Expected learning outcomes

The objective of this activity is to become familiar with the features of multiple alignment and alignment visualization programs. This includes data input and output, basic visualization and editing functions, alignment options, and differences between nucleotide and amino acid alignments.

Most importantly, you should be able to run analyses on your remote Jetstream compute node and move files to and from the remote computer and your local machine. Transferring data between your computer and a remote machine is necessary in all following labs.

About this lab

Software used in this workshop assumes that input data is aligned. If you want to use your own sequencing data during the workshop, you will need to go through the process of multiple sequence alignment (MSA). We focus here on gene sequences, which can be from targeted Sanger data or assembled genomic data. Many contemporary studies use reference-based short read alignment, but at least some of the underlying theory is the same. We do not cover short read alignment, but reference-based alignment resources are provided at the end of the tutorial.

Background

If you feel you need more background in MSA before starting the tutorial, please consult the pages linked below:

Getting started

While a large number of alignment programs have been developed, we are going to focus on MAFFT and ‎MUSCLE. Alternatives may be more accurate on small data sets, but these programs perform well even on fairly large data sets and are thus part of many phylogenomic pipelines (e.g. Yang and Smith 2014). The MAFFT algorithms and options can be viewed here. The MUSCLE user guide is found at here.

Both MAFFT and MUSCLE are available on your Jetsream node. You will transfer sequences to the Jetsream node using scp or cyberduck, run the programs as needed remotely, and then transfer back in the same way for visualization on your own laptop. Remote nodes are great for analyses, but if this is your first time using one, you will need to get accustomed to using computers without a graphical user interface (GUI).

For visualization we will use the programs SeaView or MEGA (whichever works for you). Both programs are relatively simple alignment viewers, but also allows you to estimate simple distance-based trees and invoke alignment programs. You can access help files at any time within the program by clicking on ‘Help’ in the top menu. Another good alternative is AliView. AliView is great for looking at properties of very large alignments. Tree building options in alignment viewers are not publication quality, but they can be useful if checking for contamination or homology errors.

If you have not downloaded SeaView or MEGA, do so now using the following links:

SeaView:

MEGA:

This activity is structured to be done either by yourself or with a partner. Working with a partner is a great idea!

You will run the alignment software on your remote Jetstream node. Login to your node as explained in the computer lab intro.

Create a new, empty directory named MSAlab and use the following command to copy the tutorial files there:

cp moledata/MSAlab/* MSAlab

Exercise 1: basic functions in SeaView and MEGA

Copy the 1ped.fasta file from your remote machine to your laptop. If you cannot remember how to use the scp command, take a look at the computer lab intro. The 1ped.fasta file contains alcohol dehydrogenase nucleotide sequences from a variety of organisms; modified from BAliBASE.

Start SeaView or MEGA. Load the data set 1ped.fasta by going to File > Open (in MEGA, select Align in the popup window after opening the file). Have a look at the data. Does it look like it’s already been aligned? Try some of the basic commands. To select a taxon, click on any taxon name on the left side. You can copy the sequence using the Copy selected sequences command in the edit menu. These can then be pasted into a text editor (e.g. Notepad++ on Windows or BBEdit on Mac) if needed. To select all sequences at once, on Macs you can type Command-A. On Windows or Linux, you can use Control-A. Explore the edit menu and observe how sequences can be reversed, complemented, etc. Do not close the window, but move it aside for now.

Exercise 2: comparison of two different alignment programs (MAFFT and MUSCLE) using nucleotide sequences

Programs such as MAFFT and MUSCLE and many others use flags to designate input options. These are usually a dash (-) before the command, or in the case of MAFFT a double dash (–). Sometimes programs will use a single dash with and abbreviation or a single letter to invoke and option as well as a set of double dashes for more verbose forms of those options. Examples are given throughout this lab and will become intuitive throughout the workshop.

Change directories into the MSAlab folder. Refer to the computer lab intro page if you have forgotten how to do this.

Run MAFFT

Run a progressive alignment in MAFFT on the cluster by using the command:

mafft --retree 2 1ped.fasta > mafft_dna.fasta

The breakdown of this command is:

  • mafft starts the program MAFFT
  • --retree 2 tells MAFFT to run a progressive alignment. MAFFT uses double dashes (–) for its options. You can see why it is --retree 2 on the MAFFT webpage linked above.
  • 1ped.fasta is the input file name. Place it before the > and after all flags
  • mafft_dna.fasta tells MAFFT to place the output alignment in the file mafft_dna.fasta. For many programs a > designates where to place the output. If the > symbol is unfamiliar to you, take a look back at the advanced UNIX tutorial.

Once the alignment process is completed, transfer the file to your own computer (through scp or Cyberduck) and open it in an alignment viewer (e.g. Seaview or MEGA) as in exercise 1. A new window with the aligned data will appear.

Run MUSCLE

Run a standard alignment in MUSCLE on the cluster by using the command:

muscle -log muscle_dna.log -in 1ped.fasta -out muscle_dna.fasta

The breakdown of this command is:

  • muscle starts the program MUSCLE
  • -log muscle_dna.log instructs MUSCLE to place all the output except the alignment itself to the log file called muscle_dna.log. This file will then include things like the gap penalty used, etc.
  • -in 1ped.fasta specifies the input file to MUSCLE.
  • -out muscle_dna.fasta instructs MUSCLE to place the alignment in the file muscle_dna.fasta. Note that -log are not always needed but it allows you to see the default options in MUSCLE.

Compare MAFFT and MUSCLE alignments

Once the MUSCLE alignment is done, transfer the aligned fasta file to your own computer (through scp or Cyberduck) and open it in your alignment viewer. A new window with the aligned data will appear.

Compare the alignments resulting from MAFFT and MUSCLE. Are they different? How many columns are in each the MAFFT or the MUSCLE alignment? What may be wrong with both? (Hint: these are protein coding genes).

Build 2 trees, one from each of your nucleotide alignments:

  • Go to your aligned nucleotide sequences window (for both MAFFT and MUSCLE alignments) and click on Trees > Distance Methods > NJ
  • Use a J-C distance metric
  • De-select the ignore all gap sites checkbox, then click to calculate 100 bootstraps.
  • Note: these trees are easy for helping to evaluate your alignments, but this program should never be your tree building method).

Compare the trees from both the MAFFT and MUSCLE alignments. Do the topologies and/or branch lengths differ? (Hint: look up some species names to get an idea of the expected topology!)

Exercise 3: comparison of two different alignment approaches in MAFFT using protein sequences

In this exercise we will convert the nucleotide sequences to their equivalent protein sequences and align these instead. Note that because we are running the alignment programs outside of our alignment viewers, you will not be able to convert back to the original DNA sequences post alignment. If this were to be run through SeaView or MEGA itself this could be done.

  • Return to the alignment viewer window with the unaligned 1ped.fasta sequences.
  • Click Props > View as proteins (SeaView) or select Translated Protein Sequences in the main alignment window (MEGA)
  • Click File > Save prot alignment (SeaView) or Data > Export alignment (MEGA) and save the file as 1ped_aa.fasta with Fasta as the file format
  • Transfer this file to the MSAlab folder on the remote machine
  • Run an iterative alignment in MAFFT by using the command: 
    mafft --maxiterate 1000 1ped_aa.fasta > mafft_aa_iter.fasta

    Comparing the command to the MAFFT command in exercise 2, you will notice a new option, --maxiterate 1000, which instructs MAFFT to run an iterative alignment with maximum 1000 cycles.

  • Load the mafft_aa_iter.fasta file into your alignment viewer
  • Build a tree using your protein alignment by selecting Trees > Distance Methods > NJ, selecting a Poisson distance metric, de-clicking ignore gaps and do a bootstrap test as above (SeaView) or select Phylogeny > Construct/Test Neighbor-Joining Tree, choose the mafft_aa_iter.fasta file and then select Protein sequences, followed by OK on the next window to choose the default Poisson distance metric (MEGA)
  • Using the same 1ped_aa.fasta file, employ the MAFFT automatic selection of alignment strategy by using the command: 
    mafft --auto 1ped_aa.fasta > mafft_aa_auto.fasta

    You can see that now we use the flag --auto to tell MAFFT to choose the best alignment strategy. Can you determine what strategy was employed? (hint MAFFT outputs data to the screen and the strategy should be listed near the end). Load this file into your alignment viewer.

  • Build a tree using your protein alignment by selecting Trees > Distance Methods > NJ, select the Poisson distance metric, de-select ignore gaps and use bootstrapping (SeaView) or click Phylogeny > Construct/Test Neighbor-Joining Tree, choose the mafft_aa_auto.fasta file and then select Protein sequences, followed by OK on the next window to choose the default Poisson distance metric (MEGA)

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?

Protip Try moving all three MUSCLE amino acid alignments to your laptop with scp! Assuming that the IP address of your remote machine is 123.45.67.89:

scp username@123.45.67.89:MSAlab/muscle_aa*.fasta .

Codon Alignments

This is not part of the exercises, it’s just for your future information.

As you now know, it is not appropriate to align nucleotides of protein coding regions. In the exercises above, you translated the nucleotides to amino acids which you could use to infer trees. But sometimes you want to analyze nucleotides that have been aligned by codon. (Joe Bielawski will talk about some analyses that necessitate this kind of alignment.) So how do you go from an amino acid alignment back to codon-aligned nucleotides? You can use the Pal2Nal server for this. You will upload your protein alignment and nucleotide sequences, and it will spit out the codon alignment. Please be aware that your nucleotides must be multiples of three (i.e. a full open reading frame).

Another great option is to use PRANK (Löytynoja and Goldman 2005), which can use codon-aware data structures for alignment. Many people now write their own scripts for back-translation of aligned amino acids.

Filtering

There are various approaches to filter poorly aligned sites out of your alignment (Gblocks; Castresana 2000), but in some cases filtering does not improve and can even worsen tree estimation (Tan et al. 2015). Another strategy is to integrate over alignment uncertainty in a Bayesian framework (BAli-Phy; Redelings and Suchard 2005). All strategies can be justified on some basis but will come with limitations, and it is ultimately up to you to decide the most appropriate course of action for your data.

Reference-Based Alignment

In some cases it is easier to generate low-coverage genomic data, RADseq, or other subsamples of the genome and align to some well-assembled reference, Especially for population genetics, but also in species-level phylogenetic studies (e.g. Grewe et al. 2017; Figueiró et al. 2017; Rochette et al. 2019). This process is a little different than the progressive MSA discussed here, but a common short read aligner is bwa (Li and Durbin 2009). This type of reference-based alignment can then be followed by genotyping using various variant callers, including GATK (McKenna et al. 2010, Poplin et al. 2017) and mpileup (Danecek et al. 2012) to name just two, which use the quality information from massively parallell high throughput sequencers to call variants and heterozygotes. Other genotype callers with a philosophically different approach to variant calling are ANGSD (Korneliussen et al. 2014) and graphtyper (Eggertson et al. 2017), which have many population genetic applications, but not many phylogenetic methods can leverage genotype likelihoods. These topics fall outside of the scope of our workshop, but the development teams for many of these software have fantastic tutorials that you should be able to follow with skills developed here.

References

Castresana, J. 2000. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Molecular Biology and Evolution 17:540-552.

Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H. 2012. Twelve years of SAMtools and BCFtools, GigaScience 10(2):giab008

Grewe F, Huang J-P, Leavitt SP, Lumbsch HT. 2017. Reference-based RADseq resolves robust relationships among closely related species of lichen-forming fungi using metagenomic DNA. Sci Rep. 7:9884

Eggertsson HP, Jonsson H, Kristmundsdottir S, Hjartarson E, Kehr B, Masson G, Zink F, Hjorleifsson KE, Jonasdottir A, Jonasdottir A, Jonsdottir I, Gudbjartsson DF, Melsted P, Stefansson K, Halldorsson BV. 2017. Graphtyper enables population-scale genotyping using pangenome graphs. Nat Genet 49:1654–1660.

Figueiró HV, et al. 2017. Genome-wide signatures of complex introgression and adaptive evolution in the big cats. Sci Adv. 7:1700299.

Korneliussen TS, Albrechtsen A, Nielsen R. 2014. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics. 15:356.

Li H, Durbin R. 2009. Fast and accurate short read alignment with Burrows–Wheeler transform. 25:1754-1760.

Löytynoja A, Goldman N. 2005. An algorithm for progressive multiple alignment of sequences with insertions. Proc Natl Acad Sci USA. 102:10557-10562.

McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. 2010. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20:1297-303.

Poplin R, Ruano-Rubio V, DePristo MA, Fennell TJ, Carneiro MO, Van der Auwera GA, Kling DE, Gauthier LD, Levy-Moonshine A, Roazen D, Shakir K, Thibault J, Chandran S, Whelan C, Lek M, Gabriel S, Daly MJ, Neale B, MacArthur DG, Banks E. 2017. Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv. doi:10.1101/201178.

Redelings BD, Suchard MA. 2005. Joint Bayesian estimation of alignment and phylogeny. Syst. Biol. 54:401-418.

Rochette NC, Rivera-Colón AG, Catchen JM. 2019. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. bioRxiv. doi: 10.1101/615385.

Tan G, Muffato M, Ledergerber C, Herrero J, Goldman N, Gil M, Dessimoz C. 2015. Current methods for automated filtering of multiple sequence alignments frequently worsen single-gene phylogenetic inference. Syst. Biol. 64:778-791.

Yang Y, Smith SA. 2014. Orthology inference in nonmodel organisms using transcripts and low-coverage genomes: improving accuracy and matrix occupancy for phylogenetics. Mol. Biol. Evol. 31:3081-3092.