Tangram: a comprehensive toolbox for mobile element insertion detection

Emerging Opportunities to Study Mobile Element Insertions and Their Source Elements in an Expanding Universe of Sequenced Human Genomes

Three mobile element classes, namely Alu, LINE-1 (L1), and SVA elements, remain actively mobile in human genomes and continue to produce new mobile element insertions (MEIs). Historically, MEIs have been discovered and studied using several methods, including: (1) Southern blots, (2) PCR (including PCR display), and (3) the detection of MEI copies from young subfamilies. We are now entering a new phase of MEI discovery where these methods are being replaced by whole genome sequencing and bioinformatics analysis to discover novel MEIs. We expect that the universe of sequenced human genomes will continue to expand rapidly over the next several years, both with short-read and long-read technologies. These resources will provide unprecedented opportunities to discover MEIs and study their impact on human traits and diseases. They also will allow the MEI community to discover and study the source elements that produce these new MEIs, which will facilitate our ability to study source element regulation in various tissue contexts and disease states. This, in turn, will allow us to better understand MEI mutagenesis in humans and the impact of this mutagenesis on human biology.

A second look at exome sequencing data: detecting mobile elements insertion in a rare disease cohort

About 0.3% of all variants are due to de novo mobile element insertions (MEIs). The massive development of next-generation sequencing has made it possible to identify MEIs on a large scale. We analyzed exome sequencing (ES) data from 3232 individuals (2410 probands) with developmental and/or neurological abnormalities, with MELT, a tool designed to identify MEIs. The results were filtered by frequency, impacted region and gene function. Following phenotype comparison, two candidates were identified in two unrelated probands. The first mobile element (ME) was found in a patient referred for poikilodermia. A homozygous insertion was identified in the FERMT1 gene involved in Kindler syndrome. RNA study confirmed its pathological impact on splicing. The second ME was a de novo Alu insertion in the GRIN2B gene involved in intellectual disability, and detected in a patient with a developmental disorder. The frequency of de novo exonic MEIs in our study is concordant with previous studies on ES data. This project, which aimed to identify pathological MEIs in the coding sequence of genes, confirms that including detection of MEs in the ES pipeline can increase the diagnostic rate. This work provides additional evidence that ES could be used alone as a diagnostic exam.

Retrotransposon insertion as a novel mutational cause of spinal muscular atrophy

Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disorder resulting from biallelic alterations of the SMN1 gene: deletion, gene conversion or, in rare cases, intragenic variants. The disease severity is mainly influenced by the copy number of SMN2, a nearly identical gene, which produces only low amounts of full-length (FL) mRNA. Here we describe the first example of retrotransposon insertion as a pathogenic SMN1 mutational event. The 50-year-old patient is clinically affected by SMA type III with a diagnostic odyssey spanning nearly 30 years. Despite a mild disease course, he carries a single SMN2 copy. Using Exome Sequencing and Sanger sequencing, we characterized a SINE-VNTR-Alu (SVA) type F retrotransposon inserted in SMN1 intron 7. Using RT-PCR and RNASeq experiments on lymphoblastoid cell lines, we documented the dramatic decrease of FL transcript production in the patient compared to subjects with the same SMN1 and SMN2 copy number, thus validating the pathogenicity of this SVA insertion. We described the mutant FL-SMN1-SVA transcript characterized by exon extension and showed that it is subject to degradation by nonsense-mediated mRNA decay. The stability of the SMN-SVA protein may explain the mild course of the disease. This observation exemplifies the role of retrotransposons in human genetic disorders.

Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data

Transposable elements (TEs) are classified into two classes according to their mobilization mechanism. Compared to DNA transposons that move by the "cut and paste" mechanism, retrotransposons mobilize via the "copy and paste" method. They have been an essential research topic because some of the active elements, such as Long interspersed element 1 (LINE-1), Alu, and SVA elements, have contributed to the genetic diversity of primates beyond humans. In addition, they can cause genetic disorders by altering gene expression and generating structural variations (SVs). The development and rapid technological advances in next-generation sequencing (NGS) have led to new perspectives on detecting retrotransposon-mediated SVs, especially insertions. Moreover, various computational methods have been developed based on NGS data to precisely detect the insertions and deletions in the human genome. Therefore, this review discusses details about the recently studied and utilized NGS technologies and the effective computational approaches for discovering retrotransposons through it. The final part covers a diverse range of computational methods for detecting retrotransposon insertions with human NGS data. This review will give researchers insights into understanding the TEs and how to investigate them and find connections with research interests.

Pattern matching for high precision detection of LINE-1s in human genomes

Background

Long interspersed element 1 (LINE-1 or L1) retrotransposons are mobile elements that constitute 17–20% of the human genome. Strong correlations between abnormal L1 expression and several human diseases have been reported. This has motivated increasing interest in accurate quantification of the number of L1 copies present in any given biologic specimen. A main obstacle toward this aim is that L1s are relatively long DNA segments with regions of high variability, or largely present in the human genome as truncated fragments. These particularities render traditional alignment strategies, such as seed-and-extend inefficient, as the number of segments that are similar to L1s explodes exponentially. This study uses the pattern matching methodology for more accurate identification of L1s. We validate experimentally the superiority of pattern matching for L1 detection over alternative methods and discuss some of its potential applications.

Results

Pattern matching detected full-length L1 copies with high precision, reasonable computational time, and no prior input information. It also detected truncated and significantly altered copies of L1 with relatively high precision. The method was effectively used to annotate L1s in a target genome and to calculate copy number variation with respect to a reference genome. Crucial to the success of implementation was the selection of a small set of k-mer probes from a set of sequences presenting a stable pattern of distribution in the genome. As in seed-and-extend methods, the pattern matching algorithm sowed these k-mer probes, but instead of using heuristic extensions around the seeds, the analysis was based on distribution patterns within the genome. The desired level of precision could be adjusted, with some loss of recall.

Conclusion

Pattern matching is more efficient than seed-and-extend methods for the detection of L1 segments whose characterization depends on a finite set of sequences with common areas of low variability. We propose that pattern matching may help establish correlations between L1 copy number and disease states associated with L1 mobilization and evolution.

Cas9 targeted enrichment of mobile elements using nanopore sequencing

Mobile element insertions (MEIs) are repetitive genomic sequences that contribute to genetic variation and can lead to genetic disorders. Targeted and whole-genome approaches using short-read sequencing have been developed to identify reference and non-reference MEIs; however, the read length hampers detection of these elements in complex genomic regions. Here, we pair Cas9-targeted nanopore sequencing with computational methodologies to capture active MEIs in human genomes. We demonstrate parallel enrichment for distinct classes of MEIs, averaging 44% of reads on-targeted signals and exhibiting a 13.4-54x enrichment over whole-genome approaches. We show an individual flow cell can recover most MEIs (97% L1Hs, 93% AluYb, 51% AluYa, 99% SVA_F, and 65% SVA_E). We identify seventeen non-reference MEIs in GM12878 overlooked by modern, long-read analysis pipelines, primarily in repetitive genomic regions. This work introduces the utility of nanopore sequencing for MEI enrichment and lays the foundation for rapid discovery of elusive, repetitive genetic elements.

Identification and Genotyping of Transposable Element Insertions From Genome Sequencing Data

Transposable element (TE) mobilization is a significant source of genomic variation and has been associated with various human diseases. The exponential growth of population-scale whole-genome sequencing and rapid innovations in long-read sequencing technologies provide unprecedented opportunities to study TE insertions and their functional impact in human health and disease. Identifying TE insertions, however, is challenging due to the repetitive nature of the TE sequences. Here, we review computational approaches to detecting and genotyping TE insertions using short- and long-read sequencing and discuss the strengths and weaknesses of different approaches. © 2020 Wiley Periodicals LLC.

A study of transposable element-associated structural variations (TASVs) using a de novo-assembled Korean genome

Advances in next-generation sequencing (NGS) technology have made personal genome sequencing possible, and indeed, many individual human genomes have now been sequenced. Comparisons of these individual genomes have revealed substantial genomic differences between human populations as well as between individuals from closely related ethnic groups. Transposable elements (TEs) are known to be one of the major sources of these variations and act through various mechanisms, including de novo insertion, insertion-mediated deletion, and TE–TE recombination-mediated deletion. In this study, we carried out de novo whole-genome sequencing of one Korean individual (KPGP9) via multiple insert-size libraries. The de novo whole-genome assembly resulted in 31,305 scaffolds with a scaffold N50 size of 13.23 Mb. Furthermore, through computational data analysis and experimental verification, we revealed that 182 TE-associated structural variation (TASV) insertions and 89 TASV deletions contributed 64,232 bp in sequence gain and 82,772 bp in sequence loss, respectively, in the KPGP9 genome relative to the hg19 reference genome. We also verified structural differences associated with TASVs by comparative analysis with TASVs in recent genomes (AK1 and TCGA genomes) and reported their details. Here, we constructed a new Korean de novo whole-genome assembly and provide the first study, to our knowledge, focused on the identification of TASVs in an individual Korean genome. Our findings again highlight the role of TEs as a major driver of structural variations in human individual genomes.

A novel strategy for genome analysis offers insights into the distribution and impact on genome variation of transposable elements, DNA sequences that can replicate and relocate themselves at different chromosomal regions. These sequences, also known as ‘jumping genes’, comprise up to 50% of the genome, but it has proven challenging to map them with existing techniques. Seyoung Mun of Dankook University, Cheonan, South Korea, and coworkers have developed a sequencing and computational analysis strategy that allowed them to accurately map transposable elements across the genome of a Korean individual. These data revealed hundreds of insertion and deletion events relative to an existing reference map of the genome, showing significant alterations in the chromosomal structure. The authors speculate that such widespread transposition events could potentially contribute to individual differences in gene expression and risk of disease.

Targeted nanopore sequencing by real-time mapping of raw electrical signal with UNCALLED

Conventional targeted sequencing methods eliminate many of the benefits of nanopore sequencing, such as the ability to accurately detect structural variants or epigenetic modifications. The ReadUntil method allows nanopore devices to selectively eject reads from pores in real time, which could enable purely computational targeted sequencing. However, this requires rapid identification of on-target reads while most mapping methods require computationally intensive basecalling. We present UNCALLED ( https://github.com/skovaka/UNCALLED ), an open source mapper that rapidly matches streaming of nanopore current signals to a reference sequence. UNCALLED probabilistically considers k-mers that could be represented by the signal and then prunes the candidates based on the reference encoded within a Ferragina-Manzini index. We used UNCALLED to deplete sequencing of known bacterial genomes within a metagenomics community, enriching the remaining species 4.46-fold. UNCALLED also enriched 148 human genes associated with hereditary cancers to 29.6× coverage using one MinION flowcell, enabling accurate detection of single-nucleotide polymorphisms, insertions and deletions, structural variants and methylation in these genes.

Detecting Causal Variants in Mendelian Disorders Using Whole-Genome Sequencing

Increasingly affordable sequencing technologies are revolutionizing the field of genomic medicine. It is now feasible to interrogate all major classes of variation in an individual across the entire genome for less than $1000 USD. While the generation of patient sequence information using these technologies has become routine, the analysis and interpretation of this data remains the greatest obstacle to widespread clinical implementation. This chapter summarizes the steps to identify, annotate, and prioritize variant information required for clinical report generation. We discuss methods to detect each variant class and describe strategies to increase the likelihood of detecting causal variant(s) in Mendelian disease. Lastly, we describe a sample workflow for synthesizing large amount of genetic information into concise clinical reports.

ERVcaller: identifying polymorphic endogenous retrovirus and other transposable element insertions using whole-genome sequencing data

MOTIVATION

Approximately 8% of the human genome is derived from endogenous retroviruses (ERVs). In recent years, an increasing number of human diseases have been found to be associated with ERVs. However, it remains challenging to accurately detect the full spectrum of polymorphic (unfixed) ERVs using whole-genome sequencing (WGS) data.

RESULTS

We designed a new tool, ERVcaller, to detect and genotype transposable element (TE) insertions, including ERVs, in the human genome. We evaluated ERVcaller using both simulated and real benchmark WGS datasets. Compared to existing tools, ERVcaller consistently obtained both the highest sensitivity and precision for detecting simulated ERV and other TE insertions derived from real polymorphic TE sequences. For the WGS data from the 1000 Genomes Project, ERVcaller detected the largest number of TE insertions per sample based on consensus TE loci. By analyzing the experimentally verified TE insertions, ERVcaller had 94.0% TE detection sensitivity and 96.6% genotyping accuracy. Polymerase chain reaction and Sanger sequencing in a small sample set verified 86.7% of examined insertion statuses and 100% of examined genotypes. In conclusion, ERVcaller is capable of detecting and genotyping TE insertions using WGS data with both high sensitivity and precision. This tool can be applied broadly to other species.

AVAILABILITY AND IMPLEMENTATION

http://www.uvm.edu/genomics/software/ERVcaller.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

rMETL: sensitive mobile element insertion detection with long read realignment

SUMMARY

Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Herein, we propose the Realignment-based Mobile Element insertion detection Tool for Long read (rMETL). Benchmarking results of simulated and real datasets demonstrate that rMETL enables to handle the complex signals to discover MEIs sensitively. It is suited to produce high-quality MEI callsets in many genomics studies.

AVAILABILITY AND IMPLEMENTATION

rMETL is available from https://github.com/hitbc/rMETL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

AluMine: alignment-free method for the discovery of polymorphic Alu element insertions

Background

Recently, alignment-free sequence analysis methods have gained popularity in the field of personal genomics. These methods are based on counting frequencies of short k-mer sequences, thus allowing faster and more robust analysis compared to traditional alignment-based methods.

Results

We have created a fast alignment-free method, AluMine, to analyze polymorphic insertions of Alu elements in the human genome. We tested the method on 2,241 individuals from the Estonian Genome Project and identified 28,962 potential polymorphic Alu element insertions. Each tested individual had on average 1,574 Alu element insertions that were different from those in the reference genome. In addition, we propose an alignment-free genotyping method that uses the frequency of insertion/deletion-specific 32-mer pairs to call the genotype directly from raw sequencing reads. Using this method, the concordance between the predicted and experimentally observed genotypes was 98.7%. The running time of the discovery pipeline is approximately 2 h per individual. The genotyping of potential polymorphic insertions takes between 0.4 and 4 h per individual, depending on the hardware configuration.

Conclusions

AluMine provides tools that allow discovery of novel Alu element insertions and/or genotyping of known Alu element insertions from personal genomes within few hours.

Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing

BACKGROUND

Structural variations (SVs) or copy number variations (CNVs) greatly impact the functions of the genes encoded in the genome and are responsible for diverse human diseases. Although a number of existing SV detection algorithms can detect many types of SVs using whole genome sequencing (WGS) data, no single algorithm can call every type of SVs with high precision and high recall.

RESULTS

We comprehensively evaluate the performance of 69 existing SV detection algorithms using multiple simulated and real WGS datasets. The results highlight a subset of algorithms that accurately call SVs depending on specific types and size ranges of the SVs and that accurately determine breakpoints, sizes, and genotypes of the SVs. We enumerate potential good algorithms for each SV category, among which GRIDSS, Lumpy, SVseq2, SoftSV, Manta, and Wham are better algorithms in deletion or duplication categories. To improve the accuracy of SV calling, we systematically evaluate the accuracy of overlapping calls between possible combinations of algorithms for every type and size range of SVs. The results demonstrate that both the precision and recall for overlapping calls vary depending on the combinations of specific algorithms rather than the combinations of methods used in the algorithms.

CONCLUSION

These results suggest that careful selection of the algorithms for each type and size range of SVs is required for accurate calling of SVs. The selection of specific pairs of algorithms for overlapping calls promises to effectively improve the SV detection accuracy.

On the Population Dynamics of Junk: A Review on the Population Genomics of Transposable Elements

Transposable elements (TEs) play an important role in shaping genomic organization and structure, and may cause dramatic changes in phenotypes. Despite the genetic load they may impose on their host and their importance in microevolutionary processes such as adaptation and speciation, the number of population genetics studies focused on TEs has been rather limited so far compared to single nucleotide polymorphisms (SNPs). Here, we review the current knowledge about the dynamics of transposable elements at recent evolutionary time scales, and discuss the mechanisms that condition their abundance and frequency. We first discuss non-adaptive mechanisms such as purifying selection and the variable rates of transposition and elimination, and then focus on positive and balancing selection, to finally conclude on the potential role of TEs in causing genomic incompatibilities and eventually speciation. We also suggest possible ways to better model TEs dynamics in a population genomics context by incorporating recent advances in TEs into the rich information provided by SNPs about the demography, selection, and intrinsic properties of genomes.

iMGEins: detecting novel mobile genetic elements inserted in individual genomes

Background

Recent advances in sequencing technology have allowed us to investigate personal genomes to find structural variations, which have been studied extensively to identify their association with the physiology of diseases such as cancer. In particular, mobile genetic elements (MGEs) are one of the major constituents of the human genomes, and cause genome instability by insertion, mutation, and rearrangement.

Result

We have developed a new program, iMGEins, to identify such novel MGEs by using sequencing reads of individual genomes, and to explore the breakpoints with the supporting reads and MGEs detected. iMGEins is the first MGE detection program that integrates three algorithmic components: discordant read-pair mapping, split-read mapping, and insertion sequence assembly. Our evaluation results showed its outstanding performance in detecting novel MGEs from simulated genomes, as well as real personal genomes. In detail, the average recall and precision rates of iMGEins are 96.67 and 100%, respectively, which are the highest among the programs compared. In the testing with real human genomes of the NA12878 sample, iMGEins shows the highest accuracy in detecting MGEs within 20 bp proximity of the breakpoints annotated.

Conclusion

In order to study the dynamics of MGEs in individual genomes, iMGEins was developed to accurately detect breakpoints and report inserted MGEs. Compared with other programs, iMGEins has valuable features of identifying novel MGEs and assembling the MGEs inserted.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5290-9) contains supplementary material, which is available to authorized users.

Benchmarking computational tools for polymorphic transposable element detection

Transposable elements (TEs) are an important source of human genetic variation with demonstrable effects on phenotype. Recently, a number of computational methods for the detection of polymorphic TE (polyTE) insertion sites from next-generation sequence data have been developed. The use of such tools will become increasingly important as the pace of human genome sequencing accelerates. For this report, we performed a comparative benchmarking and validation analysis of polyTE detection tools in an effort to inform their selection and use by the TE research community. We analyzed a core set of seven tools with respect to ease of use and accessibility, polyTE detection performance and runtime parameters. An experimentally validated set of 893 human polyTE insertions was used for this purpose, along with a series of simulated data sets that allowed us to assess the impact of sequence coverage on tool performance. The recently developed tool MELT showed the best overall performance followed by Mobster and then RetroSeq. PolyTE detection tools can best detect Alu insertion events in the human genome with reduced reliability for L1 insertions and substantially lowered performance for SVA insertions. We also show evidence that different polyTE detection tools are complementary with respect to their ability to detect a complete set of insertion events. Accordingly, a combined approach, coupled with manual inspection of individual results, may yield the best overall performance. In addition to the benchmarking results, we also provide notes on tool installation and usage as well as suggestions for future polyTE detection algorithm development.

Impact of non-LTR retrotransposons in the differentiation and evolution of anatomically modern humans

Background

Transposable elements are biologically important components of eukaryote genomes. In particular, non-LTR retrotransposons (N-LTRrs) played a key role in shaping the human genome throughout evolution. In this study, we compared retrotransposon insertions differentially present in the genomes of Anatomically Modern Humans, Neanderthals, Denisovans and Chimpanzees, in order to assess the possible impact of retrotransposition in the differentiation of the human lineage.

Results

We first identified species-specific N-LTRrs and established their distribution in present day human populations. These analyses shortlisted a group of N-LTRr insertions that were found exclusively in Anatomically Modern Humans. These insertions are associated with an increase in the number of transcriptional/splicing variants of those genes they inserted in. The analysis of the functionality of genes containing human-specific N-LTRr insertions reflects changes that occurred during human evolution. In particular, the expression of genes containing the most recent N-LTRr insertions is enriched in the brain, especially in undifferentiated neurons, and these genes associate in networks related to neuron maturation and migration. Additionally, we identified candidate N-LTRr insertions that have likely produced new functional variants exclusive to modern humans, whose genomic loci show traces of positive selection.

Conclusions

Our results strongly suggest that N-LTRr impacted our differentiation as a species, most likely inducing an increase in neural complexity, and have been a constant source of genomic variability all throughout the evolution of the human lineage.

Electronic supplementary material

The online version of this article (10.1186/s13100-018-0133-4) contains supplementary material, which is available to authorized users.

The Role of Transposable Elements in Speciation

Understanding the phenotypic and molecular mechanisms that contribute to genetic diversity between and within species is fundamental in studying the evolution of species. In particular, identifying the interspecific differences that lead to the reduction or even cessation of gene flow between nascent species is one of the main goals of speciation genetic research. Transposable elements (TEs) are DNA sequences with the ability to move within genomes. TEs are ubiquitous throughout eukaryotic genomes and have been shown to alter regulatory networks, gene expression, and to rearrange genomes as a result of their transposition. However, no systematic effort has evaluated the role of TEs in speciation. We compiled the evidence for TEs as potential causes of reproductive isolation across a diversity of taxa. We find that TEs are often associated with hybrid defects that might preclude the fusion between species, but that the involvement of TEs in other barriers to gene flow different from postzygotic isolation is still relatively unknown. Finally, we list a series of guides and research avenues to disentangle the effects of TEs on the origin of new species.

Genome-wide somatic variant calling using localized colored de Bruijn graphs

Reliable detection of somatic variations is of critical importance in cancer research. Here we present Lancet, an accurate and sensitive somatic variant caller, which detects SNVs and indels by jointly analyzing reads from tumor and matched normal samples using colored de Bruijn graphs. We demonstrate, through extensive experimental comparison on synthetic and real whole-genome sequencing datasets, that Lancet has better accuracy, especially for indel detection, than widely used somatic callers, such as MuTect, MuTect2, LoFreq, Strelka, and Strelka2. Lancet features a reliable variant scoring system, which is essential for variant prioritization, and detects low-frequency mutations without sacrificing the sensitivity to call longer insertions and deletions empowered by the local-assembly engine. In addition to genome-wide analysis, Lancet allows inspection of somatic variants in graph space, which augments the traditional read alignment visualization to help confirm a variant of interest. Lancet is available as an open-source program at https://github.com/nygenome/lancet.

Giuseppe Narzisi et al. present Lancet, a genome-wide somatic variant caller using localized colored de Bruijn graphs. Comparisons using real and simulated data show that Lancet has improved accuracy for single nucleotide variants and indels compared to widely used methods MuTect2, LoFreq and Strelka2.

Transposable element activity, genome regulation and human health

A convergence of novel genome analysis technologies is enabling population genomic studies of human transposable elements (TEs). Population surveys of human genome sequences have uncovered thousands of individual TE insertions that segregate as common genetic variants, i.e. TE polymorphisms. These recent TE insertions provide an important source of naturally occurring human genetic variation. Investigators are beginning to leverage population genomic data sets to execute genome-scale association studies for assessing the phenotypic impact of human TE polymorphisms. For example, the expression quantitative trait loci (eQTL) analytical paradigm has recently been used to uncover hundreds of associations between human TE insertion variants and gene expression levels. These include population-specific gene regulatory effects as well as coordinated changes to gene regulatory networks. In addition, analyses of linkage disequilibrium patterns with previously characterized genome-wide association study (GWAS) trait variants have uncovered TE insertion polymorphisms that are likely causal variants for a variety of common complex diseases. Gene regulatory mechanisms that underlie specific disease phenotypes have been proposed for a number of these trait associated TE polymorphisms. These new population genomic approaches hold great promise for understanding how ongoing TE activity contributes to functionally relevant genetic variation within and between human populations.

The Mobile Element Locator Tool (MELT): population-scale mobile element discovery and biology

Mobile element insertions (MEIs) represent ∼25% of all structural variants in human genomes. Moreover, when they disrupt genes, MEIs can influence human traits and diseases. Therefore, MEIs should be fully discovered along with other forms of genetic variation in whole genome sequencing (WGS) projects involving population genetics, human diseases, and clinical genomics. Here, we describe the Mobile Element Locator Tool (MELT), which was developed as part of the 1000 Genomes Project to perform MEI discovery on a population scale. Using both Illumina WGS data and simulations, we demonstrate that MELT outperforms existing MEI discovery tools in terms of speed, scalability, specificity, and sensitivity, while also detecting a broader spectrum of MEI-associated features. Several run modes were developed to perform MEI discovery on local and cloud systems. In addition to using MELT to discover MEIs in modern humans as part of the 1000 Genomes Project, we also used it to discover MEIs in chimpanzees and ancient (Neanderthal and Denisovan) hominids. We detected diverse patterns of MEI stratification across these populations that likely were caused by (1) diverse rates of MEI production from source elements, (2) diverse patterns of MEI inheritance, and (3) the introgression of ancient MEIs into modern human genomes. Overall, our study provides the most comprehensive map of MEIs to date spanning chimpanzees, ancient hominids, and modern humans and reveals new aspects of MEI biology in these lineages. We also demonstrate that MELT is a robust platform for MEI discovery and analysis in a variety of experimental settings.

McClintock: An Integrated Pipeline for Detecting Transposable Element Insertions in Whole-Genome Shotgun Sequencing Data

Transposable element (TE) insertions are among the most challenging types of variants to detect in genomic data because of their repetitive nature and complex mechanisms of replication . Nevertheless, the recent availability of large resequencing data sets has spurred the development of many new methods to detect TE insertions in whole-genome shotgun sequences. Here we report an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote, Saccharomyces cerevisiae We find substantial variation among McClintock component methods in their ability to detect nonreference TEs in the yeast genome, but show that nonreference TEs at nearly all biologically realistic locations can be detected in simulated data by combining multiple methods that use split-read and read-pair evidence. In general, our results reveal that split-read methods detect fewer nonreference TE insertions than read-pair methods, but generally have much higher positional accuracy. Analysis of a large sample of real yeast genomes reveals that most McClintock component methods can recover known aspects of TE biology in yeast such as the transpositional activity status of families, target preferences, and target site duplication structure, albeit with varying levels of accuracy. Our work provides a general framework for integrating and analyzing results from multiple TE detection methods, as well as useful guidance for researchers studying TEs in yeast resequencing data.

Discovery of rare, diagnostic AluYb8/9 elements in diverse human populations

BACKGROUND

Polymorphic human Alu elements are excellent tools for assessing population structure, and new retrotransposition events can contribute to disease. Next-generation sequencing has greatly increased the potential to discover Alu elements in human populations, and various sequencing and bioinformatics methods have been designed to tackle the problem of detecting these highly repetitive elements. However, current techniques for Alu discovery may miss rare, polymorphic Alu elements. Combining multiple discovery approaches may provide a better profile of the polymorphic Alu mobilome. AluYb8/9 elements have been a focus of our recent studies as they are young subfamilies (~2.3 million years old) that contribute ~30% of recent polymorphic Alu retrotransposition events. Here, we update our ME-Scan methods for detecting Alu elements and apply these methods to discover new insertions in a large set of individuals with diverse ancestral backgrounds.

RESULTS

We identified 5,288 putative Alu insertion events, including several hundred novel AluYb8/9 elements from 213 individuals from 18 diverse human populations. Hundreds of these loci were specific to continental populations, and 23 non-reference population-specific loci were validated by PCR. We provide high-quality sequence information for 68 rare AluYb8/9 elements, of which 11 have hallmarks of an active source element. Our subfamily distribution of rare AluYb8/9 elements is consistent with previous datasets, and may be representative of rare loci. We also find that while ME-Scan and low-coverage, whole-genome sequencing (WGS) detect different Alu elements in 41 1000 Genomes individuals, the two methods yield similar population structure results.

CONCLUSION

Current in-silico methods for Alu discovery may miss rare, polymorphic Alu elements. Therefore, using multiple techniques can provide a more accurate profile of Alu elements in individuals and populations. We improved our false-negative rate as an indicator of sample quality for future ME-Scan experiments. In conclusion, we demonstrate that ME-Scan is a good supplement for next-generation sequencing methods and is well-suited for population-level analyses.

STEAK: A specific tool for transposable elements and retrovirus detection in high-throughput sequencing data

The advancements of high-throughput genomics have unveiled much about the human genome highlighting the importance of variations between individuals and their contribution to disease. Even though numerous software have been developed to make sense of large genomics datasets, a major short falling of these has been the inability to cope with repetitive regions, specifically to validate structural variants and accordingly assess their role in disease. Here we describe our program STEAK, a massively parallel software designed to detect chimeric reads in high-throughput sequencing data for a broad number of applications such as identifying presence/absence, as well as discovery of transposable elements (TEs), and retroviral integrations. We highlight the capabilities of STEAK by comparing its efficacy in locating HERV-K HML-2 in clinical whole genome projects, target enrichment sequences, and in the 1000 Genomes CEU Trio to the performance of other TE and virus detecting tools. We show that STEAK outperforms other software in terms of computational efficiency, sensitivity, and specificity. We demonstrate that STEAK is a robust tool, which allows analysts to flexibly detect and evaluate TE and retroviral integrations in a diverse range of sequencing projects for both research and clinical purposes.

Population and clinical genetics of human transposable elements in the (post) genomic era

Recent technological developments-in genomics, bioinformatics and high-throughput experimental techniques-are providing opportunities to study ongoing human transposable element (TE) activity at an unprecedented level of detail. It is now possible to characterize genome-wide collections of TE insertion sites for multiple human individuals, within and between populations, and for a variety of tissue types. Comparison of TE insertion site profiles between individuals captures the germline activity of TEs and reveals insertion site variants that segregate as polymorphisms among human populations, whereas comparison among tissue types ascertains somatic TE activity that generates cellular heterogeneity. In this review, we provide an overview of these new technologies and explore their implications for population and clinical genetic studies of human TEs. We cover both recent published results on human TE insertion activity as well as the prospects for future TE studies related to human evolution and health.

Study of Transposable Elements and Their Genomic Impact

Transposable elements (TEs) have been considered traditionally as junk DNA, i.e., DNA sequences that despite representing a high proportion of genomes had no evident cellular functions. However, over the last decades, it has become undeniable that not only TE-derived DNA sequences have (and had) a fundamental role during genome evolution, but also TEs have important implications in the origin and evolution of many genomic disorders. This concise review provides a brief overview of the different types of TEs that can be found in genomes, as well as a list of techniques and methods used to study their impact and mobilization. Some of these techniques will be covered in detail in this Method Book.

Discovery of an expanded set of avian leukosis subgroup E proviruses in chickens using Vermillion, a novel sequence capture and analysis pipeline [corrected]

Transposable elements (TEs), such as endogenous retroviruses (ERVs), are common in the genomes of vertebrates. ERVs result from retroviral infections of germ-line cells, and once integrated into host DNA they become part of the host's heritable genetic material. ERVs have been ascribed positive effects on host physiology such as the generation of novel, adaptive genetic variation and resistance to infection, as well as negative effects as agents of tumorigenesis and disease. The avian leukosis virus subgroup E family (ALVE) of endogenous viruses of chickens has been used as a model system for studying the effects of ERVs on host physiology, and approximately 30 distinct ALVE proviruses have been described in the Gallus gallus genome. In this report we describe the development of a software tool, which we call Vermillion, and the use of this tool in combination with targeted next-generation sequencing (NGS) to increase the number of known proviruses belonging to the ALVE family of ERVs in the chicken genome by 4-fold, including expanding the number of known ALVE elements on chromosome 1 (Gga1) from the current 9 to a total of 40. Although we focused on the discovery of ALVE elements in chickens, with appropriate selection of target sequences Vermillion can be used to develop profiles of other families of ERVs and TEs in chickens as well as in species other than the chicken.

LINE1 insertions as a genomic risk factor for schizophrenia: Preliminary evidence from an affected family

Recent studies show that human-specific LINE1s (L1HS) play a key role in the development of the central nervous system (CNS) and its disorders, and that their transpositions within the human genome are more common than previously thought. Many polymorphic L1HS, that is, present or absent across individuals, are not annotated in the current release of the genome and are customarily termed "non-reference L1s." We developed an analytical workflow to identify L1 polymorphic insertions with next-generation sequencing (NGS) using data from a family in which SZ segregates. Our workflow exploits two independent algorithms to detect non-reference L1 insertions, performs local de novo alignment of the regions harboring predicted L1 insertions and resolves the L1 subfamily designation from the de novo assembled sequence. We found 110 non-reference L1 polymorphic loci exhibiting Mendelian inheritance, the vast majority of which are already reported in dbRIP and/or euL1db, thus, confirming their status as non-reference L1 polymorphic insertions. Four previously undetected L1 polymorphic loci were confirmed by PCR amplification and direct sequencing of the insert. A large fraction of our non-reference L1s is located within the open reading frame of protein-coding genes that belong to pathways already implicated in the pathogenesis of schizophrenia. The finding of these polymorphic variants among SZ offsprings is intriguing and suggestive of putative pathogenic role. Our data show the utility of NGS to uncover L1 polymorphic insertions, a neglected type of genetic variants with the potential to influence the risk to develop schizophrenia like SNVs and CNVs. © 2016 Wiley Periodicals, Inc.

Next-generation sequencing-based detection of germline L1-mediated transductions

Background

While active LINE-1 (L1) elements possess the ability to mobilize flanking sequences to different genomic loci through a process termed transduction influencing genomic content and structure, an approach for detecting polymorphic germline non-reference transductions in massively-parallel sequencing data has been lacking.

Results

Here we present the computational approach TIGER (Transduction Inference in GERmline genomes), enabling the discovery of non-reference L1-mediated transductions by combining L1 discovery with detection of unique insertion sequences and detailed characterization of insertion sites. We employed TIGER to characterize polymorphic transductions in fifteen genomes from non-human primate species (chimpanzee, orangutan and rhesus macaque), as well as in a human genome. We achieved high accuracy as confirmed by PCR and two single molecule DNA sequencing techniques, and uncovered differences in relative rates of transduction between primate species.

Conclusions

By enabling detection of polymorphic transductions, TIGER makes this form of relevant structural variation amenable for population and personal genome analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2670-x) contains supplementary material, which is available to authorized users.

Genome ARTIST: a robust, high-accuracy aligner tool for mapping transposon insertions and self-insertions

Background

A critical topic of insertional mutagenesis experiments performed on model organisms is mapping the hits of artificial transposons (ATs) at nucleotide level accuracy. Mapping errors may occur when sequencing artifacts or mutations as single nucleotide polymorphisms (SNPs) and small indels are present very close to the junction between a genomic sequence and a transposon inverted repeat (TIR). Another particular item of insertional mutagenesis is mapping of the transposon self-insertions and, to our best knowledge, there is no publicly available mapping tool designed to analyze such molecular events.

Results

We developed Genome ARTIST, a pairwise gapped aligner tool which works out both issues by means of an original, robust mapping strategy. Genome ARTIST is not designed to use next-generation sequencing (NGS) data but to analyze ATs insertions obtained in small to medium-scale mutagenesis experiments. Genome ARTIST employs a heuristic approach to find DNA sequence similarities and harnesses a multi-step implementation of a Smith-Waterman adapted algorithm to compute the mapping alignments. The experience is enhanced by easily customizable parameters and a user-friendly interface that describes the genomic landscape surrounding the insertion. Genome ARTIST is functional with many genomes of bacteria and eukaryotes available in Ensembl and GenBank repositories. Our tool specifically harnesses the sequence annotation data provided by FlyBase for Drosophila melanogaster (the fruit fly), which enables mapping of insertions relative to various genomic features such as natural transposons. Genome ARTIST was tested against other alignment tools using relevant query sequences derived from the D. melanogaster and Mus musculus (mouse) genomes. Real and simulated query sequences were also comparatively inquired, revealing that Genome ARTIST is a very robust solution for mapping transposon insertions.

Conclusions

Genome ARTIST is a stand-alone user-friendly application, designed for high-accuracy mapping of transposon insertions and self-insertions. The tool is also useful for routine aligning assessments like detection of SNPs or checking the specificity of primers and probes. Genome ARTIST is an open source software and is available for download at www.genomeartist.ro and at GitHub (https://github.com/genomeartist/genomeartist ).

Electronic supplementary material

The online version of this article (doi:10.1186/s13100-016-0061-0) contains supplementary material, which is available to authorized users.

Transposable element detection from whole genome sequence data

The number of software tools available for detecting transposable element insertions from whole genome sequence data has been increasing steadily throughout the last ~5 years. Some of these methods have unique features suiting them for particular use cases, but in general they follow one or more of a common set of approaches. Here, detection and filtering approaches are reviewed in the light of transposable element biology and the current state of whole genome sequencing. We demonstrate that the current state-of-the-art methods still do not produce highly concordant results and provide resources to assist future development in transposable element detection methods.

Detecting dispersed duplications in high-throughput sequencing data using a database-free approach

MOTIVATION

Dispersed duplications (DDs) such as transposon element insertions and copy number variations are ubiquitous in the human genome. They have attracted the interest of biologists as well as medical researchers due to their role in both evolution and disease. The efforts of discovering DDs in high-throughput sequencing data are currently dominated by database-oriented approaches that require pre-existing knowledge of the DD elements to be detected.

RESULTS

We present DD_DETECTION, a database-free approach to finding DD events in high-throughput sequencing data. DD_DETECTION is able to detect DDs purely from paired-end read alignments. We show in a comparative study that this method is able to compete with database-oriented approaches in recovering validated transposon insertion events. We also experimentally validate the predictions of DD_DETECTION on a human DNA sample, showing that it can find not only duplicated elements present in common databases but also DDs of novel type.

AVAILABILITY AND IMPLEMENTATION

The software presented in this article is open source and available from https://bitbucket.org/mkroon/dd_detection.

Mobster: accurate detection of mobile element insertions in next generation sequencing data

Mobile elements are major drivers in changing genomic architecture and can cause disease. The detection of mobile elements is hindered due to the low mappability of their highly repetitive sequences. We have developed an algorithm, called Mobster, to detect non-reference mobile element insertions in next generation sequencing data from both whole genome and whole exome studies. Mobster uses discordant read pairs and clipped reads in combination with consensus sequences of known active mobile elements. Mobster has a low false discovery rate and high recall rate for both L1 and Alu elements. Mobster is available at http://sourceforge.net/projects/mobster.

Discovery and characterization of Alu repeat sequences via precise local read assembly

Alu insertions have contributed to >11% of the human genome and ∼30–35 Alu subfamilies remain actively mobile, yet the characterization of polymorphic Alu insertions from short-read data remains a challenge. We build on existing computational methods to combine Alu detection and de novo assembly of WGS data as a means to reconstruct the full sequence of insertion events from Illumina paired end reads. Comparison with published calls obtained using PacBio long-reads indicates a false discovery rate below 5%, at the cost of reduced sensitivity due to the colocation of reference and non-reference repeats. We generate a highly accurate call set of 1614 completely assembled Alu variants from 53 samples from the Human Genome Diversity Project (HGDP) panel. We utilize the reconstructed alternative insertion haplotypes to genotype 1010 fully assembled insertions, obtaining >99% agreement with genotypes obtained by PCR. In our assembled sequences, we find evidence of premature insertion mechanisms and observe 5′ truncation in 16% of AluYa5 and AluYb8 insertions. The sites of truncation coincide with stem-loop structures and SRP9/14 binding sites in the Alu RNA, implicating L1 ORF2p pausing in the generation of 5′ truncations. Additionally, we identified variable AluJ and AluS elements that likely arose due to non-retrotransposition mechanisms.

PopAlu: population-scale detection of Alu polymorphisms

Alu elements are sequences of approximately 300 basepairs that together comprise more than 10% of the human genome. Due to their recent origin in primate evolution some Alu elements are polymorphic in humans, present in some individuals while absent in others. We present PopAlu, a tool to detect polymorphic Alu elements on a population scale from paired-end sequencing data. PopAlu uses read pair distance and orientation as well as split reads to identify the location and precise breakpoints of polymorphic Alus. Genotype calling enables us to differentiate between homozygous and heterozygous carriers, making the output of PopAlu suitable for use in downstream analyses such as genome-wide association studies (GWAS). We show on a simulated dataset that PopAlu calls Alu elements inserted and deleted with respect to a reference genome with high accuracy and high precision. Our analysis of real data of a human trio from the 1000 Genomes Project confirms that PopAlu is able to produce highly accurate genotype calls. To our knowledge, PopAlu is the first tool that identifies polymorphic Alu elements from multiple individuals simultaneously, pinpoints the precise breakpoints and calls genotypes with high accuracy.

Detection of Genomic Structural Variants from Next-Generation Sequencing Data

Structural variants are genomic rearrangements larger than 50 bp accounting for around 1% of the variation among human genomes. They impact on phenotypic diversity and play a role in various diseases including neurological/neurocognitive disorders and cancer development and progression. Dissecting structural variants from next-generation sequencing data presents several challenges and a number of approaches have been proposed in the literature. In this mini review, we describe and summarize the latest tools – and their underlying algorithms – designed for the analysis of whole-genome sequencing, whole-exome sequencing, custom captures, and amplicon sequencing data, pointing out the major advantages/drawbacks. We also report a summary of the most recent applications of third-generation sequencing platforms. This assessment provides a guided indication – with particular emphasis on human genetics and copy number variants – for researchers involved in the investigation of these genomic events.

Genome-wide amplification of proviral sequences reveals new polymorphic HERV-K(HML-2) proviruses in humans and chimpanzees that are absent from genome assemblies

BACKGROUND

To date, the human population census of proviruses of the Betaretrovirus-like human endogenous retroviral (HERV-K) (HML-2) family has been compiled from a limited number of complete genomes, making it certain that rare polymorphic loci are under-represented and are yet to be described.

RESULTS

Here we describe a suppression PCR-based method called genome-wide amplification of proviral sequences (GAPS) that selectively amplifies DNA fragments containing the termini of HERV-K(HML-2) proviral sequences and their flanking genomic sequences. We analysed the HERV-K(HML-2) proviral content of 101 unrelated humans, 4 common chimpanzees and three centre d'etude du polymorphisme humain (CEPH) pedigrees (44 individuals). The technique isolated HERV-K(HML-2) proviruses that had integrated in the genomes of the great apes throughout their divergence and included evolutionarily young elements still unfixed for presence/absence.

CONCLUSIONS

By examining the HERV-K(HML-2) proviral content of 145 humans we detected a new insertionally polymorphic Type I HERV-K(HML-2) provirus. We also observed provirus versus solo long terminal repeat (LTR) polymorphism within humans at a previously unreported, but ancient, locus. Finally, we report two novel chimpanzee specific proviruses, one of which is dimorphic for a provirus versus solo LTR. Thus GAPS enables the isolation of uncharacterised HERV-K(HML-2) proviral sequences and provides a direct means to assess inter-individual genetic variation associated with HERV-K(HML-2) proviruses.

T-lex2: genotyping, frequency estimation and re-annotation of transposable elements using single or pooled next-generation sequencing data

Transposable elements (TEs) constitute the most active, diverse and ancient component in a broad range of genomes. Complete understanding of genome function and evolution cannot be achieved without a thorough understanding of TE impact and biology. However, in-depth analysis of TEs still represents a challenge due to the repetitive nature of these genomic entities. In this work, we present a broadly applicable and flexible tool: T-lex2. T-lex2 is the only available software that allows routine, automatic and accurate genotyping of individual TE insertions and estimation of their population frequencies both using individual strain and pooled next-generation sequencing data. Furthermore, T-lex2 also assesses the quality of the calls allowing the identification of miss-annotated TEs and providing the necessary information to re-annotate them. The flexible and customizable design of T-lex2 allows running it in any genome and for any type of TE insertion. Here, we tested the fidelity of T-lex2 using the fly and human genomes. Overall, T-lex2 represents a significant improvement in our ability to analyze the contribution of TEs to genome function and evolution as well as learning about the biology of TEs. T-lex2 is freely available online at http://sourceforge.net/projects/tlex.

Making the difference: integrating structural variation detection tools

From prokaryotes to eukaryotes, phenotypic variation, adaptation and speciation has been associated with structural variation between genomes of individuals within the same species. Many computer algorithms detecting such variations (callers) have recently been developed, spurred by the advent of the next-generation sequencing technology. Such callers mainly exploit split-read mapping or paired-end read mapping. However, as different callers are geared towards different types of structural variation, there is still no single caller that can be considered a community standard; instead, increasingly the various callers are combined in integrated pipelines. In this article, we review a wide range of callers, discuss challenges in the integration step and present a survey of pipelines used in population genomics studies. Based on our findings, we provide general recommendations on how to set-up such pipelines. Finally, we present an outlook on future challenges in structural variation detection.