MultipleAlignment Objects
Edited: November 1, 2022; Compiled: 15 April, 2025
Contents
1 Introduction
The DNAMultipleAlignment, RNAMultipleAlignment and
AAMultipleAlignment classes allow users to represent groups
of aligned DNA, RNA or amino acid sequences as a single object. The
frame of reference for aligned sequences is static, so manipulation of
these objects is confined to be non-destructive. In practice, this
means that these objects contain slots to mask ranges of rows and
columns on the original sequence. These masks are then respected by
methods that manipulate and display the objects, allowing the user to
remove or expose columns and rows without invalidating the original
alignment.
2 Creation and masking
To create a MultipleAlignment, call the appropriate read
function to read in and parse the original alignment. There are
functions to read clustaW, Phylip and Stolkholm data formats.
library(Biostrings)
origMAlign <-
readDNAMultipleAlignment(filepath =
system.file("extdata",
"msx2_mRNA.aln",
package="Biostrings"),
format="clustal")
phylipMAlign <-
readAAMultipleAlignment(filepath =
system.file("extdata",
"Phylip.txt",
package="Biostrings"),
format="phylip")Rows can be renamed with rownames.
rownames(origMAlign)## [1] "gi|84452153|ref|NM_002449.4|" "gi|208431713|ref|NM_001135625."
## [3] "gi|118601823|ref|NM_001079614." "gi|114326503|ref|NM_013601.2|"
## [5] "gi|119220589|ref|NM_012982.3|" "gi|148540149|ref|NM_001003098."
## [7] "gi|45383056|ref|NM_204559.1|" "gi|213515133|ref|NM_001141603."rownames(origMAlign) <- c("Human","Chimp","Cow","Mouse","Rat",
"Dog","Chicken","Salmon")
origMAlign## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] -----TCCCGTCTCCGCAGCAAAAAA...TCACAATTAAAAAAAAAAAAAAAAA Human
## [2] --------------------------...------------------------- Chimp
## [3] --------------------------...------------------------- Cow
## [4] ----------------------AAAA...------------------------- Mouse
## [5] --------------------------...------------------------- Rat
## [6] --------------------------...------------------------- Dog
## [7] --------------CGGCTCCGCAGC...------------------------- Chicken
## [8] GGGGGAGACTTCAGAAGTTGTTGTCC...------------------------- SalmonTo see a more detailed version of your MultipleAlignment
object, you can use the detail method, which will show
the details of the alignment interleaved and without the rows and
columns that you have masked out.
detail(origMAlign)Applying masks is a simple matter of specifying which ranges to hide.
maskTest <- origMAlign
rowmask(maskTest) <- IRanges(start=1,end=3)
rowmask(maskTest)## NormalIRanges object with 1 range and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 3 3maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] ##########################...######################### Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ----------------------AAAA...------------------------- Mouse
## [5] --------------------------...------------------------- Rat
## [6] --------------------------...------------------------- Dog
## [7] --------------CGGCTCCGCAGC...------------------------- Chicken
## [8] GGGGGAGACTTCAGAAGTTGTTGTCC...------------------------- Salmoncolmask(maskTest) <- IRanges(start=c(1,1000),end=c(500,2343))
colmask(maskTest)## NormalIRanges object with 2 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 500 500
## [2] 1000 2343 1344maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] ##########################...######################### Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ##########################...######################### Mouse
## [5] ##########################...######################### Rat
## [6] ##########################...######################### Dog
## [7] ##########################...######################### Chicken
## [8] ##########################...######################### SalmonRemove row and column masks by assigning NULL:
rowmask(maskTest) <- NULL
rowmask(maskTest)## NormalIRanges object with 0 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>colmask(maskTest) <- NULL
colmask(maskTest)## NormalIRanges object with 0 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] -----TCCCGTCTCCGCAGCAAAAAA...TCACAATTAAAAAAAAAAAAAAAAA Human
## [2] --------------------------...------------------------- Chimp
## [3] --------------------------...------------------------- Cow
## [4] ----------------------AAAA...------------------------- Mouse
## [5] --------------------------...------------------------- Rat
## [6] --------------------------...------------------------- Dog
## [7] --------------CGGCTCCGCAGC...------------------------- Chicken
## [8] GGGGGAGACTTCAGAAGTTGTTGTCC...------------------------- SalmonWhen setting a mask, you might want to specify the rows or columns to
keep, rather than to hide. To do that, use the invert
argument. Taking the above example, we can set the exact same masks
as before by specifying their inverse and using invert=TRUE.
rowmask(maskTest, invert=TRUE) <- IRanges(start=4,end=8)
rowmask(maskTest)## NormalIRanges object with 1 range and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 3 3maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] ##########################...######################### Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ----------------------AAAA...------------------------- Mouse
## [5] --------------------------...------------------------- Rat
## [6] --------------------------...------------------------- Dog
## [7] --------------CGGCTCCGCAGC...------------------------- Chicken
## [8] GGGGGAGACTTCAGAAGTTGTTGTCC...------------------------- Salmoncolmask(maskTest, invert=TRUE) <- IRanges(start=501,end=999)
colmask(maskTest)## NormalIRanges object with 2 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 1 500 500
## [2] 1000 2343 1344maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] ##########################...######################### Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ##########################...######################### Mouse
## [5] ##########################...######################### Rat
## [6] ##########################...######################### Dog
## [7] ##########################...######################### Chicken
## [8] ##########################...######################### SalmonIn addition to being able to invert these masks, you can also choose
the way in which the ranges you provide will be merged with any
existing masks. The append argument allows you to specify
the way in which new mask ranges will interact with any existing
masks. By default, these masks will be the “union” of the new mask
and any existing masks, but you can also specify that these masks be
the mask that results from when you “intersect” the current mask and
the new mask, or that the new mask simply “replace” the current mask.
The append argument can be used in combination with the
invert argument to make things even more interesting. In
this case, the inversion of the mask will happen before it is combined
with the existing mask. For simplicity, I will only demonstrate this
on rowmask, but it also works for colmask. Before
we begin, lets set the masks back to being NULL again.
## 1st lets null out the masks so we can have a fresh start.
colmask(maskTest) <- NULL
rowmask(maskTest) <- NULLThen we can do a series of examples, starting with the default which
uses the “union” value for the append argument.
## Then we can demonstrate how the append argument works
rowmask(maskTest) <- IRanges(start=1,end=3)
maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] ##########################...######################### Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ----------------------AAAA...------------------------- Mouse
## [5] --------------------------...------------------------- Rat
## [6] --------------------------...------------------------- Dog
## [7] --------------CGGCTCCGCAGC...------------------------- Chicken
## [8] GGGGGAGACTTCAGAAGTTGTTGTCC...------------------------- Salmonrowmask(maskTest,append="intersect") <- IRanges(start=2,end=5)
maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] -----TCCCGTCTCCGCAGCAAAAAA...TCACAATTAAAAAAAAAAAAAAAAA Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ----------------------AAAA...------------------------- Mouse
## [5] --------------------------...------------------------- Rat
## [6] --------------------------...------------------------- Dog
## [7] --------------CGGCTCCGCAGC...------------------------- Chicken
## [8] GGGGGAGACTTCAGAAGTTGTTGTCC...------------------------- Salmonrowmask(maskTest,append="replace") <- IRanges(start=5,end=8)
maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] -----TCCCGTCTCCGCAGCAAAAAA...TCACAATTAAAAAAAAAAAAAAAAA Human
## [2] --------------------------...------------------------- Chimp
## [3] --------------------------...------------------------- Cow
## [4] ----------------------AAAA...------------------------- Mouse
## [5] ##########################...######################### Rat
## [6] ##########################...######################### Dog
## [7] ##########################...######################### Chicken
## [8] ##########################...######################### Salmonrowmask(maskTest,append="replace",invert=TRUE) <- IRanges(start=5,end=8)
maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] ##########################...######################### Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ##########################...######################### Mouse
## [5] --------------------------...------------------------- Rat
## [6] --------------------------...------------------------- Dog
## [7] --------------CGGCTCCGCAGC...------------------------- Chicken
## [8] GGGGGAGACTTCAGAAGTTGTTGTCC...------------------------- Salmonrowmask(maskTest,append="union") <- IRanges(start=7,end=8)
maskTest## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] ##########################...######################### Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ##########################...######################### Mouse
## [5] --------------------------...------------------------- Rat
## [6] --------------------------...------------------------- Dog
## [7] ##########################...######################### Chicken
## [8] ##########################...######################### SalmonThe function maskMotif works on MultipleAlignment
objects too, and takes the same arguments that it does
elsewhere. maskMotif is useful for masking occurances of a
string from columns where it is present in the consensus sequence.
tataMasked <- maskMotif(origMAlign, "TATA")
colmask(tataMasked)## NormalIRanges object with 5 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 811 814 4
## [2] 1180 1183 4
## [3] 1186 1191 6
## [4] 1204 1207 4
## [5] 1218 1221 4maskGaps also operates on columns and will mask collumns
based on the fraction of each column that contains gaps
min.fraction along with the width of columns that contain
this fraction of gaps min.block.width.
autoMasked <- maskGaps(origMAlign, min.fraction=0.5, min.block.width=4)
autoMasked## DNAMultipleAlignment with 8 rows and 2343 columns
## aln names
## [1] ##########################...######################### Human
## [2] ##########################...######################### Chimp
## [3] ##########################...######################### Cow
## [4] ##########################...######################### Mouse
## [5] ##########################...######################### Rat
## [6] ##########################...######################### Dog
## [7] ##########################...######################### Chicken
## [8] ##########################...######################### SalmonSometimes you may want to cast your MultipleAlignment to be a
matrix for usage eslewhere. as.matrix is supported for
these circumstances. The ability to convert one object into another
is not very unusual so why mention it? Because when you cast your
object, the masks WILL be considered so that the masked rows and
columns will be left out of the matrix object.
full = as.matrix(origMAlign)
dim(full)## [1] 8 2343partial = as.matrix(autoMasked)
dim(partial)## [1] 8 1143One example of where you might want to use as.matrix is when
using the ape package. For example if you needed to use the
dist.dna function you would want to use as.matrix
followed by as.alignment and then the
as.DNAbin to create a DNAbin object for the
dist.dna.
3 Analytic utilities
Once you have masked the sequence, you can then ask questions about the properties of that sequence. For example, you can look at the alphabet frequency of that sequence. The alphabet frequency will only be for the masked sequence.
alphabetFrequency(autoMasked)## A C G T M R W S Y K V H D B N - + .
## [1,] 260 351 296 218 0 0 0 0 0 0 0 0 0 0 0 18 0 0
## [2,] 171 271 231 128 0 0 0 0 0 0 0 0 0 0 3 339 0 0
## [3,] 277 360 275 209 0 0 0 0 0 0 0 0 0 0 0 22 0 0
## [4,] 265 343 277 226 0 0 0 0 0 0 0 0 0 0 0 32 0 0
## [5,] 251 345 287 229 0 0 0 0 0 0 0 0 0 0 0 31 0 0
## [6,] 160 285 241 118 0 0 0 0 0 0 0 0 0 0 0 339 0 0
## [7,] 224 342 273 190 0 0 0 0 0 0 0 0 0 0 0 114 0 0
## [8,] 268 289 273 262 0 0 0 0 0 0 0 0 0 0 0 51 0 0You can also calculate a consensus matrix, extract the consensus string or look at the consensus views. These methods too will all consider the masking when you run them.
consensusMatrix(autoMasked, baseOnly=TRUE)[, 84:90]## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## A 0 3 0 3 0 3 3
## C 3 0 0 1 1 1 2
## G 1 2 4 1 2 0 0
## T 1 0 1 0 1 1 0
## other 3 3 3 3 4 3 3substr(consensusString(autoMasked),80,130)## [1] "####CRGABAMGTCA-YRGCTTCTCYGTSCAWAGGCRRTGRCYTGTTYTCG"consensusViews(autoMasked)## Views on a 2343-letter BString subject
## subject: ----------------------VWVMKYYBSRST...----------------------------------
## views:
## start end width
## [1] 84 325 242 [CRGABAMGTCA-YRGCTTCTCYGTSC...GCGSCTCSGCYGGSGCYRYCCTGSGG]
## [2] 330 332 3 [CCR]
## [3] 338 1191 854 [CTGCTGCTGYCGGGVCACGGCGYYCG...TGMTAGTTTTTATGTATAAATATATA]
## [4] 1198 1241 44 [ATAAAATATAAKAC--TTTTTATAYRSCARATGTAAAAATTCAA]You can also cluster the alignments based on their distance to each
other. Because you must pass in a DNAStringSet, the clustering will
also take into account the masking. So for example, you can see how
clustering the unmasked DNAMultipleAlignment will draw a
funky looking tree.
sdist <- stringDist(as(origMAlign,"DNAStringSet"), method="hamming")## Warning in .call_fun_in_pwalign("stringDist", ...): stringDist() has moved from Biostrings to the pwalign package, and is
## formally deprecated in Biostrings >= 2.75.1. Please call
## pwalign::stringDist() to get rid of this warning.clust <- hclust(sdist, method = "single")
png(file="badTree.png")
plot(clust)
dev.off()## png
## 2
Figure 1: Funky tree produced by using unmasked strings
But, if we use the gap-masked DNAMultipleAlignment, to
remove the long uninformative regions, and then make our plot, we can
see the real relationships.
sdist <- stringDist(as(autoMasked,"DNAStringSet"), method="hamming")## Warning in .call_fun_in_pwalign("stringDist", ...): stringDist() has moved from Biostrings to the pwalign package, and is
## formally deprecated in Biostrings >= 2.75.1. Please call
## pwalign::stringDist() to get rid of this warning.clust <- hclust(sdist, method = "single")
png(file="goodTree.png")
plot(clust)
dev.off()## png
## 2fourgroups <- cutree(clust, 4)
fourgroups## Human Chimp Cow Mouse Rat Dog Chicken Salmon
## 1 2 1 1 1 2 3 4
Figure 2: A tree produced by using strings with masked gaps
In the “good” plot, the Salmon sequence is once again the most distant which is what we expect to see. A closer examination of the sequence reveals that the similarity between the mouse, rat and human sequences was being inflated by virtue of the fact that those sequences were simply much longer (had more information than) the other species represented. This is what caused the “funky” result. The relationship between the sequences in the funky tree was being driven by extra “length” in the rodent/mouse/human sequences, instead of by the similarity of the conserved regions.
4 Exporting to file
One possible export option is to write to fasta files
If you need to write your MultipleAlignment object out as a fasta
file, you can cast it to a DNAStringSet and then write
it out as a fasta file like so:
DNAStr = as(origMAlign, "DNAStringSet")
writeXStringSet(DNAStr, file="myFile.fa")One other format that is of interest is the Phylip format. The Phylip format stores the column masking of your object as well as the sequence that you are exporting. So if you have masked the sequence and you write out a Phylip file, this mask will be recorded into the file you export. As with the fasta example above, any rows that you have masked out will be removed from the exported file.
write.phylip(phylipMAlign, filepath="myFile.txt")5 Session Information
All of the output in this vignette was produced under the following conditions:
## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] Biostrings_2.76.0 GenomeInfoDb_1.44.0 XVector_0.48.0
## [4] IRanges_2.42.0 S4Vectors_0.46.0 BiocGenerics_0.54.0
## [7] generics_0.1.3 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] crayon_1.5.3 httr_1.4.7 cli_3.6.4
## [4] knitr_1.50 rlang_1.1.6 xfun_0.52
## [7] UCSC.utils_1.4.0 jsonlite_2.0.0 htmltools_0.5.8.1
## [10] sass_0.4.10 rmarkdown_2.29 evaluate_1.0.3
## [13] jquerylib_0.1.4 fastmap_1.2.0 yaml_2.3.10
## [16] lifecycle_1.0.4 bookdown_0.43 BiocManager_1.30.25
## [19] compiler_4.5.0 pwalign_1.4.0 digest_0.6.37
## [22] R6_2.6.1 GenomeInfoDbData_1.2.14 bslib_0.9.0
## [25] tools_4.5.0 cachem_1.1.0