Search

Background For microorganisms on a paper surface, the lack of water is one of the most important stress factors. A strain of Bacillus megaterium FDU301 was isolated from plaques on a paper surface using culture medium with polyethylene glycol 200 (PEG200) to simulate an arid condition. Global transcriptomic analysis of B. megaterium FDU301 grown under normal and simulated arid conditions was performed via RNA-seq technology to identify genes involved in arid stress adaptation. Results The transcriptome of B. megaterium FDU301 grown in LB medium under arid (15% PEG200 (w/w)) and normal conditions were compared. A total of 2941 genes were differentially expressed, including 1422 genes upregulated and 1519 genes downregulated under arid conditions. Oxidative stress-responsive regulatory genes perR, fur, and tipA were significantly upregulated, along with DNA protecting protein (dps), and catalase (katE). Genes related to Fe2+ uptake (feoB), sporulation stage II (spoIIB, spoIIE, spoIIGA), small acid-soluble spore protein (sspD), and biosynthesis of compatible solute ectoine (ectB, ectA) were also highly expressed to various degrees. Oxidative phosphorylation-related genes (atpB, atpE, atpF, atpH, atpA, atpG, atpD, atpC) and glycolysis-related genes (pgk, tpiA, frmA) were significantly downregulated. Conclusion This is the first report about transcriptomic analysis of a B. megaterium to explore the mechanism of arid resistance. Major changes in transcription were seen in the arid condition simulated by PEG200 (15%), with the most important one being genes related to oxidative stress. The results showed a complex mechanism for the bacteria to adapt to arid stress.

Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.

RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (, Briefings in Bioinformatics, 20 (1), ISSN:1467-5463, ISSN:1477-4054

To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data.

Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from ‘-omics’ technologies. Created from a biologist’s perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.

Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.

Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods. All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample.

To the Editor: Breast cancer is a frequently diagnosed solid tumor in women worldwide (1). MicroRNAs (miRNAs), small noncoding RNAs that regulate protein expression, are potential biomarkers in the diagnosis, prognosis, and prediction of response to treatment in breast cancer (2). We recently reported a paper-based method to extract miRNAs from plasma and circulating tumor cells (3). Here, we applied this method to plasma of patients with breast cancer and age- and gender-matched healthy controls, after treating the plasma with a commercial exosome isolation kit employing a nonspecific precipitation step; we compared the miRNA expression profiles from the plasma with and without the use of the precipitation method. After informed consent and institutional approval, we obtained peripheral blood samples from 9 patients with breast cancer and 9 healthy controls. Plasma was isolated by centrifugation at 3000 g for 10 min and stored at −80 °C. All plasma samples were visually inspected to exclude any discernible hemolysis. Then, 60 μL of Total Exosome Isolation reagent …

RNA-Seq is a widely-used method for studying the behavior of genes under different biological conditions. An essential step in an RNA-Seq study is normalization, in which raw data are adjusted to account for factors that prevent direct comparison of expression measures. Errors in normalization can have a significant impact on downstream analysis, such as inflated false positives in differential expression analysis. An under-emphasized feature of normalization is the assumptions upon which the methods rely and how the validity of these assumptions can have a substantial impact on the performance of the methods. In this paper, we explain how assumptions provide the link between raw RNA-Seq read counts and meaningful measures of gene expression. We examine normalization methods from the perspective of their assumptions, as an understanding of methodological assumptions is necessary for choosing methods appropriate for the data at hand. Furthermore, we discuss why normalization methods perform poorly when their assumptions are violated and how this causes problems in subsequent analysis. To analyze a biological experiment, researchers must select a normalization method with assumptions that are met and that produces a meaningful measure of expression for the given experiment., Comment: 20 pages, 6 figures, 1 table. Supplementary information contains 9 pages, 1 table. For associated simulation code, see https://github.com/ciaranlevans/rnaSeqAssumptions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

BACKGROUND Molecular characterization of circulating tumor cells (CTCs) holds great promise for monitoring metastatic progression and characterizing metastatic disease. However, leukocyte and red blood cell contamination of routinely isolated CTCs makes CTC-specific molecular characterization extremely challenging. METHODS Here we report the use of a paper-based medium for efficient extraction of microRNAs (miRNAs) from limited amounts of biological samples such as rare CTCs harvested from cancer patient blood. Specifically, we devised a workflow involving the use of Flinders Technology Associates (FTA)® Elute Card with a digital PCR-inspired “partitioning” method to extract and purify miRNAs from plasma and CTCs. RESULTS We demonstrated the sensitivity of this method to detect miRNA expression from as few as 3 cancer cells spiked into human blood. Using this method, background miRNA expression was excluded from contaminating blood cells, and CTC-specific miRNA expression profiles were derived from breast and colorectal cancer patients. Plasma separated out during purification of CTCs could likewise be processed using the same paper-based method for miRNA detection, thereby maximizing the amount of patient-specific information that can be derived from a single blood draw. CONCLUSIONS Overall, this paper-based extraction method enables an efficient, cost-effective workflow for maximized recovery of small RNAs from limited biological samples for downstream molecular analyses.

Pathological examination has been the gold standard for diagnosis in cancer and its role has also included the elucidation of etiology, pathogenesis, clinicopathological correlation, and prognostication. The advent of newer technologies and the realization that breast cancer is heterogeneous has shifted the focus to prognostication, with increased attention being paid to the identification of morphological features and immunohistochemical markers of prognostic relevance. However, despite the massive efforts invested in the identification of immunohistochemical biomarkers in breast cancer the majority have not proven to be of value in multivariate analyses and only estrogen receptor, progesterone receptor, and Her2/neu expression have remained essential components of pathological examination. These 3 markers were initially employed for prognostication but their role in treatment also rendered them of predictive value. Newer molecular methods, especially high-throughput technologies, have shown that even morphologically similar subtypes of breast cancer can show molecular heterogeneity; moreover, infiltrating ductal carcinoma can be separated into at least 4 molecular subtypes designated luminal (ER+, PR+, and Her2/neu–), Her2 overexpressing (ER–, PR–, and Her2/neu+), basal-like (ER–, PR–, Her2/neu–, and CK5/6+, EGFR+), and normal breast-like (ER–, PR–, and Her2/neu–), each with different clinical outcomes. The importance of proliferative gene expression in these subtypes has been demonstrated and surrogate immunohistochemical markers include ER, PR, Her2/neu, and Ki67 for the more expensive molecular tests. Molecular technologies, importantly, have not only provided further insights into the heterogeneity of breast cancer but have also opened new avenues for treatment through the identification of signaling molecules important in the proliferation and survival of the neoplastic cells. The treatment of cancer thus shifts from the conventional approach of ‘one size fits all’ to one of personalized treatment tailored to the specific characteristics of the tumor. Pathologists continue to play their traditional role in diagnosis but, as purveyors of the excised tissue, pathologists now have the additional role of identifying biomarkers responsive to therapeutic manipulation, thus playing an inextricable role as diagnostic oncologists in the management of breast cancer.

Nutrigenetics/nutrigenomics (the study of the bidirectional interactions between genes and diet) is a rapidly developing field that is changing research and practice in human nutrition. Though eventually nutrition clinicians may be able to provide personalized nutrition recommendations, in the immediate future they are most likely to use this knowledge to improve dietary recommendations for populations. Currently, estimated average requirements are used to set dietary reference intakes because scientists cannot adequately identify subsets of the population that differ in requirement for a nutrient. Recommended intake levels must exceed the actual required intake for most of the population in order to assure that individuals with the highest requirement ingest adequate amounts of the nutrient. As a result, dietary reference intake levels often are set so high that diet guidelines suggest almost unattainable intakes of some foods. Once it is possible to identify common subgroups that differ in nutrient requirements using nutrigenetic/nutrigenomic profiling, targeted interventions and recommendations can be refined. In addition, when a large variance exists in response to a nutrient, statistical analyses often argue for a null effect. If responders could be differentiated from nonre-sponders based on nutrigenetic/nutrigenomic profiling, this statistical noise could be eliminated and the sensitivity of nutrition research greatly increased.

Current techniques used to obtain lung samples have significant limitations and do not provide reproducible biomarkers of inflammation. We have developed a novel technique that allows multiple sampling methods from the same area (or multiple areas) of the lung under direct bronchoscopic vision. It allows collection of mucosal lining fluid and bronchial brushing from the same site; biopsy samples may also be taken. The novel technique takes the same time as standard procedures and can be conducted safely. Eight healthy smokers aged 40–65 years were included in this study. An absorptive filter paper was applied to the bronchial mucosa under direct vision using standard bronchoscopic techniques. Further samples were obtained from the same site using bronchial brushings. Bronchoalveolar lavage (BAL) was obtained using standard techniques. Chemokine (C-C Motif) Ligand 20 (CCL20), CCL4, CCL5, Chemokine (C-X-C Motif) Ligand 1 (CXCL1), CXCL8, CXCL9, CXCL10, CXCL11, Interleukin 1 beta (IL-1β), IL-6, Vascular endothelial growth factor (VEGF), Matrix metalloproteinase 8 (MMP-8) and MMP-9 were measured in exudate and BAL. mRNA was collected from the bronchial brushings for gene expression analysis. A greater than 10 fold concentration of all the biomarkers was detected in lung exudate in comparison to BAL. High yield of good quality RNA with RNA integrity numbers (RIN) between 7.6 and 9.3 were extracted from the bronchial brushings. The subset of genes measured were reproducible across the samples and corresponded to the inflammatory markers measured in exudate and BAL. The bronchoabsorption technique as described offers the ability to sample lung fluid direct from the site of interest without the dilution effects caused by BAL. Using this method we were able to successfully measure the concentrations of biomarkers present in the lungs as well as collect high yield mRNA samples for gene expression analysis from the same site. This technique demonstrates superior sensitivity to standard BAL for the measurement of biomarkers of inflammation. It could replace BAL as the method of choice for these measurements. This method provides a systems biology approach to studying the inflammatory markers of respiratory disease progression. NHS Health Research Authority ( 13/LO/0256 ).

Background: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an inherited vascular dementia characterised by recurrent ischemic strokes in the deep white matter. Mutations in the gene encoding the cell surface receptor, Notch3, have been identified in CADASIL patients, and accumulation of the extracellular domain of Notch3 has been demonstrated in affected vessels. Almost all CADASIL mutations alter the number of cysteine residues in the epidermal growth factor (EGF)-like repeats in the extracellular domain of the protein. Objectives: To understand the functional consequences of a recurrent CADASIL mutation on furin processing, cell surface expression, ligand binding, and activation of a downstream effector CBF1 by the Notch3 receptor. Methods: We expressed wild type and mutant Notch3 receptors in cultured cells and examined cell surface expression of the proteins. We also applied a new flow cytometry based approach to semi-quantitatively measure binding to three Notch ligands. Additionally, we used a well characterised co-culture system to examine ligand dependent activation of transcription from a CBF1-luciferase reporter construct. Results: These studies revealed subtle abnormalities in furin processing of the mutant receptor, although both heterodimeric and full length receptors are present on the cell surface, are capable of interacting with soluble forms of three ligands, Delta1, Delta4, and Jagged1, and retain the ability to activate CBF1 in a ligand dependent manner. Conclusions: By comparison with other mutant forms of Notch3, these data indicate that individual CADASIL mutations can have disparate effects on Notch3 expression and function.

A DNA microarray to monitor the expression of bacterial metabolic genes within mixed microbial communities was designed and tested. Total RNA was extracted from pure and mixed cultures containing the 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacterium Ralstonia eutropha JMP134, and the inducing agent 2,4-D. Induction of the 2,4-D catabolic genes present in this organism was readily detected 4, 7, and 24 h after the addition of 2,4-D. This strain was diluted into a constructed mixed microbial community derived from a laboratory scale sequencing batch reactor. Induction of two of five 2,4-D catabolic genes ( tfdA and tfdC ) from populations of JMP134 as low as 10 5 cells/ml was clearly detected against a background of 10 8 cells/ml. Induction of two others ( tfdB and tfdE ) was detected from populations of 10 6 cells/ml in the same background; however, the last gene, tfdF , showed no significant induction due to high variability. In another experiment, the induction of resin acid degradative genes was statistically detectable in sludge-fed pulp mill effluent exposed to dehydroabietic acid in batch experiments. We conclude that microarrays will be useful tools for the detection of bacterial gene expression in wastewaters and other complex systems.

Aims—Microarray technology has recently led to the identification of molecular prognostic subgroups in non-Hodgkin's lymphomas. To determine the usefulness of ready made macroarrays as routine diagnostic tools in haematopathology, lymph node biopsies were analysed using a cDNA macroarray containing genes involved in apoptosis, including caspases. Methods—Nine biopsy specimens were analysed using total frozen tissues: four samples of B cell follicular lymphoma, two of B cell diffuse large cell lymphoma, and three of non-neoplastic lymph nodes from benign lymphadenitis. Nine cell populations were sorted from fresh tissues: malignant B cells from two patients with follicular lymphoma and two with diffuse large cell lymphoma, reactive B cells from two benign lymph nodes, reactive T cells from one benign lymph node, and virgin (mantle zone) B cells and germinal centre B cells from benign tonsils. Immunohistochemistry (IHC) on paraffin wax sections was performed for the localisation of caspases 2, 3, 4, 7, 8, and 9. Results—In the clustered array data, sorted cells from samples sharing common histological lesions were grouped together, whereas the array/histology correlation was less satisfactory for tissues. The expression profiles of both the array and IHC methods correlated for most caspases and samples. Conclusions—Variations in array profiles of sorted cell populations can be associated with specific histological features, suggesting a possible diagnostic application of ready made apoptosis macroarrays in haematopathology.

Termites are highly eusocial insects that thrive on recalcitrant materials like wood and soil and thus play important roles in global carbon recycling and also in damaging wooden structures. Termites, such as Reticulitermes flavipes (Rhinotermitidae), owe their success to their ability to extract nutrients from lignocellulose (a major component of wood) with the help of gut-dwelling symbionts. With the aim to gain new insights into this enzymatic process we provided R. flavipes with a complex lignocellulose (wood) or pure cellulose (paper) diet and followed the resulting differential gene expression on a custom oligonucleotide-microarray platform. We identified a set of expressed sequence tags (ESTs) with differential abundance between the two diet treatments and demonstrated the source (host/symbiont) of these genes, providing novel information on termite nutritional symbiosis. Our results reveal: (1) the majority of responsive wood- and paper-abundant ESTs are from host and symbionts, respectively; (2) distinct pathways are associated with lignocellulose and cellulose feeding in both host and symbionts; and (3) sets of diet-responsive ESTs encode putative digestive and wood-related detoxification enzymes. Thus, this study illuminates the dynamics of termite nutritional symbiosis and reveals a pool of genes as potential targets for termite control and functional studies of termite-symbiont interactions.

The toxicity of pulp and paper mill effluents (PPMEs) has been greatly decreased, yet some continue to negatively affect fish reproduction. We hypothesized that PPMEs are affecting the brain resulting in decreased reproductive performance. Our goal was to use gene expression profiling to test the hypothesis that PPMEs are having an effect on neural systems in the fathead minnow (FHM; Pimephales promelas) in vivo. Sexually mature male and female FHM were exposed to 100% final biotreated PPMEs from 5 different sources for 5 days. Using an oligo-array (15K genes) we examined the effect of PPMEs on gene expression in the hypothalamus of female fish. We validated selected genes (cholecystokinin, RevErbbeta2, and urotensin I) that were identified by microarray analysis using real-time reverse-transcriptase polymerase chain reaction (RT-PCR). We compared the FHM microarray dataset to multiple microarray datasets from experiments conducted with goldfish injected with different dopaminergic pharmaceuticals to examine whether PPMEs could be affecting the dopamine system. Exposure of FHM to PPMEs resulted in varying degrees of spawning inhibition. Microarray analysis revealed surprisingly few genes in the brain that were commonly affected by the different PPMEs. Real-time RT-PCR confirmed the changes in expression for cholecystokinin, RevErbbeta2, and urotensin I. Comparison of the FHM and goldfish microarray datasets suggest that some PPMEs may be acting on the dopamine system. We show that PPMEs are neuroactive in fish and may be acting through some of the pathways in a manner similar to dopamine.

Alternative splicing is an important mechanism for increasing genetic complexity leading to multiple transcripts from single genes and gene regulation through alternative promoters. Splicing often leads to unique tissue-specific patterns of mRNAs with specific biological functions. Nuclear factor I-C (NFI-C), a member of the NFI gene family, is expressed in numerous tissues including brain, liver, spleen and heart. However, the unique dental phenotype of Nfic(-/-) mice lacking molar roots demonstrates a critical role for this transcription factor in root formation. In humans, the NFI-C gene is alternatively spliced producing 4 isoforms. However, different spliced variants have not been studied in association with tissue specificity. The main objective of this study is to identify the NFI-C isoforms expressed in dental cells/tissues, comparing them to the spliced variants in nondental cells/tissues and to analyze their relative expression levels in various cell types. Using bioinformatics, we analyzed the NFI-C gene structure, identifying 2 potential alternative promoters driving expression of selective mRNA transcripts. Our studies show the expression of 3 NFI-C transcripts with the overall splicing pattern conserved between dental and nondental cells tested. Furthermore, by quantitative real-time PCR analysis, we found that although the relative levels of these transcripts were similar in dental and nondental cells, significant differences were observed within the dental cells tested. These are the first studies to analyze the expression of NFI-C isoforms in dental versus nondental cells/tissues, finding subtle cell-/tissue-specific expression patterns that could explain the dental phenotype of Nfic(-/-) mice.

Effluents from pulp and paper mills that historically have used elemental chlorine in the bleaching process have been implicated in inhibiting reproduction in fish. Compounds with estrogenic and androgenic binding affinities have been found in these effluents, suggesting that the impairment of reproduction is through an endocrine-related mode of action. To date, a great deal of attention has been paid to phytoestrogens and resin acids that are present in mill process streams as a result of pulping trees. Estrogen and estrogen mimics interact directly with the estrogen receptor and have near immediate effects on gene transcription by turning on the expression of a unique set of genes. Using differential display (DD) RT-PCR, we examined changes in gene expression induced by exposure to paper mill effluents. Largemouth bass were exposed to 0, 10, 20, 40, and 80% paper mill effluent concentrations in large flow-through tanks for varied periods of time including 7, 28 or 56 days. Plasma hormone levels in males and females and plasma vitellogenin (Vtg) in females decreased with dose and time. Measurements of changes in gene expression using DD RT-PCR suggest that the gene expression patterns of male fish do not change much with exposure, except for the induction of a few genes including CYP 1A, a protein that is induced through the action of the Ah receptor in response to dioxin and similar polyaromatic hydrocarbons. However, in the case of females, exposure to these effluents resulted in an up-regulation of CYP 1A that was accompanied by a generalized down-regulation of genes normally expressed during the reproductive season. These antiestrogenic changes are in agreement with previous studies in bass exposed to these effluents, and could result in decreased reproductive success in affected populations.

Genomics was initiated when robotics made possible the characterisation of large numbers of DNA fragments and when ever improving computers with dedicated software were applied to the localisation in the genome of these sequences and to the analysis of their content. By enabling the generation and management of large amounts of DNA based sequences these tools have changed our perception of the genomes of living organisms. These data, as applied to humans, are contributing to the understanding of gene function, disease processes, and evolution of our species. Presently they are changing the research strategies for identifying genetic variations influencing disease susceptibility and response to treatment. These advances will have a profound impact in biomedicine.