Analysis Of Genomic Variation Research Articles

From Genome Variation to Molecular Mechanisms: What we Have Learned From Yeast Mitochondrial Genomes?

Analysis of genome variation provides insights into mechanisms in genome evolution. This is increasingly appreciated with the rapid growth of genomic data. Mitochondrial genomes (mitogenomes) are well known to vary substantially in many genomic aspects, such as genome size, sequence context, nucleotide base composition and substitution rate. Such substantial variation makes mitogenomes an excellent model system to study the mechanisms dictating mitogenome variation. Recent sequencing efforts have not only covered a rich number of yeast species but also generated genomes from abundant strains within the same species. The rich yeast genomic data have enabled detailed investigation from genome variation into molecular mechanisms in genome evolution. This mini-review highlights some recent progresses in yeast mitogenome studies.

Pathogenicity and genome-wide sequence analysis reveals relationships between soybean mosaic virus strains.

Soybean mosaic virus (SMV) is the most prevalent viral pathogen in soybean. In China, the SMV strains SC and N are used simultaneously in SMV resistance assessments of soybean cultivars, but the pathogenic relationship between them is unclear. In this study, SMV strains N1 and N3 were found to be the most closely related to SC18. Moreover, N3 was found to be more virulent than N1. A global pathotype classification revealed the highest level of genetic diversity in China. The N3 type was the most frequent and widespread worldwide, implying that SMV possibly originated in China and spread across continents through the dissemination of infected soybean. It also suggests that the enhanced virulence of N3 facilitated its spread and adaptability in diverse geographical and ecological regions worldwide. Phylogenetic analysis revealed prominent geographical associations among SMV strains/isolates, and genomic nucleotide diversity analysis and neutrality tests demonstrated that the whole SMV genome is under negative selection, with the P1 gene being under the greatest selection pressure. The results of this study will facilitate the nationwide use of SMV-resistant soybean germplasm and could provide useful insights into the molecular variability, geographical distribution, phylogenetic relationships, and evolutionary history of SMV around the world.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00705-021-05271-z.

Artificial intelligence (AI)-assisted exome reanalysis greatly aids in the identification of new positive cases and reduces analysis time in a clinical diagnostic laboratory

PurposeArtificial intelligence (AI) and variant prioritization tools for genomic variant analysis are being rapidly developed for use in clinical diagnostic testing. However, their clinical utility and reliability are currently limited. Therefore, we performed a validation of a commercial AI tool (Moon) and a comprehensive reanalysis of previously collected clinical exome sequencing cases using an open-source variant prioritization tool (Exomiser) and the now-validated AI tool to test their feasibility in clinical diagnostics. MethodsA validation study of Moon was performed with 29 positive cases determined by previous manual analysis. After validation, reanalysis was performed on 80 previously manually analyzed nondiagnostic exome cases using Moon. Finally, a comparison between Moon and Exomiser was completed regarding their ability to identify previously completed positive cases and to identify new positive cases. ResultsMoon correctly selected the causal variant(s) in 97% of manually analyzed positive cases and identified 7 new positive cases. Exomiser correctly identified the causal gene in 85% of positive cases and agreed with Moon by ranking the new gene in its top 10 list 43% of the time. ConclusionThe use of AI in diagnostic laboratories greatly enhances exome sequencing analysis by reducing analysis time and increasing the diagnostic rate.

Computational detection, analysis and interpretations of genomic variants in human diseases associated GENEMDM 2

Most of the mutations described in human MDM2 are tolerated without significantly disrupting the corresponding structural or molecular function. However, some of them are associated with a variety of human diseases, including cancer. Numerous computational methods have been developed to predict the effects of missense single nucleotide variants (SNVs). The non-synonymous single nucleotide polymorphisms affect the function of XRCC1, which impairs the ability to repair DNA and therefore increases the risk of diseases such as cancer. In this study, sequence and structure-based computational tools were used to screen the total listed coding SNPs of the MDM2 gene in order to recognize and describe them. The potential 6 ns SNP of MDM2 were identified from 29 ns SNP by consistent analysis using computational tools PolyPhen 2, SIFT, PANTHER and cSNP. The computational methods were used to systematically classify functional mutations in the regulatory and coding regions that modify the expression and function of the MDM2 enzyme. The HOPE project also made it possible to elaborate the structural effects of the substitutions of amino acids. In silico analysis predicted that rs759244097 is harmful. This study concluded that identifying this SNP will help to determine an individual's cancer susceptibility, prognosis and further treatment. Furthermore, current high-throughput sequencing efforts and the need for extensive interpretation of protein sequence variants requires more efficient and accurate computational methods in the coming years.

Abstract 14385: Genome Sequencing and Re-Engineered Heart Cell-Disease Modeling Identifies a Novel Deep Intronic Variant in a Multi-Generational Long QT Syndrome Pedigree

Introduction: Most of long QT syndrome (LQTS) is explained by pathogenic variants in KCNQ1 , KCNH2 , or SCN5A . However, ~10-20% of LQTS index cases remain genetically elusive following commercial genetic testing. Here, we identified and functionally characterized a novel LQTS genetic substrate in a multi-generational, “genotype negative” LQTS pedigree. Methods: The index case was a 40-year-old female with a history of syncope, seizures, ventricular fibrillation, and a family history of LQTS and sudden death. Although anticipated to have KCNH2- mediated LQT2, commercial genetic testing of all LQTS-causative genes was negative. Genome sequencing and variant analysis was performed on 6 affected family members. Patient-specific and CRISPR/Cas9 “gene-corrected” isogenic control induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) were generated. RT-PCR was used to determine the effects of the identified variant on splicing. Action potential duration (APD) and field potential duration (FPD) were measured using voltage-sensing dye (Fluovolt) and microelectrode array, respectively. Results: No ultra-rare, nonsynonymous heterozygous variants co-segregated among the 6 LQTS phenotype-positive individuals. Instead, a deep intronic KCNH2 variant (c.3331-316 G>T) was present in all affected individuals. RT-PCR analysis of patient-specific iPSC-CM-derived RNA revealed that c.3331-316 G>T creates a novel 89 base-pair exon residing between canonical exons 14 and 15 of KCNH2 that results in a frame-shift variant annotated as p.S1112Pfs*171. The APD 90 was significantly longer in p.S1112Pfs*171-iPSC-CMs (647 ± 12 ms, n=50) compared to isogenic control iPSC-CMs (412 ± 10 ms, n=50, p < 0.0001). Further, the FPD was significantly longer in p.S1112Pfs*171-iPSC-CMs (349 ± 8 ms, n=48) compared to isogenic control iPSC-CMs (209 ± 10 ms, n=20, p < 0.0001). Conclusions: A novel deep intronic KCNH2 variant was identified in a multi-generational genetically elusive LQTS pedigree. The patient-derived and isogenic control iPSC-CMs establish that the p.S1112Pfs*171-KCNH2 variant is the monogenetic cause for this family’s LQTS. As suggested by the phenotype, this family indeed has KCNH2 -mediated LQT2 albeit stemming from a deep intronic variant .

Evolutionary and Phenotypic Characterization of Two Spike Mutations in European Lineage 20E of SARS-CoV-2.

ABSTRACTWe have detected two mutations in the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at amino acid positions 1163 and 1167 that appeared independently in multiple transmission clusters and different genetic backgrounds. Furthermore, both mutations appeared together in a cluster of 1,627 sequences belonging to clade 20E. This cluster is characterized by 12 additional single nucleotide polymorphisms but no deletions. The available structural information on the S protein in the pre- and postfusion conformations predicts that both mutations confer rigidity, which could potentially decrease viral fitness. Accordingly, we observed reduced infectivity of this spike genotype relative to the ancestral 20E sequence in vitro, and the levels of viral RNA in nasopharyngeal swabs were not significantly higher. Furthermore, the mutations did not impact thermal stability or antibody neutralization by sera from vaccinated individuals but moderately reduce neutralization by convalescent-phase sera from the early stages of the pandemic. Despite multiple successful appearances of the two spike mutations during the first year of SARS-CoV-2 evolution, the genotype with both mutations was displaced upon the expansion of the 20I (Alpha) variant. The midterm fate of the genotype investigated was consistent with the lack of advantage observed in the clinical and experimental data.

Ensembl Genomes 2022: an expanding genome resource for non-vertebrates.

Ensembl Genomes (https://www.ensemblgenomes.org) provides access to non-vertebrate genomes and analysis complementing vertebrate resources developed by the Ensembl project (https://www.ensembl.org). The two resources collectively present genome annotation through a consistent set of interfaces spanning the tree of life presenting genome sequence, annotation, variation, transcriptomic data and comparative analysis. Here, we present our largest increase in plant, metazoan and fungal genomes since the project's inception creating one of the world's most comprehensive genomic resources and describe our efforts to reduce genome redundancy in our Bacteria portal. We detail our new efforts in gene annotation, our emerging support for pangenome analysis, our efforts to accelerate data dissemination through the Ensembl Rapid Release resource and our new AlphaFold visualization. Finally, we present details of our future plans including updates on our integration with Ensembl, and how we plan to improve our support for the microbial research community. Software and data are made available without restriction via our website, online tools platform and programmatic interfaces (available under an Apache 2.0 license). Data updates are synchronised with Ensembl's release cycle.

A multi-omics study to boost continuous bolaform sophorolipid production

Biodegradable and biobased surface active agents are renewable and environmentally friendly alternatives to petroleum derived or oleochemical surfactants. However, they are accompanied by relatively high production costs. In this study, the aim was to reduce the production costs for an innovative type of microbial biosurfactant: bolaform sophorolipids, produced by the yeast Starmerella bombicola ΔsbleΔat. A novel continuous retentostat set-up was performed whereby continuous broth microfiltration retained the biomass in the bioreactor while performing an in situ product separation of bolaform sophorolipids. Although a mean volumetric productivity of 0.56 g L−1 h−1 was achieved, it was not possible to maintain this productivity, which collapsed to almost 0 g L−1 h−1. Therefore, two process adaptations were evaluated, a sequential batch strategy and a phosphate limitation alleviation strategy. The sequential batch set-up restored the mean volumetric productivity to 0.66 g L−1 h−1 for an additional 132 h but was again followed by a productivity decline. A similar result was obtained with the phosphate limitation alleviation strategy where a mean volumetric productivity of 0.54 g L−1 h−1 was reached, but a productivity decline was also observed. Whole genome variant analysis uncovered no evidence for genomic variations for up to 1306 h of retentostat cultivation. Untargeted metabolomics analysis identified 8-hydroxyguanosine, a biomarker for oxidative RNA damage, as a key metabolite correlating with high bolaform sophorolipid productivity. This study showcases the application of a retentostat to increase bolaform sophorolipid productivity and lays the basis of a multi-omics platform for in depth investigation of microbial biosurfactant production with S. bombicola.

Genomic diversity and molecular dynamics interaction on mutational variances among RB domains of SARS-CoV-2 interplay drug inactivation.

The scientific community has been releasing whole genomic sequences of SARS-CoV-2 to facilitate the investigation of molecular features and evolutionary history. We retrieved 36 genomes of 18 prevalent countries of Asia, Europe and America for genomic diversity and mutational analysis. Besides, we studied mutations in the RBD regions of Spike (S) proteins to analyze the drug efficiency against these mutations. In this research, phylogenenetic analysis, evolutionary modeling, substitution pattern analysis, molecular docking, dynamics simulation, etc. were performed. The genomic sequences showed >99% similarity with the reference sequence of China.TN93 + G was predicted as a best nucleotide substitution model. It was revealed that effective transition from the co-existing SARS genome to the SARS-CoV-2 and a noticeable positive selection in the SARS-CoV-2 genomes occurred. Moreover, three mutations in RBD domain, Val/ Phe367, Val/ Leu 382 and Ala/ Val522, were discovered in the genomes from Netherland, Bangladesh and the USA, respectively. Molecular docking and dynamics study showed RBD with mutation Val/Leu382 had the lowest binding affinity with remdesivir. In conclusion, the SARS-CoV-2 genomes are similar, but multiple degrees of transitions and transversions occurred. The mutations cause a significant conformational change, which are needed to be investigated during drug and vaccine development.

Long-read sequencing to interrogate strain-level variation among adherent-invasive Escherichia coli isolated from human intestinal tissue.

Adherent-invasive Escherichia coli (AIEC) is a pathovar linked to inflammatory bowel diseases (IBD), especially Crohn’s disease, and colorectal cancer. AIEC are genetically diverse, and in the absence of a universal molecular signature, are defined by in vitro functional attributes. The relative ability of difference AIEC strains to colonize, persist, and induce inflammation in an IBD-susceptible host is unresolved. To evaluate strain-level variation among tissue-associated E. coli in the intestines, we develop a long-read sequencing approach to identify AIEC by strain that excludes host DNA. We use this approach to distinguish genetically similar strains and assess their fitness in colonizing the intestine. Here we have assembled complete genomes using long-read nanopore sequencing for a model AIEC strain, NC101, and seven strains isolated from the intestinal mucosa of Crohn’s disease and non-Crohn’s tissues. We show these strains can colonize the intestine of IBD susceptible mice and induce inflammatory cytokines from cultured macrophages. We demonstrate that these strains can be quantified and distinguished in the presence of 99.5% mammalian DNA and from within a fecal population. Analysis of global genomic structure and specific sequence variation within the ribosomal RNA operon provides a framework for efficiently tracking strain-level variation of closely-related E. coli and likely other commensal/pathogenic bacteria impacting intestinal inflammation in experimental settings and IBD patients.

M6A-Mediated Tumor Invasion and Methylation Modification in Breast Cancer Microenvironment.

Background We analyzed the n6-methyladenosine (m6A) modification patterns of immune cells infiltrating the tumor microenvironment of breast cancer (BC) to provide a new perspective for the early diagnosis and treatment of BC. Methods Based on 23 m6A regulatory factors, we identified m6A-related gene characteristics and m6A modification patterns in BC through unsupervised cluster analysis. To examine the differences in biological processes among various m6A modification modes, we performed genomic variation analysis. We then quantified the relative infiltration levels of different immune cell subpopulations in the tumor microenvironment of BC using the CIBERSORT algorithm and single-sample gene set enrichment analysis. Univariate Cox analysis was used to screen for m6A characteristic genes related to prognosis. Finally, we evaluated the m6A modification pattern of patients with a single BC by constructing the m6Ascore based on principal component analysis. Results We identified three different m6A modification patterns in 2128 BC samples. A higher abundance of the immune infiltration of the m6Acluster C was indicated by the results of CIBERSORT and the single-sample gene set enrichment analysis. Based on the m6A characteristic genes obtained through screening, the m6Ascore was determined. The BC patients were segregated into m6Ascore groups of low and high categories, which revealed significant survival benefits among patients with low m6Ascores. Additionally, the high-m6Ascore group had a higher mutation frequency and was associated with low PD-L1 expression, and the m6Ascore and tumor mutation burden showed a positive correlation. In addition, treatment effects were better in patients in the high-m6Ascore group. Conclusions In case of a single patient with BC, the immune cell infiltration characteristics of the tumor microenvironment and the m6A methylation modification pattern could be evaluated using the m6Ascore. Our results provide a foundation for improving personalized immunotherapy of BC.

MitoLink: A generic integrated web-based workflow system to evaluate genotype-phenotype correlations in human mitochondrial diseases: Observations from the GenomeAsia Pilot project

MitoLink is a generic, scalable and modular web-based workflow system developed to study genotype-phenotype correlations in human mitochondrial diseases. MitoLink integrates applications for assessment of genomic variation and currently houses genome-wide datasets from GenomeAsia Pilot project, gnomAD, ClinVar and DisGenNet. In this study, a reference list of nearly 3975 proteins (both nuclear and mitochondrial encoded) with mitochondrial function is reported. This protein set is mapped to disease associated variants in the GenomeAsia Pilot Project and DisGenNet and evaluated for pathogenicity as defined by ClinVar. Observations of shared genetic components in potential comorbidities are discussed from gene-disease network in Asian population, however, the platform is generic and can be applied to any population dataset. MitoLink is a unique customized workflow system that allows for systematic storage, extraction, analysis and visualization of genomic variation to understand genotype-phenotype correlations for mitochondrial diseases. Given the modularity of tool and data integration, MitoLink is a scalable system that can accommodate a diverse set of applications linked via standard data structure within the framework of Galaxy. MitoLink is built on FAIR principles and supports creation of reproducible workflows towards understanding genotype-phenotype correlations across several disease phenotypes globally.

Analysis of Genomic Copy Number Variation in Miscarriages During Early and Middle Pregnancy.

The purpose of this study was to explore the copy number variations (CNVs) associated with miscarriage during early and middle pregnancy and provide useful genetic guidance for pregnancy and prenatal diagnosis. A total of 505 fetal specimens were collected and CNV sequencing (CNV-seq) analysis was performed to determine the types and clinical significance of CNVs, and relevant medical records were collected. The chromosomal abnormality rate was 54.3% (274/505), among which the numerical chromosomal abnormality rate was 40.0% (202/505) and structural chromosomal abnormality rate was 14.3% (72/505). Chromosomal monosomy mainly occurred on sex chromosomes, and chromosomal trisomy mainly occurred on chromosomes 16, 22, 21, 15, 13, and 9. The incidence of numerical chromosomal abnormalities in ≥35 year-old age pregnant women was significantly higher than <35 year-old age group. The highest incidence of pathogenic CNV (pCNV) was found in fetuses at ≤6 weeks of pregnancy (5.26%), and the incidence of variants of unknown significance (VOUS) CNVs decreased gradually with the increase of gestational age. The rate of chromosomal abnormalities of fetuses in early pregnancy (59.5%) was higher than that of fetuses in middle pregnancy (27.2%) (p < 0.001). There were 168 genes in VOUS + pCNV regions. 41 functions and 12 pathways (p < 0.05) were enriched of these genes by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Some meaningful genetic etiology information such as genes and pathways has been obtained, it may provide useful genetic guidance for pregnancy and prenatal diagnosis.

Genomic variation and point mutations analysis of Indian COVID-19 patient samples submitted in GISAID database

Corona virus disease 2019 (COVID-19) endemic has havoc on the world; the causative virus of the pandemic is SARS CoV-2. Pharmaceutical companies and academic institutes are in continuous efforts to identify anti-viral therapy or vaccines, but the most significant challenge faced is the highly evolving genome of SARS CoV-2, which is imparting evolutionary selective benefits to the virus. To understand the viral mutations, we have retrieved nine hundred and thirty-four samples from different states of India via the GISAID database and analyzed the frequency of all types of point mutation in all structural, non-structural proteins, and accessory factors of SARS CoV-2. Spike glycol protein, nsp3, nsp6, nsp12, N and NS3 were the most evolving proteins. High frequency point mutations were Q496P (nsp2), A380V (nsp4), A994D (nsp3), L37F (nsp6), P323L & A97V (nsp12), Q57H (ns3), D614G (S), P13L (N), R203K (N), G204R (N) and S194L (N).

Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese

Premenstrual dysphoric disorder (PMDD) affects nearly 5% of women of reproductive age. Symptomatic heterogeneity, together with largely unknown genetics, has greatly hindered its effective treatment. In the present study, analysis of genomic sequencing-based copy number variations (CNVs) called from 100 kb white blood cell DNA sequence windows by means of semisupervized clustering led to the segregation of patient genomes into the D and V groups, which correlated with the depression and invasion clinical types, respectively, with 89.0% consistency. Application of diagnostic CNV features selected using the correlation-based machine learning method enabled the classification of the CNVs obtained into the D group, V group, total patient group, and control group with an average accuracy of 83.0%. The power of the diagnostic CNV features was 0.98 on average, suggesting that these CNV features could be used for the molecular diagnosis of the major clinical types of PMDD. This demonstrated concordance between the CNV profiles and clinical types of PMDD supported the validity of symptom-based diagnosis of PMDD for differentiating between its two major clinical types, as well as the predominantly genetic nature of PMDD with a host of overlaps between multiple susceptibility genes/pathways and the diagnostic CNV features as indicators of involvement in PMDD etiology.

Comprehensive Analysis to Identify Enhancer-Regulated Inflammation-Associated Genes in Lung Adenocarcinoma.

ObjectiveThe purpose of this study was to identify prognostic inflammatory markers regulated by enhancers in lung adenocarcinoma (LUAD).MethodsInflammatory indices of 490 LUAD patients in TCGA database were calculated using genomic variation analysis (GSVA). Patients were divided into high- and low-inflammatory index groups. Fraction of 22 infiltrating immune cells was estimated using the Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Gene set enrichment analysis (GSEA) was used to analyze gene enrichment. Differentially expressed genes were screened based on TCGA database. The H3K27ac ChIP-seq of A549 cells in GEO database (GSE42374) was analyzed to identify super enhancers. Kaplan–Meier method and multivariate Cox proportional hazards models were used for survival analysis. CCK8 and RT-qPCR were used for cellular level verification.ResultsInflammation was associated with better outcome in LUAD patients. Anti-cancer immune cell fractions were upregulated in high-inflammatory index group. Genes enriched in inflammation-related signaling pathways were positively correlated with high-inflammatory index group. A total of 146 upregulated genes regulated by enhancers were screened, of which five genes including GDF10, HPGDS, ABCA8, SLIT3 and ADAMTS8 had significant influence on prognosis. ChIP-seq analysis showed that TGFβ+TNFα treatment promoted the enhancer activation of the five genes. Cellular experiments revealed that there was no significant effect of TGFβ treatment on the five genes expression. TNFα treatment upregulated the five genes expression, while the BET-bromodomain inhibitor JQ1 restored the effect of TNFα. Overexpression of the five genes significantly inhibited the proliferation of A549 and H1299 cells.ConclusionGDF10, HPGDS, ABCA8, SLIT3 and ADAMTS8 were identified as enhancer-regulated prognostic inflammation-related biomarkers, and the expression of these genes inhibited proliferation of LUAD cells.

Human Adenovirus Type 7 Infections in Hubei, China During 2018-2019: Epidemic Features and Genetic Characterization of the Detected Viruses.

Human adenoviruses (HAdVs) type 7 can cause severe respiratory disease. During the period between December 2018 and August 2019, HAdV-7 infection was identified in 129 patients in Wuhan Children’s Hospital, Hubei Province, China. Samples were collected from hospitalized children and metagenomic sequencing was applied to detect the HAdV infections. Hemophagocytic lymphohistiocystosis (HLH) related to HAdV infections was observed in some patients clinically and patients were divided into two groups based on this to test the differences among clinical indicators. Genome variation, in silico restriction endonuclease analysis (REA), and phylogenetic analyses were carried out to show the genome characterization of HAdV-7 in this study. It was found that many indicators, such as all blood routine indicators, in patients of the HLH group showed significant levels. In this study, REA revealed that HAdV-7 might belong to genome 7d and genome variation analysis displayed the stable genome of HAdV. HAdV-7 is an ongoing threat to the public, and global surveillance should be established.

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease.

Numerous genetic studies have established a role for rare genomic variants in Congenital Heart Disease (CHD) at the copy number variation (CNV) and de novo variant (DNV) level. To identify novel haploinsufficient CHD disease genes, we performed an integrative analysis of CNVs and DNVs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm. We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed variation rate testing for DNVs identified in 2,489 parent-offspring trios. Our analysis revealed 21 genes which were significantly affected by rare CNVs and/or DNVs in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in small cases series or show new associations with CHD. In addition, a systems level analysis revealed affected protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes and pathways.

Robust and efficient software for reference-free genomic diversity analysis of genotyping-by-sequencing data on diploid and polyploid species.

Genotyping-by-sequencing (GBS) is a widely used and cost-effective technique for obtaining large numbers of genetic markers from populations by sequencing regions adjacent to restriction cut sites. Although a standard reference-based pipeline can be followed to analyse GBS reads, a reference genome is still not available for a large number of species. Hence, reference-free approaches are required to generate the genetic variability information that can be obtained from a GBS experiment. Unfortunately, available tools to perform de novo analysis of GBS reads face issues of usability, accuracy and performance. Furthermore, few available tools are suitable for analysing data sets from polyploid species. In this manuscript, we describe a novel algorithm to perform reference-free variant detection and genotyping from GBS reads. Nonexact searches on a dynamic hash table of consensus sequences allow for efficient read clustering and sorting. This algorithm was integrated in the Next Generation Sequencing Experience Platform (NGSEP) to integrate the state-of-the-art variant detector already implemented in this tool. We performed benchmark experiments with three different empirical data sets of plants and animals with different population structures and ploidies, and sequenced with different GBS protocols at different read depths. These experiments show that NGSEP has comparable and in some cases better accuracy and always better computational efficiency compared to existing solutions. We expect that this new development will be useful for many research groups conducting population genetic studies in a wide variety of species.

Eight soybean reference genome resources from varying latitudes and agronomic traits

Comparative analysis of multiple reference genomes representing diverse genetic backgrounds is critical for understanding the role of key alleles important in domestication and genetic breeding of important crops such as soybean. To enrich the genetic resources for soybean, we describe the generation, technical assessment, and preliminary genomic variation analysis of eight de novo reference-grade soybean genome assemblies from wild and cultivated accessions. These resources represent soybeans cultured at different latitudes and exhibiting different agronomical traits. Of these eight soybeans, five are from new accessions that have not been sequenced before. We demonstrate the usage of these genomes to identify small and large genomic variations affecting known genes as well as screening for genic PAV regions for identifying candidates for further functional studies.