High-throughput Genomic Technologies Research Articles

Metab2MeSH: annotating compounds with medical subject headings

Progress in high-throughput genomic technologies has led to the development of a variety of resources that link genes to functional information contained in the biomedical literature. However, tools attempting to link small molecules to normal and diseased physiology and published data relevant to biologists and clinical investigators, are still lacking. With metabolomics rapidly emerging as a new omics field, the task of annotating small molecule metabolites becomes highly relevant. Our tool Metab2MeSH uses a statistical approach to reliably and automatically annotate compounds with concepts defined in Medical Subject Headings, and the National Library of Medicine's controlled vocabulary for biomedical concepts. These annotations provide links from compounds to biomedical literature and complement existing resources such as PubChem and the Human Metabolome Database.

Potential value of nutrigenomics in Crohn's disease

Crohn's disease is a chronic relapsing condition that has no certain cure. Both genetic susceptibility and nutrition have key roles, but their level of involvement varies between patients. Interacting gene pathways influence the probability of disease development, but these are affected by stress and various environmental factors, including diet. In addition, the role of the gut microbiome must not be underestimated, as it is substantially altered in patients with Crohn's disease. Although an elemental diet might lead to disease remission, reintroducing real foods and sustainable diets in patients with Crohn's disease is currently difficult, and would benefit from the sensitivity and rapid feedback provided by the field of nutrigenomics. Nutrigenomics utilizes high-throughput genomics technologies to reveal changes in gene and protein expression that are modulated by the patient's nutrition. The most widely used technique thus far is transcriptomics, which permits measurement of changes in the expression of thousands of genes simultaneously in one sample. Given the volume of numbers generated in such studies, data-basing and bioinformatics are essential to ensure the correct application of nutrigenomics at the population level. These methods have been successfully applied to animal models of Crohn's disease, and the time is right to move them to human studies.

Clarifying Omics Concepts, Challenges, and Opportunities forPrunusBreeding in the Postgenomic Era

The recent sequencing of the complete genome of the peach, together with the availability of new high-throughput genome, transcriptome, proteome, and metabolome analysis technologies, offers new possibilities for Prunus breeders in what has been described as the postgenomic era. In this context, new biological challenges and opportunities for the application of these technologies in the development of efficient marker-assisted selection strategies in Prunus breeding include genome resequencing using DNA-Seq, the study of RNA regulation at transcriptional and posttranscriptional levels using tilling microarray and RNA-Seq, protein and metabolite identification and annotation, and standardization of phenotype evaluation. Additional biological opportunities include the high level of synteny among Prunus genomes. Finally, the existence of biases presents another important biological challenge in attaining knowledge from these new high-throughput omics disciplines. On the other hand, from the philosophical point of view, we are facing a revolution in the use of new high-throughput analysis techniques that may mean a scientific paradigm shift in Prunus genetics and genomics theories. The evaluation of scientific progress is another important question in this postgenomic context. Finally, the incommensurability of omics theories in the new high-throughput analysis context presents an additional philosophical challenge.

Low-Level Expression of miR-375 Correlates with Poor Outcome and Metastasis While Altering the Invasive Properties of Head and Neck Squamous Cell Carcinomas

Small, noncoding microRNAs (miRNAs) have been shown to be abnormally expressed in every tumor type examined. We used comparisons of global miRNA expression profiles of head and neck squamous cell carcinoma (HNSCC) samples and adjacent normal tissue to rank those miRNAs that were most significantly altered in our patient population. Rank Consistency Score analysis revealed miR-375 to have the most significantly lowered miRNA levels in tumors relative to matched adjacent nonmalignant tissue from the same patient among 736 miRNAs that were evaluated. This result has been previously observed by other groups; however, we extend this finding with the unique observation that low miR-375 expression levels correlate significantly with cancer survival and distant metastasis. In a study of 123 primary HNSCC patients using multivariable Cox proportional hazard ratios (HR) and 95% confidence intervals (CI), both death from disease (HR: 12.8, 95% CI: 3 to 49) and incidence of distant metastasis (HR: 8.7, 95% CI: 2 to 31) correlated with lower expression levels of miR-375 regardless of the site or stage of the tumor. In addition, we found that oral cavity tumor cell lines (eg, UMSCC1 and UMSCC47) overexpressing miR-375 were significantly less invasive in vitro than their matched empty vector controls. We conclude that miR-375 represents a potential prognostic marker of poor outcome and metastasis in HNSCC and that it may function by suppressing the tumor's invasive properties.

Controversies in modern evolutionary biology: the imperative for error detection and quality control

BackgroundThe data from high throughput genomics technologies provide unique opportunities for studies of complex biological systems, but also pose many new challenges. The shift to the genome scale in evolutionary biology, for example, has led to many interesting, but often controversial studies. It has been suggested that part of the conflict may be due to errors in the initial sequences. Most gene sequences are predicted by bioinformatics programs and a number of quality issues have been raised, concerning DNA sequencing errors or badly predicted coding regions, particularly in eukaryotes.ResultsWe investigated the impact of these errors on evolutionary studies and specifically on the identification of important genetic events. We focused on the detection of asymmetric evolution after duplication, which has been the subject of controversy recently. Using the human genome as a reference, we established a reliable set of 688 duplicated genes in 13 complete vertebrate genomes, where significantly different evolutionary rates are observed. We estimated the rates at which protein sequence errors occur and are accumulated in the higher-level analyses. We showed that the majority of the detected events (57%) are in fact artifacts due to the putative erroneous sequences and that these artifacts are sufficient to mask the true functional significance of the events.ConclusionsInitial errors are accumulated throughout the evolutionary analysis, generating artificially high rates of event predictions and leading to substantial uncertainty in the conclusions. This study emphasizes the urgent need for error detection and quality control strategies in order to efficiently extract knowledge from the new genome data.

CaGe: A Web-Based Cancer Gene Annotation System for Cancer Genomics

High-throughput genomic technologies (HGTs), including next-generation DNA sequencing (NGS), microarray, and serial analysis of gene expression (SAGE), have become effective experimental tools for cancer genomics to identify cancer-associated somatic genomic alterations and genes. The main hurdle in cancer genomics is to identify the real causative mutations or genes out of many candidates from an HGT-based cancer genomic analysis. One useful approach is to refer to known cancer genes and associated information. The list of known cancer genes can be used to determine candidates of cancer driver mutations, while cancer gene-related information, including gene expression, protein-protein interaction, and pathways, can be useful for scoring novel candidates. Some cancer gene or mutation databases exist for this purpose, but few specialized tools exist for an automated analysis of a long gene list from an HGT-based cancer genomic analysis. This report presents a new web-accessible bioinformatic tool, called CaGe, a cancer genome annotation system for the assessment of candidates of cancer genes from HGT-based cancer genomics. The tool provides users with information on cancer-related genes, mutations, pathways, and associated annotations through annotation and browsing functions. With this tool, researchers can classify their candidate genes from cancer genome studies into either previously reported or novel categories of cancer genes and gain insight into underlying carcinogenic mechanisms through a pathway analysis. We show the usefulness of CaGe by assessing its performance in annotating somatic mutations from a published small cell lung cancer study.

Transcriptional Mechanisms Underlying Apoptosis in Cerebellar Granule Neurons

Neuronal apoptosis represents an intrinsic suicide program, by which a neuron orchestrates its own destruction. Although engagement of apoptosis requires transcription and protein synthesis, the complete spectrum of genes involved in distinct temporal domains remained unknown until the advent of genomics. In the last ten years, the genome sequences and the development of high-throughput genomic technologies, such as DNA microarrays, have offered the unprecedented experimental opportunities to explore the transcriptional mechanisms underlying apoptosis from a new systems-level perspective. The present review will go over this genomic approach and illustrate its use to dissecting the multigenic program underlying neuronal apoptosis of cerebellar granule neurons.

Pronóstico genómico en el carcinoma hepatocelular

Recently, interest in hepatocellular carcinoma (HCC) has grown due to its high mortality and increased incidence. Unlike other malignancies, HCC mainly arises in the context of chronic liver injury, complicating its management and the prediction of prognosis. The Barcelona Clinic Liver Cancer (BCLC) staging classification currently offers an efficient decision-making guide in these patients. However, preoperative identification of patients with a higher risk of recurrence after resection and of those who could benefit from liver transplantation despite not meeting the Milan criteria would be useful. New high-throughput genomic technologies that can be applied to paraffin-embedded tissue have facilitated the identification of gene signatures and other biomarkers able to predict prognosis in HCC patients. None of these biomarkers, based on transcriptome, microRNAs or metilome, has been incorporated into clinical practice, although in future they may be able to complement the prognostic value of clinical and pathologic variables.

On the trail of a cereal killer: recent advances in Fusarium graminearum pathogenomics and host resistance

The ascomycete fungal pathogen Fusarium graminearum (sexual stage: Gibberella zeae) causes the devastating head blight or scab disease on wheat and barley, and cob or ear rot disease on maize. Fusarium graminearum infection causes significant crop and quality losses. In addition to roles as virulence factors during pathogenesis, trichothecene mycotoxins (e.g. deoxynivalenol) produced by this pathogen constitute a significant threat to human and animal health if consumed in respective food or feed products. In the last few years, significant progress has been made towards a better understanding of the processes involved in F. graminearum pathogenesis, toxin biosynthesis and host resistance mechanisms through the use of high-throughput genomic and phenomic technologies. In this article, we briefly review these new advances and also discuss how future research can contribute to the development of sustainable plant protection strategies against this important plant pathogen.

GeneWeaver: a web-based system for integrative functional genomics

High-throughput genome technologies have produced a wealth of data on the association of genes and gene products to biological functions. Investigators have discovered value in combining their experimental results with published genome-wide association studies, quantitative trait locus, microarray, RNA-sequencing and mutant phenotyping studies to identify gene-function associations across diverse experiments, species, conditions, behaviors or biological processes. These experimental results are typically derived from disparate data repositories, publication supplements or reconstructions from primary data stores. This leaves bench biologists with the complex and unscalable task of integrating data by identifying and gathering relevant studies, reanalyzing primary data, unifying gene identifiers and applying ad hoc computational analysis to the integrated set. The freely available GeneWeaver (http://www.GeneWeaver.org) powered by the Ontological Discovery Environment is a curated repository of genomic experimental results with an accompanying tool set for dynamic integration of these data sets, enabling users to interactively address questions about sets of biological functions and their relations to sets of genes. Thus, large numbers of independently published genomic results can be organized into new conceptual frameworks driven by the underlying, inferred biological relationships rather than a pre-existing semantic framework. An empirical ‘ontology’ is discovered from the aggregate of experimental knowledge around user-defined areas of biological inquiry.

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements

BackgroundPost-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necessitating the development of novel analysis tools that capture the resulting data structure. Outlier measurements at one or more time points present a significant challenge, while potentially valuable replicate information is often ignored by existing techniques.ResultsWe present a generative model-based Bayesian hierarchical clustering algorithm for microarray time series that employs Gaussian process regression to capture the structure of the data. By using a mixture model likelihood, our method permits a small proportion of the data to be modelled as outlier measurements, and adopts an empirical Bayes approach which uses replicate observations to inform a prior distribution of the noise variance. The method automatically learns the optimum number of clusters and can incorporate non-uniformly sampled time points. Using a wide variety of experimental data sets, we show that our algorithm consistently yields higher quality and more biologically meaningful clusters than current state-of-the-art methodologies. We highlight the importance of modelling outlier values by demonstrating that noisy genes can be grouped with other genes of similar biological function. We demonstrate the importance of including replicate information, which we find enables the discrimination of additional distinct expression profiles.ConclusionsBy incorporating outlier measurements and replicate values, this clustering algorithm for time series microarray data provides a step towards a better treatment of the noise inherent in measurements from high-throughput genomic technologies. Timeseries BHC is available as part of the R package 'BHC' (version 1.5), which is available for download from Bioconductor (version 2.9 and above) via http://www.bioconductor.org/packages/release/bioc/html/BHC.html?pagewanted=all.

B G@Sbase--a microbial gene expression and comparative genomic database

The reducing cost of high-throughput functional genomic technologies is creating a deluge of high volume, complex data, placing the burden on bioinformatics resources and tool development. The Bacterial Microarray Group at St George's (BμG@S) has been at the forefront of bacterial microarray design and analysis for over a decade and while serving as a hub of a global network of microbial research groups has developed BμG@Sbase, a microbial gene expression and comparative genomic database. BμG@Sbase (http://bugs.sgul.ac.uk/bugsbase/) is a web-browsable, expertly curated, MIAME-compliant database that stores comprehensive experimental annotation and multiple raw and analysed data formats. Consistent annotation is enabled through a structured set of web forms, which guide the user through the process following a set of best practices and controlled vocabulary. The database currently contains 86 expertly curated publicly available data sets (with a further 124 not yet published) and full annotation information for 59 bacterial microarray designs. The data can be browsed and queried using an explorer-like interface; integrating intuitive tree diagrams to present complex experimental details clearly and concisely. Furthermore the modular design of the database will provide a robust platform for integrating other data types beyond microarrays into a more Systems analysis based future.

Detection of chromosomal abnormalities using high resolution arrays in clinical cancer research

In clinical cancer research, high throughput genomic technologies are increasingly used to identify copy number aberrations. However, the admixture of tumor and stromal cells and the inherent karyotypic heterogeneity of most of the solid tumor samples make this task highly challenging. Here, we propose a robust two-step strategy to detect copy number aberrations in such a context. A spatial mixture model is first used to fit the preprocessed data. Then, a calling algorithm is applied to classify the genomic segments in three biologically meaningful states (copy loss, copy gain and modal copy). The results of a simulation study show the good properties of the proposed procedure with complex patterns of genomic aberrations. The interest of the proposed procedure in clinical cancer research is then illustrated by the analysis of real lung adenocarcinoma samples.

The Environmental Polymorphism Registry: A Unique Resource that Facilitates Translational Research of Environmental Disease

Background: Dissecting complex disease has become more feasible because of the availability of large-scale DNA resources and advances in high-throughput genomic technology. Although these tools help scientists identify potential susceptibility loci, subjects with relevant genotypes are needed for clinical phenotyping and toxicity studies.Objective: We have developed a resource of subjects and their DNA to use for translational research of environmental disease.Methods: More than 15,000 individuals of diverse sex, age, race, and ethnicity were recruited from North Carolina. DNA was isolated from their blood and coded with personal identification numbers linked to their identities. This linked resource of subjects and their DNA—the Environmental Polymorphism Registry (EPR)—allows scientists to screen for individuals with genotypes of interest and invite them to participate in follow-up studies.Discussion: The EPR is a phenotype-by-genotype resource designed to facilitate translational studies of environmental disease. Based on their genotypes, subjects are invited to participate at all levels of research, from basic laboratory ex vivo cell phenotyping experiments that require viable tissue to in vivo observational studies and clinical trials. Here we report on progress of the EPR since 2008. We also describe a major effort at the National Institute of Environmental Health Sciences (NIEHS) to investigate susceptibility loci in 87 environmental response genes and gene × environment interactions using EPR resources.Conclusion: The EPR is a unique and novel resource and is ideal for genotype-driven translational research of environmental disease. We expect that it will serve as a model for future resources. Such tools help scientists attain their ultimate goals: to identify at-risk populations and develop strategies for preventing and treating human disease.

Focusing on the Flood

Focusing on the Flood

Application of the Bayesian MMSE estimator for classification error to gene expression microarray data

With the development of high-throughput genomic and proteomic technologies, coupled with the inherent difficulties in obtaining large samples, biomedicine faces difficult small-sample classification issues, in particular, error estimation. Most popular error estimation methods are motivated by intuition rather than mathematical inference. A recently proposed error estimator based on Bayesian minimum mean square error estimation places error estimation in an optimal filtering framework. In this work, we examine the application of this error estimator to gene expression microarray data, including the suitability of the Gaussian model with normal-inverse-Wishart priors and how to find prior probabilities. We provide an implementation for non-linear classification, where closed form solutions are not available. We propose a methodology for calibrating normal-inverse-Wishart priors based on discarded microarray data and examine the performance on synthetic high-dimensional data and a real dataset from a breast cancer study. The calibrated Bayesian error estimator has superior root mean square performance, especially with moderate to high expected true errors and small feature sizes. We have implemented in C code the Bayesian error estimator for Gaussian distributions and normal-inverse-Wishart priors for both linear classifiers, with exact closed-form representations, and arbitrary classifiers, where we use a Monte Carlo approximation. Our code for the Bayesian error estimator and a toolbox of related utilities are available at http://gsp.tamu.edu/Publications/supplementary/dalton11a. Several supporting simulations are also included. ldalton@tamu.edu

Functional Cohesion of Gene Sets Determined by Latent Semantic Indexing of PubMed Abstracts

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature.AvailabilityGCAT is freely available at http://binf1.memphis.edu/gcat

A novel network-based method for measuring the functional relationship between gene sets

In the functional genomic era, a large number of gene sets have been identified via high-throughput genomic and proteomic technologies. These gene sets of interest are often related to the same or similar disorders or phenotypes, and are commonly presented as differentially expressed gene lists, co-expressed gene modules, protein complexes or signaling pathways. However, biologists are still faced by the challenge of comparing gene sets and interpreting the functional relationships between gene sets into an understanding of the underlying biological mechanisms. We introduce a novel network-based method, designated corrected cumulative rank score (CCRS), which analyzes the functional communication and physical interaction between genes, and presents an easy-to-use web-based toolkit called GsNetCom to quantify the functional relationship between two gene sets. To evaluate the performance of our method in assessing the functional similarity between two gene sets, we analyzed the functional coherence of complexes in functional catalog and identified protein complexes in the same functional catalog. The results suggested that CCRS can offer a significant advance in addressing the functional relationship between different gene sets compared with several other available tools or algorithms with similar functionality. We also conducted the case study based on our method, and succeeded in prioritizing candidate leukemia-associated protein complexes and expanding the prioritization and analysis of cancer-related complexes to other cancer types. In addition, GsNetCom provides a new insight into the communication between gene modules, such as exploring gene sets from the perspective of well-annotated protein complexes. GsNetCom is a freely available web accessible toolkit at http://bioinfo.hrbmu.edu.cn/GsNetCom.

Mass‐scale high‐throughput multiplex polymerase chain reaction for human platelet antigen single‐nucleotide polymorphisms screening of apheresis platelet donors

Treatment with human platelet antigen (HPA)-matched platelets (PLTs) is the optimal therapy for bleeding secondary to neonatal alloimmune thrombocytopenia. Recent advances in high-throughput DNA-based blood group and PLT antigen genotyping have made it possible to screen plateletpheresis donors for potential HPA-matched PLT transfusion. This prospective study evaluated genomic DNA from plateletpheresis donors for single-nucleotide polymorphisms (SNPs) associated with HPA-1, -2, -3, -4, -5, and -15 to determine whether high-throughput multiplex genomic DNA PCR and oligonucleotide extension technology can be used for mass-scale PLT antigen genotyping. Genotyping using SNP technology was confirmed using sequence-specific polymerase chain reaction (SSP-PCR). Of the 748 donors screened, 277 were found to be negative for antigens implicated in alloimmune thrombocytopenia. In addition, two donors were homozygous for HPA-1b/b and -2b/b, six donors for HPA-1b/b and -3b/b, one for HPA-2b/b and -3b/b, one for HPA-1b/b and -5b/b, 10 for HPA-1b/b and -15 b/b, four for HPA-5b/b and -15b/b, and one for HPA-2b/b and -15b/b. Retesting using SSP-PCR was conducted for 60 donors. Discrepant results occurred between SNP and SSP-PCR in less than 20% of samples for HPA-1b/1b/HPA-3b/3b, HPA-5b/5b, and HPA-15b/b. High-throughput multiplex PCR SNP and confirmatory molecular genotyping are useful for mass-scale screening of apheresis PLT donors to provide antigen-negative genotypes. Refinements to mass-scale multiplex analysis technology would reduce further the confirmatory testing needed.

Functional modelling of an equine bronchoalveolar lavage fluid proteome provides experimental confirmation and functional annotation of equine genome sequences

The equine genome sequence enables the use of high-throughput genomic technologies in equine research, but accurate identification of expressed gene products and interpreting their biological relevance require additional structural and functional genome annotation. Here, we employ the equine genome sequence to identify predicted and known proteins using proteomics and model these proteins into biological pathways, identifying 582 proteins in normal cell-free equine bronchoalveolar lavage fluid (BALF). We improved structural and functional annotation by directly confirming the in vivo expression of 558 (96%) proteins, which were computationally predicted previously, and adding Gene Ontology (GO) annotations for 174 proteins, 108 of which lacked functional annotation. Bronchoalveolar lavage is commonly used to investigate equine respiratory disease, leading us to model the associated proteome and its biological functions. Modelling of protein functions using Ingenuity Pathway Analysis identified carbohydrate metabolism, cell-to-cell signalling, cellular function, inflammatory response, organ morphology, lipid metabolism and cellular movement as key biological processes in normal equine BALF. Comparative modelling of protein functions in normal cell-free bronchoalveolar lavage proteomes from horse, human, and mouse, performed by grouping GO terms sharing common ancestor terms, confirms conservation of functions across species. Ninety-one of 92 human GO categories and 105 of 109 mouse GO categories were conserved in the horse. Our approach confirms the utility of the equine genome sequence to characterize protein networks without antibodies or mRNA quantification, highlights the need for continued structural and functional annotation of the equine genome and provides a framework for equine researchers to aid in the annotation effort.