## Warning in knitr::include_graphics(system.file("docs", "sticker.svg", package =
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1 Scanning

1.1 Background

scanMiR can be used to identify potential binding sites given a set of miRNAs and a set of transcripts. Furthermore, it determines the type of binding site and, given a KdModel object, the predicted affinity of the site.

1.2 Basic Scan

The main function used for determining matches of miRNAs in a given set of sequences is findSeedMatches. It accepts a set of DNA sequences either as a character vector or as a DNAStringSet. The miRNAs can be provided either as a character vector of seeds/miRNA sequences or as a KdModelList.

1.2.1 Using a miRNA Seed

The seed must be given in the form of a (RNA or DNA) sequence of length 7 or 8 (the 8th nucleotide being the final ‘A’ - it is added if only 7 are given). Note that the seed should be given as it would appear in a match in the target sequence (i.e. the reverse complement of how it appears in the miRNA).


# seed sequence of hsa-miR-155-5p
seed <- "AGCAUUAA"

# load a sample transcript

# run scan
matches <- findSeedMatches(SampleTranscript, seed, verbose = FALSE)
## GRanges object with 2 ranges and 1 metadata column:
##       seqnames    ranges strand |     type
##          <Rle> <IRanges>  <Rle> | <factor>
##   [1]     seq1   491-498      * |  8mer   
##   [2]     seq1   692-699      * |  7mer-m8
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

By default, a GRanges object is returned. Apart from the position of the matches, it provides information on the type of the putative binding site corresponding to the match. Setting ret = "data.frame" returns the same information as a data.frame.

1.2.2 Using a miRNA sequence

Alternatively, we can provide the full miRNA sequence, which results in additional information on supplementary 3’ pairing in the form of an aggregated score (see Section 1.3.2 for further details).

# full sequence of the mature miR-155-5p transcript

# run scan
matches <- findSeedMatches(SampleTranscript, miRNA, verbose = FALSE)
## Warning in XStringSet(pattern_seqtype, pattern): metadata columns on input
## DNAStringSet object were dropped
## GRanges object with 2 ranges and 3 metadata columns:
##       seqnames    ranges strand |     type  p3.score  note
##          <Rle> <IRanges>  <Rle> | <factor> <integer> <Rle>
##   [1]     seq1   491-498      * |  8mer           12 TDMD?
##   [2]     seq1   692-699      * |  7mer-m8         0     -
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

We can take a closer look at the alignment of the first match:

viewTargetAlignment(matches[1], miRNA, SampleTranscript)
## Warning in XStringSet(pattern_seqtype, pattern): metadata columns on input
## DNAStringSet object were dropped
## miRNA             3'-UUGGGGAUAGUGC-UAA-UCGUAAUU-5'     
##                       ||||||||||||     ||||||||        

Apart from the direct seed match (right), this representation also reveals the extensive supplementary 3’ pairing (left).

1.2.3 Using a KdModel

Finally, we can provide the miRNA in the form of a KdModel (see the vignette on KdModels for further information). In this case findSeedMatches also returns the predicted affinity value for each match. The log_kd column contains log(Kd) values multiplied by 1000, where Kd is the predicted dissociation constant of miRNA:mRNA binding for the putative binding site.

# load sample KdModel

# run scan
matches <- findSeedMatches(SampleTranscript, SampleKdModel, verbose = FALSE)
## Warning in XStringSet(pattern_seqtype, pattern): metadata columns on input
## DNAStringSet object were dropped
## GRanges object with 2 ranges and 4 metadata columns:
##       seqnames    ranges strand |     type    log_kd  p3.score  note
##          <Rle> <IRanges>  <Rle> | <factor> <integer> <integer> <Rle>
##   [1]     seq1   491-498      * |  8mer        -4868        12 TDMD?
##   [2]     seq1   692-699      * |  7mer-m8     -3702         0     -
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

Running findSeedMatches using a Kdmodel also returns matches that correspond to non-canonical binding sites. These are typically of low affinity, but may affect repression if several of them are found on the same transcript. The scan can be restricted to canonical sites using the option onlyCanonical = TRUE.

KdModel collections corresponding to all human, mouse and rat mirbase miRNAs can be obtained through the scanMiRData package.

1.3 Further Options

1.3.1 ORF length

If the transcript sequences are provided as a DNAStringSet, one may specify the length of the open reading frame region of the transcripts as a metadata column in order to distinguish between matches in the ORF and 3’UTR regions.


# generate set of random sequences
seqs <- DNAStringSet(getRandomSeq(length = 1000, n = 10))

# add vector of ORF lengths
mcols(seqs)$ORF.length <- sample(500:800, length(seqs))

# run scan
matches2 <- findSeedMatches(seqs, SampleKdModel, verbose = FALSE)
## Warning in XStringSet(pattern_seqtype, pattern): metadata columns on input
## DNAStringSet object were dropped
## GRanges object with 6 ranges and 5 metadata columns:
##       seqnames    ranges strand |          type    log_kd  p3.score  note   ORF
##          <Rle> <IRanges>  <Rle> |      <factor> <integer> <integer> <Rle> <Rle>
##   [1]     seq1   874-881      * | 7mer-m8           -4339         0     - FALSE
##   [2]     seq1   401-408      * | 6mer              -2268         0     -  TRUE
##   [3]     seq2   595-602      * | non-canonical     -1952         4     - FALSE
##   [4]     seq2   943-950      * | non-canonical     -1043         0     - FALSE
##   [5]     seq6   604-611      * | 6mer              -2057         4     - FALSE
##   [6]     seq9   654-661      * | 6mer-a1           -1547         0     - FALSE
##   -------
##   seqinfo: 10 sequences from an unspecified genome; no seqlengths

1.3.2 Supplementary 3’ pairing

Upon binding the seed regions, further complementary pairing of the target to the 3’ region of the miRNA can increase affinity and further stabilize the binding (Schirle, Sheu-Gruttadauria and MacRae, 2014). Upon finding a seed match, scanMiR performs a local alignment on the upstream region to identify such complementary 3’ binding. This is internally done by the get3pAlignment() function, the arguments to which (e.g. the maximum size of the gap between the seed binding and the complementary binding) can be passed via the findSeedMatches argument p3.params. By default, when running findSeedMatches a 3’ score is reported in the matches, which roughly corresponds to the number of consecutive matching nucleotides (adjusting for small gaps and T/G substitutions) within the constraints (see ?get3pAlignment for more detail). More information (such as the size of the miRNA and target loops between the two complementary regions) can be reported by setting findSeedMatches(..., p3.extra=TRUE). In addition, the pairing can be visualized with viewTargetAlignment:

viewTargetAlignment(matches[1], SampleKdModel, SampleTranscript)
## Warning in XStringSet(pattern_seqtype, pattern): metadata columns on input
## DNAStringSet object were dropped
## miRNA             3'-UUGGGGAUAGUGC-UAA-UCGUAAUU-5'     
##                       ||||||||||||     ||||||||        

Some forms of 3’ bindings can however lead to drastic functional consequences. For example, sufficient final complementary at the 3’ end of the miRNA can lead to Target-Directed miRNA Degradation (TDMD, Sheu-Gruttadauria, Pawlica et al., 2019). findSeedMatches will also flag such putative sites in the notes column of the matches. Finally, while circular RNAs can act as miRNA sponges, some miRNA bindings can slice their circular structure Hansen et al., 2011 and free their cargo. findSeedMatches will also flag such sites in the notes column.

1.3.3 Shadow and Overlapping Matches

The shadow argument can be used to take into account the observation that sites within the first ~15 nucleotides of the 3’UTR show poor efficiency (Grimson et al. 2007). findSeedMatches will treat matches within the first shadow positions of the UTR in the same way as matches in the ORF region. If no information on ORF lengths is provided, it will simply ignore the first shadow positions. The default setting is shadow = 0.

The parameter minDist can be used to specify the minimum distance between matches of the same miRNA (default 7). If there are multiple matches within minDist, only the highest affinity match will be considered.

1.3.4 Aggregation on the fly

With ret = "aggregated" one obtains a data.frame that contains the predicted repression for each sequence-seed-pair aggregated over all matches along with information about the types and number of matches. Parameters for aggregation can be specified using agg.params. For further details, see Section 2.

2 Aggregating Sites

2.1 Background

scanMiR implements aggregation of miRNA sites based on the biochemical model from McGeary, Lin et al. (2019). This model first predicts the occupancy of AGO-miRNA complexes at each potential binding site as a function of the measured or estimated dissociation constants (Kds). It then assumes an additive effect of the miRNA on the basal decay rate of the transcript that is proportional to this occupancy.

The key parameters of this model are:

  • a: the relative concentration of unbound AGO-miRNA complexes
  • b: the factor that multiplies with the occupancy and is added to the basal decay rate (can be interpreted as the additional repression caused by a single bound AGO)
  • c: the penalty factor for sites that are found within the ORF region

More specifically, the occupancy of a mRNA \(m\) by miRNA \(g\), with \(p\) matches in the ORF region and \(q\) matches in the 3’UTR region, is given by the following equation: \[ \begin{equation} N_{m,g} = \sum_{i=1}^{p}\left(\frac{a_g}{a_g + c_{\text{ORF}} K_{d,i}^{\text{ORF}}}\right) + \sum_{j=1}^{q}\left(\frac{a_g}{a_g + K_{d,j}^{\text{3'UTR}}}\right) \end{equation} \]

The corresponding background occupancy is estimated by substituting the average affinity of nonspecifically bound sites (i.e. \(K_d = 1.0\)): \[ \begin{equation} N_{m,g,\text{background}} = \sum_{i=1}^{p}\left(\frac{a_g}{a_g + c_{\text{ORF}}}\right) + \sum_{j=1}^{q}\left(\frac{a_g}{a_g + 1}\right) \end{equation} \] In addition to this original model, scanMiR includes a coefficient e which adjusts the Kd values based on the supplementary 3’ alignment:

\[ \begin{equation} N_{m,g} = \sum_{i=1}^{p}\left(\frac{a_g}{a_g + e_{i}c_{\text{ORF}} K_{d,i}^{\text{ORF}}}\right) + \sum_{j=1}^{q}\left(\frac{a_g}{a_g + e_{j}K_{d,j}^{\text{3'UTR}}}\right) \end{equation} \]

with \(e_i = \exp(\text{p3}\cdot\text{p3.score}_i)\). p3 is a global parameter, and \(p3.score_i\) is the 3’ alignment score (roughly corresponding to the number of matched nucleotides, by default capped to 8 and set to 0 if below 3). Of note, the default value of p3 is very small, leading to a very mild effect. The importance of complementary binding seems to depend on the miRNA, and at the moment there is no easy way to predict this from the miRNA sequence. Our conservative estimate might not attribute sufficient importance to this factor for some miRNAs.

The repression is then obtained as the log fold change of the two occupancies: \[ \text{repression} = \log(1+bN_{m,g,\text{background}}) - \log(1+bN_{m,g}) \]

Because UTR and ORF lengths have been reported to influence the efficacy of repression, scanMiR also includes an additional modifier to terms handling these effects:

\[ \text{repression}_{\text{adj}} = \text{repression}\cdot (1+f\cdot\text{UTR.length}+h\cdot\text{ORF.length}) \] While b, c, p3, f and h are considered global parameters (i.e. the same for different miRNAs and transcripts and also across experimental contexts), a is expected to be different for each miRNA in a given experimental condition. However, as shown by McGeary, Lin et al. (2019), the model performance is robust to changes in a over several orders of magnitude. Aggregation for all miRNA-transcript pairs for a given data set is therefore usually based on a single a value.

2.2 Basic Aggregation

Given a GRanges or data.frame of matches as returned by findSeedMatches, aggregation can be performed by the function aggregateMatches:

agg_matches <- aggregateMatches(matches2)
##   transcript  repression 8mer 7mer 6mer non-canonical ORF.canonical
## 1       seq1 -0.72847915    0    1    0             0             1
## 2      seq10 -0.03904096    0    0    0             1             0
## 3       seq2 -0.13713291    0    0    0             2             0
## 4       seq6 -0.14713966    0    0    1             0             0
## 5       seq9 -0.06748366    0    0    1             0             0
##   ORF.non-canonical
## 1                 0
## 2                 0
## 3                 0
## 4                 0
## 5                 0

This returns a data.frame with predicted repression values for each miRNA-transcript pair along with a count table of the different site types. If matches does not contain a log_kd column, only the count table will be returned.

scanMiR uses the following default parameter values for aggregation that have been determined by fitting and validating the model using several experimental data sets:

##            a            b            c           p3     coef_utr     coef_orf 
##     0.007726     0.573500     0.181000     0.051000    -0.171060    -0.215460 
## keepSiteInfo 
##     1.000000

Where coef_utr and coef_orf respectively correspond to the f and h in the above formula. To disable these features, they can simply be set to zero. keepSiteInfo lets you choose whether the site count table should be returned. The parameters can be passed directly to aggregateMatches, or passed to findSeedMatch when doing aggregation on-the-fly using the agg.params argument.

3 Dealing with very large scans

3.1 Multithreading

To deal with large amounts of sequences and/or seeds, both findSeedMatches and aggregateMatches support multithreading using the BiocParallel package. This can be activated by passing BP = MulticoreParam(ncores).

Depending on the system and the size of the scan (i.e. when including all non-canonical sites), mutlithreading can potentially take a large amount of memory. To avoid memory issues, the number of seeds processed simultaneously by findSeedMatches can be restricted using the n_seeds parameter. Alternatively, scan results can be saved to temporary files using the useTmpFiles argument (see ?findSeedMatches for more detail).

Note that in addition to the multithreading specified in its arguments, aggregateMatches uses the data.table package, which is often set to use multi-threading by default (see ?data.table::setDTthreads for more information). This can leave to CPU usage higher than specified through the BP argument of aggregateMatches.

3.2 Dealing with large collections of predictions

Binding sites for all miRNAs on all transcripts, especially when including non-canonical sites, can easily amount to prohibitive amounts of memory. The companion scanMiRApp package includes a class implementing fast indexed access to on-disk GenomicRanges and data.frames. The package additionally contains wrapper (e.g. for performing full transcriptome scans) for common species and for detecting enriched miRNA-target pairs, as well as a shiny interface to scanMiR.

Session info

## R version 4.2.0 RC (2022-04-21 r82226)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/
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##  [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            
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## other attached packages:
## [1] Biostrings_2.65.0   GenomeInfoDb_1.33.0 XVector_0.37.0     
## [4] IRanges_2.31.0      S4Vectors_0.35.0    BiocGenerics_0.43.0
## [7] scanMiR_1.3.0       BiocStyle_2.25.0   
## loaded via a namespace (and not attached):
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##  [4] purrr_0.3.4            colorspace_2.0-3       vctrs_0.4.1           
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## [10] utf8_1.2.2             rlang_1.0.2            jquerylib_0.1.4       
## [13] pillar_1.7.0           glue_1.6.2             DBI_1.1.2             
## [16] BiocParallel_1.31.0    GenomeInfoDbData_1.2.8 lifecycle_1.0.1       
## [19] ggseqlogo_0.1          stringr_1.4.0          zlibbioc_1.43.0       
## [22] munsell_0.5.0          gtable_0.3.0           evaluate_0.15         
## [25] labeling_0.4.2         knitr_1.38             fastmap_1.1.0         
## [28] parallel_4.2.0         fansi_1.0.3            highr_0.9             
## [31] Rcpp_1.0.8.3           scales_1.2.0           BiocManager_1.30.17   
## [34] magick_2.7.3           jsonlite_1.8.0         farver_2.1.0          
## [37] ggplot2_3.3.5          digest_0.6.29          stringi_1.7.6         
## [40] bookdown_0.26          dplyr_1.0.8            cowplot_1.1.1         
## [43] GenomicRanges_1.49.0   grid_4.2.0             cli_3.3.0             
## [46] tools_4.2.0            bitops_1.0-7           magrittr_2.0.3        
## [49] sass_0.4.1             RCurl_1.98-1.6         tibble_3.1.6          
## [52] crayon_1.5.1           pkgconfig_2.0.3        ellipsis_0.3.2        
## [55] data.table_1.14.2      assertthat_0.2.1       rmarkdown_2.14        
## [58] R6_2.5.1               compiler_4.2.0