| Title: | Computational pipeline for computing probability of modification from structure probing experiment data |
|---|---|
| Description: | This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. |
| Authors: | Alina Selega ([email protected]), Sander Granneman, Guido Sanguinetti |
| Maintainer: | Alina Selega <[email protected]> |
| License: | GPL-3 |
| Version: | 1.31.0 |
| Built: | 2024-10-30 04:27:55 UTC |
| Source: | https://github.com/bioc/BUMHMM |
This function computes posterior probabilities of chemical modification in a structure probing experiment. It uses empirical p-values, which quantify the difference between LDRs (log-ratios of drop-off rates) from treatment-control and control-control experimental comparisons, as observations in a HMM with an emission model defined as a Beta-Uniform mixture.
computeProbs(LDR_C, LDR_CT, Nc, Nt, strand, nuclPosition, analysedC, analysedCT, stretches, optimise)computeProbs(LDR_C, LDR_CT, Nc, Nt, strand, nuclPosition, analysedC, analysedCT, stretches, optimise)
LDR_C |
A matrix of transformed LDRs for control-control comparisons as returned
by |
LDR_CT |
A matrix of transformed LDRs for treatment-control comparisons as
returned by |
Nc |
Number of control experimental replicates. Must be at least 2. |
Nt |
Number of treatment experimental replicates. Must be at least 2. |
strand |
A character, indicating the plus ( |
nuclPosition |
A list where each component corresponds to a pattern (indicated by the
field |
analysedC |
The first element of the list returned by
|
analysedCT |
The second element of the list returned by
|
stretches |
An |
optimise |
An indicator variable to use the EM optimisation of shape parameters of
the Beta distribution (emission model of the HMM for the modified
state). If optimisation is to be used, the variable should be set to the
desired tolerance for parameter estimation (once the estimates stop
changing within this tolerance, the algorithm stops). Set to |
The function first computes percentiles of LDR null distributions, for each
percent from 1 to 89 and for a tenth of a percent from 90 to 100. If
multiple nucleotide patterns are used, then null distributions are computed
separately for each pattern. Then, LDRs from treatment-control comparisons
are compared to the percentiles of a corresponding null distribution via
computing the p-values, defined as 1 - closest percentile. Thus, if
the closest percentile to an LDR is 99th, then its p-value will be 0.01,
reflecting a small probability for it to be from this null distribution.
The p-values are then used as observations in a hidden Markov model.
P-values corresponding to multiple comparisons at the same nucleotide
position are considered to be independent measurements.
HMM has a binary latent state corresponding to the true state of a nucleotide, modified or unmodified. Transition probabilities are set according to the empirically derived expectations of unmodified nucleotide stretches of average length 20 and modified stretches of average length 5. Emission probabilites come from a Beta-Uniform mixture, where the Uniform component represents the conditional model for the unmodifed state, and the Beta component represents the conditional model for the modified state. The shape parameters of Beta distribution are set to (1, 10) to approximately match the likelihood under the null hypothesis (unmodified state) for the LDRs in the top quintile of the distribution.
We also provide an option to optimise the shape parameters of Beta
distribution with the EM algorithm. To do this, make the function call with
the last parameter optimise set to the desired tolerance, within
which the parameter estimates are allowed not to change at the next
iteration. The EM algorithm is run for 10 iterations and each M-step is
performed with Newton's optimisation method and is allowed 20 iterations. We
remark that during our experiments, this optimisation appeared vulnerable to
local minima. Thus, the default version of the pipeline does not use this
optimisation.
An n-by-2 matrix of posterior probabilities for all selected nucleotides. First column corresponds to the probability of the nucleotide being unmodified (given the observations) and the second column - to the probability of the nucleotide being modified. Those nucleotides not belonging to stretches for which the HMM was run are assigned an ‘NA’.
The following errors are returned if:
"Number of control and treatment replicates must be at least 2." the number of control or treatment experimental replicates is less than 2;
"All lists of positions selected for pair-wise comparisons should be non-empty." a list of nucleotides for control-control or treatment-control comparisons is empty;
"Strand should be either plus or minus, specified with a sign." strand is not specified as "+" or "-";
"The list of considered nucleotide positions should be non-empty. If patterns are not used, a vector with all positions should be provided in the first element of the list." the list of considered nucleotides (which should either correspond to patterns if addressing sequence bias or list all positions) is empty;
"The matrix of control-control (treatment-control) LDRs should have as many columns as there are control-control (treatment-control) comparisons." dimensionalities of matrices with LDRs do not agree with the number of pair-wise comparisons;
"Please provide a tolerance if shape parameters are to be optimised with EM
algorithm."
the optimise parameter was not set to a (real) tolerance value.
Alina Selega, Sander Granneman, Guido Sanguinetti
Selega et al. "Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments", Nature Methods (2016).
See Also stabiliseVariance,
selectNuclPos, findPatternPos,
and computeStretches.
library(SummarizedExperiment) Nc <- 3 Nt <- 3 t <- 1 nuclSelection <- selectNuclPos(se, Nc, Nt, t) assay(se, "dropoff_rate") <- scaleDOR(se, nuclSelection, Nc, Nt) stretches <- computeStretches(se, t) varStab <- stabiliseVariance(se, nuclSelection, Nc, Nt) LDR_C <- varStab$LDR_C LDR_CT <- varStab$LDR_CT sequence <- subject(rowData(se)$nucl) nuclPosition <- list() nuclPosition[[1]] <- 1:nchar(sequence) posteriors <- computeProbs(LDR_C, LDR_CT, Nc, Nt, '+', nuclPosition, nuclSelection$analysedC, nuclSelection$analysedCT, stretches)library(SummarizedExperiment) Nc <- 3 Nt <- 3 t <- 1 nuclSelection <- selectNuclPos(se, Nc, Nt, t) assay(se, "dropoff_rate") <- scaleDOR(se, nuclSelection, Nc, Nt) stretches <- computeStretches(se, t) varStab <- stabiliseVariance(se, nuclSelection, Nc, Nt) LDR_C <- varStab$LDR_C LDR_CT <- varStab$LDR_CT sequence <- subject(rowData(se)$nucl) nuclPosition <- list() nuclPosition[[1]] <- 1:nchar(sequence) posteriors <- computeProbs(LDR_C, LDR_CT, Nc, Nt, '+', nuclPosition, nuclSelection$analysedC, nuclSelection$analysedCT, stretches)
This function computes continuous stretches of nucleotide positions, for
which posterior probabilities are to be computed. Such positions should have
the minimum allowed coverage (as defined with the parameter t) in
all experimental replicates and a non-zero drop-off count in at least one of
the treatment replicates. The returned stretches are at least two
nucleotides long.
computeStretches(se, t)computeStretches(se, t)
se |
A |
t |
Threshold for the minimum allowed coverage. Must be non-negative. |
An IRanges object storing each stretch.
The following errors are returned if:
"The minumum coverage threshold must be non-negative." the threshold for the minimum considered coverage is negative.
Alina Selega, Sander Granneman, Guido Sanguinetti
Selega et al. "Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments", Nature Methods (2016).
t <- 1 stretches <- computeStretches(se, t)t <- 1 stretches <- computeStretches(se, t)
A matrix containing 1800x6 entries of per nucleotide coverage information from structure probing experiments.
This matrix is provided as a reference for creating a
SummarizedExperiment object for storing all structure probing data
used by the BUMHMM package. See se for the
example code.
covFilecovFile
A matrix containing the coverage information for each nucleotide position of a ribosomal RNA 18S obtained in a structure probing experiment using DMS as a probe, where:
control experimental replicate
control experimental replicate
control experimental replicate
treatment experimental replicate
treatment experimental replicate
treatment experimental replicate
Coverage information per nucleotide.
Hector, R. D. et al. "Snapshots of pre-rRNA structural flexibility reveal eukaryotic 40S assembly dynamics at nucleotide resolution." Nucleic acids research (2014).
A matrix containing 1800x6 entries of per nucleotide drop-off count information from structure probing experiments.
This matrix is provided as a reference for creating a
SummarizedExperiment object for storing all structure probing data
used by the BUMHMM package. See se for the
example code.
docFiledocFile
A matrix containing the drop-off count information for each nucleotide position of a ribosomal RNA 18S obtained in a structure probing experiment using DMS as a probe, where:
control experimental replicate
control experimental replicate
control experimental replicate
treatment experimental replicate
treatment experimental replicate
treatment experimental replicate
Drop-off count information per nucleotide.
Hector, R. D. et al. "Snapshots of pre-rRNA structural flexibility reveal eukaryotic 40S assembly dynamics at nucleotide resolution." Nucleic acids research (2014).
A matrix containing 1800x6 entries of per nucleotide drop-off rate information from structure probing experiments.
This matrix is provided as a reference for creating a
SummarizedExperiment object for storing all structure probing data
used by the BUMHMM package. See se for the
example code.
dorFiledorFile
A matrix containing the drop-off rate information for each nucleotide position of a ribosomal RNA 18S obtained in a structure probing experiment using DMS as a probe, where:
control experimental replicate
control experimental replicate
control experimental replicate
treatment experimental replicate
treatment experimental replicate
treatment experimental replicate
Drop-off rate information per nucleotide.
Hector, R. D. et al. "Snapshots of pre-rRNA structural flexibility reveal eukaryotic 40S assembly dynamics at nucleotide resolution." Nucleic acids research (2014).
This function finds all occurrences of a nucleotide pattern in the sequence.
For each occurrence, the function returns the index of the middle
nucleotide, computed as: ceiling(length(pattern) / 2). The function
supports data for the plus and minus DNA strands; for the minus strand, all
patterns are turned to complementary sequence.
findPatternPos(patterns, sequence, strand)findPatternPos(patterns, sequence, strand)
patterns |
A list of nucleotide permutations of length |
sequence |
A |
strand |
A character, indicating the plus ( |
This function uses stringi::stri_locate_all_fixed().
This function aims to assist with addressing sequence bias in structure probing data. The sequence in the neighbourhood of a nucleotide is assumed to have an effect on its structural state. By considering sequence patterns of a certain length (specified by the user), this function finds indices of the middle nucleotide of each pattern's occurrences within the sequence. We then separately analyse the nucleotides occurring in the middle of each pattern, taking into account sequence dependency.
This function returns a list where each component corresponds to a pattern
(indicated by the field names) and contains indices of the middle
nucleotides of that pattern's occurrences within the sequence.
The following errors are returned if:
"Strand should be either plus or minus, specified with a sign." strand is not specified as "+" or "-";
"The sequence should be non-empty." provided sequence is empty;
"The list of patterns should be non-empty." the list of patterns to search for in the sequence is empty.
Alina Selega, Sander Granneman, Guido Sanguinetti
Selega et al. "Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments", Nature Methods (2016).
See also nuclPerm.
library(SummarizedExperiment) ## Extract the DNA sequence from se sequence <- subject(rowData(se)$nucl) ## Generate patterns of length 3 n <- 3 patterns <- nuclPerm(n) ## Find positions of pattern occurrences nuclPosition <- findPatternPos(patterns, sequence, '+')library(SummarizedExperiment) ## Extract the DNA sequence from se sequence <- subject(rowData(se)$nucl) ## Generate patterns of length 3 n <- 3 patterns <- nuclPerm(n) ## Find positions of pattern occurrences nuclPosition <- findPatternPos(patterns, sequence, '+')
This function creates all permutations of 4 nucleobases (A, T, G, C) of
length n.
nuclPerm(n)nuclPerm(n)
n |
Length of the pattern. |
This function uses gtools::permutations().
A vector of characters with each element being a nucleobase pattern of
length n.
The following error is returned if:
"The length of patterns provided is not a positive number." the provided length of patterns to be generated is not positive.
Alina Selega, Sander Granneman, Guido Sanguinetti
Selega et al. "Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments", Nature Methods (2016).
nuclPerm(3)nuclPerm(3)
The function extracts all nucleotides selected for control-control and
treatment-control comparisons and scales them to have a common median across
all replicates. This value is computed as the median of all drop-off rates
of the selected nucleotides in control replicate samples. The function
requires the output of selectNuclPos, which holds
lists of nucleotide positions selected for pair-wise comparisons.
scaleDOR(se, nuclSelection, Nc, Nt)scaleDOR(se, nuclSelection, Nc, Nt)
se |
A |
nuclSelection |
A list returned by |
Nc |
Number of control replicate samples. Must be at least 2. |
Nt |
Number of treatment replicate samples. Must be at least 2. |
Returns a modified n-by-m matrix of drop-off rates, scaled
per replicate to have the same median value, where n is the number of
nucleotides and m is the total number of replicate samples.
The following errors are returned if:
"Number of control and treatment replicates must be at least 2." the number of control or treatment experimental replicates is less than 2;
"Nucleotide selection should have two elements." "All lists of positions selected for pair-wise comparisons or for which posteriors will be computed should be non-empty." the list containing positions of nucleotides selected for control-control and treatment-control comparisons does not have 2 elements or any of the elements is empty;
"Drop-off rate matrix should not have any NA entries." the drop-off rate matrix has NA entries.
Alina Selega, Sander Granneman, Guido Sanguinetti
Selega et al. "Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments", Nature Methods (2016).
See Also selectNuclPos.
Nc <- 3 Nt <- 3 t <- 1 nuclSelection <- selectNuclPos(se, Nc, Nt, t) dorFile <- scaleDOR(se, nuclSelection, Nc, Nt)Nc <- 3 Nt <- 3 t <- 1 nuclSelection <- selectNuclPos(se, Nc, Nt, t) dorFile <- scaleDOR(se, nuclSelection, Nc, Nt)
A SummarizedExperiment container storing the genomic sequence
of the yeast rRNA 18S and three matrices with coverage, drop-off count, and
drop-off rate information for each nucleotide position in three replicates
of control and treatment DMS structure probing experiments.
sese
An instance of the SummarizedExperiment class storing data
obtained in a structure probing experiment for the yeast rRNA 18S using the
DMS chemical probing agent. The data can be accessed in the Gene Expression
Omnibus with the code GSE52878.
The available data types are summarised in three assays: coverage, drop-off count of the reverse transcriptase, and drop-off rate at each nucleotide position. The coverage and drop-off count data are obtained in a random-primed paired-end sequencing structure probing experiment. The drop-off rate is computed as the ratio between the drop-off count and coverage at each nucleotide position. Control (no reagent added) and treatment experiments are available in triplicates.
The data from each assay is summarised in a rectangular matrix. The rows
represent nucleotide position and the columns represent experimental
replicates. The replicates are labelled as "control" or "treatment", which
is stored as column data and can be accessed through the field
replicate. The row data stores the associated DNA sequence,
represented with a DNAString object.
The SummarizedExperiment object can be created from individual
matrices and the sequence string as follows. Below:
seqa string storing the genomic sequence
covFilea matrix storing coverage information, with rows as nucleotide positions and columns as experimental replicates (control replicates come first, followed by treatment replicates)
docFilea matrix storing drop-off count information,
arranged as covFile
dorFilea matrix storing drop-off rate information,
arranged as covFile
The seq, covFile, docFile, and dorFile
are also provided as accompanying data sets for reference.
Example code:
library(BUMHMM) library(Biostrings) library(SummarizedExperiment)
dna = DNAString(seq) se <- SummarizedExperiment( list( coverage=as.matrix(covFile), dropoff_count=as.matrix(docFile), dropoff_rate=as.matrix(dorFile) ), colData=DataFrame( replicate=rep(c("control", "treatment"), each=3) ), rowData=DataFrame( nucl=Views(dna, successiveIRanges(rep(1, nchar(dna)))) )) colnames(se) <- c('C1', 'C2', 'C3', 'T1', 'T2', 'T3')
RNA structure probing data.
Hector, R. D. et al. "Snapshots of pre-rRNA structural flexibility reveal eukaryotic 40S assembly dynamics at nucleotide resolution." Nucleic acids research (2014).
This function selects pairs of nucleotide positions for computing log-ratios of control-control and treatment-control replicate comparisons.
selectNuclPos(se, Nc, Nt, t)selectNuclPos(se, Nc, Nt, t)
se |
A |
Nc |
Number of control experimental replicates. Must be at least 2. |
Nt |
Number of treatment experimental replicates. Must be at least 2. |
t |
Threshold for the minimum allowed coverage. Must be non-negative. |
This function uses combn.
analysedC |
List where each element corresponds to a control-control replicate
comparison. Each element holds indices of nucleotides that have coverage
>= |
analysedCT |
List where each element corresponds to a treatment-control replicate
comparison. Each element holds indices of nucleotides that have coverage
>= |
The following errors are returned if:
"The number of experimental replicates must be at least 2." the number of control or treatment experimental replicates is less than 2;
"The minumum coverage threshold must be non-negative." the threshold for the minimum considered coverage is negative;
"The coverage and drop-off count matrices should not have NA entries." the coverage and drop-off count matrices have NA entries.
Alina Selega, Sander Granneman, Guido Sanguinetti
Selega et al. "Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments", Nature Methods (2016).
selectNuclPos(se, 3, 3, 1)selectNuclPos(se, 3, 3, 1)
A string containing genomic sequence of a ribosomal RNA 18S.
This string is provided as a reference for creating a
SummarizedExperiment object for storing all structure probing data
used by the BUMHMM package. See se for the
example code.
seqseq
A string containing genomic sequence of a ribosomal RNA 18S.
Genomic sequence.
RDN18-1, S000006482, Saccharomyces Genome Database.
This function computes the log drop-off rate ratios (LDRs) for control-control and treatment-control comparisons and applies a transformation to them that reduces their variance as a function of coverage.
stabiliseVariance(se, nuclSelection, Nc, Nt)stabiliseVariance(se, nuclSelection, Nc, Nt)
se |
A |
nuclSelection |
A list returned by |
Nc |
Number of control experimental replicates. Must be at least 2. |
Nt |
Number of treatment experimental replicates. Must be at least 2. |
The variance is reduced by sorting all LDRs in the null distribution by the average coverage and splitting the data in bins. Each bin spans a coverage of 100; or, if maximum coverage is not larger by 100 than the minimum coverage, the range is set to their difference divided by 10.
For each bin, the 95th quantile of LDRs with subtracted mean and the average coverage are computed. Then non-linear least squares are used to fit the following model (with parameters k, b): f = 1/sqrt(n) * k + b, for f - quantiles, n - mean coverage in the bin.
All LDRs are then rescaled by this model according to their corresponding average coverage in the pair of replicates.
LDR_C |
A matrix of transformed LDRs for control-control comparisons. The matrix
rows correspond to nucleotide positions and columns to a control-control
comparison. Only those positions selected for a pair-wise comparison
will be assigned a value; the rest will be left as an |
LDR_CT |
A matrix of transformed LDRs for treatment-control comparisons. The
matrix rows correspond to nucleotide positions and columns to a
treatment-control comparison. Only those positions selected for a
pair-wise comparison will be assigned a value; the rest will be left as
an |
The following errors are returned if:
"Number of control and treatment replicates must be at least 2." the number of control or treatment experimental replicates is less than 2;
"All lists of positions selected for pair-wise comparisons should be non-empty." a list of nucleotides for control-control or treatment-control comparisons is empty;
"The coverage and drop-off count matrices should not have NA entries." the coverage and drop-off count matrices have NA entries;
"Unable to fit the model for correcting the coverage bias."
The function nls could not execute successfully. The 95th
quantiles of the LDR distribution in each bin could be equal to 0 or not
have enough elements. This would happen if not enough nucleotides end up in
a bin; e.g. one nucleotide per bin.
Alina Selega, Sander Granneman, Guido Sanguinetti
Selega et al. "Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments", Nature Methods (2016).
See Also selectNuclPos.
library(SummarizedExperiment) Nc <- 3 Nt <- 3 t <- 1 nuclSelection <- selectNuclPos(se, Nc, Nt, t) assay(se, "dropoff_rate") <- scaleDOR(se, nuclSelection, Nc, Nt) varStab <- stabiliseVariance(se, nuclSelection, Nc, Nt)library(SummarizedExperiment) Nc <- 3 Nt <- 3 t <- 1 nuclSelection <- selectNuclPos(se, Nc, Nt, t) assay(se, "dropoff_rate") <- scaleDOR(se, nuclSelection, Nc, Nt) varStab <- stabiliseVariance(se, nuclSelection, Nc, Nt)