RepeatModeler is a de novo transposable element (TE) family identification and modeling package. At the heart of RepeatModeler are three de-novo repeat finding programs ( RECON, RepeatScout and LtrHarvest/Ltr_retriever ) which employ complementary computational methods for identifying repeat element boundaries and family relationships from sequence data.
RepeatModeler assists in automating the runs of the various algorithms given a genomic database, clustering redundant results, refining and classifying the families and producing a high quality library of TE families suitable for use with RepeatMasker and ultimately for submission to the Dfam database (http://dfam.org).
RepeatModeler: Robert Hubley, Arian Smit - Institute for Systems Biology
LTR Pipeline Extensions: Jullien M. Flynn - Cornell University
There are two supported paths to installing RepeatModeler on a UNIX-based server. RepeatModeler may be installed from source as described in the "Source Distribution Installation" instructions below, or using one of our Dfam-TETools container images ( Docker or Singularity ). The containers include RepeatModeler, it's prerequisites and additional TE analysis tools/utilities used by Dfam. Information on the Dfam-TETools container may be found here: https://github.com/Dfam-consortium/TETools
Source Distribution Installation
Perl Available at http://www.perl.org/get.html. Developed and tested with version 5.8.8.
RepeatMasker & Libraries Developed and tested with open-4.0.9. The program is available at http://www.repeatmasker.org/RMDownload.html and is distributed with Dfam - an open database of transposable element families.
RECON - De Novo Repeat Finder, Bao Z. and Eddy S.R. Developed and tested with our patched version of RECON ( 1.08 ). The 1.08 version fixes problems with running RECON on 64 bit machines and supplies a workaround to a division by zero bug along with some buffer overrun fixes. The program is available at: http://www.repeatmasker.org/RepeatModeler/RECON-1.08.tar.gz. The original version is available at http://eddylab.org/software/recon/.
RepeatScout - De Novo Repeat Finder, Price A.L., Jones N.C. and Pevzner P.A. Developed and tested with our multiple sequence version of RepeatScout ( 1.0.6 ). This version is available at http://www.repeatmasker.org/RepeatScout-1.0.6.tar.gz
TRF - Tandem Repeat Finder, G. Benson et al. You can obtain a free copy at http://tandem.bu.edu/trf/trf.html RepeatModeler requires version 4.0.9 or higher.
RMBlast - A modified version of NCBI Blast for use with RepeatMasker and RepeatModeler. Precompiled binaries and source can be found at http://www.repeatmasker.org/RMBlast.html We recommend using 2.9.0-p2 or higher.
Optional. Additional search engine:
ABBlast - Sequence Search Engine, W. Gish et al. Developed and tested with 2.0 [04-May-2006]. The latest versions of ABBlast may be downloaded from: http://blast.advbiocomp.com/licensing/
Optional. Required for running LTR structural search pipeline:
LtrHarvest - The LtrHarvest program is part of the GenomeTools suite. We have developed this release of RepeatModeler on GenomeTools version 1.5.9 available for download from here: http://genometools.org/pub/ NOTE: use the "make threads=yes" build options to enable multi-threaded runs.
Ltr_retriever - A LTR discovery post-processing and filtering tool. We recommend using version 2.6 or higher from here: https://github.com/oushujun/LTR_retriever/releases
MAFFT - A multiple sequence alignment program. We developed and tested RepeatModeler using mafft version 7.407. Please use this verison or higher from here: https://mafft.cbrc.jp/alignment/software/
CD-HIT - A sequence clustering package. We developed and tested RepeatModeler using version 4.8.1. Please use this version or higher from: http://weizhongli-lab.org/cd-hit/
Ninja - A tool for large-scale neighbor-joining phylogeny inference and clustering. We developed and tested RepeatModeler using Ninja version "0.95-cluster_only". Please obtain a copy from: https://github.com/TravisWheelerLab/NINJA/releases/tag/0.95-cluster_only
- Obtain the source distribution
Uncompress and expand the distribution archive:
tar -zxvf RepeatModeler-open-#.#.#.tar.gz
tar -xvf RepeatModeler-open-#.#.#.tar
Configure for your site:Automatic:
Run the "configure" script interactively with prompts for each setting:
Run the "configure" script with supplied paramters:
perl ./configure -rscout_dir .. -recon_dir ..
- Edit the configuration file "RepModelConfig.pm"
Use the "configuration overrides" command line options with the RepeatModeler programs. e.g:
./RepeatModeler -rscout_dir .. -recon_dir ..
In this example we first downloaded elephant sequences from Genbank ( approx 11MB ) into a file called elephant.fa.
Create a Database for RepeatModeler
RepeatModeler uses a NCBI BLASTDB or a ABBlast XDF database ( depending on the search engine used ) as input to the repeat modeling pipeline. A utility is provided to assist the user in creating a single database from several types of input structures.
<RepeatModelerPath>/BuildDatabase -name elephant elephant.fa
Run "BuildDatabase" without any options in order to see the full documentation on this utility. There are several options which make it easier to import multiple sequence files into one database.
TIP: It is a good idea to place your datafiles and run this program suite from a local disk rather than over NFS. This will greatly improve runtime as the filesystem access is considerable
RepeatModeler runs several compute intensive programs on the input sequence. For best results run this on a single machine with a moderate amount of memory > 32GB and multiple processors.
Our setup is Xeon(R) CPU E5-2680 v4 @ 2.40GHz - 28 cores, 128GB RAM. To specify a run using 20 parallel jobs, and including the new LTR discovery pipeline:
nohup <RepeatModelerPath>/RepeatModeler -database elephant -pa 20 -LTRStruct >& run.out &
The nohup is used on our machines when running long ( > 3-4 hour ) jobs. The log output is saved to a file and the process is backgrounded. For typical runtimes ( can be > 2 days with this configuration on a well assembled mammalian genome ) see the run statistics section of this file. It is important to save the log output for later usage.
It contains the random number generator seed so that the sampling process may be reproduced if necessary. In addition the log file contains details about the progress of the run for later assesment of peformance or debuging problems.
Interpret the results
RepeatModeler produces a voluminous amount of temporary files stored in a directory created at runtime named like:
RM_<PID>.<DATE> ie. "RM_5098.MonMar141305172005"
and remains after each run for debugging purposes or for the purpose of resuming runs if a failure occures. At the succesful completion of a run, two files are generated:
<database_name>-families.fa : Consensus sequences <database_name>-families.stk : Seed alignments
The seed alignment file is in a Dfam compatible Stockholm format and may be uploaded to the Dfam database by submiting the data to email@example.com. In the near future we will provide a tool for uploading families directly to the database.
The fasta format is useful for running quick custom library searches using RepeatMasker. Ie.:
<RepeatMaskerPath>/RepeatMasker -lib <database_name>-families.fa mySequence.fa
Other files produced in the working directory include:
RM_<PID>.<DATE>/ consensi.fa families.stk round-1/ sampleDB-#.fa : The genomic sample used in this round sampleDB-#.fa.lfreq : The RepeatScout lmer table sampleDB-#.fa.rscons: The RepeatScout generated consensi sampleDB-#.fa.rscons.filtered : The simple repeat/low complexity filtered version of *.rscons consensi.fa : The final consensi db for this round family-#-cons.html : A visualization of the model refinement process. This can be opened in web browsers that support zooming. ( such as firefox ). This is used to track down problems with the Refiner.pl index.html : A HTML index to all the family-#-cons.html files. round-2/ sampleDB-#.fa : The genomic sample used in this round msps.out : The output of the sample all-vs-all comparison summary/ : The RECON output directory eles : The RECON family output consensi.fa : Same as above family-#-cons.html : Same as above index.html : Same as above round-3/ Same as round-2 .. round-n/
Recover from a failure
If for some reason RepeatModeler fails, you may restart an analysis starting from the last round it was working on. The -recoverDir [ResultDir] option allows you to specify a diretory ( i.e RM_
RepeatModeler is designed to run on assemblies rather than genome reads. At the start of a run a quick analysis is performed on the input database to ascertain the assembly N50. A histogram of contig size is also displayed.
RepeatModeler employs symmetric multiprocessing parallelism, therefore should be run on a single machine per-assembly.
It is not recommended that a genome be run in a batched fashion nor the results of multiple RepeatModeler runs on the same genome be naively combined. Doing so will generate a combined library that is largely redundant. The -genomeSampleSizeMax parameter is provided for the purpose of increasing the amount of the genome sampled while avoiding rediscovery of families.
Please see the RELEASE-NOTES file for more details.
Benchmarks and statistics for runs of RepeatModeler on several sample genomes.
RepeatModeler 2.0 ( RECON + RepeatScout + LTRStruct ):
|Genome||Genome DB Size (bp)||Runtime (hh:mm)*||Models Built|
|D. melanogaster||164 Mbp||12:56||734|
|D. rerio||1.4 Gbp||40:36||3851|
|O. sativa||375 Mbp||37:23||2648|
- Analysis run on a CentOS 7.6.1810 Linux system with Intel... processors. D. melanogaster was run using 8 parallel jobs ( -pa 8 ) while D. rerio and O.sativa were run with 16 parallel jobs.
RepeatModeler 1.0.3 ( RECON + RepeatScout ): Genome DB Sample*** Run Time* Models Models % Sample Genome Size (bp) Size (bp) (hh:mm) Built Classified Masked** ---------- ----------- ---------- ---------- ------- ----------- -------- Marmoset 3.0 Bbp 228 Mbp 55:37 582 564 36.4 Zebrafinch 1.2 Bbp 222 Mbp 66:29 178 75 8.6 * Analysis run on a 4 processor P4, 2.4Ghz, 3GB RAM, machine running Red Hat Linux. ** Includes simple repeats and low complexity DNA. Results obtained with RepeatMasker open-3.2.5, WUBlast and the -lib option. *** Sample size does not include 40 Mbp used in the RepeatScout analysis. This 40 Mbp is randomly chosen and may overlap 0-100% of the sample used in the RECON analysis. RepeatModeler 1.0.2 ( RECON + RepeatScout ): Genome DB Sample*** Run Time* Models Models % Sample Genome Size (bp) Size (bp) (hh:mm) Built Classified Masked** ---------- ----------- ---------- ---------- ------- ----------- -------- Human HG18 3.1 Bbp 238 Mbp 46:36 614 611 35.66 Platypus 1.9 Bbp 220 Mbp 76:02 540 457 Zebrafinch 1.3 Bbp 220 Mbp 63:57 233 104 9.41 Sea Urchin 867 Mbp 220 Mbp 40:03 1830 360 33.85 diatom 32,930,227 32,930,227 4:41 128 35 2.86 Rabbit 11,770,949 11,770,949 3:14 83 72 31.30 * Analysis run on a 4 processor P4, 2.4Ghz, 3GB RAM, machine running Red Hat Linux. ** Includes simple repeats and low complexity DNA. Results obtained with RepeatMasker open-3.1.9, WUBlast and the -lib option. *** Sample size does not include 40 Mbp used in the RepeatScout analysis. This 40 Mbp is randomly chosen and may overlap 0-100% of the sample used in the RECON analysis. RepeatModeler 1.0.0 ( RECON ): Genome DB Sample Run Time* Models Models % Sample Genome Size (bp) Size (bp) (hh:mm) Built Classified Masked** ---------- ----------- ---------- ---------- ------- ----------- -------- Opossum 3.5 Billion 319 Mbp 140:52 1137 467 52.55 Armadillo 47,332,136 47,332,136 6:20 121 92 36.07 Platypus 14,768,992 14,768,992 3:46 18 13 40.69 Rabbit 11,770,949 11,770,949 2:17 20 16 28.67 Elephant 11,550,090 11,550,090 1:21 34 28 37.08 * Analysis run on a 4 processor P4, 2.4Ghz, 3GB RAM, machine running Red Hat Linux. ** Includes simple repeats and low complexity DNA. Results obtained with RepeatMasker open-3.0.9, WUBlast and the -lib option.
Arnie Kas for the work done on the original MultAln.pm.
Andy Siegel for statistics consultations.
Thanks so much to Warren Gish for his invaluable assistance and consultation on his ABBlast program suite.
Alkes Price and Pavel Pevzner for assistance with RepeatScout and hosting my multi-sequence version of RepeatScout.
Shujun Ou, and Ning Jiang for discussions and assistance with using LTR_retreiver.
This work was supported by the NIH ( R44 HG02244-02), ( RO1 HG002939 ), ( U24 HG010136 ), and the Institute for Systems Biology.
This work is licensed under the Open Source License v2.1.
To view a copy of this license, visit http://www.opensource.org/licenses/osl-2.1.php or see the LICENSE file contained in this distribution.