In functionalgenomics/atena: Analysis of Transposable Elements

library(knitr)

options(width=80)

knitr::opts_chunk$set(
  collapse=TRUE,
  comment="")

What are transposable elements

Transposable elements (TEs) are autonomous mobile genetic elements. They are DNA sequences that have, or once had, the ability to mobilize within the genome either directly or through an RNA intermediate [@payer2019transposable]. TEs can be categorized into two classes based on the intermediate substrate propagating insertions (RNA or DNA). Class I TEs, also called retrotransposons, first transcribe an RNA copy that is then reverse transcribed to cDNA before inserting in the genome. In turn, these can be divided into long terminal repeat (LTR) retrotransposons, which refer to endogenous retroviruses (ERVs), and non-LTR retrotransposons, which include long interspersed element class 1 (LINE-1 or L1) and short interspersed elements (SINEs). Class II TEs, also known as DNA transposons, directly excise themselves from one location before reinsertion. TEs are further split into families and subfamilies depending on various structural features [@goerner2018computational; @guffanti2018novel].

Most TEs have lost the capacity for generating new insertions over their evolutionary history and are now fixed in the human population. Their insertions have resulted in a complex distribution of interspersed repeats comprising almost half (50%) of the human genome [@payer2019transposable].

TE expression has been observed in association with physiological processes in a wide range of species, including humans where it has been described to be important in early embryonic pluripotency and development. Moreover, aberrant TE expression has been associated with diseases such as cancer, neurodegenerative disorders, and infertility [@payer2019transposable].

Currently available methods for quantifying TE expression

The study of TE expression faces one main challenge: given their repetitive nature, the majority of TE-derived reads map to multiple regions of the genome and these multi-mapping reads are consequently discarded in standard RNA-seq data processing pipelines. For this reason, specific software packages for the quantification of TE expression have been developed [@goerner2018computational], such as TEtranscripts [@jin2015tetranscripts], ERVmap [@tokuyama2018ervmap] and Telescope [@bendall2019telescope]. The main differences between these three methods are the following:

• TEtranscripts [@jin2015tetranscripts] reassigns multi-mapping reads to TEs proportionally to their relative abundance, which is estimated using an expectation-maximization (EM) algorithm.

• ERVmap [@tokuyama2018ervmap] is based on selective filtering of multi-mapping reads. It applies filters that consist in discarding reads when the ratio of sum of hard and soft clipping to the length of the read (base pair) is greater than or equal to 0.02, the ratio of the edit distance to the sequence read length (base pair) is greater or equal to 0.02 and/or the difference between the alignment score from BWA (field AS) and the suboptimal alignment score from BWA (field XS) is less than 5.

• Telescope [@bendall2019telescope] reassigns multi-mapping reads to TEs using their relative abundance, which like in TEtranscripts, is also estimated using an EM algorithm. The main differences with respect to TEtranscripts are: (1) Telescope works with an additional parameter for each TE that estimates the proportion of multi-mapping reads that need to be reassigned to that TE; (2) that reassignment parameter is optimized during the EM algorithm jointly with the TE relative abundances, using a Bayesian maximum a posteriori (MAP) estimate that allows one to use prior values on these two parameters; and (3) using the final estimates on these two parameters, multi-mapping reads can be flexibly reassigned to TEs using different strategies, where the default one is to assign a multi-mapping read to the TE with largest estimated abundance and discard those multi-mapping reads with ties on those largest abundances.

Because these tools were only available outside R and Bioconductor, the atena package provides a complete re-implementation in R of these three methods to facilitate the integration of TE expression quantification into Bioconductor workflows for the analysis of RNA-seq data.

TEs annotations

Another challenge in TE expression quantification is the lack of complete TE annotations due to the difficulty to correctly place TEs in genome assemblies [@goerner2018computational]. One of the main sources of TE annotations are RepeatMasker annotations, available for instance at the RepeatMasker track of the UCSC Genome Browser. atena can fetch RepeatMasker annotations with the function annotaTEs() and flexibly parse them by using a parsing function provided through the parameter parsefun. Examples of parsefun included in atena are:

• rmskidentity(): returns RepeatMasker annotations without any modification.
• rmskbasicparser(): filters out non-TE repeats and elements without strand information from RepeatMasker annotations. Then assigns a unique id to each elements based on their repeat name.
• OneCodeToFindThemAll(): implementation of the "One Code To Find Them All" algorithm by @bailly2014one, for parsing RepeatMasker output files.
• rmskatenaparser(): attempts to reconstruct fragmented TEs by assembling together fragments from the same TE that are close enough. For LTR class TEs, tries to reconstruct full-length and partial TEs following the LTR - internal region - LTR structure.

Both, the rmskatenaparser() and OneCodeToFindThemAll() parser functions attempt to address the annotation fragmentation present in the output files of the RepeatMasker software (i.e. presence of multiple hits, such as homology-based matches, corresponding to a unique copy of an element). This is highly frequent for TEs of the LTR class, where the consensus sequences are split separately into the LTR and internal regions, causing RepeatMasker to also report these two regions of the TE as two separate elements. These two functions try to identify these and other cases of fragmented annotations and assemble them together into single elements. To do so, the assembled elements must satisfy certain criteria. These two parser functions differ in those criteria, as well as in the approach for finding equivalences between LTR and internal regions to reconstruct LTR retrotransposons. The rmskatenaparser() function is also much faster than OneCodeToFindThemAll().

Retrieving and parsing TE annotations

As an example, let's retrieve TE annotations for Drosophila melanogaster dm6 genome version. By setting rmskidentity() as argument to the parsefun parameter, RepeatMasker annotations are retrieved intact as a GRanges object.

library(atena)
library(BiocParallel)

rmskann <- annotaTEs(genome="dm6", parsefun=rmskidentity)
rmskann

We can see that we obtained annotations for r length(rmskann) elements. Now, let's fetch the same RepeatMasker annotations, but process them using the OneCodeToFindThemAll parser function [@bailly2014one]. We set the parameter strict=FALSE to avoid applying a filter of minimum 80% identity with the consensus sequence and minimum 80 bp length. The insert parameter is set to 500, meaning that two elements with the same name are merged if they are closer than 500 bp in the annotations. The BPPARAM parameter allows one to run calculations in parallel using the functionality of the BiocParallel Bioconductor package. In this particular example, we are setting the BPPARAM parameter to SerialParam(progress=FALSE) to disable parallel calculations and progress reporting, but a common setting if we want to run calculations in parallel would be BPPARAM=Multicore(workers=ncores, progress=TRUE), which would use ncores parallel threads of execution and report progress on the calculations.

teann <- annotaTEs(genome="dm6", parsefun=OneCodeToFindThemAll, strict=FALSE,
                   insert=500, BPPARAM=SerialParam(progress=FALSE))
length(teann)
teann[1]

As expected, we get a lower number of elements in the annotations, because repeats that are not TEs have been removed. Furthermore, some fragmented regions of TEs have been assembled together.

This time, the resulting teann object is of class GRangesList. Each element of the list represents an assembled TE containing a GRanges object of length one, if the TE could not be not assembled with another element, or of length greater than one, if two or more fragments were assembled together into a single TE.

We can get more information of the parsed annotations by accessing the metadata columns with mcols():

mcols(teann)

There is information about the reconstruction status of the TE (Status column), the relative length of the reconstructed TE (RelLength) and the repeat class of the TE (Class). The relative length is calculated by adding the length (in base pairs) of all fragments from the same assembled TE, and dividing that sum by the length (in base pairs) of the consensus sequence. For full-length and partially reconstructed LTR TEs, the consensus sequence length used is the one resulting from adding twice the consensus sequence length of the long terminal repeat (LTR) and the one from the corresponding internal region. For solo-LTRs, the consensus sequence length of the long terminal repeat is used.

We can get an insight into the composition of the assembled annotations using the information from the status column. Let's look at the absolute frequencies of the status and class of TEs in the annotations.

library(RColorBrewer)

pal1 <- brewer.pal(6, "Pastel2")
pal2 <- brewer.pal(length(unique(mcols(teann)$Class)), "Set2")

par(mfrow = c(1,2), mar = c(5,4,3,1))
bp1 <- barplot(table(mcols(teann)$Status), col = pal1, border = "black",
        main = "TEs by status", cex.axis=0.8, xaxt = "n")
grid(nx=NA, ny=NULL)
axis(1, at=bp1, labels = FALSE, las=1, line=0, lwd = 0, lwd.ticks = 1) 
par(xpd=TRUE)
text(x= bp1[, 1] - 0.3, y = 10, labels=names(table(mcols(teann)$Status)), 
     srt=40, offset = 1.7, cex = 0.8, pos = 1)
par(xpd=FALSE)

bp2 <- barplot(table(mcols(teann)$Class), col = pal2, border = "black",
        main = "TEs by class", cex.axis=0.8, xaxt = "n",
        ylim = c(0,max(table(mcols(teann)$Status))))
grid(nx=NA, ny=NULL)
axis(1, at=bp2, labels = FALSE, las=1, line=0, lwd = 0, lwd.ticks = 1) 
par(xpd=TRUE)
text(x= bp2[, 1] - 0.1, y = 10, labels=names(table(mcols(teann)$Class)), 
     srt=35, offset = 1.2, cex = 0.8, pos = 1)
par(xpd=FALSE)

Here, full-lengthLTR are reconstructed LTR retrotransposons following the LTR - internal region (int) - LTR structure. Partially reconstructed LTR TEs are partialLTR_down (internal region followed by a downstream LTR) and partialLTR_up (LTR upstream of an internal region). int and LTR correspond to internal and solo-LTR regions, respectively. Finally, the noLTR refers to TEs of other classes (not LTR), as well as TEs of class LTR which could not be identified as either internal or long terminal repeat regions based on their name.

Moreover, the atena package provides getter functions to retrieve TEs of a specific class, using a specific relative length threshold. Those TEs with higher relative lengths are more likely to have intact open reading frames, making them more interesting for expression quantification and functional analyses. For example, to get LINEs with a minimum of 0.9 relative length, we can use the getLINEs() function. We use the TE annotations in teann we obtained before and set the relLength to 0.9.

rmskLINE <- getLINEs(teann, relLength=0.9)
length(rmskLINE)
rmskLINE[1]

To get LTR retrotransposons, we can use the function getLTRs(). This function also allows to get one or more specific types of reconstructed TEs. To get full-length, partial LTRs and other fragments that could not be reconstructed, we can:

rmskLTR <- getLTRs(teann, relLength=0.8, fullLength=TRUE, partial=TRUE,
                   otherLTR=TRUE)
length(rmskLTR)
rmskLTR[1]

To obtain DNA transposons and SINEs, one can use the getDNAtransposons() and getSINEs() functions, respectively.

TE expression quantification

Quantification of TE expression with atena consists in the following two steps:

1. Building of a parameter object for one of the available quantification methods.

2. Calling the TE expression quantification method qtex() using the previously built parameter object.

The dataset that will be used to illustrate how to quantify TE expression with atena is a published RNA-seq dataset of Drosophila melanogaster available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) under accession GSE47006). The two selected samples are: a piwi knockdown and a piwi control (GSM1142845 and GSM1142844). These files have been subsampled. The piwi-associated silencing complex (piRISC) silences TEs in the Drosophila ovary, hence the knockdown of piwi causes the de-repression of TEs. Here we show how the expression of full-length LTR retrotransposons present in rmskLTR can be easily quantified using atena.

Building a parameter object

A parameter object is build calling a specific function for the quantification method we want to use. Independenty of each method, all parameter object constructor functions require that the first two arguments specify the BAM files and the TE annotation, respectively.

ERVmap

To use the ERVmap method in atena we should first build an object of the class ERVmapParam using the function ERVmapParam(). The singleEnd parameter is set to TRUE since the example BAM files are single-end. The ignoreStrand parameter works analogously to the same parameter in the function summarizeOverlaps() from package r Biocpkg("GenomicAlignments") and should be set to TRUE whenever the RNA library preparation protocol was stranded.

One of the filters applied by the ERVmap method compares the alignment score of a given primary alignment, stored in the AS tag of a SAM record, to the largest alignment score among every other secondary alignment, known as the suboptimal alignment score. The original ERVmap software assumes that input BAM files are generated using the Burrows-Wheeler Aligner (BWA) software [@li2009fast], which stores suboptimal alignment scores in the XS tag. Although AS is an optional tag, most short-read aligners provide this tag with alignment scores in BAM files. However, the suboptimal alignment score, stored in the XS tag by BWA, is either stored in a different tag or not stored at all by other short-read aligner software, such as STAR [@dobin2013star].

To enable using ERVmap on BAM files produced by short-read aligner software other than BWA, atena allows the user to set the argument suboptimalAlignmentTag to one of the following three possible values:

• The name of a tag different to XS that stores the suboptimal alignment score.

• The value "none", which will trigger the calculation of the suboptimal alignment score by searching for the largest value stored in the AS tag among all available secondary alignments.

• The value "auto" (default), by which atena will first extract the name of the short-read aligner software from the BAM file and if that software is BWA, then suboptimal alignment scores will be obtained from the XS tag. Otherwise, it will trigger the calculation previously explained for suboptimalAlignemntTag="none".

Finally, this filter is applied by comparing the difference between alignment and suboptimal alignment scores to a cutoff value, which by default is 5 but can be modified using the parameter suboptimalAlignmentCutoff. The default value 5 is the one employed in the original ERVmap software that assumes the BAM file was generated with BWA and for which lower values are interpreted as "equivalent to second best match has one or more mismatches than the best match" [@tokuyama2018ervmap, pg. 12571]. From a different perspective, in BWA the mismatch penalty has a value of 4 and therefore, a suboptimalAlignmentCutoff value of 5 only retains those reads where the suboptimal alignment has at least 1 mismatch more than the best match. Therefore, the suboptimalAlignmentCutoff value is specific to the short-read mapper software and we recommend to set this value according to the mismatch penalty of that software. Another option is to set suboptimalAlignmentCutoff=NA, which prevents the filtering of reads based on this criteria, as set in the following example.

bamfiles <- list.files(system.file("extdata", package="atena"),
                       pattern="*.bam", full.names=TRUE)
empar <- ERVmapParam(bamfiles, 
                     teFeatures=rmskLTR, 
                     singleEnd=TRUE, 
                     ignoreStrand=TRUE, 
                     suboptimalAlignmentCutoff=NA)
empar

In the case of paired-end BAM files (singleEnd=FALSE), two additional arguments can be specified, strandMode and fragments:

• strandMode defines the behavior of the strand getter when internally reading the BAM files with the GAlignmentPairs() function. See the help page of strandMode in the r Biocpkg("GenomicAlignments") package for further details.

• fragments controls how read filtering and counting criteria are applied to the read mates in a paired-end read. To use the original ERVmap algorithm [@tokuyama2018ervmap] one should set fragments=TRUE (default when singleEnd=FALSE), which filters and counts each mate of a paired-end read independently (i.e., two read mates overlapping the same feature count twice on that feature, treating paired-end reads as if they were single-end). On the other hand, when fragments=FALSE, if the two read mates pass the filtering criteria and overlap the same feature, they count once on that feature. If either read mate fails to pass the filtering criteria, then both read mates are discarded.

An additional functionality with respect to the original ERVmap software is the integration of gene and TE expression quantification. The original ERVmap software doesn't quantify TE and gene expression coordinately and this can potentially lead to counting twice reads that simultaneously overlap a gene and a TE. In atena, gene expression is quantified based on the approach used in the TEtranscripts software [@jin2015tetranscripts]: unique reads are preferably assigned to genes, whereas multi-mapping reads are preferably assigned to TEs.

In case that a unique read does not overlap a gene or a multi-mapping read does not overlap a TE, atena searches for overlaps with TEs or genes, respectively. Given the different treatment of unique and multi-mapping reads, atena requires the information regarding the unique or multi-mapping status of a read. This information is obtained from the presence of secondary alignments in the BAM file or, alternatively, from the NH tag in the BAM file (number of reported alignments that contain the query in the current SAM record). Therefore, either secondary alignments or the NH tag need to be present for gene expression quantification.

The original ERVmap approach does not discard any read overlapping gene annotations. However, this can be changed using the parameter geneCountMode, which by default geneCountMode="all" and follows the behavior in the original ERVmap method. On the contrary, by setting geneCountMode="ervmap", atena also applies the filtering criteria employed to quantify TE expression to the reads overlapping gene annotations.

Finally, atena also allows one to aggregate TE expression quantifications. By default, the names of the input GRanges or GRangesList object given in the teFeatures parameter are used to aggregate quantifications. However, the aggregateby parameter can be used to specify other column names in the feature annotations to be used to aggregate TE counts, for example at the sub-family level.

Telescope

To use the Telescope method for TE expression quantification, the TelescopeParam() function is used to build a parameter object of the class TelescopeParam.

As in the case of ERVmapParam(), the aggregateby argument, which should be a character vector of column names in the annotation, determines the columns to be used to aggregate TE expression quantifications. This way, atena provides not only quantifications at the subfamily level, but also allows to quantify TEs at the desired level (family, class, etc.), including locus based quantifications. For such a use case, the object with the TE annotations should include a column with unique identifiers for each TE locus and the aggregateby argument should specify the name of that column. When aggregateby is not specified, the names() of the object containing TE annotations are used to aggregate quantifications.

Here, TE quantifications will be aggregated according to the names() of the rmskLTR object.

bamfiles <- list.files(system.file("extdata", package="atena"),
                       pattern="*.bam", full.names=TRUE)
tspar <- TelescopeParam(bfl=bamfiles, 
                        teFeatures=rmskLTR, 
                        singleEnd=TRUE, 
                        ignoreStrand=TRUE)
tspar

In case of paired-end data (singleEnd=FALSE), the argument usage is similar to that of ERVmapParam(). In relation to the BAM file, Telescope follows the same approach as the ERVmap method: when fragments=FALSE, only mated read pairs from opposite strands are considered, while when fragments=TRUE, same-strand pairs, singletons, reads with unmapped pairs and other fragments are also considered by the algorithm. However, there is one important difference with respect to the counting approach followed by ERVmap: when fragments=TRUE mated read pairs mapping to the same element are counted once, whereas in the ERVmap method they are counted twice.

As in the ERVmap method from atena, the gene expression quantification method in Telescope is based on the approach of the TEtranscripts software [@jin2015tetranscripts]. This way, atena provides the possibility to integrate TE expression quantification by Telescope with gene expression quantification. As in the case of the ERVmap method implemented in atena, either secondary alignments or the NH tag are required for gene expression quantification.

TEtranscripts

Finally, the third method available is TEtranscripts. First, the TEtranscriptsParam() function is called to build a parameter object of the class TEtranscriptsParam. The usage of the aggregateby argument is the same as in TelescopeParam() and ERVmapParam(). Locus based quantifications in the TEtranscripts method from atena is possible because the TEtranscripts algorithm actually computes TE quantifications at the locus level and then sums up all instances of each TE subfamily to provide expression at the subfamily level. By avoiding this last step, atena can provide TE expression quantification at the locus level using the TEtranscripts method. For such a use case, the object with the TE annotations should include a column with unique identifiers for each TE and the aggregateby argument should specify the name of that column.

In this example, the aggregateby argument will be set to aggregateby="repName" in order to aggregate quantifications at the repeat name level. Moreover, gene expression will also be quantified. To do so, gene annotations are loaded from a TxDb object.

library(TxDb.Dmelanogaster.UCSC.dm6.ensGene)

txdb <- TxDb.Dmelanogaster.UCSC.dm6.ensGene
gannot <- exonsBy(txdb, by="gene")
length(gannot)

bamfiles <- list.files(system.file("extdata", package="atena"),
                       pattern="*.bam", full.names=TRUE)
ttpar <- TEtranscriptsParam(bamfiles, 
                            teFeatures=rmskLTR,
                            geneFeatures=gannot,
                            singleEnd=TRUE, 
                            ignoreStrand=TRUE, 
                            aggregateby="repName")
ttpar

For paired-end data, where would set singleEnd=FALSE, the fragments parameter has the same purpose as in TelescopeParam(). We can also extract the TEs and gene combined feature set using the features() function on the parameter object. A metadata column called isTE is added to enable distinguishing TEs from gene annotations.

features(ttpar)
mcols(features(ttpar))
table(mcols(features(ttpar))$isTE)

Regarding gene expression quantification, atena has implemented the approach of the original TEtranscripts software [@jin2015tetranscripts]. As in the case of the ERVmap and Telescope methods from atena, either secondary alignments or the NH tag are required.

Following the gene annotation processing present in the TEtranscripts algorithm, in case that geneFeatures contains a metadata column named "type", only the elements with type="exon" are considered for quantification. If those elements are grouped through a GRangesList object, then counts are aggregated at the level of those GRangesList elements, such as genes or transcripts. This also applies to the ERVmap and Telescope methods implemented in atena when gene features are present. Let's see an example of this processing:

## Create a toy example of gene annotations
geneannot <- GRanges(seqnames=rep("2L", 8),
                     ranges=IRanges(start=c(1,20,45,80,110,130,150,170),
                                    width=c(10,20,35,10,5,15,10,25)),
                     strand="*", 
                     type=rep("exon",8))
names(geneannot) <- paste0("gene",c(rep(1,3),rep(2,4),rep(3,1)))
geneannot
ttpar2 <- TEtranscriptsParam(bamfiles, 
                             teFeatures=rmskLTR, 
                             geneFeatures=geneannot, 
                             singleEnd=TRUE, 
                             ignoreStrand=TRUE)
mcols(features(ttpar2))
features(ttpar2)[!mcols(features(ttpar2))$isTE]

Quantifying expression

Finally, to quantify TE expression we call the qtex() method using one of the previously defined parameter objects (ERVmapParam, TEtranscriptsParam or TelescopeParam) according to the quantification method we want to use. As with the OneCodeToFindThemAll() function described before, here we can also use the BPPARAM parameter to perform calculations in parallel.

The qtex() method returns a SummarizedExperiment object containing the resulting quantification of expression in an assay slot. Additionally, when a data.frame, or DataFrame, object storing phenotypic data is passed to the qtex() function through the phenodata parameter, this will be included as column data in the resulting SummarizedExperiment object and the row names of these phenotypic data will be set as column names in the output SummarizedExperiment object.

In the current example, the call to quantify TE expression using the ERVmap method would be the following:

emq <- qtex(empar)

emq
colSums(assay(emq))

In the case of the Telescope method, the call would be as follows:

tsq <- qtex(tspar)

tsq
colSums(assay(tsq))

For the TEtranscripts method, TE expression is quantified by using the following call:

ttq <- qtex(ttpar)

ttq
colSums(assay(ttq))

As mentioned, TE expression quantification is provided at the repeat name level.

Accesing expression quantifications and metadata

The qtex() function returns a SummarizedExperiment object that, on the one hand, stores the quantified expression in its assay data.

head(assay(ttq))

On the other hand, it contains metadata about the features that may be useful to select subsets of the quantified data and extract and explore the feature annotations, using the function rowData() on this SummarizedExperiment object.

rowData(ttq)

Because we have aggregated quantifications by RepName the number of TE quantified features has been substantially reduced with respect to the original number of TE features.

table(rowData(ttq)$isTE)

Let's say we want to select full-length LTRs features, this could be a way of doing it.

temask <- rowData(ttq)$isTE
fullLTRs <- rowData(ttq)$Status == "full-lengthLTR"
fullLTRs <- (sapply(fullLTRs, sum, na.rm=TRUE) == 1) &
            (lengths(rowData(ttq)$Status) == 1)
sum(fullLTRs)
rowData(ttq)[fullLTRs, ]

Note also that since we restricted expression quantification to LTRs, we do have only quantification for that TE class.

table(rowData(ttq)$Class[temask])

Session information

sessionInfo()

References

functionalgenomics/atena documentation built on Nov. 4, 2024, 7:33 p.m.