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RepeatModeler2: automated genomic discovery of transposable element families
Jullien M. Flynn1,*, Robert Hubley2,*, Clément Goubert1, Jeb Rosen2, Andrew G. Clark1, Cédric
Feschotte1, Arian F. Smit2
*these authors contributed equally
Co-corresponding authors:
Andrew G. Clark ac347@cornell.edu
Cédric Feschotte cf458@cornell.edu
Arian F. Smit asmit@systemsbiology.org
1 Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
2 Institute for Systems Biology, Seattle, WA 98109, USA
Significance
Genome sequences are being produced for more and more eukaryotic species. The bulk of
these genomes is composed of parasitic, self-mobilizing transposable elements (TEs) that play
important roles in organismal evolution. Thus there is a pressing need for developing software
that can accurately identify the diverse set of TEs dispersed in genome sequences. Here we
introduce RepeatModeler2, an easy-to-use package for the curation of reference TE libraries
which can be applied to any eukaryotic species. Through several major improvements over the
previous version, RepeatModeler2 is able to produce libraries that recapitulate the known
composition of three model species with some of the most complex TE landscapes. Thus
RepeatModeler2 will greatly enhance the discovery and annotation of TEs in genome
sequences.
Abstract
The accelerating pace of genome sequencing throughout the tree of life is driving the need for
improved unsupervised annotation of genome components such as transposable elements
(TEs). Because the types and sequences of TEs are highly variable across species, automated
TE discovery and annotation are challenging and time-consuming tasks. A critical first step is
the de novo identification and accurate compilation of sequence models representing all the
unique TE families dispersed in the genome. Here we introduce RepeatModeler2, a new
pipeline that greatly facilitates this process. This new program brings substantial improvements
over the original version of RepeatModeler, one of the most widely used tools for TE discovery.
In particular, this version incorporates a module for structural discovery of complete LTR
retroelements, which are widespread in eukaryotic genomes but recalcitrant to automated
identification because of their size and sequence complexity. We benchmarked
RepeatModeler2 on three model species with diverse TE landscapes and high-quality, manually
curated TE libraries: Drosophila melanogaster (fruit fly), Danio rerio (zebrafish), and Oryza
sativa (rice). In these three species, RepeatModeler2 identified approximately three times more
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consensus sequences matching with >95% sequence identity and sequence coverage to the
manually curated sequences than the original RepeatModeler. As expected, the greatest
improvement is for LTR retroelements. The program had an extremely low false positive rate
when applied to simulated genomes devoid of TEs. Thus, RepeatModeler2 represents a
valuable addition to the genome annotation toolkit that will enhance the identification and study
of TEs in eukaryotic genome sequences. RepeatModeler2 is available as source code or a
containerized package under an open license (https://github.com/Dfam-
consortium/RepeatModeler, https://github.com/Dfam-consortium/TETools).
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Introduction
Most eukaryotic genomes contain a large number of interspersed repeats that by and large
represent copies of transposable elements (TEs) at varying stages of evolutionary decay (Smit
1999; Consortium and International Human Genome Sequencing Consortium 2001; Jurka et al.
2007; Huang et al. 2012; Bourque et al. 2018). TEs are genomic sequences capable of
mobilization and replication, generating complex patterns of repeats that account for up to 85%
of eukaryotic genome content (International Wheat Genome Sequencing Consortium (IWGSC)
et al. 2018). Different organisms have diverse TE landscapes, including a wide range of
abundances, activity levels, and sequence degradation levels (Hua-Van et al. 2005; Smit 2012).
As mutagens and major contributors to the organization, rearrangement, and regulation of the
genome, TEs have had a profound impact on organismal evolution (reviewed in (Bourque et al.
2018)). Our understanding of the biological impact of TEs has grown steadily through the study
of both model and non-model organisms from which whole genome sequences can now be
routinely assembled. With each new species sequenced comes the challenge of identifying its
unique set of TE families, which remains a tedious and largely manual endeavor. Yet, the
accurate identification of TEs and other repeats is prerequisite to nearly all other genomic
analysis, including the annotation of genes (Yandell and Ence 2012).
What makes TEs so potent in remodeling the genome but also challenging to annotate is their
diversity in structures and sequences, which greatly vary across species (Huang et al. 2012;
Bourque et al. 2018). There are two major classes of TEs (reviewed in (Finnegan 1989; Wicker
et al. 2007; Piégu et al. 2015); https://www.dfam.org/classification): class I retroelements
replicate and transpose via an RNA intermediate; while class II elements (or DNA transposons)
are mobilized via a DNA intermediate. Class I elements include long and short interspersed
elements (LINEs, SINEs) and long terminal repeat (LTR) retrotransposons. The most common
class II transposons are TIR (terminal inverted repeats) elements, which transpose via a “cut-
and-paste” mechanism (Feschotte and Pritham 2007). But other class II elements, such as
Helitrons, also use replicative mechanisms (Thomas and Pritham 2015; Grabundzija et al.
2018). Within each class, TE sequences are extremely diverse and evolve rapidly (Wicker et al.
2007; Arkhipova 2017; Bourque et al. 2018). Additionally, once integrated in the host genome
each element is subject to mutations, such as point mutations, and a vast array of
rearrangements, including internal deletions, truncations, and nested insertions. The vast
sequence diversity of TEs combined with the complexity of mutations that occur after insertion
makes automated TE identification and classification a daunting task.
The most elementary level of classification of TEs is the family, which designates interspersed
genomic copies derived from the amplification of an ancestral progenitor sequence (Wicker et
al. 2007). Each TE family can be represented by a consensus sequence approximating that of
the ancestral progenitor. Such consensus sequence can be recreated from a multiple alignment
of individual genomic copies (or “seeds”) from which each ancestral nucleotide can be inferred
based on a majority-rule along the alignment. Similarly, the seed alignment may be used to
generate a profile Hidden Markov Model for each family. Consensus TE sequences and HMMs
are used for many downstream applications in the study and annotation of genomes. Notably
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they are used to annotate or “mask” the genome using RepeatMasker, which is a prerequisite
for gene annotation (Yandell and Ence 2012). Consensus sequences are generally stored in
widely used databases such as Repbase (Bao et al. 2015) or along with seed alignments and
HMMs in Dfam (Hubley et al. 2016). Seed alignments and accurate sequence models are
critical for reconstructing the evolutionary history of TEs and are used for a variety of biological
studies including the study of TE invasions and regulation (e.g. (Kofler et al. 2015)). Years of
manual curation have resulted in high quality consensus libraries for a limited set of species,
mostly model organisms (Lerat et al. 2003; Hubley et al. 2016; Stitzer et al. 2019).
The number of whole-genome assemblies for species throughout the tree of life continues to
grow at a very fast rate , and efforts are underway to produce thousands more (Koepfli et al.
2015; Lewin et al. 2018). Long-read sequencing technologies are improving the quality of
genome assemblies, especially in highly repetitive regions (e.g. (Chang and Larracuente 2019)).
These developments bring a pressing need to improve tools that automate the discovery and
annotation of TEs. Although there are dozens of tools that tackle one aspect of de novo
identification or one class of TE (Saha et al. 2008a; b), there are very few easy-to-use programs
that can produce a comprehensive library of TE family seed alignments and consensus
sequences from a genome assembly.
RepeatModeler was released in 2008 by Hubley and Smit and is one of the most widely used
TE discovery tools (cited 1462 times in publications as of 11/21/2019). RepeatModeler
constructs seed alignments and consensus sequences for genome-wide repeat families de
novo. However, the original version of RepeatModeler, like other existing TE-discovery
software, falls short of producing a complete, non-redundant library of full-length consensus
sequences. The most problematic issue is the representation of what should be a unique
contiguous consensus sequence for a given TE family into many fragmented and partially
redundant sequences in the output library. This issue, in turn, can hamper the classification of
the TE families, inflates the number of actual TE families in the genome, and confounds
genome annotation and downstream analyses. LTR retroelements are particularly recalcitrant to
automated TE identification because of their size (up to 20 Kbp) and complexity in sequence
and organization, which is driven in part by their ability to recombine within and between families
(Vargiu et al. 2016). Yet these elements are widespread and often extremely abundant and
diverse in eukaryotic genomes. For instance, the maize reference genome harbors >100,000
LTR elements falling into ~20,000 distinct families accounting for about half of the genomic DNA
(Jiao et al. 2017).
To address these issues we developed a new version of RepeatModeler. Notably, we integrated
an optional module dedicated to the identification of LTR elements in the genome through their
structural characteristics (Ou and Jiang 2018; Ellinghaus et al. 2008). By benchmarking on three
diverse model species, we demonstrate that RepeatModeler2 is a substantial improvement over
the previous version both in terms of detection sensitivity and consensus sequence quality. The
open-source package is designed to run on a single, multi-processor computer and is available
as a source distribution or Docker/Singularity container for easier installation.
(https://github.com/Dfam-consortium/RepeatModeler).
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Methods
RepeatModeler2 overview
RepeatModeler is a pipeline for automated de novo identification of TEs that employs two
distinct discovery algorithms, RepeatScout (Price et al. 2005) and RECON (Bao 2002), followed
by consensus building and classification steps. In addition, RepeatModeler2 now includes the
LTRharvest (Ellinghaus et al. 2008) and LTR_retriever (Ou and Jiang 2018) tools. Our tool
takes advantage of the unique strengths of each approach as well as providing a tractable
solution to analyzing large datasets such as whole genome assemblies. For instance,
RepeatScout uses high frequency sequence word counts to identify interspersed repeat seeds
(short regions of putative homology) and then performs an iterative extension of a multiple
alignment around the aligned seeds, similar to the seed and extend phase of the BLAST
pairwise alignment algorithm. While RepeatScout’s implementation of this algorithm is fast, the
program input is currently limited to ~1 Gbp and the alignment scoring system ( +1/-1, and non-
affine gap penalty) limits the divergence of discovered families. Despite these limitations,
RepeatScout serves well as a fast method to discover the youngest and most abundant families
given a small sequence sample from a genome. RECON, on the other hand, provides
sophisticated and TE biology-aware clustering and relationship determination approaches to
generate TE families from exhaustive inter-genome alignments. RECON’s approach requires a
computationally intensive but sensitive alignment (sophisticated scoring matrices, and affine gap
penalties) and detects older TE families quite well.
In order to comprehensively identify TE families in a genome we chose to employ a sampling
and iterative (sample, mask, identify) search strategy (Figure 1). We begin by supplying
RepeatScout with a random 40 Mbp sample of the genome to quickly identify young and
abundant families. In each successive round we mask a new genomic sample using all
previously discovered TE families to avoid re-discovery and allowing for larger successive
sample sizes as the computational burden of self-comparison is reduced on pre-masked
sequence. The second and subsequent rounds all employ the RECON approach starting with a
3 Mbp sample (without replacement), tripling the sample size between rounds and continuing
until a sample size maximum or round limit is reached (default: 243 Mbp, or 5 rounds). For an
average mammalian genome of 3 Gbp this would sample ~13% of the genome.
The new RepeatModeler2 pipeline now includes an additional structural discovery approach to
assist in the discovery LTR retrotransposons. Due to their unique structure and biology (two
long terminal repeat flanking a large 5-18 kb internal region) LTRs are often identified as
fragments with disassociated LTR and internal regions or missing the internal segment
completely using the RepeatScout/RECON methodologies. The LTR discovery is run on the
complete unmasked genomic sequence and as such produces high quality LTR families with
some redundancy to the previously discovered families. Therefore, we follow up LTR discovery
with a merging and redundancy removal process as described in the LTR module section.
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Due to constraints in the methods employed by TE discovery algorithms, their output is often
either in the form of a complete or partial set of genome annotations (location ranges within the
input sequence representing instances of a particular repeat), or is simply the pre-calculated
consensus for each discovered TE family. In addition, the quality of these pre-calculated
consensus sequences may vary considerably. A basic goal of RepeatModeler has been to
produce a high-quality seed alignment and consensus sequence for each TE family; therefore
we developed a seed alignment refinement method (see Refiner section below) which is
employed on all families produced by the de novo tools.
Once all rounds of discovery/refinement/merging are complete, the final library is run through a
simple classification step (see Classification section for details), where each family is assigned
(if possible) to a known TE class using the unified RepeatMasker/Dfam classification
nomenclature.
Figure 1: RepeatModeler2 Flow Diagram.
Refiner description
Given a set of putatively related TE family instances the RepeatModeler Refiner tool attempts to
build a high-quality seed alignment and derive from it a consensus sequence for the family.
Refiner bootstraps the generation of a seed alignment by selecting as the initial consensus, the
sequence which scores the best against all others. From this initial alignment a new consensus
Figure 1.
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is generated and the process is repeated until the consensus stabilizes. Refiner employs an
algorithm to identify low scoring subregions in the seed alignment, often caused by common
indels relative to the consensus, and resolves them by globally aligning the sequences within
the subregion and updating the consensus (see Supplemental Material). The consensus is not
simply based on alignment column majority rule, rather each consensus position represents the
highest scoring base using a scoring matrix similar to those developed for RepeatMasker, which
reflects observed neutral substitution patterns in mammals. For instance, the algorithm is aware
of the rapid decay of CpG sites to CpA and TpG dinucleotides in most eukaryotes due to
accelerated deamination of methylated cytosines (Sved and Bird 1990; Colot and Rossignol
1999) and calls a CG pair given enough instances of aligned CA and TG dinucleotides (see
supplementary data for details). The final output of the analysis is a consensus sequence and a
seed alignment in Stockholm format. The latter can be used for generating profile Hidden
Markov Models (Wheeler and Eddy 2013) and preserves the provenance of the family’s
representative sequences.
LTR module description
RepeatModeler2 uses the LTRharvest (Ellinghaus et al. 2008) package for structural LTR
detection for both its overall sensitivity and speed (Ou et al. 2018, Ou et al. 2019). LTRharvest
is both a discovery and annotation algorithm that does not attempt to group LTR instances into
families. In addition, any region in the genome demonstrating LTR-like structure (flanking
repetitive sequence of the correct size, with the correct intervening sequence) is often
incorrectly identified as an LTR instance. To solve this problem, Ou and Jiang developed
LTR_retriever (Ou and Jiang 2018), a package for filtering false positive results, resolving
nested (mosaic) annotations, and identifying internal regions of LTRs. Some genomes have
challenging nested structures that are not always resolved by LTR_retriever, so we
implemented an optional parameter (-LTRMaxSeqLen) that sets the maximum allowable LTR
internal length to avoid inclusion of missed mosaic internal elements in the seed alignment (see
supplementary material).
We use LTR_retriever’s redundant library and perform our own clustering and consensus
building process. Since LTR elements frequently contain internal deletions, and this often
results in “over-splitting” elements when clustering with CD-HIT (Li and Godzik 2006), we
implemented a clustering approach that scores alignment gaps as a reduced (single position)
penalty. This step involves a multiple sequence alignment with MAFFT (Katoh et al. 2002),
followed by nearest neighbor clustering into families with Ninja (Wheeler 2009). Families are
then refined and consensus sequences generated in a similar fashion to results from
RepeatScout/RECON.
Combining libraries and reducing redundancy
Combining results from multiple tools is a difficult but essential step for the production of a
comprehensive and non-redundant library of TE families. The RepeatScout and RECON
analysis rounds reduce redundancy by masking out previously identified families with each
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iteration. With the addition of the LTR structural module as an independent analysis on the
genome, we cannot avoid generating redundant LTR TE families. We tackled this problem by
clustering the sequences between the modules with CD-HIT. Whenever RepeatModeler
sequences cluster with an LTR pipeline sequence, we retain the LTR pipeline family as the
representative. In addition, in RepeatModeler2 we extended this method to
RepeatScout/RECON-produced families by labeling closely matching sequences as putative
subfamilies (with a link to the accepted representative). Users can then choose whether to
remove these subfamilies depending on the goals of their analyses.
Classification
RepeatModeler contains a basic homology-based classification module (RepeatClassifier)
which compares the TE families generated by the various de novo tools to both the
RepeatMasker Repeat Protein DB and to the RepeatMasker libraries (e.g. Dfam and/or
RepBase). The Repeat Protein DB is a set of TE-derived coding sequences that covers a wide
range of TE classes and organisms. As is often the case with a search against all known TE
sequences, there will be a high number of false positive or partial matches. RepeatClassifier
uses a combination of score and overlap filters to produce a reduced set of high confidence
results. If there is a concordance in classification among the filtered results, RepeatClassifier
will label the family using the RepeatMasker/Dfam classification system and adjust the
orientation (if necessary). Remaining families are labeled “Unknown” if a call cannot be made.
Benchmarking
We benchmarked RepeatModeler2 on model species that have high-quality reference TE
libraries: D. melanogaster, D. rerio, and O. sativa. We used Repbase
for the D. melanogaster (release 20181026) and D. rerio (release 14.01) libraries, and the
manually-improved library for O. sativa from (Ou et al. 2019). We used RepeatMasker and
parseRM (https://github.com/4ureliek/Parsing-RepeatMasker-Outputs/blob/master/parseRM.pl)
to estimate the percentage of the genome masked by each subclass for the manually-curated
and RepeatModeler2 libraries.
An important aspect of our pipeline is its ability to produce accurate consensus sequences
corresponding to unique TE families. Thus, we also assessed the quality of our families by
comparing their sequences with the sequences of the manually-curated consensus sequences
of reference libraries. We used RepeatMasker (v 1.332) with the reference library, and the
RepeatModeler2 output library as the subject. We then used a custom bash script
(https://github.com/jmf422/TE_annotation/blob/master/get_family_summary_paper.sh) to
assess the sequence identity and coverage of matches between the libraries. We classify
families as being “perfect”, “good”, “present”, or “not found” based on the following definitions.
“Perfect” families are those for which one sequence in our de novo library matches with >95%
nucleotide similarity and >95% length coverage to a family consensus in the reference library.
“Good” families are those in which multiple overlapping sequences in our output library match
with >95% nucleotide similarity and >95% coverage to the curated consensus. A family is
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considered “present” if one or multiple library sequences align with >80% similarity and >80%
coverage to the reference consensus sequence. Below these thresholds, we consider a family
“not found” (although there may be fragments present in our output library).
Results and Discussion
We benchmarked RepeatModeler2 on three model species (fruit fly, zebrafish, rice) with diverse
TE landscapes for which reference TE libraries have been extensively curated over decades of
study (Figure 2). As a first assessment of the ability of RepeatModeler2 to capture known TEs in
each of these genomes, we use each output library to run RepeatMasker against the cognate
genome assembly and measured the percent of the genome masked by each major TE
subclass. We also counted the number of sequences in each library falling within each
subclass. RepeatModeler2 was able to recover accurately the contrasted TE landscapes of
these species (compare with curated libraries in Figure 2).
As previously documented (e.g. Lerat et al. 2003), our results show that the genome of the fruit
fly D. melanogaster is dominated by retrotransposons, especially LTR retroelements. This is
reflected both by the amount of genomic DNA covered by these elements and by the number of
unique families (Figure 2). The zebrafish, D. rerio, is dominated by class II, DNA-TIR
transposons, but also exhibits a very diverse assortment of LTR retroelements with many
unique families (Howe et al. 2013). While our RepeatModeler2 library captures this general
composition, it defines about twice less LTR families but twice more TIR families than in the
original reference library. These differences may be caused by 1) a less stringent definition of
LTR families in the reference library compared to the RM2 library; and 2) the fact that DNA-TIR
elements are only identified by the RECON/RepeatScout module of RepeatModeler2, which
tend to produce shorter, more fragmented sequences than the LTR structural module. Therefore
the number of sequences classified as DNA-TIRs by RepeatModeler2 may be inflated and may
rather represent variants or fragments of the same family (see Figure 3). The genome of rice, O.
sativa, is known to contain almost equal proportions and numbers of DNA-TIR and LTR
elements (Ou et al. 2019), and this profile is recovered by our RepeatModeler2 library (Figure
2). In summary, RepeatModeler2 produces libraries that recapitulate the major TE subclass
composition of these three model species.
Next we assess the ability of RepeatModeler2 to accurately capture the diversity and sequence
of unique TE families. RepeatModeler2 produced 766, 3851, and 2648 library sequences for D.
melanogaster, D. rerio, and O.sativa, respectively - all comparable to the number of individual
sequences in the reference libraries (Table 1). In addition, RepeatModeler2 labels families that
cluster within 20% similarity as “putative subfamily”, thus we also provided the number of
sequences without the inclusion of subfamilies (Table 1). A TE library with more sequences is
not necessarily more useful since it often indicates redundancy and fragmented sequences.
Since we use a redundancy-removal step, RepeatModeler2 did not produce drastically more
family models than the previous version of the program.
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The most significant improvement of RepeatModeler2 over the previous release of the program
is in the quality (accuracy) of the family consensus sequences delivered in the output library
(Figure 3). We labelled the sequence matches between the RepeatModeler2 and reference
libraries as “perfect”, “good”, or “present” based on the level of sequence similarity and
coverage (Figure 3A, see also Methods). If the family did not meet the minimum criteria for
being counted as “present”, it was reported as “not found”. RepeatModeler2 produced 2.9 to 4.7
times more “perfect” families than the first version of RepeatModeler, and had more sequences
closely matching the reference libraries overall (Figure 3B). Most of this improvement can be
attributed to perfect LTR element families that are identified by RepeatModeler2, but were
previously missed (Figure 3C). The evaluation criteria we used for families were relatively strict,
probably explaining the large number of families “not found”. Indeed, when masking each
genome with the cognate library, we were able to recapitulate the genomic proportions of each
subclass as generally obtained with the reference library (Figure 2).
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Figure2: Benchmarking of RepeatModeler2 on three model species. Top panel: genome composition (top) and
number of families (bottom) of each TE subclass for the reference libraries. Bottom panel: genome composition (top)
and number of families (bottom) of each TE subclass for the RepeatModeler2 library.
Table 1: Number of sequences produced from RepeatModeler and present in the manually-curated libraries. The
second column indicates the total number of families produced by RepeatModeler2 and the third column is the same
except not including those annotated as subfamilies. The fourth column is the number of sequences in the curated
library, and the fifth column is the number of sequences produced from RepeatModeler1.
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Species
RM2
Families
RM2 Families
(no
subfamilies)
Curated
library
RM1 families
D. melanogaster
734
509
207
699
D. rerio
3851
3147
2322
2503
O. sativa
2648
2284
2431
1652
Figure 3: Evaluation family by family for RepeatModeler1 and RepeatModeler2. (A) Definitions of “Perfect”, “Good”,
and “Present” families. “Perfect” families are those for which one sequence in our de novo library matches >95% in
sequence identity and coverage to a family in the reference library. “Good” families are those in which multiple
overlapping library sequences with alignments >95% similar to the reference consensus make up the >95%
sequence coverage of the element. Finally, a family is considered “present” if one or multiple library sequences align
with >80% similarity to the reference consensus sequence and cover >80% of the sequence. Otherwise, we consider
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a family “not found” (although there may be fragments present) (B) Summary of families found by the last release of
RepeatModeler (RM1) and RepeatModeler2 (RM2). (C) Number of perfect families by subclass for each benchmark
species.
Eukaryotic genomes contain complex structure and tandem repeats, which may result in false
positives for TE discovery software. We assessed the false positive rate of RepeatModeler2 by
running it on artificially-generated genomes devoid of TEs simulated by GARLIC (Caballero et
al. 2014) for D. melanogaster and D. rerio. GARLIC generates background sequences with
realistic complexity, isochore structure and tandem repeat content as the modeled genome.
RepeatModeler2 produced only one false positive family on the D. melanogaster artificial
genome and five false positive families on the D. rerio artificial genome. No false positive
families were produced from the LTR module. These results suggest that the rate of false
positives generated by RepeatModeler2 is very low.
In addition to LTR retroelements, other types of TEs are sometimes considered challenging to
identify ab initio with automated approaches. In particular, MITEs (miniature inverted-repeat
transposable elements) can be challenging because they are typically non-coding and may be
highly diverged in sequence from from their parental DNA:TIR element (Feschotte et al. 2002;
Han and Wessler 2010). The zebrafish and rice genomes are known to contain a large number
of MITE families (Jiang et al. 2004), but these elements did not appear under-represented in the
RepeatModeler2 libraries generated for these two species (Figure 2). In comparison to the
reference libraries, RepeatModeler2 also performed well with the identification of Helitrons,
which also pose particular challenges for automated discovery (reviewed in (Thomas and
Pritham 2015)) (Figure 2). Thus, RepeatModeler2 appears capable of recovering a wide
diversity of TEs which have been traditionally considered recalcitrant to ab initio identification.
We anticipate that additional improvements will further enhance the current version of the
pipeline. Because of the modular architecture of RepeatModeler2, it should be relatively
straightforward to add other modules tailored to the discovery of specific subclass of elements
such as those dedicated to the identification of MITEs (Han and Wessler 2010) or Helitrons
(Yang and Bennetzen 2009; Xiong et al. 2014). It is also important to emphasize that the
classifier currently used by RepeatModeler2 remains rudimentary as it strictly relies on detection
of sequence homology to known TEs and protein domains. This limitation hampers the ability to
classify non-coding elements or elements with sequences highly diverged from those annotated
in the databases. Integrating additional features used for TE classification, such as conserved
TIR sequence motifs or target site duplications, as implemented in other TE classifiers
(Feschotte et al. 2009) will further improve the ability of RepeatModeler2 to deliver high-quality
libraries.
The genome annotation community is in pressing need of a TE discovery program that is easy
to use, has been thoroughly benchmarked, and can be applied to almost any eukaryotic species
(Hoen et al. 2015). We believe that RepeatModeler2 will meet this demand. Other TE discovery
programs exist, but either focus on finding instances in the genome instead of family consensus
sequence building (e.g. Berthelier et al. 2018, Ou et al. 2019), or are challenging to install and
.CC-BY 4.0 International licensenot certified by peer review) is the author/funder. It is made available under a The copyright holder for this preprint (which wasthis version posted November 26, 2019. . https://doi.org/10.1101/856591doi: bioRxiv preprint
14
use (Flutre et al. 2011). RepeatModeler2 is easy to install and run, as we provide a container
version to avoid installing independently all dependencies. We anticipate that the application of
RepeatModeler2 to existing and future genome assemblies will result in more consistent
genome annotations and improved TE family models, which will enhance a wide array of
genomic analyses including but not limited to TE biology.
Acknowledgements
We thank Arnie Kas, Warren Gish, Alkes Price, Pavel Pevzner, Shujun Ou, and Ning Jiang for
assistance with dependencies used by RepeatModeler. We thank Andy Siegel for statistics
consultations in the development of RepeatModeler. This work was supported by NIH grants
U01-HG009391 and R35-GM122550 to CF, and NHGRI grant U24 HG010136 and NHGRI R01
HG002939 to AFS. JMF was supported by a NSERC PGSD graduate fellowship.
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SUPPLEMENTARY MATERIALS
LTRPipeline
The development of RepeatModeler2 was motivated by author JMF working on annotating
transposable elements in Drosophila with CG, AGC, and CF. In Drosophila, LTR elements are
the most abundant subclass of TEs and are often present in heterochromatic regions where
they frequently contain nested structures. JMF developed a bash pipeline incorporating
RepeatModeler and an LTR-specific module, which resulted in greatly improved TE libraries for
Drosophila. We found that this pipeline also performed better than RepeatModeler in other
species we tested it on such as zebrafish. We believed that the TE community at large could
benefit from the incorporation of the LTR pipeline, so we partnered with the authors of
RepeatModeler (RH, JR, and AFS) to incorporate the LTR pipeline into an improved software
we call RepeatModeler2.
One of the main troubleshooting issues with incorporating a structural LTR identification
program was that it identifies instances of LTR elements in the genome, whereas the goal of the
RepeatModeler is to identify TE families. In order to remove redundancy and have one seed
alignment (and consensus) per family, sequence similarity clustering is used. LTR_retriever
performs similarity clustering with CD-HIT to accomplish this. However, we noticed that the CD-
HIT clustered output of LTR_retriever still contained redundancy. We hypothesized that this was
because of CD-HIT’s gap scoring policy, as it scores each base pair of an indel as a penalty,
rather than the indel as a single penalty, which is more biologically relevant (Flynn et al. 2015).
This issue is especially evident for LTR elements, which commonly contain internal deletions.
We originally used a multiple sequence alignment with MAFFT followed by nearest-neighbor
clustering with MOTHUR (Schloss et al. 2009). MOTHUR clustering scores indels as a single
gap penalty, and we found it worked to effectively cluster LTR elements into accurate family
groups. In later versions of the pipeline, we made a custom clustering script with NINJA using
the same procedure as MOTHUR. Instead of using the longest sequence in the cluster as the
family representative as LTR_retriever does, we incorporated the Refiner module from
RepeatModeler to build seed alignments and a family consensus from the cluster members.
LTR internal length filtering
LTR elements often acquire nested insertions. In RepeatModeler2 we utilize the LTR_retriever
analysis tool, which attempts to remove nested insertions; however we found that some nested
insertions still remain. This can reduce the quality of the LTR library. One way to prevent LTR
elements with nested insertions from being included in the LTR library is to impose a maximum
length of the internal sequence (optional parameter –LTRMaxSeqLen). For example, in
Drosophila, almost all true LTR internal elements are <10 kb and it is known that nested LTR
elements are common in heterochromatic regions, we would use –LTRMaxSeqLen 10000.
Since using this parameter could potentially bias the results, we only recommend using it if the
max LTR length is known or if nested insertions are a problem in the particular analysis.
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21
Figure S1: RepeatModeler Refiner module flowchart.
Family Refinement
The RepeatModeler Refiner tool produces a multiple sequence alignment (seed alignment) from
putatively related instances of a TE family. Refiner uses an iterative process (Figure S1) to
build and then improve the seed alignment for the family. This process is bootstrapped by first
performing a full pairwise comparison of all instance sequences to each other and selecting, as
the initial consensus, the instance which aligns best to all others. It is possible that the initial
sequence chosen does not align to a small portion of the input sequences. In this case the
unaligned sequences are maintained in a candidate pool for possible inclusion in further
iterations of consensus refinement. The pairwise alignments to the chosen sequence are
combined into the initial seed alignment and novel CpG-adjusted consensus caller is used to
generate an updated consensus for the family.
The consensus caller used in RepeatModeler differs from a standard majority-rule consensus
caller in two ways: it scores each possible ancestral base or IUB code to the seed alignment
column using a neutral substitution matrix, and it looks for overrepresentation of common CpG
mutation products to correctly identify the ancestral state of CpG dimers. The first step uses a
matrix (Figure S2) that reflects observed neutral DNA substitution patterns. This matrix and
similar matrices used by RepeatMasker were derived from studies of neutrally decaying DNA
transposon families in mammals.
# A R G C Y T K M S W N -
A 9 0 -8 -15 -16 -17 -13 -3 -11 -4 -2 -6
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22
R 2 1 1 -15 -15 -16 -7 -6 -6 -7 -2 -6
G -4 3 10 -14 -14 -15 -2 -9 -2 -9 -2 -6
C -15 -14 -14 10 3 -4 -9 -2 -2 -9 -2 -6
Y -16 -15 -15 1 1 2 -6 -7 -6 -7 -2 -6
T -17 -16 -15 -8 0 9 -3 -13 -11 -4 -2 -6
K -11 -6 -2 -11 -7 -3 -2 -11 -6 -7 -2 -6
M -3 -7 -11 -2 -6 -11 -11 -2 -6 -7 -2 -6
S -9 -5 -2 -2 -5 -9 -5 -5 -2 -9 -2 -6
W -4 -8 -11 -11 -8 -4 -8 -8 -11 -4 -2 -6
N -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -1 -6
- -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 -6 3
Figure S2: Neutral substitution matrix used by the RepeatModeler consensus caller. Rows and columns
represent the ancestral and current nucleic acids respectively. IUPAC codes, as well as a standard gap
(“-”) characters are included.
After the score for each possible ancestral base and at each column in the alignment is
calculated, a 2 bp sliding window is run over the alignment and the highest dimer score is
calculated for window. Using the truth table (Table S3), an alternative score is developed for
the hypothesis that the ancestral dimer was a CpG (CG_Score). If the total CG_Score is higher
than the matrix score for the window the consensus is changed to a “CG” at these two positions
in the alignment.
Observation
Matrix Score
CG Score
Description
TG
+6
+12
Direct result of current strand CpG
deaminating the C and converting to a TG.
CA
+6
+12
Indirect result of CpG on opposite strand
converting to TG and an incorrect repair of
the current strand.
TA
-8
-5
Two step result. CG -> TG followed by a
common transition of either forward strand
TG -> TA or reverse strand CA -> TA.
TT
-19
-13
CpG transition + transversion. E.g. CG ->
TG -> TT.
TC
-18
-13
(dito) CG -> TG -> TC.
AA
-19
-13
(dito) CG -> CA -> AA.
GA
-18
-13
(dito) CG -> CA -> GA.
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23
Table S3: Dimer score table for calling CpG sites. This table represents the dimers which are most likely
to be generated by mutation at CpG site. For these cases a modified matrix score ( CG Score ) is used to
calculate the alternative score for the alignment columns.
The updated consensus is re-aligned to all seed alignment sequences as well as the candidate
pool and the process is repeated until the consensus sequence stabilizes or a maximum
iteration count is exceeded. At this stage the seed alignment may often contain short regions of
misalignment caused by tandem duplications or deletions within the original consensus choice.
We identify these regions by calculating seed alignment quality using a sliding window approach
(Ruzzo and Tompa 1999) and considering each region independently for consensus refinement.
If there is a majority sequence length within the region, the consensus is called from only the
sequences of this length, otherwise an all-vs-all global alignment is performed and the
sequence scoring best against all others is used to align and then call the new sub-region
consensus. The sub-regions consensi are replaced in the full-consensus and the original
consensus refinement process is repeated until the consensus stabilizes.
Benchmarking Parameters
The benchmarking analysis was performed on a research group-shared CentOS 7.6.1810 Linux
machine (Intel(R) Xeon(R) CPU E7-4830 v4 @ 2.00GHz, 112 cores, 504 GB RAM).
RepeatModeler utilizes a seeded random number generator in the selection of the genomic
samples. The seed is automatically chosen and displayed at runtime to facilitate reproducibility
(using the -srand option). The runtime, and seeds are shown in Table S2. RepeatModeler
1.0.11 benchmarks were generated using only the “-database” parameter using the program
defaults. The new “-LTRStruct” option to RepeatModeler2 was used in all benchmark runs. In
addition for D. melanogaster, the -LTRMaxSeqLen 10000 was used.
Species
Assembly
size (Mb)
RM Seed #
RM
cores
RM
runtime
RM2 Seed #
RM2
cores
RM2
runtime
D.
melanogaster
164
1572903982
8
62:26:16
1570222393
8
12:55:59
D. rerio
1372
1570465657
16
33:12:10
1570222833
16
40:35:56
O. sativa
375
1570476015
16
81:03:54
1570222971
16
37:22:46
Table S2: benchmarking information. RM = RepeatModeler1.0.11, RM2 = RepeatModeler 2.
Genome assembly versions used for benchmarking:
D. melanogaster - PBcr-BLASR Celera 8.1 assembly (Koren et al. 2012)
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24
D. rerio - danRer10: https://www.ncbi.nlm.nih.gov/assembly/GCF_000002035.5/
O. sativa - Verson 7.0
http://rice.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/pseudo
molecules/version_7.0/all.dir/all.con
The scripts used to produce the benchmarking statistics may be found here:
(https://github.com/Dfam-consortium/RepeatModeler ). RepeatModeler2 was configured using
the following versions of dependent packages:
- Mafft 7.407
- RepeatMasker 4.0.9-p2
- RECON 1.0.8
- RepeatScout 1.0.6
- RMBLast 2.9.0-p2
- TRF 4.0.9
- Ninja 0.97-cluster_only
- CD-HIT 4.8.1
- GenomeTools 1.5.10
- LTR_retriever 2.6
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