Advanced sequencing technologies: methods and goals

Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions

Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.

Next generation plant biostimulants & genome sequencing strategies for sustainable agriculture development

The best environment for plant growth and development contains certain essential metabolites. A broad category of metabolites known as "plant biostimulants" (PBs) includes biomolecules such as proteins, carbohydrates, lipids, and other secondary metabolites related to groups of terpenes, specific nitrogen-containing compounds, and benzene ring-conjugated compounds. The formation of biomolecules depends on both biotic and abiotic factors, such as the release of PB by plants, animals, and microorganisms, or it can result from the control of temperature, humidity, and pressure in the atmosphere, in the case of humic substances (HSs). Understanding the genomic outputs of the concerned organism (may be plants or others than them) becomes crucial for identifying the underlying behaviors that lead to the synthesis of these complex compounds. For the purposes of achieving the objectives of sustainable agriculture, detailed research on PBs is essential because they aid in increasing yield and other growth patterns of agro-economic crops. The regulation of homeostasis in the plant-soil-microbe system for the survival of humans and other animals is mediated by the action of plant biostimulants, as considered essential for the growth of plants. The genomic size and gene operons for functional and regulation control have so far been revealed through technological implementations, but important gene annotations are still lacking, causing a delay in revealing the information. Next-generation sequencing techniques, such as nanopore, nanoball, and Illumina, are essential in troubleshooting the information gaps. These technical advancements have greatly expanded the candidate gene openings. The secondary metabolites being important precursors need to be studied in a much wider scale for accurate calculations of biochemical reactions, taking place inside and outside the synthesized living cell. The present review highlights the sequencing techniques to provide a foundation of opportunity generation for agricultural sustainability.

Silica Nanowires-Filled Glass Microporous Sensor for the Ultrasensitive Detection of Deoxyribonucleic Acid

DNA carries genetic information and can serve as an important biomarker for the early diagnosis and assessment of the disease prognosis. Here, we propose a bottom-up assembly method for a silica nanowire-filled glass microporous (SiNWs@GMP) sensor and develop a universal sensing platform for the ultrasensitive and specific detection of DNA. The three-dimensional network structure formed by SiNWs provides them with highly abundant and accessible binding sites, allowing for the immobilization of a large amount of capture probe DNA, thereby enabling more target DNA to hybridize with the capture probe DNA to improve detection performance. Therefore, the SiNWs@GMP sensor achieves ultrasensitive detection of target DNA. In the detection range of 1 aM to 100 fM, there is a good linear relationship between the decrease rate of current signal and the concentration of target DNA, and the detection limit is as low as 1 aM. The developed SiNWs@GMP sensor can distinguish target DNA sequences that are 1-, 3-, and 5-mismatched, and specifically recognize target DNA from complex mixed solution. Furthermore, based on this excellent selectivity and specificity, we validate the universality of this sensing strategy by detecting DNA (H1N1 and H5N1) sequences associated with the avian influenza virus. By replacing the types of nucleic acid aptamers, it is expected to achieve a wide range and low detection limit sensitive detection of various biological molecules. The results indicate that the developed universal sensing platform has ultrahigh sensitivity, excellent selectivity, stability, and acceptable reproducibility, demonstrating its potential application in DNA bioanalysis.

Nanoconfined Electrokinetic Chromatography (NEC): Gradient Separation and Sensing of Short DNA Fragments at the Single-Molecule Level

Glass nanopipets have been demonstrated to be a powerful tool for the sensing and discrimination of biomolecules, such as DNA strands with different lengths or configurations. Despite progress made in nanopipet-based sensors, it remains challenging to develop effective strategies that separate and sense in one operation. In this study, we demonstrate an agarose gel-filled nanopipet that enables hyphenated length-dependent separation and electrochemical sensing of short DNA fragments based on the electrokinetic flow of DNA molecules in the nanoconfined channel at the tip of the nanopipet. This nanoconfined electrokinetic chromatography (NEC) method is used to distinguish the mixture of DNA strands without labels, and the ionic current signals measured in real time show that the mixed DNA strands pass through the tip hole in order according to the molecular weight. With NEC, gradient separation and electrochemical measurement of biomolecules can be achieved simultaneously at the single-molecule level, which is further applied for programmable gene delivery into single living cells. Overall, NEC provides a multipurpose platform integrating separation, sensing, single-cell delivery, and manipulation, which may bring new insights into advanced bioapplication.

Review: Wildlife forensic genetics-Biological evidence, DNA markers, analytical approaches, and challenges

Wildlife-related crimes are the second most prevalent lawbreaking offense globally. This illicit trade encompasses hunting, breeding and trafficking. Besides diminishing many species and their habitats and ecosystems, hindering the economic development of local communities that depend on them, undermining the rule of law and financing terrorism, various cross-species transmissions (zoonoses) of pathogens, including COVID-19, can be attributed to wildlife crimes. Wildlife forensics applies interdisciplinary scientific analyses to support law enforcement in investigating wildlife crimes. Its main objectives are to identify the taxonomic species in question, determine if a crime has been committed, link a suspect to the crime and support the conviction and prosecution of the perpetrator. This article reviews wildlife crime and its implications, wildlife forensic science investigation, common forms of wildlife biological evidence, including DNA, wildlife DNA techniques and challenges in wildlife forensic genetics. The article also reviews the contributions of genetic markers such as short tandem repeat (STR) and mitochondrial DNA (mtDNA) markers, which provide the probative genetic data representing the bulk of DNA evidence for solving wildlife crime. This review provides an overview of wildlife DNA databases, which are critical for searching and matching forensic DNA profiles and sequences and establishing how frequent forensic DNA profiles and sequences are in a particular population or geographic region. As such, this review will contain an in-depth analysis of the current status of wildlife forensic genetics, and it will be of general interest to wildlife and conservation biologists, law enforcement officers, and academics interested in combating crimes against wildlife using animal forensic DNA methods.

Leveraging machine learning for taxonomic classification of emerging astroviruses

Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.

Application of high-throughput sequencing technologies and analytical tools for pathogen detection in urban water systems: Progress and future perspectives

The ubiquitous presence of pathogenic microorganisms, such as viruses, bacteria, fungi, and protozoa, in urban water systems poses a significant risk to public health. The emergence of infectious waterborne diseases mediated by urban water systems has become one of the leading global causes of mortality. However, the detection and monitoring of these pathogenic microorganisms have been limited by the complexity and diversity in the environmental samples. Conventional methods were restricted by long assay time, high benchmarks of identification, and narrow application sceneries. Novel technologies, such as high-throughput sequencing technologies, enable potentially full-spectrum detection of trace pathogenic microorganisms in complex environmental matrices. This review discusses the current state of high-throughput sequencing technologies for identifying pathogenic microorganisms in urban water systems with a concise summary. Furthermore, future perspectives in pathogen research emphasize the need for detection methods with high accuracy and sensitivity, the establishment of precise detection standards and procedures, and the significance of bioinformatics software and platforms. We have compiled a list of pathogens analysis software/platforms/databases that boast robust engines and high accuracy for preference. We highlight the significance of analyses by combining targeted and non-targeted sequencing technologies, short and long reads technologies, sequencing technologies, and bioinformatic tools in pursuing upgraded biosafety in urban water systems.

Evaluation of Different SNP Analysis Software and Optimal Mining Process in Tree Species

Single nucleotide polymorphism (SNP) is one of the most widely used molecular markers to help researchers understand the relationship between phenotypes and genotypes. SNP calling mainly consists of two steps, including read alignment and locus identification based on statistical models, and various software have been developed and applied in this issue. Meanwhile, in our study, very low agreement (<25%) was found among the prediction results generated by different software, which was much less consistent than expected. In order to obtain the optimal protocol of SNP mining in tree species, the algorithm principles of different alignment and SNP mining software were discussed in detail. And the prediction results were further validated based on in silico and experimental methods. In addition, hundreds of validated SNPs were provided along with some practical suggestions on program selection and accuracy improvement were provided, and we wish that these results could lay the foundation for the subsequent analysis of SNP mining.

Genome Evolution and the Future of Phylogenomics of Non-Avian Reptiles

Non-avian reptiles comprise a large proportion of amniote vertebrate diversity, with squamate reptiles-lizards and snakes-recently overtaking birds as the most species-rich tetrapod radiation. Despite displaying an extraordinary diversity of phenotypic and genomic traits, genomic resources in non-avian reptiles have accumulated more slowly than they have in mammals and birds, the remaining amniotes. Here we review the remarkable natural history of non-avian reptiles, with a focus on the physical traits, genomic characteristics, and sequence compositional patterns that comprise key axes of variation across amniotes. We argue that the high evolutionary diversity of non-avian reptiles can fuel a new generation of whole-genome phylogenomic analyses. A survey of phylogenetic investigations in non-avian reptiles shows that sequence capture-based approaches are the most commonly used, with studies of markers known as ultraconserved elements (UCEs) especially well represented. However, many other types of markers exist and are increasingly being mined from genome assemblies in silico, including some with greater information potential than UCEs for certain investigations. We discuss the importance of high-quality genomic resources and methods for bioinformatically extracting a range of marker sets from genome assemblies. Finally, we encourage herpetologists working in genomics, genetics, evolutionary biology, and other fields to work collectively towards building genomic resources for non-avian reptiles, especially squamates, that rival those already in place for mammals and birds. Overall, the development of this cross-amniote phylogenomic tree of life will contribute to illuminate interesting dimensions of biodiversity across non-avian reptiles and broader amniotes.

Normalization of clonal diversity in gene therapy studies using shape constrained splines

Viral vectors are used to insert genetic material into semirandom genomic positions of hematopoietic stem cells which, after reinfusion into patients, regenerate the entire hematopoietic system. Hematopoietic cells originating from genetically modified stem cells will harbor insertions in specific genomic positions called integration sites, which represent unique genetic marks of clonal identity. Therefore, the analysis of vector integration sites present in the genomic DNA of circulating cells allows to determine the number of clones in the blood ecosystem. Shannon diversity index is adopted to evaluate the heterogeneity of the transduced population of gene corrected cells. However, this measure can be affected by several technical variables such as the DNA amount used and the sequencing depth of the library analyzed and therefore the comparison across samples may be affected by these confounding factors. We developed an advanced spline-regression approach that leverages on confounding effects to provide a normalized entropy index. Our proposed method was first validated and compared with two state of the art approaches in a specifically designed in vitro assay. Subsequently our approach allowed to observe the expected impact of vector genotoxicity on entropy level decay in an in vivo model of hematopoietic stem cell gene therapy based on tumor prone mice.

Use of Next-Generation Sequencing for Identifying Mitochondrial Disorders

Mitochondria are major contributors to ATP synthesis, generating more than 90% of the total cellular energy production through oxidative phosphorylation (OXPHOS): metabolite oxidation, such as the β-oxidation of fatty acids, and the Krebs's cycle. OXPHOS inadequacy due to large genetic lesions in mitochondrial as well as nuclear genes and homo- or heteroplasmic point mutations in mitochondrially encoded genes is a characteristic of heterogeneous, maternally inherited genetic disorders known as mitochondrial disorders that affect multisystemic tissues and organs with high energy requirements, resulting in various signs and symptoms. Several traditional diagnostic approaches, including magnetic resonance imaging of the brain, cardiac testing, biochemical screening, variable heteroplasmy genetic testing, identifying clinical features, and skeletal muscle biopsies, are associated with increased risks, high costs, a high degree of false-positive or false-negative results, or a lack of precision, which limits their diagnostic abilities for mitochondrial disorders. Variable heteroplasmy levels, mtDNA depletion, and the identification of pathogenic variants can be detected through genetic sequencing, including the gold standard Sanger sequencing. However, sequencing can be time consuming, and Sanger sequencing can result in the missed recognition of larger structural variations such as CNVs or copy-number variations. Although each sequencing method has its own limitations, genetic sequencing can be an alternative to traditional diagnostic methods. The ever-growing roster of possible mutations has led to the development of next-generation sequencing (NGS). The enhancement of NGS methods can offer a precise diagnosis of the mitochondrial disorder within a short period at a reasonable expense for both research and clinical applications.

Molecular research progress and improvement approach of fruit quality traits in cucumber

Recent molecular studies revealed new opportunities to improve cucumber fruit quality. However, the fruit color and spine traits molecular basis remain vague despite the vast sources of genetic diversity. Cucumber is agriculturally, economically and nutritionally important vegetable crop. China produces three-fourths of the world's total cucumber production. Cucumber fruit quality depends on a number of traits such as the fruit color (peel and flesh color), spine (density, size and color), fruit shape, fruit size, defects, texture, firmness, taste, maturity stage and nutritional composition. Fruit color and spine traits determine critical quality attributes and have been the interest of researchers at the molecular level. Evaluating the molecular mechanisms of fruit quality traits is important to improve production and quality of cucumber varieties. Genes and qualitative trait locus (QTL) that are responsible for cucumber fruit color and fruit spine have been identified. The purpose of this paper is to reveal the molecular research progress of fruit color and spines as key quality traits of cucumber. The markers and genes identified so far could help for marker-assisted selection of the fruit color and spine trait in cucumber breeding and its associated nutritional improvement. Based on the previous studies, peel color and spine density as examples, we proposed a comprehensive approach for cucumber fruit quality traits improvement. Moreover, the markers and genes can be useful to facilitate cloning-mediated genetic breeding in cucumber. However, in the era of climate change, increased human population and high-quality demand of consumers, studies on molecular mechanisms of cucumber fruit quality traits are limited.

Single-molecule, hybridization-based strategies for short nucleic acids detection and recognition with nanopores

DNA nanotechnology has seen large developments over the last 30 years through the combination of detection and discovery of DNAs, and solid phase synthesis to increase the chemical functionalities on nucleic acids, leading to the emergence of novel and sophisticated in features, nucleic acids-based biopolymers. Arguably, nanopores developed for fast and direct detection of a large variety of molecules, are part of a revolutionary technological evolution which led to cheaper, smaller and considerably easier to use devices enabling DNA detection and sequencing at the single-molecule level. Through their versatility, the nanopore-based tools proved useful biomedicine, nanoscale chemistry, biology and physics, as well as other disciplines spanning materials science to ecology and anthropology. This mini-review discusses the progress of nanopore- and hybridization-based DNA detection, and explores a range of state-of-the-art applications afforded through the combination of certain synthetically-derived polymers mimicking nucleic acids and nanopores, for the single-molecule biophysics on short DNA structures.

Exploiting Single-Cell Tools in Gene and Cell Therapy

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Empirical evaluation of methods for de novo genome assembly

Technologies for next-generation sequencing (NGS) have stimulated an exponential rise in high-throughput sequencing projects and resulted in the development of new read-assembly algorithms. A drastic reduction in the costs of generating short reads on the genomes of new organisms is attributable to recent advances in NGS technologies such as Ion Torrent, Illumina, and PacBio. Genome research has led to the creation of high-quality reference genomes for several organisms, and de novo assembly is a key initiative that has facilitated gene discovery and other studies. More powerful analytical algorithms are needed to work on the increasing amount of sequence data. We make a thorough comparison of the de novo assembly algorithms to allow new users to clearly understand the assembly algorithms: overlap-layout-consensus and de-Bruijn-graph, string-graph based assembly, and hybrid approach. We also address the computational efficacy of each algorithm's performance, challenges faced by the assem- bly tools used, and the impact of repeats. Our results compare the relative performance of the different assemblers and other related assembly differences with and without the reference genome. We hope that this analysis will contribute to further the application of de novo sequences and help the future growth of assembly algorithms.

Highly Accurate Chip-Based Resequencing of SARS-CoV-2 Clinical Samples

SARS-CoV-2 has infected over 128 million people worldwide, and until a vaccine is developed and widely disseminated, vigilant testing and contact tracing are the most effective ways to slow the spread of COVID-19. Typical clinical testing only confirms the presence or absence of the virus, but rather, a simple and rapid testing procedure that sequences the entire genome would be impactful and allow for tracing the spread of the virus and variants, as well as the appearance of new variants. However, traditional short read sequencing methods are time consuming and expensive. Herein, we describe a tiled genome array that we developed for rapid and inexpensive full viral genome resequencing, and we have applied our SARS-CoV-2-specific genome tiling array to rapidly and accurately resequence the viral genome from eight clinical samples. We have resequenced eight samples acquired from patients in Wyoming that tested positive for SARS-CoV-2. We were ultimately able to sequence over 95% of the genome of each sample with greater than 99.9% average accuracy.

A Hitchhiker's Guide to Click-Chemistry with Nucleic Acids

Click chemistry is an immensely powerful technique for the fast and efficient covalent conjugation of molecular entities. Its broad scope has positively impacted on multiple scientific disciplines, and its implementation within the nucleic acid field has enabled researchers to generate a wide variety of tools with application in biology, biochemistry, and biotechnology. Azide-alkyne cycloadditions (AAC) are still the leading technology among click reactions due to the facile modification and incorporation of azide and alkyne groups within biological scaffolds. Application of AAC chemistry to nucleic acids allows labeling, ligation, and cyclization of oligonucleotides efficiently and cost-effectively relative to previously used chemical and enzymatic techniques. In this review, we provide a guide to inexperienced and knowledgeable researchers approaching the field of click chemistry with nucleic acids. We discuss in detail the chemistry, the available modified-nucleosides, and applications of AAC reactions in nucleic acid chemistry and provide a critical view of the advantages, limitations, and open-questions within the field.

The Use of Whole Genome and Exome Sequencing for Newborn Screening: Challenges and Opportunities for Population Health

Newborn screening (NBS) is a population-based program with a goal of reducing the burden of disease for conditions with significant clinical impact on neonates. Screening tests were originally developed and implemented one at a time, but newer methods have allowed the use of multiplex technologies to expand additions more rapidly to standard panels. Recent improvements in next-generation sequencing are also evolving rapidly from first focusing on individual genes, then panels, and finally all genes as encompassed by whole exome and genome sequencing. The intersection of these two technologies brings the revolutionary possibility of identifying all genetic disorders in newborns, allowing implementation of therapies at the optimum time regardless of symptoms. This article reviews the history of newborn screening and early studies examining the use of whole genome and exome sequencing as a screening tool. Lessons learned from these studies are discussed, along with technical, ethical, and societal challenges to broad implementation.

Candidate gene association of gene expression data in sugarcane contrasting for sucrose content

Association mapping of gene expression data, generated from transcriptome and proteome studies, provides a means of understanding the functional significance and trait association potential of candidate genes. In this study, we applied candidate gene association mapping to validate sugarcane genes, using data from the starch and sucrose metabolism pathway, transcriptome, and proteome. We performed multiplex PCR targeted amplicon sequencing of 109 candidate genes, using NGS technology. A range of statistical models, both single-locus and multi-locus, were compared for minimization of false positives in association mapping of four sugar-related traits with different heritability. The Fixed and random model Circulating Probability Unification model effectively suppressed false positives for both low- and high-heritability traits. We identified favorable alleles of the candidate genes involved in signalling and transcriptional regulation. The results will support genetic improvement of sugarcane and may help clarify the genetic architecture of sugar-related traits.

Scorpion primer PCR analysis for genotyping of allele variants of thiopurine s‑methyltransferase*3

Thiopurine S-methyltransferase (TPMT) plays an important role in the metabolism of thiopurines. Mutations in the TPMT gene can affect drug activity, which may have adverse effects in humans. Thus, genotyping can help elucidate genetic determinants of drug response to thiopurines and optimize the selection of drug therapies for individual patients, effectively avoiding palindromia during maintenance treatment caused by insufficient dosing and the serious side effects caused by excessive doses. The current available detection methods used for TPMT*3B and TPMT*3C are complex, costly and time-consuming. Therefore, innovative detection methods for TPMT genotyping are urgently required. The aim of the present study was to establish and optimize a simple, specific and timesaving TPMT genotyping method. Using the principles of Web-based Allele-Specific PCR and competitive real-time fluorescent allele-specific PCR (CRAS-PCR), two pairs of Scorpion primers were designed for the detection of TPMT*3B and *3C, respectively, and a mutation in TPMT*3A was inferred based on data from TPMT*3B and *3C. In total, 226 samples from volunteers living in Chongqing were used for CRAS-PCR to detect TPMT*3 mutations. Results showed that nine (3.98%) were mutant (MT) heterozygotes and none were MT homozygotes for TPMT*3C, and no TPMT*3A and TPMT*3B mutations were found. Three TPMT*3C MT heterozygotes were randomly selected for DNA sequencing, and CRAS-PCR results were consistent with the sequencing results. In conclusion, in order to improve simplicity, specificity and efficiency, the present study established and optimized CRAS-PCR assays for commonly found mutant alleles of TPMT*3A (G460A and A719G), TPMT*3B (G460A), and TPMT*3C (A719G).

Cultivating DNA Sequencing Technology After the Human Genome Project

When the Human Genome Project was completed in 2003, automated Sanger DNA sequencing with fluorescent dye labels was the dominant technology. Several nascent alternative methods based on older ideas that had not been fully developed were the focus of technical researchers and companies. Funding agencies recognized the dynamic nature of technology development and that, beyond the Human Genome Project, there were growing opportunities to deploy DNA sequencing in biological research. Consequently, the National Human Genome Research Institute of the National Institutes of Health created a program-widely known as the Advanced Sequencing Technology Program-that stimulated all stages of development of new DNA sequencing methods, from innovation to advanced manufacturing and production testing, with the goal of reducing the cost of sequencing a human genome first to $100,000 and then to $1,000. The events of this period provide a powerful example of how judicious funding of academic and commercial partners can rapidly advance core technology developments that lead to profound advances across the scientific landscape.

Nanopipettes: a potential tool for DNA detection

As the information in DNA is of practical value for clinical diagnosis, it is important to develop efficient and rapid methods for DNA detection. In the past decades, nanopores have been extensively explored for DNA detection due to their low cost and high efficiency. As a sub-group of the solid-state nanopore, nanopipettes exhibit great potential for DNA detection which is ascribed to their stability, ease of fabrication and good compatibility with other technologies, compared with biological and traditional solid-state nanopores. Herein, the review systematically summarizes the recent progress in DNA detection with nanopipettes and highlights those studies dedicated to improve the performance of DNA detection using nanopipettes through different approaches, including reducing the rate of DNA translocation, improving the spatial resolution of sensing nanopipettes, and controlling DNA molecules through novel techniques. Besides, some new perspectives of the integration of nanopipettes with other technologies are reviewed.

Ion torrent high throughput mitochondrial genome sequencing (HTMGS)

The implementation and popularity of next generation sequencing (NGS) has led to the development of various rapid whole mitochondrial genome sequencing techniques. We summarise an efficient and cost-effective NGS approach for mitochondrial genomic DNA in humans using the Ion Torrent platform, and further discuss our bioinformatics pipeline for streamlined variant calling. Ion 316 chips were utilised with the Ion Torrent semi-conductor platform Personal Genome Machine (PGM) to perform tandem sequencing of mitochondrial genomes from the core pedigree (n = 315) of the Norfolk Island Health Study. Key improvements from commercial methods focus on the initial PCR step, which currently requires extensive optimisation to ensure the accurate and reproducible elongation of each section of the complete mitochondrial genome. Dual-platform barcodes were incorporated into our protocol thereby extending its potential application onto Illumina-based systems. Our bioinformatics pipeline consists of a modified version of GATK best practices tailored for mitochondrial genomic data. When compared with current commercial methods, our method, termed high throughput mitochondrial genome sequencing (HTMGS), allows high multiplexing of samples and the use of alternate library preparation reagents at a lower cost per sample (~1.7 times) when compared to current commercial methodologies. Our HTMGS methodology also provides robust mitochondrial sequencing data (>450X average coverage) that can be applied and modified to suit various study designs. On average, we were able to identify ~30 variants per sample with 572 variants observed across 315 samples. We have developed a high throughput sequencing and analysis method targeting complete mitochondrial genomes; with the potential to be platform agnostic with analysis options that adhere to current best practices.

The Status and Impact of Clinical Tumor Genome Sequencing

Since the discovery that DNA alterations initiate tumorigenesis, scientists and clinicians have been exploring ways to counter these changes with targeted therapeutics. The sequencing of tumor DNA was initially limited to highly actionable hot spots-areas of the genome that are frequently altered and have an approved matched therapy in a specific tumor type. Large-scale genome sequencing programs quickly developed technological improvements that enabled the deployment of whole-exome and whole-genome sequencing technologies at scale for pristine sample materials in research environments. However, the turning point for precision medicine in oncology was the innovations in clinical laboratories that improved turnaround time, depth of coverage, and the ability to reliably sequence archived, clinically available samples. Today, tumor genome sequencing no longer suffers from significant technical or financial hurdles, and the next opportunity for improvement lies in the optimal utilization of the technologies and data for many different tumor types.

A theoretical analysis of single molecule protein sequencing via weak binding spectra

We propose and theoretically study an approach to massively parallel single molecule peptide sequencing, based on single molecule measurement of the kinetics of probe binding (Havranek, et al., 2013) to the N-termini of immobilized peptides. Unlike previous proposals, this method is robust to both weak and non-specific probe-target affinities, which we demonstrate by applying the method to a range of randomized affinity matrices consisting of relatively low-quality binders. This suggests a novel principle for proteomic measurement whereby highly non-optimized sets of low-affinity binders could be applicable for protein sequencing, thus shifting the burden of amino acid identification from biomolecular design to readout. Measurement of probe occupancy times, or of time-averaged fluorescence, should allow high-accuracy determination of N-terminal amino acid identity for realistic probe sets. The time-averaged fluorescence method scales well to weakly-binding probes with dissociation constants of tens or hundreds of micromolar, and bypasses photobleaching limitations associated with other fluorescence-based approaches to protein sequencing. We argue that this method could lead to an approach with single amino acid resolution and the ability to distinguish many canonical and modified amino acids, even using highly non-optimized probe sets. This readout method should expand the design space for single molecule peptide sequencing by removing constraints on the properties of the fluorescent binding probes.

BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions

Big datasets, accumulated from biomedical and agronomic studies, provide the potential to identify genes that control complex human diseases and agriculturally important traits through genome-wide association studies (GWAS). However, big datasets also lead to extreme computational challenges, especially when sophisticated statistical models are employed to simultaneously reduce false positives and false negatives. The newly developed fixed and random model circulating probability unification (FarmCPU) method uses a bin method under the assumption that quantitative trait nucleotides (QTNs) are evenly distributed throughout the genome. The estimated QTNs are used to separate a mixed linear model into a computationally efficient fixed effect model (FEM) and a computationally expensive random effect model (REM), which are then used iteratively. To completely eliminate the computationally expensive REM, we replaced REM with FEM by using Bayesian information criteria. To eliminate the requirement that QTNs be evenly distributed throughout the genome, we replaced the bin method with linkage disequilibrium information. The new method is called Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Both real and simulated data analyses demonstrated that BLINK improves statistical power compared to FarmCPU, in addition to remarkably reducing computing time. Now, a dataset with one million individuals and one-half million markers can be analyzed within three hours, instead of one week using FarmCPU.

Identification and Analysis of Genes Associated with Inherited Retinal Diseases

Inherited retinal diseases (IRDs) display a very high degree of clinical and genetic heterogeneity, which poses challenges in finding the underlying defects in known IRD-associated genes and in identifying novel IRD-associated genes. Knowledge on the molecular and clinical aspects of IRDs has increased tremendously in the last decade. Here, we outline the state-of-the-art techniques to find the causative genetic variants, with special attention for next-generation sequencing which can combine molecular diagnostics and retinal disease gene identification. An important aspect is the functional assessment of rare variants with RNA and protein effects which can only be predicted in silico. We therefore describe the in vitro assessment of putative splice defects in human embryonic kidney cells. In addition, we outline the use of stem cell technology to generate photoreceptor precursor cells from patients' somatic cells which can subsequently be used for RNA and protein studies. Finally, we outline the in silico methods to interpret the causality of variants associated with inherited retinal disease and the registry of these variants.

Scalable fabrication of nanopores in membranes via thermal annealing of Au nanoparticles

Nanopores are promising candidates for versatile sensing of micro- and nanomaterials. However, the fabrication of isolated nanopores with optimal dimensions and distributions requires complex processes that involve the use of high-cost equipment. Herein, we report a scalable fabrication of isolated conical nanopores with adjustable dimensions and distribution densities on a Si₃N₄ membrane via thermal annealing of Au nanoparticles (AuNPs). The AuNP-dispersed solution was dropped and evaporated on the membrane, while the pH value and concentration of AuNPs controlled the zeta potential difference and the distribution density of the attached AuNPs. The optimized thermal annealing directly fabricated conical nanopores at the positions of the AuNPs because of the quasi-liquid state of the AuNPs and their interaction with the Si₃N₄ lattices. The 50, 100, and 200 nm AuNPs enabled one-step fabrication of 8-, 26-, and 63 nm nanopores, while the inter-distances and distribution densities were controllable over the membrane. The physicochemical analyses elucidated the underlying mechanisms of direct nanopore formation, and the precise adjustment of thermal annealing developed three unique nanopores that differently interacted with the AuNPs: (1) Au-residue-embedded nanopores, (2) isolated nanopores, and (3) nanopores with the remaining Au droplet. The AuNPs-driven fabrication of versatile nanopore membranes enables new applications for sensing and transporting small-scale materials.

Development of a Simple and Cost-Effective Method Based on T7 Endonuclease Cleavage for Detection of Single Nucleotide Polymorphisms

AIMS

Single nucleotide polymorphisms (SNP) can be used as genetic markers and for risk assessment of allele-linked diseases, which can provide information for clinical diagnosis. Large-scale microarray and next-generation sequencing methods have made genome-wide SNP genotyping possible. However, in addition to their high cost, these techniques are dependent on having specialized equipment. Thus, there is a need for a simple genotyping method that can be implemented in a resource-limited environment.

METHODS

We developed a strategy for SNP genotyping based on T7 Endonuclease I cleavage and an enzyme-linked microparticle immune assay. Using this method, we genotyped two common SNP sites (rs11526468 and rs12979860). The quality of the genotyping process was validated.

RESULTS

Although a 70% false-negative rate was observed, no false-positive reactions were found. Therefore, multiple parallel repeat reactions can offset the possibility of mutation detection failure.

DISCUSSION

This method employs a duplicate reagent-dependent procedure, and therefore has the potential for integration into a portable kit for field utilization.

The Role of Lipid Interactions in Simulations of the α-Hemolysin Ion-Channel-Forming Toxin

Molecular dynamics simulations were performed to describe the function of the ion-channel-forming toxin α-hemolysin (αHL) in lipid membranes that were composed of either 1,2-diphytanoyl-sn-glycero-3-phospho-choline or 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-choline. The simulations highlight the importance of lipid type in maintaining αHL structure and function, enabling direct comparison to experiments for biosensing applications. We determined that although the two lipids studied are similar in structure, 1,2-diphytanoyl-sn-glycero-3-phospho-choline membranes better match the hydrophobic thickness of αHL compared to 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-choline membranes. This hydrophobic match is essential to maintaining proper alignment of β-sheet loops at the trans entrance of αHL, which, when disrupted, creates an additional constriction to ion flow that decreases the channel current below experimental values and creates greater variability in channel conductance. Agreement with experiments was further improved with sufficient lipid membrane equilibration and allowed the discrimination of subtle αHL conduction states with lipid type. Finally, we explore the effects of truncating the extramembrane cap of αHL and its role in maintaining proper alignment of αHL in the membrane and channel conductance. Our results demonstrate the essential role of lipid type and lipid-protein interactions in simulations of αHL and will considerably improve the interpretation of experimental data.

Gene editing in the context of an increasingly complex genome

The reporting of the first draft of the human genome in 2000 brought with it much hope for the future in what was felt as a paradigm shift toward improved health outcomes. Indeed, we have now mapped the majority of variation across human populations with landmark projects such as 1000 Genomes; in cancer, we have catalogued mutations across the primary carcinomas; whilst, for other diseases, we have identified the genetic variants with strongest association. Despite this, we are still awaiting the genetic revolution in healthcare to materialise and translate itself into the health benefits for which we had hoped. A major problem we face relates to our underestimation of the complexity of the genome, and that of biological mechanisms, generally. Fixation on DNA sequence alone and a 'rigid' mode of thinking about the genome has meant that the folding and structure of the DNA molecule -and how these relate to regulation- have been underappreciated. Projects like ENCODE have additionally taught us that regulation at the level of RNA is just as important as that at the spatiotemporal level of chromatin.In this review, we chart the course of the major advances in the biomedical sciences in the era pre- and post the release of the first draft sequence of the human genome, taking a focus on technology and how its development has influenced these. We additionally focus on gene editing via CRISPR/Cas9 as a key technique, in particular its use in the context of complex biological mechanisms. Our aim is to shift the mode of thinking about the genome to that which encompasses a greater appreciation of the folding of the DNA molecule, DNA- RNA/protein interactions, and how these regulate expression and elaborate disease mechanisms.Through the composition of our work, we recognise that technological improvement is conducive to a greater understanding of biological processes and life within the cell. We believe we now have the technology at our disposal that permits a better understanding of disease mechanisms, achievable through integrative data analyses. Finally, only with greater understanding of disease mechanisms can techniques such as gene editing be faithfully conducted.

Family specific genetic predisposition to breast cancer: results from Tunisian whole exome sequenced breast cancer cases

BACKGROUND

A family history of breast cancer has long been thought to indicate the presence of inherited genetic events that predispose to this disease. In North Africa, many specific epidemio-genetic characteristics have been observed in breast cancer families when compared to Western populations. Despite these specificities, the majority of breast cancer genetics studies performed in North Africa remain restricted to the investigation of the BRCA1 and BRCA2 genes. Thus, comprehensive data at a whole exome or whole genome level from local patients are lacking.

METHODS

A whole exome sequencing (WES) of seven breast cancer Tunisian families have been performed using a family-based approach. We focused our analysis on BC-TN-F001 family that included two affected members that have been sequenced using WES. Relevant variants identified in BC-TN-F001 have been confirmed using Sanger sequencing. Then, we conducted an integrative analysis by combining our results with those from other WES studies in order to figure out the genetic transmission model of the newly identified genes. Biological network construction and protein-protein interactions analyses have been performed to decipher the molecular mechanisms likely accounting for the role of these genes in breast cancer risk.

RESULTS

Sequencing, filtering strategies, and validation analysis have been achieved. For BC-TN-F001, no deleterious mutations have been identified on known breast cancer genes. However, 373 heterozygous, exonic and rare variants have been identified on other candidate genes. After applying several filters, 12 relevant high-risk variants have been selected. Our results showed that these variants seem to be inherited in a family specific model. This hypothesis has been confirmed following a thorough analysis of the reported WES studies. Enriched biological process and protein-protein interaction networks resulted in the identification of four novel breast cancer candidate genes namely MMS19, DNAH3, POLK and KATB6.

CONCLUSIONS

In this first WES application on Tunisian breast cancer patients, we highlighted the impact of next generation sequencing technologies in the identification of novel breast cancer candidate genes which may bring new insights into the biological mechanisms of breast carcinogenesis. Our findings showed that the breast cancer predisposition in non-BRCA families may be ethnic and/or family specific.

AutoSeqMan: batch assembly of contigs for Sanger sequences

With the wide application of DNA sequencing technology, DNA sequences are still increasingly generated through the Sanger sequencing platform. SeqMan (in the LaserGene package) is an excellent program with an easy-to-use graphical user interface (GUI) employed to assemble Sanger sequences into contigs. However, with increasing data size, larger sample sets and more sequenced loci make contig assemble complicated due to the considerable number of manual operations required to run SeqMan. Here, we present the 'autoSeqMan' software program, which can automatedly assemble contigs using SeqMan scripting language. There are two main modules available, namely, 'Classification' and 'Assembly'. Classification first undertakes preprocessing work, whereas Assembly generates a SeqMan script to consecutively assemble contigs for the classified files. Through comparison with manual operation, we showed that autoSeqMan saved substantial time in the preprocessing and assembly of Sanger sequences. We hope this tool will be useful for those with large sample sets to analyze, but with little programming experience. It is freely available at https://github.com/ Sun-Yanbo/autoSeqMan.

Solid-State Nanopore

Solid-state nanopore has captured the attention of many researchers due to its characteristic of nanoscale. Now, different fabrication methods have been reported, which can be summarized into two broad categories: "top-down" etching technology and "bottom-up" shrinkage technology. Ion track etching method, mask etching method chemical solution etching method, and high-energy particle etching and shrinkage method are exhibited in this report. Besides, we also discussed applications of solid-state nanopore fabrication technology in DNA sequencing, protein detection, and energy conversion.

Highly accurate fluorogenic DNA sequencing with information theory-based error correction

Eliminating errors in next-generation DNA sequencing has proved challenging. Here we present error-correction code (ECC) sequencing, a method to greatly improve sequencing accuracy by combining fluorogenic sequencing-by-synthesis (SBS) with an information theory-based error-correction algorithm. ECC embeds redundancy in sequencing reads by creating three orthogonal degenerate sequences, generated by alternate dual-base reactions. This is similar to encoding and decoding strategies that have proved effective in detecting and correcting errors in information communication and storage. We show that, when combined with a fluorogenic SBS chemistry with raw accuracy of 98.1%, ECC sequencing provides single-end, error-free sequences up to 200 bp. ECC approaches should enable accurate identification of extremely rare genomic variations in various applications in biology and medicine.

Open sharing of genomic data: Who does it and why?

We explored the characteristics and motivations of people who, having obtained their genetic or genomic data from Direct-To-Consumer genetic testing (DTC-GT) companies, voluntarily decide to share them on the publicly accessible web platform openSNP. The study is the first attempt to describe open data sharing activities undertaken by individuals without institutional oversight. In the paper we provide a detailed overview of the distribution of the demographic characteristics and motivations of people engaged in genetic or genomic open data sharing. The geographical distribution of the respondents showed the USA as dominant. There was no significant gender divide, the age distribution was broad, educational background varied and respondents with and without children were equally represented. Health, even though prominent, was not the respondents' primary or only motivation to be tested. As to their motivations to openly share their data, 86.05% indicated wanting to learn about themselves as relevant, followed by contributing to the advancement of medical research (80.30%), improving the predictability of genetic testing (76.02%) and considering it fun to explore genotype and phenotype data (75.51%). Whereas most respondents were well aware of the privacy risks of their involvement in open genetic data sharing and considered the possibility of direct, personal repercussions troubling, they estimated the risk of this happening to be negligible. Our findings highlight the diversity of DTC-GT consumers who decide to openly share their data. Instead of focusing exclusively on health-related aspects of genetic testing and data sharing, our study emphasizes the importance of taking into account benefits and risks that stretch beyond the health spectrum. Our results thus lend further support to the call for a broader and multi-faceted conceptualization of genomic utility.

A genome-wide SNP-based genetic map and QTL mapping for agronomic traits in Chinese cabbage

The aim of this work was to construct a high-resolution genetic map for the dissection of complex morphological and agronomic traits in Chinese cabbage (Brassica rapa L. syn. B. campestris). Chinese cabbage, an economically important vegetable, is a good model plant for studies on the evolution of morphologic variation. Herein, two high-generation inbred Chinese cabbage lines, 'Huangxiaoza' and 'Bqq094-11', were crossed. Then restriction-site-associated DNA sequencing (RAD-seq) was performed on the parents and 120 F₂ individuals. A genetic map containing 711 bins representing 3985 single nucleotide polymorphism (SNP) markers was constructed. By using WinQTL with composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) analysis via QTLNetwork, quantitative trait loci (QTL) linked to 16 genetic traits related to plant size, color and leaf characteristics were mapped to 10 linkage groups. The high density genetic map and QTL identified for morphological and agronomic traits lay the groundwork for functional gene mapping, map-based cloning and marker-assisted selection (MAS) in Chinese cabbage.

Computational complexity of algorithms for sequence comparison, short-read assembly and genome alignment

A multitude of algorithms for sequence comparison, short-read assembly and whole-genome alignment have been developed in the general context of molecular biology, to support technology development for high-throughput sequencing, numerous applications in genome biology and fundamental research on comparative genomics. The computational complexity of these algorithms has been previously reported in original research papers, yet this often neglected property has not been reviewed previously in a systematic manner and for a wider audience. We provide a review of space and time complexity of key sequence analysis algorithms and highlight their properties in a comprehensive manner, in order to identify potential opportunities for further research in algorithm or data structure optimization. The complexity aspect is poised to become pivotal as we will be facing challenges related to the continuous increase of genomic data on unprecedented scales and complexity in the foreseeable future, when robust biological simulation at the cell level and above becomes a reality.

Genomics pipelines and data integration: challenges and opportunities in the research setting

INTRODUCTION

The emergence and mass utilization of high-throughput (HT) technologies, including sequencing technologies (genomics) and mass spectrometry (proteomics, metabolomics, lipids), has allowed geneticists, biologists, and biostatisticians to bridge the gap between genotype and phenotype on a massive scale. These new technologies have brought rapid advances in our understanding of cell biology, evolutionary history, microbial environments, and are increasingly providing new insights and applications towards clinical care and personalized medicine. Areas covered: The very success of this industry also translates into daunting big data challenges for researchers and institutions that extend beyond the traditional academic focus of algorithms and tools. The main obstacles revolve around analysis provenance, data management of massive datasets, ease of use of software, interpretability and reproducibility of results. Expert commentary: The authors review the challenges associated with implementing bioinformatics best practices in a large-scale setting, and highlight the opportunity for establishing bioinformatics pipelines that incorporate data tracking and auditing, enabling greater consistency and reproducibility for basic research, translational or clinical settings.

Plant Microbe Interactions in Post Genomic Era: Perspectives and Applications

Deciphering plant-microbe interactions is a promising aspect to understand the benefits and the pathogenic effect of microbes and crop improvement. The advancement in sequencing technologies and various 'omics' tool has impressively accelerated the research in biological sciences in this area. The recent and ongoing developments provide a unique approach to describing these intricate interactions and test hypotheses. In the present review, we discuss the role of plant-pathogen interaction in crop improvement. The plant innate immunity has always been an important aspect of research and leads to some interesting information like the adaptation of unique immune mechanisms of plants against pathogens. The development of new techniques in the post - genomic era has greatly enhanced our understanding of the regulation of plant defense mechanisms against pathogens. The present review also provides an overview of beneficial plant-microbe interactions with special reference to Agrobacterium tumefaciens-plant interactions where plant derived signal molecules and plant immune responses are important in pathogenicity and transformation efficiency. The construction of various Genome-scale metabolic models of microorganisms and plants presented a better understanding of all metabolic interactions activated during the interactions. This review also lists the emerging repertoire of phytopathogens and its impact on plant disease resistance. Outline of different aspects of plant-pathogen interactions is presented in this review to bridge the gap between plant microbial ecology and their immune responses.

Bioinformatics Approach in Plant Genomic Research

The advance in genomics technology leads to the dramatic change in plant biology research. Plant biologists now easily access to enormous genomic data to deeply study plant high-density genetic variation at molecular level. Therefore, fully understanding and well manipulating bioinformatics tools to manage and analyze these data are essential in current plant genome research. Many plant genome databases have been established and continued expanding recently. Meanwhile, analytical methods based on bioinformatics are also well developed in many aspects of plant genomic research including comparative genomic analysis, phylogenomics and evolutionary analysis, and genome-wide association study. However, constantly upgrading in computational infrastructures, such as high capacity data storage and high performing analysis software, is the real challenge for plant genome research. This review paper focuses on challenges and opportunities which knowledge and skills in bioinformatics can bring to plant scientists in present plant genomics era as well as future aspects in critical need for effective tools to facilitate the translation of knowledge from new sequencing data to enhancement of plant productivity.

A comparison of tools for the simulation of genomic next-generation sequencing data

Computer simulation of genomic data has become increasingly popular for assessing and validating biological models or for gaining an understanding of specific data sets. Several computational tools for the simulation of next-generation sequencing (NGS) data have been developed in recent years, which could be used to compare existing and new NGS analytical pipelines. Here we review 23 of these tools, highlighting their distinct functionality, requirements and potential applications. We also provide a decision tree for the informed selection of an appropriate NGS simulation tool for the specific question at hand.

GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction

Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.

A Review of Whole-Exome Sequencing Efforts Toward Hereditary Breast Cancer Susceptibility Gene Discovery

Inherited genetic risk factors contribute toward breast cancer (BC) onset. BC risk variants can be divided into three categories of penetrance (high, moderate, and low) that reflect the probability of developing the disease. Traditional BC susceptibility gene discovery approaches that searched for high- and moderate-risk variants in familial BC cases have had limited success; to date, these risk variants explain only ∼30% of familial BC cases. Next-generation sequencing technologies can be used to search for novel high and moderate BC risk variants, and this manuscript reviews 12 familial BC whole-exome sequencing efforts. Study design, filtering strategies, and segregation and validation analyses are discussed. Overall, only a modest number of novel BC risk genes were identified, and 90% and 97% of the exome-sequenced families and cases, respectively, had no BC risk variants reported. It is important to learn from these studies and consider alternate strategies in order to make further advances. The discovery of new BC susceptibility genes is critical for improved risk assessment and to provide insight toward disease mechanisms for the development of more effective therapies.