Method Article
Creation of a comprehensive repeat library for a newly sequenced parasitic worm genome
https://doi.org/10.1038/protex.2018.054
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It is useful to mask repeat elements within the assembly of a newly sequenced genome for tasks such as gene-finding, but also for the purpose of understanding evolution of repetitive elements. To do this, one must first construct a library of the repeat elements in the genome. We present a protocol for creating a comprehensive repeat library for the genome of a newly sequenced parasitic worm, based on merging repeat libraries made by RepeatModeler, TransposonPSI and LTRharvest.
repeat-finding
repeat library
repeat libraries
RepeatMasker
RepeatModeler
Transposon PSI
LTRharvest
LTRdigest
transposons
transposable elements
repetitive elements
repetitive DNA
repeats
parasites
genomes
assemblies
parasitic worms
helminths
nematodes
flatworms
platyhelminths
Nematoda
Platyhelminthes
It is useful to mask repeat elements within the assembly of a newly sequenced genome for tasks such as gene-finding, but also for the purpose of understanding evolution of repetitive elements. To do this, one must first construct a library of the repeat elements in the genome. We present a protocol for creating a comprehensive repeat library for the genome of a newly sequenced parasitic worm, based on merging repeat libraries made by RepeatModeler, TransposonPSI and LTRharvest.
RepeatModeler uses results from three other programs, RECON (Bao & Eddy 2002), RepeatScout (Price et al. 2005), and Tandem Repeats Finder (TRF; Benson, 1999) to build a repeat library for an assembly. RepeatScout is good for finding highly conserved repetitive elements, while RECON can find less highly conserved elements.
TransposonPSI is a program for finding repeats in genomes, by using PSI-BLAST (Altschul et al. 1997) to search for sequence matches at the DNA or protein level to proteins encoded by transposable elements. It can be useful for finding transposable elements that are very degenerate, and so may not be found by other repeat-finding software. The types of transposable elements found by TransposonPSI include LTR retroelements (e.g. gypsy and copia), non-LTR retroelements (e.g. LINE retrotransposon ORFs), DNA transposons (e.g. cryptons and other families), and helitrons.
The LTRharvest software can be used to find LTR retrotransposons in a genome assembly.
While many analyses of genomes use a single repeat-finding approach to produce a repeat library, we believe that by making a non-redundant library based on a combination of three different software packages (LTRharvest, TransposonPSI, RepeatModeler), our protocol produces a more complete repeat library.
Computer cluster.
Build a repeat library for the newly sequenced genome using RepeatModeler (
RepeatModeler).Build a second repeat library for the newly sequenced genome using TransposonPSI (
TransposonPSI). TransposonPSI uses PSI-BLAST to search for sequence matches at the DNA or protein level to proteins encoded by transposable elements. Remove sequences of <50 bp from the TransposonPSI repeat library.Build a third repeat library for the newly sequenced genome, by using LTRharvest (Ellinghaus et al. 2008) to identify long terminal repeat (LTR) retrotransposons. To remove likely false positives, and make a library of full-length (or mostly full-length) LTR retrotransposon sequences, run LTRdigest on the LTRharvest results, with protein HMMs from Pfam (Finn et al. 2016) (those Pfam domains listed in tables B1 and B2 of Steinbiss et al. 2009) and GyDB (Llorens et al. 2011). To further remove likely false positives, remove any LTR retrotransposon candidates without domain hits.
For each of the three repeat libraries from (1), (2), (3), classify the repeats using RepeatClassifier (part of the RepeatModeler software).
Merge the repeat libraries from RepeatModeler, TransposonPSI and LTRharvest by using USEARCH v7 (Edgar 2010) to cluster the candidate sequences with ≥80% identity, and remove all but one sequence for each cluster. The resultant repeat library should be non-redundant.
To try to remove protein-coding genes from non-transposable element genes (e.g. collagen repeats), then filter the non-redundant library by removing repeats that were classified as ‘unknown’ (by RepeatClassifier) and that have BLASTN (Altschul et al. 1997) hits of E≤0.001 against known protein-coding genes and ncRNA genes from nematodes and platyhelminths (DNA sequences for C. elegans protein-coding and ncRNA transcripts from WormBase WS235 (Howe et al. 2016), and for S. mansoni and E. multilocularis protein-coding transcripts from GeneDB (Logan-Klumpler et al. 2012)).
If it is required to repeat-mask the genome of interest, the non-redundant library from (6) above can be used to mask repeats in the genome using RepeatMasker (
www.repeatmasker.org), with the –s (sensitive) option. This will mask low complexity DNA and simple repeats as well as transposable elements.RepeatModeler
When running RepeatModeler, it may be necessary to rename the sequences in assembly to have simple names eg. C1, C2, C3, etc. as RepeatModeler gets confused by unusual characters or sequence names.
If your assembly has lots of smallish scaffolds (eg. average scaffold size 10kb), then it might be a good idea to cut down run-time by running RepeatModeller on just the subset of scaffolds that are quite long (eg. >=100 kb).
We find RepeatModeler can take several days or even a week or two to run on a large genome. For a 65-Mbase genome, it ran overnight and we reserved 3000 Mbyte of memory (RAM) for it. If RepeatModeler crashes for no obvious reason, it may have ran out of memory, so it may be worth trying re-running it with lots (e.g. 20,000 Mbyte) of memory.
RepeatModeler uses RepeatScout, and RepeatScout will often find tandem repeats and low complexity sequences, which may be present in the RepeatModeler output file, and you might want to remove them.
TransposonPSI
We find TransposonPSI takes a long time (e.g. 1 week) for some assemblies with lots of scaffolds (e.g. ~100,000 scaffolds). To get around this, we glue together short scaffolds into long scaffolds, with 1000 'N's between each pair of short scaffolds.
RepeatClassifier
Running RepeatClassifier seems to require a lot of memory (RAM), e.g. if a repeat library is 0.5 Mbase, it needs 100 Mbyte RAM and takes 1 day; if 5-10 Mbase then 5000 Mbyte and 2 days; if 20-50 Mbase then 5000 Mbyte and >2 days. Sometimes RepeatClassifier takes a long time and produces a lot of large temporary output files; in this case the repeat library can be split up into smaller files of 500 sequences each, and RepeatClassifier run on each separately.
Merged repeat library
We find that for very fragmented genome assemblies, there appear to be quite a lot of low complexity sequences in the merged repeat library, which you may wish to identify (e.g. using Tandem Repeats Finder; Benson 1999) and remove.
RepeatMasker
When running RepeatMasker, it may be necessary to rename the repeats in the repeat library if the sequence names are too long to use for RepeatMasker (does not accept names of >80 characters).
The output is a repeat library. This is a fasta file of repeats for the genome of interest, each repeat classified by type (by RepeatClassifier).
If RepeatMasker is used to mask the genome with the repeat library, it produces a masked version of the genome, as well as a table showing the positions of repeats in the genome assembly.
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids res. 25, 3389-402 (1997).
Bao, Z. & Eddy S. R. Automated de novo identification of repeat sequence families in sequenced genomes. Genome res. 12, 1269-76 (2002).
Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic acids res. 27, 573-80 (1999).
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460-2461 (2010).
Ellinghaus, D. et al. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC bioinformatics 9, 18 (2008).
Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic acids res. 44, D279-285 (2016).
Howe, K. L. et al. WormBase 2016: expanding to enable helminth genomic research. Nucleic acids res. 44, D774-780 (2016).
Llorens, C. et al. The Gypsy Database (GyDB) of mobile genetic elements: release 2.0. Nucleic acids res. 39, D70-74 (2011).
Logan-Klumpler, F. J. et al. GeneDB--an annotation database for pathogens. Nucleic acids res. 40, D98-108 (2012).
Price, A. L. et al. De novo identification of repeat families in large genomes. Bioinformatics Suppl 1, i351-8 (2005).
Steinbiss, S. et al. Fine-grained annotation and classification of de novo predicted LTR retrotransposons. Nucleic acids res. 37, 7002-7013 (2009).
We thank James Cotton and Diogo Ribeiro for advice on running RepeatModeler, and to Sascha Steinbiss for advice on LTRharvest and LTRdigest.
The authors declare no competing financial interests.
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