The complete genome of an individual by massively parallel DNA sequencing

From old markers to next generation: reconstructing the history of the peopling of Sardinia

CONTEXT

For many years the Sardinian population has been the object of numerous studies because of its unique genetic structure. Despite the extreme abundance of papers, various aspects of the peopling and genetic structure of Sardinia still remain uncertain and sometimes controversial.

OBJECTIVE

We reviewed what has emerged from different studies, focussing on some still open questions, such as the origin of Sardinians, their relationship with the Corsican population, and the intra-regional genetic heterogeneity.

METHODS

The various issues have been addressed through the analysis of classical markers, molecular markers and, finally, genomic data through next generation sequencing.

RESULTS AND CONCLUSIONS

Although the most ancient human remains date back to the end of the Palaeolithic, Mesolithic populations brought founding lineages that left evident traces in the modern population. Then, with the Neolithic, the island underwent an important demographic expansion. Subsequently, isolation and genetic drift contributed to maintain a significant genetic heterogeneity, but preserving the overall homogeneity on a regional scale. At the same time, isolation and genetic drift contributed to differentiate Sardinia from Corsica, which saw an important gene flow from the mainland. However, the isolation did not prevent gene flow from the neighbouring populations whose contribution are still recognisable in the genome of Sardinians.

Unexpected organellar locations of ESCRT machinery in Giardia intestinalis and complex evolutionary dynamics spanning the transition to parasitism in the lineage Fornicata

BACKGROUND

Comparing a parasitic lineage to its free-living relatives is a powerful way to understand how that evolutionary transition to parasitism occurred. Giardia intestinalis (Fornicata) is a leading cause of gastrointestinal disease world-wide and is famous for its unusual complement of cellular compartments, such as having peripheral vacuoles instead of typical endosomal compartments. Endocytosis plays an important role in Giardia's pathogenesis. Endosomal sorting complexes required for transport (ESCRT) are membrane-deforming proteins associated with the late endosome/multivesicular body (MVB). MVBs are ill-defined in G. intestinalis, and roles for identified ESCRT-related proteins are not fully understood in the context of its unique endocytic system. Furthermore, components thought to be required for full ESCRT functionality have not yet been documented in this species.

RESULTS

We used genomic and transcriptomic data from several Fornicata species to clarify the evolutionary genome streamlining observed in Giardia, as well as to detect any divergent orthologs of the Fornicata ESCRT subunits. We observed differences in the ESCRT machinery complement between Giardia strains. Microscopy-based investigations of key components of ESCRT machinery such as GiVPS36 and GiVPS25 link them to peripheral vacuoles, highlighting these organelles as simplified MVB equivalents. Unexpectedly, we show ESCRT components associated with the endoplasmic reticulum and, for the first time, mitosomes. Finally, we identified the rare ESCRT component CHMP7 in several fornicate representatives, including Giardia and show that contrary to current understanding, CHMP7 evolved from a gene fusion of VPS25 and SNF7 domains, prior to the last eukaryotic common ancestor, over 1.5 billion years ago.

CONCLUSIONS

Our findings show that ESCRT machinery in G. intestinalis is far more varied and complete than previously thought, associates to multiple cellular locations, and presents changes in ESCRT complement which pre-date adoption of a parasitic lifestyle.

Application of NGS Technology in Understanding the Pathology of Autoimmune Diseases

NGS technologies have transformed clinical diagnostics and broadly used from neonatal emergencies to adult conditions where the diagnosis cannot be made based on clinical symptoms. Autoimmune diseases reveal complicate molecular background and traditional methods could not fully capture them. Certainly, NGS technologies meet the needs of modern exploratory research, diagnostic and pharmacotherapy. Therefore, the main purpose of this review was to briefly present the application of NGS technology used in recent years in the understanding of autoimmune diseases paying particular attention to autoimmune connective tissue diseases. The main issues are presented in four parts: (a) panels, whole-genome and -exome sequencing (WGS and WES) in diagnostic, (b) Human leukocyte antigens (HLA) as a diagnostic tool, (c) RNAseq, (d) microRNA and (f) microbiome. Although all these areas of research are extensive, it seems that epigenetic impact on the development of systemic autoimmune diseases will set trends for future studies on this area.

The Value of Cardiac Magnetic Resonance Imaging in Identification of Rare Diseases Mimicking Hypertrophic Cardiomyopathy

Background: The cardiac Magnetic Resonance Imaging (MRI) characteristics of rare diseases with the hypertrophic cardiomyopathy (HCM) phenotype are not well defined. Methods: Seventy-three sequential patients and 34 of their relatives, who have the HCM phenotype, were included. All subjects underwent cardiac MRI and genetic testing. Results: Of these 107 patients with phenotypic HCM, seven rare diseases were identified: four cases with LAMP2, one case with PRKAG2, one case with TTR mutation, and one case with senile systemic amyloidosis. Subjects with rare diseases had diffuse LGE, and the percentage of those with LGE was significantly higher than that of other HCM (median: 18.9%, interquartile range (IQR): 14.05 to 28.2% versus 7.8%, IQR: 4.41 to 14.56%; p = 0.003). Additionally, global T1 and ECV were significantly higher in subjects with rare diseases (global T1: 1423.1 ± 93.3 ms versus 1296.2 ± 66.6 ms; global ECV: 44.3 ± 11.5% versus 29.9 ± 4.5%; all p < 0.001). Conclusions: Cardiac MRI suggests the existence of distinct imaging characteristics, including via LGE and T1 mapping, among rare diseases that mimic HCM and HCM itself.

Discovery of genomic variation across a generation

Over the past 30 years (the timespan of a generation), advances in genomics technologies have revealed tremendous and unexpected variation in the human genome and have provided increasingly accurate answers to long-standing questions of how much genetic variation exists in human populations and to what degree the DNA complement changes between parents and offspring. Tracking the characteristics of these inherited and spontaneous (or de novo) variations has been the basis of the study of human genetic disease. From genome-wide microarray and next-generation sequencing scans, we now know that each human genome contains over 3 million single nucleotide variants when compared with the ~ 3 billion base pairs in the human reference genome, along with roughly an order of magnitude more DNA—approximately 30 megabase pairs (Mb)—being ‘structurally variable’, mostly in the form of indels and copy number changes. Additional large-scale variations include balanced inversions (average of 18 Mb) and complex, difficult-to-resolve alterations. Collectively, ~1% of an individual’s genome will differ from the human reference sequence. When comparing across a generation, fewer than 100 new genetic variants are typically detected in the euchromatic portion of a child’s genome. Driven by increasingly higher-resolution and higher-throughput sequencing technologies, newer and more accurate databases of genetic variation (for instance, more comprehensive structural variation data and phasing of combinations of variants along chromosomes) of worldwide populations will emerge to underpin the next era of discovery in human molecular genetics.

Temporal dynamics of teleost populations during the Pleistocene: a report from publicly available genome data

Background

Global climate oscillation, as a selection dynamic, is an ecologically important element resulting in global biodiversity. During the glacial geological periods, most organisms suffered detrimental selection pressures (such as food shortage and habitat loss) and went through population declines. However, during the mild interglacial periods, many species re-flourished. These temporal dynamics of effective population sizes (N_e) provide essential information for understanding and predicting evolutionary outcomes during historical and ongoing global climate changes.

Results

Using high-quality genome assemblies and corresponding sequencing data, we applied the Pairwise Sequentially Markovian Coalescent (PSMC) method to quantify N_e changes of twelve representative teleost species from approximately 10 million years ago (mya) to 10 thousand years ago (kya). These results revealed multiple rounds of population contraction and expansion in most of the examined teleost species during the Neogene and the Quaternary periods. We observed that 83% (10/12) of the examined teleosts had experienced a drastic decline in N_e before the last glacial period (LGP, 110–12 kya), slightly earlier than the reported pattern of N_e changes in 38 avian species. In comparison with the peaks, almost all of the examined teleosts maintained long-term lower N_e values during the last few million years. This is consistent with increasingly dramatic glaciation during this period.

Conclusion

In summary, these findings provide a more comprehensive understanding of the historical N_e changes in teleosts. Results presented here could lead to the development of appropriate strategies to protect species in light of ongoing global climate changes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07816-7.

Approach to genetic diagnosis of inborn errors of immunity through next-generation sequencing

Patients with inborn errors of immunity (IEI) present with a heterogeneous clinical and immunological phenotype, therefore a correct molecular diagnosis is crucial for the classification and subsequent therapeutic management. On the other hand, IEI are a group of rare congenital diseases with highly diverse features and, in most cases, an as yet unknown genetic etiology. Next generation sequencing has facilitated genetic examinations of rare inherited disorders during the recent years, thus allowing a suitable molecular diagnosis in the IEI patients. This review aimed to investigate the current findings about these techniques in the field of IEI, suggesting an efficient stepwise approach to molecular diagnosis of inborn errors of immunity.

Dealing with Pseudogenes in Molecular Diagnostics in the Next Generation Sequencing Era

Presence of pseudogenes is a dreadful issue in next generation sequencing (NGS), because their contamination can interfere with the detection of variants in the genuine gene and generate false positive and false negative variants.In this chapter we focus on issues related to the application of NGS strategies for analysis of genes with pseudogenes in a clinical setting. The degree to which a pseudogene impacts the ability to accurately detect and map variants in its parent gene depends on the degree of similarity (homology) with the parent gene itself. Hereby, target enrichment and mapping strategies are crucial factors to avoid "contaminating" pseudogene sequences. For target enrichment, we describe advantages and disadvantages of PCR- and capture-based strategies. For mapping strategies, we discuss crucial parameters that need to be considered to accurately distinguish sequences of functional genes from pseudogenic sequences. Finally, we discuss some examples of genes associated with Mendelian disorders, for which interesting NGS approaches are described to avoid interference with pseudogene sequences.

Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data

BACKGROUND

Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005-0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection.

RESULT

Here, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs (< 2 Mbp) were detected. There was also significant variability in their ability to identify CNVs in the sex chromosomes. Overall, BIC-seq2 was found to be the best method in terms of statistical performance. However, its significant drawback was by far the slowest runtime among the methods (> 3 h) compared with FREEC (~ 3 min), which we considered the second-best method.

CONCLUSIONS

Our comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.

Machine learning-integrated omics for the risk and safety assessment of nanomaterials

With the advancement in nanotechnology, we are experiencing transformation in world order with deep insemination of nanoproducts from basic necessities to advanced electronics, health care products and medicines. Therefore, nanoproducts, however, can have negative side effects and must be strictly monitored to avoid negative outcomes. Future toxicity and safety challenges regarding nanomaterial incorporation into consumer products, including rapid addition of nanomaterials with diverse functionalities and attributes, highlight the limitations of traditional safety evaluation tools. Currently, artificial intelligence and machine learning algorithms are envisioned for enhancing and improving the nano-bio-interaction simulation and modeling, and they extend to the post-marketing surveillance of nanomaterials in the real world. Thus, hyphenation of machine learning with biology and nanomaterials could provide exclusive insights into the perturbations of delicate biological functions after integration with nanomaterials. In this review, we discuss the potential of combining integrative omics with machine learning in profiling nanomaterial safety and risk assessment and provide guidance for regulatory authorities as well.

Molecular Genetic Basis of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a genetic disease of the myocardium characterized by a hypertrophic left ventricle with a preserved or increased ejection fraction. Cardiac hypertrophy is often asymmetrical, which is associated with left ventricular outflow tract obstruction. Myocyte hypertrophy, disarray, and myocardial fibrosis constitute the histological features of HCM. HCM is a relatively benign disease but an important cause of sudden cardiac death in the young and heart failure in the elderly. Pathogenic variants (PVs) in genes encoding protein constituents of the sarcomeres are the main causes of HCM. PVs exhibit a gradient of effect sizes, as reflected in their penetrance and variable phenotypic expression of HCM. MYH7 and MYBPC3, encoding β-myosin heavy chain and myosin binding protein C, respectively, are the two most common causal genes and responsible for ≈40% of all HCM cases but a higher percentage of HCM in large families. PVs in genes encoding protein components of the thin filaments are responsible for ≈5% of the HCM cases. Whereas pathogenicity of the genetic variants in large families has been firmly established, ascertainment causality of the PVs in small families and sporadic cases is challenging. In the latter category, PVs are best considered as probabilistic determinants of HCM. Deciphering the genetic basis of HCM has enabled routine genetic testing and has partially elucidated the underpinning mechanism of HCM as increased number of the myosin molecules that are strongly bound to actin. The discoveries have led to the development of mavacamten that targets binding of the myosin molecule to actin filaments and imparts beneficial clinical effects. In the coming years, the yield of the genetic testing is expected to be improved and the so-called missing causal gene be identified. The advances are also expected to enable development of additional specific therapies and editing of the mutations in HCM.

Gut microbiome investigation in celiac disease: from methods to its pathogenetic role

Our body is inhabited by a variety of microbes (microbiota), mainly bacteria, that outnumber our own cells. Until recently, most of what we knew about the human microbiota was based on culture methods, whereas a large part of the microbiota is uncultivable, and consequently previous information was limited. The advent of culture-independent methods and, particularly, of next-generation sequencing (NGS) methodology, marked a turning point in studies of the microbiota in terms of its composition and of the genes encoded by these microbes (microbiome). The microbiome is influenced predominantly by environmental factors that cause a large inter-individual variability (~20%) being its heritability only 1.9%. The gut microbiome plays a relevant role in human physiology, and its alteration ("dysbiosis") has been linked to a variety of inflammatory gut diseases, including celiac disease (CD). CD is a chronic, immune-mediated disorder that is triggered by both genetic (mainly HLA-DQ2/DQ8 haplotypes) and environmental factors (gluten), but, in recent years, a large body of experimental evidence suggested that the gut microbiome is an additional contributing factor to the pathogenesis of CD. In this review, we summarize the literature that has investigated the gut microbiome associated with CD, the methods and biological samples usually employed in CD microbiome investigations and the putative pathogenetic role of specific microbial alterations in CD. In conclusion, both gluten-microbe and host-microbe interactions drive the gluten-mediated immune response. However, it remains to be established whether the CD-associated dysbiosis is the consequence of the disease, a simple concomitant association or a concurring causative factor.

Genetic basis of mitochondrial diseases

Mitochondrial disorders are monogenic disorders characterized by a defect in oxidative phosphorylation and caused by pathogenic variants in one of over 340 different genes. The implementation of whole-exome sequencing has led to a revolution in their diagnosis, duplicated the number of associated disease genes, and significantly increased the diagnosed fraction. However, the genetic etiology of a substantial fraction of patients exhibiting mitochondrial disorders remains unknown, highlighting limitations in variant detection and interpretation, which calls for improved computational and DNA sequencing methods, as well as the addition of OMICS tools. More intriguingly, this also suggests that some pathogenic variants lie outside of the protein-coding genes and that the mechanisms beyond the Mendelian inheritance and the mtDNA are of relevance. This review covers the current status of the genetic basis of mitochondrial diseases, discusses current challenges and perspectives, and explores the contribution of factors beyond the protein-coding regions and monogenic inheritance in the expansion of the genetic spectrum of disease.

New Insights Into the Cancer-Microbiome-Immune Axis: Decrypting a Decade of Discoveries

The past decade has witnessed groundbreaking advances in the field of microbiome research. An area where immense implications of the microbiome have been demonstrated is tumor biology. The microbiome affects tumor initiation and progression through direct effects on the tumor cells and indirectly through manipulation of the immune system. It can also determine response to cancer therapies and predict disease progression and survival. Modulation of the microbiome can be harnessed to potentiate the efficacy of immunotherapies and decrease their toxicity. In this review, we comprehensively dissect recent evidence regarding the interaction of the microbiome and anti-tumor immune machinery and outline the critical questions which need to be addressed as we further explore this dynamic colloquy.

Genetic ancestry plays a central role in population pharmacogenomics

Recent studies have pointed out the essential role of genetic ancestry in population pharmacogenetics. In this study, we analyzed the whole-genome sequencing data from The 1000 Genomes Project (Phase 3) and the pharmacogenetic information from Drug Bank, PharmGKB, PharmaADME, and Biotransformation. Here we show that ancestry-informative markers are enriched in pharmacogenetic loci, suggesting that trans-ancestry differentiation must be carefully considered in population pharmacogenetics studies. Ancestry-informative pharmacogenetic loci are located in both protein-coding and non-protein-coding regions, illustrating that a whole-genome analysis is necessary for an unbiased examination over pharmacogenetic loci. Finally, those ancestry-informative pharmacogenetic loci that target multiple drugs are often a functional variant, which reflects their importance in biological functions and pathways. In summary, we develop an efficient algorithm for an ultrahigh-dimensional principal component analysis. We create genetic catalogs of ancestry-informative markers and genes. We explore pharmacogenetic patterns and establish a high-accuracy prediction panel of genetic ancestry. Moreover, we construct a genetic ancestry pharmacogenomic database Genetic Ancestry PhD (http://hcyang.stat.sinica.edu.tw/databases/genetic_ancestry_phd/).

Hsin-Chou Yang et al. examine population structure in several genomic databases and identify that pharmacogenetic loci are enriched for markers of genetic ancestry. Their results suggest that genetic ancestry must be carefully considered in population pharmacogenetics studies.

KRGDB: the large-scale variant database of 1722 Koreans based on whole genome sequencing

Since 2012, the Center for Genome Science of the Korea National Institute of Health (KNIH) has been sequencing complete genomes of 1722 Korean individuals. As a result, more than 32 million variant sites have been identified, and a large proportion of the variant sites have been detected for the first time. In this article, we describe the Korean Reference Genome Database (KRGDB) and its genome browser. The current version of our database contains both single nucleotide and short insertion/deletion variants. The DNA samples were obtained from four different origins and sequenced in different sequencing depths (10× coverage of 63 individuals, 20× coverage of 194 individuals, combined 10× and 20× coverage of 135 individuals, 30× coverage of 230 individuals and 30× coverage of 1100 individuals). The major features of the KRGDB are that it contains information on the Korean genomic variant frequency, frequency difference between the Korean and other populations and the variant functional annotation (such as regulatory elements in ENCODE regions and coding variant functions) of the variant sites. Additionally, we performed the genome-wide association study (GWAS) between Korean genome variant sites for the 30×230 individuals and three major common diseases (diabetes, hypertension and metabolic syndrome). The association results are displayed on our browser. The KRGDB uses the MySQL database and Apache-Tomcat web server adopted with Java Server Page (JSP) and is freely available at http://coda.nih.go.kr/coda/KRGDB/index.jsp.

Availability: http://coda.nih.go.kr/coda/KRGDB/index.jsp

Genomic Variation, Evolvability, and the Paradox of Mental Illness

Twentieth-century genetics was hard put to explain the irregular behavior of neuropsychiatric disorders. Autism and schizophrenia defy a principle of natural selection; they are highly heritable but associated with low reproductive success. Nevertheless, they persist. The genetic origins of such conditions are confounded by the problem of variable expression, that is, when a given genetic aberration can lead to any one of several distinct disorders. Also, autism and schizophrenia occur on a spectrum of severity, from mild and subclinical cases to the overt and disabling. Such irregularities reflect the problem of missing heritability; although hundreds of genes may be associated with autism or schizophrenia, together they account for only a small proportion of cases. Techniques for higher resolution, genomewide analysis have begun to illuminate the irregular and unpredictable behavior of the human genome. Thus, the origins of neuropsychiatric disorders in particular and complex disease in general have been illuminated. The human genome is characterized by a high degree of structural and behavioral variability: DNA content variation, epistasis, stochasticity in gene expression, and epigenetic changes. These elements have grown more complex as evolution scaled the phylogenetic tree. They are especially pertinent to brain development and function. Genomic variability is a window on the origins of complex disease, neuropsychiatric disorders, and neurodevelopmental disorders in particular. Genomic variability, as it happens, is also the fuel of evolvability. The genomic events that presided over the evolution of the primate and hominid lineages are over-represented in patients with autism and schizophrenia, as well as intellectual disability and epilepsy. That the special qualities of the human genome that drove evolution might, in some way, contribute to neuropsychiatric disorders is a matter of no little interest.

Genistein Reduces the Risk of Local Mammary Cancer Recurrence and Ameliorates Alterations in the Gut Microbiota in the Offspring of Obese Dams

The risk of recurrence of estrogen receptor-positive breast cancer remains constant, even 20 years after diagnosis. Recurrence may be more likely in patients pre-programmed for it already in the womb, such as in the daughters born to obese mothers. Maternal obesity persistently alters offspring’s gut microbiota and impairs tumor immune responses. To investigate if the gut dysbiosis is linked to increased risk of mammary cancer recurrence in the offspring of obese rat dams, we fed adult offspring genistein which is known to have beneficial effects on the gut bacteria. However, the effects of genistein on breast cancer remain controversial. We found that genistein intake after tamoxifen response prevented the increased risk of local recurrence in the offspring of obese dams but had no effect on the control offspring. A significant increase in the abundance of inflammatory Prevotellaceae and Enterobacteriaceae, and a reduction in short-chain fatty acid producing Clostridiaceae was observed in the offspring of obese dams. Genistein supplementation reversed these changes as well as reversed increased gut metabolite N-acetylvaline levels which are linked to increased all-cause mortality. Genistein supplementation also reduced genotoxic tyramine levels, increased metabolites improving pro-resolving phase of inflammation, and reversed the elevated tumor mRNA expression of multiple immunosuppressive genes in the offspring of obese dams. If translatable to breast cancer patients, attempts to prevent breast cancer recurrences might need to focus on dietary modifications which beneficially modify the gut microbiota.

Comparison of the two up-to-date sequencing technologies for genome assembly: HiFi reads of Pacific Biosciences Sequel II system and ultralong reads of Oxford Nanopore

BACKGROUND

The availability of reference genomes has revolutionized the study of biology. Multiple competing technologies have been developed to improve the quality and robustness of genome assemblies during the past decade. The 2 widely used long-read sequencing providers-Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT)-have recently updated their platforms: PacBio enables high-throughput HiFi reads with base-level resolution of >99%, and ONT generated reads as long as 2 Mb. We applied the 2 up-to-date platforms to a single rice individual and then compared the 2 assemblies to investigate the advantages and limitations of each.

RESULTS

The results showed that ONT ultralong reads delivered higher contiguity, producing a total of 18 contigs of which 10 were assembled into a single chromosome compared to 394 contigs and 3 chromosome-level contigs for the PacBio assembly. The ONT ultralong reads also prevented assembly errors caused by long repetitive regions, for which we observed a total of 44 genes of false redundancies and 10 genes of false losses in the PacBio assembly, leading to over- or underestimation of the gene families in those long repetitive regions. We also noted that the PacBio HiFi reads generated assemblies with considerably fewer errors at the level of single nucleotides and small insertions and deletions than those of the ONT assembly, which generated an average 1.06 errors per kb and finally engendered 1,475 incorrect gene annotations via altered or truncated protein predictions.

CONCLUSIONS

It shows that both PacBio HiFi reads and ONT ultralong reads had their own merits. Further genome reference constructions could leverage both techniques to lessen the impact of assembly errors and subsequent annotation mistakes rooted in each.

Draft Genome of the Common Snapping Turtle, Chelydra serpentina, a Model for Phenotypic Plasticity in Reptiles

Turtles are iconic reptiles that inhabit a range of ecosystems from oceans to deserts and climates from the tropics to northern temperate regions. Yet, we have little understanding of the genetic adaptations that allow turtles to survive and reproduce in such diverse environments. Common snapping turtles, Chelydra serpentina, are an ideal model species for studying adaptation to climate because they are widely distributed from tropical to northern temperate zones in North America. They are also easy to maintain and breed in captivity and produce large clutch sizes, which makes them amenable to quantitative genetic and molecular genetic studies of traits like temperature-dependent sex determination. We therefore established a captive breeding colony and sequenced DNA from one female using both short and long reads. After trimming and filtering, we had 209.51Gb of Illumina reads, 25.72Gb of PacBio reads, and 21.72 Gb of Nanopore reads. The assembled genome was 2.258 Gb in size and had 13,224 scaffolds with an N50 of 5.59Mb. The longest scaffold was 27.24Mb. BUSCO analysis revealed 97.4% of core vertebrate genes in the genome. We identified 3.27 million SNPs in the reference turtle, which indicates a relatively high level of individual heterozygosity. We assembled the transcriptome using RNA-Seq data and used gene prediction software to produce 22,812 models of protein coding genes. The quality and contiguity of the snapping turtle genome is similar to or better than most published reptile genomes. The genome and genetic variants identified here provide a foundation for future studies of adaptation to climate.

EYS is a major gene involved in retinitis pigmentosa in Japan: genetic landscapes revealed by stepwise genetic screening

Next-generation sequencing (NGS) has greatly advanced the studies of causative genes and variants of inherited diseases. While it is sometimes challenging to determine the pathogenicity of identified variants in NGS, the American College of Medical Genetics and Genomics established the guidelines to help the interpretation. However, as to the genetic screenings for patients with retinitis pigmentosa (RP) in Japan, none of the previous studies utilized the guidelines. Considering that EYS is the major causative gene of RP in Japan, we conducted stepwise genetic screening of 220 Japanese patients with RP utilizing the guidelines. Step 1-4 comprised the following, in order: Sanger sequencing for two major EYS founder mutations; targeted sequencing of all coding regions of EYS; whole genome sequencing; Sanger sequencing for Alu element insertion in RP1, a recently determined founder mutation for RP. Among the detected variants, 2, 19, 173, and 1 variant(s) were considered pathogenic and 8, 41, 44, and 5 patients were genetically solved in step 1, 2, 3, and 4, respectively. Totally, 44.5% (98/220) of the patients were genetically solved, and 50 (51.0%) were EYS-associated and 5 (5.1%) were Alu element-associated. Among the unsolved 122 patients, 22 had at least one possible pathogenic variant.

Complete genome sequence and bioinformatics analysis of nine Egyptian females with clinical information from different geographic regions in Egypt

Egyptians are at a crossroad between Africa and Eurasia, providing useful genomic resources for analyzing both genetic and environmental factors for future personalized medicine. Two personal Egyptian whole genomes have been published previously by us and here nine female whole genome sequences with clinical information have been added to expand the genomic resource of Egyptian personal genomes. Here we report the analysis of whole genomes of nine Egyptian females from different regions using Illumina short-read sequencers. At 30x sequencing coverage, we identified 12 SNPs that were shared in most of the subjects associated with obesity which are concordant with their clinical diagnosis. Also, we found mtDNA mutation A4282G is common in all the samples and this is associated with chronic progressive external ophthalmoplegia (CPEO). Haplogroup and Admixture analyses revealed that most Egyptian samples are close to the other north Mediterranean, Middle Eastern, and European, respectively, possibly reflecting the into-Africa influx of human migration. In conclusion, we present whole-genome sequences of nine Egyptian females with personal clinical information that cover the diverse regions of Egypt. Although limited in sample size, the whole genomes data provides possible geno-phenotype candidate markers that are relevant to the region's diseases.

Clinical Interpretation and Management of Genetic Variants

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The human genome contains approximately 4 million variants, whose population frequencies vary according to the ethnic backgrounds.

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Genetic diversity of humans in part determines interindividual variability in susceptibility to diseases, response to therapy, and the clinical outcomes.

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Genetic variants exert a gradient of biological and clinical effect sizes. In general, variants with the largest effect sizes are responsible for the single-gene disorders, whereas those with moderate and modest effect sizes are responsible for oligogenic and polygenic diseases, respectively.

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A phenotype is the consequence of nonlinear stochastic interactions among multiple genetic and nongenetic determinants.

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Discerning pathogenicity of the genetic variants, identified through genetic testing, in the clinical phenotype is challenging and requires complementary expertise in human molecular genetics and clinical medicine.

Genetic variants are major determinants of susceptibility to disease, response to therapy, and clinical outcomes. Advances in the short-read sequencing technologies, despite some shortcomings, have enabled identification of the vast majority of the genetic variants in each genome. The major challenge is in identifying the pathogenic variants in cardiovascular diseases. The yield of the genetic testing has been limited because of technological shortcomings and our incomplete understanding of the genetic basis of cardiovascular disorders. To advance the field, a shift to long-read sequencing platforms is necessary. In addition, to discern the pathogenic variants, genetic diseases should be considered as a continuum and the genetic variants as probabilistic factors with a gradient of effect sizes. Moreover, disease-specific physician-scientists with expertise in the clinical medicine and molecular genetics are best equipped to discern functional and clinical significance of the genetic variants. The changes would be expected to enhance clinical utilities of the genetic discoveries.

Consequences of 22q11.2 Microdeletion on the Genome, Individual and Population Levels

Chromosomal 22q11.2 deletion syndrome (22q11.2DS) (ORPHA: 567) caused by microdeletion in chromosome 22 is the most common chromosomal microdeletion disorder in humans. Despite the same change on the genome level, like in the case of monozygotic twins, phenotypes are expressed differently in 22q11.2 deletion individuals. The rest of the genome, as well as epigenome and environmental factors, are not without influence on the variability of phenotypes. The penetrance seems to be more genotype specific than deleted locus specific. The transcript levels of deleted genes are not usually reduced by 50% as assumed due to haploinsufficiency. 22q11.2DS is often an undiagnosed condition, as each patient may have a different set out of 180 possible clinical manifestations. Diverse dysmorphic traits are present in patients from different ethnicities, which makes diagnosis even more difficult. 22q11.2 deletion syndrome serves as an example of a genetic syndrome that is not easy to manage at all stages: diagnosis, consulting and dealing with.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

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

Long-read sequencing is promising for the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high yields and performance simultaneously due to the complex SV signatures implied by noisy long reads. We propose cuteSV, a sensitive, fast, and scalable long-read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and employs a clustering-and-refinement method to implement sensitive SV detection. Benchmarks on simulated and real long-read sequencing datasets demonstrate that cuteSV has higher yields and scaling performance than state-of-the-art tools. cuteSV is available at https://github.com/tjiangHIT/cuteSV.

Progress in Neoantigen Targeted Cancer Immunotherapies

Immunotherapies that harness the immune system to kill cancer cells have showed significant therapeutic efficacy in many human malignancies. A growing number of studies have highlighted the relevance of neoantigens in recognizing cancer cells by intrinsic T cells. Cancer neoantigens are a direct consequence of somatic mutations presenting on the surface of individual cancer cells. Neoantigens are fully cancer-specific and exempt from central tolerance. In addition, neoantigens are important targets for checkpoint blockade therapy. Recently, technological innovations have made neoantigen discovery possible in a variety of malignancies, thus providing an impetus to develop novel immunotherapies that selectively enhance T cell reactivity for the destruction of cancer cells while leaving normal tissues unharmed. In this review, we aim to introduce the methods of the identification of neoantigens, the mutational patterns of human cancers, related clinical trials, neoantigen burden and sensitivity to immune checkpoint blockade. Moreover, we focus on relevant challenges of targeting neoantigens for cancer treatment.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

Genomic Diagnosis for Pediatric Disorders: Revolution and Evolution

Powerful, recent advances in technologies to analyze the genome have had a profound impact on the practice of medical genetics, both in the laboratory and in the clinic. Increasing utilization of genome-wide testing such as chromosomal microarray analysis and exome sequencing have lead a shift toward a "genotype-first" approach. Numerous techniques are now available to diagnose a particular syndrome or phenotype, and while traditional techniques remain efficient tools in certain situations, higher-throughput technologies have become the de facto laboratory tool for diagnosis of most conditions. However, selecting the right assay or technology is challenging, and the wrong choice may lead to prolonged time to diagnosis, or even a missed diagnosis. In this review, we will discuss current core technologies for the diagnosis of classic genetic disorders to shed light on the benefits and disadvantages of these strategies, including diagnostic efficiency, variant interpretation, and secondary findings. Finally, we review upcoming technologies posed to impart further changes in the field of genetic diagnostics as we move toward "genome-first" practice.

Privacy challenges and research opportunities for genomic data sharing

The sharing of genomic data holds great promise in advancing precision medicine and providing personalized treatments and other types of interventions. However, these opportunities come with privacy concerns, and data misuse could potentially lead to privacy infringement for individuals and their blood relatives. With the rapid growth and increased availability of genomic datasets, understanding the current genome privacy landscape and identifying the challenges in developing effective privacy-protecting solutions are imperative. In this work, we provide an overview of major privacy threats identified by the research community and examine the privacy challenges in the context of emerging direct-to-consumer genetic-testing applications. We additionally present general privacy-protection techniques for genomic data sharing and their potential applications in direct-to-consumer genomic testing and forensic analyses. Finally, we discuss limitations in current privacy-protection methods, highlight possible mitigation strategies and suggest future research opportunities for advancing genomic data sharing.

[Anonymity of gamete donation and genetic testing]

Development of genetic testing direct-to-consumer (DTC) for recreational purposes, although prohibited in France, is a real challenge to the current practice of gamete donation. Indeed, anonymity is a fundamental principle contributing to the ethics of donation. This principle is weakened due to the availability to the general public of these tests on the Internet. Several thousands of people are conceived by gamete donation worldwide, some of whom do not know how they were conceived. Gamete donors should be informed that their anonymity is no longer guaranteed, as they can be found by homologies of their DNA, or that of a parent or a child, potentially available in databases. Thus, adults conceived by gamete donation but not informed by their parents can discover their way of conception. Recipients of gamete donation should also be informed that their child's DNA will establish the biological discrepancy and they should be encouraged to disclose the conception to their child. Several countries now allow children conceived by donation to obtain donor's identity. In France, the Bioethics Law is currently being finalized and will now allow access to donor's identity for people conceived by gamete donation.

The Role of Host Genetic Factors in Coronavirus Susceptibility: Review of Animal and Systematic Review of Human Literature

BACKGROUND

The recent SARS-CoV-2 pandemic raises many scientific and clinical questions. One set of questions involves host genetic factors that may affect disease susceptibility and pathogenesis. New work is emerging related to SARS-CoV-2; previous work has been conducted on other coronaviruses that affect different species.

OBJECTIVES

We aimed to review the literature on host genetic factors related to coronaviruses, with a systematic focus on human studies.

METHODS

We conducted a PubMed-based search and analysis for articles relevant to host genetic factors in coronavirus. We categorized articles, summarized themes related to animal studies, and extracted data from human studies for analyses.

RESULTS

We identified 1,187 articles of potential relevance. Forty-five studies were related to human host genetic factors related to coronavirus, of which 35 involved analysis of specific genes or loci; aside from one meta-analysis on respiratory infections, all were candidate-driven studies, typically investigating small number of research subjects and loci. Multiple significant loci were identified, including 16 related to susceptibility to coronavirus (of which 7 identified protective alleles), and 16 related to outcomes or clinical variables (of which 3 identified protective alleles). The types of cases and controls used varied considerably; four studies used traditional replication/validation cohorts. Of the other studies, 28 involved both human and non-human host genetic factors related to coronavirus, 174 involved study of non-human (animal) host genetic factors related to coronavirus, 584 involved study of non-genetic host factors related to coronavirus, including involving immunopathogenesis, 16 involved study of other pathogens (not coronavirus), 321 involved other studies of coronavirus, and 18 studies were assigned to the other categories and removed.

KEY FINDINGS

We have outlined key genes and loci from animal and human host genetic studies that may bear investigation in the nascent host genetic factor studies of COVID-19. Previous human studies to date have been limited by issues that may be less impactful on current endeavors, including relatively low numbers of eligible participants and limited availability of advanced genomic methods.

How have advances in genetic technology modified movement disorder nosology?

The role of genetics and its technological development have been fundamental in advancing the field of movement disorders, opening the door to precision medicine. Starting from the revolutionary discovery of the locus of the Huntington's disease gene, we review the milestones of genetic discoveries in movement disorders and their impact on clinical practice and research efforts. Before the 1980s, early techniques did not allow the identification of genetic alteration in complex diseases. Further advances increasingly defined a large number of pathogenic genetic alterations. Moreover, these techniques allowed epigenomic, transcriptomic and microbiome analyses. In the 2020s, these new technologies are poised to displace phenotype-based classifications towards a nosology based on genetic/biological data. Advances in genetic technologies are engineering a reversal of the phenotype-to-genotype order of nosology development, replacing convergent clinicopathological disease models with the genotypic divergence required for future precision medicine applications.

Toward a Symbiotic Perspective on Public Health: Recognizing the Ambivalence of Microbes in the Anthropocene

Microbes evolve in complex environments that are often fashioned, in part, by human desires. In a global perspective, public health has played major roles in structuring how microbes are perceived, cultivated, and destroyed. The germ theory of disease cast microbes as enemies of the body and the body politic. Antibiotics have altered microbial development by providing stringent natural selection on bacterial species, and this has led to the formation of antibiotic-resistant bacterial strains. Public health perspectives such as "Precision Public Health" and "One Health" have recently been proposed to further manage microbial populations. However, neither of these take into account the symbiotic relationships that exist between bacterial species and between bacteria, viruses, and their eukaryotic hosts. We propose a perspective on public health that recognizes microbial evolution through symbiotic associations (the hologenome theory) and through lateral gene transfer. This perspective has the advantage of including both the pathogenic and beneficial interactions of humans with bacteria, as well as combining the outlook of the "One Health" model with the genomic methodologies utilized in the "Precision Public Health" model. In the Anthropocene, the conditions for microbial evolution have been altered by human interventions, and public health initiatives must recognize both the beneficial (indeed, necessary) interactions of microbes with their hosts as well as their pathogenic interactions.

Omenn Syndrome Identified by Newborn Screening

Severe combined immunodeficiency (SCID) encompasses a group of genetic defects. T cell development is universally affected and has alteration of B and/or NK cells. We present the case of a 5-day-old boy with combined heterozygous frame shift (c.256_257del, p.(Lys86Valfs*33)) and missense (c.1186C>T, p.(Arg396Cys)) variations in the RAG1 gene. He was admitted to our institution because of 0 TREC on Newborn Screen and worsening rash. Initially thought to have Omenn syndrome versus maternal engraftment with graft versus host disease, DNA analysis identified the noted mutations and he subsequently received a bone marrow transplant from a matched sibling.

Automated Flow Synthesis of Tumor Neoantigen Peptides for Personalized Immunotherapy

High-throughput genome sequencing and computation have enabled rapid identification of targets for personalized medicine, including cancer vaccines. Synthetic peptides are an established mode of cancer vaccine delivery, but generating the peptides for each patient in a rapid and affordable fashion remains difficult. High-throughput peptide synthesis technology is therefore urgently needed for patient-specific cancer vaccines to succeed in the clinic. Previously, we developed automated flow peptide synthesis technology that greatly accelerates the production of synthetic peptides. Herein, we show that this technology permits the synthesis of high-quality peptides for personalized medicine. Automated flow synthesis produces 30-mer peptides in less than 35 minutes and 15- to 16-mer peptides in less than 20 minutes. The purity of these peptides is comparable with or higher than the purity of peptides produced by other methods. This work illustrates how automated flow synthesis technology can enable customized peptide therapies by accelerating synthesis and increasing purity. We envision that implementing this technology in clinical settings will greatly increase capacity to generate clinical-grade peptides on demand, which is a key step in reaching the full potential of personalized vaccines for the treatment of cancer and other diseases.

LanceletDB: an integrated genome database for lancelet, comparing domain types and combination in orthologues among lancelet and other species

Lancelet (amphioxus) represents the most basally divergent extant chordate (cephalochordates) that diverged from the other two chordate lineages (urochordates and vertebrates) more than half a billion years ago. As it occupies a key position in evolution, it is considered as one of the best proxies for understanding the chordate ancestral state. Thus, the construction of a database with multiple lancelet genomes and gene annotation data, including protein domains, is urgently needed to investigate the loss and gain of domains in orthologues among species, especially ancient domain types (non-vertebrate-specific domains) and novel domain combination, which is helpful for providing new insight into the chordate ancestral state and vertebrate evolution. Here, we present an integrated genome database for lancelet, LanceletDB, which provides reference haploid genome sequence and annotation data for lancelet (Branchiostoma belcheri), including gene models and annotation, protein domain types, gene expression pattern in embryogenesis, different expression sequence tag sets and alternative polyadenylation (APA) sites profiled by the sequencing APA sites method. Especially, LanceletDB allows comparison of domain types and combination in orthologues among type species so as to decode the ancient domain types and novel domain combination during evolution. We also integrated the released diploid lancelet genome annotation data (Branchiostoma floridae) to expand LanceletDB and extend its usefulness. These data are available through the search and analysis page, basic local alignment search tool page and genome browser to provide an integrated display.

A brief history of human disease genetics

A primary goal of human genetics is to identify DNA sequence variants that influence biomedical traits, particularly those related to the onset and progression of human disease. Over the past 25 years, progress in realizing this objective has been transformed by advances in technology, foundational genomic resources and analytical tools, and by access to vast amounts of genotype and phenotype data. Genetic discoveries have substantially improved our understanding of the mechanisms responsible for many rare and common diseases and driven development of novel preventative and therapeutic strategies. Medical innovation will increasingly focus on delivering care tailored to individual patterns of genetic predisposition.

Reverse engineering gene networks using global-local shrinkage rules

Inferring gene regulatory networks from high-throughput 'omics' data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

Long walk to genomics: History and current approaches to genome sequencing and assembly

Genomes represent the starting point of genetic studies. Since the discovery of DNA structure, scientists have devoted great efforts to determine their sequence in an exact way. In this review we provide a comprehensive historical background of the improvements in DNA sequencing technologies that have accompanied the major milestones in genome sequencing and assembly, ranging from early sequencing methods to Next-Generation Sequencing platforms. We then focus on the advantages and challenges of the current technologies and approaches, collectively known as Third Generation Sequencing. As these technical advancements have been accompanied by progress in analytical methods, we also review the bioinformatic tools currently employed in de novo genome assembly, as well as some applications of Third Generation Sequencing technologies and high-quality reference genomes.

A strategy for building and using a human reference pangenome

In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.

A strategy for building and using a human reference pangenome

In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a usable human pangenome as well as to build relevant tools for genome graph infrastructures. During the meeting, the group held several intense and productive discussions covering a diverse set of topics, including advantages of graph genomes over a linear reference representation, design of new methods that can leverage graph-based data structures, and novel visualization and annotation approaches for pangenomes. Additionally, the participants self-organized themselves into teams that worked intensely over a three-day period to build a set of pipelines and tools for specific pangenomic applications. A summary of the questions raised and the tools developed are reported in this manuscript.

Establishment and characterization of a new malignant peritoneal mesothelioma cell line, KOG-1, from the ascitic fluid of a patient with pemetrexed chemotherapy resistance

Malignant peritoneal mesothelioma (MPeM) is a rare and aggressive form of malignant mesothelioma. Sufficient biological tools for studying the functional characteristics of this cancer have not been developed. Therefore, in this study, a novel human cancer cell line, KOG-1, was established from ascites fluids isolated from a 39-year-old Japanese woman with pemetrexed-resistant MPeM. Cells were dendritic or linear immediately after thawing, showed a jigsaw puzzle-like and spindle arrangement during growth, and formed monolayers without contact inhibition in two-dimensional (2D) culture. The population doubling time was 13.7 h. Karyotypic and molecular genetic analyses showed that chromosome numbers ranged from 62 to 142, with a peak of 73 with complicated copy number alterations. No germline BAP1 pathogenic variant was detected. Cells expressed various tumor markers of mesothelioma, such as calretinin, podoplanin, and Wilms tumor 1 (WT-1). Drug sensitivity and resistance testing with a set of 36 drugs using 2D and three-dimensional (3D) culture models demonstrated that KOG-1 cells showed high and low sensitivity to pemetrexed under 2D and 3D culture conditions, respectively, whereas control ovarian cancer cell lines showed low sensitivity to pemetrexed under both culture conditions. This newly established cell line will be a valuable biological resource to expand the feasibility of functional studies as well as drug testing for potential therapeutic purposes in MPeM.

dnAQET: a framework to compute a consolidated metric for benchmarking quality of de novo assemblies

Background

Accurate de novo genome assembly has become reality with the advancements in sequencing technology. With the ever-increasing number of de novo genome assembly tools, assessing the quality of assemblies has become of great importance in genome research. Although many quality metrics have been proposed and software tools for calculating those metrics have been developed, the existing tools do not produce a unified measure to reflect the overall quality of an assembly.

Results

To address this issue, we developed the de novo Assembly Quality Evaluation Tool (dnAQET) that generates a unified metric for benchmarking the quality assessment of assemblies. Our framework first calculates individual quality scores for the scaffolds/contigs of an assembly by aligning them to a reference genome. Next, it computes a quality score for the assembly using its overall reference genome coverage, the quality score distribution of its scaffolds and the redundancy identified in it. Using synthetic assemblies randomly generated from the latest human genome build, various builds of the reference genomes for five organisms and six de novo assemblies for sample NA24385, we tested dnAQET to assess its capability for benchmarking quality evaluation of genome assemblies. For synthetic data, our quality score increased with decreasing number of misassemblies and redundancy and increasing average contig length and coverage, as expected. For genome builds, dnAQET quality score calculated for a more recent reference genome was better than the score for an older version. To compare with some of the most frequently used measures, 13 other quality measures were calculated. The quality score from dnAQET was found to be better than all other measures in terms of consistency with the known quality of the reference genomes, indicating that dnAQET is reliable for benchmarking quality assessment of de novo genome assemblies.

Conclusions

The dnAQET is a scalable framework designed to evaluate a de novo genome assembly based on the aggregated quality of its scaffolds (or contigs). Our results demonstrated that dnAQET quality score is reliable for benchmarking quality assessment of genome assemblies. The dnQAET can help researchers to identify the most suitable assembly tools and to select high quality assemblies generated.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6070-x) contains supplementary material, which is available to authorized users.