# An introduction to the atena package

Beatriz Calvo-Serra1* and Robert Castelo1**

1Dept. of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain

* beatriz.calvo@upf.edu
** robert.castelo@upf.edu

#### 24 October 2023

#### Abstract

The `atena` package provides methods to quantify the expression of transposable elements within R and Bioconductor.

#### Package

atena 1.8.0

# 1 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).

# 2 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.

# 3 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). The gold standard for TE annotations are RepeatMasker annotations, available through the RepeatMasker track in UCSC Genome Browser. `atena` can fetch RepeatMasker annotations with the function `annotaTEs()`. Moreover, this function can parse TE annotations by applying `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.
- `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.
- `OneCodeToFindThemAll()`: implementation of the `OneCodeToFindThemAll.pl` (Bailly-Bechet, Haudry, and Lerat 2014) tool for parsing RepeatMasker output files.

Both `rmskatenaparser()` and `OneCodeToFindThemAll()` functions try to address the annotation fragmentation present in the output file of the RepeatMasker software (i.e. presence of multiple hits (homology-based matches) corresponding to a unique copy of an element). This is highly frequent for LTR class TEs, where the consensus sequences are split into LTR and internal regions separately, causing RepeatMasker to also report these two regions of a TE as separate elements. These two functions attempt 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. Differences in these criteria, as well as different approaches for finding equivalences between LTR and internal regions to reconstruct LTR retrotransposons, is what differences these two functions.

## 3.1 Retrieving and parsing TE annotations

As an example, let’s retrieve TE annotations for Drosophila melanogaster dm6 genome version. By setting `rmskidentity()` as `parsefun`, the RepeatMasker annotations are not modified:

    library(atena)
    library(GenomicRanges)
    rmskid <- annotaTEs(genome = "dm6", parsefun = rmskidentity)
    rmskid
    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 get annotations with 137555 elements.

Now, let’s obtain the same annotations but processes them using the `rmskatenaparser` function. We set 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.

    rmskat <- annotaTEs(genome = "dm6", parsefun = rmskatenaparser, strict = FALSE,
                        insert = 500)
    loading from cache
    rmskat[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

How many elements are present in the annotations?

    length(rmskat)
    [1] 22848

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.

The resulting `rmskat` object is of class `GRangesList`. Each element of the list represents an assembled TE containing a `GRanges` object of length 1 (when the TE was not assembled with another element) or length > 1 (when 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(rmskat)
    DataFrame with 22848 rows and 3 columns
                             status Rel_length       Class
                        <character>  <numeric> <character>
    IDEFIX_LTR.1                LTR   0.225589         LTR
    DNAREP1_DM.2              noLTR   0.176768         DNA
    DNAREP1_DM.3              noLTR   0.151515         DNA
    DNAREP1_DM.4              noLTR   0.419192         DNA
    DNAREP1_DM.5              noLTR   0.409091         DNA
    ...                         ...        ...         ...
    Gypsy12_I-int.22844       noLTR 0.18211323         LTR
    PROTOP.22845              noLTR 0.20848214         DNA
    TAHRE.22846               noLTR 0.06518207        LINE
    TART.22847                noLTR 0.00780548        LINE
    FW_DM.22848               noLTR 0.08823529        LINE

There is information about the reconstruction status of the TE (status column), the relative length of the reconstructed TE (Rel_length) and the repeat class of the TE (Class). The relative length is computed by adding the length (in base pairs) of all fragments from the same assembled TE and dividing the 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.

Figure 1: 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 useful 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 previously obtained object (`rmskat`) with annotations parsed with the `rmskatenaparser()` function, and set the `relLength` to 0.9.

    rmskLINE <- getLINEs(rmskat, relLength = 0.9)
    length(rmskLINE)
    [1] 355
    rmskLINE[1]
    GRangesList object of length 1:
    $LINEJ1_DM.6
    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(rmskat, relLength = 0.8, full_length = TRUE, partial = TRUE,
                       otherLTR = TRUE)
    length(rmskLTR)
    [1] 1711
    rmskLTR[1]
    GRangesList object of length 1:
    $`BLOOD_I-int.20`
    GRanges object with 5 ranges and 11 metadata columns:
          seqnames        ranges strand |   swScore  milliDiv  milliDel  milliIns
             <Rle>     <IRanges>  <Rle> | <integer> <numeric> <numeric> <numeric>
      [1]    chr2L 347941-348153      - |      3447         3        43         0
      [2]    chr2L 348188-348355      - |      3447         3        43         0
      [3]    chr2L 348356-354968      - |     60509         0         0         0
      [4]    chr2L 354969-355181      - |      3434         5        43         0
      [5]    chr2L 355216-355383      - |      3434         5        43         0
           genoLeft     repName    repClass   repFamily  repStart    repEnd
          <integer> <character> <character> <character> <integer> <integer>
      [1] -23165559   BLOOD_LTR         LTR       Gypsy         0       399
      [2] -23165357   BLOOD_LTR         LTR       Gypsy       213       186
      [3] -23158744 BLOOD_I-int         LTR       Gypsy         0      6613
      [4] -23158531   BLOOD_LTR         LTR       Gypsy         0       399
      [5] -23158329   BLOOD_LTR         LTR       Gypsy       213       186
            repLeft
          <integer>
      [1]       187
      [2]         2
      [3]         1
      [4]       187
      [5]         2
      -------
      seqinfo: 1870 sequences (1 circular) from dm6 genome

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

# 4 Using atena to quantify TE expression

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 (accession no. 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, thus, the knockdown of piwi causes the de-repression of TEs.

Here, the expression of full-length LTR retrotransposons present in `rmskLTR` will be quantified.

## 4.1 Building a parameter object for 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)
    empar
    ERVmapParam object
    # BAM files (2): control_KD.bam, piwi_KD.bam
    # features (1711): BLOOD_I-int.20, ..., NOMAD_LTR.22823
    # 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.

## 4.2 Building a parameter object for 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
    TelescopeParam object
    # BAM files (2): control_KD.bam, piwi_KD.bam
    # features (CompressedGRangesList length 1711): BLOOD_I-int.20, ..., NOMAD_LTR.22823
    # aggregated by: CompressedGRangesList names
    # 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 from `atena`, either secondary alignments or the `NH` tag are required for gene expression quantification.

## 4.3 Building a parameter object for 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
    txdb_genes <- exonsBy(txdb, by = "gene")
    length(txdb_genes)
    [1] 17807

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

    ttpar
    TEtranscriptsParam object
    # BAM files (2): control_KD.bam, piwi_KD.bam
    # features (CompressedGRangesList length 86732): BLOOD_I-int.20, ..., FBgn0286941
    # aggregated by: repName
    # single-end; unstranded

For paired-end data (`singleEnd=FALSE`), the usage of the `fragments` argument is the same as in `TelescopeParam()`.

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 the analysis. Then, exon counts are summarized to the gene level in a `GRangesList` object. This also applies to the ERVmap and Telescope methods for `atena` when gene features are present. Let’s see an example of this processing:

    # Creating an example of gene annotations
    annot_gen <- 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))
    # Setting gene ids
    names(annot_gen) <- paste0("gene",c(rep(1,3),rep(2,4),rep(3,1)))
    annot_gen
    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
    ttpar_gen <- TEtranscriptsParam(bamfiles,
                                    teFeatures = rmskLTR,
                                    geneFeatures = annot_gen,
                                    singleEnd = TRUE,
                                    ignoreStrand=TRUE)
    ttpar_gen
    TEtranscriptsParam object
    # BAM files (2): control_KD.bam, piwi_KD.bam
    # features (CompressedGRangesList length 1714): BLOOD_I-int.20, ..., gene3
    # aggregated by: CompressedGRangesList names
    # single-end; unstranded

Let’s see the result of the gene annotation processing:

    features_tt <- atena::features(ttpar_gen)
    features_tt[!attributes(features_tt)$isTE$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      isTE        type
            <integer> <logical> <character>
      gene1      <NA>     FALSE        exon
      gene1      <NA>     FALSE        exon
      gene1      <NA>     FALSE        exon
      -------
      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      isTE        type
            <integer> <logical> <character>
      gene2      <NA>     FALSE        exon
      gene2      <NA>     FALSE        exon
      gene2      <NA>     FALSE        exon
      gene2      <NA>     FALSE        exon
      -------
      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      isTE        type
            <integer> <logical> <character>
      gene3      <NA>     FALSE        exon
      -------
      seqinfo: 1870 sequences (1 circular) from dm6 genome

## 4.4 Quantify TE expression with `qtex()`

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. 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: 1711 2
    metadata(0):
    assays(1): counts
    rownames(1711): BLOOD_I-int.20 ROO_LTR.23 ... MDG1_LTR.22807
      NOMAD_LTR.22823
    rowData names(4): status Rel_length Class isTE
    colnames(2): control_KD piwi_KD
    colData names(0):
    colSums(assay(emq))
    control_KD    piwi_KD
           137        131

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

    tsq <- qtex(tspar)

    tsq
    class: RangedSummarizedExperiment
    dim: 1712 2
    metadata(0):
    assays(1): counts
    rownames(1712): BLOOD_I-int.20 ROO_LTR.23 ... NOMAD_LTR.22823
      no_feature
    rowData names(4): status Rel_length Class isTE
    colnames(2): control_KD piwi_KD
    colData names(0):
    colSums(assay(tsq))
    control_KD    piwi_KD
           150        126

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

    ttq <- qtex(ttpar)

    ttq
    class: RangedSummarizedExperiment
    dim: 85131 2
    metadata(0):
    assays(1): counts
    rownames(85131): ACCORD2_I-int ACCORD2_LTR ... FBgn0286941 FBgn0286941
    rowData names(6): status Rel_length ... exon_name isTE
    colnames(2): control_KD piwi_KD
    colData names(0):
    colSums(assay(ttq))
    control_KD    piwi_KD
           150        133

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

# 5 Session information

    sessionInfo()
    R version 4.3.1 (2023-06-16)
    Platform: x86_64-pc-linux-gnu (64-bit)
    Running under: Ubuntu 22.04.3 LTS

    Matrix products: default
    BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
    LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

    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] stats4    stats     graphics  grDevices utils     datasets  methods
    [8] base

    other attached packages:
     [1] XVector_0.42.0
     [2] TxDb.Dmelanogaster.UCSC.dm6.ensGene_3.12.0
     [3] GenomicFeatures_1.54.0
     [4] AnnotationDbi_1.64.0
     [5] RColorBrewer_1.1-3
     [6] atena_1.8.0
     [7] SummarizedExperiment_1.32.0
     [8] Biobase_2.62.0
     [9] GenomicRanges_1.54.0
    [10] GenomeInfoDb_1.38.0
    [11] IRanges_2.36.0
    [12] S4Vectors_0.40.0
    [13] BiocGenerics_0.48.0
    [14] MatrixGenerics_1.14.0
    [15] matrixStats_1.0.0
    [16] knitr_1.44
    [17] BiocStyle_2.30.0

    loaded via a namespace (and not attached):
     [1] DBI_1.1.3                     bitops_1.0-7
     [3] biomaRt_2.58.0                rlang_1.1.1
     [5] magrittr_2.0.3                compiler_4.3.1
     [7] RSQLite_2.3.1                 png_0.1-8
     [9] vctrs_0.6.4                   stringr_1.5.0
    [11] pkgconfig_2.0.3               crayon_1.5.2
    [13] fastmap_1.1.1                 dbplyr_2.3.4
    [15] magick_2.8.1                  ellipsis_0.3.2
    [17] utf8_1.2.4                    Rsamtools_2.18.0
    [19] promises_1.2.1                rmarkdown_2.25
    [21] purrr_1.0.2                   bit_4.0.5
    [23] xfun_0.40                     zlibbioc_1.48.0
    [25] cachem_1.0.8                  jsonlite_1.8.7
    [27] progress_1.2.2                blob_1.2.4
    [29] later_1.3.1                   DelayedArray_0.28.0
    [31] BiocParallel_1.36.0           interactiveDisplayBase_1.40.0
    [33] parallel_4.3.1                prettyunits_1.2.0
    [35] R6_2.5.1                      bslib_0.5.1
    [37] stringi_1.7.12                SQUAREM_2021.1
    [39] rtracklayer_1.62.0            jquerylib_0.1.4
    [41] Rcpp_1.0.11                   bookdown_0.36
    [43] httpuv_1.6.12                 Matrix_1.6-1.1
    [45] tidyselect_1.2.0              abind_1.4-5
    [47] yaml_2.3.7                    codetools_0.2-19
    [49] curl_5.1.0                    lattice_0.22-5
    [51] tibble_3.2.1                  shiny_1.7.5.1
    [53] withr_2.5.1                   KEGGREST_1.42.0
    [55] evaluate_0.22                 BiocFileCache_2.10.0
    [57] xml2_1.3.5                    Biostrings_2.70.0
    [59] pillar_1.9.0                  BiocManager_1.30.22
    [61] filelock_1.0.2                generics_0.1.3
    [63] RCurl_1.98-1.12               BiocVersion_3.18.0
    [65] hms_1.1.3                     sparseMatrixStats_1.14.0
    [67] xtable_1.8-4                  glue_1.6.2
    [69] tools_4.3.1                   AnnotationHub_3.10.0
    [71] BiocIO_1.12.0                 GenomicAlignments_1.38.0
    [73] XML_3.99-0.14                 grid_4.3.1
    [75] GenomeInfoDbData_1.2.11       restfulr_0.0.15
    [77] cli_3.6.1                     rappdirs_0.3.3
    [79] fansi_1.0.5                   S4Arrays_1.2.0
    [81] dplyr_1.1.3                   sass_0.4.7
    [83] digest_0.6.33                 SparseArray_1.2.0
    [85] rjson_0.2.21                  memoise_2.0.1
    [87] htmltools_0.5.6.1             lifecycle_1.0.3
    [89] httr_1.4.7                    mime_0.12
    [91] bit64_4.0.5

# 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): 3593–9.

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.