Classes for DNA and RNA sequences containing modified nucleotides
Modstrings 1.16.0
Most nucleic acids, regardless of their being DNA or RNA, contain modified nucleotides, which enhances the normal function of encoding genetic information. They have usually a regulatory function and/or modify folding behavior and molecular interactions.
RNA are nearly always post-transcriptionally modified. Most prominent examples are of course ribsomal RNA (rRNA) and transfer RNA (tRNA), but in recent years mRNA was also discovered to be post-transcriptionally modified. In addition, many small and long non-coding RNAs are also modified.
In many resources, like the tRNAdb (Jühling et al. 2009) or the modomics database (Boccaletto et al. 2018), modified nucleotides are repertoried. However in the Bioconductor context these information were not accessible, since they rely extensively on special characters in the RNA modification alphabet.
Therefore, the ModRNAString class was implemented extending the BString
class from the Biostrings (H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy 2017) package. It can store RNA sequences
containing special characters of the RNA modification alphabet and thus can
store location and identity of modifications. Functions for conversion to a
tabular format are implemented as well.
The implemented classes inherit most of the functions from the parental
BString class and it derivatives, which allows them to behave like the
normal XString classes within the Bioconductor context. Most of the
functionality is directly inherited and derived from the Biostrings package.
Since a DNA modification alphabet also exists, a ModDNAString class was
implemented as well. For details on the available letters have a look at the
RNA modification and
[DNA modification](ModDNAString-alphabet.html alphabet vignettes.
ModRNAString objectIn principle ModRNAString and ModDNAString objects can be created as any
other XString object. However encoding issue will most certainly come into
play, depending on the modification, the operation system and probably the R
version. This is not a problem of how the data is internally used, but how the
letter is transfered from the console to R and back.
library(Modstrings)
library(GenomicRanges)# This works
mr <- ModRNAString("ACGU7")
# This might work on Linux, but does not on Windows
ModRNAString("ACGU≈")## 5-letter ModRNAString object
## seq: ACGU≈# This cause a misinterpretation on Windows. Omega gets added as O.
# This modifys the information from yW-72 (7-aminocarboxypropylwyosine) to
# m1I (1-methylinosine)
ModRNAString("ACGUΩ")## 5-letter ModRNAString object
## seq: ACGUΩTo eliminate this issue the function modifyNucleotide() is implemented,
which can use short names or the nomenclature of a modification to add it at the
desired position.
head(shortName(ModRNAString()))## [1] "m1Am" "m1Gm" "m1Im" "m1acp3Y" "m1A" "m1G"head(nomenclature(ModRNAString()))## [1] "01A" "01G" "019A" "1309U" "1A" "1G"r <- RNAString("ACGUG")
mr2 <- modifyNucleotides(r,5L,"m7G")
mr2## 5-letter ModRNAString object
## seq: ACGU7mr3 <- modifyNucleotides(r,5L,"7G",nc.type = "nc")
mr3## 5-letter ModRNAString object
## seq: ACGU7In addition, one can also use the alphabet() function and subset to the
desired modifications.
mr4 <- ModRNAString(paste0("ACGU",alphabet(ModRNAString())[33L]))
mr4## 5-letter ModRNAString object
## seq: ACGUBTo offer a more streamlined functionality, which can take more information as
input, the function combineIntoModstrings() is implemented. It takes a
XString object and a GRanges object with a mod column and returns a
ModString object. The information in the mod column must match the short
name or nomenclature of the particular modification of interest as returned by
the shortName() or nomenclature() functions as seen above.
gr <- GRanges("1:5", mod = "m7G")
mr5 <- combineIntoModstrings(r, gr)
mr5## 5-letter ModRNAString object
## seq: ACGU7combineIntoModstrings() is also implemented for ModStringSet objects.
rs <- RNAStringSet(list(r,r,r,r,r))
names(rs) <- paste0("Sequence", seq_along(rs))
gr2 <- GRanges(seqnames = names(rs)[c(1L,1L,2L,3L,3L,4L,5L,5L)],
ranges = IRanges(start = c(4L,5L,5L,4L,5L,5L,4L,5L),
width = 1L),
mod = c("D","m7G","m7G","D","m7G","m7G","D","m7G"))
gr2## GRanges object with 8 ranges and 1 metadata column:
## seqnames ranges strand | mod
## <Rle> <IRanges> <Rle> | <character>
## [1] Sequence1 4 * | D
## [2] Sequence1 5 * | m7G
## [3] Sequence2 5 * | m7G
## [4] Sequence3 4 * | D
## [5] Sequence3 5 * | m7G
## [6] Sequence4 5 * | m7G
## [7] Sequence5 4 * | D
## [8] Sequence5 5 * | m7G
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengthsmrs <- combineIntoModstrings(rs, gr2)
mrs## A ModRNAStringSet instance of length 5
## width seq names
## [1] 5 ACGD7 Sequence1
## [2] 5 ACGU7 Sequence2
## [3] 5 ACGD7 Sequence3
## [4] 5 ACGU7 Sequence4
## [5] 5 ACGD7 Sequence5The reverse operation is also available via the function separate(), which
allows the positions of modifications to be transfered into a tabular format.
gr3 <- separate(mrs)
rs2 <- RNAStringSet(mrs)
gr3## GRanges object with 8 ranges and 1 metadata column:
## seqnames ranges strand | mod
## <Rle> <IRanges> <Rle> | <character>
## [1] Sequence1 4 + | D
## [2] Sequence1 5 + | m7G
## [3] Sequence2 5 + | m7G
## [4] Sequence3 4 + | D
## [5] Sequence3 5 + | m7G
## [6] Sequence4 5 + | m7G
## [7] Sequence5 4 + | D
## [8] Sequence5 5 + | m7G
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengthsrs2## RNAStringSet object of length 5:
## width seq names
## [1] 5 ACGUG Sequence1
## [2] 5 ACGUG Sequence2
## [3] 5 ACGUG Sequence3
## [4] 5 ACGUG Sequence4
## [5] 5 ACGUG Sequence5modifyNucleotides() and therefore also combineIntoModstrings() requires,
that the nucleotides to be modified match the originating base for the
modification. The next chunk fails, since the originating base for m7G is of
course G.
modifyNucleotides(r,4L,"m7G")## Error: Modification type does not match the originating base:
## U != G for m7GCalls for both functions check the sanity for this operation, so that the next
bit is always TRUE.
r <- RNAString("ACGUG")
mr2 <- modifyNucleotides(r,5L,"m7G")
r == RNAString(mr2)## [1] TRUEModString objectsModString objects can be directly compared to RNAString or DNAString
objects depending on the type (ModRNA to RNA and ModDNA to DNA).
r == ModRNAString(r)## [1] TRUEr == mr## [1] FALSErs == ModRNAStringSet(rs)## [1] TRUE TRUE TRUE TRUE TRUErs == c(mrs[1L:3L],rs[4L:5L])## [1] FALSE FALSE FALSE TRUE TRUEModString objectsModString objects can be converted into each other. However any conversion
will remove any information on modifications and revert each nucleotide back to
its originating nucleotide.
RNAString(mr)## 5-letter RNAString object
## seq: ACGUGModStringQuality information can be encoded alongside ModString objects by combining it
with a XStringQuality object inside a QualityScaledModStringSet object. Two
class are implemented: QualityScaledModRNAStringSet and
QualityScaledModDNAStringSet. They are usable as expected from a
QualityScaledXStringSet object.
qmrs <- QualityScaledModRNAStringSet(mrs,
PhredQuality(c("!!!!h","!!!!h","!!!!h",
"!!!!h","!!!!h")))
qmrs## A QualityScaledModRNAStringSet instance containing:
##
## A ModRNAStringSet instance of length 5
## width seq names
## [1] 5 ACGD7 Sequence1
## [2] 5 ACGU7 Sequence2
## [3] 5 ACGD7 Sequence3
## [4] 5 ACGU7 Sequence4
## [5] 5 ACGD7 Sequence5
##
## PhredQuality object of length 5:
## width seq
## [1] 5 !!!!h
## [2] 5 !!!!h
## [3] 5 !!!!h
## [4] 5 !!!!h
## [5] 5 !!!!hThey can also be constructed/deconstructed using the functions
combineIntoModstrings() and separate() and use an additional metadata column
named quality. For quality information to persist during construction, set the
argument with.qualities = TRUE. If a QualityScaledModStringSet is used as an
input to separate, the quality information are returned in the quality column.
We choose to avoid clashes with the score column and not to recycle it.
qgr <- separate(qmrs)
qgr## GRanges object with 8 ranges and 2 metadata columns:
## seqnames ranges strand | mod quality
## <Rle> <IRanges> <Rle> | <character> <integer>
## [1] Sequence1 4 + | D 71
## [2] Sequence1 5 + | m7G 0
## [3] Sequence2 5 + | m7G 71
## [4] Sequence3 4 + | D 0
## [5] Sequence3 5 + | m7G 71
## [6] Sequence4 5 + | m7G 0
## [7] Sequence5 4 + | D 71
## [8] Sequence5 5 + | m7G 71
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengthscombineIntoModstrings(mrs,qgr, with.qualities = TRUE)## A QualityScaledModRNAStringSet instance containing:
##
## A ModRNAStringSet instance of length 5
## width seq names
## [1] 5 ACGD7 Sequence1
## [2] 5 ACGU7 Sequence2
## [3] 5 ACGD7 Sequence3
## [4] 5 ACGU7 Sequence4
## [5] 5 ACGD7 Sequence5
##
## PhredQuality object of length 5:
## width seq
## [1] 5 !!!h!
## [2] 5 !!!!h
## [3] 5 !!!!h
## [4] 5 !!!!!
## [5] 5 !!!hhModString objects to/from fileThe nucleotide sequences with modifications can be saved to a fasta or
fastq file using the functions writeModStringSet(). Reading of these files
is achieved using readModRNAStringSet() or readModDNAStringSet(). In case of
fastq files, the sequences can be automatically read as a
QualityScaledModRNAStringSet using readQualityScaledModRNAStringSet()
function.
writeModStringSet(mrs, file = "test.fasta")
# note the different function name. Otherwise empty qualities will be written
writeQualityScaledModStringSet(qmrs, file = "test.fastq")
mrs2 <- readModRNAStringSet("test.fasta", format = "fasta")
mrs2## A ModRNAStringSet instance of length 5
## width seq names
## [1] 5 ACGD7 Sequence1
## [2] 5 ACGU7 Sequence2
## [3] 5 ACGD7 Sequence3
## [4] 5 ACGU7 Sequence4
## [5] 5 ACGD7 Sequence5qmrs2 <- readQualityScaledModRNAStringSet("test.fastq")
qmrs2## A QualityScaledModRNAStringSet instance containing:
##
## A ModRNAStringSet instance of length 5
## width seq names
## [1] 5 ACGD7 Sequence1
## [2] 5 ACGU7 Sequence2
## [3] 5 ACGD7 Sequence3
## [4] 5 ACGU7 Sequence4
## [5] 5 ACGD7 Sequence5
##
## PhredQuality object of length 5:
## width seq
## [1] 5 !!!!h
## [2] 5 !!!!h
## [3] 5 !!!!h
## [4] 5 !!!!h
## [5] 5 !!!!hSince these functions are specifically designed to work with the modified
nucleotides within the sequence, they are slower than the analogous functions
from the Biostrings package. This is the result of a purely R based
implementation, whereas Biostrings functions are spead up through a C
backend. This is a potential improvement for future developments, but
currently special sequence files are limited, so it is not a priority.
Pattern matching is implemented as well as expected for XString objects.
matchPattern("U7",mr)## Views on a 5-letter ModRNAString subject
## subject: ACGU7
## views:
## start end width
## [1] 4 5 2 [U7]vmatchPattern("D7",mrs)## MIndex object of length 5
## $Sequence1
## IRanges object with 1 range and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 4 5 2
##
## $Sequence2
## IRanges object with 0 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
##
## $Sequence3
## IRanges object with 1 range and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 4 5 2
##
## $Sequence4
## IRanges object with 0 ranges and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
##
## $Sequence5
## IRanges object with 1 range and 0 metadata columns:
## start end width
## <integer> <integer> <integer>
## [1] 4 5 2mrl <- unlist(mrs)
matchLRPatterns("7ACGU","U7ACG",100L,mrl)## Views on a 25-letter ModRNAString subject
## subject: ACGD7ACGU7ACGD7ACGU7ACGD7
## views:
## start end width
## [1] 5 23 19 [7ACGU7ACGD7ACGU7ACG]In principle post-translational modifications of proteins could also be implemented. However, a one letter alphabet of post-translational modifications must be developed first. If you are already aware of such an alphabet and want to use it in a Bioconductor context, let us know.
This is a quick example showing how sequence information containing modified
nucleotides can be imported into an R session using the Modstrings package.
The file needs to be UTF-8 encoded.
# read the lines
test <- readLines(system.file("extdata","test.fasta",package = "Modstrings"),
encoding = "UTF-8")
head(test,2L)## [1] "> tRNA | Ala | AGC | Saccharomyces cerevisiae | cytosolic"
## [2] "-GGGCGUGUKGCGUAGDC-GGD--AGCGCRCUCCCUUIGCOPGGGAGAG-------------------GDCUCCGGTPCGAUUCCGGACUCGUCCACCA"# keep every second line as sequence, the other one as name
names <- test[seq.int(from = 1L, to = 104L, by = 2L)]
seq <- test[seq.int(from = 2L, to = 104L, by = 2L)]
# sanitize input. This needs to be adapt to the individual case
names <- gsub(" ","_",
gsub("> ","",
gsub(" \\| ","-",
names)))
seq <- gsub("-","",gsub("_","",seq))
names(seq) <- names# sanitize special characters to Modstrings equivalent
seq <- sanitizeFromModomics(seq)
seq <- ModRNAStringSet(seq)
seq## A ModRNAStringSet instance of length 52
## width seq names
## [1] 76 GGGCGUGUKGCGUAGDCGGDAGC...PCGAUUCCGGACUCGUCCACCA tRNA-Ala-AGC-Saccha...
## [2] 75 GCUCGCGUKLCGUAADGGCAACG...PCG"CCCCCAUCGUGAGUGCCA tRNA-Arg-UCU-Saccha...
## [3] 76 PUCCUCGUKLCCCAADGGDCACG...PCA"GUCCUGGCGGGGAAGCCA tRNA-Arg-ICG-Saccha...
## [4] 77 GACUCCAUGLCCAAGDDGGDDAA...PCA"CCCUCACUGGGGUCGCCA tRNA-Asn-GUU-Saccha...
## [5] 75 UCCGUGAUAGUUPAADGGDCAGA...PCAAUUCCCCGUCGCGGAGCCA tRNA-Asp-GUC-Saccha...
## ... ... ...
## [48] 76 GCUCUCUUAGCUUAADGGDUAAA...PCAAAUCAUGGAGAGAGUACCA tRNA-Arg-NCU-Saccha...
## [49] 90 GGAUGGUUGACUGAGDGGDUUAA...PCAAAUCCUACAUCAUCCGCCA tRNA-Ser-UGA-Saccha...
## [50] 90 GGAUGGUUGACUGAGDGGDUUAA...PCAAAUCCUACAUCAUCCGCCA tRNA-Ser-UGA-Saccha...
## [51] 73 GUAAAUAUAAUUUAADGGDAAAA...PCAAAUCUUAGUAUUUACACCA tRNA-Thr-UAG-Saccha...
## [52] 74 AAGGAUAUAGUUUAADGGDAAAA...PCGAAUCUCUUUAUCCUUGCCA tRNA-Trp-!CA-Saccha...# convert the contained modifications into a tabular format
separate(seq)## GRanges object with 567 ranges and 1 metadata column:
## seqnames ranges strand | mod
## <Rle> <IRanges> <Rle> | <character>
## [1] tRNA-Ala-AGC-Sacchar.. 9 + | m1G
## [2] tRNA-Ala-AGC-Sacchar.. 16 + | D
## [3] tRNA-Ala-AGC-Sacchar.. 20 + | D
## [4] tRNA-Ala-AGC-Sacchar.. 26 + | m2,2G
## [5] tRNA-Ala-AGC-Sacchar.. 34 + | I
## ... ... ... ... . ...
## [563] tRNA-Trp-!CA-Sacchar.. 33 + | cmnm5U
## [564] tRNA-Trp-!CA-Sacchar.. 36 + | xA
## [565] tRNA-Trp-!CA-Sacchar.. 38 + | Y
## [566] tRNA-Trp-!CA-Sacchar.. 52 + | m5U
## [567] tRNA-Trp-!CA-Sacchar.. 53 + | Y
## -------
## seqinfo: 47 sequences from an unspecified genome; no seqlengthssessionInfo()## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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] GenomicRanges_1.52.0 Modstrings_1.16.0 Biostrings_2.68.0
## [4] GenomeInfoDb_1.36.0 XVector_0.40.0 IRanges_2.34.0
## [7] S4Vectors_0.38.0 BiocGenerics_0.46.0 BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.6.2 crayon_1.5.2 cli_3.6.1
## [4] knitr_1.42 rlang_1.1.0 xfun_0.39
## [7] stringi_1.7.12 jsonlite_1.8.4 glue_1.6.2
## [10] RCurl_1.98-1.12 htmltools_0.5.5 sass_0.4.5
## [13] rmarkdown_2.21 evaluate_0.20 jquerylib_0.1.4
## [16] bitops_1.0-7 fastmap_1.1.1 lifecycle_1.0.3
## [19] yaml_2.3.7 bookdown_0.33 stringr_1.5.0
## [22] BiocManager_1.30.20 compiler_4.3.0 digest_0.6.31
## [25] R6_2.5.1 GenomeInfoDbData_1.2.10 magrittr_2.0.3
## [28] bslib_0.4.2 tools_4.3.0 zlibbioc_1.46.0
## [31] cachem_1.0.7Boccaletto, Pietro, Magdalena A. Machnicka, Elzbieta Purta, Pawel Piatkowski, Blazej Baginski, Tomasz K. Wirecki, Valérie de Crécy-Lagard, et al. 2018. “MODOMICS: A Database of Rna Modification Pathways. 2017 Update.” Nucleic Acids Research 46 (D1): D303–D307. https://doi.org/10.1093/nar/gkx1030.
H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy. 2017. “Biostrings.” Bioconductor. https://doi.org/10.18129/B9.bioc.Biostrings.
Jühling, Frank, Mario Mörl, Roland K. Hartmann, Mathias Sprinzl, Peter F. Stadler, and Joern Pütz. 2009. “TRNAdb 2009: Compilation of tRNA Sequences and tRNA Genes.” Nucleic Acids Research 37: D159–D162. https://doi.org/10.1093/nar/gkn772.