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.
RNAmodR.ML implements additional classes from the base package
to train and use machine learning models. The package contains an example
workflow for random forest models with the
(Wright & Ziegler 2017). In addition classes for using
deep learning models generated with the
keras package are also implemented
(Allaire & Chollet 2018). Classes for other machine learning
models can also be easily implemented.
The current version of the RNAmodR.Data package is available from Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("RNAmodR.ML") library(RNAmodR.ML)
ModifierML class extends the
Modifier class from the
and adds one slot,
mlModel, a getter/setter
For different types of models
ModifierMLModel derived classes are available,
which currently are:
ModifierMLrangerfor models generated with the
rangerpackage (Wright & Ziegler 2017)
ModifierMLkerasfor models generated with the
keraspackage (Allaire & Chollet 2018)
An trained model can be used to create a
ModifierMLModel object. The generated
ModifierMLModel object can then be set for the
ModifierML object using the
For more details, please have a look at the vignette.
Marvin N. Wright and Andreas Ziegler (2017): "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R". Journal of Statistical Software 77 (1): 1-17. https://doi.org/10.18637/jss.v077.i01
JJ Allaire and François Chollet (2018): "keras: R Interface to 'Keras'". R package version 2.2.4 (https://CRAN.R-project.org/package=keras)
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