LoRTE: Detecting transposon-induced genomic variants using low coverage PacBio long read sequences
© The Author(s). 2017
Received: 19 January 2017
Accepted: 17 March 2017
Published: 8 April 2017
Population genomic analysis of transposable elements has greatly benefited from recent advances of sequencing technologies. However, the short size of the reads and the propensity of transposable elements to nest in highly repeated regions of genomes limits the efficiency of bioinformatic tools when Illumina or 454 technologies are used. Fortunately, long read sequencing technologies generating read length that may span the entire length of full transposons are now available. However, existing TE population genomic softwares were not designed to handle long reads and the development of new dedicated tools is needed.
LoRTE is the first tool able to use PacBio long read sequences to identify transposon deletions and insertions between a reference genome and genomes of different strains or populations. Tested against simulated and genuine Drosophila melanogaster PacBio datasets, LoRTE appears to be a reliable and broadly applicable tool to study the dynamic and evolutionary impact of transposable elements using low coverage, long read sequences.
LoRTE is an efficient and accurate tool to identify structural genomic variants caused by TE insertion or deletion. LoRTE is available for download at http://www.egce.cnrs-gif.fr/?p=6422
KeywordsTransposable element Structural variation Population genomic Long read sequence
Transposable elements (TEs), which represent an essential part of eukaryotic and prokaryotic genomes, play important roles in genome size, structure and functions [1, 2]. TE identification and annotation remains one of the most challenging task in computational genomics [3, 4] but our knowledge of the TE diversity and dynamics among genomes has greatly benefited from the recent advance of sequencing technologies . Specifically, comparison of closely related strains or species using short read sequencing technologies enabled new insights into TE dynamic and their roles in generating structural genomic variation. Two different approaches with their associated computational tools have been developed to achieve this goal, see [5, 6] for exhaustive descriptions of the different strategies. Briefly, the first approach is based on the direct assembly of the repeated fraction of the reads using highly abundant k-mer : RepARK  or Tedna . Other tools such as RepeatExplorer  or dnaPipeTE  used low-coverage sub-samples of the reads in order to retrieve and specifically assemble the highly repeated elements. All these tools have the advantage to give a good picture of the global TE abundance and diversity. However they do not provide the exact genomic positions of each TE, preventing the identification of the presence/absence of given TE copies between related populations or species. The second approach is implemented in programs that have been specifically developed to detect transposon presence/absence between a reference genome and Illumina or 454 short read sequences [10–13]. The global architecture of these softwares is similar: 1. New insertions are detected by retrieving the reads that do not map on the reference genomes but that align both on a TE consensus sequence and a unique region in the genome. 2. Deletions are detected by identifying reads that align on the two flanking sequences of a given TE present in the reference genome indicating that the locus not contains anymore the sequence of the TE copy. Programs like the Transposon Insertion and Depletion AnaLyzer (TIDAL) also take advantage of the presence of paired end sequences on Illumina reads to identify the deleted locus . This later approach has been extensively tested and benchmarked on diverse Drosophila datasets leading to mixed results. Indeed, comparison of respective performance of each program indicated that a very small fraction of the TE presence/absence was identified by all programs [12, 13]. For example, the comparison of TIDAL , TEMP , LnB  and CnT  on Drosophila Synthetic Population Resource (DGRP) strains  revealed that only 3% of the calls are predicted in common by the different programs. Thus, a large majority of the predictions are program-specific and PCR validations of the calls lead to substantial levels of false positive (around 40%) . These limitations are mainly due to the fact that TEs tend to insert preferentially in highly repetitive regions. The short length of Illumina reads prevents the precise identification and mapping of these TEs nested in one another. Additionally, the precise breakpoint prediction required the use of specific softwares . Interestingly, long read sequencing technologies such as those provided by PacBio or MinION technologies are now generating read length that may span the entire length of full transposons and their associated flanking genomic sequences. However, existing programs are not designed to deal with long read sequences and the implementation of new methods is thus required. Here we present LoRTE (Long Read Transposable Element), the first tool for population genomic analyses of TE presence/absence between a reference genome and PacBio long read sequences.
The first module is designed to verify the presence/absence in the PacBio reads of a list of annotated TEs in the reference genome (Fig. 1a). Briefly, the program acquires the flanking sequences of each TEs and align them on the reference genomes using MEGABLAST  (not shown in Fig. 1a). The length of the flanking sequences is specified by the user (default = 200 bp). At this stage, a filter verifies if the TE is correctly annotated and if the flanking sequences map uniquely on the genome. TE wrongly annotated or located in region too much enriched in repeats are categorized as “irresolvable locus” in the final output file. The remaining 3′ and 5′ flanking sequences are aligned on the PacBio read using MEGABLAST (Fig. 1a). All the sequences located between a 3′ and 5′ flanking sequences in the same orientation, and in a specified window size in the PacBio reads are extracted. These extracted sequences are then searched with BLASTN against the TE consensus sequences. For a given locus if the sequence matches to the same TE consensi, the TE is considered as “TE Present” in the read. Sequences <50 nt without any match on the TE consensi correspond to a deletion (“TE absent”). “Possible polymorphism” locus corresponds to a situation in which a given TE is “absent” in some reads and “present” in some others (heterozygosity or true polymorphism if the DNA of several organisms have been pooled and sequenced). Finally some locus are characterized as “ambiguous negative” if the extracted sequences between the 3′ and the 5′ flanking are >50 nt but do not match with a TE consensus sequences. This latter case may correspond to partially deleted TEs.
The second step aims to identify new TE insertions present in the reads but absent in the reference genome. The program removes from the PacBio reads the segments of sequences corresponding to the TEs identified by the first module. Then, the TE consensi are aligned using BLASTN on the reads to identify all the remaining TEs. The flanking 5′ and 3′ ends of these putative new TE insertions are extracted and aligned using MEGABLAST on the reference genome. All the sequences between a 5′ and 3′ ends, in the same orientation, and in a specified window size are extracted and the program verifies if they match with a TE consensus using BLASTN. If the extracted sequences are <50 nt and do not resemble to a given consensus the program considers these cases as new insertions in the reads. “New polymorphic TE insertion” corresponds to a situation in which a new previously identified TE insertion in step 1 is “present” in some reads but “absent” in some others. Finally, all the reads testifying for a new insertion for the same locus are clustered together.
To assess the performance and accuracy, we have tested LoRTE on two Drosophila melanogaster datasets: (i) Benchmark of the program is monitored by random insertion of 250 TEs and random deletion of 100 TEs in the reference genome (release 5) before its segmentation in pieces of 3 to 30 kb in length. More realistic, error-prone, PacBio reads have also been generated using the PBSIM software with default parameters except –length-min = 1000  (ii) genuine PacBio reads of pooled 1950 adult males of the ISO1 strains (same stock used in the official reference assembly)  with a sequencing depth of 90× (average read length: 10,040 bp).
In order to identify false positives, LoRTE predictions are then compared with the genome assembly of the PacBio reads. Reads and the Falcon assembly  are available at https://github.com/PacificBiosciences/DevNet/wiki/Drosophila-sequence-and-assembly. To test the impact of the coverage on the performance of LoRTE we have sub-sampled the datasets to lower coverages (from 1× to 40×). For these experiments, we have used a list of 4239 annotated TEs  and corresponding TE consensi obtained from FlyBase FB2016_04 release (http://flybase.org/) and RepBase version 31/01/2014 (http://www.girinst.org/repbase/). Input and raw output files used in this study are available at http://www.egce.cnrs-gif.fr/?p=6422
LoRTE predictions on the ISO1 PacBio reads have been evaluated using the de novo 90× Falcon assembly. For the new TE insertions and deletions, each 3′ and 5′ flanking sequences of the corresponding predictions in the PacBio reads are aligned on the Falcon assembly using MEGABLAST. The sequences located between these 3′ and 5′ flanking sequences are extracted and searched with BLASTN against the TE consensus sequences. BLAST output files are then manually compared with the LoRTE calls to estimate the validity of each prediction.
We then tested the ability of LoRTE to detect the insertions/deletions made on the synthetic datasets. Figure 2b displays the percentage of insertions/deletions detected by LoRTE with respect to the read coverage. LoRTE detected 98% of the deletions and 100% of the insertion from coverage of 9× and did not generated false positive calls, whatever the coverage. We have also tested LoRTE with the synthetic datasets generated by the PBSIM software  that simulates the size distribution and the high error rate of genuine PacBio reads. With a coverage of 10×, we obtained very similar results using error-free and PBSIM error-prone PacBio reads. The detection of the deletion appears slightly less efficient with error-prone reads, mainly because the alignments of the flanking 5′ and 3′ sequences of each TE locus generate some misalignments. This phenomenon leads to the extraction of some sequences located between these 5′ 3′ that are longer than the threshold of 50 nt. Consequently, these loci appear as «ambiguous negative >50 nt» or «possible polymorphism» rather than «TE absent». By relaxing the threshold at 100 nt, most of these loci now appear as «TE absent». However, on real PacBio reads, a relaxation of this threshold could generate false positives or an overestimation of the level of polymorphism. Taken together, these results strengthen the reliability of LoRTE, even in a context of low coverage PacBio datasets.
Taken together, our results indicate that LoRTE is an efficient and accurate tool to identify structural genomic variants caused by TE insertion or deletion among closely related populations or strains. Here, we demonstrated that LoRTE performs well even at low coverage PacBio read (<10×) providing a cost effective tool to study the dynamics and impact of TEs in natural populations.
The authors wish to thank Nicolas Pollet and Jean-Michel Rossignol for their helpful comments.
Availability of data and materials
Project name: LoRTE
Project home page: http://www.egce.cnrs-gif.fr/?p=6422
Availability of data and materials: http://www.egce.cnrs-gif.fr/?p=6422
Operating system: UNIX/LINUX 64-bit
Programming language: Python 2.7
Other requirements: BLAST+
ED and JF coded and tested the software, JF designed the study and drafted the manuscript. Both authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Fedoroff NV. Presidential address. Transposable elements, epigenetics, and genome evolution. Science. 2012;338(6108):758–67.View ArticlePubMedGoogle Scholar
- Hua-Van A, Le Rouzic A, Boutin TS, Filee J, Capy P. The struggle for life of the genome’s selfish architects. Biol Direct. 2011;6:19.View ArticlePubMedPubMed CentralGoogle Scholar
- Lerat E. Identifying repeats and transposable elements in sequenced genomes: how to find your way through the dense forest of programs. Heredity (Edinb). 2010;104(6):520–33.View ArticleGoogle Scholar
- Koch P, Platzer M, Downie BR. RepARK--de novo creation of repeat libraries from whole-genome NGS reads. Nucleic Acids Res. 2014;42(9):e80.View ArticlePubMedPubMed CentralGoogle Scholar
- Ewing AD. Transposable element detection from whole genome sequence data. Mob DNA. 2015;6(1):24.View ArticlePubMedPubMed CentralGoogle Scholar
- Rishishwar L, Mariño-Ramírez L, Jordan IK. Benchmarking computational tools for polymorphic transposable element detection. Briefings Bioinf. 2016. bbw072. https://academic.oup.com/bib/article-abstract/doi/10.1093/bib/bbw072/2562836/Benchmarkingcomputational-tools-for-polymorphic?redirectedFrom=fulltext.
- Zytnicki M, Akhunov E, Quesneville H. Tedna: a transposable element de novo assembler. Bioinformatics. 2014;30(18):2656–8.View ArticlePubMedGoogle Scholar
- Novak P, Neumann P, Pech J, Steinhaisl J, Macas J. RepeatExplorer: a Galaxy-based web server for genome-wide characterization of eukaryotic repetitive elements from next-generation sequence reads. Bioinformatics. 2013;29(6):792–3.View ArticlePubMedGoogle Scholar
- Goubert C, Modolo L, Vieira C, ValienteMoro C, Mavingui P, Boulesteix M. De novo assembly and annotation of the Asian tiger mosquito (Aedes albopictus) repeatome with dnaPipeTE from raw genomic reads and comparative analysis with the yellow fever mosquito (Aedes aegypti). Genome Biol Evol. 2015;7(4):1192–205.View ArticlePubMedPubMed CentralGoogle Scholar
- Fiston-Lavier AS, Barron MG, Petrov DA, Gonzalez J. T-lex2: genotyping, frequency estimation and re-annotation of transposable elements using single or pooled next-generation sequencing data. Nucleic Acids Res. 2015;43(4):e22.View ArticlePubMedGoogle Scholar
- Kofler R, Betancourt AJ, Schlotterer C. Sequencing of pooled DNA samples (Pool-Seq) uncovers complex dynamics of transposable element insertions in Drosophila melanogaster. PLoS Genet. 2012;8(1):e1002487.View ArticlePubMedPubMed CentralGoogle Scholar
- Rahman R, Chirn GW, Kanodia A, Sytnikova YA, Brembs B, Bergman CM, Lau NC. Unique transposon landscapes are pervasive across Drosophila melanogaster genomes. Nucleic Acids Res. 2015;43(22):10655–72.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhuang J, Wang J, Theurkauf W, Weng Z. TEMP: a computational method for analyzing transposable element polymorphism in populations. Nucleic Acids Res. 2014;42(11):6826–38.View ArticlePubMedPubMed CentralGoogle Scholar
- Linheiro RS, Bergman CM. Whole genome resequencing reveals natural target site preferences of transposable elements in Drosophila melanogaster. PLoS One. 2012;7(2):e30008.View ArticlePubMedPubMed CentralGoogle Scholar
- Cridland JM, Macdonald SJ, Long AD, Thornton KR. Abundance and distribution of transposable elements in two Drosophila QTL mapping resources. Mol Biol Evol. 2013;30(10):2311–27.View ArticlePubMedPubMed CentralGoogle Scholar
- Mackay TF, Richards S, Stone EA, Barbadilla A, Ayroles JF, Zhu D, Casillas S, Han Y, Magwire MM, Cridland JM. The Drosophila melanogaster genetic reference panel. Nature. 2012;482(7384):173–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Hénaff E, Zapata L, Casacuberta JM, Ossowski S. Jitterbug: somatic and germline transposon insertion detection at single-nucleotide resolution. BMC Genomics. 2015;16(1):768.View ArticlePubMedPubMed CentralGoogle Scholar
- Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Res. 2008;36(Web Server issue):W5–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Ono Y, Asai K, Hamada M. PBSIM: PacBio reads simulator—toward accurate genome assembly. Bioinformatics. 2013;29(1):119–21.View ArticlePubMedGoogle Scholar
- Kim KE, Peluso P, Babayan P, Yeadon PJ, Yu C, Fisher WW, Chin C-S, Rapicavoli NA, Rank DR, Li J. Long-read, whole-genome shotgun sequence data for five model organisms. Sci Data. 2014;1.140045
- Chin C-S, Peluso P, Sedlazeck FJ, Nattestad M, Concepcion GT, Clum A, Dunn C, O’Malley R, Figueroa-Balderas R, Morales-Cruz A. Phased diploid genome assembly with single-molecule real-time sequencing. Nat Methods. 2016;13(12):1050–4.View ArticlePubMedGoogle Scholar
- Quesneville H, Bergman CM, Andrieu O, Autard D, Nouaud D, Ashburner M, Anxolabehere D. Combined evidence annotation of transposable elements in genome sequences. PLoS Comput Biol. 2005;1(2):e22.View ArticlePubMed CentralGoogle Scholar
- Ragagnin GT, Bernardo LP, Loreto EL. Unraveling the evolutionary scenario of the hobo element in populations of Drosophila melanogaster and D. simulans in South America using the TPE repeats as markers. Genet Mol Biol. 2016;39(1):145–50.View ArticlePubMedPubMed CentralGoogle Scholar