miRNA affinity models and the KdModel class
26 April 2022
Abstract
This vignettes introduces the KdModel and KdModelList classes used for storing miRNA 12-mer affinities and predicting the dissociation constant of specific sites.Package
scanMiR 1.2.0
Contents
1 12-mer dissociation rates
McGeary, Lin et al. (2019) used
RNA bind-n-seq (RBNS) to empirically determine the affinities (i.e. dissoiation
rates) of selected miRNAs towards random 12-nucleotide sequences (termed
12-mers). As expected, bound sequences typically exhibited complementarity to
the miRNA seed region (positions 2-8 from the miRNA’s 5’ end), but the study
also revealed non-canonical bindings and the importance of flanking
di-nucleotides. Based on these data, the authors developed a model which
predicted 12-mer dissociation rates (KD) based on the miRNA sequence. ScanMiR
encodes a compressed version of these prediction in the form of a KdModel
object.
The 12-mer is defined as the 8 nucleotides opposite the miRNA’s extended seed region plus flanking dinucleotides on either side:
## Warning in knitr::include_graphics(system.file("docs", "12mer.png", package =
## "scanMiR")): It is highly recommended to use relative paths for images. You had
## absolute paths: "/tmp/RtmpMATF3z/Rinste54c68cda8d0/scanMiR/docs/12mer.png"
2 KdModels
The KdModel class contains the information concerning the sequence (12-mer)
affinity of a given miRNA, and is meant to compress and make easily manipulable
the dissociation constants (Kd) predictions from
McGeary, Lin et al. (2019).
We can take a look at the example KdModel:
library(scanMiR)
data(SampleKdModel)
SampleKdModel## A `KdModel` for hsa-miR-155-5p (Conserved across mammals)
## Sequence: UUAAUGCUAAUCGUGAUAGGGGUU
## Canonical target seed: AGCATTA(A)In addition to the information necessary to predict the binding affinity to any
given 12-mer sequence, the model contains, minimally, the name and sequence of
the miRNA. Since the KdModel class extends the list class, any further
information can be stored:
SampleKdModel$myVariable <- "test"An overview of the binding affinities can be obtained with the following plot:
plotKdModel(SampleKdModel, what="seeds")
The plot gives the -log(Kd) values of the top 7-mers (including both canonical and non-canonical sites), with or without the final “A” vis-à-vis the first miRNA nucleotide.
To predict the dissociation constant (and binding type, if any) of a given
12-mer sequence, you can use the assignKdType function:
assignKdType("ACGTACGTACGT", SampleKdModel)## type log_kd
## 1 non-canonical 0# or using multiple sequences:
assignKdType(c("CTAGCATTAAGT","ACGTACGTACGT"), SampleKdModel)## type log_kd
## 1 8mer -5129
## 2 non-canonical 0The log_kd column contains log(Kd) values multiplied by 1000 and stored as an integer (which is more economical when dealing with millions of sites). In the example above, -5129 means -5.129, or a dissociation constant of 0.0059225. The smaller the values, the stronger the relative affinity.
2.1 KdModelLists
A KdModelList object is simply a collection of KdModel objects. We can
build one in the following way:
# we create a copy of the KdModel, and give it a different name:
mod2 <- SampleKdModel
mod2$name <- "dummy-miRNA"
kml <- KdModelList(SampleKdModel, mod2)
kml## An object of class "KdModelList"
## [[1]]
## A `KdModel` for hsa-miR-155-5p (Conserved across mammals)
## Sequence: UUAAUGCUAAUCGUGAUAGGGGUU
## Canonical target seed: AGCATTA(A)
## [[2]]
## A `KdModel` for dummy-miRNA (Conserved across mammals)
## Sequence: UUAAUGCUAAUCGUGAUAGGGGUU
## Canonical target seed: AGCATTA(A)summary(kml)## A `KdModelList` object containing binding affinity models from 2 miRNAs.
##
## Low-confidence Poorly conserved
## 0 0
## Conserved across mammals Conserved across vertebrates
## 2 0Beyond operations typically performed on a list (e.g. subsetting), some specific slots of the respective KdModels can be accessed, for example:
conservation(kml)## hsa-miR-155-5p dummy-miRNA
## Conserved across mammals Conserved across mammals
## 4 Levels: Low-confidence Poorly conserved ... Conserved across vertebrates3 Creating a KdModel object
KdModel objects are meant to be created from a table assigning a log_kd
values to 12-mer target sequences, as produced by the CNN from McGeary, Lin et
al. (2019). For the purpose of example, we create such a dummy table:
kd <- dummyKdData()
head(kd)## X12mer log_kd
## 1 AAAGCAAAAAAA -0.428
## 2 CAAGCACAAACA -0.404
## 3 GAAGCAGAAAGA -0.153
## 4 TAAGCATAAATA -1.375
## 5 ACAGCAACAAAC -0.448
## 6 CCAGCACCAACC -0.274A KdModel object can then be created with:
mod3 <- getKdModel(kd=kd, mirseq="TTAATGCTAATCGTGATAGGGGTT", name = "my-miRNA")Alternatively, the kd argument can also be the path to the output file of the
CNN (and if mirseq and name are in the table, they can be omitted).
4 Common KdModel collections
The scanMiRData package contains
KdModel collections corresponding to all human, mouse and rat mirbase miRNAs.
5 Under the hood
When calling getKdModel, the dissociation constants are stored as an
lightweight overfitted linear model, with base KDs coefficients (stored as
integers in object$mer8) for each 1024 partially-matching 8-mers (i.e. at
least 4 consecutive matching nucleotides) to which are added 8-mer-specific
coefficients (stored in object$fl) that are multiplied with a flanking score
generated by the flanking di-nucleotides. The flanking score is calculated
based on the di-nucleotide effects experimentally measured by McGeary, Lin et
al. (2019). To save space, the actual 8-mer sequences are not stored but
generated when needed in a deterministic fashion. The 8-mers can be obtained,
in the right order, with the getSeed8mers function.
Session info
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## 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
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scanMiR_1.2.0 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.2 xfun_0.30 bslib_0.3.1
## [4] purrr_0.3.4 colorspace_2.0-3 vctrs_0.4.1
## [7] generics_0.1.2 htmltools_0.5.2 stats4_4.2.0
## [10] yaml_2.3.5 utf8_1.2.2 rlang_1.0.2
## [13] jquerylib_0.1.4 pillar_1.7.0 glue_1.6.2
## [16] DBI_1.1.2 BiocParallel_1.30.0 BiocGenerics_0.42.0
## [19] GenomeInfoDbData_1.2.8 lifecycle_1.0.1 ggseqlogo_0.1
## [22] stringr_1.4.0 zlibbioc_1.42.0 Biostrings_2.64.0
## [25] munsell_0.5.0 gtable_0.3.0 evaluate_0.15
## [28] labeling_0.4.2 knitr_1.38 IRanges_2.30.0
## [31] fastmap_1.1.0 GenomeInfoDb_1.32.0 parallel_4.2.0
## [34] fansi_1.0.3 highr_0.9 Rcpp_1.0.8.3
## [37] scales_1.2.0 BiocManager_1.30.17 S4Vectors_0.34.0
## [40] magick_2.7.3 jsonlite_1.8.0 XVector_0.36.0
## [43] farver_2.1.0 ggplot2_3.3.5 digest_0.6.29
## [46] stringi_1.7.6 bookdown_0.26 dplyr_1.0.8
## [49] cowplot_1.1.1 GenomicRanges_1.48.0 grid_4.2.0
## [52] cli_3.3.0 tools_4.2.0 bitops_1.0-7
## [55] magrittr_2.0.3 sass_0.4.1 RCurl_1.98-1.6
## [58] tibble_3.1.6 crayon_1.5.1 pkgconfig_2.0.3
## [61] ellipsis_0.3.2 data.table_1.14.2 assertthat_0.2.1
## [64] rmarkdown_2.14 R6_2.5.1 compiler_4.2.0