The discovery of human genetic variations and their use as disease markers: past, present and future

Genomic Mosaicism of the Brain: Origin, Impact, and Utility

Genomic mosaicism describes the phenomenon where some but not all cells within a tissue harbor unique genetic mutations. Traditionally, research focused on the impact of genomic mosaicism on clinical phenotype-motivated by its involvement in cancers and overgrowth syndromes. More recently, we increasingly shifted towards the plethora of neutral mosaic variants that can act as recorders of cellular lineage and environmental exposures. Here, we summarize the current state of the field of genomic mosaicism research with a special emphasis on our current understanding of this phenomenon in brain development and homeostasis. Although the field of genomic mosaicism has a rich history, technological advances in the last decade have changed our approaches and greatly improved our knowledge. We will provide current definitions and an overview of contemporary detection approaches for genomic mosaicism. Finally, we will discuss the impact and utility of genomic mosaicism.

Selection and direct biomarkers of reproductive capacity of breeding boars

Efficient management of pig reproduction is paramount for the sustainability and productivity of the global pork industry. Modern artificial insemination (AI) breeding programs have greatly benefited from the integration of advanced selection methods and biomarkers to enhance the reproductive performance of boars. While traditional selection methods have relied soley on boar phenotype, such as growth rate and conformation, modern pig breeding has shifted more and more toward molecular and genetic tools, which are still complemented by phenotypic traits. These methods encompass genomics, transcriptomics, and proteomics. Biomarkers serve as critical indicators of boar reproductive capacity. They can help to identify individuals with superior fertility and aid in the early identification of potential fertility issues, allowing for proactive management strategies. This review summarizes current knowledge of various biomarkers associated with semen quality, sperm function, and overall reproductive fitness in boars. Furthermore, we explore advanced technologies and their potential applications in uncovering novel selection methods and biomarkers for predicting boar fertility. A comprehensive understanding of selection criteria and biomarkers governing boar reproductive capacity is essential for developing effective breeding programs to enhance swine reproductive performance.

Critical assessment of on-premise approaches to scalable genome analysis

BACKGROUND

Plummeting DNA sequencing cost in recent years has enabled genome sequencing projects to scale up by several orders of magnitude, which is transforming genomics into a highly data-intensive field of research. This development provides the much needed statistical power required for genotype-phenotype predictions in complex diseases.

METHODS

In order to efficiently leverage the wealth of information, we here assessed several genomic data science tools. The rationale to focus on on-premise installations is to cope with situations where data confidentiality and compliance regulations etc. rule out cloud based solutions. We established a comprehensive qualitative and quantitative comparison between BCFtools, SnpSift, Hail, GEMINI, and OpenCGA. The tools were compared in terms of data storage technology, query speed, scalability, annotation, data manipulation, visualization, data output representation, and availability.

RESULTS

Tools that leverage sophisticated data structures are noted as the most suitable for large-scale projects in varying degrees of scalability in comparison to flat-file manipulation (e.g., BCFtools, and SnpSift). Remarkably, for small to mid-size projects, even lightweight relational database.

CONCLUSION

The assessment criteria provide insights into the typical questions posed in scalable genomics and serve as guidance for the development of scalable computational infrastructure in genomics.

GWAS-identified telomere length associated genetic variants predict risk of recurrence of HPV-positive oropharyngeal cancer after definitive radiotherapy

BACKGROUND

Lymphocyte telomere length (LTL)-related genetic variants may modulate LTL and affect recurrence of squamous cell carcinoma of the oropharynx (SCCOP).

METHODS

A total of 1013 patients with incident SCCOP were recruited and genotyped for 16 genome-wide association study (GWAS)-identified TL-related polymorphisms. Of these patients, 489 had tumour HPV16 status determination. Univariate and multivariate analyses were performed to evaluate associations.

FINDINGS

Of the 16 TL-related polymorphisms, four were significantly associated with LTL: rs1920116, rs3027234, rs6772228, and rs11125529, and the patients with putatively favourable genotypes had approximately 1.5-3 times the likelihood of shorter LTL compared with patients with the corresponding risk genotypes. Moreover, patients with one to four favourable genotypes of the four combined polymorphisms had approximately 3-11 times the likelihood of shorter LTL compared with patients with no favourable genotype. The four LTL-related polymorphisms were significantly associated with approximately 40% reduced risk (for favourable genotypes) or doubled risk (for risk genotypes) of recurrence, and similar but more pronounced associations were observed in patients with tumour HPV16-positive SCCOP. Similarly, patients with one to four risk genotypes had significantly approximately 2.5-4 times increased recurrence risk compared with patients with no risk genotype, and similar but more pronounced associations were observed in patients with tumour HPV16-positive SCCOP.

INTERPRETATION

Four LTL-related polymorphisms individually or jointly modify LTL and risk of recurrence of SCCOP, particularly HPV-positive SCCOP. These LTL-related polymorphisms could have potential to further stratify patients with HPV-positive SCCOP for individualized treatment and better survival.

FUNDING

Not applicable.

Chemical Sensors using Single-Molecule Electrical Measurements

Driven by the digitization and informatization of contemporary society, electrical sensors are developing toward minimal structure, intelligent function, and high detection resolution. Single-molecule electrical measurement techniques have been proven to be capable of label-free molecular recognition and detection, which opens a new strategy for the design of efficient single-molecule detection sensors. In this review, we outline the main advances and potentials of single-molecule electronics for qualitative identification and recognition assays at the single-molecule level. Strategies for single-molecule electro-sensing and its main applications are reviewed, mainly in the detection of ions, small molecules, oligomers, genetic materials, and proteins. This review summarizes the remaining challenges in the current development of single-molecule electrical sensing and presents some potential perspectives for this field.

Establishment and characterization of patient-derived xenograft of a rare pediatric anaplastic pleomorphic xanthoastrocytoma (PXA) bearing a CDC42SE2-BRAF fusion

Pleomorphic xanthoastrocytoma (PXA) is a rare subset of primary pediatric glioma with 70% 5-year disease free survival. However, up to 20% of cases present with local recurrence and malignant transformation into more aggressive type anaplastic PXA (AXPA) or glioblastoma. The understanding of disease etiology and mechanisms driving PXA and APXA are limited, and there is no standard of care. Therefore, development of relevant preclinical models to investigate molecular underpinnings of disease and to guide novel therapeutic approaches are of interest. Here, for the first time we established, and characterized a patient-derived xenograft (PDX) from a leptomeningeal spread of a patient with recurrent APXA bearing a novel CDC42SE2-BRAF fusion. An integrated -omics analysis was conducted to assess model fidelity of the genomic, transcriptomic, and proteomic/phosphoproteomic landscapes. A stable xenoline was derived directly from the patient recurrent tumor and maintained in 2D and 3D culture systems. Conserved histology features between the PDX and matched APXA specimen were maintained through serial passages. Whole exome sequencing (WES) demonstrated a high degree of conservation in the genomic landscape between PDX and matched human tumor, including small variants (Pearson's r = 0.794-0.839) and tumor mutational burden (~ 3 mutations/MB). Large chromosomal variations including chromosomal gains and losses were preserved in PDX. Notably, chromosomal gain in chromosomes 4-9, 17 and 18 and loss in the short arm of chromosome 9 associated with homozygous 9p21.3 deletion involving CDKN2A/B locus were identified in both patient tumor and PDX sample. Moreover, chromosomal rearrangement involving 7q34 fusion; CDC42SE-BRAF t (5;7) (q31.1, q34) (5:130,721,239, 7:140,482,820) was identified in the PDX tumor, xenoline and matched human tumor. Transcriptomic profile of the patient's tumor was retained in PDX (Pearson r = 0.88) and in xenoline (Pearson r = 0.63) as well as preservation of enriched signaling pathways (FDR Adjusted P < 0.05) including MAPK, EGFR and PI3K/AKT pathways. The multi-omics data of (WES, transcriptome, and reverse phase protein array (RPPA) was integrated to deduce potential actionable pathways for treatment (FDR < 0.05) including KEGG01521, KEGG05202, and KEGG05200. Both xenoline and PDX were resistant to the MEK inhibitors trametinib or mirdametinib at clinically relevant doses, recapitulating the patient's resistance to such treatment in the clinic. This set of APXA models will serve as a preclinical resource for developing novel therapeutic regimens for rare anaplastic PXAs and pediatric high-grade gliomas bearing BRAF fusions.

Comparative Analyses of Turkish Variome and Widely Used Genomic Variation Databases for the Evaluation of Rare Sequence Variants in Turkish Individuals: Idiopathic Hypogonadotropic Hypogonadism as a Disease Model

Objective

With the increasing use of whole-exome sequencing, one of the challenges in identifying the causal allele for a Mendelian disease is the lack of availability of population-specific human genetic variation reference databases. The people of Turkey were not represented in GnomAD or other publicly available large databases until recently, when the first comprehensive genomic variation database, Turkish Variome (TRV), was published. The aim of this study was to evaluate whether TRV or other publicly available large genomic variation databases can reliably be used for rare disease variant evaluation in Turkish individuals.

Methods

Sixty non-disease-causing, non-synonymous variants (minor allele frequencies >1%) were identified in 58 genes that are known to be associated with idiopathic hypogonadotropic hypogonadism from a large Turkish patient cohort. The allelic frequencies of these variants were then compared with those in various public genomic variation databases, including TRV.

Results

Our cohort variants showed the highest correlations with those in the TRV, Iranome, and The Greater Middle East Variome, in decreasing order.

Conclusion

These results suggest that the TRV is the appropriate database to use for rare genomic variant evaluations in the Turkish population. Our data also suggest that variomes from geographic neighborhoods may serve as substitute references for populations devoid of their own genomic variation databases.

Genomic medicine on the frontier of precision medicine

Genomic medicine has created a great deal of hope since the completion of the Human Genome Project (HGP). Genomic medicine promises disease prevention and early diagnosis in the context of precision medicine. Precision medicine as a scientific discipline has introduced as an evolution in medicine. The rapid growth of high-development technologies permits the assessment of biological systems. Study of the integrated profiles of omics, such as genome, transcriptome, proteome and other omics information lead to significant advances in personalized and precision medicine. In the context of precision medicine, pharmacogenomics can play an important role in order to discriminate responders and non-responders to medications and avoiding toxicity and achieving the optimum dose. So precision medicine in accordance with genomic medicine will transform medicine from conventional evidence-based medicine in the diagnosis and treatment towards precision based-medicine. In this review, we have summarized the related issues for genomic medicine and precision medicine.

Leveraging Mann-Whitney U test on large-scale genetic variation data for analysing malaria genetic markers

Background

The malaria risk analysis of multiple populations is crucial and of great importance whilst compressing limitations. However, the exponential growth in diversity and accumulation of genetic variation data obtained from malaria-infected patients through Genome-Wide Association Studies opens up unprecedented opportunities to explore the significant differences between genetic markers (risk factors), particularly in the resistance or susceptibility of populations to malaria risk. Thus, this study proposes using statistical tests to analyse large-scale genetic variation data, comprising 20,854 samples from 11 populations within three continents: Africa, Oceania, and Asia.

Methods

Even though statistical tests have been utilized to conduct case–control studies since the 1950s to link risk factors to a particular disease, several challenges faced, including the choice of data (ordinal vs. non-ordinal) and test (parametric vs. non-parametric). This study overcomes these challenges by adopting the Mann–Whitney U test to analyse large-scale genetic variation data; to explore the statistical significance of markers between populations; and to further identify the highly differentiated markers.

Results

The findings of this study revealed a significant difference in the genetic markers between populations (p < 0.01) in all the case groups and most control groups. However, for the highly differentiated genetic markers, a significant difference (p < 0.01) was present for most genetic markers with varying p-values between the populations in the case and control groups. Moreover, several genetic markers were observed to have very significant differences (p < 0.001) across all populations, while others exist between certain specific populations. Also, several genetic markers have no significant differences between populations.

Conclusions

These findings further support that the genetic markers contribute differently between populations towards malaria resistance or susceptibility, thus showing differences in the likelihood of malaria infection. In addition, this study demonstrated the robustness of the Mann–Whitney U test in analysing genetic markers in large-scale genetic variation data, thereby indicating an alternative method to explore genetic markers in other complex diseases. The findings hold great promise for genetic markers analysis, and the pipeline emphasized in this study can fully be reproduced to analyse new data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-022-04104-x.

New neural network classification method for individuals ancestry prediction from SNPs data

Artificial Neural Network (ANN) algorithms have been widely used to analyse genomic data. Single Nucleotide Polymorphisms(SNPs) represent the genetic variations, the most common in the human genome, it has been shown that they are involved in many genetic diseases, and can be used to predict their development. Developing ANN to handle this type of data can be considered as a great success in the medical world. However, the high dimensionality of genomic data and the availability of a limited number of samples can make the learning task very complicated. In this work, we propose a New Neural Network classification method based on input perturbation. The idea is first to use SVD to reduce the dimensionality of the input data and to train a classification network, which prediction errors are then reduced by perturbing the SVD projection matrix. The proposed method has been evaluated on data from individuals with different ancestral origins, the experimental results have shown the effectiveness of the proposed method. Achieving up to 96.23% of classification accuracy, this approach surpasses previous Deep learning approaches evaluated on the same dataset.

The Role of Neurod Genes in Brain Development, Function, and Disease

Transcriptional regulation is essential for the correct functioning of cells during development and in postnatal life. The basic Helix-loop-Helix (bHLH) superfamily of transcription factors is well conserved throughout evolution and plays critical roles in tissue development and tissue maintenance. A subgroup of this family, called neural lineage bHLH factors, is critical in the development and function of the central nervous system. In this review, we will focus on the function of one subgroup of neural lineage bHLH factors, the Neurod family. The Neurod family has four members: Neurod1, Neurod2, Neurod4, and Neurod6. Available evidence shows that these four factors are key during the development of the cerebral cortex but also in other regions of the central nervous system, such as the cerebellum, the brainstem, and the spinal cord. We will also discuss recent reports that link the dysfunction of these transcription factors to neurological disorders in humans.

Prediction and interpretation of rare missense variant in OTOG associated with hearing loss

OTOG encodes for otogelin, a component of the tectorial membrane. This gene is associated with nonprogressive mild-to-moderate hearing loss. However, no studies have yet identified the association between OTOG variation and severe-to-profound hearing loss. Therefore, to address this issue, a family-based whole-exome sequencing strategy (WES) was carried out. Two unrelated Iranian families with non-syndromic hearing loss were identified, and WES was conducted on one selected candidate from each family. As a result, a rare homozygous missense variant, OTOG (c.C2383T:p.R795C), was detected in both of the subjected probands, and segregation analysis confirmed the c.C2383T variant in seven cases of severe-to-profound hearing loss. Additionally, the results from the protein modeling demonstrated that the altered position of a few disulfide bonds in the TIL domain may have a deleterious impact on protein stability and normal functionality. In conclusion, it seems that the homozygosity of the OTOG c.C2383T mutation sheds light on hearing loss pathobiology. Nevertheless, further studies are required to unravel the precise function of OTOG mutation, which is potentially associated with severe-to-profound hearing loss.

Personalized Medicine in Oncology in the Developing World: Barriers and Concepts to Improve Status Quo

Personalized medicine (PM) has revolutionized oncology management in high human development indexed countries. By interrogating both disease and host factors through a variety of tools, oncologists have been able to better target an individual's cancer, leading to improved outcomes. But both the tools used to define these variables, such as next generation sequencing, large immunohistochemical and fluorescence in situ hybridization (FISH) panels, and the weapons employed against each target are extremely expensive. The expenses have to be measured as not only the direct cost to the patient but also the cost to the system to develop and deploy the necessary infrastructure to optimally use them. However, the concepts of predictive, timely prevention and PM have demonstrated improvement in patient's satisfaction and cost effectiveness. In this paper we will summarize the relevant barriers and challenges that limit the implementation of PM in the developing world with an emphasis on the challenges in Nigeria and Nepal.

The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.

Exon Coverage Variations Between Cancer Tissues and Adjacent Non-Cancerous Tissues are Prognostic Factors in Gastric Cancer

Introduction

Gastric cancer is highly heterogeneous both clinically and pathologically and is one of the leading causes of cancer-related deaths worldwide. Genomic coverage variations, also known as copy number variations (CNVs), play a critical role in the carcinogenesis of gastric cancer. Many studies have demonstrated that DNA CNVs are important factors affecting the expression of protein-encoding genes in the gastric cancer genome.

Methods

Thirty gastric cancer patients from a Chinese population were enrolled. Genomic DNA was extracted from gastric cancer tissue and matched adjacent non-cancerous tissue from each patient. A panel of 1,021 genes including 3300 exons was designed and subjected to next-generation sequencing. Copy numbers of each gene and exon were calculated for each tissue. Coverage variations between gastric cancer tissue and matched adjacent non-cancerous tissue were also calculated, and we examined the correlation between overall survival of patients and coverage variation type for each exon.

Results

DNA from cancerous tissue and corresponding adjacent non-cancerous tissue were significantly different with respect to the pattern of gene copy number. Exon copy numbers were highly consistent among non-cancerous samples and confirmed that non-cancerous tissue contain diploid genomes. In contrast, the gene coverage pattern among cancerous tissue showed significant differences and confirmed that gastric cancer is a genetically heterogeneous disease. Numerous exon coverage variations were identified in gastric cancer tissue compared with matched, adjacent non-cancerous tissue. Overall survival between patients with and without coverage variations in regions of NOTCH2, NTRK3, ERBB2 and RERE exons exhibited significant differences. This is consistent with previous reports and indicates that these findings may have prognostic value.

Conclusion

Our results confirm that gastric cancer is a genetically heterogeneous disease. Exon coverage variations between cancer tissue and their adjacent non-cancerous tissue were shown to be associated with prognosis in gastric cancer.

Human gene and disease associations for clinical-genomics and precision medicine research

We are entering the era of personalized medicine in which an individual's genetic makeup will eventually determine how a doctor can tailor his or her therapy. Therefore, it is becoming critical to understand the genetic basis of common diseases, for example, which genes predispose and rare genetic variants contribute to diseases, and so on. Our study focuses on helping researchers, medical practitioners, and pharmacists in having a broad view of genetic variants that may be implicated in the likelihood of developing certain diseases. Our focus here is to create a comprehensive database with mobile access to all available, authentic and actionable genes, SNPs, and classified diseases and drugs collected from different clinical and genomics databases worldwide, including Ensembl, GenCode, ClinVar, GeneCards, DISEASES, HGMD, OMIM, GTR, CNVD, Novoseek, Swiss-Prot, LncRNADisease, Orphanet, GWAS Catalog, SwissVar, COSMIC, WHO, and FDA. We present a new cutting-edge gene-SNP-disease-drug mobile database with a smart phone application, integrating information about classified diseases and related genes, germline and somatic mutations, and drugs. Its database includes over 59 000 protein-coding and noncoding genes; over 67 000 germline SNPs and over a million somatic mutations reported for over 19 000 protein-coding genes located in over 1000 regions, published with over 3000 articles in over 415 journals available at the PUBMED; over 80 000 ICDs; over 123 000 NDCs; and over 100 000 classified gene-SNP-disease associations. We present an application that can provide new insights into the information about genetic basis of human complex diseases and contribute to assimilating genomic with phenotypic data for the availability of gene-based designer drugs, precise targeting of molecular fingerprints for tumor, appropriate drug therapy, predicting individual susceptibility to disease, diagnosis, and treatment of rare illnesses are all a few of the many transformations expected in the decade to come.

Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine

Background

The last decade has seen a dramatic increase in the availability of scientific data, where human-related biological databases have grown not only in count but also in volume, posing unprecedented challenges in data storage, processing, analysis, exchange, and curation. Next generation sequencing (NGS) advancements have facilitated and accelerated the process of identifying genetic variations. Adopting NGS with Whole-Genome and RNA sequencing in a diagnostic context has the potential to improve disease-risk detection in support of precision medicine and drug discovery. Several bioinformatics pipelines have been developed to strengthen variant interpretation by efficiently processing and analyzing sequence data, whereas many published results show how genomics data can be proactively incorporated into medical practices and improve utilization of clinical information. To utilize the wealth of genomics and health, there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern technology.

Results

Our focus here is to create a comprehensive database with mobile access to actionable genes and classified diseases, considered the foundation for clinical genomics and precision medicine. We present a publicly available iOS app, PAS-Gen, which invites global users to freely download it on iPhone and iPad devices, quickly adopt its easy to use interface, and search for genes and related diseases. PAS-Gen was developed using Swift, XCODE, and PHP scripting that uses Web and MySQL database servers, which includes over 59,000 protein-coding and non-coding genes, and over 90,000 classified gene-disease associations. PAS-Gen is founded on the clinical and scientific premise that easier healthcare and genomics data sharing will accelerate future medical discoveries.

Conclusions

We present a cutting-edge gene-disease database with a smart phone application, integrating information on classified diseases and related genes. The PAS-Gen app will assist researchers, medical practitioners, and pharmacists by providing a broad and view of genes that may be implicated in the likelihood of developing certain diseases. This tool with accelerate users’ abilities to understand the genetic basis of human complex diseases and by assimilating genomic and phenotypic data will support future work to identify gene-specific designer drugs, target precise molecular fingerprints for tumors, suggest appropriate drug therapies, predict individual susceptibility to disease, and diagnose and treat rare illnesses.

Comparison of INDEL Calling Tools with Simulation Data and Real Short-Read Data

Insertions and deletions (INDELs) comprise a significant proportion of human genetic variation, and recent papers have revealed that many human diseases may be attributable to INDELs. With the development of next-generation sequencing (NGS) technology, many statistical/computational tools have been developed for calling INDELs. However, there are differences among those tools, and comparisons among them have been limited. In order to better understand these inter-tool differences, five popular and publicly available INDEL calling tools-GATK HaplotypeCaller, Platypus, VarScan2, Scalpel, and GotCloud-were evaluated using simulation data, 1000 Genomes Project data, and family-based sequencing data. The accuracy of INDEL calling by each tool was mainly evaluated by concordance rates. Family-based sequencing data, which consisted of 49 individuals from eight Korean families, were used to calculate Mendelian error rates. Our comparison results show that GATK HaplotypeCaller usually performs the best and that joint calling with Platypus can lead to additional improvements in accuracy. The result of this study provides important information regarding future directions for the variant detection and the algorithms development.

MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants

The amount of genetic variation discovered in human populations is growing rapidly leading to challenging computational tasks, such as variant calling. Standard methods for addressing this problem include read mapping, a computationally expensive procedure; thus, mapping-free tools have been proposed in recent years. These tools focus on isolated, biallelic SNPs, providing limited support for multi-allelic SNPs and short insertions and deletions of nucleotides (indels). Here we introduce MALVA, a mapping-free method to genotype an individual from a sample of reads. MALVA is the first mapping-free tool able to genotype multi-allelic SNPs and indels, even in high-density genomic regions, and to effectively handle a huge number of variants. MALVA requires one order of magnitude less time to genotype a donor than alignment-based pipelines, providing similar accuracy. Remarkably, on indels, MALVA provides even better results than the most widely adopted variant discovery tools.

•

MALVA is able to genotype multi-allelic SNPs and indels without mapping reads

•

MALVA calls correctly more indels than the most widely adopted genotyping pipelines

•

Mapping-free approaches are as accurate as alignment-based ones, while being faster

Label-free discrimination of single nucleotide changes in DNA by reflectometric interference Fourier transform spectroscopy

Phenotypic variation - such as disease susceptibility and differential drug response - has a strong genetic component. Substantial effort has therefore been made to identify causal genomic variants explaining such variation among humans. Point mutations (PMs), which are single nucleotide changes in the genome, have been identified to be the most abundant form of causal genomic variants, making them useful, reliable diagnostic markers. Methods developed to genotype PMs have moved towards solid-phase assays, which not only show greater sensitivity and specificity, but also enable scalability and faster processing time. Most current assays are, however, based on fluorescent probes, which makes them relatively expensive. To develop a more cost-effective label-free genotyping method, we used a porous silicon (PSi) base as an efficient support for DNA biosensing and coupled it with reflectometric interference Fourier transform spectroscopy (RIFTS). To assess the versatility of this approach, we tested both a single nucleotide substitution in VKORC1 (-1639G > A; rs9923231) and a single nucleotide insertion in BRCA1 (5382insC; rs80357906). We demonstrate that the PSi-RIFTS method can efficiently detect both PM types with high sensitivity where hybridization of complementary DNA can be quantifiably differentiated from mismatch and non-complementary hybridization events. In addition, we show that the PSi base with immobilized DNA not only can be re-used to type further samples, but it also remains stable for 14 days, suggesting its potential for high-throughput applications.

Chromosomal aberrations in renal cell carcinoma: An overview with implications for clinical practice

Chromosomal instability and aberrations are known in many cancers including renal cell carcinoma. Detailed understanding of these changes has led to an improved drug discovery and continued developments in other therapeutic options. Chromosomal aberrations have a potential to be used to monitor disease including prognostication. There has been a growing experience in cytogenetic techniques and gap between clinic and laboratory has narrowed significantly in the recent past. Nevertheless, more work on validation of these techniques, establishing threshold and interobserver agreement needs to be carried out for these diagnostic/prognostic tests before utilizing them in clinics as a part of “personalized medicine” care. The review presented here is a summary of common genetic disorders in renal cancer and some of acquired genetic changes which can be used as biomarkers. The review also describes basics of commonly used genetic techniques for wider clinical community involved in the management of renal cancer.

Noise cancellation using total variation for copy number variation detection

BACKGROUND

Due to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount data's noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm.

RESULTS

To investigate the performance of the proposed denoising method, we computed sensitivities, false discovery rates and specificities of CNV detection when employing denoising, using both simulated and real data. We also compared the performance of the proposed denoising method, Taut String, with that of the commonly used approaches such as moving average (MA) and discrete wavelet transforms (DWT) in terms of sensitivity of detecting true CNVs and time complexity. The results show that Taut String works better than DWT and MA and has a better power to identify very narrow CNVs. The ability of Taut String denoising in preserving CNV segments' breakpoints and narrow CNVs increases the detection accuracy of segmentation algorithms, resulting in higher sensitivities and lower false discovery rates.

CONCLUSIONS

In this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection.

Role of Genetics and Epigenetics in the Pathogenesis of Alzheimer's Disease and Frontotemporal Dementia

Alzheimer's disease (AD) and frontotemporal dementia (FTD) represent the first cause of dementia in senile and pre-senile population, respectively. A percentage of cases have a genetic cause, inherited with an autosomal dominant pattern of transmission. The majority of cases, however, derive from complex interactions between a number of genetic and environmental factors. Gene variants may act as risk or protective factors. Their combination with a variety of environmental exposures may result in increased susceptibility to these diseases or may influence their course. The scenario is even more complicated considering the effect of epigenetics, which encompasses mechanisms able to alter the expression of genes without altering the DNA sequence. In this review, an overview of the current genetic and epigenetic progresses in AD and FTD will be provided, with particular focus on 1) causative genes, 2) genetic risk factors and disease modifiers, and 3) epigenetics, including methylation, non-coding RNAs and chromatin remodeling.

Single Nucleotide Polymorphism Genotyping in Single-Molecule Electronic Circuits

Establishing low-cost, high-throughput, simple, and accurate single nucleotide polymorphism (SNP) genotyping techniques is beneficial for understanding the intrinsic relationship between individual genetic variations and their biological functions on a genomic scale. Here, a straightforward and reliable single-molecule approach is demonstrated for precise SNP authentication by directly measuring the fluctuations in electrical signals in an electronic circuit, which is fabricated from a high-gain field-effect silicon nanowire decorated with a single hairpin DNA, in the presence of different target DNAs. By simply comparing the proportion difference of a probe-target duplex structure throughout the process, this study implements allele-specific and accurate SNP detection. These results are supported by the statistical analyses of different dynamic parameters such as the mean lifetime and the unwinding probability of the duplex conformation. In comparison with conventional polymerase chain reaction and optical methods, this convenient and label-free method is complementary to existing optical methods and also shows several advantages, such as simple operation and no requirement for fluorescent labeling, thus promising a futuristic route toward the next-generation genotyping technique.

An evaluation of copy number variation detection tools for cancer using whole exome sequencing data

BACKGROUND

Recently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors' complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.

RESULTS

In this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly.

CONCLUSIONS

The limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

DNA methylation-based variation between human populations

Several studies have proved that DNA methylation affects regulation of gene expression and development. Epigenome-wide studies have reported variation in methylation patterns between populations, including Caucasians, non-Caucasians (Blacks), Hispanics, Arabs, and numerous populations of the African continent. Not only has DNA methylation differences shown to impact externally visible characteristics, but is also a potential biomarker for underlying racial health disparities between human populations. Ethnicity-related methylation differences set their mark during early embryonic development. Genetic variations, such as single-nucleotide polymorphisms and environmental factors, such as age, dietary folate, socioeconomic status, and smoking, impacts DNA methylation levels, which reciprocally impacts expression of phenotypes. Studies show that it is necessary to address these external influences when attempting to differentiate between populations since the relative impacts of these factors on the human methylome remain uncertain. The present review summarises several reported attempts to establish the contribution of differential DNA methylation to natural human variation, and shows that DNA methylation could represent new opportunities for risk stratification and prevention of several diseases amongst populations world-wide. Variation of methylation patterns between human populations is an exciting prospect which inspires further valuable research to apply the concept in routine medical and forensic casework. However, trans-generational inheritance needs to be quantified to decipher the proportion of variation contributed by DNA methylation. The future holds thorough evaluation of the epigenome to understand quantification, heritability, and the effect of DNA methylation on phenotypes. In addition, methylation profiling of the same ethnic groups across geographical locations will shed light on conserved methylation differences in populations.

Detection of microRNA in tumor cells using exonuclease III and graphene oxide-regulated signal amplification

In this study, we developed a label-free, ultrasensitive graphene oxide (GO)-based probe for the detection of oligonucleotides by laser desorption/ionization mass spectrometry (LDI-MS). On the basis of simple π-π stacking and electrostatic interactions between rhodamine 6G (R6G) and GO, we prepared the nanocomposite R6G-modified GO (R6G-GO). Signal intensities of R6G increased in mass spectra in the presence of single-stranded oligonucleotides under pulsed laser irradiation (355 nm) of R6G-GO. In addition, the signal intensity of R6G was stronger in the presence of short oligonucleotides. Because small oligonucleotides improve the LDI efficiency of R6G on GO, we designed an enzyme-amplified signal transduction probe system for the detection of microRNA (miRNA). After specific digestion of the probe DNA (pDNA) strand from pDNA/miRNA-hybridized complexes by exonuclease III (Exo III), the resulting small oligonucleotide fragments increased the R6G signal during LDI-MS of R6G-GO. In addition, the signal intensity of the R6G ions increased with increasing concentrations of the target miRNA. Coupling this enzyme reaction and R6G-GO with LDI-MS enabled the detection of miRNA at concentrations of the femtomolar (fM) level. We also demonstrated the analysis of miRNA in tumor cells and utilized this R6G-GO probe in the detection of a single-nucleotide polymorphism (SNP) in the Arg249Ser unit of the TP53 gene. This simple, rapid, and sensitive detection system based on the coupling of functional GO with LDI-MS appears to have great potential as a tool for the bioanalyses of oligonucleotides and proteins.

Lane-by-lane sequencing using Illumina's Genome Analyzer II

Next-generation sequencing has become an essential tool in molecular biology that has been successfully applied to a broad variety of experimental approaches. While several platforms for next-generation sequencing exist, the most commonly used approach is sequencing-by-synthesis, implemented on Illumina's Genome Analyzer II (GAII) and HiSeq2000 systems. A key constraint of these sequencers is the need to run multiple lanes of samples with identical parameters as part of a single flowcell. Here, we present a series of modifications to the Illumina Genome Analyzer II, along with a script generating tool, that allow users to run the GAII in a lane-by-lane manner. Any number of lanes can be run at one time. Repeated use of the same flowcell on multiple sequencing runs does not appreciably reduce the intensity, cluster density, or accuracy of the run. These modifications will enable smaller-scale experiments with unusual design parameters to be run routinely on the GAII.

Pairomics, the omics way to mate choice

The core aspects of the biology and evolution of sexual reproduction are reviewed with a focus on the diploid, sexually reproducing, outbreeding, polymorphic, unspecialized, altricial and cultural human species. Human mate choice and pair bonding are viewed as central to individuals' lives and to the evolution of the species, and genetic assistance in reproduction is viewed as a universal human right. Pairomics is defined as an emerging branch of the omics science devoted to the study of mate choice at the genomic level and its consequences for present and future generations. In pairomics, comprehensive genetic information of individual genomes is stored in a database. Computational tools are employed to analyze the mating schemes and rules that govern mating among the members of the database. Mating models and algorithms simulate the outcomes of mating any given genome with each of a number of genomes represented in the database. The analyses and simulations may help to understand mating schemes and their outcomes, and also contribute a new cue to the multicued schemes of mate choice. The scientific, medical, evolutionary, ethical, legal and social implications of pairomics are far reaching. The use of genetic information as a search tool in mate choice may influence our health, lifestyle, behavior and culture. As knowledge on genomics, population genetics and gene-environment interactions, as well as the size of genomic databases expand, so does the ability of pairomics to investigate and predict the consequences of mate choice for the present and future generations.

Genotyping-by-sequencing (GBS): a novel, efficient and cost-effective genotyping method for cattle using next-generation sequencing

High-throughput genotyping methods have increased the analytical power to study complex traits but high cost has remained a barrier for large scale use in animal improvement. We have adapted genotyping-by-sequencing (GBS) used in plants for genotyping 47 animals representing 7 taurine and indicine breeds of cattle from the US and Africa. Genomic DNA was digested with different enzymes, ligated to adapters containing one of 48 unique bar codes and sequenced by the Illumina HiSeq 2000. PstI was the best enzyme producing 1.4 million unique reads per animal and initially identifying a total of 63,697 SNPs. After removal of SNPs with call rates of less than 70%, 51,414 SNPs were detected throughout all autosomes with an average distance of 48.1 kb, and 1,143 SNPs on the X chromosome at an average distance of 130.3 kb, as well as 191 on unmapped contigs. If we consider only the SNPs with call rates of 90% and over, we identified 39,751 on autosomes, 850 on the X chromosome and 124 on unmapped contigs. Of these SNPs, 28,843 were not tightly linked to other SNPs. Average marker density per autosome was highly correlated with chromosome size (coefficient of correlation  = −0.798, r²  = 0.637) with higher density in smaller chromosomes. Average SNP call rate was 86.5% for all loci, with 53.0% of the loci having call rates >90% and the average minor allele frequency being 0.212. Average observed heterozygosity ranged from 0.046–0.294 among individuals, and from 0.064–0.197 among breeds, with Brangus showing the highest diversity as expected. GBS technique is novel, flexible, sufficiently high-throughput, and capable of providing acceptable marker density for genomic selection or genome-wide association studies at roughly one third of the cost of currently available genotyping technologies.

Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.

Development of strategies for SNP detection in RNA-seq data: application to lymphoblastoid cell lines and evaluation using 1000 Genomes data

Next-generation RNA sequencing (RNA-seq) maps and analyzes transcriptomes and generates data on sequence variation in expressed genes. There are few reported studies on analysis strategies to maximize the yield of quality RNA-seq SNP data. We evaluated the performance of different SNP-calling methods following alignment to both genome and transcriptome by applying them to RNA-seq data from a HapMap lymphoblastoid cell line sample and comparing results with sequence variation data from 1000 Genomes. We determined that the best method to achieve high specificity and sensitivity, and greatest number of SNP calls, is to remove duplicate sequence reads after alignment to the genome and to call SNPs using SAMtools. The accuracy of SNP calls is dependent on sequence coverage available. In terms of specificity, 89% of RNA-seq SNPs calls were true variants where coverage is >10X. In terms of sensitivity, at >10X coverage 92% of all expected SNPs in expressed exons could be detected. Overall, the results indicate that RNA-seq SNP data are a very useful by-product of sequence-based transcriptome analysis. If RNA-seq is applied to disease tissue samples and assuming that genes carrying mutations relevant to disease biology are being expressed, a very high proportion of these mutations can be detected.

Personalized medicine in cardiovascular diseases

Personalized medicine is a novel medical model with all decisions and practices being tailored to individual patients in whatever ways possible. In the era of genomics, personalized medicine combines the genetic information for additional benefit in preventive and therapeutic strategies. Personalized medicine may allow the physician to provide a better therapy for patients in terms of efficiency, safety and treatment length to reduce the associated costs. There was a remarkable growth in scientific publication on personalized medicine within the past few years in the cardiovascular field. However, so far, only very few cardiologists in the USA are incorporating personalized medicine into clinical treatment. We review the concepts, strengths, limitations and challenges of personalized medicine with a particular focus on cardiovascular diseases (CVDs). There are many challenges from both scientific and policy perspectives to personalized medicine, which can overcome them by comprehensive concept and understanding, clinical application, and evidence based practices. Individualized medicine serves a pivotal role in the evolution of national and global healthcare reform, especially, in the CVDs fields. Ultimately, personalized medicine will affect the entire landscape of health care system in the near future.

Androgen receptor polyglutamine repeat number: models of selection and disease susceptibility

Variation in polyglutamine repeat number in the androgen receptor (AR CAGn) is negatively correlated with the transcription of androgen-responsive genes and is associated with susceptibility to an extensive list of human disease. Only a small portion of the heritability for many of these diseases is explained by conventional SNP-based genome-wide association studies, and the forces shaping AR CAGn among humans remains largely unexplored. Here, we propose evolutionary models for understanding selection at the AR CAG locus, namely balancing selection, sexual conflict, accumulation-selection, and antagonistic pleiotropy. We evaluate these models by examining AR CAGn-linked susceptibility to eight extensively studied diseases representing the diverse physiological roles of androgens, and consider the costs of these diseases by their frequency and fitness effects. Five diseases could contribute to the distribution of AR CAGn observed among contemporary human populations. With support for disease susceptibilities associated with long and short AR CAGn, balancing selection provides a useful model for studying selection at this locus. Gender-specific differences AR CAGn health effects also support this locus as a candidate for sexual conflict over repeat number. Accompanied by the accumulation of AR CAGn in humans, these models help explain the distribution of repeat number in contemporary human populations.

CD6 and syntaxin binding protein 6 variants and response to tumor necrosis factor alpha inhibitors in Danish patients with rheumatoid arthritis

BACKGROUND

TNFα inhibitor therapy has greatly improved the treatment of patients with rheumatoid arthritis, however at least 30% do not respond. We aimed to investigate insertions and deletions (INDELS) associated with response to TNFα inhibitors in patients with rheumatoid arthritis (RA).

METHODOLOGY AND PRINCIPAL FINDINGS

In the DANBIO Registry we identified 237 TNFα inhibitor naïve patients with RA (81% women; median age 56 years; disease duration 6 years) who initiated treatment with infliximab (n=160), adalimumab (n=56) or etanercept (n=21) between 1999 and 2008 according to national treatment guidelines. Clinical response was assessed at week 26 using EULAR response criteria. Based on literature, we selected 213 INDELS potentially related to RA and treatment response using the GeneVa® (Compugen) in silico database of 350,000 genetic variations in the human genome. Genomic segments were amplified by polymerase chain reaction (PCR), and genotyped by Sanger sequencing or fragment analysis. We tested the association between genotypes and EULAR good response versus no response, and EULAR good response versus moderate/no response using Fisher's exact test. At baseline the median DAS28 was 5.1. At week 26, 68 (29%) patients were EULAR good responders, while 81 (34%) and 88 (37%) patients were moderate and non-responders, respectively. A 19 base pair insertion within the CD6 gene was associated with EULAR good response vs. no response (OR=4.43, 95% CI: 1.99-10.09, p=7.211×10(-5)) and with EULAR good response vs. moderate/no response (OR=4.54, 95% CI: 2.29-8.99, p=3.336×10(-6)). A microsatellite within the syntaxin binding protein 6 (STXBP6) was associated with EULAR good response vs. no response (OR=4.01, 95% CI: 1.92-8.49, p=5.067×10(-5)).

CONCLUSION

Genetic variations within CD6 and STXBP6 may influence response to TNFα inhibitors in patients with RA.

A chromosome 19 locus positively influences the number of retrieved oocytes during stimulated cycles in Brazilian women

PURPOSE

To evaluate if several genetic loci that are associated with variation in normal menopause age and early menopause can account for a poor response to controlled ovarian stimulation.

METHODS

A total of 71 patients age ≤35 years old who were undergoing intracytoplasmic sperm injection were genotyped for four genetic variants that are associated with normal variation in menopausal age and early menopause. The patients were divided into two groups based upon treatment response: a poor responder group (PR group, n = 21) and a normal responder group (NR group, n = 50). The genetic variants rs244715, rs9379896, rs4806660 and rs16991615 were analyzed.

RESULTS

There was no significant difference in the incidence of the genetic variants between the NR and PR group. The risk allele for the chromosome 19 variant (rs4806660) demonstrated a protective effect for a poor ovarian response. The presence of a risk allele was associated with an increased response to COS, which resulted in an elevated number of follicles (Coef: 2.54, P = 0.041) and retrieved oocytes (Coef: 1.41, P = 0.041).

CONCLUSIONS

The genetic variants rs244715, rs9379896, rs4806660 and rs16991615 are not risk factors for poor ovarian response in Brazilian women. In contrast, rs4806660 is associated with higher number of follicles and retrieved oocytes. rs4806660 may be associated with an increased response to gonadotrophin stimulation in this population.

Exploring the implications of INDELs in neuropsychiatric genetics: challenges and perspectives

The decade passed after publishing the Human Genome first draft faced an enormous growth at the understanding of the genomic variation among different subjects, populations, and groups of patients. Single nucleotide polymorphisms (SNPs) and insertion or deletions (INDELs) have been increasingly recognized as a major type of genetic variations, with potential impact in protein activities and gene expression changes observed in complex genetic traits, like neuropsychiatric diseases. INDELs represent the second most common class of variations after SNPs, but there is still an important gap between the number of INDELs reported and the actual knowledge about the functional implications of such variations. There are approximately 10 million SNPs already reported, and the human populations are expected to collectively harbor at least 1.6-2.5 million INDELs. One of the major challenges is to find better platforms to screen for INDELs in a high throughput manner. The discordance in between the data from different studies might be explained by the diverse approaches employed to sequence the genomes with variable platforms. Short INDEL variations increased the scope of genetic markers in human genetic diseases, and various studies showed that common microdeletions and smaller INDELs might be highly associated with neuropsychiatric diseases such as schizophrenia, autism, mental retardation, and Alzheimer disease. The rapidly increasing amount of resequencing, genotyping, and personal genome data generated by large-scale genetic human projects require the development of integrated bioinformatics tools able to efficiently manage and analyze these genetic data. Our group is currently dealing with different approaches that might optimize sequencing and bioinformatics analyses of short INDELs to broaden our research capabilities of identifying those intriguing genetic variations. Hopefully, INDELs might become a new trend in association studies in neuropsychiatric genetics since so far the level of significant and positive associations with the standard SNPs reported presents limited predictive application.

Applications of second generation sequencing technologies in complex disorders

Second generation sequencing (2ndGS) technologies generate unprecedented amounts of sequence data very rapidly and at relatively limited costs, allowing the sequence of a human genome to be completed in a few weeks. The principle is on the basis of generating millions of relatively short reads from amplified single DNA fragments using iterative cycles of nucleotide extensions. However, the data generated on this scale present new challenges in interpretation, data analysis and data management. 2ndGS technologies are becoming widespread and are profoundly impacting biomedical research. Common applications include whole-genome sequencing, target resequencing, characterization of structural and copy number variation, profiling epigenetic modifications, transcriptome sequencing and identification of infectious agents. New methodologies and instruments that will enable to sequence the complete human genome in less than a day at a cost of less than $1,000 are currently in development.