Long-read-based human genomic structural variation detection with cuteSV

Identification of Candidate Genes Related to Hybrid Sterility by Genomic Structural Variation and Transcriptome Analyses in Cattle-yak

Hybrids between closely related but genetically incompatible species are often inviable or sterile. Cattle-yak, an interspecific hybrid of yak and cattle, exhibits male-specific sterility, which limits the fixation of its desired traits and prevents genetic improvement in yak through crossbreeding. Transcriptome profiles of testicular tissues have been generated in cattle, yak, and cattle-yak; however, the genetic variations underlying differential gene expression associated with hybrid sterility have yet to be elucidated. We detected differences in the cellular composition and gene expression of testes from yak and cattle-yak at 3 mo of age, 10 mo of age and adulthood. Histological analysis revealed that the most advanced germ cells were gonocytes (prospermatogonia) at 3 mo and spermatocytes at 10 mo. Complete spermatogenesis occurred in the seminiferous tubules of adult yak, whereas only spermatogonia and a limited number of spermatocytes were detected in the testis of adult cattle-yak. Transcriptome analysis revealed 180, 6310, and 6112 differentially expressed genes (DEGs) in yak and cattle-yak at each stage, respectively. Next, we examined the spermatogenic cell types in the backcross generation (BC1) and detected the appearance of round spermatids, indicating the partial recovery of spermatogenesis in these animals. Compared with those in cattle-yak, 272 DEGs were identified in the testes of BC1 animals. Notably, we discovered that the expression of X chromosome-linked (X-linked) genes was upregulated in the testis of cattle-yak compared with yak, suggesting a possible abnormality in the process of meiotic sex chromosome inactivation (MSCI) in hybrid animals. We next screened DEGs harboring structural variations (SVs) and identified a list of SV genes associated with spermatogonial development, meiotic recombination, and double-strand break (DSB) repair. Furthermore, we found that the SV genes ESCO2 (establishment of sister chromatid cohesion N-acetyltransferase 2) and BRDT (bromodomain testis associated) may be involved in meiotic arrest of cattle-yak spermatocytes. Overall, our research provides a valuable database for identifying structural variant loci that contribute to hybrid sterility.

Genome-wide association testing beyond SNPs

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Biparental graph strategy to represent and analyze hybrid plant genomes

Hybrid plants are found extensively in the wild, and they often demonstrate superior performance of complex traits over their parents and other selfing plants. This phenomenon, known as heterosis, has been extensively applied in plant breeding for decades. However, the process of decoding hybrid plant genomes has seriously lagged due to the challenges associated with genome assembly and the lack of appropriate methodologies for their subsequent representation and analysis. Here, we present the assembly and analysis of 2 hybrids, an intraspecific hybrid between 2 maize (Zea mays ssp. mays) inbred lines and an interspecific hybrid between maize and its wild relative teosinte (Z. mays ssp. parviglumis), utilizing a combination of PacBio High Fidelity sequencing and chromatin conformation capture sequencing data. The haplotypic assemblies are well phased at chromosomal scale, successfully resolving the complex loci with extensive parental structural variations (SVs). By integrating into a biparental genome graph, the haplotypic assemblies can facilitate downstream short-read-based SV calling and allele-specific gene expression analysis, demonstrating outstanding advantages over a single linear genome. Our work offers a comprehensive workflow that aims to facilitate the decoding of numerous hybrid plant genomes, particularly those with unknown or inaccessible parentage, thereby enhancing our understanding of genome evolution and heterosis.

Detection of mosaic and population-level structural variants with Sniffles2

Calling structural variations (SVs) is technically challenging, but using long reads remains the most accurate way to identify complex genomic alterations. Here we present Sniffles2, which improves over current methods by implementing a repeat aware clustering coupled with a fast consensus sequence and coverage-adaptive filtering. Sniffles2 is 11.8 times faster and 29% more accurate than state-of-the-art SV callers across different coverages (5-50×), sequencing technologies (ONT and HiFi) and SV types. Furthermore, Sniffles2 solves the problem of family-level to population-level SV calling to produce fully genotyped VCF files. Across 11 probands, we accurately identified causative SVs around MECP2, including highly complex alleles with three overlapping SVs. Sniffles2 also enables the detection of mosaic SVs in bulk long-read data. As a result, we identified multiple mosaic SVs in brain tissue from a patient with multiple system atrophy. The identified SV showed a remarkable diversity within the cingulate cortex, impacting both genes involved in neuron function and repetitive elements.

Performance of somatic structural variant calling in lung cancer using Oxford Nanopore sequencing technology

BACKGROUND

Lung cancer is a heterogeneous disease and the primary cause of cancer-related mortality worldwide. Somatic mutations, including large structural variants, are important biomarkers in lung cancer for selecting targeted therapy. Genomic studies in lung cancer have been conducted using short-read sequencing. Emerging long-read sequencing technologies are a promising alternative to study somatic structural variants, however there is no current consensus on how to process data and call somatic events. In this study, we preformed whole genome sequencing of lung cancer and matched non-tumour samples using long and short read sequencing to comprehensively benchmark three sequence aligners and seven structural variant callers comprised of generic callers (SVIM, Sniffles2, DELLY in generic mode and cuteSV) and somatic callers (Severus, SAVANA, nanomonsv and DELLY in somatic modes).

RESULTS

Different combinations of aligners and variant callers influenced somatic structural variant detection. The choice of caller had a significant influence on somatic structural variant detection in terms of variant type, size, sensitivity, and accuracy. The performance of each variant caller was assessed by comparing to somatic structural variants identified by short-read sequencing. When compared to somatic structural variants detected with short-read sequencing, more events were detected with long-read sequencing. The mean recall of somatic variant events identified by long-read sequencing was higher for the somatic callers (72%) than generic callers (53%). Among the somatic callers when using the minimap2 aligner, SAVANA and Severus achieved the highest recall at 79.5% and 79.25% respectively, followed by nanomonsv with a recall of 72.5%.

CONCLUSION

Long-read sequencing can identify somatic structural variants in clincal samples. The longer reads have the potential to improve our understanding of cancer development and inform personalized cancer treatment.

FindCSV: a long-read based method for detecting complex structural variations

BACKGROUND

Structural variations play a significant role in genetic diseases and evolutionary mechanisms. Extensive research has been conducted over the past decade to detect simple structural variations, leading to the development of well-established detection methods. However, recent studies have highlighted the potentially greater impact of complex structural variations on individuals compared to simple structural variations. Despite this, the field still lacks precise detection methods specifically designed for complex structural variations. Therefore, the development of a highly efficient and accurate detection method is of utmost importance.

RESULT

In response to this need, we propose a novel method called FindCSV, which leverages deep learning techniques and consensus sequences to enhance the detection of SVs using long-read sequencing data. Compared to current methods, FindCSV performs better in detecting complex and simple structural variations.

CONCLUSIONS

FindCSV is a new method to detect complex and simple structural variations with reasonable accuracy in real and simulated data. The source code for the program is available at https://github.com/nwpuzhengyan/FindCSV .

Complex genetic variation in nearly complete human genomes

Diverse sets of complete human genomes are required to construct a pangenome reference and to understand the extent of complex structural variation. Here, we sequence 65 diverse human genomes and build 130 haplotype-resolved assemblies (130 Mbp median continuity), closing 92% of all previous assembly gaps1,2 and reaching telomere-to-telomere (T2T) status for 39% of the chromosomes. We highlight complete sequence continuity of complex loci, including the major histocompatibility complex (MHC), SMN1/SMN2, NBPF8, and AMY1/AMY2, and fully resolve 1,852 complex structural variants (SVs). In addition, we completely assemble and validate 1,246 human centromeres. We find up to 30-fold variation in α-satellite high-order repeat (HOR) array length and characterize the pattern of mobile element insertions into α-satellite HOR arrays. While most centromeres predict a single site of kinetochore attachment, epigenetic analysis suggests the presence of two hypomethylated regions for 7% of centromeres. Combining our data with the draft pangenome reference1 significantly enhances genotyping accuracy from short-read data, enabling whole-genome inference3 to a median quality value (QV) of 45. Using this approach, 26,115 SVs per sample are detected, substantially increasing the number of SVs now amenable to downstream disease association studies.

Haplotype-based pangenomes reveal genetic variations and climate adaptations in moso bamboo populations

Moso bamboo (Phyllostachys edulis), an ecologically and economically important forest species in East Asia, plays vital roles in carbon sequestration and climate change mitigation. However, intensifying climate change threatens moso bamboo survival. Here we generate high-quality haplotype-based pangenome assemblies for 16 representative moso bamboo accessions and integrated these assemblies with 427 previously resequenced accessions. Characterization of the haplotype-based pangenome reveals extensive genetic variation, predominantly between haplotypes rather than within accessions. Many genes with allele-specific expression patterns are implicated in climate responses. Integrating spatiotemporal climate data reveals more than 1050 variations associated with pivotal climate factors, including temperature and precipitation. Climate-associated variations enable the prediction of increased genetic risk across the northern and western regions of China under future emissions scenarios, underscoring the threats posed by rising temperatures. Our integrated haplotype-based pangenome elucidates moso bamboo's local climate adaptation mechanisms and provides critical genomic resources for addressing intensifying climate pressures on this essential bamboo. More broadly, this study demonstrates the power of long-read sequencing in dissecting adaptive traits in climate-sensitive species, advancing evolutionary knowledge to support conservation.

Personalized pangenome references

Pangenomes reduce reference bias by representing genetic diversity better than a single reference sequence. Yet when comparing a sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with by filtering rare variants. However, this blunt heuristic both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach that imputes a personalized pangenome subgraph by sampling local haplotypes according to k-mer counts in the reads. We implement the approach in the vg toolkit ( https://github.com/vgteam/vg ) for the Giraffe short-read aligner and compare its accuracy to state-of-the-art methods using human pangenome graphs from the Human Pangenome Reference Consortium. This reduces small variant genotyping errors by four times relative to the Genome Analysis Toolkit and makes short-read structural variant genotyping of known variants competitive with long-read variant discovery methods.

MEHunter: transformer-based mobile element variant detection from long reads

SUMMARY

Mobile genetic elements (MEs) are heritable mutagens that significantly contribute to genetic diseases. The advent of long-read sequencing technologies, capable of resolving large DNA fragments, offers promising prospects for the comprehensive detection of ME variants (MEVs). However, achieving high precision while maintaining recall performance remains challenging mainly brought by the variable length and similar content of MEV signatures, which are often obscured by the noise in long reads. Here, we propose MEHunter, a high-performance MEV detection approach utilizing a fine-tuned transformer model adept at identifying potential MEVs with fragmented features. Benchmark experiments on both simulated and real datasets demonstrate that MEHunter consistently achieves higher accuracy and sensitivity than the state-of-the-art tools. Furthermore, it is capable of detecting novel potentially individual-specific MEVs that have been overlooked in published population projects.

AVAILABILITY AND IMPLEMENTATION

MEHunter is available from https://github.com/120L021101/MEHunter.

CSV-Filter: a deep learning-based comprehensive structural variant filtering method for both short and long reads

MOTIVATION

Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are needed.

RESULTS

We developed a novel deep learning-based SV filtering tool, CSV-Filter, for both short and long reads. CSV-Filter uses a novel multi-level grayscale image encoding method based on CIGAR strings of the alignment results and employs image augmentation techniques to improve SV feature extraction. CSV-Filter also utilizes self-supervised learning networks for transfer as classification models, and employs mixed-precision operations to accelerate training. The experiments showed that the integration of CSV-Filter with popular SV detection tools could considerably reduce false positive SVs for short and long reads, while maintaining true positive SVs almost unchanged. Compared with DeepSVFilter, a SV filtering tool for short reads, CSV-Filter could recognize more false positive calls and support long reads as an additional feature.

AVAILABILITY AND IMPLEMENTATION

https://github.com/xzyschumacher/CSV-Filter.

A telomere-to-telomere cotton genome assembly reveals centromere evolution and a Mutator transposon-linked module regulating embryo development

Assembly of complete genomes can reveal functional genetic elements missing from draft sequences. Here we present the near-complete telomere-to-telomere and contiguous genome of the cotton species Gossypium raimondii. Our assembly identified gaps and misoriented or misassembled regions in previous assemblies and produced 13 centromeres, with 25 chromosomal ends having telomeres. In contrast to satellite-rich Arabidopsis and rice centromeres, cotton centromeres lack phased CENH3 nucleosome positioning patterns and probably evolved by invasion from long terminal repeat retrotransposons. In-depth expression profiling of transposable elements revealed a previously unannotated DNA transposon (MuTC01) that interacts with miR2947 to produce trans-acting small interfering RNAs (siRNAs), one of which targets the newly evolved LEC2 (LEC2b) to produce phased siRNAs. Systematic genome editing experiments revealed that this tripartite module, miR2947-MuTC01-LEC2b, controls the morphogenesis of complex folded embryos characteristic of Gossypium and its close relatives in the cotton tribe. Our study reveals a trans-acting siRNA-based tripartite regulatory pathway for embryo development in higher plants.

Comprehensive identification of pathogenic variants in retinoblastoma by long- and short-read sequencing

Retinoblastoma (RB) is the most common intraocular malignancy in childhood. The causal variants in RB are mostly characterized by previously used short-read sequencing (SRS) analysis, which has technical limitations in identifying structural variants (SVs) and phasing information. Long-read sequencing (LRS) technology has advantages over SRS in detecting SVs, phased genetic variants, and methylation. In this study, we comprehensively characterized the genetic landscape of RB using combinatorial LRS and SRS of 16 RB tumors and 16 matched blood samples. We detected a total of 232 somatic SVs, with an average of 14.5 SVs per sample across the whole genome in our cohort. We identified 20 distinct pathogenic variants disrupting RB1 gene, including three novel small variants and five somatic SVs. We found more somatic SVs were detected from LRS than SRS (140 vs. 122) in RB samples with WGS data, particularly the insertions (18 vs. 1). Furthermore, our analysis shows that, with the exception of one sample who lacked the methylation data, all samples presented biallelic inactivation of RB1 in various forms, including two cases with the biallelic hypermethylated promoter and four cases with compound heterozygous mutations which were missing in SRS analysis. By inferring relative timing of somatic events, we reveal the genetic progression that RB1 disruption early and followed by copy number changes, including amplifications of Chr2p and deletions of Chr16q, during RB tumorigenesis. Altogether, we characterize the comprehensive genetic landscape of RB, providing novel insights into the genetic alterations and mechanisms contributing to RB initiation and development. Our work also establishes a framework to analyze genomic landscape of cancers based on LRS data.

VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing

Structural variants (SVs) significantly contribute to human genome diversity and play a crucial role in precision medicine. Although advancements in single-molecule long-read sequencing offer a groundbreaking resource for SV detection, identifying SV breakpoints and sequences accurately and robustly remains challenging. We introduce VolcanoSV, an innovative hybrid SV detection pipeline that utilizes both a reference genome and local de novo assembly to generate a phased diploid assembly. VolcanoSV uses phased SNPs and unique k-mer similarity analysis, enabling precise haplotype-resolved SV discovery. VolcanoSV is adept at constructing comprehensive genetic maps encompassing SNPs, small indels, and all types of SVs, making it well-suited for human genomics studies. Our extensive experiments demonstrate that VolcanoSV surpasses state-of-the-art assembly-based tools in the detection of insertion and deletion SVs, exhibiting superior recall, precision, F1 scores, and genotype accuracy across a diverse range of datasets, including low-coverage (10x) datasets. VolcanoSV outperforms assembly-based tools in the identification of complex SVs, including translocations, duplications, and inversions, in both simulated and real cancer data. Moreover, VolcanoSV is robust to various evaluation parameters and accurately identifies breakpoints and SV sequences.

Diagnostic utility of DNA methylation analysis in genetically unsolved pediatric epilepsies and CHD2 episignature refinement

Sequence-based genetic testing identifies causative variants in ~ 50% of individuals with developmental and epileptic encephalopathies (DEEs). Aberrant changes in DNA methylation are implicated in various neurodevelopmental disorders but remain unstudied in DEEs. We interrogate the diagnostic utility of genome-wide DNA methylation array analysis on peripheral blood samples from 582 individuals with genetically unsolved DEEs. We identify rare differentially methylated regions (DMRs) and explanatory episignatures to uncover causative and candidate genetic etiologies in 12 individuals. Using long-read sequencing, we identify DNA variants underlying rare DMRs, including one balanced translocation, three CG-rich repeat expansions, and four copy number variants. We also identify pathogenic variants associated with episignatures. Finally, we refine the CHD2 episignature using an 850 K methylation array and bisulfite sequencing to investigate potential insights into CHD2 pathophysiology. Our study demonstrates the diagnostic yield of genome-wide DNA methylation analysis to identify causal and candidate variants as 2% (12/582) for unsolved DEE cases.

Pan-genome analysis of 13 Spinacia accessions reveals structural variations associated with sex chromosome evolution and domestication traits in spinach

Structural variations (SVs) are major genetic variants that can be involved in the origin, adaptation and domestication of species. However, the identification and characterization of SVs in Spinacia species are rare due to the lack of a pan-genome. Here, we report eight chromosome-scale assemblies of cultivated spinach and its two wild species. After integration with five existing assemblies, we constructed a comprehensive Spinacia pan-genome and identified 193 661 pan-SVs, which were genotyped in 452 Spinacia accessions. Our pan-SVs enabled genome-wide association study identified signals associated with sex and clarified the evolutionary direction of spinach. Most sex-linked SVs (86%) were biased to occur on the Y chromosome during the evolution of the sex-linked region, resulting in reduced Y-linked gene expression. The frequency of pan-SVs among Spinacia accessions further illustrated the contribution of these SVs to domestication, such as bolting time and seed dormancy. Furthermore, compared with SNPs, pan-SVs act as efficient variants in genomic selection (GS) because of their ability to capture missing heritability information and higher prediction accuracy. Overall, this study provides a valuable resource for spinach genomics and highlights the potential utility of pan-SV in crop improvement and breeding programmes.

Increased AID results in mutations at the CRLF2 locus implicated in Latin American ALL health disparities

Activation-induced cytidine deaminase (AID) is a B cell-specific mutator required for antibody diversification. However, it is also implicated in the etiology of several B cell malignancies. Evaluating the AID-induced mutation load in patients at-risk for certain blood cancers is critical in assessing disease severity and treatment options. We have developed a digital PCR (dPCR) assay that allows us to quantify mutations resulting from AID modification or DNA double-strand break (DSB) formation and repair at sites known to be prone to DSBs. Implementation of this assay shows that increased AID levels in immature B cells increase genome instability at loci linked to chromosomal translocation formation. This includes the CRLF2 locus that is often involved in translocations associated with a subtype of acute lymphoblastic leukemia (ALL) that disproportionately affects Hispanics, particularly those with Latin American ancestry. Using dPCR, we characterize the CRLF2 locus in B cell-derived genomic DNA from both Hispanic ALL patients and healthy Hispanic donors and found increased mutations in both, suggesting that vulnerability to DNA damage at CRLF2 may be driving this health disparity. Our ability to detect and quantify these mutations will potentiate future risk identification, early detection of cancers, and reduction of associated cancer health disparities.

Comprehensive assessment of long-read sequencing platforms and calling algorithms for detection of copy number variation

Copy number variations (CNVs) play pivotal roles in disease susceptibility and have been intensively investigated in human disease studies. Long-read sequencing technologies offer opportunities for comprehensive structural variation (SV) detection, and numerous methodologies have been developed recently. Consequently, there is a pressing need to assess these methods and aid researchers in selecting appropriate techniques for CNV detection using long-read sequencing. Hence, we conducted an evaluation of eight CNV calling methods across 22 datasets from nine publicly available samples and 15 simulated datasets, covering multiple sequencing platforms. The overall performance of CNV callers varied substantially and was influenced by the input dataset type, sequencing depth, and CNV type, among others. Specifically, the PacBio CCS sequencing platform outperformed PacBio CLR and Nanopore platforms regarding CNV detection recall rates. A sequencing depth of 10x demonstrated the capability to identify 85% of the CNVs detected in a 50x dataset. Moreover, deletions were more generally detectable than duplications. Among the eight benchmarked methods, cuteSV, Delly, pbsv, and Sniffles2 demonstrated superior accuracy, while SVIM exhibited high recall rates.

macroH2A1 drives nucleosome dephasing and genome instability in histone humanized yeast

In addition to replicative histones, eukaryotic genomes encode a repertoire of non-replicative variant histones, providing additional layers of structural and epigenetic regulation. Here, we systematically replace individual replicative human histones with non-replicative human variant histones using a histone replacement system in yeast. We show that variants H2A.J, TsH2B, and H3.5 complement their respective replicative counterparts. However, macroH2A1 fails to complement, and its overexpression is toxic in yeast, negatively interacting with yeast's native histones and kinetochore genes. To isolate yeast with macroH2A1 chromatin, we uncouple the effects of its macro and histone fold domains, revealing that both domains suffice to override native nucleosome positioning. Furthermore, both uncoupled constructs of macroH2A1 exhibit lower nucleosome occupancy, decreased short-range chromatin interactions (<20 kb), disrupted centromeric clustering, and increased chromosome instability. Our observations demonstrate that lack of a canonical histone H2A dramatically alters chromatin organization in yeast, leading to genome instability and substantial fitness defects.

A comprehensive review of deep learning-based variant calling methods

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data

BACKGROUND

Structural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection.

RESULTS

This study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines' detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking .

CONCLUSIONS

This study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.

HapKled: a haplotype-aware structural variant calling approach for Oxford nanopore sequencing data

Introduction: Structural Variants (SVs) are a type of variation that can significantly influence phenotypes and cause diseases. Thus, the accurate detection of SVs is a vital part of modern genetic analysis. The advent of long-read sequencing technology ushers in a new era of more accurate and comprehensive SV calling, and many tools have been developed to call SVs using long-read data. Haplotype-tagging is a procedure that can tag haplotype information on reads and can thus potentially improve the SV detection; nevertheless, few methods make use of this information. In this article, we introduce HapKled, a new SV detection tool that can accurately detect SVs from Oxford Nanopore Technologies (ONT) long-read alignment data. Methods: HapKled utilizes haplotype information underlying alignment data by conducting haplotype-tagging using Whatshap on the reads to improve the detection performance, with three unique calling mechanics including altering clustering conditions according to haplotype information of signatures, determination of similar SVs based on haplotype information, and slack filtering conditions based on haplotype quality. Results: In our evaluations, HapKled outperformed state-of-the-art tools and can deliver better SV detection results on both simulated and real sequencing data. The code and experiments of HapKled can be obtained from https://github.com/CoREse/HapKled. Discussion: With the superb SV detection performance that HapKled can deliver, HapKled could be useful in bioinformatics research, clinical diagnosis, and medical research and development.

Simultaneous de novo calling and phasing of genetic variants at chromosome-scale using NanoStrand-seq

The successful accomplishment of the first telomere-to-telomere human genome assembly, T2T-CHM13, marked a milestone in achieving completeness of the human reference genome. The upcoming era of genome study will focus on fully phased diploid genome assembly, with an emphasis on genetic differences between individual haplotypes. Most existing sequencing approaches only achieved localized haplotype phasing and relied on additional pedigree information for further whole-chromosome scale phasing. The short-read-based Strand-seq method is able to directly phase single nucleotide polymorphisms (SNPs) at whole-chromosome scale but falls short when it comes to phasing structural variations (SVs). To shed light on this issue, we developed a Nanopore sequencing platform-based Strand-seq approach, which we named NanoStrand-seq. This method allowed for de novo SNP calling with high precision (99.52%) and acheived a superior phasing accuracy (0.02% Hamming error rate) at whole-chromosome scale, a level of performance comparable to Strand-seq for haplotype phasing of the GM12878 genome. Importantly, we demonstrated that NanoStrand-seq can efficiently resolve the MHC locus, a highly polymorphic genomic region. Moreover, NanoStrand-seq enabled independent direct calling and phasing of deletions and insertions at whole-chromosome level; when applied to long genomic regions of SNP homozygosity, it outperformed the strategy that combined Strand-seq with bulk long-read sequencing. Finally, we showed that, like Strand-seq, NanoStrand-seq was also applicable to primary cultured cells. Together, here we provided a novel methodology that enabled interrogation of a full spectrum of haplotype-resolved SNPs and SVs at whole-chromosome scale, with broad applications for species with diploid or even potentially polypoid genomes.

Insights into spinach domestication from genome sequences of two wild spinach progenitors, Spinacia turkestanica and Spinacia tetrandra

Cultivated spinach (Spinacia oleracea) is a dioecious species. We report high-quality genome sequences for its two closest wild relatives, Spinacia turkestanica and Spinacia tetrandra, which are also dioecious, and are used to study the genetics of spinach domestication. Using a combination of genomic approaches, we assembled genomes of both these species and analyzed them in comparison with the previously assembled S. oleracea genome. These species diverged c. 6.3 million years ago (Ma), while cultivated spinach split from S. turkestanica 0.8 Ma. In all three species, all six chromosomes include very large gene-poor, repeat-rich regions, which, in S. oleracea, are pericentromeric regions with very low recombination rates in both male and female genetic maps. We describe population genomic evidence that the similar regions in the wild species also recombine rarely. We characterized 282 structural variants (SVs) that have been selected during domestication. These regions include genes associated with leaf margin type and flowering time. We also describe evidence that the downy mildew resistance loci of cultivated spinach are derived from introgression from both wild spinach species. Collectively, this study reveals the genome architecture of spinach assemblies and highlights the importance of SVs during the domestication of cultivated spinach.

Population genomics highlights structural variations in local adaptation to saline coastal environments in woolly grape

Structural variations (SVs) are a feature of plant genomes that has been largely unexplored despite their significant impact on plant phenotypic traits and local adaptation to abiotic and biotic stress. In this study, we employed woolly grape (Vitis retordii), a species native to the tropical and subtropical regions of East Asia with both coastal and inland habitats, as a valuable model for examining the impact of SVs on local adaptation. We assembled a haplotype-resolved chromosomal reference genome for woolly grape, and conducted population genetic analyses based on whole-genome sequencing (WGS) data from coastal and inland populations. The demographic analyses revealed recent bottlenecks in all populations and asymmetric gene flow from the inland to the coastal population. In total, 1,035 genes associated with plant adaptive regulation for salt stress, radiation, and environmental adaptation were detected underlying local selection by SVs and SNPs in the coastal population, of which 37.29% and 65.26% were detected by SVs and SNPs, respectively. Candidate genes such as FSD2, RGA1, and AAP8 associated with salt tolerance were found to be highly differentiated and selected during the process of local adaptation to coastal habitats in SV regions. Our study highlights the importance of SVs in local adaptation; candidate genes related to salt stress and climatic adaptation to tropical and subtropical environments are important genomic resources for future breeding programs of grapevine and its rootstocks.

Rapid evolution of piRNA clusters in the Drosophila melanogaster ovary

The piRNA pathway is a highly conserved mechanism to repress transposable element (TE) activity in the animal germline via a specialized class of small RNAs called piwi-interacting RNAs (piRNAs). piRNAs are produced from discrete genomic regions called piRNA clusters (piCs). Although the molecular processes by which piCs function are relatively well understood in Drosophila melanogaster, much less is known about the origin and evolution of piCs in this or any other species. To investigate piC origin and evolution, we use a population genomic approach to compare piC activity and sequence composition across eight geographically distant strains of D. melanogaster with high-quality long-read genome assemblies. We perform annotations of ovary piCs and genome-wide TE content in each strain. Our analysis uncovers extensive variation in piC activity across strains and signatures of rapid birth and death of piCs. Most TEs inferred to be recently active show an enrichment of insertions into old and large piCs, consistent with the previously proposed "trap" model of piC evolution. In contrast, a small subset of active LTR families is enriched for the formation of new piCs, suggesting that these TEs have higher proclivity to form piCs. Thus, our findings uncover processes leading to the origin of piCs. We propose that piC evolution begins with the emergence of piRNAs from individual insertions of a few select TE families prone to seed new piCs that subsequently expand by accretion of insertions from most other TE families during evolution to form larger "trap" clusters. Our study shows that TEs themselves are the major force driving the rapid evolution of piCs.

Fine mapping a QTL for BYDV-PAV resistance in maize

Barley yellow dwarf (BYD) is one of the economically most important virus diseases of cereals worldwide, causing yield losses up to 80%. The means to control BYD are limited, and the use of genetically resistant cultivars is the most economical and environmentally friendly approach. The objectives of this study were i) to identify the causative gene for BYD virus (BYDV)-PAV resistance in maize, ii) to identify single nucleotide polymorphisms and/or structural variations in the gene sequences, which may cause differing susceptibilities to BYDV-PAV of maize inbreds, and iii) to characterize the effect of BYDV-PAV infection on gene expression of susceptible, tolerant, and resistant maize inbreds. Using two biparental mapping populations, we could reduce a previously published quantitative trait locus for BYDV-PAV resistance in maize to ~ 0.3 Mbp, comprising nine genes. Association mapping and gene expression analysis further reduced the number of candidate genes for BYDV-PAV resistance in maize to two: Zm00001eb428010 and Zm00001eb428020. The predicted functions of these genes suggest that they confer BYDV-PAV resistance either via interfering with virus replication or by inducing reactive oxygen species signaling. The gene sequence of Zm00001eb428010 is affected by a 54 bp deletion in the 5`-UTR and a protein altering variant in BYDV-PAV-resistant maize inbreds but not in BYDV-PAV-susceptible and -tolerant inbreds. This finding suggests that altered abundance and/or properties of the proteins encoded by Zm00001eb428010 may lead to BYDV-PAV resistance.

The Utility of Long-Read Sequencing in Diagnosing Genetic Autosomal Recessive Parkinson's Disease: a genetic screening study

Background

Mutations within the genes PRKN and PINK1 are the leading cause of early onset autosomal recessive Parkinson's disease (PD). However, the genetic cause of most early-onset PD (EOPD) cases still remains unresolved. Long-read sequencing has successfully identified many pathogenic structural variants that cause disease, but this technology has not been widely applied to PD. We recently identified the genetic cause of EOPD in a pair of monozygotic twins by uncovering a complex structural variant that spans over 7 Mb, utilizing Oxford Nanopore Technologies (ONT) long-read sequencing. In this study, we aimed to expand on this and assess whether a second variant could be detected with ONT long-read sequencing in other unresolved EOPD cases reported to carry one heterozygous variant in PRKN or PINK1.

Methods

ONT long-read sequencing was performed on patients with one reported PRKN/PINK1 pathogenic variant. EOPD patients with an age at onset younger than 50 were included in this study. As a positive control, we also included EOPD patients who had already been identified to carry two known PRKN pathogenic variants. Initial genetic testing was performed using either short-read targeted panel sequencing for single nucleotide variants and multiplex ligation-dependent probe amplification (MLPA) for copy number variants.

Results

48 patients were included in this study (PRKN "one-variant" n = 24, PINK1 "one-variant" n = 12, PRKN "two-variants" n = 12). Using ONT long-read sequencing, we detected a second pathogenic variant in six PRKN "one-variant" patients (26%, 6/23) but none in the PINK1 "one-variant" patients (0%, 0/12). Long-read sequencing identified one case with a complex inversion, two instances of structural variant overlap, and three cases of duplication. In addition, in the positive control PRKN "two-variants" group, we were able to identify both pathogenic variants in PRKN in all the patients (100%, 12/12).

Conclusions

This data highlights that ONT long-read sequencing is a powerful tool to identify a pathogenic structural variant at the PRKN locus that is often missed by conventional methods. Therefore, for cases where conventional methods fail to detect a second variant for EOPD, long-read sequencing should be considered as an alternative and complementary approach.

Leaf: an ultrafast filter for population-scale long-read SV detection

Advances in sequencing technology have facilitated population-scale long-read structural variant (SV) detection. Arguably, one of the main challenges in population-scale analysis is developing effective computational pipelines. Here, we present a new filter-based pipeline for population-scale long-read SV detection. It better captures SV signals at an early stage than conventional assembly-based or alignment-based pipelines. Assessments in this work suggest that the filter-based pipeline helps better resolve intra-read rearrangements. Moreover, it is also more computationally efficient than conventional pipelines and thus may facilitate population-scale long-read applications.

Expanding the genetic toolbox for the obligate human pathogen Streptococcus pyogenes

Genetic tools form the basis for the study of molecular mechanisms. Despite many recent advances in the field of genetic engineering in bacteria, genetic toolsets remain scarce for non-model organisms, such as the obligatory human pathogen Streptococcus pyogenes. To overcome this limitation and enable the straightforward investigation of gene functions in S. pyogenes, we have developed a comprehensive genetic toolset. By adapting and combining different tools previously applied in other Gram-positive bacteria, we have created new replicative and integrative plasmids for gene expression and genetic manipulation, constitutive and inducible promoters as well as fluorescence reporters for S. pyogenes. The new replicative plasmids feature low- and high-copy replicons combined with different resistance cassettes and a standardized multiple cloning site for rapid cloning procedures. We designed site-specific integrative plasmids and verified their integration by nanopore sequencing. To minimize the effect of plasmid integration on bacterial physiology, we screened publicly available RNA-sequencing datasets for transcriptionally silent sites. We validated this approach by designing the integrative plasmid pSpy0K6 targeting the transcriptionally silent gene SPy_1078. Analysis of the activity of different constitutive promoters indicated a wide variety of strengths, with the lactococcal promoter P 23 showing the strongest activity and the synthetic promoter P xylS2 showing the weakest activity. Further, we assessed the functionality of three inducible regulatory elements including a zinc- and an IPTG-inducible promoter as well as an erythromycin-inducible riboswitch that showed low-to-no background expression and high inducibility. Additionally, we demonstrated the applicability of two codon-optimized fluorescent proteins, mNeongreen and mKate2, as reporters in S. pyogenes. We therefore adapted the chemically defined medium called RPMI4Spy that showed reduced autofluorescence and enabled efficient signal detection in plate reader assays and fluorescence microscopy. Finally, we developed a plasmid-based system for genome engineering in S. pyogenes featuring the counterselection marker pheS*, which enabled the scarless deletion of the sagB gene. This new toolbox simplifies previously laborious genetic manipulation procedures and lays the foundation for new methodologies to study gene functions in S. pyogenes, leading to a better understanding of its virulence mechanisms and physiology.

Deletion variants calling in third-generation sequencing data based on a dual-attention mechanism

Deletion is a crucial type of genomic structural variation and is associated with numerous genetic diseases. The advent of third-generation sequencing technology has facilitated the analysis of complex genomic structures and the elucidation of the mechanisms underlying phenotypic changes and disease onset due to genomic variants. Importantly, it has introduced innovative perspectives for deletion variants calling. Here we propose a method named Dual Attention Structural Variation (DASV) to analyze deletion structural variations in sequencing data. DASV converts gene alignment information into images and integrates them with genomic sequencing data through a dual attention mechanism. Subsequently, it employs a multi-scale network to precisely identify deletion regions. Compared with four widely used genome structural variation calling tools: cuteSV, SVIM, Sniffles and PBSV, the results demonstrate that DASV consistently achieves a balance between precision and recall, enhancing the F1 score across various datasets. The source code is available at https://github.com/deconvolution-w/DASV.

SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies

Structural variation (SV) is an important form of genomic variation that influences gene function and expression by altering the structure of the genome. Although long-read data have been proven to better characterize SVs, SVs detected from noisy long-read data still include a considerable portion of false-positive calls. To accurately detect SVs in long-read data, we present SVDF, a method that employs a learning-based noise filtering strategy and an SV signature-adaptive clustering algorithm, for effectively reducing the likelihood of false-positive events. Benchmarking results from multiple orthogonal experiments demonstrate that, across different sequencing platforms and depths, SVDF achieves higher calling accuracy for each sample compared to several existing general SV calling tools. We believe that, with its meticulous and sensitive SV detection capability, SVDF can bring new opportunities and advancements to cutting-edge genomic research.

The super-pangenome of Populus unveils genomic facets for its adaptation and diversification in widespread forest trees

Understanding the underlying mechanisms and links between genome evolution and adaptive innovations stands as a key goal in evolutionary studies. Poplars, among the world's most widely distributed and cultivated trees, exhibit extensive phenotypic diversity and environmental adaptability. In this study, we present a genus-level super-pangenome comprising 19 Populus genomes, revealing the likely pivotal role of private genes in facilitating local environmental and climate adaptation. Through the integration of pangenomes with transcriptomes, methylomes, and chromatin accessibility mapping, we unveil that the evolutionary trajectories of pangenes and duplicated genes are closely linked to local genomic landscapes of regulatory and epigenetic architectures, notably CG methylation in gene-body regions. Further comparative genomic analyses have enabled the identification of 142 202 structural variants across species that intersect with a significant number of genes and contribute substantially to both phenotypic and adaptive divergence. We have experimentally validated a ∼180-bp presence/absence variant affecting the expression of the CUC2 gene, crucial for leaf serration formation. Finally, we developed a user-friendly web-based tool encompassing the multi-omics resources associated with the Populus super-pangenome (http://www.populus-superpangenome.com). Together, the present pioneering super-pangenome resource in forest trees not only aids in the advancement of breeding efforts of this globally important tree genus but also offers valuable insights into potential avenues for comprehending tree biology.

Comparative evaluation of SNVs, indels, and structural variations detected with short- and long-read sequencing data

Short- and long-read sequencing technologies are routinely used to detect DNA variants, including SNVs, indels, and structural variations (SVs). However, the differences in the quality and quantity of variants detected between short- and long-read data are not fully understood. In this study, we comprehensively evaluated the variant calling performance of short- and long-read-based SNV, indel, and SV detection algorithms (6 for SNVs, 12 for indels, and 13 for SVs) using a novel evaluation framework incorporating manual visual inspection. The results showed that indel-insertion calls greater than 10 bp were poorly detected by short-read-based detection algorithms compared to long-read-based algorithms; however, the recall and precision of SNV and indel-deletion detection were similar between short- and long-read data. The recall of SV detection with short-read-based algorithms was significantly lower in repetitive regions, especially for small- to intermediate-sized SVs, than that detected with long-read-based algorithms. In contrast, the recall and precision of SV detection in nonrepetitive regions were similar between short- and long-read data. These findings suggest the need for refined strategies, such as incorporating multiple variant detection algorithms, to generate a more complete set of variants using short-read data.

Characterizing structural variants based on graph-genotyping provides insights into pig domestication and local adaption

Structural variants (SVs), such as deletions (DELs) and insertions (INSs), contribute substantially to pig genetic diversity and phenotypic variation. Using a library of SVs discovered from long-read primary assemblies and short-read sequenced genomes, we map pig genomic SVs with a graph-based method for re-genotyping SVs in 402 genomes. Our results demonstrate that those SVs harboring specific trait-associated genes may greatly shape pig domestication and local adaptation. Further characterization of SVs reveals that some population-stratified SVs may alter the transcription of genes by affecting regulatory elements. We identify that the genotypes of two DELs (296-bp DEL, chr7: 52,172,101-52,172,397; 278-bp DEL, chr18: 23,840,143-23,840,421) located in muscle-specific enhancers are associated with the expression of target genes related to meat quality (FSD2) and muscle fiber hypertrophy (LMOD2 and WASL) in pigs. Our results highlight the role of SVs in domestic porcine evolution, and the identified candidate functional genes and SVs are valuable resources for future genomic research and breeding programs in pigs.

Integration of transcriptomics and long-read genomics prioritizes structural variants in rare disease

Rare structural variants (SVs) - insertions, deletions, and complex rearrangements - can cause Mendelian disease, yet they remain difficult to accurately detect and interpret. We sequenced and analyzed Oxford Nanopore long-read genomes of 68 individuals from the Undiagnosed Disease Network (UDN) with no previously identified diagnostic mutations from short-read sequencing. Using our optimized SV detection pipelines and 571 control long-read genomes, we detected 716 long-read rare (MAF < 0.01) SV alleles per genome on average, achieving a 2.4x increase from short-reads. To characterize the functional effects of rare SVs, we assessed their relationship with gene expression from blood or fibroblasts from the same individuals, and found that rare SVs overlapping enhancers were enriched (LOR = 0.46) near expression outliers. We also evaluated tandem repeat expansions (TREs) and found 14 rare TREs per genome; notably these TREs were also enriched near overexpression outliers. To prioritize candidate functional SVs, we developed Watershed-SV, a probabilistic model that integrates expression data with SV-specific genomic annotations, which significantly outperforms baseline models that don't incorporate expression data. Watershed-SV identified a median of eight high-confidence functional SVs per UDN genome. Notably, this included compound heterozygous deletions in FAM177A1 shared by two siblings, which were likely causal for a rare neurodevelopmental disorder. Our observations demonstrate the promise of integrating long-read sequencing with gene expression towards improving the prioritization of functional SVs and TREs in rare disease patients.

De novo and somatic structural variant discovery with SVision-pro

Long-read-based de novo and somatic structural variant (SV) discovery remains challenging, necessitating genomic comparison between samples. We developed SVision-pro, a neural-network-based instance segmentation framework that represents genome-to-genome-level sequencing differences visually and discovers SV comparatively between genomes without any prerequisite for inference models. SVision-pro outperforms state-of-the-art approaches, in particular, the resolving of complex SVs is improved, with low Mendelian error rates, high sensitivity of low-frequency SVs and reduced false-positive rates compared with SV merging approaches.

Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data

Long-read sequencing offers long contiguous DNA fragments, facilitating diploid genome assembly and structural variant (SV) detection. Efficient and robust algorithms for SV identification are crucial with increasing data availability. Alignment-based methods, favored for their computational efficiency and lower coverage requirements, are prominent. Alternative approaches, relying solely on available reads for de novo genome assembly and employing assembly-based tools for SV detection via comparison to a reference genome, demand significantly more computational resources. However, the lack of comprehensive benchmarking constrains our comprehension and hampers further algorithm development. Here we systematically compare 14 read alignment-based SV calling methods (including 4 deep learning-based methods and 1 hybrid method), and 4 assembly-based SV calling methods, alongside 4 upstream aligners and 7 assemblers. Assembly-based tools excel in detecting large SVs, especially insertions, and exhibit robustness to evaluation parameter changes and coverage fluctuations. Conversely, alignment-based tools demonstrate superior genotyping accuracy at low sequencing coverage (5-10×) and excel in detecting complex SVs, like translocations, inversions, and duplications. Our evaluation provides performance insights, highlighting the absence of a universally superior tool. We furnish guidelines across 31 criteria combinations, aiding users in selecting the most suitable tools for diverse scenarios and offering directions for further method development.

Benchmarking long-read aligners and SV callers for structural variation detection in Oxford nanopore sequencing data

Structural variants (SVs) are one of the significant types of DNA mutations and are typically defined as larger-than-50-bp genomic alterations that include insertions, deletions, duplications, inversions, and translocations. These modifications can profoundly impact the phenotypic characteristics and contribute to disorders like cancer, response to treatment, and infections. Four long-read aligners and five SV callers have been evaluated using three Oxford Nanopore NGS human genome datasets in terms of precision, recall, and F1-score statistical metrics, depth of coverage, and speed of analysis. The best SV caller regarding recall, precision, and F1-score when matched with different aligners at different coverage levels tend to vary depending on the dataset and the specific SV types being analyzed. However, based on our findings, Sniffles and CuteSV tend to perform well across different aligners and coverage levels, followed by SVIM, PBSV, and SVDSS in the last place. The CuteSV caller has the highest average F1-score (82.51%) and recall (78.50%), and Sniffles has the highest average precision value (94.33%). Minimap2 as an aligner and Sniffles as an SV caller act as a strong base for the pipeline of SV calling because of their high speed and reasonable accomplishment. PBSV has a lower average F1-score, precision, and recall and may generate more false positives and overlook some actual SVs. Our results are valuable in the comprehensive evaluation of popular SV callers and aligners as they provide insight into the performance of several long-read aligners and SV callers and serve as a reference for researchers in selecting the most suitable tools for SV detection.

Long-read sequencing unveils novel somatic variants and methylation patterns in the genetic information system of early lung cancer

Lung cancer poses a global health challenge, necessitating advanced diagnostics for improved outcomes. Intensive efforts are ongoing to pinpoint early detection biomarkers, such as genomic variations and DNA methylation, to elevate diagnostic precision. We conducted long-read sequencing on cancerous and adjacent non-cancerous tissues from a patient with lung adenocarcinoma. We identified somatic structural variations (SVs) specific to lung cancer by integrating data from various SV calling methods and differentially methylated regions (DMRs) that were distinct between these two tissue samples, revealing a unique methylation pattern associated with lung cancer. This study discovered over 40,000 somatic SVs and over 180,000 DMRs linked to lung cancer. We identified approximately 700 genes of significant relevance through comprehensive analysis, including genes intricately associated with many lung cancers, such as NOTCH1, SMOC2, CSMD2, and others. Furthermore, we observed that somatic SVs and DMRs were substantially enriched in several pathways, such as axon guidance signaling pathways, which suggests a comprehensive multi-omics impact on lung cancer progression across various biological investigation levels. These datasets can potentially serve as biomarkers for early lung cancer detection and may hold significant value in clinical diagnosis and treatment applications.

Assessing the efficacy of target adaptive sampling long-read sequencing through hereditary cancer patient genomes

Innovations in sequencing technology have led to the discovery of novel mutations that cause inherited diseases. However, many patients with suspected genetic diseases remain undiagnosed. Long-read sequencing technologies are expected to significantly improve the diagnostic rate by overcoming the limitations of short-read sequencing. In addition, Oxford Nanopore Technologies (ONT) offers adaptive sampling and computationally driven target enrichment technology. This enables more affordable intensive analysis of target gene regions compared to standard non-selective long-read sequencing. In this study, we developed an efficient computational workflow for target adaptive sampling long-read sequencing (TAS-LRS) and evaluated it through application to 33 genomes collected from suspected hereditary cancer patients. Our workflow can identify single nucleotide variants with nearly the same accuracy as the short-read platform and elucidate complex forms of structural variations. We also newly identified several SINE-R/VNTR/Alu (SVA) elements affecting the APC gene in two patients with familial adenomatous polyposis, as well as their sites of origin. In addition, we demonstrated that off-target reads from adaptive sampling, which is typically discarded, can be effectively used to accurately genotype common single-nucleotide polymorphisms (SNPs) across the entire genome, enabling the calculation of a polygenic risk score. Furthermore, we identified allele-specific MLH1 promoter hypermethylation in a Lynch syndrome patient. In summary, our workflow with TAS-LRS can simultaneously capture monogenic risk variants including complex structural variations, polygenic background as well as epigenetic alterations, and will be an efficient platform for genetic disease research and diagnosis.

When Livestock Genomes Meet Third-Generation Sequencing Technology: From Opportunities to Applications

Third-generation sequencing technology has found widespread application in the genomic, transcriptomic, and epigenetic research of both human and livestock genetics. This technology offers significant advantages in the sequencing of complex genomic regions, the identification of intricate structural variations, and the production of high-quality genomes. Its attributes, including long sequencing reads, obviation of PCR amplification, and direct determination of DNA/RNA, contribute to its efficacy. This review presents a comprehensive overview of third-generation sequencing technologies, exemplified by single-molecule real-time sequencing (SMRT) and Oxford Nanopore Technology (ONT). Emphasizing the research advancements in livestock genomics, the review delves into genome assembly, structural variation detection, transcriptome sequencing, and epigenetic investigations enabled by third-generation sequencing. A comprehensive analysis is conducted on the application and potential challenges of third-generation sequencing technology for genome detection in livestock. Beyond providing valuable insights into genome structure analysis and the identification of rare genes in livestock, the review ventures into an exploration of the genetic mechanisms underpinning exemplary traits. This review not only contributes to our understanding of the genomic landscape in livestock but also provides fresh perspectives for the advancement of research in this domain.

The Genome of Vitis zhejiang-adstricta Strengthens the Protection and Utilization of the Endangered Ancient Grape Endemic to China

Vitis zhejiang-adstricta (V. zhejiang-adstricta) is one of the most important and endangered wild grapes. It is a national key protected wild, rare and endangered ancient grape endemic to China and used as a candidate material for resistance breeding owing to its excellent significant disease resistance. Here, we present a high-quality chromosome-level assembly of V. zhejiang-adstricta (IB-VB-01), comprising 506.66 Mb assembled into 19 pseudo-chromosomes. The contig N50 length is 3.91 Mb with 31,196 annotated protein-coding genes. Comparative genome and evolutionary analyses illustrated that V. zhejiang-adstricta has a specific position in the evolution of East Asian Vitis and shared a common ancestor with Vitis vinifera during the divergence of the two species about 10.42 (between 9.34 and 11.12) Mya. The expanded gene families compared with those in plants were related to disease resistance, and constructed gene families were related to plant growth and primary metabolism. With the analysis of gene family expansion and contraction, the evolution of environmental adaptability and especially the NBS-LRR gene family of V. zhejiang-adstricta was elucidated based on the pathways of resistance genes (R genes), unique genes and structural variations. The near-complete and accurate diploid V. zhejiang-adstricta reference genome obtained herein serves as an important complement to wild grape genomes and will provide valuable genomic resources for investigating the genomic architecture of V. zhejiang-adstricta as well as for improving disease resistance breeding strategies in grape.

Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast

Microbes are increasingly employed as cell factories to produce biomolecules. This often involves the expression of complex heterologous biosynthesis pathways in host strains. Achieving maximal product yields and avoiding build-up of (toxic) intermediates requires balanced expression of every pathway gene. However, despite progress in metabolic modeling, the optimization of gene expression still heavily relies on trial-and-error. Here, we report an approach for in vivo, multiplexed Gene Expression Modification by LoxPsym-Cre Recombination (GEMbLeR). GEMbLeR exploits orthogonal LoxPsym sites to independently shuffle promoter and terminator modules at distinct genomic loci. This approach facilitates creation of large strain libraries, in which expression of every pathway gene ranges over 120-fold and each strain harbors a unique expression profile. When applied to the biosynthetic pathway of astaxanthin, an industrially relevant antioxidant, a single round of GEMbLeR improved pathway flux and doubled production titers. Together, this shows that GEMbLeR allows rapid and efficient gene expression optimization in heterologous biosynthetic pathways, offering possibilities for enhancing the performance of microbial cell factories.

A sequence-aware merger of genomic structural variations at population scale

Merging structural variations (SVs) at the population level presents a significant challenge, yet it is essential for conducting comprehensive genotypic analyses, especially in the era of pangenomics. Here, we introduce PanPop, a tool that utilizes an advanced sequence-aware SV merging algorithm to efficiently merge SVs of various types. We demonstrate that PanPop can merge and optimize the majority of multiallelic SVs into informative biallelic variants. We show its superior precision and lower rates of missing data compared to alternative software solutions. Our approach not only enables the filtering of SVs by leveraging multiple SV callers for enhanced accuracy but also facilitates the accurate merging of large-scale population SVs. These capabilities of PanPop will help to accelerate future SV-related studies.

VariantDetective: an accurate all-in-one pipeline for detecting consensus bacterial SNPs and SVs

MOTIVATION

Genomic variations comprise a spectrum of alterations, ranging from single nucleotide polymorphisms (SNPs) to large-scale structural variants (SVs), which play crucial roles in bacterial evolution and species diversification. Accurately identifying SNPs and SVs is beneficial for subsequent evolutionary and epidemiological studies. This study presents VariantDetective (VD), a novel, user-friendly, and all-in-one pipeline combining SNP and SV calling to generate consensus genomic variants using multiple tools.

RESULTS

The VD pipeline accepts various file types as input to initiate SNP and/or SV calling, and benchmarking results demonstrate VD's robustness and high accuracy across multiple tested datasets when compared to existing variant calling approaches.

AVAILABILITY AND IMPLEMENTATION

The source code, test data, and relevant information for VD are freely accessible at https://github.com/OLF-Bioinformatics/VariantDetective under the MIT License.

Phased genomics reveals hidden somatic mutations and provides insight into fruit development in sweet orange

Although revisiting the discoveries and implications of genetic variations using phased genomics is critical, such efforts are still lacking. Somatic mutations represent a crucial source of genetic diversity for breeding and are especially remarkable in heterozygous perennial and asexual crops. In this study, we focused on a diploid sweet orange (Citrus sinensis) and constructed a haplotype-resolved genome using high fidelity (HiFi) reads, which revealed 10.6% new sequences. Based on the phased genome, we elucidate significant genetic admixtures and haplotype differences. We developed a somatic detection strategy that reveals hidden somatic mutations overlooked in a single reference genome. We generated a phased somatic variation map by combining high-depth whole-genome sequencing (WGS) data from 87 sweet orange somatic varieties. Notably, we found twice as many somatic mutations relative to a single reference genome. Using these hidden somatic mutations, we separated sweet oranges into seven major clades and provide insight into unprecedented genetic mosaicism and strong positive selection. Furthermore, these phased genomics data indicate that genomic heterozygous variations contribute to allele-specific expression during fruit development. By integrating allelic expression differences and somatic mutations, we identified a somatic mutation that induces increases in fruit size. Applications of phased genomics will lead to powerful approaches for discovering genetic variations and uncovering their effects in highly heterozygous plants. Our data provide insight into the hidden somatic mutation landscape in the sweet orange genome, which will facilitate citrus breeding.

A centromere map based on super pan-genome highlights the structure and function of rice centromeres

Rice (Oryza sativa) is a significant crop worldwide with a genome shaped by various evolutionary factors. Rice centromeres are crucial for chromosome segregation, and contain some unreported genes. Due to the diverse and complex centromere region, a comprehensive understanding of rice centromere structure and function at the population level is needed. We constructed a high-quality centromere map based on the rice super pan-genome consisting of a 251-accession panel comprising both cultivated and wild species of Asian and African rice. We showed that rice centromeres have diverse satellite repeat CentO, which vary across chromosomes and subpopulations, reflecting their distinct evolutionary patterns. We also revealed that long terminal repeats (LTRs), especially young Gypsy-type LTRs, are abundant in the peripheral CentO-enriched regions and drive rice centromere expansion and evolution. Furthermore, high-quality genome assembly and complete telomere-to-telomere (T2T) reference genome enable us to obtain more centromeric genome information despite mapping and cloning of centromere genes being challenging. We investigated the association between structural variations and gene expression in the rice centromere. A centromere gene, OsMAB, which positively regulates rice tiller number, was further confirmed by expression quantitative trait loci, haplotype analysis and clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 methods. By revealing the new insights into the evolutionary patterns and biological roles of rice centromeres, our finding will facilitate future research on centromere biology and crop improvement.

pathMap: a path-based mapping tool for long noisy reads with high sensitivity

With the rapid development of single-molecule sequencing (SMS) technologies, the output read length is continuously increasing. Mapping such reads onto a reference genome is one of the most fundamental tasks in sequence analysis. Mapping sensitivity is becoming a major concern since high sensitivity can detect more aligned regions on the reference and obtain more aligned bases, which are useful for downstream analysis. In this study, we present pathMap, a novel k-mer graph-based mapper that is specifically designed for mapping SMS reads with high sensitivity. By viewing the alignment chain as a path containing as many anchors as possible in the matched k-mer graph, pathMap treats chaining as a path selection problem in the directed graph. pathMap iteratively searches the longest path in the remaining nodes; more candidate chains with high quality can be effectively detected and aligned. Compared to other state-of-the-art mapping methods such as minimap2 and Winnowmap2, experiment results on simulated and real-life datasets demonstrate that pathMap obtains the number of mapped chains at least 11.50% more than its closest competitor and increases the mapping sensitivity by 17.28% and 13.84% of bases over the next-best mapper for Pacific Biosciences and Oxford Nanopore sequencing data, respectively. In addition, pathMap is more robust to sequence errors and more sensitive to species- and strain-specific identification of pathogens using MinION reads.

Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data

Structural Variants (SVs) are a crucial type of genetic variant that can significantly impact phenotypes. Therefore, the identification of SVs is an essential part of modern genomic analysis. In this article, we present kled, an ultra-fast and sensitive SV caller for long-read sequencing data given the specially designed approach with a novel signature-merging algorithm, custom refinement strategies and a high-performance program structure. The evaluation results demonstrate that kled can achieve optimal SV calling compared to several state-of-the-art methods on simulated and real long-read data for different platforms and sequencing depths. Furthermore, kled excels at rapid SV calling and can efficiently utilize multiple Central Processing Unit (CPU) cores while maintaining low memory usage. The source code for kled can be obtained from https://github.com/CoREse/kled.