Detecting Alu insertions from high-throughput sequencing data

An Auto-Reading probe system for detecting deletion mutations In liquid biopsy with direct quantification of mutation abundance

Background

Deletion mutations have been confirmed to be closely related to the occurrence and progression of different hereditary diseases and tumors. Specifically, the deletion of a small number of bases is more challenging to be captured and differentiated. In non-invasive prenatal testing (NIPT) and liquid biopsy targeting circulating tumor DNA, obtaining accurate mutation abundance in targeted DNA is a crucial step in the detection process. However, the quantification of mutation abundance with existing methods is not accurate enough.

Results

Herein, we developed the " Auto-Reading" probe detection system based on our previous work. Through theoretical modeling and experimental calculations, we verified the successful application of our system in NIPT and early cancer diagnosis, enabling effective discrimination of different mutant abundances.

Significance

Our method overcomes the interference of reaction concentrations on signal detection, allowing direct quantification of mutation abundance without the need for purification of PCR products. The detection system is cost-effective and feasible for laboratory use. We believe the system will facilitate broad applications in mutation detection.

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.

Evaluation of domain of unknown function 1220 (DUF1220) for detection of human genome by quantitative polymerase chain reaction: Potential use in assessing the biodistribution of transplanted therapeutic human cells

The biodistribution profile of cell-based therapy products in animal models is important for evaluation of their safety and efficacy. Because of its quantitative nature and sensitivity, the quantitative polymerase chain reaction (qPCR) is a useful method for detecting and quantifying xenogeneic cell-derived DNA in animal models, thereby allowing a biodistribution profile to be established. Although the restriction endonuclease family from Arthrobacter luteus (Alu) of repetitive elements in human genome sequences has been used to assess the biodistribution of human cells, high background signals are detected. In the present study, we evaluate the potential of domain of unknown function 1220 (DUF1220), which is a human lineage-specific, multiple-copy gene similar to Alu sequences, for such analysis. Using qPCR analysis for DUF1220, human genome could be detected against a mouse genome background at a level comparable to that of Alu sequences with no detectable background signals. Moreover, using this approach, the human genome could be distinguished from the cynomolgus monkey genome. Further investigation of the quantitative aspects of this DUF1220-based qPCR assay might prove its usefulness for biodistribution studies of human cells transplanted into animals in the future.

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.

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.

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.

A polymorphic Alu insertion that mediates distinct disease-associated deletions

Large deletions that are associated with insertions of Alu-derived sequence represent a rare, but potentially unique class of alterations. Whether they form by a one-step mechanism or by a primary insertion step followed by an independent secondary deletion step is not clear. We resolved two disease-associated SPAST deletions, which involve distinct exons by long range PCR. Alu-derived sequence was observed between the breakpoints in both cases. The intronic regions that represent the targets of potentially involved Alu retrotransposition events overlapped. Microsatellite- and SNP-based haplotyping indicated that both deletions originated on one and the same founder allele. Our data suggest that the deletions are best explained by two-step insertion-deletion scenarios for which a single Alu retrotransposition event represents the shared primary step. This Alu then mediated one of the deletions by non-homologous end joining and the other by non-allelic homologous recombination. Our findings thus strongly argue for temporal separation of insertion and deletion in Alu insertion-associated deletions. They also suggest that certain Alu integrations confer a general increase in local genomic instability, and that this explains why they are usually not detected during the probably short time that precedes the rearrangements they mediate.

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.

The Wukong Terminal-Repeat Retrotransposon in Miniature (TRIM) Elements in Diverse Maize Germplasm

TRIMs (terminal-repeat retrotransposons in miniature), which are characterized by their small size, have been discovered in all investigated vascular plants and even in animals. Here, we identified a highly conservative TRIM family referred to as Wukong elements in the maize genome. The Wukong family shows a distinct pattern of tandem arrangement in the maize genome suggesting a high rate of unequal crossing over. Estimation of insertion times implies a burst of retrotransposition activity of the Wukong family after the allotetraploidization of maize. Using next-generation sequencing data, we detected 87 new Wukong insertions in parents of the maize NAM population relative to the B73 reference genome and found abundant insertion polymorphism of Wukong elements in 75 re-sequenced maize lines, including teosinte, landraces, and improved lines. These results suggest that Wukong elements possessed a persistent retrotransposition activity throughout maize evolution. Moreover, the phylogenetic relationships among 76 maize inbreds and their relatives based on insertion polymorphisms of Wukong elements should provide us with reliable molecular markers for biodiversity and genetics studies.

Assembly and characterization of novel Alu inserts detected from next-generation sequencing data

Repetitive elements generally, and Alu inserts specifically are a large contributor to the recent evolution of the human genome. By assembling the sequences of novel Alu inserts using their respective subfamily consensus sequences as references, we found an exponential decay in the Alu subfamily call enrichment with increased number of sequence variants (Pearson correlation [Formula: see text], [Formula: see text]). By mapping the sequences of these inserts to a human reference genome, we infer the reference Alu sources of a subset of the novel Alus, of which 85% were previously shown to be active. We also evaluate relationships between the loci of the novel inserts and their inferred sources.

MOSAIK: a hash-based algorithm for accurate next-generation sequencing short-read mapping

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery. All variant discovery benefits from an accurate description of the read placement confidence. To this end, MOSAIK uses a neural-network based training scheme to provide well-calibrated mapping quality scores, demonstrated by a correlation coefficient between MOSAIK assigned and actual mapping qualities greater than 0.98. In order to ensure that studies of any genome are supported, a training pipeline is provided to ensure optimal mapping quality scores for the genome under investigation. MOSAIK is multi-threaded, open source, and incorporated into our command and pipeline launcher system GKNO (http://gkno.me).

MOSAIK: a hash-based algorithm for accurate next-generation sequencing short-read mapping

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery. All variant discovery benefits from an accurate description of the read placement confidence. To this end, MOSAIK uses a neural-network based training scheme to provide well-calibrated mapping quality scores, demonstrated by a correlation coefficient between MOSAIK assigned and actual mapping qualities greater than 0.98. In order to ensure that studies of any genome are supported, a training pipeline is provided to ensure optimal mapping quality scores for the genome under investigation. MOSAIK is multi-threaded, open source, and incorporated into our command and pipeline launcher system GKNO (http://gkno.me).