AGE: defining breakpoints of genomic structural variants at single-nucleotide resolution, through optimal alignments with gap excision

Duplications and Retrogenes Are Numerous and Widespread in Modern Canine Genomic Assemblies

Recent years have seen a dramatic increase in the number of canine genome assemblies available. Duplications are an important source of evolutionary novelty and are also prone to misassembly. We explored the duplication content of nine canine genome assemblies using both genome self-alignment and read-depth approaches. We find that 8.58% of the genome is duplicated in the canFam4 assembly, derived from the German Shepherd Dog Mischka, including 90.15% of unplaced contigs. Highlighting the continued difficulty in properly assembling duplications, less than half of read-depth and assembly alignment duplications overlap, but the mCanLor1.2 Greenland wolf assembly shows greater concordance. Further study shows the presence of multiple segments that have alignments to four or more duplicate copies. These high-recurrence duplications correspond to gene retrocopies. We identified 3,892 candidate retrocopies from 1,316 parental genes in the canFam4 assembly and find that ∼8.82% of duplicated base pairs involve a retrocopy, confirming this mechanism as a major driver of gene duplication in canines. Similar patterns are found across eight other recent canine genome assemblies, with metrics supporting a greater quality of the PacBio HiFi mCanLor1.2 assembly. Comparison between the wolf and other canine assemblies found that 92% of retrocopy insertions are shared between assemblies. By calculating the number of generations since genome divergence, we estimate that new retrocopy insertions appear, on average, in 1 out of 3,514 births. Our analyses illustrate the impact of retrogene formation on canine genomes and highlight the variable representation of duplicated sequences among recently completed canine assemblies.

Human pangenome analysis of sequences missing from the reference genome reveals their widespread evolutionary, phenotypic, and functional roles

Nonreference sequences (NRSs) are DNA sequences present in global populations but absent in the current human reference genome. However, the extent and functional significance of NRSs in the human genomes and populations remains unclear. Here, we de novo assembled 539 genomes from five genetically divergent human populations using long-read sequencing technology, resulting in the identification of 5.1 million NRSs. These were merged into 45284 unique NRSs, with 29.7% being novel discoveries. Among these NRSs, 38.7% were common across the five populations, and 35.6% were population specific. The use of a graph-based pangenome approach allowed for the detection of 565 transcript expression quantitative trait loci on NRSs, with 426 of these being novel findings. Moreover, 26 NRS candidates displayed evidence of adaptive selection within human populations. Genes situated in close proximity to or intersecting with these candidates may be associated with metabolism and type 2 diabetes. Genome-wide association studies revealed 14 NRSs to be significantly associated with eight phenotypes. Additionally, 154 NRSs were found to be in strong linkage disequilibrium with 258 phenotype-associated SNPs in the GWAS catalogue. Our work expands the understanding of human NRSs and provides novel insights into their functions, facilitating evolutionary and biomedical researches.

Precise characterization of somatic complex structural variations from tumor/control paired long-read sequencing data with nanomonsv

We present our novel software, nanomonsv, for detecting somatic structural variations (SVs) using tumor and matched control long-read sequencing data with a single-base resolution. The current version of nanomonsv includes two detection modules, Canonical SV module, and Single breakend SV module. Using tumor/control paired long-read sequencing data from three cancer and their matched lymphoblastoid lines, we demonstrate that Canonical SV module can identify somatic SVs that can be captured by short-read technologies with higher precision and recall than existing methods. In addition, we have developed a workflow to classify mobile element insertions while elucidating their in-depth properties, such as 5' truncations, internal inversions, as well as source sites for 3' transductions. Furthermore, Single breakend SV module enables the detection of complex SVs that can only be identified by long-reads, such as SVs involving highly-repetitive centromeric sequences, and LINE1- and virus-mediated rearrangements. In summary, our approaches applied to cancer long-read sequencing data can reveal various features of somatic SVs and will lead to a better understanding of mutational processes and functional consequences of somatic SVs.

Common genetic risk factors in ASD and ADHD co-occurring families

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two major neurodevelopmental disorders that frequently co-occur. However, the genetic mechanism of the co-occurrence remains unclear. The New Jersey Language and Autism Genetics Study (NJLAGS) collected more than 100 families with at least one member affected by ASD. NJLAGS families show a high prevalence of ADHD and provide a good opportunity to study shared genetic risk factors for ASD and ADHD. The linkage study of the NJLAGS families revealed regions on chromosomes 12 and 17 that are significantly associated with ADHD. Using whole-genome sequencing data on 272 samples from 73 NJLAGS families, we identified potential risk genes for ASD and ADHD. Within the linkage regions, we identified 36 genes that are associated with ADHD using a pedigree-based gene prioritization approach. KDM6B (Lysine Demethylase 6B) is the highest-ranking gene, which is a known risk gene for neurodevelopmental disorders, including ASD and ADHD. At the whole-genome level, we identified 207 candidate genes from the analysis of both small variants and structure variants, including both known and novel genes. Using enrichment and protein-protein interaction network analyses, we identified gene ontology terms and pathways enriched for ASD and ADHD candidate genes, such as cilia function and cation channel activity. Candidate genes and pathways identified in our study improve the understanding of the genetic etiology of ASD and ADHD and will lead to new diagnostic or therapeutic interventions for ASD and ADHD in the future.

Recent, full-length gene retrocopies are common in canids

Gene retrocopies arise from the reverse transcription and insertion into the genome of processed mRNA transcripts. Although many retrocopies have acquired mutations that render them functionally inactive, most mammals retain active LINE-1 sequences capable of producing new retrocopies. New retrocopies, referred to as retro copy number variants (retroCNVs), may not be identified by standard variant calling techniques in high-throughput sequencing data. Although multiple functional FGF4 retroCNVs have been associated with skeletal dysplasias in dogs, the full landscape of canid retroCNVs has not been characterized. Here, retroCNV discovery was performed on a whole-genome sequencing data set of 293 canids from 76 breeds. We identified retroCNV parent genes via the presence of mRNA-specific 30-mers, and then identified retroCNV insertion sites through discordant read analysis. In total, we resolved insertion sites for 1911 retroCNVs from 1179 parent genes, 1236 of which appeared identical to their parent genes. Dogs had on average 54.1 total retroCNVs and 1.4 private retroCNVs. We found evidence of expression in testes for 12% (14/113) of the retroCNVs identified in six Golden Retrievers, including four chimeric transcripts, and 97 retroCNVs also had significantly elevated F ST across dog breeds, possibly indicating selection. We applied our approach to a subset of human genomes and detected an average of 4.2 retroCNVs per sample, highlighting a 13-fold relative increase of retroCNV frequency in dogs. Particularly in canids, retroCNVs are a largely unexplored source of genetic variation that can contribute to genome plasticity and that should be considered when investigating traits and diseases.

Combined use of Oxford Nanopore and Illumina sequencing yields insights into soybean structural variation biology

BACKGROUND

Structural variants (SVs), including deletions, insertions, duplications, and inversions, are relatively long genomic variations implicated in a diverse range of processes from human disease to ecology and evolution. Given their complex signatures, tendency to occur in repeated regions, and large size, discovering SVs based on short reads is challenging compared to single-nucleotide variants. The increasing availability of long-read technologies has greatly facilitated SV discovery; however, these technologies remain too costly to apply routinely to population-level studies. Here, we combined short-read and long-read sequencing technologies to provide a comprehensive population-scale assessment of structural variation in a panel of Canadian soybean cultivars.

RESULTS

We used Oxford Nanopore long-read sequencing data (~12× mean coverage) for 17 samples to both benchmark SV calls made from Illumina short-read data and predict SVs that were subsequently genotyped in a population of 102 samples using Illumina data. Benchmarking results show that variants discovered using Oxford Nanopore can be accurately genotyped from the Illumina data. We first use the genotyped deletions and insertions for population genetics analyses and show that results are comparable to those based on single-nucleotide variants. We observe that the population frequency and distribution within the genome of deletions and insertions are constrained by the location of genes. Gene Ontology and PFAM domain enrichment analyses also confirm previous reports that genes harboring high-frequency deletions and insertions are enriched for functions in defense response. Finally, we discover polymorphic transposable elements from the deletions and insertions and report evidence of the recent activity of a Stowaway MITE.

CONCLUSIONS

We show that structural variants discovered using Oxford Nanopore data can be genotyped with high accuracy from Illumina data. Our results demonstrate that long-read and short-read sequencing technologies can be efficiently combined to enhance SV analysis in large populations, providing a reusable framework for their study in a wider range of samples and non-model species.

CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data

Copy number variation (CNV), is defined as repetitions or deletions of genomic segments of 1 Kb to 5 Mb, and is a major trigger for human disease. The high-throughput and low-cost characteristics of next-generation sequencing technology provide the possibility of the detection of CNVs in the whole genome, and also greatly improve the clinical practicability of next-generation sequencing (NGS) testing. However, current methods for the detection of CNVs are easily affected by sequencing and mapping errors, and uneven distribution of reads. In this paper, we propose an improved approach, CNV-MEANN, for the detection of CNVs, involving changing the structure of the neural network used in the MFCNV method. This method has three differences relative to the MFCNV method: (1) it utilizes a new feature, mapping quality, to replace two features in MFCNV, (2) it considers the influence of the loss categories of CNV on disease prediction, and refines the output structure, and (3) it uses a mind evolutionary algorithm to optimize the backpropagation (neural network) neural network model, and calculates individual scores for each genome bin to predict CNVs. Using both simulated and real datasets, we tested the performance of CNV-MEANN and compared its performance with those of seven widely used CNV detection methods. Experimental results demonstrated that the CNV-MEANN approach outperformed other methods with respect to sensitivity, precision, and F1-score. The proposed method was able to detect many CNVs that other approaches could not, and it reduced the boundary bias. CNV-MEANN is expected to be an effective method for the analysis of changes in CNVs in the genome.

LongAGE: defining breakpoints of genomic structural variants through optimal and memory efficient alignments of long reads

Summary

Defining the precise location of structural variations (SVs) at single-nucleotide breakpoint resolution is a challenging problem due to large gaps in alignment. Previously, Alignment with Gap Excision (AGE) enabled us to define breakpoints of SVs at single-nucleotide resolution; however, AGE requires a vast amount of memory when aligning a pair of long sequences. To address this, we developed a memory-efficient implementation—LongAGE—based on the classical Hirschberg algorithm. We demonstrate an application of LongAGE for resolving breakpoints of SVs embedded into segmental duplications on Pacific Biosciences (PacBio) reads that can be longer than 10 kb. Furthermore, we observed different breakpoints for a deletion and a duplication in the same locus, providing direct evidence that such multi-allelic copy number variants (mCNVs) arise from two or more independent ancestral mutations.

Availability and implementation

LongAGE is implemented in C++ and available on Github at https://github.com/Coaxecva/LongAGE.

Supplementary information

Supplementary data are available at Bioinformatics online.

Deletion Detection Method Using the Distribution of Insert Size and a Precise Alignment Strategy

Homozygous and heterozygous deletions commonly exist in the human genome. For current structural variation detection tools, it is significant to determine whether a deletion is homozygous or heterozygous. However, the problems of sequencing errors, micro-homologies, and micro-insertions prohibit common alignment tools from identifying accurate breakpoint locations, and often result in detecting false structural variations. In this study, we present a novel deletion detection tool called Sprites2. Comparing with Sprites, Sprites2 makes the following modifications: (1) The distribution of insert size is used in Sprites2, which can identify the type of deletions and improve the accuracy of deletion calls. (2) A precise alignment method based on AGE (one algorithm simultaneously aligning 5' and 3' ends between two sequences) is adopted in Sprites2 to identify breakpoints, which is helpful to resolve the problems introduced by sequencing errors, micro-homologies, and micro-insertions. In order to test and verify the performance of Sprites2, some simulated and real datasets are adopted in our experiments, and Sprites2 is compared with five popular tools. The experimental results show that Sprites2 can improve the performance of deletion detection. Sprites2 can be downloaded from https://github.com/zhangzhen/sprites2.

Long-read assembly of a Great Dane genome highlights the contribution of GC-rich sequence and mobile elements to canine genomes

Technological advances have allowed improvements in genome reference sequence assemblies. Here, we combined long- and short-read sequence resources to assemble the genome of a female Great Dane dog. This assembly has improved continuity compared to the existing Boxer-derived (CanFam3.1) reference genome. Annotation of the Great Dane assembly identified 22,182 protein-coding gene models and 7,049 long noncoding RNAs, including 49 protein-coding genes not present in the CanFam3.1 reference. The Great Dane assembly spans the majority of sequence gaps in the CanFam3.1 reference and illustrates that 2,151 gaps overlap the transcription start site of a predicted protein-coding gene. Moreover, a subset of the resolved gaps, which have an 80.95% median GC content, localize to transcription start sites and recombination hotspots more often than expected by chance, suggesting the stable canine recombinational landscape has shaped genome architecture. Alignment of the Great Dane and CanFam3.1 assemblies identified 16,834 deletions and 15,621 insertions, as well as 2,665 deletions and 3,493 insertions located on secondary contigs. These structural variants are dominated by retrotransposon insertion/deletion polymorphisms and include 16,221 dimorphic canine short interspersed elements (SINECs) and 1,121 dimorphic long interspersed element-1 sequences (LINE-1_Cfs). Analysis of sequences flanking the 3' end of LINE-1_Cfs (i.e., LINE-1_Cf 3'-transductions) suggests multiple retrotransposition-competent LINE-1_Cfs segregate among dog populations. Consistent with this conclusion, we demonstrate that a canine LINE-1_Cf element with intact open reading frames can retrotranspose its own RNA and that of a SINEC_Cf consensus sequence in cultured human cells, implicating ongoing retrotransposon activity as a driver of canine genetic variation.

Analysis of Structural Variants Reveal Novel Selective Regions in the Genome of Meishan Pigs by Whole Genome Sequencing

Structural variants (SVs) represent essential forms of genetic variation, and they are associated with various phenotypic traits in a wide range of important livestock species. However, the distribution of SVs in the pig genome has not been fully characterized, and the function of SVs in the economic traits of pig has rarely been studied, especially for most domestic pig breeds. Meishan pig is one of the most famous Chinese domestic pig breeds, with excellent reproductive performance. Here, to explore the genome characters of Meishan pig, we construct an SV map of porcine using whole-genome sequencing data and report 33,698 SVs in 305 individuals of 55 globally distributed pig breeds. We perform selective signature analysis using these SVs, and a number of candidate variants are successfully identified. Especially for the Meishan pig, 64 novel significant selection regions are detected in its genome. A 140-bp deletion in the Indoleamine 2,3-Dioxygenase 2 (IDO2) gene, is shown to be associated with reproduction traits in Meishan pig. In addition, we detect two duplications only existing in Meishan pig. Moreover, the two duplications are separately located in cytochrome P450 family 2 subfamily J member 2 (CYP2J2) gene and phospholipase A2 group IVA (PLA2G4A) gene, which are related to the reproduction trait. Our study provides new insights into the role of selection in SVs' evolution and how SVs contribute to phenotypic variation in pigs.

RKDOSCNV: A Local Kernel Density-Based Approach to the Detection of Copy Number Variations by Using Next-Generation Sequencing Data

Copy number variations (CNVs) are significant causes of many human cancers and genetic diseases. The detection of CNVs has become a common method by which to analyze human diseases using next-generation sequencing (NGS) data. However, effective detection of insignificant CNVs is still a challenging task. In this study, we propose a new detection method, RKDOSCNV, to meet the need. RKDOSCNV uses kernel density estimation method to evaluate the local kernel density distribution of each read depth segment (RDS) based on an expanded nearest neighbor (k-nearest neighbors, reverse nearest neighbors, and shared nearest neighbors of each RDS) data set, and assigns a relative kernel density outlier score (RKDOS) for each RDS. According to the RKDOS profile, RKDOSCNV predicts the candidate CNVs by choosing a reasonable threshold, which it uses split read approach to correct the boundaries of candidate CNVs. The performance of RKDOSCNV is assessed by comparing it with several current popular methods via experiments with simulated and real data at different tumor purity levels. The experimental results verify that the performance of RKDOSCNV is superior to that of several other methods. In summary, RKDOSCNV is a simple and effective method for the detection of CNVs from whole genome sequencing (WGS) data, especially for samples with low tumor purity.

Combining copy number, methylation markers, and mutations as a panel for endometrial cancer detection via intravaginal tampon collection

OBJECTIVE

We aimed to assess whether endometrial cancer (EC) can be detected in shed DNA collected with vaginal tampon by analyzing copy number, methylation markers, and mutations.

METHODS

Tampons were collected prior to hysterectomy from 38 EC patients and 28 women with benign indications. Extracted tampon DNA underwent the following: 1) low-coverage whole genome sequencing (LC-WGS) to assess copy number, 2) pyrosequencing to measure percent promotor methylation of HOXA9, RASSF1, and CDH13 and 3) next generation sequencing (NGS) to identify mutations in 19 genes associated with EC identified through The Cancer Genome Atlas. Sensitivity and specificity for each test and test combinations were calculated.

RESULTS

Methylation analysis yielded the highest specificities but lowest sensitivities (37-40% sensitivity; 100% specificity for HOXA9, RASSF1 and HTR1B) while mutation analysis had improved sensitivity (50% sensitivity; 83% specificity). Only one "false positive" result for copy number variants was identified among women with benign surgical indications, which was based on detection of copy number changes, and associated with a leiomyosarcoma that was only recognized at hysterectomy. Considering any of the 3 biomarker classes as a positive, resulted in a sensitivity of 92% and specificity of 86%. Mutation analysis did not add sensitivity to the combination of analysis of copy number and methylation.

CONCLUSIONS

This study demonstrates a proof-of-principle for non-invasive yet precise detection of endometrial cancer. We propose that with improved biomarker testing, it may be possible to develop a clinically useful test for detecting EC.

High throughput genotyping of structural variations in a complex plant genome using an original Affymetrix® axiom® array

Background

Insertions/deletions (InDels) and more specifically presence/absence variations (PAVs) are pervasive in several species and have strong functional and phenotypic effect by removing or drastically modifying genes. Genotyping of such variants on large panels remains poorly addressed, while necessary for approaches such as association mapping or genomic selection.

Results

We have developed, as a proof of concept, a new high-throughput and affordable approach to genotype InDels. We first identified 141,000 InDels by aligning reads from the B73 line against the genome of three temperate maize inbred lines (F2, PH207, and C103) and reciprocally. Next, we designed an Affymetrix® Axiom® array to target these InDels, with a combination of probes selected at breakpoint sites (13%) or within the InDel sequence, either at polymorphic (25%) or non-polymorphic sites (63%) sites. The final array design is composed of 662,772 probes and targets 105,927 InDels, including PAVs ranging from 35 bp to 129kbp. After Affymetrix® quality control, we successfully genotyped 86,648 polymorphic InDels (82% of all InDels interrogated by the array) on 445 maize DNA samples with 422,369 probes. Genotyping InDels using this approach produced a highly reliable dataset, with low genotyping error (~ 3%), high call rate (~ 98%), and high reproducibility (> 95%). This reliability can be further increased by combining genotyping of several probes calling the same InDels (< 0.1% error rate and > 99.9% of call rate for 5 probes). This “proof of concept” tool was used to estimate the kinship matrix between 362 maize lines with 57,824 polymorphic InDels. This InDels kinship matrix was highly correlated with kinship estimated using SNPs from Illumina 50 K SNP arrays.

Conclusions

We efficiently genotyped thousands of small to large InDels on a sizeable number of individuals using a new Affymetrix® Axiom® array. This powerful approach opens the way to studying the contribution of InDels to trait variation and heterosis in maize. The approach is easily extendable to other species and should contribute to decipher the biological impact of InDels at a larger scale.

Predicting Local Inversions Using Rectangle Clustering and Representative Rectangle Prediction

As a specific type of structural variation, inversions are enjoying particular traction as a result of their established role in evolution. Using third-generation sequencing technology to predict inversions is growing in interest, but many such methods focus on improving sensitivity, giving rise to either too many false positives or very long running times. In this paper, we propose a new framework for inversion detection based on a combination of two novel theoretical models: rectangle clustering and representative rectangle prediction. This combination can automatically filter out false positive inversion predictions while retaining correct ones, leading to a method that has both high sensitivity and high positive prediction values (PPV). Further, this new framework can run very fast on available data. Our software can be freely obtained at https://github.com/UTbioinf/RigInv.

Sequencing XMET genes to promote genotype-guided risk assessment and precision medicine

High-throughput next generation sequencing (NGS) is a shotgun approach applied in a parallel fashion by which the genome is fragmented and sequenced through small pieces and then analyzed either by aligning to a known reference genome or by de novo assembly without reference genome. This technology has led researchers to conduct an explosion of sequencing related projects in multidisciplinary fields of science. However, due to the limitations of sequencing-based chemistry, length of sequencing reads and the complexity of genes, it is difficult to determine the sequences of some portions of the human genome, leaving gaps in genomic data that frustrate further analysis. Particularly, some complex genes are difficult to be accurately sequenced or mapped because they contain high GC-content and/or low complexity regions, and complicated pseudogenes, such as the genes encoding xenobiotic metabolizing enzymes and transporters (XMETs). The genetic variants in XMET genes are critical to predicate inter-individual variability in drug efficacy, drug safety and susceptibility to environmental toxicity. We summarized and discussed challenges, wet-lab methods, and bioinformatics algorithms in sequencing "complex" XMET genes, which may provide insightful information in the application of NGS technology for implementation in toxicogenomics and pharmacogenomics.

Accelerated rates of large-scale mutations in the presence of copper and nickel

Mutation rate variation has been under intense investigation for decades. Despite these efforts, little is known about the extent to which environmental stressors accelerate mutation rates and influence the genetic load of populations. Moreover, most studies on stressors have focused on unicellular organisms and point mutations rather than large-scale deletions and duplications (copy number variations [CNVs]). We estimated mutation rates in Daphnia pulex exposed to low levels of environmental stressors as well as the effect of selection on de novo mutations. We conducted a mutation accumulation (MA) experiment in which selection was minimized, coupled with an experiment in which a population was propagated under competitive conditions in a benign environment. After an average of 103 generations of MA propagation, we sequenced 60 genomes and found significantly accelerated rates of deletions and duplications in MA lines exposed to ecologically relevant concentrations of metals. Whereas control lines had gene deletion and duplication rates comparable to other multicellular eukaryotes (1.8 × 10-6 per gene per generation), the presence of nickel and copper increased these rates fourfold. The realized mutation rate under selection was reduced to 0.4× that of control MA lines, providing evidence that CNVs contribute to mutational load. Our CNV breakpoint analysis revealed that nonhomologous recombination associated with regions of DNA fragility is the primary source of CNVs, plausibly linking metal-induced DNA strand breaks with higher CNV rates. Our findings suggest that environmental stress, in particular multiple stressors, can have profound effects on large-scale mutation rates and mutational load of multicellular organisms.

MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples

Currently a few tools are capable of detecting genome-wide Copy Number Variations (CNVs) based on sequencing of multiple samples. Although aberrations in mate pair insertion sizes provide additional hints for the CNV detection based on multiple samples, the majority of the current tools rely only on the depth of coverage. Here, we propose a new algorithm (MSeq-CNV) which allows detecting common CNVs across multiple samples. MSeq-CNV applies a mixture density for modeling aberrations in depth of coverage and abnormalities in the mate pair insertion sizes. Each component in this mixture density applies a Binomial distribution for modeling the number of mate pairs with aberration in the insertion size and also a Poisson distribution for emitting the read counts, in each genomic position. MSeq-CNV is applied on simulated data and also on real data of six HapMap individuals with high-coverage sequencing, in 1000 Genomes Project. These individuals include a CEU trio of European ancestry and a YRI trio of Nigerian ethnicity. Ancestry of these individuals is studied by clustering the identified CNVs. MSeq-CNV is also applied for detecting CNVs in two samples with low-coverage sequencing in 1000 Genomes Project and six samples form the Simons Genome Diversity Project.

Jointly aligning a group of DNA reads improves accuracy of identifying large deletions

Performing sequence alignment to identify structural variants, such as large deletions, from genome sequencing data is a fundamental task, but current methods are far from perfect. The current practice is to independently align each DNA read to a reference genome. We show that the propensity of genomic rearrangements to accumulate in repeat-rich regions imposes severe ambiguities in these alignments, and consequently on the variant calls-with current read lengths, this affects more than one third of known large deletions in the C. Venter genome. We present a method to jointly align reads to a genome, whereby alignment ambiguity of one read can be disambiguated by other reads. We show this leads to a significant improvement in the accuracy of identifying large deletions (≥20 bases), while imposing minimal computational overhead and maintaining an overall running time that is at par with current tools. A software implementation is available as an open-source Python program called JRA at https://bitbucket.org/jointreadalignment/jra-src.

Sequence analysis of European maize inbred line F2 provides new insights into molecular and chromosomal characteristics of presence/absence variants

BACKGROUND

Maize is well known for its exceptional structural diversity, including copy number variants (CNVs) and presence/absence variants (PAVs), and there is growing evidence for the role of structural variation in maize adaptation. While PAVs have been described in this important crop species, they have been only scarcely characterized at the sequence level and the extent of presence/absence variation and relative chromosomal landscape of inbred-specific regions remain to be elucidated.

RESULTS

De novo genome sequencing of the French F2 maize inbred line revealed 10,044 novel genomic regions larger than 1 kb, making up 88 Mb of DNA, that are present in F2 but not in B73 (PAV). This set of maize PAV sequences allowed us to annotate PAV content and to analyze sequence breakpoints. Using PAV genotyping on a collection of 25 temperate lines, we also analyzed Linkage Disequilibrium in PAVs and flanking regions, and PAV frequencies within maize genetic groups.

CONCLUSIONS

We highlight the possible role of MMEJ-type double strand break repair in maize PAV formation and discover 395 new genes with transcriptional support. Pattern of linkage disequilibrium within PAVs strikingly differs from this of flanking regions and is in accordance with the intuition that PAVs may recombine less than other genomic regions. We show that most PAVs are ancient, while some are found only in European Flint material, thus pinpointing structural features that may be at the origin of adaptive traits involved in the success of this material. Characterization of such PAVs will provide useful material for further association genetic studies in European and temperate maize.

Copy Number Variations Detection: Unravelling the Problem in Tangible Aspects

In the midst of the important genomic variants associated to the susceptibility and resistance to complex diseases, Copy Number Variations (CNV) has emerged as a prevalent class of structural variation. Following the flood of next-generation sequencing data, numerous tools publicly available have been developed to provide computational strategies to identify CNV at improved accuracy. This review goes beyond scrutinizing the main approaches widely used for structural variants detection in general, including Split-Read, Paired-End Mapping, Read-Depth, and Assembly-based. In this paper, (1) we characterize the relevant technical details around the detection of CNV, which can affect the estimation of breakpoints and number of copies, (2) we pinpoint the most important insights related to GC-content and mappability biases, and (3) we discuss the paramount caveats in the tools evaluation process. The points brought out in this study emphasize common assumptions, a variety of possible limitations, valuable insights, and directions for desirable contributions to the state-of-the-art in CNV detection tools.

Genome-wide analysis of structural variants reveals genetic differences in Chinese pigs

Pigs have experienced long-term selections, resulting in dramatic phenotypic changes. Structural variants (SVs) are reported to exert extensive impacts on phenotypic changes. We built a high resolution and informative SV map based on high-depth sequencing data from 66 Chinese domestic and wild pigs. We inferred the SV formation mechanisms in the pig genome and used SVs as materials to perform a population-level analysis. We detected the selection signals on chromosome X for northern Chinese domestic pigs, as well as the differentiated loci across the whole genome. Analysis showed that these loci differ between southern and northern Chinese domestic pigs. Our results based on SVs provide new insights into genetic differences in Chinese pigs.

Detection Copy Number Variants from NGS with Sparse and Smooth Constraints

It is known that copy number variations (CNVs) are associated with complex diseases and particular tumor types, thus reliable identification of CNVs is of great potential value. Recent advances in next generation sequencing (NGS) data analysis have helped manifest the richness of CNV information. However, the performances of these methods are not consistent. Reliably finding CNVs in NGS data in an efficient way remains a challenging topic, worthy of further investigation. Accordingly, we tackle the problem by formulating CNVs identification into a quadratic optimization problem involving two constraints. By imposing the constraints of sparsity and smoothness, the reconstructed read depth signal from NGS is anticipated to fit the CNVs patterns more accurately. An efficient numerical solution tailored from alternating direction minimization (ADM) framework is elaborated. We demonstrate the advantages of the proposed method, namely ADM-CNV, by comparing it with six popular CNV detection methods using synthetic, simulated, and empirical sequencing data. It is shown that the proposed approach can successfully reconstruct CNV patterns from raw data, and achieve superior or comparable performance in detection of the CNVs compared to the existing counterparts.

CNV discovery for milk composition traits in dairy cattle using whole genome resequencing

Background

Copy number variations (CNVs) are important and widely distributed in the genome. CNV detection opens a new avenue for exploring genes associated with complex traits in humans, animals and plants. Herein, we present a genome-wide assessment of CNVs that are potentially associated with milk composition traits in dairy cattle.

Results

In this study, CNVs were detected based on whole genome re-sequencing data of eight Holstein bulls from four half- and/or full-sib families, with extremely high and low estimated breeding values (EBVs) of milk protein percentage and fat percentage. The range of coverage depth per individual was 8.2–11.9×. Using CNVnator, we identified a total of 14,821 CNVs, including 5025 duplications and 9796 deletions. Among them, 487 differential CNV regions (CNVRs) comprising ~8.23 Mb of the cattle genome were observed between the high and low groups. Annotation of these differential CNVRs were performed based on the cattle genome reference assembly (UMD3.1) and totally 235 functional genes were found within the CNVRs. By Gene Ontology and KEGG pathway analyses, we found that genes were significantly enriched for specific biological functions related to protein and lipid metabolism, insulin/IGF pathway-protein kinase B signaling cascade, prolactin signaling pathway and AMPK signaling pathways. These genes included INS, IGF2, FOXO3, TH, SCD5, GALNT18, GALNT16, ART3, SNCA and WNT7A, implying their potential association with milk protein and fat traits. In addition, 95 CNVRs were overlapped with 75 known QTLs that are associated with milk protein and fat traits of dairy cattle (Cattle QTLdb).

Conclusions

In conclusion, based on NGS of 8 Holstein bulls with extremely high and low EBVs for milk PP and FP, we identified a total of 14,821 CNVs, 487 differential CNVRs between groups, and 10 genes, which were suggested as promising candidate genes for milk protein and fat traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3636-3) contains supplementary material, which is available to authorized users.

PSE-HMM: genome-wide CNV detection from NGS data using an HMM with Position-Specific Emission probabilities

Background

Copy Number Variation (CNV) is envisaged to be a major source of large structural variations in the human genome. In recent years, many studies apply Next Generation Sequencing (NGS) data for the CNV detection. However, still there is a necessity to invent more accurate computational tools.

Results

In this study, mate pair NGS data are used for the CNV detection in a Hidden Markov Model (HMM). The proposed HMM has position specific emission probabilities, i.e. a Gaussian mixture distribution. Each component in the Gaussian mixture distribution captures a different type of aberration that is observed in the mate pairs, after being mapped to the reference genome. These aberrations may include any increase (decrease) in the insertion size or change in the direction of mate pairs that are mapped to the reference genome. This HMM with Position-Specific Emission probabilities (PSE-HMM) is utilized for the genome-wide detection of deletions and tandem duplications. The performance of PSE-HMM is evaluated on a simulated dataset and also on a real data of a Yoruban HapMap individual, NA18507.

Conclusions

PSE-HMM is effective in taking observation dependencies into account and reaches a high accuracy in detecting genome-wide CNVs. MATLAB programs are available at http://bs.ipm.ir/softwares/PSE-HMM/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1296-y) contains supplementary material, which is available to authorized users.

MGP-HMM: Detecting genome-wide CNVs using an HMM for modeling mate pair insertion sizes and read counts

MOTIVATION

Association of Copy Number Variation (CNV) with schizophrenia, autism, developmental disabilities and fatal diseases such as cancer is verified. Recent developments in Next Generation Sequencing (NGS) have facilitated the CNV studies. However, many of the current CNV detection tools are not capable of discriminating tandem duplication from non-tandem duplications.

RESULTS

In this study, we propose MGP-HMM as a tool which besides detecting genome-wide deletions discriminates tandem duplications from non-tandem duplications. MGP-HMM takes mate pair abnormalities into account and predicts the digitized number of tandem or non-tandem copies. Abnormalities in the mate pair directions and insertion sizes, after being mapped to the reference genome, are elucidated using a Hidden Markov Model (HMM). For this purpose, a Mixture Gaussian density with time-dependent parameters is applied for emitting mate pair insertion sizes from HMM states. Indeed, depending on observed abnormalities in mate pair insertion size or its orientation, each component in the mixture density will have different parameters. MGP-HMM also applies a Poisson distribution for modeling read depth data. This parametric modeling of the mate pair reads enables us to estimate the length of CNVs precisely, which is an advantage over methods which rely only on read depth approach for the CNV detection. Hidden state of the proposed HMM is the digitized copy number of a genomic segment and states correspond to the multipliers of the mixture Gaussian components. The accuracy of our model is validated on a set of next generation sequencing real and simulated data and is compared to other tools.

Elevated variant density around SV breakpoints in germline lineage lends support to error-prone replication hypothesis

Copy number variants (CNVs) are a class of structural variants that may involve complex genomic rearrangements (CGRs) and are hypothesized to have additional mutations around their breakpoints. Understanding the mechanisms underlying CNV formation is fundamental for understanding the repair and mutation mechanisms in cells, thereby shedding light on evolution, genomic disorders, cancer, and complex human traits. In this study, we used data from the 1000 Genomes Project to analyze hundreds of loci harboring heterozygous germline deletions in the subjects NA12878 and NA19240. By utilizing synthetic long-read data (longer than 2 kbp) in combination with high coverage short-read data and, in parallel, by comparing with parental genomes, we interrogated the phasing of these deletions with the flanking tens of thousands of heterozygous SNPs and indels. We found that the density of SNPs/indels flanking the breakpoints of deletions (in-phase variants) is approximately twice as high as the corresponding density for the variants on the haplotype without deletion (out-of-phase variants). This fold change was even larger for the subset of deletions with signatures of replication-based mechanism of formation. The allele frequency (AF) spectrum for deletions is enriched for rare events; and the AF spectrum for in-phase SNPs is shifted toward this deletion spectrum, thus offering evidence consistent with the concomitance of the in-phase SNPs/indels with the deletion events. These findings therefore lend support to the hypothesis that the mutational mechanisms underlying CNV formation are error prone. Our results could also be relevant for resolving mutation-rate discrepancies in human and to explain kataegis.

An NGS Workflow Blueprint for DNA Sequencing Data and Its Application in Individualized Molecular Oncology

Next-generation sequencing (NGS) technologies that have advanced rapidly in the past few years possess the potential to classify diseases, decipher the molecular code of related cell processes, identify targets for decision-making on targeted therapy or prevention strategies, and predict clinical treatment response. Thus, NGS is on its way to revolutionize oncology. With the help of NGS, we can draw a finer map for the genetic basis of diseases and can improve our understanding of diagnostic and prognostic applications and therapeutic methods. Despite these advantages and its potential, NGS is facing several critical challenges, including reduction of sequencing cost, enhancement of sequencing quality, improvement of technical simplicity and reliability, and development of semiautomated and integrated analysis workflow. In order to address these challenges, we conducted a literature research and summarized a four-stage NGS workflow for providing a systematic review on NGS-based analysis, explaining the strength and weakness of diverse NGS-based software tools, and elucidating its potential connection to individualized medicine. By presenting this four-stage NGS workflow, we try to provide a minimal structural layout required for NGS data storage and reproducibility.

Structural variation discovery in the cancer genome using next generation sequencing: computational solutions and perspectives

Somatic Structural Variations (SVs) are a complex collection of chromosomal mutations that could directly contribute to carcinogenesis. Next Generation Sequencing (NGS) technology has emerged as the primary means of interrogating the SVs of the cancer genome in recent investigations. Sophisticated computational methods are required to accurately identify the SV events and delineate their breakpoints from the massive amounts of reads generated by a NGS experiment. In this review, we provide an overview of current analytic tools used for SV detection in NGS-based cancer studies. We summarize the features of common SV groups and the primary types of NGS signatures that can be used in SV detection methods. We discuss the principles and key similarities and differences of existing computational programs and comment on unresolved issues related to this research field. The aim of this article is to provide a practical guide of relevant concepts, computational methods, software tools and important factors for analyzing and interpreting NGS data for the detection of SVs in the cancer genome.

Sprites: detection of deletions from sequencing data by re-aligning split reads

MOTIVATION

Advances of next generation sequencing technologies and availability of short read data enable the detection of structural variations (SVs). Deletions, an important type of SVs, have been suggested in association with genetic diseases. There are three types of deletions: blunt deletions, deletions with microhomologies and deletions with microsinsertions. The last two types are very common in the human genome, but they pose difficulty for the detection. Furthermore, finding deletions from sequencing data remains challenging. It is highly appealing to develop sensitive and accurate methods to detect deletions from sequencing data, especially deletions with microhomology and deletions with microinsertion.

RESULTS

We present a novel method called Sprites (SPlit Read re-alIgnment To dEtect Structural variants) which finds deletions from sequencing data. It aligns a whole soft-clipping read rather than its clipped part to the target sequence, a segment of the reference which is determined by spanning reads, in order to find the longest prefix or suffix of the read that has a match in the target sequence. This alignment aims to solve the problem of deletions with microhomologies and deletions with microinsertions. Using both simulated and real data we show that Sprites performs better on detecting deletions compared with other current methods in terms of F-score.

AVAILABILITY AND IMPLEMENTATION

Sprites is open source software and freely available at https://github.com/zhangzhen/sprites

CONTACT

jxwang@mail.csu.edu.cnSupplementary data: Supplementary data are available at Bioinformatics online.

Structural Variant Detection by Large-scale Sequencing Reveals New Evolutionary Evidence on Breed Divergence between Chinese and European Pigs

In this study, we performed a genome-wide SV detection among the genomes of thirteen pigs from diverse Chinese and European originated breeds by next genetation sequencing, and constrcuted a single-nucleotide resolution map involving 56,930 putative SVs. We firstly identified a SV hotspot spanning 35 Mb region on the X chromosome specifically in the genomes of Chinese originated individuals. Further scrutinizing this region by large-scale sequencing data of extra 111 individuals, we obtained the confirmatory evidence on our initial finding. Moreover, thirty five SV-related genes within the hotspot region, being of importance for reproduction ability, rendered significant different evolution rates between Chinese and European originated breeds. The SV hotspot identified herein offers a novel evidence for assessing phylogenetic relationships, as well as likely explains the genetic difference of corresponding phenotypes and features, among Chinese and European pig breeds. Furthermore, we employed various SVs to infer genetic structure of individuls surveyed. We found SVs can clearly detect the difference of genetic background among individuals. This clues us that genome-wide SVs can capture majority of geneic variation and be applied into cladistic analyses. Characterizing whole genome SVs demonstrated that SVs are significantly enriched/depleted with various genomic features.

Discovery, genotyping and characterization of structural variation and novel sequence at single nucleotide resolution from de novo genome assemblies on a population scale

Background

Comprehensive recognition of genomic variation in one individual is important for understanding disease and developing personalized medication and treatment. Many tools based on DNA re-sequencing exist for identification of single nucleotide polymorphisms, small insertions and deletions (indels) as well as large deletions. However, these approaches consistently display a substantial bias against the recovery of complex structural variants and novel sequence in individual genomes and do not provide interpretation information such as the annotation of ancestral state and formation mechanism.

Findings

We present a novel approach implemented in a single software package, AsmVar, to discover, genotype and characterize different forms of structural variation and novel sequence from population-scale de novo genome assemblies up to nucleotide resolution. Application of AsmVar to several human de novo genome assemblies captures a wide spectrum of structural variants and novel sequences present in the human population in high sensitivity and specificity.

Conclusions

Our method provides a direct solution for investigating structural variants and novel sequences from de novo genome assemblies, facilitating the construction of population-scale pan-genomes. Our study also highlights the usefulness of the de novo assembly strategy for definition of genome structure.

Electronic supplementary material

The online version of this article (doi:10.1186/s13742-015-0103-4) contains supplementary material, which is available to authorized users.

Local sequence assembly reveals a high-resolution profile of somatic structural variations in 97 cancer genomes

Genomic structural variations (SVs) are pervasive in many types of cancers. Characterizing their underlying mechanisms and potential molecular consequences is crucial for understanding the basic biology of tumorigenesis. Here, we engineered a local assembly-based algorithm (laSV) that detects SVs with high accuracy from paired-end high-throughput genomic sequencing data and pinpoints their breakpoints at single base-pair resolution. By applying laSV to 97 tumor-normal paired genomic sequencing datasets across six cancer types produced by The Cancer Genome Atlas Research Network, we discovered that non-allelic homologous recombination is the primary mechanism for generating somatic SVs in acute myeloid leukemia. This finding contrasts with results for the other five types of solid tumors, in which non-homologous end joining and microhomology end joining are the predominant mechanisms. We also found that the genes recursively mutated by single nucleotide alterations differed from the genes recursively mutated by SVs, suggesting that these two types of genetic alterations play different roles during cancer progression. We further characterized how the gene structures of the oncogene JAK1 and the tumor suppressors KDM6A and RB1 are affected by somatic SVs and discussed the potential functional implications of intergenic SVs.

Analysis of deletion breakpoints from 1,092 humans reveals details of mutation mechanisms

Investigating genomic structural variants at basepair resolution is crucial for understanding their formation mechanisms. We identify and analyze 8,943 deletion breakpoints in 1,092 samples from the 1000 Genomes Project. We find breakpoints have more nearby SNPs and indels than the genomic average, likely a consequence of relaxed selection. By investigating the correlation of breakpoints with DNA methylation, Hi-C interactions, and histone marks and the substitution patterns of nucleotides near them, we find that breakpoints with the signature of non-allelic homologous recombination (NAHR) are associated with open chromatin. We hypothesize that some NAHR deletions occur without DNA replication and cell division, in embryonic and germline cells. In contrast, breakpoints associated with non-homologous (NH) mechanisms often have sequence micro-insertions, templated from later replicating genomic sites, spaced at two characteristic distances from the breakpoint. These micro-insertions are consistent with template-switching events and suggest a particular spatiotemporal configuration for DNA during the events.

MetaSV: an accurate and integrative structural-variant caller for next generation sequencing

Summary: Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes.

Availability and implementation: Code in Python is at http://bioinform.github.io/metasv/.

Contact:rd@bina.com

Supplementary information:Supplementary data are available at Bioinformatics online.

G-CNV: A GPU-Based Tool for Preparing Data to Detect CNVs with Read-Depth Methods

Copy number variations (CNVs) are the most prevalent types of structural variations (SVs) in the human genome and are involved in a wide range of common human diseases. Different computational methods have been devised to detect this type of SVs and to study how they are implicated in human diseases. Recently, computational methods based on high-throughput sequencing (HTS) are increasingly used. The majority of these methods focus on mapping short-read sequences generated from a donor against a reference genome to detect signatures distinctive of CNVs. In particular, read-depth based methods detect CNVs by analyzing genomic regions with significantly different read-depth from the other ones. The pipeline analysis of these methods consists of four main stages: (i) data preparation, (ii) data normalization, (iii) CNV regions identification, and (iv) copy number estimation. However, available tools do not support most of the operations required at the first two stages of this pipeline. Typically, they start the analysis by building the read-depth signal from pre-processed alignments. Therefore, third-party tools must be used to perform most of the preliminary operations required to build the read-depth signal. These data-intensive operations can be efficiently parallelized on graphics processing units (GPUs). In this article, we present G-CNV, a GPU-based tool devised to perform the common operations required at the first two stages of the analysis pipeline. G-CNV is able to filter low-quality read sequences, to mask low-quality nucleotides, to remove adapter sequences, to remove duplicated read sequences, to map the short-reads, to resolve multiple mapping ambiguities, to build the read-depth signal, and to normalize it. G-CNV can be efficiently used as a third-party tool able to prepare data for the subsequent read-depth signal generation and analysis. Moreover, it can also be integrated in CNV detection tools to generate read-depth signals.

Novel variation and de novo mutation rates in population-wide de novo assembled Danish trios

Building a population-specific catalogue of single nucleotide variants (SNVs), indels and structural variants (SVs) with frequencies, termed a national pan-genome, is critical for further advancing clinical and public health genetics in large cohorts. Here we report a Danish pan-genome obtained from sequencing 10 trios to high depth (50 × ). We report 536k novel SNVs and 283k novel short indels from mapping approaches and develop a population-wide de novo assembly approach to identify 132k novel indels larger than 10 nucleotides with low false discovery rates. We identify a higher proportion of indels and SVs than previous efforts showing the merits of high coverage and de novo assembly approaches. In addition, we use trio information to identify de novo mutations and use a probabilistic method to provide direct estimates of 1.27e−8 and 1.5e−9 per nucleotide per generation for SNVs and indels, respectively.

The generation of a national pan-genome, a population-specific catalogue of genetic variation, may advance the impact of clinical genetics studies. Here the Besenbacher et al. carry out deep sequencing and de novo assembly of 10 parent–child trios to generate a Danish pan-genome that provides insight into structural variation, de novo mutation rates and variant calling.

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.

Vindel: a simple pipeline for checking indel redundancy

Background

With the advance of next generation sequencing (NGS) technologies, a large number of insertion and deletion (indel) variants have been identified in human populations. Despite much research into variant calling, it has been found that a non-negligible proportion of the identified indel variants might be false positives due to sequencing errors, artifacts caused by ambiguous alignments, and annotation errors.

Results

In this paper, we examine indel redundancy in dbSNP, one of the central databases for indel variants, and develop a standalone computational pipeline, dubbed Vindel, to detect redundant indels. The pipeline first applies indel position information to form candidate redundant groups, then performs indel mutations to the reference genome to generate corresponding indel variant substrings. Finally the indel variant substrings in the same candidate redundant groups are compared in a pairwise fashion to identify redundant indels. We applied our pipeline to check for redundancy in the human indels in dbSNP. Our pipeline identified approximately 8% redundancy in insertion type indels, 12% in deletion type indels, and overall 10% for insertions and deletions combined. These numbers are largely consistent across all human autosomes. We also investigated indel size distribution and adjacent indel distance distribution for a better understanding of the mechanisms generating indel variants.

Conclusions

Vindel, a simple yet effective computational pipeline, can be used to check whether a set of indels are redundant with respect to those already in the database of interest such as NCBI’s dbSNP. Of the approximately 5.9 million indels we examined, nearly 0.6 million are redundant, revealing a serious limitation in the current indel annotation. Statistics results prove the consistency of the pipeline on indel redundancy detection for all 22 chromosomes. Apart from the standalone Vindel pipeline, the indel redundancy check algorithm is also implemented in the web server http://bioinformatics.cs.vt.edu/zhanglab/indelRedundant.php.

Electronic supplementary material

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Ulysses: accurate detection of low-frequency structural variations in large insert-size sequencing libraries

MOTIVATION

The detection of structural variations (SVs) in short-range Paired-End (PE) libraries remains challenging because SV breakpoints can involve large dispersed repeated sequences, or carry inherent complexity, hardly resolvable with classical PE sequencing data. In contrast, large insert-size sequencing libraries (Mate-Pair libraries) provide higher physical coverage of the genome and give access to repeat-containing regions. They can thus theoretically overcome previous limitations as they are becoming routinely accessible. Nevertheless, broad insert size distributions and high rates of chimerical sequences are usually associated to this type of libraries, which makes the accurate annotation of SV challenging.

RESULTS

Here, we present Ulysses, a tool that achieves drastically higher detection accuracy than existing tools, both on simulated and real mate-pair sequencing datasets from the 1000 Human Genome project. Ulysses achieves high specificity over the complete spectrum of variants by assessing, in a principled manner, the statistical significance of each possible variant (duplications, deletions, translocations, insertions and inversions) against an explicit model for the generation of experimental noise. This statistical model proves particularly useful for the detection of low frequency variants. SV detection performed on a large insert Mate-Pair library from a breast cancer sample revealed a high level of somatic duplications in the tumor and, to a lesser extent, in the blood sample as well. Altogether, these results show that Ulysses is a valuable tool for the characterization of somatic mosaicism in human tissues and in cancer genomes.

Characterization of structural variants with single molecule and hybrid sequencing approaches

MOTIVATION

Structural variation is common in human and cancer genomes. High-throughput DNA sequencing has enabled genome-scale surveys of structural variation. However, the short reads produced by these technologies limit the study of complex variants, particularly those involving repetitive regions. Recent 'third-generation' sequencing technologies provide single-molecule templates and longer sequencing reads, but at the cost of higher per-nucleotide error rates.

RESULTS

We present MultiBreak-SV, an algorithm to detect structural variants (SVs) from single molecule sequencing data, paired read sequencing data, or a combination of sequencing data from different platforms. We demonstrate that combining low-coverage third-generation data from Pacific Biosciences (PacBio) with high-coverage paired read data is advantageous on simulated chromosomes. We apply MultiBreak-SV to PacBio data from four human fosmids and show that it detects known SVs with high sensitivity and specificity. Finally, we perform a whole-genome analysis on PacBio data from a complete hydatidiform mole cell line and predict 1002 high-probability SVs, over half of which are confirmed by an Illumina-based assembly.

Gustaf: Detecting and correctly classifying SVs in the NGS twilight zone

MOTIVATION

The landscape of structural variation (SV) including complex duplication and translocation patterns is far from resolved. SV detection tools usually exhibit low agreement, are often geared toward certain types or size ranges of variation and struggle to correctly classify the type and exact size of SVs.

RESULTS

We present Gustaf (Generic mUlti-SpliT Alignment Finder), a sound generic multi-split SV detection tool that detects and classifies deletions, inversions, dispersed duplications and translocations of ≥ 30 bp. Our approach is based on a generic multi-split alignment strategy that can identify SV breakpoints with base pair resolution. We show that Gustaf correctly identifies SVs, especially in the range from 30 to 100 bp, which we call the next-generation sequencing (NGS) twilight zone of SVs, as well as larger SVs >500 bp. Gustaf performs better than similar tools in our benchmark and is furthermore able to correctly identify size and location of dispersed duplications and translocations, which otherwise might be wrongly classified, for example, as large deletions.

PolyTB: a genomic variation map for Mycobacterium tuberculosis

Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) is the second major cause of death from an infectious disease worldwide. Recent advances in DNA sequencing are leading to the ability to generate whole genome information in clinical isolates of M. tuberculosis complex (MTBC). The identification of informative genetic variants such as phylogenetic markers and those associated with drug resistance or virulence will help barcode Mtb in the context of epidemiological, diagnostic and clinical studies. Mtb genomic datasets are increasingly available as raw sequences, which are potentially difficult and computer intensive to process, and compare across studies. Here we have processed the raw sequence data (>1500 isolates, eight studies) to compile a catalogue of SNPs (n = 74,039, 63% non-synonymous, 51.1% in more than one isolate, i.e. non-private), small indels (n = 4810) and larger structural variants (n = 800). We have developed the PolyTB web-based tool (http://pathogenseq.lshtm.ac.uk/polytb) to visualise the resulting variation and important meta-data (e.g. in silico inferred strain-types, location) within geographical map and phylogenetic views. This resource will allow researchers to identify polymorphisms within candidate genes of interest, as well as examine the genomic diversity and distribution of strains. PolyTB source code is freely available to researchers wishing to develop similar tools for their pathogen of interest.

A deterministic analysis of genome integrity during neoplastic growth in Drosophila

The development of cancer has been associated with the gradual acquisition of genetic alterations leading to a progressive increase in malignancy. In various cancer types this process is enabled and accelerated by genome instability. While genome sequencing-based analysis of tumor genomes becomes increasingly a standard procedure in human cancer research, the potential necessity of genome instability for tumorigenesis in Drosophila melanogaster has, to our knowledge, never been determined at DNA sequence level. Therefore, we induced formation of tumors by depletion of the Drosophila tumor suppressor Polyhomeotic and subjected them to genome sequencing. To achieve a highly resolved delineation of the genome structure we developed the Deterministic Structural Variation Detection (DSVD) algorithm, which identifies structural variations (SVs) with high accuracy and at single base resolution. The employment of long overlapping paired-end reads enables DSVD to perform a deterministic, i.e. fragment size distribution independent, identification of a large size spectrum of SVs. Application of DSVD and other algorithms to our sequencing data reveals substantial genetic variation with respect to the reference genome reflecting temporal separation of the reference and laboratory strains. The majority of SVs, constituted by small insertions/deletions, is potentially caused by erroneous replication or transposition of mobile elements. Nevertheless, the tumor did not depict a loss of genome integrity compared to the control. Altogether, our results demonstrate that genome stability is not affected inevitably during sustained tumor growth in Drosophila implying that tumorigenesis, in this model organism, can occur irrespective of genome instability and the accumulation of specific genetic alterations.

Discovery of recurrent structural variants in nasopharyngeal carcinoma

We present the discovery of genes recurrently involved in structural variation in nasopharyngeal carcinoma (NPC) and the identification of a novel type of somatic structural variant. We identified the variants with high complexity mate-pair libraries and a novel computational algorithm specifically designed for tumor-normal comparisons, SMASH. SMASH combines signals from split reads and mate-pair discordance to detect somatic structural variants. We demonstrate a >90% validation rate and a breakpoint reconstruction accuracy of 3 bp by Sanger sequencing. Our approach identified three in-frame gene fusions (YAP1-MAML2, PTPLB-RSRC1, and SP3-PTK2) that had strong levels of expression in corresponding NPC tissues. We found two cases of a novel type of structural variant, which we call “coupled inversion,” one of which produced the YAP1-MAML2 fusion. To investigate whether the identified fusion genes are recurrent, we performed fluorescent in situ hybridization (FISH) to screen 196 independent NPC cases. We observed recurrent rearrangements of MAML2 (three cases), PTK2 (six cases), and SP3 (two cases), corresponding to a combined rate of structural variation recurrence of 6% among tested NPC tissues.

Coval: improving alignment quality and variant calling accuracy for next-generation sequencing data

Accurate identification of DNA polymorphisms using next-generation sequencing technology is challenging because of a high rate of sequencing error and incorrect mapping of reads to reference genomes. Currently available short read aligners and DNA variant callers suffer from these problems. We developed the Coval software to improve the quality of short read alignments. Coval is designed to minimize the incidence of spurious alignment of short reads, by filtering mismatched reads that remained in alignments after local realignment and error correction of mismatched reads. The error correction is executed based on the base quality and allele frequency at the non-reference positions for an individual or pooled sample. We demonstrated the utility of Coval by applying it to simulated genomes and experimentally obtained short-read data of rice, nematode, and mouse. Moreover, we found an unexpectedly large number of incorrectly mapped reads in ‘targeted’ alignments, where the whole genome sequencing reads had been aligned to a local genomic segment, and showed that Coval effectively eliminated such spurious alignments. We conclude that Coval significantly improves the quality of short-read sequence alignments, thereby increasing the calling accuracy of currently available tools for SNP and indel identification. Coval is available at http://sourceforge.net/projects/coval105/.

Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.

Analysis of variable retroduplications in human populations suggests coupling of retrotransposition to cell division

In primates and other animals, reverse transcription of mRNA followed by genomic integration creates retroduplications. Expressed retroduplications are either “retrogenes” coding for functioning proteins, or expressed “processed pseudogenes,” which can function as noncoding RNAs. To date, little is known about the variation in retroduplications in terms of their presence or absence across individuals in the human population. We have developed new methodologies that allow us to identify “novel” retroduplications (i.e., those not present in the reference genome), to find their insertion points, and to genotype them. Using these methods, we catalogued and analyzed 174 retroduplication variants in almost one thousand humans, which were sequenced as part of Phase 1 of The 1000 Genomes Project Consortium. The accuracy of our data set was corroborated by (1) multiple lines of sequencing evidence for retroduplication (e.g., depth of coverage in exons vs. introns), (2) experimental validation, and (3) the fact that we can reconstruct a correct phylogenetic tree of human subpopulations based solely on retroduplications. We also show that parent genes of retroduplication variants tend to be expressed at the M-to-G1 transition in the cell cycle and that M-to-G1 expressed genes have more copies of fixed retroduplications than genes expressed at other times. These findings suggest that cell division is coupled to retrotransposition and, perhaps, is even a requirement for it.

Barnacle: detecting and characterizing tandem duplications and fusions in transcriptome assemblies

Background

Chimeric transcripts, including partial and internal tandem duplications (PTDs, ITDs) and gene fusions, are important in the detection, prognosis, and treatment of human cancers.

Results

We describe Barnacle, a production-grade analysis tool that detects such chimeras in de novo assemblies of RNA-seq data, and supports prioritizing them for review and validation by reporting the relative coverage of co-occurring chimeric and wild-type transcripts. We demonstrate applications in large-scale disease studies, by identifying PTDs in MLL, ITDs in FLT3, and reciprocal fusions between PML and RARA, in two deeply sequenced acute myeloid leukemia (AML) RNA-seq datasets.

Conclusions

Our analyses of real and simulated data sets show that, with appropriate filter settings, Barnacle makes highly specific predictions for three types of chimeric transcripts that are important in a range of cancers: PTDs, ITDs, and fusions. High specificity makes manual review and validation efficient, which is necessary in large-scale disease studies. Characterizing an extended range of chimera types will help generate insights into progression, treatment, and outcomes for complex diseases.