Lab 4: Multiple Sequence Alignment (MSA)

Transcription

1 Lab 4: Multiple Sequence Alignment (MSA) The objective of this lab is to become familiar with the features of several multiple alignment and visualization tools, including the data input and output, basic visualization and editing functions, alignment options, and different between nucleotide and amino acid alignments. A large number of alignment tools are available, but we don t have time to go through them all one by one. Thus we are going to focus on two of them: MAFFT and MUSCLE, since multiple studies have proved both to be of the best performance. For visualization purpose, we use the program Jalview, from which you can invoke these two alignment tools. You are allowed to finish the lab by yourself or with a partner. [Exercise 1] Jalview: Basic Functions 1. Start Jalview 2. choose the file 1ped.fasta in the data directory by going to File > Input alignment > from File. Have a look at the data. Are the sequences well-aligned? 3. Try some basic commands (1) To select a taxa, click on the taxon name on the left side (2) To select all sequences at once, use CTRL-A (3) To deselect all, go to Select > Deselect All (4) To move selected sequences to another point in the data set, hightlight the sequences and use the arrow keys on your keyboard to move the sequences up/down/left/right-ward. 4. You can edit the sequences manually by a) Left click and drag to select where you wish to begin editing b) Right click the hightlighted sequence and then choose Select>Edit>Edit Sequence c) Enter the characters you want to insert, or a space to denote a gap 5. You can undo changes by choosing Edit>Undo

2 6. Close the file with/without saving. [Exercise 2] MSA: MAFFT and MUSCLE Comparison [Part 1: MAFFT] 1. Run a progressive alignment in MAFFT by entering the command mafft retree 2 1ped.fasta > mafft_dna.fasta 2. Once the alignment process is completed, open the result in Jalview. The concensus graph shows the percentage of agreement for each column of the alignment. [Part 2: MUSCLE] 1. Run a standard alignment in MUSCLE by entering the command muscle verbose log muscle_dna.log in 1ped.fasta out muscle_dna.fasta Note that verbose and log are not necessary but you can use them to see the default options in MUSCLE. 2. Open the results in Jalview [Part C: Comparison] 1. Compare the alignment resulting from the previous two parts. Are they different? Which one do you prefer, the MAFFT or MUSCLE alignment? What may be wrong with both? (Hint: this file contains protein-coding genes) 2. Build 2 trees, one from each of your alignments. Go to the alignment window (for both MAFFT and MUSCLE alignments), then click Calculate > Calculate Tree > Neighbor Joining Using %Identity (Note: these trees are great for helping to evaluate your alignments, but this program should never be your tree-building choice) 3. Compare the trees from both alignments. Do the topologies and/or branch lengths differ? [Exercise 3] MAFFT for Nucleotide and Protein Sequence In this exercise we will convert the nucleotide sequences to their equivalent protein sequences

3 and align these instead. Note that because we are running the alignment programs externally to JalView you cannot convert back to the original DNA sequences post alignment. MAFFT and MUSCLE run through the program SeaView can do this, but will not be covered here. [Part 1: Iterative Strategy usin MAFFT] 1. Find the original 1ped.fasta window. 2. Click Calculate > translate cdna 3. Click File > Save as to save the translated sequence as 1ped_aa.fasta 4. Run an iterative alignment in MAFFT by using the command: mafft --maxiterate ped_aa.fasta > mafft_aa_iter.fasta Load this resulted file into JalView. Notice that two new graphs appear along with the alignment: conservation and quality. Conservation measures the number of changes in the physio-chemical properties of the amino acids in any given column of the alignment. Quality is a score that measures the likelihood of changes in each column, given the substitution matrix used to calculate the alignment. For more detail, click Help > Documentation > Alignment Annotations. 5. Build a tree out using your nucleotide alignment by selecting Calculate > Calculate Tree > Neighbor Joining using BLOSUM62. [Part 2: Automatic Strategy usin MAFFT] 1. Find the original 1ped.fasta window, then click Calculate > translate cdna 2. Click File > Save as and save the file as 1ped_aa.fasta with Fasta as the file format. 3. Employ the MAFFT automatic selection of alignment strategy by using the command: mafft --auto 1ped_aa.fasta > mafft_aa_auto.fasta Can you determine which kind of strategy was employed? (hint: mafft outputs data to the screen and the strategy should be listed near the end). Load this file into JalView. Notice that also two new graphs appear along with the alignment: conservation and quality. 4. Build a tree out using your nucleotide alignment by selecting Calculate > Calculate Tree > Neighbor Joining using BLOSUM62.

4 [Part 3: Comparison] Compare amino acid alignments and trees. Which one do you prefer? Does it make sense to align protein-coding sequences using the protein translation, or should you instead build alignments from nucleotide sequences? [Exercise 4] MUSCLE: Does the gap penalty affect the alignment? Here we will run MUSCLE with different gap penalties to observe how this changes the alignment results. [Part 1] 1. Run MUSCLE with a gap-open penalty of -20: muscle verbose log muscle_gap-20.log in 1ped_aa.fasta out muscle_aa_gap-20.fasta gapopen Load the alignment results into Jalview and build a tree as above. [Part 2] 1. Run MUSCLE with a gap penalty of -1: muscle -verbose -log muscle_gap-1.log -in 1ped_aa.fasta -out muscle_aa_gap-1.fasta -gapopen Load the alignment into JalView and build a tree. [Part 3] 1. Run MUSCLE with the default gap penalty: muscle -verbose -log muscle_defgap.log -in 1ped_aa.fasta -out muscle_aa.fasta 2. Load the alignment into JalView and build a tree. [Part 4] Compare the modified gap penalty alignments to the default one. Which one of the three alignments do you prefer, and why? Which has the most gaps? Can you guess the default gap penalty? Can you find in the log files of MUSCLE the gap penalty used?

5 [Exercise 5] BMGE: Trimming Alignments Trimming of an alignment removes the ambiguous columns later analysis. We shall use BMGE for this task. This program calculates column entropy to determine columns that are within biologically expected variation. Any columns above a given entropy threshold are removed. 1. We shall trim the MUSCLE amino acid alignment (muscle_aa.fasta) using BMGE with a conserved entropy threshold of 0.7 and use the BLOSUM62 matrix to determine substitution weights: java -jar BMGE.jar -i muscle_aa.fasta -ofaa muscle_trimmed.fasta -h.7 -m BLOSUM62 -t AA How many columns were removed by BMGE? 2. Load the resulting alignment into JalView and build a tree. Compare to the untrimmed MUSCLE alignment. Has the tree changed? Have the branch lengths changed? Which alignment do you prefer? [Exercise 6] Loading sequences from a public database Jalview can search the EMBL, PDB, PFAM, and Uniprot databases and load sequences so that you may align and analyze them. Try searching through one of these databases and finding sequences you are interested in working with. Make a note of the accession numbers and enter them in step 4, or use the numbers provided below. 1. Close all alignments you have open in Jalview. 2. Click on File > Fetch Sequence(s). 3. Click EMBL in the drop-down menu (or a different database). 4. Enter X53828; X53829; X53930; X5831 (or the numbers of the sequences you found yourself) in the box and then submit 5. Save these sequences in a variety of different formats to analyze as you wish. [Exercise 7] Saving alignments as graphics You may need an image of your alignment for publication. Jalview will allow you to save one in

6 HTML, EPS or PNG format. Open any of the alignments you have worked with so far. 1. To wrap the alignment on the page, click on Format > Wrap. 2. Click on File > Export > HTML and enter a name for your new image.