Era Of Genome Sequencing Research Articles

Incidental Findings in the Era of Whole Genome Sequencing?

AbstractThe rise of technologies that can inexpensively sequence entire genomes means that researchers and clinicians have access to ever vaster stores of genomic data, some of which could be of great use to research participants or patients, and most of which, at least for today, will be of little, uncertain, or no use. Those facts are essential features of a new ethical territory we are now entering with genetics research. As we explore that territory, we should try to be as clear as possible about the issues at hand.Clarity about the ethical issues that delineate this new territory requires that we be wary about too hastily importing into it concepts from the old territory. We want to suggest a way in which the term “incidental findings” can impede our ability to see clearly some of the most important issues we face. To show how that term can sometimes obscure more than it illuminates, we focus on how it is being deployed in the context of research. Although it may have been useful, when considering older forms of genetic research, to speak about researchers stumbling across clinically significant findings, and although that way of speaking has certainly not yet become wholly obsolete, it is ever less appropriate as the technology becomes ever more powerful.

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

Update on genetics of pre-eclampsia

Over half of the familial predisposition to pre-eclampsia can be attributed to genetic factors in the mother and/or fetus. The search for genetic susceptibility variants has progressed through candidate gene studies, family-based linkage studies, and most recently genome-wide association studies (GWAS). It is unlikely that any single variant has a large effect on pre-eclampsia susceptibility; studies involving thousands of samples are required to detect variants with small effect size. These exceed the resources of most individual research groups, and collaborative approaches are likely to be more fruitful. Collaboration has included meta-analysis of existing data, and recent publications offer some support to a role for thrombophilic polymorphisms in pre-eclampsia. A small number of GWAS have been published so far; the lack of replication of positive GWAS results in an independent population has been frustrating. This may be due to false positive results in the original GWAS, or lack of statistical power in the replication set. A further concern is that the pre-eclampsia syndrome is a common end-point to multiple pathologies with differing underlying genetic susceptibility, requiring ever larger sample sizes for their detection. In this climate, researchers should make every effort to record the phenotypic characteristics of their cohorts, to enable meta-analysis of independent GWAS results. The InterPregGen consortium of groups from Europe and Central Asia is conducting GWAS analysis of maternal and fetal genes in 13,000 pre-eclamptic pregnancies. This study will provide the opportunity to analyse maternal-fetal gene interactions in addition to their individual effects. Developments arising from the ever-falling costs of DNA sequencing include deep sequencing to identify rare variants with large effect sizes. The era of whole genome sequencing is likely to supplant the GWAS approach, creating challenges for data analysis, but with the potential to provide greater insights into the genetic basis of pre-eclampsia.

Insertion site mapping for repeated elements in Mycobacterium tuberculosis

Insertion elements not only act as genetic markers for differentiation of bacteria but their movement in bacterial genomes likely plays an essential role in changing the physical and biochemical traits of the organisms when adapting to new environments. Genomic Insertion Site mapping of transposable elements could shed light on the putative altered function of adjacent genes. In the era of whole genome sequencing where repeat elements are difficult to sequence with short read technologies and in the absence of high throughput technologies especially in poorer resource settings, an alternative approach to their characterisation is needed. A rapid and simple method of insertion site mapping that uses Insertion Sequence 6110 (IS6110) fluorescent amplified fragment length polymorphism (FAFLP) PCR as a foundation and then uses additional selective bases to reduce the number of fragments generated was developed. This was applied to Mycobacterium tuberculosis H37Rv sequenced strain to compare the experimental data with the in silico results. This was successfully achieved for all but two of the sixteen fragments generated by FAFLP and demonstrated that, by using this technique, insertion sites can be mapped onto the genomes of M. tuberculosis.

Multimodal Dynamic Profiling of Healthy and Diseased States for Future Personalized Health Care

Personalized medicine is rapidly emerging as the future direction in the post–genome sequencing era. Early applications have concentrated on variant-based assessment of individual disease risks. Technological advances and dropping costs now allow for expanding the domain of personalized medicine to include multimodal and dynamic monitoring of individuals (including transcriptomic, proteomic, and metabolomic longitudinal profiling). Such efforts may lead to effective diagnostic and precise monitoring tools that can be applied in a clinical setting. Clinical Pharmacology & Therapeutics (2013); 93 1, 29–32. doi:10.1038/clpt.2012.204

A RubisCO-like protein links SAM metabolism with isoprenoid biosynthesis

Functional assignment of uncharacterized proteins is a challenge in the era of large-scale genome sequencing. Here, we combine in extracto-NMR, proteomics, and transcriptomics with a newly developed (knock-out) metabolomics platform to determine a potential physiological role for a ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO)-like protein (RLP) from Rhodospirillum rubrum. Our studies unravelled an unexpected link in bacterial central carbon metabolism between S-adenosylmethionine (SAM)-dependent polyamine metabolism and isoprenoid biosynthesis and also provide an alternative approach to assign enzyme function at the organismic level.

Improving the efficiency and relevance of evidence-based recommendations in the era of whole-genome sequencing: an EGAPP methods update.

To provide an update on recent revisions to Evaluation of Genomic Applications in Practice and Prevention (EGAPP) methods designed to improve efficiency, and an assessment of the implications of whole genome sequencing for evidence-based recommendation development. Improvements to the EGAPP approach include automated searches for horizon scanning, a quantitative ranking process for topic prioritization, and the development of a staged evidence review and evaluation process. The staged process entails (i) triaging tests with minimal evidence of clinical validity, (ii) using and updating existing reviews, (iii) evaluating clinical validity prior to analytic validity or clinical utility, (iv) using decision modeling to assess potential clinical utility when direct evidence is not available. EGAPP experience to date suggests the following approaches will be critical for the development of evidence based recommendations in the whole genome sequencing era: (i) use of triage approaches and frameworks to improve efficiency, (ii) development of evidence thresholds that consider the value of further research, (iii) incorporation of patient preferences, and (iv) engagement of diverse stakeholders. The rapid advances in genomics present a significant challenge to traditional evidence based medicine, but also an opportunity for innovative approaches to recommendation development.

Epidemia de Clostridium difficile, Chile 2012: Informe de la Sociedad Chilena de Infectología. Una aproximación científico histórica

A Summary Report from the Chilean Society for Infectious Diseases regarding the presence of a Clostridium difficile epidemic with several fatalities in Chile's premier emergency public hospital in Santiago is used to make a scientific historical analysis of the situation. This Summary Report identifies several hygienic and sanitary shortcomings that may have played a role in triggering this major epidemic. These include deficiencies in hand washing policies, overcrowding of beds in wards, relaxation of infection control policies, antimicrobial therapy mismanagement and lack of laboratory support. The relevance of these shortcomings to the epidemic is further supported by the lack of any laboratory evidence for the presence of hypertoxigenic strains of C. difficile. In an era of whole genome sequencing of pathogens to guide therapy, prevention, and epidemiological studies of infectious diseases, it is illuminating and sobering, as this report so clearly demonstrates, to realize that many epidemics of hospital infections still result from breakdowns in classical and ancillary asepsis and infection control measures developed in the nineteenth century by Semmelweis, Nightingale and Lister. As the Summary Report suggests, such hygienic breakdowns in countries like Chile are usually brought about by lack of implementation and regulation of national hospital infection control policies resulting from the shift of economic resources from the public to the private sector, despite the former being responsible for health care of 80% of the population.

The role of phenotype in gene discovery in the whole genome sequencing era

As whole genome sequence becomes a routine component of gene discovery studies in humans, we will have an exhaustive catalog of genetic variation and the challenge becomes understanding the phenotypic consequences of these variants. Statistical genetic methods and analytical approaches that are concerned with optimizing phenotypes for gene discovery for complex traits offer two general categories of advantages. They may increase power to localize genes of interest and also aid in interpreting associations between genetic variants and disease outcomes by suggesting potential mechanisms and pathways through which genes may affect outcomes. Such phenotype optimization approaches include use of allied phenotypes such as symptoms or ages of onset to reduce genetic heterogeneity within a set of cases, study of quantitative risk factors or endophenotypes, joint analyses of related phenotypes, and derivation of new phenotypes designed to extract independent measures underlying the correlations among a set of related phenotypes through approaches such as principal components. New opportunities are also presented by technological advances that permit efficient collection of hundreds or thousands of phenotypes on an individual, including phenotypes more proximal to the level of gene action such as levels of gene expression, microRNAs, or metabolic and proteomic profiles.

Detecting Rare Variants for Quantitative Traits Using Nuclear Families

With the advent of sequencing technology opening up a new era of personal genome sequencing, huge amounts of rare variant data have suddenly become available to researchers seeking genetic variants related to human complex disorders. There is an urgent need for the development of novel statistical methods to analyze rare variants in a statistically powerful manner. While a number of statistical tests have already been developed to analyze collapsed rare variants identified by association tests in case-control studies, to date, only two FBAT tests-for-rare (described in the updated FBAT version v2.0.4) have applied collapsing methods analogously in family-based designs. For further research in this area, this study aims to introduce three new beta-determined weight tests for detecting rare variants for quantitative traits in nuclear families. In addition to evaluating the performance of these new methods, it also evaluates that of the two FBAT tests-for-rare, using extensive simulations of situations with and without linkage disequilibrium. Results from these simulations suggest that the four tests using beta-determined weights outperform the two collapsing methods used in FBAT (-v0 and -v1). In addition, both the linear combination method (detailed in the FBAT menu v2.0.4) and the multiple regression method (mixing LASSO and Ridge penalties) performed better than the other two beta-determined weight tests we proposed. Following testing and evaluation, we submitted four new beta-determined weight methods of statistical analysis in a computer program to the Comprehensive R Archive Network (CRAN) for general use.

Context-dependent individualization of nucleotides and virtual genomic hybridization allow the precise location of human SNPs

We have entered the era of individual genomic sequencing, and can already see exponential progress in the field. It is of utmost importance to exclude false-positive variants from reported datasets. However, because of the nature of the used algorithms, this task has not been optimized to the required level of precision. This study presents a unique strategy for identifying SNPs, called COIN-VGH, that largely minimizes the presence of false-positives in the generated data. The algorithm was developed using the X-chromosome-specific regions from the previously sequenced genomes of Craig Venter and James Watson. The algorithm is based on the concept that a nucleotide can be individualized if it is analyzed in the context of its surrounding genomic sequence. COIN-VGH consists of defining the most comprehensive set of nucleotide strings of a defined length that map with 100% identity to a unique position within the human reference genome (HRG). Such set is used to retrieve sequence reads from a query genome (QG), allowing the production of a genomic landscape that represents a draft HRG-guided assembly of the QG. This landscape is analyzed for specific signatures that indicate the presence of SNPs. The fidelity of the variation signature was assessed using simulation experiments by virtually altering the HRG at defined positions. Finally, the signature regions identified in the HRG and in the QG reads are aligned and the precise nature and position of the corresponding SNPs are detected. The advantages of COIN-VGH over previous algorithms are discussed.

Isolation and analysis of high quality nuclear DNA with reduced organellar DNA for plant genome sequencing and resequencing

BackgroundHigh throughput sequencing (HTS) technologies have revolutionized the field of genomics by drastically reducing the cost of sequencing, making it feasible for individual labs to sequence or resequence plant genomes. Obtaining high quality, high molecular weight DNA from plants poses significant challenges due to the high copy number of chloroplast and mitochondrial DNA, as well as high levels of phenolic compounds and polysaccharides. Multiple methods have been used to isolate DNA from plants; the CTAB method is commonly used to isolate total cellular DNA from plants that contain nuclear DNA, as well as chloroplast and mitochondrial DNA. Alternatively, DNA can be isolated from nuclei to minimize chloroplast and mitochondrial DNA contamination.ResultsWe describe optimized protocols for isolation of nuclear DNA from eight different plant species encompassing both monocot and eudicot species. These protocols use nuclei isolation to minimize chloroplast and mitochondrial DNA contamination. We also developed a protocol to determine the number of chloroplast and mitochondrial DNA copies relative to the nuclear DNA using quantitative real time PCR (qPCR). We compared DNA isolated from nuclei to total cellular DNA isolated with the CTAB method. As expected, DNA isolated from nuclei consistently yielded nuclear DNA with fewer chloroplast and mitochondrial DNA copies, as compared to the total cellular DNA prepared with the CTAB method. This protocol will allow for analysis of the quality and quantity of nuclear DNA before starting a plant whole genome sequencing or resequencing experiment.ConclusionsExtracting high quality, high molecular weight nuclear DNA in plants has the potential to be a bottleneck in the era of whole genome sequencing and resequencing. The methods that are described here provide a framework for researchers to extract and quantify nuclear DNA in multiple types of plants.

Websites of note

Websites of note

Genetics for the Practicing Dermatologist

In the era of robust genome sequencing, a working understanding of genetics has become important for the clinician. For the dermatologist, understanding the flow of genetic information from genotype to phenotype can aid in the delivery of effective patient care. In this article, we will review concepts in genetics and the human genome and how they contribute to clinical dermatology.

Whole-Genome Sequencing (WGS) and the Changing Landscape of Bacterial Genetics

Shotgun sequencing of Haemophilus influenzae in 1995 is traditionally considered to be the dawn of the modern era of whole genome sequencing (WGS). In 2004, the introduction of pyrosequencing based (454/Roche) massively parallel sequencing (MPS) brought about the second wave of advances in WGS. The advent of NextGen sequencing technologies is making great impacts in many areas of research and is already changing the landscape of bacterial genetics as well. Genetic mapping of mutations in bacteria is an arduous process taking anywhere from months to years. Because of the availability of rapid and low-cost WGS technologies, mapping mutations directly without resorting to traditional genetic approaches is now feasible even in bacteria for which genetic mapping systems are not available.

Assessing the impact of transcriptomics, proteomics and metabolomics on fungal phytopathology.

SUMMARY Peer-reviewed literature is today littered with exciting new tools and techniques that are being used in all areas of biology and medicine. Transcriptomics, proteomics and, more recently, metabolomics are three of these techniques that have impacted on fungal plant pathology. Used individually, each of these techniques can generate a plethora of data that could occupy a laboratory for years. When used in combination, they have the potential to comprehensively dissect a system at the transcriptional and translational level. Transcriptomics, or quantitative gene expression profiling, is arguably the most familiar to researchers in the field of fungal plant pathology. Microarrays have been the primary technique for the last decade, but others are now emerging. Proteomics has also been exploited by the fungal phytopathogen community, but perhaps not to its potential. A lack of genome sequence information has frustrated proteomics researchers and has largely contributed to this technique not fulfilling its potential. The coming of the genome sequencing era has partially alleviated this problem. Metabolomics is the most recent of these techniques to emerge and is concerned with the non-targeted profiling of all metabolites in a given system. Metabolomics studies on fungal plant pathogens are only just beginning to appear, although its potential to dissect many facets of the pathogen and disease will see its popularity increase quickly. This review assesses the impact of transcriptomics, proteomics and metabolomics on fungal plant pathology over the last decade and discusses their futures. Each of the techniques is described briefly with further reading recommended. Key examples highlighting the application of these technologies to fungal plant pathogens are also reviewed.

Phylogenetic understanding of clonal populations in an era of whole genome sequencing

Phylogenetic hypotheses using whole genome sequences have the potential for unprecedented accuracy, yet a failure to understand issues associated with discovery bias, character sampling, and strain sampling can lead to highly erroneous conclusions. For microbial pathogens, phylogenies derived from whole genome sequences are becoming more common, as large numbers of characters distributed across entire genomes can yield extremely accurate phylogenies, particularly for strictly clonal populations. The availability of whole genomes is increasing as new sequencing technologies reduce the cost and time required for genome sequencing. Until entire sample collections can be fully sequenced, harnessing the phylogenetic power from whole genome sequences in more than a small subset of fully sequenced strains requires the integration of whole genome and partial genome genotyping data. Such integration involves discovering evolutionarily stable polymorphic characters by whole genome comparisons, then determining allelic states across a wide panel of isolates using high-throughput genotyping technologies. Here, we demonstrate how such an approach using single nucleotide polymorphisms (SNPs) yields highly accurate, but biased phylogenetic reconstructions and how the accuracy of the resulting tree is compromised by incomplete taxon and character sampling. Despite recent phylogenetic work detailing the strengths and biases of integrating whole genome and partial genome genotype data, these issues are relatively new and remain poorly understood by many researchers. Here, we revisit these biases and provide strategies for maximizing phylogenetic accuracy. Although we write this review with bacterial pathogens in mind, these concepts apply to any clonally reproducing population or indeed to any evolutionarily stable marker that is inherited in a strictly clonal manner. Understanding the ways in which current and emerging technologies can be used to maximize phylogenetic knowledge is advantageous only with a complete understanding of the strengths and weaknesses of these methods.

Metagenomics and autoimmune liver disease: searching for the unknown

Metagenomics and autoimmune liver disease: searching for the unknown

Targeting the Human Cancer Pathway Protein Interaction Network by Structural Genomics

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Genome Annotation in Plants and Fungi: EuGene as a Model Platform

In this era of whole genome sequencing, reliable genome annotations (identification of functional regions) are the cornerstones for many subsequent analyses. Not only is careful annotation important for studying the gene and gene family content of a genome and its host, but also for wide-scale transcriptome and proteome analyses attempting to de- scribe a certain biological process or to get a global picture of a cell's behavior. Although the number of sequenced ge- nomes is increasing thanks to the application of new technologies, genome-wide analyses will critically depend on the quality of the genome annotations. However, the annotation process is more complicated in the plant field than in the animal field because of the limited funding that leads to much fewer experimental data and less annotation expertise. This situation calls for highly automated annotation platforms that can make the best use of all available data, experimental or not. We discuss how the gene prediction (the process of predicting protein gene structures in genomic sequences) research field increasingly shifts from methods that typically exploited one or two types of data to more integrative approaches that simultaneously deal with various experimental, statistical, or other in silico evidence. We illustrate the importance of inte- grative approaches for producing high-quality automatic annotations of genomes of plants and algae as well as of fungi that live in close association with plants using the platform EuGene as an example.