Last edited: 08 May 2020
## crop=NULL or FALSE => fix vignette rendering based on yihui/knitr#1796 ## added also error=FALSE to include_graphics knitr::opts_chunk$set( collapse = TRUE, tidy = FALSE, ## comment = "#>", error = FALSE, warning = FALSE, message = FALSE, crop = NULL ) ## check the output type out_type <- knitr::opts_knit$get("rmarkdown.pandoc.to") if (is.null(out_type)) out_type <- "html" ## add styling if (out_type == "html") { BiocStyle::markdown() ## BiocStyle::markdown(css.files = c('custom.css')) } else if (out_type == "latex") { BiocStyle::latex() }
An alignment of DNA or amino acid sequences is commonly represented in the form of a position weight matrix (PWM), a $J \times W$ matrix in which position $(j,w)$ gives the probability of observing nucleotide $j$ in position $w$ of an alignment of length $W$. Here $J$ denotes the number of letters in the alphabet from which the sequences were derived. An important summary measure of a given position weight matrix is its information content profile [@Schneider1986]. The information content at position $w$ of the motif is given by
$$ IC(w) = \log_2(J) + \sum_{j=1}^J p_{wj}\log_2(p_{wj}) = \log_2(J) - entropy(w). $$
The information content is measured in bits and, in the case of DNA sequences, ranges from 0 to 2 bits. A position in the motif at which all nucleotides occur with equal probability has an information content of 0 bits, while a position at which only a single nucleotide can occur has an information content of 2 bits. The information content at a given position can therefore be thought of as giving a measure of the tolerance for substitutions in that position: Positions that are highly conserved and thus have a low tolerance for substitutions correspond to high information content, while positions with a high tolerance for substitutions correspond to low information content.
Sequence logos are a graphical representation of sequence alignments developed by [@Schneider1990]. Each logo consists of stacks of symbols, one stack for each position in the sequence. The overall height of the stack is proportional to the information content at that position, while the height of symbols within the stack indicates the relative frequency of each amino or nucleic acid at that position. In general, a sequence logo provides a richer and more precise description of, for example, a binding site, than would a consensus sequence.
The r Biocpkg("seqLogo")
package provides an R implementation for plotting
such sequence logos for alignments consisting of DNA sequences. Before
being able to access this functionality, the user is required to load
the package using the library()
command:
library(seqLogo)
pwm-class
The r Biocpkg("seqLogo")
package defines the class pwm
which can
be used to represent position weight matrices. An instance of this
class can be constructed from a simple matrix or a data frame using the
function makePWM()
:
mFile <- system.file("extdata/pwm1", package="seqLogo") m <- read.table(mFile) m p <- makePWM(m)
makePWM()
checks that all column probabilities add up to 1.0
and also obtains the information content profile and consensus sequence
for the position weight matrix. These can then be accessed through the
corresponding slots of the created object:
slotNames(p) pwm(p) ic(p) consensus(p)
The seqLogo()
function plots sequence logos.
The position weight matrix for which the sequence logo is to be plotted,
pwm
. This may be either an instance of class pwm
, as defined by the
package r Biocpkg("seqLogo")
, a matrix
, or a data.frame
.
A logical
ic.scale
indicating whether the height
of each column is to be proportional to its information content, as
originally proposed by [@Schneider1986]. If ic.scale=FALSE
,
all columns have the same height.
The call seqLogo(p)
produces the sequence logo shown in figure
\@ref(seqlogo1). Alternatively, we can use seqLogo(p, ic.scale=FALSE)
to obtain the sequence logo shown in figure \@ref(seqlogo2) in which
all columns have the same height.
seqLogo(p)
seqLogo(p, ic.scale=FALSE)
It is also possible to change the default colors by providing a named character
vector as a fill
argument seqLogo
function.
seqLogo(p, fill=c(A="#4daf4a", C="#377eb8", G="#ffd92f", T="#e41a1c"), ic.scale=FALSE)
The RNA logos are supported as well. In this particular case, the seqLogo
will either accept fill
colors specified for c("A", "C", "G", "U")
letters
or c("A", "C", "G", "T")
and uses the color specified in element "T"
for letter "U".
r <- makePWM(m, alphabet="RNA") seqLogo(r, ic.scale=FALSE)
The following features of the programming approach employed in
r Biocpkg("seqLogo")
may be of interest to users.
Class/method object-oriented programming. Like many other Bioconductor
packages, r Biocpkg("seqLogo")
has adopted the
S4 class/method objected-oriented programming approach presented in
[@Chambers1998]. In particular, a new class, pwm
, is defined to represent
a position weight matrix. The plot method for this class is set to produce
the sequence logo corresponding to this class.
Use of the grid
package. The grid
package is used to draw the sequence
letters from graphical primitives. We note that this should make it easy
to extend the package to amino acid sequences.
The following is the session info that generated this vignette:
sessionInfo()
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