An introduction to the atena package

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 (Payer and Burns 2019). 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 (Goerner-Potvin and Bourque 2018; Guffanti et al. 2018).

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 (Payer and Burns 2019).

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 (Payer and Burns 2019).

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 (Goerner-Potvin and Bourque 2018), such as TEtranscripts (Jin et al. 2015), ERVmap (Tokuyama et al. 2018) and Telescope (Bendall et al. 2019). The main differences between these three methods are the following:

  • TEtranscripts (Jin et al. 2015) reassigns multi-mapping reads to TEs proportionally to their relative abundance, which is estimated using an expectation-maximization (EM) algorithm.

  • ERVmap (Tokuyama et al. 2018) 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 (Bendall et al. 2019) 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 (Goerner-Potvin and Bourque 2018). 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 Bailly-Bechet, Haudry, and Lerat (2014), 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
GRanges object with 137555 ranges and 11 metadata columns:
                   seqnames    ranges strand |   swScore  milliDiv  milliDel
                      <Rle> <IRanges>  <Rle> | <integer> <numeric> <numeric>
       [1]            chr2L     2-154      + |       778       167         7
       [2]            chr2L   313-408      + |       296       174       207
       [3]            chr2L   457-612      + |       787       170         7
       [4]            chr2L   771-866      + |       296       174       207
       [5]            chr2L  915-1070      + |       787       170         7
       ...              ...       ...    ... .       ...       ...       ...
  [137551] chrUn_DS486004v1    99-466      - |      3224        14         0
  [137552] chrUn_DS486005v1    1-1001      + |       930        48         0
  [137553] chrUn_DS486008v1     1-488      + |      4554         0         0
  [137554] chrUn_DS486008v1   489-717      - |      2107         9         0
  [137555] chrUn_DS486008v1  717-1001      - |      2651         3         0
            milliIns  genoLeft      repName      repClass     repFamily
           <numeric> <integer>  <character>   <character>   <character>
       [1]        20 -23513558     HETRP_DM     Satellite     Satellite
       [2]        42 -23513304     HETRP_DM     Satellite     Satellite
       [3]        19 -23513100     HETRP_DM     Satellite     Satellite
       [4]        42 -23512846     HETRP_DM     Satellite     Satellite
       [5]        19 -23512642     HETRP_DM     Satellite     Satellite
       ...       ...       ...          ...           ...           ...
  [137551]         3      -535 ROVER-LTR_DM           LTR         Gypsy
  [137552]         1         0  (TATACATA)n Simple_repeat Simple_repeat
  [137553]         0      -513    NOMAD_LTR           LTR         Gypsy
  [137554]         0      -284   ACCORD_LTR           LTR         Gypsy
  [137555]         0         0       DMRT1A          LINE            R1
            repStart    repEnd   repLeft
           <integer> <integer> <integer>
       [1]      1519      1669      -203
       [2]      1519      1634      -238
       [3]      1516      1669      -203
       [4]      1519      1634      -238
       [5]      1516      1669      -203
       ...       ...       ...       ...
  [137551]         0       367         1
  [137552]         1      1000         0
  [137553]        31       518         0
  [137554]      -123       435       207
  [137555]         0      5183      4899
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

We can see that we obtained annotations for 137555 elements. Now, let’s fetch the same RepeatMasker annotations, but process them using the OneCodeToFindThemAll parser function (Bailly-Bechet, Haudry, and Lerat 2014). 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)
[1] 22538
teann[1]
GRangesList object of length 1:
$IDEFIX_LTR.1
GRanges object with 1 range and 11 metadata columns:
      seqnames    ranges strand |   swScore  milliDiv  milliDel  milliIns
         <Rle> <IRanges>  <Rle> | <integer> <numeric> <numeric> <numeric>
  [1]    chr2L 9726-9859      + |       285       235        64        15
       genoLeft     repName    repClass   repFamily  repStart    repEnd
      <integer> <character> <character> <character> <integer> <integer>
  [1] -23503853  IDEFIX_LTR         LTR       Gypsy       425       565
        repLeft
      <integer>
  [1]        29
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

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)
DataFrame with 22538 rows and 3 columns
                               Status RelLength       Class
                          <character> <numeric> <character>
IDEFIX_LTR.1                      LTR  0.225589         LTR
DNAREP1_DM.2                    noLTR  0.419192         DNA
LINEJ1_DM.3                     noLTR  0.997211        LINE
DNAREP1_DM.4                    noLTR  0.861953         DNA
BS2.5                           noLTR  0.126880        LINE
...                               ...       ...         ...
QUASIMODO_I-int.22534           noLTR 0.0882838         LTR
ROVER-I_DM.22535      partialLTR_down 0.0636786         LTR
NOMAD_LTR.22536                 noLTR 0.9420849         LTR
ACCORD_LTR.22537                noLTR 0.4103943         LTR
DMRT1A.22538                    noLTR 0.0549875        LINE

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.

Composition of parsed TE annotations.

Composition of parsed TE annotations.

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)
[1] 355
rmskLINE[1]
GRangesList object of length 1:
$LINEJ1_DM.3
GRanges object with 1 range and 11 metadata columns:
      seqnames      ranges strand |   swScore  milliDiv  milliDel  milliIns
         <Rle>   <IRanges>  <Rle> | <integer> <numeric> <numeric> <numeric>
  [1]    chr2L 47514-52519      + |     43674         5         0         0
       genoLeft     repName    repClass   repFamily  repStart    repEnd
      <integer> <character> <character> <character> <integer> <integer>
  [1] -23461193   LINEJ1_DM        LINE      Jockey         2      5007
        repLeft
      <integer>
  [1]        13
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

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)
[1] 1408
rmskLTR[1]
GRangesList object of length 1:
$`ROO_I-int.11`
GRanges object with 4 ranges and 11 metadata columns:
      seqnames        ranges strand |   swScore  milliDiv  milliDel  milliIns
         <Rle>     <IRanges>  <Rle> | <integer> <numeric> <numeric> <numeric>
  [1]    chr2L 976935-977362      + |      3968         5         0         0
  [2]    chr2L 977363-983449      + |     54257         1        13         1
  [3]    chr2L 983448-984084      + |      5412         5        19         0
  [4]    chr2L 984085-984512      + |      3968         5         0         0
       genoLeft     repName    repClass   repFamily  repStart    repEnd
      <integer> <character> <character> <character> <integer> <integer>
  [1] -22536350     ROO_LTR         LTR         Pao         1       428
  [2] -22530263   ROO_I-int         LTR         Pao         1      6166
  [3] -22529628   ROO_I-int         LTR         Pao      7608      8256
  [4] -22529200     ROO_LTR         LTR         Pao         1       428
        repLeft
      <integer>
  [1]         0
  [2]      2090
  [3]         0
  [4]         0
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

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 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 (Li and Durbin 2009), 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 (Dobin et al. 2013).

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” (Tokuyama et al. 2018, 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)
ℹ Locating BAM files
ℹ Processing features
✔ Parameter object successfully created
empar
ERVmapParam object
# BAM files (2): control_KD.bam, piwi_KD.bam
# features (1408): ACCORD2_I-int.18752, ..., ZAM_LTR.21390
# single-end, unstranded

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 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 (Tokuyama et al. 2018) 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 (Jin et al. 2015): 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)
ℹ Locating BAM files
ℹ Processing features
✔ Parameter object successfully created
tspar
TelescopeParam object
# BAM files (2): control_KD.bam, piwi_KD.bam
# features (CompressedGRangesList length 1408): ACCORD2_I-int.18752, ..., ZAM_LTR.21390
# single-end; unstranded

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 (Jin et al. 2015). 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)
[1] 17807
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")
ℹ Locating BAM files
ℹ Processing features
✔ Parameter object successfully created
ttpar
TEtranscriptsParam object
# BAM files (2): control_KD.bam, piwi_KD.bam
# features (CompressedGRangesList length 19215): ACCORD2_I-int.18752, ..., ZAM_LTR.21390
# aggregated by: repName
# single-end; unstranded

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)
GRangesList object of length 19215:
$`ACCORD2_I-int.18752`
GRanges object with 5 ranges and 15 metadata columns:
                      seqnames          ranges strand |   swScore  milliDiv
                         <Rle>       <IRanges>  <Rle> | <integer> <numeric>
  ACCORD2_I-int.18752     chrY 2683299-2683474      - |      1419         0
  ACCORD2_I-int.18752     chrY 2683475-2685353      - |      8817        30
  ACCORD2_I-int.18752     chrY 2685348-2688057      - |     22228        20
  ACCORD2_I-int.18752     chrY 2688056-2689854      - |      6698        75
  ACCORD2_I-int.18752     chrY 2689855-2690073      - |      1821         5
                       milliDel  milliIns  genoLeft       repName    repClass
                      <numeric> <numeric> <integer>   <character> <character>
  ACCORD2_I-int.18752         6        23   -983878   ACCORD2_LTR         LTR
  ACCORD2_I-int.18752        98         0   -981999 ACCORD2_I-int         LTR
  ACCORD2_I-int.18752         1         0   -979295 ACCORD2_I-int         LTR
  ACCORD2_I-int.18752        57        41   -977498 ACCORD2_I-int         LTR
  ACCORD2_I-int.18752         0        18   -977279   ACCORD2_LTR         LTR
                        repFamily  repStart    repEnd   repLeft   exon_id
                      <character> <integer> <integer> <integer> <integer>
  ACCORD2_I-int.18752       Gypsy        42       173         1      <NA>
  ACCORD2_I-int.18752       Gypsy         9      7203      5142      <NA>
  ACCORD2_I-int.18752       Gypsy      2371      4841      2128      <NA>
  ACCORD2_I-int.18752       Gypsy      5197      2015         1      <NA>
  ACCORD2_I-int.18752       Gypsy         0       215         1      <NA>
                        exon_name        type  isTE
                      <character> <character> <Rle>
  ACCORD2_I-int.18752        <NA>        <NA>  TRUE
  ACCORD2_I-int.18752        <NA>        <NA>  TRUE
  ACCORD2_I-int.18752        <NA>        <NA>  TRUE
  ACCORD2_I-int.18752        <NA>        <NA>  TRUE
  ACCORD2_I-int.18752        <NA>        <NA>  TRUE
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

$`ACCORD2_I-int.18766`
GRanges object with 6 ranges and 15 metadata columns:
                      seqnames          ranges strand |   swScore  milliDiv
                         <Rle>       <IRanges>  <Rle> | <integer> <numeric>
  ACCORD2_I-int.18766     chrY 2737073-2737248      - |      1521         0
  ACCORD2_I-int.18766     chrY 2737249-2739132      - |      9404        31
  ACCORD2_I-int.18766     chrY 2739127-2741836      - |     23688        20
  ACCORD2_I-int.18766     chrY 2741835-2742273      - |      2682        64
  ACCORD2_I-int.18766     chrY 2742370-2743581      - |      5660        69
  ACCORD2_I-int.18766     chrY 2743582-2743800      - |      1947         5
                       milliDel  milliIns  genoLeft       repName    repClass
                      <numeric> <numeric> <integer>   <character> <character>
  ACCORD2_I-int.18766         6        23   -930104   ACCORD2_LTR         LTR
  ACCORD2_I-int.18766        97         1   -928220 ACCORD2_I-int         LTR
  ACCORD2_I-int.18766         2         0   -925516 ACCORD2_I-int         LTR
  ACCORD2_I-int.18766        32        46   -925079 ACCORD2_I-int         LTR
  ACCORD2_I-int.18766        20        42   -923771 ACCORD2_I-int         LTR
  ACCORD2_I-int.18766         0        18   -923552   ACCORD2_LTR         LTR
                        repFamily  repStart    repEnd   repLeft   exon_id
                      <character> <integer> <integer> <integer> <integer>
  ACCORD2_I-int.18766       Gypsy        42       173         1      <NA>
  ACCORD2_I-int.18766       Gypsy         6      7206      5142      <NA>
  ACCORD2_I-int.18766       Gypsy      2371      4841      2128      <NA>
  ACCORD2_I-int.18766       Gypsy      5197      2015      1583      <NA>
  ACCORD2_I-int.18766       Gypsy      5845      1367         1      <NA>
  ACCORD2_I-int.18766       Gypsy         0       215         1      <NA>
                        exon_name        type  isTE
                      <character> <character> <Rle>
  ACCORD2_I-int.18766        <NA>        <NA>  TRUE
  ACCORD2_I-int.18766        <NA>        <NA>  TRUE
  ACCORD2_I-int.18766        <NA>        <NA>  TRUE
  ACCORD2_I-int.18766        <NA>        <NA>  TRUE
  ACCORD2_I-int.18766        <NA>        <NA>  TRUE
  ACCORD2_I-int.18766        <NA>        <NA>  TRUE
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

$`ACCORD2_I-int.2712`
GRanges object with 7 ranges and 15 metadata columns:
                     seqnames          ranges strand |   swScore  milliDiv
                        <Rle>       <IRanges>  <Rle> | <integer> <numeric>
  ACCORD2_I-int.2712    chr2R 2204406-2204447      + |       246       119
  ACCORD2_I-int.2712    chr2R 2204527-2207748      + |     15834        93
  ACCORD2_I-int.2712    chr2R 2207743-2208758      + |      6631        72
  ACCORD2_I-int.2712    chr2R 2208789-2209734      + |      2458        83
  ACCORD2_I-int.2712    chr2R 2209729-2211681      + |     12355        81
  ACCORD2_I-int.2712    chr2R 2211689-2211834      + |       556       102
  ACCORD2_I-int.2712    chr2R 2212158-2212233      + |       475       107
                      milliDel  milliIns  genoLeft       repName    repClass
                     <numeric> <numeric> <integer>   <character> <character>
  ACCORD2_I-int.2712         0         0 -23082489 ACCORD2_I-int         LTR
  ACCORD2_I-int.2712        62        18 -23079188 ACCORD2_I-int         LTR
  ACCORD2_I-int.2712        65         8 -23078178 ACCORD2_I-int         LTR
  ACCORD2_I-int.2712       103         7 -23077202 ACCORD2_I-int         LTR
  ACCORD2_I-int.2712        57         9 -23075255 ACCORD2_I-int         LTR
  ACCORD2_I-int.2712        30       123 -23075102   ACCORD2_LTR         LTR
  ACCORD2_I-int.2712         0        13 -23074703   ACCORD2_LTR         LTR
                       repFamily  repStart    repEnd   repLeft   exon_id
                     <character> <integer> <integer> <integer> <integer>
  ACCORD2_I-int.2712       Gypsy      1326      1367      5845      <NA>
  ACCORD2_I-int.2712       Gypsy      1522      4950      2262      <NA>
  ACCORD2_I-int.2712       Gypsy      5159      6236       976      <NA>
  ACCORD2_I-int.2712       Gypsy      3851      4950      2262      <NA>
  ACCORD2_I-int.2712       Gypsy      5159      7209         3      <NA>
  ACCORD2_I-int.2712       Gypsy         1       132        83      <NA>
  ACCORD2_I-int.2712       Gypsy       141       215         0      <NA>
                       exon_name        type  isTE
                     <character> <character> <Rle>
  ACCORD2_I-int.2712        <NA>        <NA>  TRUE
  ACCORD2_I-int.2712        <NA>        <NA>  TRUE
  ACCORD2_I-int.2712        <NA>        <NA>  TRUE
  ACCORD2_I-int.2712        <NA>        <NA>  TRUE
  ACCORD2_I-int.2712        <NA>        <NA>  TRUE
  ACCORD2_I-int.2712        <NA>        <NA>  TRUE
  ACCORD2_I-int.2712        <NA>        <NA>  TRUE
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

...
<19212 more elements>
mcols(features(ttpar))
DataFrame with 19215 rows and 4 columns
                             Status RelLength       Class      isTE
                        <character> <numeric> <character> <logical>
ACCORD2_I-int.18752  full-lengthLTR  0.887595         LTR      TRUE
ACCORD2_I-int.18766  full-lengthLTR  0.868882         LTR      TRUE
ACCORD2_I-int.2712  partialLTR_down  0.968464         LTR      TRUE
ACCORD2_I-int.6123   full-lengthLTR  1.000000         LTR      TRUE
ACCORD2_LTR.19682             noLTR  1.000000         LTR      TRUE
...                             ...       ...         ...       ...
TRANSPAC_I-int.8329  full-lengthLTR  0.999810         LTR      TRUE
TRANSPAC_I-int.9501  full-lengthLTR  1.000000         LTR      TRUE
ZAM_I-int.2573       full-lengthLTR  0.998459         LTR      TRUE
ZAM_I-int.7530       full-lengthLTR  0.861987         LTR      TRUE
ZAM_LTR.21390                 noLTR  0.957627         LTR      TRUE
table(mcols(features(ttpar))$isTE)

FALSE  TRUE 
17807  1408

Regarding gene expression quantification, atena has implemented the approach of the original TEtranscripts software (Jin et al. 2015). 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
GRanges object with 8 ranges and 1 metadata column:
        seqnames    ranges strand |        type
           <Rle> <IRanges>  <Rle> | <character>
  gene1       2L      1-10      * |        exon
  gene1       2L     20-39      * |        exon
  gene1       2L     45-79      * |        exon
  gene2       2L     80-89      * |        exon
  gene2       2L   110-114      * |        exon
  gene2       2L   130-144      * |        exon
  gene2       2L   150-159      * |        exon
  gene3       2L   170-194      * |        exon
  -------
  seqinfo: 1 sequence from an unspecified genome; no seqlengths
ttpar2 <- TEtranscriptsParam(bamfiles, 
                             teFeatures=rmskLTR, 
                             geneFeatures=geneannot, 
                             singleEnd=TRUE, 
                             ignoreStrand=TRUE)
ℹ Locating BAM files
ℹ Processing features
✔ Parameter object successfully created
mcols(features(ttpar2))
DataFrame with 1411 rows and 4 columns
                             Status RelLength       Class      isTE
                        <character> <numeric> <character> <logical>
ACCORD2_I-int.18752  full-lengthLTR  0.887595         LTR      TRUE
ACCORD2_I-int.18766  full-lengthLTR  0.868882         LTR      TRUE
ACCORD2_I-int.2712  partialLTR_down  0.968464         LTR      TRUE
ACCORD2_I-int.6123   full-lengthLTR  1.000000         LTR      TRUE
ACCORD2_LTR.19682             noLTR  1.000000         LTR      TRUE
...                             ...       ...         ...       ...
ZAM_I-int.7530       full-lengthLTR  0.861987         LTR      TRUE
ZAM_LTR.21390                 noLTR  0.957627         LTR      TRUE
gene1                            NA        NA          NA     FALSE
gene2                            NA        NA          NA     FALSE
gene3                            NA        NA          NA     FALSE
features(ttpar2)[!mcols(features(ttpar2))$isTE]
GRangesList object of length 3:
$gene1
GRanges object with 3 ranges and 13 metadata columns:
        seqnames    ranges strand |   swScore  milliDiv  milliDel  milliIns
           <Rle> <IRanges>  <Rle> | <integer> <numeric> <numeric> <numeric>
  gene1    chr2L      1-10      * |      <NA>        NA        NA        NA
  gene1    chr2L     20-39      * |      <NA>        NA        NA        NA
  gene1    chr2L     45-79      * |      <NA>        NA        NA        NA
         genoLeft     repName    repClass   repFamily  repStart    repEnd
        <integer> <character> <character> <character> <integer> <integer>
  gene1      <NA>        <NA>        <NA>        <NA>      <NA>      <NA>
  gene1      <NA>        <NA>        <NA>        <NA>      <NA>      <NA>
  gene1      <NA>        <NA>        <NA>        <NA>      <NA>      <NA>
          repLeft        type  isTE
        <integer> <character> <Rle>
  gene1      <NA>        exon FALSE
  gene1      <NA>        exon FALSE
  gene1      <NA>        exon FALSE
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

$gene2
GRanges object with 4 ranges and 13 metadata columns:
        seqnames    ranges strand |   swScore  milliDiv  milliDel  milliIns
           <Rle> <IRanges>  <Rle> | <integer> <numeric> <numeric> <numeric>
  gene2    chr2L     80-89      * |      <NA>        NA        NA        NA
  gene2    chr2L   110-114      * |      <NA>        NA        NA        NA
  gene2    chr2L   130-144      * |      <NA>        NA        NA        NA
  gene2    chr2L   150-159      * |      <NA>        NA        NA        NA
         genoLeft     repName    repClass   repFamily  repStart    repEnd
        <integer> <character> <character> <character> <integer> <integer>
  gene2      <NA>        <NA>        <NA>        <NA>      <NA>      <NA>
  gene2      <NA>        <NA>        <NA>        <NA>      <NA>      <NA>
  gene2      <NA>        <NA>        <NA>        <NA>      <NA>      <NA>
  gene2      <NA>        <NA>        <NA>        <NA>      <NA>      <NA>
          repLeft        type  isTE
        <integer> <character> <Rle>
  gene2      <NA>        exon FALSE
  gene2      <NA>        exon FALSE
  gene2      <NA>        exon FALSE
  gene2      <NA>        exon FALSE
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

$gene3
GRanges object with 1 range and 13 metadata columns:
        seqnames    ranges strand |   swScore  milliDiv  milliDel  milliIns
           <Rle> <IRanges>  <Rle> | <integer> <numeric> <numeric> <numeric>
  gene3    chr2L   170-194      * |      <NA>        NA        NA        NA
         genoLeft     repName    repClass   repFamily  repStart    repEnd
        <integer> <character> <character> <character> <integer> <integer>
  gene3      <NA>        <NA>        <NA>        <NA>      <NA>      <NA>
          repLeft        type  isTE
        <integer> <character> <Rle>
  gene3      <NA>        exon FALSE
  -------
  seqinfo: 1870 sequences (1 circular) from dm6 genome

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
class: RangedSummarizedExperiment 
dim: 1408 2 
metadata(0):
assays(1): counts
rownames(1408): ACCORD2_I-int.18752 ACCORD2_I-int.18766 ...
  ZAM_I-int.7530 ZAM_LTR.21390
rowData names(4): Status RelLength Class isTE
colnames(2): control_KD piwi_KD
colData names(0):
colSums(assay(emq))
control_KD    piwi_KD 
        95         75

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

tsq <- qtex(tspar)
tsq
class: RangedSummarizedExperiment 
dim: 1408 2 
metadata(0):
assays(1): counts
rownames(1408): ACCORD2_I-int.18752 ACCORD2_I-int.18766 ...
  ZAM_I-int.7530 ZAM_LTR.21390
rowData names(4): Status RelLength Class isTE
colnames(2): control_KD piwi_KD
colData names(0):
colSums(assay(tsq))
control_KD    piwi_KD 
       144          3

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

ttq <- qtex(ttpar)
ttq
class: RangedSummarizedExperiment 
dim: 17917 2 
metadata(0):
assays(1): counts
rownames(17917): ACCORD2_I-int ACCORD2_LTR ... FBgn0286940 FBgn0286941
rowData names(4): Status RelLength Class isTE
colnames(2): control_KD piwi_KD
colData names(0):
colSums(assay(ttq))
control_KD    piwi_KD 
       149        133

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))
              control_KD piwi_KD
ACCORD2_I-int          0       0
ACCORD2_LTR            0       0
ACCORD_LTR             0       0
BATUMI_I-int           0       0
BATUMI_LTR             0       0
BEL_I-int              0       0

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)
DataFrame with 17917 rows and 4 columns
                                    Status RelLength       Class      isTE
                           <CharacterList> <numeric> <character> <logical>
ACCORD2_I-int              partialLTR_down  0.968464         LTR      TRUE
ACCORD2_LTR           full-lengthLTR,noLTR  0.928405         LTR      TRUE
ACCORD_LTR            full-lengthLTR,noLTR  0.879776         LTR      TRUE
BATUMI_I-int     int,partialLTR_down,noLTR  0.934641         LTR      TRUE
BATUMI_LTR    full-lengthLTR,partialLTR_up  0.963187         LTR      TRUE
...                                    ...       ...         ...       ...
FBgn0286937                             NA        NA          NA     FALSE
FBgn0286938                             NA        NA          NA     FALSE
FBgn0286939                             NA        NA          NA     FALSE
FBgn0286940                             NA        NA          NA     FALSE
FBgn0286941                             NA        NA          NA     FALSE

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)

FALSE  TRUE 
17807   110

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)
[1] 14
rowData(ttq)[fullLTRs, ]
DataFrame with 14 rows and 4 columns
                       Status RelLength       Class      isTE
              <CharacterList> <numeric> <character> <logical>
BEL_LTR        full-lengthLTR  0.969638         LTR      TRUE
BLASTOPIA_LTR  full-lengthLTR  0.990062         LTR      TRUE
BLOOD_LTR      full-lengthLTR  0.991468         LTR      TRUE
BURDOCK_LTR    full-lengthLTR  0.982514         LTR      TRUE
Chouto_LTR     full-lengthLTR  0.972210         LTR      TRUE
...                       ...       ...         ...       ...
Gypsy6_LTR     full-lengthLTR  0.899885         LTR      TRUE
Gypsy8_LTR     full-lengthLTR  1.000000         LTR      TRUE
Gypsy9_LTR     full-lengthLTR  0.975136         LTR      TRUE
Invader3_LTR   full-lengthLTR  0.989059         LTR      TRUE
TRANSPAC_LTR   full-lengthLTR  0.999943         LTR      TRUE

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

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

LTR 
110

Session information

sessionInfo()
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        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: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] TxDb.Dmelanogaster.UCSC.dm6.ensGene_3.12.0
 [2] GenomicFeatures_1.59.1                    
 [3] AnnotationDbi_1.69.0                      
 [4] RColorBrewer_1.1-3                        
 [5] BiocParallel_1.41.0                       
 [6] atena_1.13.0                              
 [7] SummarizedExperiment_1.37.0               
 [8] Biobase_2.67.0                            
 [9] GenomicRanges_1.59.1                      
[10] GenomeInfoDb_1.43.2                       
[11] IRanges_2.41.1                            
[12] S4Vectors_0.45.2                          
[13] BiocGenerics_0.53.3                       
[14] generics_0.1.3                            
[15] MatrixGenerics_1.19.0                     
[16] matrixStats_1.4.1                         
[17] knitr_1.49                                
[18] BiocStyle_2.35.0                          

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1         dplyr_1.1.4              blob_1.2.4              
 [4] filelock_1.0.3           Biostrings_2.75.1        bitops_1.0-9            
 [7] fastmap_1.2.0            RCurl_1.98-1.16          BiocFileCache_2.15.0    
[10] GenomicAlignments_1.43.0 XML_3.99-0.17            digest_0.6.37           
[13] mime_0.12                lifecycle_1.0.4          KEGGREST_1.47.0         
[16] RSQLite_2.3.8            magrittr_2.0.3           compiler_4.4.2          
[19] rlang_1.1.4              sass_0.4.9               tools_4.4.2             
[22] utf8_1.2.4               yaml_2.3.10              rtracklayer_1.67.0      
[25] S4Arrays_1.7.1           bit_4.5.0                curl_6.0.1              
[28] DelayedArray_0.33.2      abind_1.4-8              withr_3.0.2             
[31] purrr_1.0.2              sys_3.4.3                grid_4.4.2              
[34] fansi_1.0.6              cli_3.6.3                rmarkdown_2.29          
[37] crayon_1.5.3             httr_1.4.7               rjson_0.2.23            
[40] DBI_1.2.3                cachem_1.1.0             zlibbioc_1.52.0         
[43] parallel_4.4.2           BiocManager_1.30.25      XVector_0.47.0          
[46] restfulr_0.0.15          vctrs_0.6.5              Matrix_1.7-1            
[49] jsonlite_1.8.9           bit64_4.5.2              maketools_1.3.1         
[52] jquerylib_0.1.4          glue_1.8.0               codetools_0.2-20        
[55] BiocVersion_3.21.1       BiocIO_1.17.1            UCSC.utils_1.3.0        
[58] tibble_3.2.1             pillar_1.9.0             rappdirs_0.3.3          
[61] htmltools_0.5.8.1        GenomeInfoDbData_1.2.13  R6_2.5.1                
[64] dbplyr_2.5.0             sparseMatrixStats_1.19.0 evaluate_1.0.1          
[67] lattice_0.22-6           AnnotationHub_3.15.0     png_0.1-8               
[70] Rsamtools_2.23.1         memoise_2.0.1            SQUAREM_2021.1          
[73] bslib_0.8.0              Rcpp_1.0.13-1            SparseArray_1.7.2       
[76] xfun_0.49                buildtools_1.0.0         pkgconfig_2.0.3

References

Bailly-Bechet, Marc, Annabelle Haudry, and Emmanuelle Lerat. 2014. “‘One Code to Find Them All’: A Perl Tool to Conveniently Parse RepeatMasker Output Files.” Mobile DNA 5 (1): 1–15.
Bendall, Matthew L, Miguel De Mulder, Luis Pedro Iñiguez, Aarón Lecanda-Sánchez, Marcos Pérez-Losada, Mario A Ostrowski, R Brad Jones, et al. 2019. “Telescope: Characterization of the Retrotranscriptome by Accurate Estimation of Transposable Element Expression.” PLoS Computational Biology 15 (9): e1006453.
Dobin, Alexander, Carrie A Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, and Thomas R Gingeras. 2013. “STAR: Ultrafast Universal RNA-Seq Aligner.” Bioinformatics 29 (1): 15–21.
Goerner-Potvin, Patricia, and Guillaume Bourque. 2018. “Computational Tools to Unmask Transposable Elements.” Nature Reviews Genetics 19 (11): 688–704.
Guffanti, Guia, Andrew Bartlett, Torsten Klengel, Claudia Klengel, Richard Hunter, Gennadi Glinsky, and Fabio Macciardi. 2018. “Novel Bioinformatics Approach Identifies Transcriptional Profiles of Lineage-Specific Transposable Elements at Distinct Loci in the Human Dorsolateral Prefrontal Cortex.” Molecular Biology and Evolution 35 (10): 2435–53.
Jin, Ying, Oliver H Tam, Eric Paniagua, and Molly Hammell. 2015. “TEtranscripts: A Package for Including Transposable Elements in Differential Expression Analysis of RNA-Seq Datasets.” Bioinformatics 31 (22): 3593–99.
Li, Heng, and Richard Durbin. 2009. “Fast and Accurate Short Read Alignment with Burrows–Wheeler Transform.” Bioinformatics 25 (14): 1754–60.
Payer, Lindsay M, and Kathleen H Burns. 2019. “Transposable Elements in Human Genetic Disease.” Nature Reviews Genetics 20 (12): 760–72.
Tokuyama, Maria, Yong Kong, Eric Song, Teshika Jayewickreme, Insoo Kang, and Akiko Iwasaki. 2018. “ERVmap Analysis Reveals Genome-Wide Transcription of Human Endogenous Retroviruses.” Proceedings of the National Academy of Sciences 115 (50): 12565–72.