segmentSeq: methods for identifying small RNA loci from high-throughput sequencing data
05 November, 2024
Package
segmentSeq 2.41.1
Contents
1 Introduction
High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods we propose allows the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery.
We approach the problem by considering a large set of potential segments upon the genome and counting the number of tags that match to that segment in multiple sequencing experiments (that may or may not contain replication). We then adapt the empirical Bayesian methods implemented in the baySeq package Hardcastle2010 to establish, for a given segment, the likelihood that the count data in that segment is similar to background levels, or that it is similar to the regions to the left or right of that segment. We then rank all the potential segments in order of increasing likelihood of similarity and reject those segments for which there is a high likelihood of similarity with the background or the regions to the left or right of the segment. This gives us a large list of overlapping segments. We reduce this list to identify non-overlapping loci by choosing, for a set of overlapping segments, the segment which has the lowest likelihood of similarity with either background or the regions to the left or right of that segment and rejecting all other segments that overlap with this segment. For fuller details of the method, see Hardcastle et al. Hardcastle2011.
2 Preparation
We begin by loading the segmentSeq package.
library(segmentSeq)Note that because the experiments that segmentSeq is designed to analyse are usually massive, we should use (if possible) parallel processing as implemented by the parallel package. If using this approach, we need to begin by define a cluster. The following command will use eight processors on a single machine; see the help page for makeCluster for more information. If we don’t want to parallelise, we can proceed anyway with a NULL cluster.
if(FALSE) { # set to FALSE if you don't want parallelisation
numCores <- min(8, detectCores())
cl <- makeCluster(numCores)
} else {
cl <- NULL
}The readGeneric function is able to read in tab-delimited files which have appropriate column names, and create an alignmentData object. Alternatively, if the appropriate column names are not present, we can specify which columns to use for the data. In either case, to use this function we pass a character vector of files, together with information on which data are to be treated as replicates to the function. We also need to define the lengths of the chromosome and specifiy the chromosome names as a character. The data here, drawn from text files in the ‘data’ directory of the segmentSeq package are taken from the first million bases of an alignment to chromosome 1 and the first five hundred thousand bases of an alignment to chromosome 2 of Arabidopsis thaliana in a sequencing experiment where libraries ‘SL9’ and ‘SL10’ are replicates, as are ‘SL26’ and ‘SL32’. Libraries ‘SL9’ and ‘SL10’ are sequenced from an Argonaute 6 IP, while ‘SL26’ and ‘SL32’ are an Argonaute 4 IP.
A similar function, readBAM performs the same operation on files in the BAM format. Please consult the help page for further details.
datadir <- system.file("extdata", package = "segmentSeq")
libfiles <- c("SL9.txt", "SL10.txt", "SL26.txt", "SL32.txt")
libnames <- c("SL9", "SL10", "SL26", "SL32")
replicates <- c("AGO6", "AGO6", "AGO4", "AGO4")
aD <- readGeneric(files = libfiles, dir = datadir,
replicates = replicates, libnames = libnames,
polyLength = 10, header = TRUE, gap = 200)
#> Reading files........done!
#> Analysing tags...........done!
aD
#> An object of class "alignmentData"
#> 3149 rows and 4 columns
#>
#> Slot "libnames":
#> [1] "SL9" "SL10" "SL26" "SL32"
#>
#> Slot "replicates":
#> [1] AGO6 AGO6 AGO4 AGO4
#> Levels: AGO4 AGO6
#>
#> Slot "alignments":
#> GRanges object with 3149 ranges and 2 metadata columns:
#> seqnames ranges strand | tag multireads
#> <Rle> <IRanges> <Rle> | <character> <numeric>
#> [1] >Chr1 265-284 - | AAATGAAGATAAACCATCCA 1
#> [2] >Chr1 405-427 - | AAGGAGTAAGAATGACAATA.. 1
#> [3] >Chr1 406-420 - | AAGAATGACAATAAA 1
#> [4] >Chr1 600-623 + | AAGGATTGGTGGTTTGAAGA.. 1
#> [5] >Chr1 665-688 + | ATCCTTGTAGCACACATTTT.. 1
#> ... ... ... ... . ... ...
#> [3145] >Chr1 991569-991589 - | CCGATAAACGCATACTTCCCT 1
#> [3146] >Chr1 992039-992054 - | AAGGAAATTAGAAAAT 1
#> [3147] >Chr1 995357-995372 + | AGAGACATGGGCGACA 1
#> [3148] >Chr1 995493-995507 + | AAACTCGTGAAGAAG 1
#> [3149] >Chr1 995817-995840 - | AGAGATCAAGTATATAGAAT.. 1
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths
#>
#> Slot "data":
#> Matrix with 3149 rows.
#> SL9 SL10 SL26 SL32
#> 1 1 0 0 0
#> 2 0 0 0 2
#> 3 0 1 0 0
#> 4 0 1 0 0
#> 5 7 1 0 0
#> ... ... ... ... ...
#> 3145 1 0 0 0
#> 3146 0 1 0 0
#> 3147 0 1 0 0
#> 3148 0 1 0 0
#> 3149 1 0 0 0
#>
#> Slot "libsizes":
#> [1] 1193 1598 1818 1417Next, we process this alignmentData object to produce a segData object. This segData object contains a set of potential segments on the genome defined by the start and end points of regions of overlapping alignments in the alignmentData object. It then evaluates the number of tags that hit in each of these segments.
sD <- processAD(aD, cl = cl)
#> Chromosome: >Chr1
#> Finding start-stop co-ordinates...done!
#> 1452 candidate loci found.
sD
#> GRanges object with 1452 ranges and 1 metadata column:
#> seqnames ranges strand | chunk
#> <Rle> <IRanges> <Rle> | <Rle>
#> [1] >Chr1 265-284 * | 1
#> [2] >Chr1 265-420 * | 1
#> [3] >Chr1 265-623 * | 1
#> [4] >Chr1 265-688 * | 1
#> [5] >Chr1 265-830 * | 1
#> ... ... ... ... . ...
#> [1448] >Chr1 992039-992054 * | 171
#> [1449] >Chr1 995357-995372 * | 172
#> [1450] >Chr1 995357-995507 * | 172
#> [1451] >Chr1 995493-995507 * | 172
#> [1452] >Chr1 995817-995840 * | 173
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths
#> An object of class "lociData"
#> 1452 rows and 4 columns
#>
#> Slot "replicates"
#> AGO6 AGO6 AGO4 AGO4
#> Slot "groups":
#> list()
#>
#> Slot "data":
#> SL9 SL10 SL26 SL32
#>
#> Slot "annotation":
#> data frame with 0 columns and 0 rows
#>
#> Slot "locLikelihoods" (stored on log scale):
#> Matrix with 0 rows.
#> <0 x 0 matrix>We can now construct a segment map from these potential segments.
3 Segmentation by heuristic methods
A fast method of segmentation can be achieved by exploiting the bimodality of the densities of small RNAs in the potential segments. In this approach, we assign each potential segment to one of two clusters for each replicate group, either as a segment or a null based on the density of sequence tags within that segment. We then combine these clusterings for each replicate group to gain a consensus segmentation map.
hS <- heuristicSeg(sD = sD, aD = aD, RKPM = 1000, largeness = 1e8, getLikes = TRUE, cl = cl)
#> Number of candidate loci: 1452
#> Evaluating candidate loci...done.
#> >Chr1
#> Strand *
#> Checking overlaps.....done.
#> Selecting loci...done!
#> Extending loci.....done!
#> Finding priors...
#> Warning in getPriors.NB(mD, verbose = TRUE, cl = cl): The '@replicates' slot is
#> not a factor; converting now.
#> done.
#> Getting likelihoods for replicate group AGO4...
#> Length of priorReps:1014
#> Length of priorSubset:507
#> Length of subset:507
#> Length of postRows:507
#> ...
#> ...done!
#> Getting likelihoods for replicate group AGO6...
#> Length of priorReps:1014
#> Length of priorSubset:507
#> Length of subset:507
#> Length of postRows:507
#> ...
#> ...done!4 Segmentation by empirical Bayesian methods
A more refined approach to the problem uses an existing segment map (or, if not provided, a segment map defined by the hS function) to acquire empirical distributions on the density of sequence tags within a segment. We can then estimate posterior likelihoods for each potential segment as being either a true segment or a null. We then identify all potential segments in the with a posterior likelihood of being a segment greater than some value ‘lociCutoff’ and containing no subregion with a posterior likelihood of being a null greater than ‘nullCutoff’. We then greedily select the longest segments satisfying these criteria that do not overlap with any other such segments in defining our segmentation map.
cS <- classifySeg(sD = sD, aD = aD, cD = hS, cl = cl)
#> Finding candidate priors...done.
#> Finding priors...
#> Warning in getPriors.NB(prepD, samplesize = samplesize, verbose = TRUE, : The
#> '@replicates' slot is not a factor; converting now.
#> done.
#> Segmentation split into 1 parts.
#> Establishing likelihoods of loci; Part 1 of 1
#> Establishing likelihoods of loci...
#> ...for replicate group AGO4......done.
#> ...for replicate group AGO6......done.
#> Warning in getPriors.NB(curNullsWithin, samplesize = samplesize, verbose =
#> FALSE, : The '@replicates' slot is not a factor; converting now.
#> Warning in getPriors.NB(nullSegPriors, samplesize = samplesize, verbose =
#> FALSE, : The '@replicates' slot is not a factor; converting now.
#> Establishing likelihoods of nulls; Part 1 of 1
#> ...for replicate group AGO4...Length of priorReps:0
#> Length of priorSubset:84
#> Length of subset:84
#> Length of postRows:84
#> .
#> Length of priorReps:0
#> Length of priorSubset:3061
#> Length of subset:3061
#> Length of postRows:3061
#> .
#> ...done.
#> ...for replicate group AGO6...Length of priorReps:0
#> Length of priorSubset:93
#> Length of subset:93
#> Length of postRows:93
#> .
#> Length of priorReps:0
#> Length of priorSubset:3111
#> Length of subset:3111
#> Length of postRows:3111
#> .
#> ...done.
#> Strand *
#> Checking overlaps.....done.
#> Selecting loci...done!
#> Extending loci.....done!
#> Finding priors...
#> Warning in getPriors.NB(mD, verbose = TRUE, cl = cl): The '@replicates' slot is
#> not a factor; converting now.
#> done.
#> Getting likelihoods for replicate group AGO4...
#> Length of priorReps:130
#> Length of priorSubset:65
#> Length of subset:65
#> Length of postRows:65
#> ...
#> ...done!
#> Getting likelihoods for replicate group AGO6...
#> Length of priorReps:130
#> Length of priorSubset:65
#> Length of subset:65
#> Length of postRows:65
#> ...
#> ...done!
cS
#> GRanges object with 65 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] >Chr1 1-264 *
#> [2] >Chr1 265-839 *
#> [3] >Chr1 840-967 *
#> [4] >Chr1 968-17054 *
#> [5] >Chr1 17055-17738 *
#> ... ... ... ...
#> [61] >Chr1 789508-789548 *
#> [62] >Chr1 789549-944194 *
#> [63] >Chr1 944195-944222 *
#> [64] >Chr1 944223-958610 *
#> [65] >Chr1 958611-959152 *
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths
#> An object of class "lociData"
#> 65 rows and 4 columns
#>
#> Slot "replicates"
#> AGO6 AGO6 AGO4 AGO4
#> Slot "groups":
#> list()
#>
#> Slot "data":
#> AGO6.1 AGO6.2 AGO4.1 AGO4.2
#> [1,] 0 0 0 0
#> [2,] 31 29 51 85
#> [3,] 24 18 14 0
#> [4,] 2 3 0 0
#> [5,] 121 120 147 560
#> 60 more rows...
#>
#> Slot "annotation":
#> data frame with 0 columns and 65 rows
#>
#> Slot "locLikelihoods" (stored on log scale):
#> Matrix with 65 rows.
#> AGO4 AGO6
#> 1 0.031548 0.034761
#> 2 0.8338 0.9883
#> 3 0.61923 0.85384
#> 4 0.014835 0.019089
#> 5 0.95559 0.84837
#> ... ... ...
#> 61 0.66692 0.93945
#> 62 0.0076827 0.024958
#> 63 0.98159 0.86769
#> 64 0.12483 0.018825
#> 65 0.32536 0.99319
#>
#> Expected number of loci in each replicate group
#> AGO4 AGO6
#> 29.40546 36.18992By one of these methods, we finally acquire an annotated lociData object, with the annotations describing the co-ordinates of each segment.
We can use this lociData object, in combination with the alignmentData object, to plot the segmented genome.
par(mfrow = c(2,1), mar = c(2,6,2,2))
plotGenome(aD, hS, chr = ">Chr1", limits = c(1, 1e5),
showNumber = FALSE, cap = 50)
#> Computing plot.....done!
plotGenome(aD, cS, chr = ">Chr1", limits = c(1, 1e5),
showNumber = FALSE, cap = 50)
#> Computing plot.....done!
Figure 1: The segmented genome (first \(10^5\) bases of chromosome 1)
Given the calculated likelihoods, we can filter the segmented genome by controlling on likelihood, false discovery rate, or familywise error rate
loci <- selectLoci(cS, FDR = 0.05)
loci
#> GRanges object with 34 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] >Chr1 265-839 *
#> [2] >Chr1 840-967 *
#> [3] >Chr1 17739-17909 *
#> [4] >Chr1 17910-18157 *
#> [5] >Chr1 18158-18553 *
#> ... ... ... ...
#> [30] >Chr1 758302-758848 *
#> [31] >Chr1 758849-759196 *
#> [32] >Chr1 789508-789548 *
#> [33] >Chr1 944195-944222 *
#> [34] >Chr1 958611-959152 *
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths
#> An object of class "lociData"
#> 34 rows and 4 columns
#>
#> Slot "replicates"
#> AGO6 AGO6 AGO4 AGO4
#> Slot "groups":
#> list()
#>
#> Slot "data":
#> AGO6.1 AGO6.2 AGO4.1 AGO4.2
#> [1,] 31 29 51 85
#> [2,] 24 18 14 0
#> [3,] 16 21 0 17
#> [4,] 239 173 162 177
#> [5,] 301 302 1096 349
#> 29 more rows...
#>
#> Slot "annotation":
#> data frame with 0 columns and 34 rows
#>
#> Slot "locLikelihoods" (stored on log scale):
#> Matrix with 34 rows.
#> AGO4 AGO6
#> 1 0.8338 0.9883
#> 2 0.61923 0.85384
#> 3 0.6483 0.88199
#> 4 0.98546 0.99732
#> 5 0.98467 0.99843
#> ... ... ...
#> 30 0.98027 0.96982
#> 31 0.56102 0.98788
#> 32 0.66692 0.93945
#> 33 0.98159 0.86769
#> 34 0.32536 0.99319
#>
#> Expected number of loci in each replicate group
#> AGO4 AGO6
#> 25.33357 32.75701The lociData objects can now be examined for differential expression with the baySeq package.
First we define the possible models of differential expression on the data. In this case, the models are of non-differential expression and pairwise differential expression.
groups(cS) <- list(NDE = c(1,1,1,1), DE = c("AGO6", "AGO6", "AGO4", "AGO4"))Then we get empirical distributions on the parameter space of the data.
cS <- getPriors(cS, cl = cl)
#> Finding priors...
#> Warning in getPriors(cS, cl = cl): The '@replicates' slot is not a factor;
#> converting now.
#> done.Then we get the posterior likelihoods of the data conforming to each model. Since the cS object contains null regions as well as true loci, we will use the nullData = TRUE option to distinguish between non-differentially expressed loci and non-expressed regions. By default, the loci likelihoods calculated earlier will be used to weight the initial parameter fit in order to detect null data.
cS <- getLikelihoods(cS, nullData = TRUE, cl = cl)
#> Finding posterior likelihoods...Length of priorReps:0
#> Length of priorSubset:65
#> Length of subset:65
#> Length of postRows:65
#> Analysing part 1 of 1
#> Preparing data....................................................................done.
#> Estimating likelihoods......done!
#> .
#> done.We can examine the highest likelihood non-expressed (‘null’) regions
topCounts(cS, NULL, number = 3)
#> seqnames start end width strand AGO6.1 AGO6.2 AGO4.1 AGO4.2
#> NA.61 >Chr1 789549 944194 154646 * 13 37 29 29
#> NA.28 >Chr1 372735 423138 50404 * 0 0 0 0
#> NA.45 >Chr1 552693 587497 34805 * 0 0 0 0
#> likes FDR. FWER.
#> NA.61 0.9781566 0.02184336 0.02184336
#> NA.28 0.9754303 0.02320653 0.04587638
#> NA.45 0.9753956 0.02367247 0.06935198The highest likelihood expressed but non-differentially expressed regions
topCounts(cS, "NDE", number = 3)
#> seqnames start end width strand AGO6.1 AGO6.2 AGO4.1 AGO4.2 likes
#> NA.14 >Chr1 77151 77519 369 * 176 147 275 131 0.9307905
#> NA.56 >Chr1 758302 758848 547 * 167 166 269 134 0.8751151
#> NA.42 >Chr1 550077 550655 579 * 5 11 0 19 0.8663408
#> FDR.NDE FWER.NDE
#> NA.14 0.06920947 0.06920947
#> NA.56 0.09704717 0.18545113
#> NA.42 0.10925117 0.29432304And the highest likelihood differentially expressed regions
topCounts(cS, "DE", number = 3)
#> seqnames start end width strand AGO6.1 AGO6.2 AGO4.1 AGO4.2 likes
#> NA.48 >Chr1 634297 634350 54 * 65 90 12 17 0.9950597
#> NA.16 >Chr1 238359 238417 59 * 9 9 0 0 0.9914359
#> NA.13 >Chr1 76799 77150 352 * 6 21 0 0 0.9784148
#> DE FDR.DE FWER.DE
#> NA.48 AGO6>AGO4 0.004940330 0.00494033
#> NA.16 AGO6>AGO4 0.006752209 0.01346211
#> NA.13 AGO6>AGO4 0.011696524 0.03475668Finally, to be a good citizen, we stop the cluster we started earlier:
if(!is.null(cl))
stopCluster(cl)5 Bibliography
Thomas J. Hardcastle and Krystyna A. Kelly. baySeq: Empirical Bayesian Methods For Identifying Differential Expression In Sequence Count Data. BMC Bioinformatics (2010).
Thomas J. Hardcastle and Krystyna A. Kelly and David C. Baulcombe. Identifying small RNA loci from high-throughput sequencing data. Bioinformatics (2012).
6 Session Info
sessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> 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
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] parallel stats4 stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] segmentSeq_2.41.1 ShortRead_1.65.0
#> [3] GenomicAlignments_1.43.0 SummarizedExperiment_1.37.0
#> [5] Biobase_2.67.0 MatrixGenerics_1.19.0
#> [7] matrixStats_1.4.1 Rsamtools_2.23.0
#> [9] Biostrings_2.75.0 XVector_0.47.0
#> [11] BiocParallel_1.41.0 GenomicRanges_1.59.0
#> [13] GenomeInfoDb_1.43.0 IRanges_2.41.0
#> [15] S4Vectors_0.45.0 BiocGenerics_0.53.1
#> [17] generics_0.1.3 baySeq_2.41.0
#> [19] BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] xfun_0.49 bslib_0.8.0 hwriter_1.3.2.1
#> [4] latticeExtra_0.6-30 lattice_0.22-6 tools_4.5.0
#> [7] bitops_1.0-9 highr_0.11 Matrix_1.7-1
#> [10] RColorBrewer_1.1-3 lifecycle_1.0.4 GenomeInfoDbData_1.2.13
#> [13] compiler_4.5.0 deldir_2.0-4 statmod_1.5.0
#> [16] tinytex_0.54 codetools_0.2-20 htmltools_0.5.8.1
#> [19] sass_0.4.9 yaml_2.3.10 crayon_1.5.3
#> [22] jquerylib_0.1.4 DelayedArray_0.33.1 cachem_1.1.0
#> [25] limma_3.63.1 magick_2.8.5 abind_1.4-8
#> [28] locfit_1.5-9.10 digest_0.6.37 bookdown_0.41
#> [31] fastmap_1.2.0 grid_4.5.0 cli_3.6.3
#> [34] SparseArray_1.7.1 magrittr_2.0.3 S4Arrays_1.7.1
#> [37] edgeR_4.5.0 UCSC.utils_1.3.0 rmarkdown_2.29
#> [40] pwalign_1.3.0 httr_1.4.7 jpeg_0.1-10
#> [43] interp_1.1-6 png_0.1-8 evaluate_1.0.1
#> [46] knitr_1.48 rlang_1.1.4 Rcpp_1.0.13-1
#> [49] BiocManager_1.30.25 jsonlite_1.8.9 R6_2.5.1
#> [52] zlibbioc_1.53.0