BIN-ALI-MSA

Multile Sequence Alignment

 

Keywords:  Multiple sequence alignment

 

 

Caution!

This unit is under development. There is some contents here but it is incomplete and/or may change significantly: links may lead to nowhere, the contents is likely going to be rearranged, and objectives, deliverables etc. may be incomplete or missing. Do not work with this material until it is updated to "live" status.

 

Abstract

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This unit ...

Prerequisites

You need to complete the following units before beginning this one:

• BIN-ALI-Optimal_sequence_alignment
• BIN-ALI-PSI-BLAST
• FND-STA-Information_theory

 

Objectives

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Outcomes

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Deliverables

• Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
• Journal: Document your progress in your course journal.
• Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.

 

Evaluation

Evaluation: NA

This unit is not evaluated for course marks.

 

Contents

Task:

• Read the introductory notes on concepts of multiple sequence alignments.

Multiple sequence alignments (MSAs) are further useful to resolve ambiguities in the precise placement of "indels" and to ensure that columns in alignments actually contain amino acids that evolve in a similar context. MSAs serve as input for

• functional annotation;
• protein homology modelling;
• phylogenetic analyses, and
• sensitive homology searches in databases.

Multiple Sequence Alignment

 

In order to perform a multiple sequence alignment, we obviously need a set of homologous sequences. This is not trivial. All interpretation of MSA results depends absolutely on how the input sequences were chosen. Should we include only orthologues, or paralogues as well? Should we include only species with fully sequenced genomes, or can we tolerate that some orthologous genes are possibly missing for a species? Should we include all sequences we can lay our hands on, or should we restrict the selection to a manageable number of representative sequences? All of these choices influence our interpretation:

• orthologues are expected to be functionally and structurally conserved;
• paralogues may have divergent function but have similar structure;
• missing genes may make paralogs look like orthologs; and
• selection bias may weight our results toward sequences that are over-represented and do not provide a fair representation of evolutionary divergence.

 

 

Computing an MSA in R

 

Let's use the Bioconductor msa package to align the sequences we have. Study and run the following code

Task:

• Return to your RStudio session.
• Make sure you have saved myDB as instructed previously.
• Bring code and data resources up to date:
  • pull the most recent version of the project from GitHub
  • type init() to lod the most recent files and functions
  • re-merge your current myDB
• Study and work through the code in the Multiple sequence alignments section of the BCH441_A04.R script.
• Note that the final task asks you to print out some results and bring them to class for the next quiz.

 

Sequence alignment editors

 

Really excellent software tools have been written that help you visualize and manually curate multiple sequence alignments. If anything, I think they tend to do too much. Past versions of the course have used Jalview, but I have heard good things of AliView (and if you are on a Mac seqotron might interest you, but I only cover software that is free and runs on all three major platforms).

Right now, I am just mentioning the two alignment editors. If you have experience with comparing them, let us know.

• [Jalview] an integrated MSA editor and sequence annotation workbench from the Barton lab in Dundee. Lots of functions.
• [AliView] from Uppsala: fast, lean, looks to be very practical.

 

Jalview: alignment editor

Geoff Barton's lab in Dundee has developed an integrated MSA editor and sequence annotation workbench with a number of very useful functions. It is written in Java and should run on Mac, Linux and Windows platforms without modifications.

Waterhouse et al. (2009) Jalview Version 2--a multiple sequence alignment editor and analysis workbench. Bioinformatics 25:1189-91. (pmid: 19151095)

[ PubMed ] [ DOI ] UNLABELLED: Jalview Version 2 is a system for interactive WYSIWYG editing, analysis and annotation of multiple sequence alignments. Core features include keyboard and mouse-based editing, multiple views and alignment overviews, and linked structure display with Jmol. Jalview 2 is available in two forms: a lightweight Java applet for use in web applications, and a powerful desktop application that employs web services for sequence alignment, secondary structure prediction and the retrieval of alignments, sequences, annotation and structures from public databases and any DAS 1.53 compliant sequence or annotation server. AVAILABILITY: The Jalview 2 Desktop application and JalviewLite applet are made freely available under the GPL, and can be downloaded from www.jalview.org.

We will quickly install Jalview and explore its features in other assignments.

Task:

1. Navigate to the Jalview homepage click on the Download link, and install Jalview on your computer. For Mac OS X, use the Install Jalview Only link.
2. Start Jalview. A number of windows that showcase the program's abilities will load, you can close these.
3. Select File → Input Alignment → from File and open the APSES_proteins.mfa file you have prepared above. An alignment window with sequences should appear.
4. Choose Web Service → Alignment → Tcoffee with Defaults to run a Tcoffee MSA remotely at the Barton lab. The program should execute remotely and download the aligned results into a new window. Scroll along the window to get a sense of what has and hasn't been aligned.
5. Select File → Input Alignment → from File and open the APSES_proteins_muscle.mfa file you have prepared above. An alignment window with your Muscle alignment should appear.
6. Compare the two alignments and get a sense for how similar or different they are.

Computing alignments

try two MSA's algorithms and load them in Jalview.
Locally: which one do you prefer? Modify the consensus. Annotate domains.

The EBI has a very convenient page to access a number of MSA algorithms. This is especially convenient when you want to compare, e.g. T-Coffee and Muscle and MAFFT results to see which regions of your alignment are robust. You could use any of these tools, just paste your sequences into a Webform, download the results and load into Jalview. Easy.

But even easier is to calculate the alignments directly from Jalview. available. (Not today. Bummer.)

No. Claculate an external alignment and import.

Calculate a MAFFT alignment using the Jalview Web service option

Task:

1. In Jalview, select Web Service → Alignment → MAFFT with defaults.... The alignment is calculated in a few minutes and displayed in a new window.

Calculate a MAFFT alignment when the Jalview Web service is NOT available

Task:

1. In Jalview, select File → Output to Textbox → FASTA
2. Copy the sequences.
3. Navigate to the MAFFT Input form at the EBI.
4. Paste your sequences into the form.
5. Click on Submit.
6. Close the Jalview sequence window and either save your MAFFT alignment to file and load in Jalview, or simply 'File → Input Alignment → from Textbox, paste and click New Window.

In any case, you should now have an alignment.

Task:

1. Choose Colour → Hydrophobicity and → by Conservation. Then adjust the slider left or right to see which columns are highly conserved. You will notice that the Swi6 sequence that was supposed to align only to the ankyrin domains was in fact aligned to other parts of the sequence as well. This is one part of the MSA that we will have to correct manually and a common problem when aligning sequences of different lengths.

R code: load alignment and compute information scores

As discussed in the lecture, Shannon information is calculated as the difference between expected and observed entropy, where entropy is the negative sum over probabilities times the log of those probabilities:

*a review of regex range characters +?*{min,max}, and greedy.
*build an AT-hook motif matcher https://en.wikipedia.org/wiki/AT-hook

Here we compute Shannon information scores for aligned positions of the APSES domain, and plot the values in R. You can try this with any part of your alignment, but I have used only the aligned residues for the APSES domain for my example. This is a good choice for a first try, since there are (almost) no gaps.

Task:

1. Export only the sequences of the aligned APSES domains to a file on your computer, in FASTA format as explained below. You could call this: Mbp1_All_APSES.fa.
   1. Use your mouse and clik and drag to select the aligned APSES domains in the alignment window.
   2. Copy your selection to the clipboard.
   3. Use the main menu (not the menu of your alignment window) and select File → Input alignment → from Textbox; paste the selection into the textbox and click New Window.
   4. Use File → save as to save the aligned siequences in multi-FASTA format under the filename you want in your R project directory.

1. Explore the R-code below. Be sure that you understand it correctly. Note that this code does not implement any sampling bias correction, so positions with large numbers of gaps will receive artificially high scores (the alignment looks like the gap charecter were a conserved character).

# CalculateInformation.R
# Calculate Shannon information for positions in a multiple sequence alignment.
# Requires: an MSA in multi FASTA format

# It is good practice to set variables you might want to change
# in a header block so you don't need to hunt all over the code
# for strings you need to update.
#
setwd("/your/R/working/directory")
mfa      <- "MBP1_All_APSES.fa"

# ================================================
#    Read sequence alignment fasta file
# ================================================

# read MFA datafile using seqinr function read.fasta()
library(seqinr)
tmp  <- read.alignment(mfa, format="fasta")
MSA  <- as.matrix(tmp)  # convert the list into a characterwise matrix
                        # with appropriate row and column names using
                        # the seqinr function as.matrix.alignment()
                        # You could have a look under the hood of this
                        # function to understand beter how to convert a
                        # list into something else ... simply type
                        # "as.matrix.alignment" - without the parentheses
                        # to retrieve the function source code (as for any
                        # function btw).

### Explore contents of and access to the matrix of sequences
MSA
MSA[1,]
MSA[,1]
length(MSA[,1])


# ================================================
#    define function to calculate entropy
# ================================================

entropy <- function(v) { # calculate shannon entropy for the aa vector v
	                     # Note: we are not correcting for small sample sizes
	                     # here. Thus if there are a large number of gaps in
	                     # the alignment, this will look like small entropy
	                     # since only a few amino acids are present. In the
	                     # extreme case: if a position is only present in
	                     # one sequence, that one amino acid will be treated
	                     # as 100% conserved - zero entropy. Sampling error
	                     # corrections are discussed eg. in Schneider et al.
	                     # (1986) JMB 188:414
	l <- length(v)
	a <- rep(0, 21)      # initialize a vector with 21 elements (20 aa plus gap)
	                     # the set the name of each row to the one letter
	                     # code. Through this, we can access a row by its
	                     # one letter code.
	names(a)  <- unlist(strsplit("acdefghiklmnpqrstvwy-", ""))

	for (i in 1:l) {       # for the whole vector of amino acids
		c <- v[i]          # retrieve the character
		a[c] <- a[c] + 1   # increment its count by one
	} # note: we could also have used the table() function for this

	tot <- sum(a) - a["-"] # calculate number of observed amino acids
	                       # i.e. subtract gaps
	a <- a/tot             # frequency is observations of one amino acid
	                       # divided by all observations. We assume that
	                       # frequency equals probability.
	a["-"] <- 0
	for (i in 1:length(a)) {
		if (a[i] != 0) { # if a[i] is not zero, otherwise leave as is.
			             # By definition, 0*log(0) = 0  but R calculates
			             # this in parts and returns NaN for log(0).
			a[i] <- a[i] * (log(a[i])/log(2)) # replace a[i] with
			                                  # p(i) log_2(p(i))
		}
	}
	return(-sum(a)) # return Shannon entropy
}

# ================================================
#    calculate entropy for reference distribution
#    (from UniProt, c.f. Assignment 2)
# ================================================

refData <- c(
    "A"=8.26,
    "Q"=3.93,
    "L"=9.66,
    "S"=6.56,
    "R"=5.53,
    "E"=6.75,
    "K"=5.84,
    "T"=5.34,
    "N"=4.06,
    "G"=7.08,
    "M"=2.42,
    "W"=1.08,
    "D"=5.45,
    "H"=2.27,
    "F"=3.86,
    "Y"=2.92,
    "C"=1.37,
    "I"=5.96,
    "P"=4.70,
    "V"=6.87
    )

### Calculate the entropy of this distribution

H.ref <- 0
for (i in 1:length(refData)) {
	p <- refData[i]/sum(refData) # convert % to probabilities
    H.ref <- H.ref - (p * (log(p)/log(2)))
}

# ================================================
#    calculate information for each position of
#    multiple sequence alignment
# ================================================

lAli <- dim(MSA)[2] # length of row in matrix is second element of dim(<matrix>).
I <- rep(0, lAli)   # initialize result vector
for (i in 1:lAli) {
	I[i] = H.ref - entropy(MSA[,i])  # I = H_ref - H_obs
}

### evaluate I
I
quantile(I)
hist(I)
plot(I)

# you can see that we have quite a large number of columns with the same,
# high value ... what are these?

which(I > 4)
MSA[,which(I > 4)]

# And what is in the columns with low values?
MSA[,which(I < 1.5)]


# ===================================================
#    plot the information
#    (c.f. Assignment 5, see there for explanations)
# ===================================================

IP <- (I-min(I))/(max(I) - min(I) + 0.0001)
nCol <- 15
IP <- floor(IP * nCol) + 1
spect <- colorRampPalette(c("#DD0033", "#00BB66", "#3300DD"), bias=0.6)(nCol)
# lets set the information scores from single informations to grey. We
# change the highest level of the spectrum to grey.
#spect[nCol] <- "#CCCCCC"
Icol <- vector()
for (i in 1:length(I)) {
	Icol[i] <- spect[ IP[i] ]
}

plot(1,1, xlim=c(0, lAli), ylim=c(-0.5, 5) ,
     type="n", bty="n", xlab="position in alignment", ylab="Information (bits)")

# plot as rectangles: height is information and color is coded to information
for (i in 1:lAli) {
   rect(i, 0, i+1, I[i], border=NA, col=Icol[i])
}

# As you can see, some of the columns reach very high values, but they are not
# contiguous in sequence. Are they contiguous in structure? We will find out in
# a later assignment, when we map computed values to structure.

Plot of information vs. sequence position produced by the R script above, for an alignment of Mbp1 ortholog APSES domains.

Calculating conservation scores

Task:

• Study this code carefully, execute it, section by section and make sure you understand all of it. Ask on the list if anything is not clear.

# BiostringsExample.R
# Short tutorial on sequence alignment with the Biostrings package.
# Boris Steipe for BCH441, 2013 - 2014
#
setwd("~/path/to/your/R_files/")
setwd("~/Documents/07.TEACHING/37-BCH441 Bioinformatics 2014/05-Materials/Assignment_5 data")

# Biostrings is a package within the bioconductor project.
# bioconducter packages have their own installation system,
# they are normally not installed via CRAN.

# First, you load the BioConductor installer...
source("http://bioconductor.org/biocLite.R")

# Then you can install the Biostrings package and all of its dependencies.
biocLite("Biostrings")

# ... and load the library.
library(Biostrings)

# Some basic (technical) information is available ...
library(help=Biostrings)

# ... but for more in depth documentation, use the
# so called "vignettes" that are provided with every R package.
browseVignettes("Biostrings")

# In this code, we mostly use functions that are discussed in the
# pairwise alignement vignette.
(# Read in two fasta files - you will need to edit this for YFO
sacce <- readAAStringSet("mbp1-sacce.fa", format="fasta")

# "USTMA" is used only as an example here - modify for YFO  :-)
ustma <- readAAStringSet("mbp1-ustma.fa", format="fasta")

sacce
names(sacce)
names(sacce) <- "Mbp1 SACCE"
names(ustma) <- "Mbp1 USTMA" # Example only ... modify for YFO

width(sacce)
as.character(sacce)

# Biostrings takes a sophisticated approach to sequence alignment ...
?pairwiseAlignment

# ... but the use in practice is quite simple:
ali <- pairwiseAlignment(sacce, ustma, substitutionMatrix = "BLOSUM50")
ali

pattern(ali)
subject(ali)

writePairwiseAlignments(ali)

p <- aligned(pattern(ali))
names(p) <- "Mbp1 SACCE aligned"
s <- aligned(subject(ali))
names(s) <- "Mbp1 USTMA aligned"

# don't overwrite your EMBOSS .fal files
writeXStringSet(p, "mbp1-sacce.R.fal", append=FALSE, format="fasta")
writeXStringSet(s, "mbp1-ustma.R.fal", append=FALSE, format="fasta")

# Done.

• Compare the alignments you received from the EMBOSS server, and that you computed using R. Are they approximately the same? Exactly? You did use different matrices and gap parameters, so minor differences are to be expected. But by and large you should get the same alignments.

We will now use the aligned sequences to compute a graphical display of alignment quality.

Task:

• Study this code carefully, execute it, section by section and make sure you understand all of it. Ask on the list if anything is not clear.

# aliScore.R
# Evaluating an alignment with a sliding window score
# Boris Steipe, October 2012. Update October 2013
setwd("~/path/to/your/R_files/")

# Scoring matrices can be found at the NCBI.
# ftp://ftp.ncbi.nih.gov/blast/matrices/BLOSUM62

# It is good practice to set variables you might want to change
# in a header block so you don't need to hunt all over the code
# for strings you need to update.
#
fa1      <- "mbp1-sacce.R.fal"
fa2      <- "mbp1-ustma.R.fal"
code1    <- "SACCE"
code2    <- "USTMA"
mdmFile  <- "BLOSUM62.mdm"
window   <- 9   # window-size (should be an odd integer)

# ================================================
#    Read data files
# ================================================

# read fasta datafiles using seqinr function read.fasta()
install.packages("seqinr")
library(seqinr)
tmp  <- unlist(read.fasta(fa1, seqtype="AA", as.string=FALSE, seqonly=TRUE))
seq1 <- unlist(strsplit(as.character(tmp), split=""))

tmp  <- unlist(read.fasta(fa2, seqtype="AA", as.string=FALSE, seqonly=TRUE))
seq2 <- unlist(strsplit(as.character(tmp), split=""))

if (length(seq1) != length(seq2)) {
	print("Error: Sequences have unequal length!")
	}

lSeq <- length(seq1)

# ================================================
#    Read scoring matrix
# ================================================

MDM <- read.table(mdmFile, skip=6)

# This is a dataframe. Study how it can be accessed:

MDM
MDM[1,]
MDM[,1]
MDM[5,5]   # Cys-Cys
MDM[20,20] # Val-Val
MDM[,"W"]  # the tryptophan column
MDM["R","W"]  # Arg-Trp pairscore
MDM["W","R"]  # Trp-Arg pairscore: pairscores are symmetric

colnames(MDM)  # names of columns
rownames(MDM)  # names of rows
colnames(MDM)[3]   # third column
rownames(MDM)[12]  # twelfth row

# change the two "*" names to "-" so we can use them to score
# indels of the alignment. This is a bit of a hack, since this
# does not reflect the actual indel penalties (which is, as you)
# remember from your lectures, calculated as a gap opening
# + gap extension penalty; it can't be calculated in a pairwise
# manner) EMBOSS defaults for BLODSUM62 are opening -10 and
# extension -0.5 i.e. a gap of size 3 (-11.5) has approximately
# the same penalty as a 3-character score of "-" matches (-12)
# so a pairscore of -4 is not entirely unreasonable.

colnames(MDM)[24]
rownames(MDM)[24]
colnames(MDM)[24] <- "-"
rownames(MDM)[24] <- "-"
colnames(MDM)[24]
rownames(MDM)[24]
MDM["Q", "-"]
MDM["-", "D"]
# so far so good.

# ================================================
#    Tabulate pairscores for alignment
# ================================================


# It is trivial to create a pairscore vector along the
# length of the aligned sequences.

PS <- vector()
for (i in 1:lSeq) {
   aa1 <- seq1[i]
   aa2 <- seq2[i]
   PS[i] = MDM[aa1, aa2]
}

PS


# The same vector could be created - albeit perhaps not so
# easy to understand - with the expression ...
MDM[cbind(seq1,seq2)]



# ================================================
#    Calculate moving averages
# ================================================

# In order to evaluate the alignment, we will calculate a
# sliding window average over the pairscores. Somewhat surprisingly
# R doesn't (yet) have a native function for moving averages: options
# that are quoted are:
#   - rollmean() in the "zoo" package http://rss.acs.unt.edu/Rdoc/library/zoo/html/rollmean.html
#   - MovingAverages() in "TTR"  http://rss.acs.unt.edu/Rdoc/library/TTR/html/MovingAverages.html
#   - ma() in "forecast"  http://robjhyndman.com/software/forecast/
# But since this is easy to code, we shall implement it ourselves.

PSma <- vector()           # will hold the averages
winS <- floor(window/2)    # span of elements above/below the centre
winC <- winS+1             # centre of the window

# extend the vector PS with zeros (virtual observations) above and below
PS <- c(rep(0, winS), PS , rep(0, winS))

# initialize the window score for the first position
winScore <- sum(PS[1:window])

# write the first score to PSma
PSma[1] <- winScore

# Slide the window along the sequence, and recalculate sum()
# Loop from the next position, to the last position that does not exceed the vector...
for (i in (winC + 1):(lSeq + winS)) {
   # subtract the value that has just dropped out of the window
   winScore <- winScore - PS[(i-winS-1)]
   # add the value that has just entered the window
   winScore <- winScore + PS[(i+winS)]
   # put score into PSma
   PSma[i-winS] <- winScore
}

# convert the sums to averages
PSma <- PSma / window

# have a quick look at the score distributions

boxplot(PSma)
hist(PSma)

# ================================================
#    Plot the alignment scores
# ================================================

# normalize the scores
PSma <- (PSma-min(PSma))/(max(PSma) - min(PSma) + 0.0001)
# spread the normalized values to a desired range, n
nCol <- 10
PSma <- floor(PSma * nCol) + 1

# Assign a colorspectrum to a vector (with a bit of colormagic,
# don't worry about that for now). Dark colors are poor scores,
# "hot" colors are high scores
spect <- colorRampPalette(c("black", "red", "yellow", "white"), bias=0.4)(nCol)

# Color is an often abused aspect of plotting. One can use color to label
# *quantities* or *qualities*. For the most part, our pairscores measure amino
# acid similarity. That is a quantity and with the spectrum that we just defined
# we associte the measured quantities with the color of a glowing piece
# of metal: we start with black #000000, then first we ramp up the red
# (i.e. low-energy) part of the visible spectrum to red #FF0000, then we
# add and ramp up the green spectrum giving us yellow #FFFF00 and finally we
# add blue, giving us white #FFFFFF. Let's have a look at the spectrum:

s <- rep(1, nCol)
barplot(s, col=spect, axes=F, main="Color spectrum")

# But one aspect of our data is not quantitatively different: indels.
# We valued indels with pairscores of -4. But indels are not simply poor alignment,
# rather they are non-alignment. This means stretches of -4 values are really
# *qualitatively* different. Let's color them differently by changing the lowest
# level of the spectrum to grey.

spect[1] <- "#CCCCCC"
barplot(s, col=spect, axes=F, main="Color spectrum")

# Now we can display our alignment score vector with colored rectangles.

# Convert the integers in PSma to color values from spect
PScol <- vector()
for (i in 1:length(PSma)) {
	PScol[i] <- spect[ PSma[i] ]  # this is how a value from PSma is used as an index of spect
}

# Plot the scores. The code is similar to the last assignment.
# Create an empty plot window of appropriate size
plot(1,1, xlim=c(-100, lSeq), ylim=c(0, 2) , type="n", yaxt="n", bty="n", xlab="position in alignment", ylab="")

# Add a label to the left
text (-30, 1, adj=1, labels=c(paste("Mbp1:\n", code1, "\nvs.\n", code2)), cex=0.9 )

# Loop over the vector and draw boxes  without border, filled with color.
for (i in 1:lSeq) {
   rect(i, 0.9, i+1, 1.1, border=NA, col=PScol[i])
}

# Note that the numbers along the X-axis are not sequence numbers, but numbers
# of the alignment, i.e. sequence number + indel length. That is important to
# realize: if you would like to add the annotations from the last assignment
# which I will leave as an exercise, you need to map your sequence numbering
# into alignment numbering. Let me know in case you try that but need some help.

 

Further reading, links and resources

 

Notes

 

Self-evaluation

 

 

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If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.

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About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2017-08-05

Version:

0.1

Version history:

• 0.1 First stub

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