Multiple sequence alignment

Last updated on 2025-06-24 | Edit this page

Overview

Questions

  • How do you align multiple sequences?
  • Why is it important to properly align sequences?

Objectives

  • Use mafft to align multiple sequences.
  • Test different algorithms.

Introduction

In this episode, we are exploring multiple sequence alignment (MSA). In the first task, you are going to use mafft to align homologs of RpoB, the β subunit of the bacterial RNA polymerase. It is a long, multi-domain protein, suitable for showing issues related to MSA.

In the second task, you will trim that alignment to remove poorly aligned regions.

But first, go to your own folder and create a phylogenetics subfolder. You will use the alignments for other tutorials as well.

Task 1: Use different flavors of MAFFT and compare the results

Start by finding the data, which is a fasta file containing 19 sequences of RpoB, gathered by aligning RpoB from E. coli to a very small database present at NCBI, the landmark database. These sequences are a subset of the sequences gathered in the previous exercise on BLAST.

The file is available in /proj/g2020004/nobackup/3MK013/data/.

Challenge 1.1: prepare the terrain

The course base folder is at /proj/g2020004/nobackup/3MK013. Go to your own folder, create a phylogenetics subfolder, and move into it. Also, start the interactive session, for 4 hours. The session name is uppmax2025-3-4 and the cluster is snowy.

Remember those commands?

BASH 

ssh -Y
cd
mkdir
ls
interactive

BASH 

ssh -Y <username>@rackham.uppmax.uu.se

Remember to replace <username> by your name.

BASH 

interactive -A <session> -M <cluster> -t <hh::mm::ss>
cd <basefolder>/<username>
mkdir <folder>
cd <folder>

Challenge 1.2: copy the file

Copy the file to your newly created phylogenetics folder. Use a relative path.

BASH 

cp
pwd
ls ../..

Use the ../../ to see what’s two folders up, and then data/rpoB/rpoB.fasta

Challenge 1.1: prepare the terrain (continued)

Renaming sequences

Look at the accession ids of the fasta sequences: they are not very informative.

BASH 

grep '>' rpoB.fasta | head -n 5

OUTPUT 

>WP_000263098.1 MULTISPECIES: DNA-directed RNA polymerase subunit beta [Enterobacteriaceae]
>WP_011070610.1 DNA-directed RNA polymerase subunit beta [Shewanella oneidensis]
>WP_003114335.1 DNA-directed RNA polymerase subunit beta [Pseudomonas aeruginosa]
>WP_002228870.1 DNA-directed RNA polymerase subunit beta [Neisseria meningitidis]
>WP_003436174.1 MULTISPECIES: DNA-directed RNA polymerase subunit beta [Eubacteriales]

Keep the taxonomic name following the accession id, separated by _, using a bit of sed magic. Save the resulting file for later use, and show the headers again.

BASH 

sed "/^>/ s/>\([^ ]*\) \([^\[]*\)\[\(.*\)\]/>\\3_\\1/"  rpoB.fasta | sed "s/ /_/g" > rpoB_renamed.fasta
grep '>' rpoB_renamed.fasta | head -n 5

Nerd alert: dissecting the sed magic

This is optional reading.

The sed command matches (and possibly substitutes) strings (chains of characters). In that case, the goal is to simplify the header by putting the taxonomic group first but keeping it informative enough by adding the accession number. The strategy is to match what is between the > and the first space, then what is between square parentheses, and put them back, separated by an underscore.

The sed command first matches only lines that start with a > (/^>/). It then substitutes (general pattern s/<something>/<something else>/) a text with another one. The first part is to match the accession id, between (escaped) square brackets, which comes after the > at the beginning of the line. This is expressed as >\([^ ]*\): match any number of non-space characters ([^ ]*) and put it in memory (what is between \( and \)). Then, the description is matched by \([^\[]*\), any number of characters that are not an opening bracket [, and put into memory. Finally, the taxonomic description is matched: \[\(.*\)\], that is, any number of characters between square brackets is stored into memory. The whole line is then replaced with a >, the third match into memory, followed by an _ and the content of the first match into memory >\\3_\\1. Then, all the input is passed through sed again, to replace any space with an underscore: s/ /_/g and the output is stored in a different file.

OUTPUT 

>Enterobacteriaceae_WP_000263098.1
>Shewanella_oneidensis_WP_011070610.1
>Pseudomonas_aeruginosa_WP_003114335.1
>Neisseria_meningitidis_WP_002228870.1
>Eubacteriales_WP_003436174.1

It looks better.

Alignment with progressive algorithm

Use mafft with a progressive algorithm to align the sequences.

Challenge 1.3: Run mafft with progressive algorithm

Use the FFT-NS-2 algorithm from mafft to align the renamed sequences. Also, record the time it takes for mafft to complete the task.

Use the module command to load bioinfo-tools and MAFFT. Use time to record the time.

The help obtained through mafft -h is not very informative about algorithms, so check the mafft webpage.

mafft actually has one executable program for each algorithm, all starting with mafft-. A way to display them all is to type that and push the Tab key twice to see all possibilities.

BASH 

module load <general module> <mafft module>
time mafft-<algorithm> <fasta_file> > rpoB.fftns.aln

OUTPUT 

...
[...]
Strategy:
 FFT-NS-2 (Fast but rough)
 Progressive method (guide trees were built 2 times.)

If unsure which option to use, try 'mafft --auto input > output'.
For more information, see 'mafft --help', 'mafft --man' and the mafft page.

The default gap scoring scheme has been changed in version 7.110 (2013 Oct).
It tends to insert more gaps into gap-rich regions than previous versions.
To disable this change, add the --leavegappyregion option.


real	0m1.125s
user	0m0.818s
sys	0m0.181s

The last line is the output of the time command. It took 1.125 seconds to complete this time.

Type mafft and try tab-complete to see all versions of mafft.

Try the command time

Alignment with iterative algorithm

Now use one of the supposedly better iterative algorithm of mafft to align the same sequences. Choose the E-INS-i algorithm which is suited for proteins that have highly conserved motifs interspersed with less conserved ones.

Take a few minutes to read upon the different alignment strategies on the page above.

Challenge 1.4

Use the superior E-INS-i algorithm from mafft to align the renamed sequences. Also, record the time it takes for mafft to complete the task.

BASH 

time mafft-<better algo> <fasta file> > rpoB.einsi.aln

OUTPUT 

[...]
Strategy:
 E-INS-i (Suitable for sequences with long unalignable regions, very slow)
 Iterative refinement method (<16) with LOCAL pairwise alignment with generalized affine gap costs (Altschul 1998)

If unsure which option to use, try 'mafft --auto input > output'.
For more information, see 'mafft --help', 'mafft --man' and the mafft page.

The default gap scoring scheme has been changed in version 7.110 (2013 Oct).
It tends to insert more gaps into gap-rich regions than previous versions.
To disable this change, add the --leavegappyregion option.

Parameters for the E-INS-i option have been changed in version 7.243 (2015 Jun).
To switch to the old parameters, use --oldgenafpair, instead of --genafpair.


real	0m7.367s
user	0m7.022s
sys	0m0.244s

It now took 7.36 seconds to complete this time, i.e. 6 times more than with the progressive algorithm. It doesn’t make a big difference now, but with hundreds of sequences it will make one.

Alignment visualization

You will now inspect the two resulting alignment methods. There are no convenient way to do that from Uppmax, and the easiest solution is to download the alignments on your computer and to use either seaview (you will need to install it on your computer) or an online alignment viewer like AlignmentViewer.

Arrange the two windows on top of each other. Change the fontsize (Props -> Fontsize in Seaview) to 8 to see a larger portion of the alignment.

Challenge

Can you spot differences? Which alignment is longer?

Hint: try to scroll to position 800-900. What do you see there? How are the blocks arranged?

Use scp to copy files from Uppmax to your computer. scp allows wildcards, but you probably need to escape the *.

BASH 

scp <username>@rackham.uppmax.uu.se:/<absolute path to phylogenetics folder>/rpoB.\*.aln <localfolder>/
Alignments shown in seaview

If you have time over, spend it exploring the different options of Seaview/AlignmentViewer.

Task 2: Trim the alignment

Often, some regions of the alignment don’t align properly, either because they contain low complexity segments (hinges in proteins) or evolved through repeated insertions/deletions, which alignment program cannot handle properly. It is thus good practice to remove (trim) these regions, as they are likely to worsen the quality of the subsequent phylogenetic trees. On the other hand, trimming too much of the alignment removes also potentially valuable information. There is thus a balance to be found.

In this part, you will use two different alignment trimmers, TrimAl and ClipKIT, on the results of mafft’s E-INS-i algorithm.

Trimming with TrimAl

First, load the module and look at the list of options available with trimAl.

BASH 

module load trimAl
trimal -h

trimAl offers a lot of different options. You are going to explore two different: gap threshold (-gt) and the heuristic method -automated1, which automatically decides between three automated methods, -gappyout, -strict and -strictplus, based on the properties of the alignment. The gap threshold methods removes columns that contain a fraction of sequences lower than the cut-off.

For comparison purposes, you will be adding an html output.

Challenge 2.1: TrimAl with gap threshold

Use trimAl to remove positions in the alignment that have more than 40% gaps.

The “gap threshold” is actually expressed as fraction of non-gap residues.

BASH 

trimal -in <input aln> -out rpoB.einsi.trimalgt.aln -gt <cut-off> -htmlout <html_output>

Challenge 2.2: TrimAl with automated trimming

Use trimAl with the automated heuristic algorithm.

Add a -automated1 option and rerun as above, choosing a different output file.

Challenge 2.3: Compare the results

Get the files (both alignments and html files) to your own laptop and visualize the results, by opening the html files with your browser and the alignment files with seaview or the viewer you used above.

On your own laptop, go inside the folder where you want to import the files. Use scp. On some OS it is necessary to escape the wildcard *. If the output says something about no matches found, try that.

As before, if you do not have access to a terminal on your windows laptop, use MobaXterm and Session > SFTP to copy files to your computer.

Trimming with ClipKIT

ClipKIT is one of the more recent tools to trim multiple sequence alignments. In a nutshell, it tries to preseve phylogenetically-informative sites, rather than trimming gappy regions. Although it also has multiple options and modes, you will only use the default mode, smart-gap.

Challenge 2.4: Use ClipKIT

To get an idea of the modes and options, load the ClipKIT module and look at the help page:

BASH 

module load ClipKIT
clipkit -h

Then run ClipKIT, explicitly using the smart-gap mode. Compare how much ClipKIT has trimmed the original alignment compared to trimAl.

BASH 

module load ClipKIT
clipkit -h

BASH 

clipkit <input aln> -m <mode> -l -o <output file>

OUTPUT 

---------------------
| Output Statistics |
---------------------
Original length: 2043
Number of sites kept: 1543
Number of sites trimmed: 500
Percentage of alignment trimmed: 24.474%

Execution time: 0.379s

Comparing all the results

Import the data and inspect the three alignments.

Challenge 2.5: Import data and compare results

Copy the alignment file and visualize with seaview or the web-based visualization tool.

Use scp as above.

Then use seaview or another viewer to visualize and compare results.

The three alignments on top of each other look like this. Click on this link to better see the figure.

Alignments shown in seaview

It is of course difficult to draw conclusions based on this figure, but can you spot some trends? What alignment is more likely to generate good results?