvkylefise ciiqvlqskh pgdfgadaqg amnkalelfr kdmasnykel
  gfqg
  	

• The simplest way to get your sequence into FASTA format is to use the FASTA format display option in Genbank. Once you are on the page containing the DNA or protein sequence that you want, you can get your sequence into this format by clicking on the "FASTA" link below the "NCBI Reference Sequence" information. You can then either cut and paste the formatted sequence into a text file, or use the pull down menu next to the "Send To" button and change it to "File." If you then click on the button, it will download the sequence on your screen to the file name you specify.
• Note that you don't need to worry about spaces and returns in most sequence analysis programs because they ignore them and only read the "letters" in the file.

4. Use the BLAST program (at NCBI or elsewhere) to search the non-redundant (nr) database of proteins (Protein BLAST or BLAST-P) for homologs from other species. All BLAST does is search all available databases for other proteins that are the most similar to the one you entered. It then gives you an output with "matching" sequences ranked by which is the most similar to the one you entered. Be sure to check the alignment on the BLAST readout to make sure the two sequence do not just share a small stretch of similarity, but look to actually be from homologous proteins. You should try to find your gene in at least 3 or 4 other organisms in order to construct a meaningful sequence alignment. (Note: Some programs "max out" if you try to put in too many sequences at one time, so you may have to do them in batches.)
5. Use a multiple sequence alignment program (like CLUSTAL W from Expasy or other sources) to do a "multiple sequence alignment" to compare sequences from multiple species. Your input file should be a list of FASTA formatted sequences representing the same gene in different organisms. All this kind of program does is line up the proteins (using their most similar regions) so that you can tally up the differences in the sequences.
6. Use the alignment program (or any other tree-building program) to generate a distance matrix or a tree. A distance matrix just assigns a "distance value" (which is a reflection of how related the two sequences are based on "counting" differences in the alignment) to each pair of sequences you entered. That is, those animals with a higher "distance value" are less closely related than those with a lower "distance value." Use the distance matrix you generated to build a tree yourself. Or you can enter the sequences into an alignment program that will build the tree for you.
   • Note: This "counting" is not purely counting differences, but weights different changes with different values based on how rare or common that kind of change is, etc. So it is a little bit more sophisticated than just counting differences. That is why it is great to have a computer program to help you out!
7. How does this data support or disprove your hypothesis? Possible explanations?