RNAmodR.ML: detecting patterns of post-transcriptional modifications using machine learning
Introduction
Post-transcriptional modifications can be found abundantly in rRNA and tRNA and can be detected classically via several strategies. However, difficulties arise if the identity and the position of the modified nucleotides is to be determined at the same time. Classically, a primer extension, a form of reverse transcription (RT), would allow certain modifications to be accessed by blocks during the RT changes or changes in the cDNA sequences. Other modification would need to be selectively treated by chemical reactions to influence the outcome of the reverse transcription.
With the increased availability of high throughput sequencing, these classical methods were adapted to high throughput methods allowing more RNA molecules to be accessed at the same time. However, patterns of some modification cannot be detected by accessing small number of parameters. For these cases machine learning models can be trained on data from positions known to be modified in order to detect additional modified positions.
To extend the functionality of the RNAmodR package and
classical detection strategies used for RiboMethSeq or AlkAnilineSeq
(see RNAmodR.RiboMethSeq and
RNAmodR.AlkAnilineSeq packages) towards detection through
machine learning models, RNAmodR.ML provides classes and an
example workflow.
Using RNAmodR.ML
## Warning: replacing previous import 'utils::findMatches' by
## 'S4Vectors::findMatches' when loading 'ExperimentHubData'The ModifierML class extends the Modifier
class from the RNAmodR package and adds one slot,
mlModel, a getter/setter
getMLModel/setMLModel, an additional
useMLModel function to be called from the
aggregate function.
The slot mlModel can either be an empty character or
contain a name of a ModifierMLModel class, which is loaded
upon creation of a ModifierML object, and serves as a
wrapper around a machine learning model. For different types of machine
learning models ModifierMLModel derived classes are
available, which currently are:
ModifierMLrangerfor models generated with therangerpackage (Wright and Ziegler 2017)ModifierMLkerasfor models generated with thekeraspackage (Allaire and Chollet 2018)
To illustrate how to develop a machine learning model with help from
the RNAmodR.ML package, an example is given below.
Development of new Modifier class
As an example for this vignette, we will try to detect D positions in
AlkAnilineSeq data. First define a specific ModifierML
class loading pileup and coverage data. In this example, the RNA
specific RNAModifierML class is used.
setClass("ModMLExample",
contains = c("RNAModifierML"),
prototype = list(mod = c("D"),
score = "score",
dataType = c("PileupSequenceData",
"CoverageSequenceData"),
mlModel = character(0)))
# constructor function for ModMLExample
ModMLExample <- function(x, annotation = NA, sequences = NA, seqinfo = NA,
...){
RNAmodR:::Modifier("ModMLExample", x = x, annotation = annotation,
sequences = sequences, seqinfo = seqinfo, ...)
}
setClass("ModSetMLExample",
contains = "ModifierSet",
prototype = list(elementType = "ModMLExample"))
# constructor function for ModSetMLExample
ModSetMLExample <- function(x, annotation = NA, sequences = NA, seqinfo = NA,
...){
RNAmodR:::ModifierSet("ModMLExample", x, annotation = annotation,
sequences = sequences, seqinfo = seqinfo, ...)
}Since the mlModel slot contains an empty character, the
creation of the object will not automatically trigger a search for
modifications. However, it will aggregate the data in the format we want
to use. The aggregate_example function is just an example
and the aggregation of the data is part of the model building. (See
(#Summary))
Getting training data
To gather training data, we just create a ModMLExample
object and let it do its job.
## Import genomic features from the file as a GRanges object ... OK
## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ...## Warning in .makeTxDb_normarg_chrominfo(chrominfo): genome version information
## is not available for this TxDb object## OK
## Loading Pileup data from BAM files ... OK
## Loading Coverage data from BAM files ... OK
## Aggregating data and calculating scores ... Starting to search for 'Dihydrouridine' ...## Warning: ML model not set. Skipped search for modifications ...## done.Afterwards we need to load/create coordinates for positions known to be modified as well as positions known to be unmodified.
data("dmod",package = "RNAmodR.ML")
# we just select the next U position from known positions
nextUPos <- function(gr){
nextU <- lapply(seq.int(1L,2L),
function(i){
subseq <- subseq(sequences(me)[dmod$Parent], start(dmod)+3L)
pos <- start(dmod) + 2L +
vapply(strsplit(as.character(subseq),""),
function(y){which(y == "U")[i]},integer(1))
ans <- dmod
ranges(ans) <- IRanges(start = pos, width = 1L)
ans
})
nextU <- do.call(c,nextU)
nextU$mod <- NULL
unique(nextU)
}
nondmod <- nextUPos(dmod)
nondmod <- nondmod[!(nondmod %in% dmod)]
coord <- unique(c(dmod,nondmod))
coord <- coord[order(as.integer(coord$Parent))]With these coordinates the aggregated data of the
ModMLExample can be subset to a training data set using the
function trainingData.
Training a model
How a specific model can be trained or what kind of strategies can be
employed to successfully train a model, is out of scope for the
vignette. For this example a random forest is trained using the
functionality provided by the ranger package.
Constructing a ‘ModifierMLModel’
Now, the trained model can be used to create a new
ModifierMLModel class and object.
setClass("ModifierMLexample",
contains = c("ModifierMLranger"),
prototype = list(model = model))
ModifierMLexample <- function(...){
new("ModifierMLexample")
}
mlmodel <- ModifierMLexample()To be able to use the model via the ModifierMLModel
class, we also need to define an accessor to the predictions made by the
model. This function is called useModel and is already
prefined for the ModifierMLModel classes mentioned above in
secion Using RNAmodR.ML.
## Method Definition:
##
## function (x, y)
## {
## data <- getAggregateData(y)
## model <- x@model
## if (!is(model, "ranger")) {
## stop("model is not a ranger model")
## }
## unlisted_data <- unlist(data, use.names = FALSE)
## p <- predict(x@model, data.frame(unlisted_data))
## relist(p$predictions, data)
## }
## <bytecode: 0x560b762bcdc0>
## <environment: namespace:RNAmodR.ML>
##
## Signatures:
## x y
## target "ModifierMLranger" "ModifierML"
## defined "ModifierMLranger" "ModifierML"If the results of these function is not usable for a specific model,
it can redefined for your needs. As defined by RNAmodR.ML
the function returns a NumericList along the aggregated
data of the ModifierML object.
Setting and using the model
The generated ModifierMLexample wrapper can now be set
for the ModifierML object using the setMLModel
function. (If a model is saved as part of a package, this step ist not
necessary, because it can be part of the class definition)
In order for the prediction data to be usable, another function has
to be implemented to save the predictions in the aggregated data. The
function is called useMLModel.
setMethod(f = "useMLModel",
signature = signature(x = "ModMLExample"),
definition =
function(x){
predictions <- useModel(getMLModel(x), x)
data <- getAggregateData(x)
unlisted_data <- unlist(data, use.names = FALSE)
unlisted_data$score <- unlist(predictions)
x@aggregate <- relist(unlisted_data,data)
x
}
)By re-running the aggregate function and force an update
of the data, the predictions are made and used to populate the
score column as outlined above.
Performance
During the model building phase some form of a performance
measurement usually is included. In addition to these model specific
measurements, RNAmodR.ML includes the functionality from
the ROCR package inherited from the RNAmodR
package. With this the performance of a model can evaluted over the
training set or any coordinates.
Performance of the maching learning model to distinguish unmodified from modified nucleotides.
Using a ModifierML class
Since we want to use the ModifierML object to detect
modifications, we also need to define the findMod function.
Based on the information on the performance, we set a threshold of
0.8 for the prediction score for detecting D modifications.
In the example below this threshold is hardcoded in the
find_mod_example function, but can also be implemented
using the settings function.
setMethod(
f = "findMod",
signature = signature(x = "ModMLExample"),
definition =
function(x){
find_mod_example(x, 25L)
}
)Now we can redfine the ModMLExample class with the slot
mlModel already set to the ModifierMLexample
class. The ModMLExample is now complete.
rm(me)
setClass("ModMLExample",
contains = c("RNAModifierML"),
prototype = list(mod = c("D"),
score = "score",
dataType = c("PileupSequenceData",
"CoverageSequenceData"),
mlModel = "ModifierMLexample"))
me <- ModMLExample(files[[1]], annotation, sequences)## Import genomic features from the file as a GRanges object ... OK
## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ...## Warning in .makeTxDb_normarg_chrominfo(chrominfo): genome version information
## is not available for this TxDb object## OK
## Loading Pileup data from BAM files ... OK
## Loading Coverage data from BAM files ... OK
## Aggregating data and calculating scores ... Starting to search for 'Dihydrouridine' ... done.The detected modifications can be access using the
modifications function.
## GRangesList object of length 37:
## $`1`
## GRanges object with 2 ranges and 5 metadata columns:
## seqnames ranges strand | mod source type
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## [1] Q0020_15S_RRNA 48 + | D RNAmodR.ML RNAMOD
## [2] Q0020_15S_RRNA 323 + | D RNAmodR.ML RNAMOD
## score Parent
## <numeric> <character>
## [1] 0.936333 1
## [2] 0.832700 1
## -------
## seqinfo: 60 sequences from an unspecified genome; no seqlengths
##
## $`3`
## GRanges object with 1 range and 5 metadata columns:
## seqnames ranges strand | mod source type score
## <Rle> <IRanges> <Rle> | <character> <character> <character> <numeric>
## [1] RDN18-1 229 + | D RNAmodR.ML RNAMOD 0.819467
## Parent
## <character>
## [1] 3
## -------
## seqinfo: 60 sequences from an unspecified genome; no seqlengths
##
## $`4`
## GRanges object with 4 ranges and 5 metadata columns:
## seqnames ranges strand | mod source type score
## <Rle> <IRanges> <Rle> | <character> <character> <character> <numeric>
## [1] RDN25-1 2 + | D RNAmodR.ML RNAMOD 0.860233
## [2] RDN25-1 748 + | D RNAmodR.ML RNAMOD 0.818367
## [3] RDN25-1 1720 + | D RNAmodR.ML RNAMOD 0.828700
## [4] RDN25-1 2571 + | D RNAmodR.ML RNAMOD 0.810800
## Parent
## <character>
## [1] 4
## [2] 4
## [3] 4
## [4] 4
## -------
## seqinfo: 60 sequences from an unspecified genome; no seqlengths
##
## ...
## <34 more elements>Refining a model
Some of the modification found look reasonable. However, some of the positions seem to be noise.
Visualization of sequence data
Several options exist to improve the model: Either the threshold
applied to the prediction score can be raised to a higher value, like
0.9 or the model can maybe retrained by including these
position in another training data set. In addition, the training data
might be improved in general by higher sequencing depth.
nonValidMod <- mod[c("1","4")]
nonValidMod[["18"]] <- nonValidMod[["18"]][2]
nonValidMod[["26"]] <- nonValidMod[["26"]][2]
nonValidMod <- unlist(nonValidMod)
nonValidMod <- nonValidMod[,"Parent"]
coord <- unique(c(dmod,nondmod,nonValidMod))
coord <- coord[order(as.integer(coord$Parent))]As an example, a new model is trained including the wrongly identified positions from the previous model as unmodified positions.
trainingData <- trainingData(me,coord)
trainingData <- unlist(trainingData, use.names = FALSE)
trainingData$labels <- as.integer(trainingData$labels)model2 <- ranger(labels ~ ., data.frame(trainingData), num.trees = 2000)
setClass("ModifierMLexample2",
contains = c("ModifierMLranger"),
prototype = list(model = model2))
ModifierMLexample2 <- function(...){
new("ModifierMLexample2")
}
mlmodel2 <- ModifierMLexample2()
me2 <- me
setMLModel(me2) <- mlmodel2
me2 <- aggregate(me2, force = TRUE)After updating the ModifierMLexample class and
aggregating the data again the performance looks a bit better…
Performance aggregation of multiple samples and strategies.
… and leads to a better result for detecting D modifications. Some positions are not detected anymore, which suggest that this model is still not an optimal solution and other factors could and should be improved upon as suggested above.
setMethod(
f = "findMod",
signature = signature(x = "ModMLExample"),
definition =
function(x){
find_mod_example(x, 25L)
}
)
me2 <- modify(me2, force = TRUE)
modifications(me2)## GRanges object with 46 ranges and 5 metadata columns:
## seqnames ranges strand | mod source type
## <Rle> <IRanges> <Rle> | <character> <character> <character>
## [1] tA-AGC-D 47 + | D RNAmodR.ML RNAMOD
## [2] tA-TGC-A 47 + | D RNAmodR.ML RNAMOD
## [3] tC-GCA-B 19 + | D RNAmodR.ML RNAMOD
## [4] tC-GCA-B 46 + | D RNAmodR.ML RNAMOD
## [5] tE-CTC-D 20 + | D RNAmodR.ML RNAMOD
## ... ... ... ... . ... ... ...
## [42] tV-CAC-D 47 + | D RNAmodR.ML RNAMOD
## [43] tW-CCA-G1 16 + | D RNAmodR.ML RNAMOD
## [44] tW-CCA-G1 46 + | D RNAmodR.ML RNAMOD
## [45] tY-GTA-D 21 + | D RNAmodR.ML RNAMOD
## [46] tY-GTA-D 22 + | D RNAmodR.ML RNAMOD
## score Parent
## <numeric> <character>
## [1] 0.961825 6
## [2] 0.954383 7
## [3] 0.921766 8
## [4] 0.947067 8
## [5] 0.847392 11
## ... ... ...
## [42] 0.929375 57
## [43] 0.806283 59
## [44] 0.826408 59
## [45] 0.906250 60
## [46] 0.802633 60
## -------
## seqinfo: 60 sequences from an unspecified genome; no seqlengthsIn addition to training a single model, several models can be trained
and combined to a ModifierSet.
An overall performance over several models can be analyzed or the individual performance compaired.
plotROC(mse, dmod, score= "score",
plot.args = list(avg = "threshold", spread.estimate = "stderror"))
Performance average across models
If several models are trained and each provides useful information,
these can be package into a single ModifierMLModel class to
combine the output of several models. Some of the functions outlined
above need, e.g. useMLModel and/or useModel,
to be modified to provide one or more scores for detecting a
modification.
Packaging
If the created models can be saved to file, they can also be included in a package. This would allow others to use the models and the models can potentially be improved upon with increasing version numbers.
Summary
RNAmodR.ML provides the interface for machine learning
models to be used with RNAmodR to detect modified
nucleotides in high throughput sequencing data. For your own work
defining a working model might take some time. We hope that by using
RNAmodR.ML the steps surrounding this crucial step might
become a bit easier.
However, if some steps or design choices made for
RNAmodR.ML do not suit your need, let us know.
Contributions are always welcome as well.
Hints
We also want to provide some additional hints and suggestions for developing machine learning models.
- the aggregate function is used in the example above as a feature
engineering tool. You might want to skip this step, if you want to use a
deep learning model for example with
keras. - If you don’t want to engage in a feature enginering step and just
want to aggregate the sequence data as is, just do a custom
cbindon the data from theSequenceDataobjects (cbindworks onSequenceDataobjects, if they are of the same type. Convert each of them to aCompressedSplitDataFrameListusingas(x,"CompressedSplitDataFrameList")). - in a deep learning context, if a coordinate is selected without a
flanking value, e.g. when using
trainingData, a 2D tensor is returned (sample, values). This can be converted into a 3D tensor by providing a flanking value.
Sessioninfo
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ranger_0.17.0 Rsamtools_2.23.1 RNAmodR.Data_1.20.0
## [4] ExperimentHubData_1.33.0 AnnotationHubData_1.37.0 futile.logger_1.4.3
## [7] ExperimentHub_2.15.0 AnnotationHub_3.15.0 BiocFileCache_2.15.0
## [10] dbplyr_2.5.0 RNAmodR.ML_1.21.0 RNAmodR_1.21.0
## [13] Modstrings_1.23.0 Biostrings_2.75.1 XVector_0.47.0
## [16] rtracklayer_1.67.0 GenomicRanges_1.59.1 GenomeInfoDb_1.43.2
## [19] IRanges_2.41.1 S4Vectors_0.45.2 BiocGenerics_0.53.3
## [22] generics_0.1.3 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] BiocIO_1.17.1 bitops_1.0-9
## [3] filelock_1.0.3 tibble_3.2.1
## [5] graph_1.85.0 XML_3.99-0.17
## [7] rpart_4.1.23 lifecycle_1.0.4
## [9] httr2_1.0.7 lattice_0.22-6
## [11] ensembldb_2.31.0 OrganismDbi_1.49.0
## [13] backports_1.5.0 magrittr_2.0.3
## [15] Hmisc_5.2-0 sass_0.4.9
## [17] rmarkdown_2.29 jquerylib_0.1.4
## [19] yaml_2.3.10 RUnit_0.4.33
## [21] Gviz_1.51.0 DBI_1.2.3
## [23] buildtools_1.0.0 RColorBrewer_1.1-3
## [25] abind_1.4-8 zlibbioc_1.52.0
## [27] purrr_1.0.2 AnnotationFilter_1.31.0
## [29] biovizBase_1.55.0 RCurl_1.98-1.16
## [31] nnet_7.3-19 VariantAnnotation_1.53.0
## [33] rappdirs_0.3.3 GenomeInfoDbData_1.2.13
## [35] AnnotationForge_1.49.0 maketools_1.3.1
## [37] codetools_0.2-20 DelayedArray_0.33.2
## [39] xml2_1.3.6 tidyselect_1.2.1
## [41] UCSC.utils_1.3.0 matrixStats_1.4.1
## [43] base64enc_0.1-3 GenomicAlignments_1.43.0
## [45] jsonlite_1.8.9 Formula_1.2-5
## [47] tools_4.4.2 progress_1.2.3
## [49] stringdist_0.9.12 Rcpp_1.0.13-1
## [51] glue_1.8.0 gridExtra_2.3
## [53] SparseArray_1.7.2 BiocBaseUtils_1.9.0
## [55] xfun_0.49 MatrixGenerics_1.19.0
## [57] dplyr_1.1.4 withr_3.0.2
## [59] formatR_1.14 BiocManager_1.30.25
## [61] fastmap_1.2.0 latticeExtra_0.6-30
## [63] fansi_1.0.6 caTools_1.18.3
## [65] digest_0.6.37 mime_0.12
## [67] R6_2.5.1 colorspace_2.1-1
## [69] gtools_3.9.5 jpeg_0.1-10
## [71] dichromat_2.0-0.1 biomaRt_2.63.0
## [73] RSQLite_2.3.8 utf8_1.2.4
## [75] data.table_1.16.2 prettyunits_1.2.0
## [77] httr_1.4.7 htmlwidgets_1.6.4
## [79] S4Arrays_1.7.1 pkgconfig_2.0.3
## [81] gtable_0.3.6 blob_1.2.4
## [83] sys_3.4.3 htmltools_0.5.8.1
## [85] RBGL_1.83.0 ProtGenerics_1.39.0
## [87] scales_1.3.0 Biobase_2.67.0
## [89] png_0.1-8 colorRamps_2.3.4
## [91] knitr_1.49 lambda.r_1.2.4
## [93] rstudioapi_0.17.1 reshape2_1.4.4
## [95] rjson_0.2.23 checkmate_2.3.2
## [97] curl_6.0.1 biocViews_1.75.0
## [99] cachem_1.1.0 stringr_1.5.1
## [101] KernSmooth_2.23-24 BiocVersion_3.21.1
## [103] parallel_4.4.2 foreign_0.8-87
## [105] AnnotationDbi_1.69.0 restfulr_0.0.15
## [107] pillar_1.9.0 grid_4.4.2
## [109] vctrs_0.6.5 gplots_3.2.0
## [111] cluster_2.1.6 htmlTable_2.4.3
## [113] evaluate_1.0.1 GenomicFeatures_1.59.1
## [115] cli_3.6.3 compiler_4.4.2
## [117] futile.options_1.0.1 rlang_1.1.4
## [119] crayon_1.5.3 interp_1.1-6
## [121] plyr_1.8.9 stringi_1.8.4
## [123] deldir_2.0-4 BiocParallel_1.41.0
## [125] BiocCheck_1.43.2 txdbmaker_1.3.1
## [127] munsell_0.5.1 lazyeval_0.2.2
## [129] Matrix_1.7-1 BSgenome_1.75.0
## [131] hms_1.1.3 bit64_4.5.2
## [133] ggplot2_3.5.1 KEGGREST_1.47.0
## [135] SummarizedExperiment_1.37.0 ROCR_1.0-11
## [137] memoise_2.0.1 bslib_0.8.0
## [139] bit_4.5.0