An introduction to the atena package
29 October 2024
Abstract
The atena package provides methods to quantify the expression of transposable elements within R and Bioconductor.
Package
atena 1.12.0
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). 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().
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 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 genomeWe 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 genomeAs 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 LINEThere 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.
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 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 genomeTo 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 genomeTo obtain DNA transposons and SINEs, one can use the getDNAtransposons() and
getSINEs() functions, respectively.
4 TE expression quantification
Quantification of TE expression with atena consists in the following two
steps:
Building of a parameter object for one of the available quantification methods.
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.
4.1 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.
4.1.1 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
XSthat 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
AStag among all available secondary alignments.The value “auto” (default), by which
atenawill 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 theXStag. Otherwise, it will trigger the calculation previously explained forsuboptimalAlignemntTag="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, unstrandedIn the case of paired-end BAM files (singleEnd=FALSE), two additional
arguments can be specified, strandMode and fragments:
strandModedefines the behavior of the strand getter when internally reading the BAM files with theGAlignmentPairs()function. See the help page ofstrandModein the GenomicAlignments package for further details.fragmentscontrols 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 setfragments=TRUE(default whensingleEnd=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, whenfragments=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.
4.1.2 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; unstrandedIn 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.
4.1.3 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] 17807bamfiles <- 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; unstrandedFor 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