Massively parallel sequencing approaches for characterization of structural variation

Clinical exome sequencing findings in 1589 patients

Clinical exome sequencing (CES) is important for the diagnosis of Mendelian diseases, which are clinically and etiologically heterogeneous. Sharing of large amounts of CES data associated with clinical findings will increase the accuracy of variant interpretation. We performed a retrospective study to state the diagnostic yield of CES in 1589 patients with a wide phenotypic spectrum. CES was performed using the Sophia Clinical Exome Sequencing Kit with 4493 genes, followed by sequencing on a NextSeq 500 system. The diagnosis rate was 36.8% when only pathogenic and likely pathogenic variants were included. Consanguineous unions and positive family history were associated with a high diagnostic yield. The neurological disease group had the highest number of patients. The groups with high diagnosis rates were ear, eye, and muscle disease groups. Seven candidate genes (EFHC2, HSPB3, FAAH2, ITGB1, GYG2, CD177, and CSTF2T) that are not yet associated with human diseases were identified. Owing to the high diagnostic yield of CES compared with that of other genetic tests, it can be used as a standard diagnostic test in patients with rare genetic disorders that require a wide differential diagnosis, especially in laboratories with limited resources.

Detection of Structural Variants by NGS: Revealing Missing Alleles in Lysosomal Storage Diseases

Lysosomal storage diseases (LSDs) are a heterogeneous group of rare multisystem metabolic disorders occurring mostly in infancy and childhood, characterized by a gradual accumulation of non-degraded substrates inside the cells. Although biochemical enzymatic assays are considered the gold standard for diagnosis of symptomatic patients, genotyping is a requirement for inclusion in enzyme replacement programs and is a prerequisite for carrier tests in relatives and DNA-based prenatal diagnosis. The emerging next-generation sequencing (NGS) technologies are now offering a powerful diagnostic tool for genotyping LSDs patients by providing faster, cheaper, and higher-resolution testing options, and are allowing to unravel, in a single integrated workflow SNVs, small insertions and deletions (indels), as well as major structural variations (SVs) responsible for the pathology. Here, we summarize the current knowledge about the most recurrent and private SVs involving LSDs-related genes, review advantages and drawbacks related to the use of the NGS in the SVs detection, and discuss the challenges to bring this type of analysis in clinical diagnostics.

Oral prodrug of remdesivir parent GS-441524 is efficacious against SARS-CoV-2 in ferrets

Remdesivir is an antiviral approved for COVID-19 treatment, but its wider use is limited by intravenous delivery. An orally bioavailable remdesivir analog may boost therapeutic benefit by facilitating early administration to non-hospitalized patients. This study characterizes the anti-SARS-CoV-2 efficacy of GS-621763, an oral prodrug of remdesivir parent nucleoside GS-441524. Both GS-621763 and GS-441524 inhibit SARS-CoV-2, including variants of concern (VOC) in cell culture and human airway epithelium organoids. Oral GS-621763 is efficiently converted to plasma metabolite GS-441524, and in lungs to the triphosphate metabolite identical to that generated by remdesivir, demonstrating a consistent mechanism of activity. Twice-daily oral administration of 10 mg/kg GS-621763 reduces SARS-CoV-2 burden to near-undetectable levels in ferrets. When dosed therapeutically against VOC P.1 gamma γ, oral GS-621763 blocks virus replication and prevents transmission to untreated contact animals. These results demonstrate therapeutic efficacy of a much-needed orally bioavailable analog of remdesivir in a relevant animal model of SARS-CoV-2 infection.

Remdesivir is an approved antiviral treatment for COVID-19, but it needs to be administered intravenously. Here, Cox et al. show that GS-621763, a prodrug of remdesivir parent nucleoside GS-441524 has good oral bioavailability and inhibits SARS-CoV-2 and variants of concerns in ferrets.

Constitutive Activation of RAS/MAPK Pathway Cooperates with Trisomy 21 and Is Therapeutically Exploitable in Down Syndrome B-cell Leukemia

PURPOSE

Children with Down syndrome (constitutive trisomy 21) that develop acute lymphoblastic leukemia (DS-ALL) have a 3-fold increased likelihood of treatment-related mortality coupled with a higher cumulative incidence of relapse, compared with other children with B-cell acute lymphoblastic leukemia (B-ALL). This highlights the lack of suitable treatment for Down syndrome children with B-ALL.

EXPERIMENTAL DESIGN

To facilitate the translation of new therapeutic agents into clinical trials, we built the first preclinical cohort of patient-derived xenograft (PDX) models of DS-ALL, comprehensively characterized at the genetic and transcriptomic levels, and have proven its suitability for preclinical studies by assessing the efficacy of drug combination between the MEK inhibitor trametinib and conventional chemotherapy agents.

RESULTS

Whole-exome and RNA-sequencing experiments revealed a high incidence of somatic alterations leading to RAS/MAPK pathway activation in our cohort of DS-ALL, as well as in other pediatric B-ALL presenting somatic gain of the chromosome 21 (B-ALL+21). In murine and human B-cell precursors, activated KRAS^G12D functionally cooperates with trisomy 21 to deregulate transcriptional networks that promote increased proliferation and self renewal, as well as B-cell differentiation blockade. Moreover, we revealed that inhibition of RAS/MAPK pathway activation using the MEK1/2 inhibitor trametinib decreased leukemia burden in several PDX models of B-ALL+21, and enhanced survival of DS-ALL PDX in combination with conventional chemotherapy agents such as vincristine.

CONCLUSIONS

Altogether, using novel and suitable PDX models, this study indicates that RAS/MAPK pathway inhibition represents a promising strategy to improve the outcome of Down syndrome children with B-cell precursor leukemia.

An update on genetic risk assessment and prevention: the role of genetic testing panels in breast cancer

Introduction: In the past 5 years, multi-gene panels have replaced the practice of BRCA1 and BRCA2 genetic testing in cases of suspected inherited breast cancer susceptibility. A variety of genes have been included on these panels without certainty of their clinical utility. Pertinent current and historical literature was reviewed to provide an up-to-date snapshot of the changing landscape of the use of gene panel tests in the context of breast cancer. Areas covered: Following a recent review of the evidence, 10 genes have been found to have definitive evidence of increased breast cancer risk with variable penetrance. Here, we review the recent changes to the practice of multi-gene panel use in breast cancer diagnoses, including an update on next generation sequencing, alternative models of genetic testing, considerations when ordering these panel tests, and recommendations for management in identified carriers for a variety of genes. A comparison of screening recommendations and carrier frequencies from recent studies is also explored. Lastly, we consider what the future of hereditary oncologic genetic testing holds. Expert opinion: The transition to multi-gene panels in breast cancer patients has improved the likelihood of capturing a rare variant in a well-established gene associated with hereditary breast cancer (e.g. BRCA1 and BRCA2, TP53). There is also an increase in the likelihood of uncovering an uncertain result. This could be in the form of a variant of uncertain significance, or a pathogenic variant in a gene with questionable breast cancer risk-association. Concurrently, a changing landscape of who orders genetic tests will improve access to genetic testing. This pervasiveness of genetic testing must be accompanied with increased genetic literacy in all health-care providers, and access to support from genetics professionals for management of patients and at-risk family members.

Natural Hosts and Genetic Diversity of the Emerging Tomato Leaf Curl New Delhi Virus in Spain

Knowledge about the host range and genetic structure of emerging plant viruses provides insights into fundamental ecological and evolutionary processes, and from an applied perspective, facilitates the design and implementation of sustainable disease control measures. Tomato leaf curl New Delhi virus (ToLCNDV) is an emerging whitefly transmitted begomovirus that is rapidly spreading and inciting economically important diseases in cucurbit crops of the Mediterranean basin. Genetic characterization of the ToLCNDV Mediterranean populations has shown that they are monophyletic in cucurbit plants. However, the extent to which other alternative (cultivated and wild) hosts may affect ToLCNDV genetic population structure and virus prevalence remains unknown. In this study a total of 683 samples from 13 cultivated species, and 203 samples from 24 wild species from three major cucurbit-producing areas of Spain (Murcia, Alicante and Castilla-La Mancha) from five cropping seasons (2012-2016) were analyzed for ToLCNDV infection. Except for watermelon, ToLCNDV was detected in all cultivated-cucurbit species as well as in tomato. Among weeds, Ecballium elaterium, Datura stramonium, Sonchus oleraceus, and Solanum nigrum were identified as alternative ToLCNDV plant hosts, which could act as new potential sources of virus inoculum. Furthermore, we performed full-genome deep-sequencing of 80 ToLCNDV isolates from different hosts, location and cropping year. Our phylogenetic analysis supports a Mediterranean virus population that is genetically very homogeneous, with no clustering pattern, and clearly different from Asian virus populations. Additionally, D. stramonium displayed higher levels of within-host genetic diversity than cultivated plants, and this variability appeared to increase with time. These results suggest that the potential ToLCNDV adaptive evolution occurring in wild plant hosts could serve as a source of virus genetic variability, thereby affecting the genetic structure and spatial-temporal dynamics of the viral population.

TIA: algorithms for development of identity-linked SNP islands for analysis by massively parallel DNA sequencing

Background

Single nucleotide polymorphisms (SNPs) located within the human genome have been shown to have utility as markers of identity in the differentiation of DNA from individual contributors. Massively parallel DNA sequencing (MPS) technologies and human genome SNP databases allow for the design of suites of identity-linked target regions, amenable to sequencing in a multiplexed and massively parallel manner. Therefore, tools are needed for leveraging the genotypic information found within SNP databases for the discovery of genomic targets that can be evaluated on MPS platforms.

Results

The SNP island target identification algorithm (TIA) was developed as a user-tunable system to leverage SNP information within databases. Using data within the 1000 Genomes Project SNP database, human genome regions were identified that contain globally ubiquitous identity-linked SNPs and that were responsive to targeted resequencing on MPS platforms. Algorithmic filters were used to exclude target regions that did not conform to user-tunable SNP island target characteristics. To validate the accuracy of TIA for discovering these identity-linked SNP islands within the human genome, SNP island target regions were amplified from 70 contributor genomic DNA samples using the polymerase chain reaction. Multiplexed amplicons were sequenced using the Illumina MiSeq platform, and the resulting sequences were analyzed for SNP variations. 166 putative identity-linked SNPs were targeted in the identified genomic regions. Of the 309 SNPs that provided discerning power across individual SNP profiles, 74 previously undefined SNPs were identified during evaluation of targets from individual genomes. Overall, DNA samples of 70 individuals were uniquely identified using a subset of the suite of identity-linked SNP islands.

Conclusions

TIA offers a tunable genome search tool for the discovery of targeted genomic regions that are scalable in the population frequency and numbers of SNPs contained within the SNP island regions. It also allows the definition of sequence length and sequence variability of the target region as well as the less variable flanking regions for tailoring to MPS platforms. As shown in this study, TIA can be used to discover identity-linked SNP islands within the human genome, useful for differentiating individuals by targeted resequencing on MPS technologies.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2133-2) contains supplementary material, which is available to authorized users.

A systematic review on the genetics of male infertility in the era of next-generation sequencing

OBJECTIVES

To identify the role of next-generation sequencing (NGS) in male infertility, as advances in NGS technologies have contributed to the identification of novel genes responsible for a wide variety of human conditions and recently has been applied to male infertility, allowing new genetic factors to be discovered.

MATERIALS AND METHODS

PubMed was searched for combinations of the following terms: 'exome', 'genome', 'panel', 'sequencing', 'whole-exome sequencing', 'whole-genome sequencing', 'next-generation sequencing', 'azoospermia', 'oligospermia', 'asthenospermia', 'teratospermia', 'spermatogenesis', and 'male infertility', to identify studies in which NGS technologies were used to discover variants causing male infertility.

RESULTS

Altogether, 23 studies were found in which the primary mode of variant discovery was an NGS-based technology. These studies were mostly focused on patients with quantitative sperm abnormalities (non-obstructive azoospermia and oligospermia), followed by morphological and motility defects. Combined, these studies uncover variants in 28 genes causing male infertility discovered by NGS methods.

CONCLUSIONS

Male infertility is a condition that is genetically heterogeneous, and therefore remarkably amenable to study by NGS. Although some headway has been made, given the high incidence of this condition despite its detrimental effect on reproductive fitness, there is significant potential for further discoveries.

MLPA-Based Analysis of Copy Number Variation in Plant Populations

Copy number variants (CNVs) are intraspecies duplications/deletions of large DNA segments (>1 kb). A growing number of reports highlight the functional and evolutionary impact of CNV in plants, increasing the need for appropriate tools that enable locus-specific CNV genotyping on a population scale. Multiplex ligation-dependent probe amplification (MLPA) is considered a gold standard in genotyping CNV in humans. Consequently, numerous commercial MLPA assays for CNV-related human diseases have been created. We routinely genotype complex multiallelic CNVs in human and plant genomes using the modified MLPA procedure based on fully synthesized oligonucleotide probes (90-200 nt), which greatly simplifies the design process and allows for the development of custom assays. Here, we present a step-by-step protocol for gene-specific MLPA probe design, multiplexed assay setup and data analysis in a copy number genotyping experiment in plants. As a case study, we present the results of a custom assay designed to genotype the copy number status of 12 protein coding genes in a population of 80 Arabidopsis accessions. The genes were pre-selected based on whole genome sequencing data and are localized in the genomic regions that display different levels of population-scale variation (non-variable, biallelic, or multiallelic, as well as CNVs overlapping whole genes or their fragments). The presented approach is suitable for population-scale validation of the CNV regions inferred from whole genome sequencing data analysis and for focused analysis of selected genes of interest. It can also be very easily adopted for any plant species, following optimization of the template amount and design of the appropriate control probes, according to the general guidelines presented in this paper.

Error baseline rates of five sample preparation methods used to characterize RNA virus populations

Individual RNA viruses typically occur as populations of genomes that differ slightly from each other due to mutations introduced by the error-prone viral polymerase. Understanding the variability of RNA virus genome populations is critical for understanding virus evolution because individual mutant genomes may gain evolutionary selective advantages and give rise to dominant subpopulations, possibly even leading to the emergence of viruses resistant to medical countermeasures. Reverse transcription of virus genome populations followed by next-generation sequencing is the only available method to characterize variation for RNA viruses. However, both steps may lead to the introduction of artificial mutations, thereby skewing the data. To better understand how such errors are introduced during sample preparation, we determined and compared error baseline rates of five different sample preparation methods by analyzing in vitro transcribed Ebola virus RNA from an artificial plasmid-based system. These methods included: shotgun sequencing from plasmid DNA or in vitro transcribed RNA as a basic “no amplification” method, amplicon sequencing from the plasmid DNA or in vitro transcribed RNA as a “targeted” amplification method, sequence-independent single-primer amplification (SISPA) as a “random” amplification method, rolling circle reverse transcription sequencing (CirSeq) as an advanced “no amplification” method, and Illumina TruSeq RNA Access as a “targeted” enrichment method. The measured error frequencies indicate that RNA Access offers the best tradeoff between sensitivity and sample preparation error (1.4⁻⁵) of all compared methods.

Genome Sequencing

Genome sequencing is an important step toward correlating genotypes with phenotypic characters. Sequencing technologies are important in many fields in the life sciences, including functional genomics, transcriptomics, oncology, evolutionary biology, forensic sciences, and many more. The era of sequencing has been divided into three generations. First generation sequencing involved sequencing by synthesis (Sanger sequencing) and sequencing by cleavage (Maxam-Gilbert sequencing). Sanger sequencing led to the completion of various genome sequences (including human) and provided the foundation for development of other sequencing technologies. Since then, various techniques have been developed which can overcome some of the limitations of Sanger sequencing. These techniques are collectively known as "Next-generation sequencing" (NGS), and are further classified into second and third generation technologies. Although NGS methods have many advantages in terms of speed, cost, and parallelism, the accuracy and read length of Sanger sequencing is still superior and has confined the use of NGS mainly to resequencing genomes. Consequently, there is a continuing need to develop improved real time sequencing techniques. This chapter reviews some of the options currently available and provides a generic workflow for sequencing a genome.

Using VarScan 2 for Germline Variant Calling and Somatic Mutation Detection

The identification of small sequence variants remains a challenging but critical step in the analysis of next-generation sequencing data. Our variant calling tool, VarScan 2, employs heuristic and statistic thresholds based on user-defined criteria to call variants using SAMtools mpileup data as input. Here, we provide guidelines for generating that input, and describe protocols for using VarScan 2 to (1) identify germline variants in individual samples; (2) call somatic mutations, copy number alterations, and LOH events in tumor-normal pairs; and (3) identify germline variants, de novo mutations, and Mendelian inheritance errors in family trios. Further, we describe a strategy for variant filtering that removes likely false positives associated with common sequencing- and alignment-related artifacts.

Genome-Wide Detection of CNVs and Their Association with Meat Tenderness in Nelore Cattle

Brazil is one of the largest beef producers and exporters in the world with the Nelore breed representing the vast majority of Brazilian cattle (Bos taurus indicus). Despite the great adaptability of the Nelore breed to tropical climate, meat tenderness (MT) remains to be improved. Several factors including genetic composition can influence MT. In this article, we report a genome-wide analysis of copy number variation (CNV) inferred from Illumina® High Density SNP-chip data for a Nelore population of 723 males. We detected >2,600 CNV regions (CNVRs) representing ≈6.5% of the genome. Comparing our results with previous studies revealed an overlap in ≈1400 CNVRs (>50%). A total of 1,155 CNVRs (43.6%) overlapped 2,750 genes. They were enriched for processes involving guanosine triphosphate (GTP), previously reported to influence skeletal muscle physiology and morphology. Nelore CNVRs also overlapped QTLs for MT reported in other breeds (8.9%, 236 CNVRs) and from a previous study with this population (4.1%, 109 CNVRs). Two CNVRs were also proximal to glutathione metabolism genes that were previously associated with MT. Genome-wide association study of CN state with estimated breeding values derived from meat shear force identified 6 regions, including a region on BTA3 that contains genes of the cAMP and cGMP pathway. Ten CNVRs that overlapped regions associated with MT were successfully validated by qPCR. Our results represent the first comprehensive CNV study in Bos taurus indicus cattle and identify regions in which copy number changes are potentially of importance for the MT phenotype.

Next generation sequencing of DNA-launched Chikungunya vaccine virus

Chikungunya virus (CHIKV) represents a pandemic threat with no approved vaccine available. Recently, we described a novel vaccination strategy based on iDNA® infectious clone designed to launch a live-attenuated CHIKV vaccine from plasmid DNA in vitro or in vivo. As a proof of concept, we prepared iDNA plasmid pCHIKV-7 encoding the full-length cDNA of the 181/25 vaccine. The DNA-launched CHIKV-7 virus was prepared and compared to the 181/25 virus. Illumina HiSeq2000 sequencing revealed that with the exception of the 3' untranslated region, CHIKV-7 viral RNA consistently showed a lower frequency of single-nucleotide polymorphisms than the 181/25 RNA including at the E2-12 and E2-82 residues previously identified as attenuating mutations. In the CHIKV-7, frequencies of reversions at E2-12 and E2-82 were 0.064% and 0.086%, while in the 181/25, frequencies were 0.179% and 0.133%, respectively. We conclude that the DNA-launched virus has a reduced probability of reversion mutations, thereby enhancing vaccine safety.

A decade of structural variants: description, history and methods to detect structural variation

In the past decade, the view on genomic structural variation (SV) has been changed completely. SVs, previously considered rare events, are now recognized as the largest source of interindividual genetic variation affecting more bases than single nucleotide polymorphisms, variable number of tandem repeats and other small genetic variants. They have also been shown to play a role in phenotypic variation and in disease. In this review, the authors will provide an introduction to SV; a short historical perspective on the research of this source of genomic variation; a description of the types of structural variants, and on how they may have arisen; and an overview on methods of detecting structural variants, focusing on the analysis of high-throughput sequencing data.

Diagnosis of fetal submicroscopic chromosomal abnormalities in failed array CGH samples: copy number by sequencing as an alternative to microarrays for invasive fetal testing

OBJECTIVES

Array comparative genomic hybridization (CGH) has become the technology of choice for high-resolution prenatal whole genome analysis. Limitations of microarrays are mainly related to the analog nature of the analysis, and poor-quality DNA can result in failed quality metrics with these platforms. We examined a cohort of abnormal fetuses with failed array CGH results using a next-generation sequencing algorithm, CNV-Seq. We assessed the ability of the platform to handle suboptimal prenatal samples and generate interpretable molecular karyotypes.

METHODS

Nine samples obtained from abnormal fetuses and one from a normal control fetus were sequenced using an Illumina GAIIx. A segmentation algorithm for sequencing data was used to determine regional copy number data on the sequencing datasets.

RESULTS

Phred quality scores were satisfactory for analysis of all samples. CNV-Seq identified both large- and small-scale abnormalities in the cohort, and normal results were obtained for fetuses for which microarray data were previously uninterpretable. No variants of uncertain significance were detected. Analysis of the digital sequencing datasets offered some advantages over array CGH output.

CONCLUSIONS

Using next-generation sequencing for the detection of genomic copy number variants may be advantageous for poor-quality, invasively-acquired prenatal samples. CNV-Seq could become a potential alternative to array CGH in this setting.

Molecular diagnostics in soft tissue sarcomas and gastrointestinal stromal tumors

Soft tissue sarcomas are rare malignant heterogenous tumors of mesenchymal origin with over fifty subtypes. The use of hematoxylin and eosin stained sections (and immunohistochemistry) in the morphologic assessment of these tumors has been the bane of clinical diagnosis until recently. The last decade has witnessed considerable progress in the understanding and application of molecular techniques in refining the current understanding of soft tissue sarcomas and gastrointestinal stromal tumors beyond the limits of traditional approaches. Indeed, the identification of reciprocal chromosomal translocations and fusion genes in some subsets of sarcomas with potential implications in the pathogenesis, diagnosis and treatment has been revolutionary. The era of molecular targeted therapy presents a platform that continues to drive biomarker discovery and personalized medicine in soft tissue sarcomas and gastrointestinal stromal tumors. In this review, we highlight how the different molecular techniques have enhanced the diagnosis of these tumors with prognostic and therapeutic implications.

Making the difference: integrating structural variation detection tools

From prokaryotes to eukaryotes, phenotypic variation, adaptation and speciation has been associated with structural variation between genomes of individuals within the same species. Many computer algorithms detecting such variations (callers) have recently been developed, spurred by the advent of the next-generation sequencing technology. Such callers mainly exploit split-read mapping or paired-end read mapping. However, as different callers are geared towards different types of structural variation, there is still no single caller that can be considered a community standard; instead, increasingly the various callers are combined in integrated pipelines. In this article, we review a wide range of callers, discuss challenges in the integration step and present a survey of pipelines used in population genomics studies. Based on our findings, we provide general recommendations on how to set-up such pipelines. Finally, we present an outlook on future challenges in structural variation detection.

LipidSeq: a next-generation clinical resequencing panel for monogenic dyslipidemias

We report the design of a targeted resequencing panel for monogenic dyslipidemias, LipidSeq, for the purpose of replacing Sanger sequencing in the clinical detection of dyslipidemia-causing variants. We also evaluate the performance of the LipidSeq approach versus Sanger sequencing in 84 patients with a range of phenotypes including extreme blood lipid concentrations as well as additional dyslipidemias and related metabolic disorders. The panel performs well, with high concordance (95.2%) in samples with known mutations based on Sanger sequencing and a high detection rate (57.9%) of mutations likely to be causative for disease in samples not previously sequenced. Clinical implementation of LipidSeq has the potential to aid in the molecular diagnosis of patients with monogenic dyslipidemias with a high degree of speed and accuracy and at lower cost than either Sanger sequencing or whole exome sequencing. Furthermore, LipidSeq will help to provide a more focused picture of monogenic and polygenic contributors that underlie dyslipidemia while excluding the discovery of incidental pathogenic clinically actionable variants in nonmetabolism-related genes, such as oncogenes, that would otherwise be identified by a whole exome approach, thus minimizing potential ethical issues.

Visualization and probability-based scoring of structural variants within repetitive sequences

MOTIVATION

Repetitive sequences account for approximately half of the human genome. Accurately ascertaining sequences in these regions with next generation sequencers is challenging, and requires a different set of analytical techniques than for reads originating from unique sequences. Complicating the matter are repetitive regions subject to programmed rearrangements, as is the case with the antigen-binding domains in the Immunoglobulin (Ig) and T-cell receptor (TCR) loci.

RESULTS

We developed a probability-based score and visualization method to aid in distinguishing true structural variants from alignment artifacts. We demonstrate the usefulness of this method in its ability to separate real structural variants from false positives generated with existing upstream analysis tools. We validated our approach using both target-capture and whole-genome experiments. Capture sequencing reads were generated from primary lymphoid tumors, cancer cell lines and an EBV-transformed lymphoblast cell line over the Ig and TCR loci. Whole-genome sequencing reads were from a lymphoblastoid cell-line.

AVAILABILITY

We implement our method as an R package available at https://github.com/Eitan177/targetSeqView. Code to reproduce the figures and results are also available.

Building a genome analysis pipeline to predict disease risk and prevent disease

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice.

The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation

Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.

A survey of tools for variant analysis of next-generation genome sequencing data

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

A model-based clustering method for genomic structural variant prediction and genotyping using paired-end sequencing data

Structural variation (SV) has been reported to be associated with numerous diseases such as cancer. With the advent of next generation sequencing (NGS) technologies, various types of SV can be potentially identified. We propose a model based clustering approach utilizing a set of features defined for each type of SV events. Our method, termed SVMiner, not only provides a probability score for each candidate, but also predicts the heterozygosity of genomic deletions. Extensive experiments on genome-wide deep sequencing data have demonstrated that SVMiner is robust against the variability of a single cluster feature, and it significantly outperforms several commonly used SV detection programs. SVMiner can be downloaded from http://cbc.case.edu/svminer/.

A forward genetic screen identifies eukaryotic translation initiation factor 3, subunit H (eIF3h), as an enhancer of variegation in the mouse

We have used a forward genetic screen to identify genes required for transgene silencing in the mouse. Previously these genes were found using candidate-based sequencing, a slow and labor-intensive process. Recently, whole-exome deep sequencing has accelerated our ability to find the causative point mutations, resulting in the discovery of novel and sometimes unexpected genes. Here we report the identification of translation initiation factor 3, subunit H (eIF3h) in two modifier of murine metastable epialleles (Mommes) lines. Mice carrying mutations in this gene have not been reported previously, and a possible involvement of eIF3h in transcription or epigenetic regulation has not been considered.

The Caulobacter crescentus phage phiCbK: genomics of a canonical phage

Background

The bacterium Caulobacter crescentus is a popular model for the study of cell cycle regulation and senescence. The large prolate siphophage phiCbK has been an important tool in C. crescentus biology, and has been studied in its own right as a model for viral morphogenesis. Although a system of some interest, to date little genomic information is available on phiCbK or its relatives.

Results

Five novel phiCbK-like C. crescentus bacteriophages, CcrMagneto, CcrSwift, CcrKarma, CcrRogue and CcrColossus, were isolated from the environment. The genomes of phage phiCbK and these five environmental phage isolates were obtained by 454 pyrosequencing. The phiCbK-like phage genomes range in size from 205 kb encoding 318 proteins (phiCbK) to 280 kb encoding 448 proteins (CcrColossus), and were found to contain nonpermuted terminal redundancies of 10 to 17 kb. A novel method of terminal ligation was developed to map genomic termini, which confirmed termini predicted by coverage analysis. This suggests that sequence coverage discontinuities may be useable as predictors of genomic termini in phage genomes. Genomic modules encoding virion morphogenesis, lysis and DNA replication proteins were identified. The phiCbK-like phages were also found to encode a number of intriguing proteins; all contain a clearly T7-like DNA polymerase, and five of the six encode a possible homolog of the C. crescentus cell cycle regulator GcrA, which may allow the phage to alter the host cell’s replicative state. The structural proteome of phage phiCbK was determined, identifying the portal, major and minor capsid proteins, the tail tape measure and possible tail fiber proteins. All six phage genomes are clearly related; phiCbK, CcrMagneto, CcrSwift, CcrKarma and CcrRogue form a group related at the DNA level, while CcrColossus is more diverged but retains significant similarity at the protein level.

Conclusions

Due to their lack of any apparent relationship to other described phages, this group is proposed as the founding cohort of a new phage type, the phiCbK-like phages. This work will serve as a foundation for future studies on morphogenesis, infection and phage-host interactions in C. crescentus.

Current mutation discovery approaches in Retinitis Pigmentosa

With a worldwide prevalence of about 1 in 3500-5000 individuals, Retinitis Pigmentosa (RP) is the most common form of hereditary retinal degeneration. It is an extremely heterogeneous group of genetically determined retinal diseases leading to progressive loss of vision due to impairment of rod and cone photoreceptors. RP can be inherited as an autosomal-recessive, autosomal-dominant, or X-linked trait. Non-Mendelian inheritance patterns such as digenic, maternal (mitochondrial) or compound heterozygosity have also been reported. To date, more than 65 genes have been implicated in syndromic and non-syndromic forms of RP, which account for only about 60% of all RP cases. Due to this high heterogeneity and diversity of inheritance patterns, the molecular diagnosis of syndromic and non-syndromic RP is very challenging, and the heritability of 40% of total RP cases worldwide remains unknown. However new sequencing methodologies, boosted by the human genome project, have contributed to exponential plummeting in sequencing costs, thereby making it feasible to include molecular testing for RP patients in routine clinical practice within the coming years. Here, we summarize the most widely used state-of-the-art technologies currently applied for the molecular diagnosis of RP, and address their strengths and weaknesses for the molecular diagnosis of such a complex genetic disease.