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Asset Tag: Variant detection

July 7, 2019  |  

STR-realigner: a realignment method for short tandem repeat regions.

In the estimation of repeat numbers in a short tandem repeat (STR) region from high-throughput sequencing data, two types of strategies are mainly taken: a strategy based on counting repeat patterns included in sequence reads spanning the region and a strategy based on estimating the difference between the actual insert size and the insert size inferred from paired-end reads. The quality of sequence alignment is crucial, especially in the former approaches although usual alignment methods have difficulty in STR regions due to insertions and deletions caused by the variations of repeat numbers.We proposed a new dynamic programming based realignment method named STR-realigner that considers repeat patterns in STR regions as prior knowledge. By allowing the size change of repeat patterns with low penalty in STR regions, accurate realignment is expected. For the performance evaluation, publicly available STR variant calling tools were applied to three types of aligned reads: synthetically generated sequencing reads aligned with BWA-MEM, those realigned with STR-realigner, those realigned with ReviSTER, and those realigned with GATK IndelRealigner. From the comparison of root mean squared errors between estimated and true STR region size, the results for the dataset realigned with STR-realigner are better than those for other cases. For real data analysis, we used a real sequencing dataset from Illumina HiSeq 2000 for a parent-offspring trio. RepeatSeq and lobSTR were applied to the sequence reads for these individuals aligned with BWA-MEM, those realigned with STR-realigner, ReviSTER, and GATK IndelRealigner. STR-realigner shows the best performance in terms of consistency of the size of estimated STR regions in Mendelian inheritance. Root mean squared error values were also calculated from the comparison of these estimated results with STR region sizes obtained from high coverage PacBio sequencing data, and the results from the realigned sequencing data with STR-realigner showed the least (the best) root mean squared error value.The effectiveness of the proposed realignment method for STR regions was verified from the comparison with an existing method on both simulation datasets and real whole genome sequencing dataset.

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July 7, 2019  |  

SRinversion: a tool for detecting short inversions by splitting and re-aligning poorly mapped and unmapped sequencing reads.

Rapid development in sequencing technologies has dramatically improved our ability to detect genetic variants in human genome. However, current methods have variable sensitivities in detecting different types of genetic variants. One type of such genetic variants that is especially hard to detect is inversions. Analysis of public databases showed that few short inversions have been reported so far. Unlike reads that contain small insertions or deletions, which will be considered through gap alignment, reads carrying short inversions often have poor mapping quality or are unmapped, thus are often not further considered. As a result, the majority of short inversions might have been overlooked and require special algorithms for their detection.Here, we introduce SRinversion, a framework to analyze poorly mapped or unmapped reads by splitting and re-aligning them for the purpose of inversion detection. SRinversion is very sensitive to small inversions and can detect those less than 10?bp in size. We applied SRinversion to both simulated data and high-coverage sequencing data from the 1000 Genomes Project and compared the results with those from Pindel, BreakDancer, DELLY, Gustaf and MID. A better performance of SRinversion was achieved for both datasets for the detection of small inversions.SRinversion is implemented in Perl and is publicly available at http://paed.hku.hk/genome/software/SRinversion/index.html CONTACT: yangwl@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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July 7, 2019  |  

svclassify: a method to establish benchmark structural variant calls.

The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). High-quality benchmark small variant calls for the pilot National Institute of Standards and Technology (NIST) Reference Material (NA12878) have been developed by the Genome in a Bottle Consortium, but no similar high-quality benchmark SV calls exist for this genome. Since SV callers output highly discordant results, we developed methods to combine multiple forms of evidence from multiple sequencing technologies to classify candidate SVs into likely true or false positives. Our method (svclassify) calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate SVs as likely true or false positives.We first used pedigree analysis to develop a set of high-confidence breakpoint-resolved large deletions. We then used svclassify to cluster and classify these deletions as well as a set of high-confidence deletions from the 1000 Genomes Project and a set of breakpoint-resolved complex insertions from Spiral Genetics. We find that likely SVs cluster separately from likely non-SVs based on our annotations, and that the SVs cluster into different types of deletions. We then developed a supervised one-class classification method that uses a training set of random non-SV regions to determine whether candidate SVs have abnormal annotations different from most of the genome. To test this classification method, we use our pedigree-based breakpoint-resolved SVs, SVs validated by the 1000 Genomes Project, and assembly-based breakpoint-resolved insertions, along with semi-automated visualization using svviz.We find that candidate SVs with high scores from multiple technologies have high concordance with PCR validation and an orthogonal consensus method MetaSV (99.7 % concordant), and candidate SVs with low scores are questionable. We distribute a set of 2676 high-confidence deletions and 68 high-confidence insertions with high svclassify scores from these call sets for benchmarking SV callers. We expect these methods to be particularly useful for establishing high-confidence SV calls for benchmark samples that have been characterized by multiple technologies.

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July 7, 2019  |  

Collection and storage of HLA NGS genotyping data for the 17th International HLA and Immunogenetics Workshop.

For over 50?years, the International HLA and Immunogenetics Workshops (IHIW) have advanced the fields of histocompatibility and immunogenetics (H&I) via community sharing of technology, experience and reagents, and the establishment of ongoing collaborative projects. Held in the fall of 2017, the 17th IHIW focused on the application of next generation sequencing (NGS) technologies for clinical and research goals in the H&I fields. NGS technologies have the potential to allow dramatic insights and advances in these fields, but the scope and sheer quantity of data associated with NGS raise challenges for their analysis, collection, exchange and storage. The 17th IHIW adopted a centralized approach to these issues, and we developed the tools, services and systems to create an effective system for capturing and managing these NGS data. We worked with NGS platform and software developers to define a set of distinct but equivalent NGS typing reports that record NGS data in a uniform fashion. The 17th IHIW database applied our standards, tools and services to collect, validate and store those structured, multi-platform data in an automated fashion. We have created community resources to enable exploration of the vast store of curated sequence and allele-name data in the IPD-IMGT/HLA Database, with the goal of creating a long-term community resource that integrates these curated data with new NGS sequence and polymorphism data, for advanced analyses and applications. Copyright © 2017 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

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July 7, 2019  |  

A comprehensive model of DNA fragmentation for the preservation of High Molecular Weight DNA

During DNA extraction the DNA molecule undergoes physical and chemical shearing, causing the DNA to fragment into shorter and shorter pieces. Under common laboratory conditions this fragmentation yields DNA fragments of 5-35 kilobases (kb) in length. This fragment length is more than sufficient for DNA sequencing using short-read technologies which generate reads 50-600 bp in length, but insufficient for long-read sequencing and linked reads where fragment lengths of more than 40 kb may be desirable. This study provides a theoretical framework for quality management to ensure access to high molecular weight DNA in samples. Shearing can be divided into physical and chemical shearing which generate different patterns of fragmentation. Exposure to physical shearing creates a characteristic fragment length where DNA fragments are cut in half by shear stress. This characteristic length can be measured using gel electrophoresis or instruments for DNA fragment analysis. Chemical shearing generates randomly distributed fragment lengths visible as a smear of DNA below the peak fragment length. By measuring the peak of DNA fragment length and the proportion of very short DNA fragments both sources of shearing can be measured using commonly used laboratory techniques, providing a suitable quantification of DNA integrity of DNA for sequencing with long-read technologies.

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July 7, 2019  |  

Supergene evolution triggered by the introgression of a chromosomal inversion.

Supergenes are groups of tightly linked loci whose variation is inherited as a single Mendelian locus and are a common genetic architecture for complex traits under balancing selection [1-8]. Supergene alleles are long-range haplotypes with numerous mutations underlying distinct adaptive strategies, often maintained in linkage disequilibrium through the suppression of recombination by chromosomal rearrangements [1, 5, 7-9]. However, the mechanism governing the formation of supergenes is not well understood and poses the paradox of establishing divergent functional haplotypes in the face of recombination. Here, we show that the formation of the supergene alleles encoding mimicry polymorphism in the butterfly Heliconius numata is associated with the introgression of a divergent, inverted chromosomal segment. Haplotype divergence and linkage disequilibrium indicate that supergene alleles, each allowing precise wing-pattern resemblance to distinct butterfly models, originate from over a million years of independent chromosomal evolution in separate lineages. These “superalleles” have evolved from a chromosomal inversion captured by introgression and maintained in balanced polymorphism, triggering supergene inheritance. This mode of evolution involving the introgression of a chromosomal rearrangement is likely to be a common feature of complex structural polymorphisms associated with the coexistence of distinct adaptive syndromes. This shows that the reticulation of genealogies may have a powerful influence on the evolution of genetic architectures in nature. Copyright © 2018 Elsevier Ltd. All rights reserved.

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July 7, 2019  |  

FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods.

Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE .

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July 7, 2019  |  

GtTR: Bayesian estimation of absolute tandem repeat copy number using sequence capture and high throughput sequencing.

Tandem repeats comprise significant proportion of the human genome including coding and regulatory regions. They are highly prone to repeat number variation and nucleotide mutation due to their repetitive and unstable nature, making them a major source of genomic variation between individuals. Despite recent advances in high throughput sequencing, analysis of tandem repeats in the context of complex diseases is still hindered by technical limitations. We report a novel targeted sequencing approach, which allows simultaneous analysis of hundreds of repeats. We developed a Bayesian algorithm, namely – GtTR – which combines information from a reference long-read dataset with a short read counting approach to genotype tandem repeats at population scale. PCR sizing analysis was used for validation.We used a PacBio long-read sequenced sample to generate a reference tandem repeat genotype dataset with on average 13% absolute deviation from PCR sizing results. Using this reference dataset GtTR generated estimates of VNTR copy number with accuracy within 95% high posterior density (HPD) intervals of 68 and 83% for capture sequence data and 200X WGS data respectively, improving to 87 and 94% with use of a PCR reference. We show that the genotype resolution increases as a function of depth, such that the median 95% HPD interval lies within 25, 14, 12 and 8% of the its midpoint copy number value for 30X, 200X WGS, 395X and 800X capture sequence data respectively. We validated nine targets by PCR sizing analysis and genotype estimates from sequencing results correlated well with PCR results.The novel genotyping approach described here presents a new cost-effective method to explore previously unrecognized class of repeat variation in GWAS studies of complex diseases at the population level. Further improvements in accuracy can be obtained by improving accuracy of the reference dataset.

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July 7, 2019  |  

Meeting report: mobile genetic elements and genome plasticity 2018

The Mobile Genetic Elements and Genome Plasticity conference was hosted by Keystone Symposia in Santa Fe, NM USA, February 11–15, 2018. The organizers were Marlene Belfort, Evan Eichler, Henry Levin and Lynn Maquat. The goal of this conference was to bring together scientists from around the world to discuss the function of transposable elements and their impact on host species. Central themes of the meeting included recent innovations in genome analysis and the role of mobile DNA in disease and evolution. The conference included 200 scientists who participated in poster presentations, short talks selected from abstracts, and invited talks. A total of 58 talks were organized into eight sessions and two workshops. The topics varied from mechanisms of mobilization, to the structure of genomes and their defense strategies to protect against transposable elements.

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July 7, 2019  |  

A universal SNP and small-indel variant caller using deep neural networks.

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.

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July 7, 2019  |  

STRetch: detecting and discovering pathogenic short tandem repeat expansions.

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Most existing tools for detecting STR variation with short reads do so within the read length and so are unable to detect the majority of pathogenic expansions. Here we present STRetch, a new genome-wide method to scan for STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting STR expansions using short-read whole-genome sequencing data at known pathogenic loci as well as novel STR loci. STRetch is open source software, available from github.com/Oshlack/STRetch .

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July 7, 2019  |  

Mitochondrial genomes of two diplectanids (Platyhelminthes: Monogenea) expose paraphyly of the order Dactylogyridea and extensive tRNA gene rearrangements.

Recent mitochondrial phylogenomics studies have reported a sister-group relationship of the orders Capsalidea and Dactylogyridea, which is inconsistent with previous morphology- and molecular-based phylogenies. As Dactylogyridea mitochondrial genomes (mitogenomes) are currently represented by only one family, to improve the phylogenetic resolution, we sequenced and characterized two dactylogyridean parasites, Lamellodiscus spari and Lepidotrema longipenis, belonging to a non-represented family Diplectanidae.The L. longipenis mitogenome (15,433 bp) contains the standard 36 flatworm mitochondrial genes (atp8 is absent), whereas we failed to detect trnS1, trnC and trnG in L. spari (14,614 bp). Both mitogenomes exhibit unique gene orders (among the Monogenea), with a number of tRNA rearrangements. Both long non-coding regions contain a number of different (partially overlapping) repeat sequences. Intriguingly, these include putative tRNA pseudogenes in a tandem array (17 trnV pseudogenes in L. longipenis, 13 trnY pseudogenes in L. spari). Combined nucleotide diversity, non-synonymous/synonymous substitutions ratio and average sequence identity analyses consistently showed that nad2, nad5 and nad4 were the most variable PCGs, whereas cox1, cox2 and cytb were the most conserved. Phylogenomic analysis showed that the newly sequenced species of the family Diplectanidae formed a sister-group with the Dactylogyridae + Capsalidae clade. Thus Dactylogyridea (represented by the Diplectanidae and Dactylogyridae) was rendered paraphyletic (with high statistical support) by the nested Capsalidea (represented by the Capsalidae) clade.Our results show that nad2, nad5 and nad4 (fast-evolving) would be better candidates than cox1 (slow-evolving) for species identification and population genetics studies in the Diplectanidae. The unique gene order pattern further suggests discontinuous evolution of mitogenomic gene order arrangement in the Class Monogenea. This first report of paraphyly of the Dactylogyridea highlights the need to generate more molecular data for monogenean parasites, in order to be able to clarify their relationships using large datasets, as single-gene markers appear to provide a phylogenetic resolution which is too low for the task.

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July 7, 2019  |  

Picky comprehensively detects high-resolution structural variants in nanopore long reads.

Acquired genomic structural variants (SVs) are major hallmarks of cancer genomes, but they are challenging to reconstruct from short-read sequencing data. Here we exploited the long reads of the nanopore platform using our customized pipeline, Picky ( https://github.com/TheJacksonLaboratory/Picky ), to reveal SVs of diverse architecture in a breast cancer model. We identified the full spectrum of SVs with superior specificity and sensitivity relative to short-read analyses, and uncovered repetitive DNA as the major source of variation. Examination of genome-wide breakpoints at nucleotide resolution uncovered micro-insertions as the common structural features associated with SVs. Breakpoint density across the genome is associated with the propensity for interchromosomal connectivity and was found to be enriched in promoters and transcribed regions of the genome. Furthermore, we observed an over-representation of reciprocal translocations from chromosomal double-crossovers through phased SVs. We demonstrate that Picky analysis is an effective tool for comprehensive detection of SVs in cancer genomes from long-read data.

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July 7, 2019  |  

iMGEins: detecting novel mobile genetic elements inserted in individual genomes.

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.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.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.

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