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Many complex diseases such as cancer are associated with changes in biological pathways and molecular networks rather than being caused by single gene alterations. A major challenge in the diagnosis and treatment of such diseases is to identify characteristic aberrancies in the biological pathways and molecular network activities and elucidate their relationship to the disease. This review presents recent progress in using high-throughput biological assays to decipher aberrant pathways and network activities. In particular, this review provides specific examples in which high-throughput data have been applied to identify relationships between diseases and aberrant pathways and network activities. The achievements in this field have been remarkable, but many challenges have yet to be addressed.

More than 70 years after the first ex situ genebanks have been established, major efforts in this field are still concerned with issues related to further completion of individual collections and securing of their storage. Attempts regarding valorization of ex situ collections for plant breeders have been hampered by the limited availability of phenotypic and genotypic information. With the advent of molecular marker technologies first efforts were made to fingerprint genebank accessions, albeit on a very small scale and mostly based on inadequate DNA marker systems. Advances in DNA sequencing technology and the development of high-throughput systems for multiparallel interrogation of thousands of single nucleotide polymorphisms (SNPs) now provide a suite of technological platforms facilitating the analysis of several hundred of Gigabases per day using state-of-the-art sequencing technology or, at the same time, of thousands of SNPs. The present review summarizes recent developments regarding the deployment of these technologies for the analysis of plant genetic resources, in order to identify patterns of genetic diversity, map quantitative traits and mine novel alleles from the vast amount of genetic resources maintained in genebanks around the world. It also refers to the various shortcomings and bottlenecks that need to be overcome to leverage the full potential of high-throughput DNA analysis for the targeted utilization of plant genetic resources.

Life expectancy has dramatically increased in industrialized nations over the last 200 hundred years. The aging of populations carries over to clinical research and leads to an increasing representation of elderly and multimorbid individuals in study populations. Clinical research in these populations is complicated by the fact that individuals are likely to experience several potential disease endpoints that prevent some disease-specific endpoint of interest from occurrence. Large developments in competing risks methodology have been achieved over the last decades, but we assume that recognition of competing risks in the clinical community is still marginal. It is the aim of this article to address translational aspects of competing risks to the clinical community. We describe clinical populations where competing risks issues may arise. We then discuss the importance of agreement between the competing risks methodology and the study aim, in particular the distinction between etiologic and prognostic research questions. In a review of 50 clinical studies performed in individuals susceptible to competing risks published in high-impact clinical journals, we found competing risks issues in 70% of all articles. Better recognition of issues related to competing risks and of statistical methods that deal with competing risks in accordance with the aim of the study is needed. Copyright (C) 2011 John Wiley & Sons, Ltd.

Ortholog identification is used in gene functional annotation, species phylogeny estimation, phylogenetic profile construction and many other analyses. Bioinformatics methods for ortholog identification are commonly based on pairwise protein sequence comparisons between whole genomes. Phylogenetic methods of ortholog identification have also been developed; these methods can be applied to protein data sets sharing a common domain architecture or which share a single functional domain but differ outside this region of homology. While promiscuous domains represent a challenge to all orthology prediction methods, overall structural similarity is highly correlated with proximity in a phylogenetic tree, conferring a degree of robustness to phylogenetic methods. In this article, we review the issues involved in orthology prediction when data sets include sequences with structurally heterogeneous domain architectures, with particular attention to automated methods designed for high-throughput application, and present a case study to illustrate the challenges in this area.

Orthology is a powerful refinement of homology that allows us to describe more precisely the evolution of genomes and understand the function of the genes they contain. However, because orthology is not concerned with genomic position, it is limited in its ability to describe genes that are likely to have equivalent roles in different genomes. Because of this limitation, the concept of ‘positional orthology’ has emerged, which describes the relation between orthologous genes that retain their ancestral genomic positions. In this review, we formally define this concept, for which we introduce the shorter term ‘toporthology’, with respect to the evolutionary events experienced by a gene’s ancestors. Through a discussion of recent studies on the role of genomic context in gene evolution, we show that the distinction between orthology and toporthology is biologically significant. We then review a number of orthology prediction methods that take genomic context into account and thus that may be used to infer the important relation of toporthology.

The statistical language R and its Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by some analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing systems offer a solution to this issue. The Simple Parallel R Interface (SPRINT) is a package that provides biostatisticians with easy access to High Performance Computing systems and allows the addition of parallelized functions to R. Previous work has established that the SPRINT implementation of an R permutation testing function has close to optimal scaling on up to 512 processors on a supercomputer. Access to supercomputers, however, is not always possible, and so the work presented here compares the performance of the SPRINT implementation on a supercomputer with benchmarks on a range of platforms including cloud resources and a common desktop machine with multiprocessing capabilities. Copyright (C) 2011 John Wiley & Sons, Ltd.

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.

With the growing number of microRNAs (miRNAs) being identified each year, more innovative molecular tools are required to efficiently characterize these small RNAs in living animal systems. Caenorhabditis elegans is a powerful model to study how miRNAs regulate gene expression and control diverse biological processes during development and in the adult. Genetic strategies such as large-scale miRNA deletion studies in nematodes have been used with limited success since the majority of miRNA genes do not exhibit phenotypes when individually mutated. Recent work has indicated that miRNAs function in complex regulatory networks with other small RNAs and protein-coding genes, and therefore the challenge will be to uncover these functional redundancies. The use of miRNA inhibitors such as synthetic antisense 2′-O-methyl oligoribonucleotides is emerging as a promising in vivo approach to dissect out the intricacies of miRNA regulation.

Despite the common assumption that orthologs usually share the same function, there have been various reports of divergence between orthologs, even among species as close as mammals. The comparison of mouse and human is of special interest, because mouse is often used as a model organism to understand human biology. We review the literature on evidence for divergence between human and mouse orthologous genes, and discuss it in the context of biomedical research.

Pluripotent embryonic stem (ES) cells are specialized cells with a dynamic chromatin structure, which is intimately connected with their pluripotency and physiology. In recent years somatic cells have been reprogrammed to a pluripotent state through over-expression of a defined set of transcription factors. These cells, known as induced pluripotent stem (iPS) cells, recapitulate ES cell properties and can be differentiated to apparently all cell lineages, making iPS cells a suitable replacement for ES cells in future regenerative medicine. Chromatin modifiers play a key function in establishing and maintaining pluripotency, therefore, elucidating the mechanisms controlling chromatin structure in both ES and iPS cells is of utmost importance to understanding their properties and harnessing their therapeutic potential. In this review, we discuss recent studies that provide a genome-wide view of the chromatin structure signature in ES cells and iPS cells and that highlight the central role of histone modifiers and chromatin remodelers in pluripotency maintenance and induction.

Fertilization of the oocyte by the sperm results in the formation of a totipotent zygote, in which the maternal and paternal chromatin is enclosed in two pronuclei undergoing distinct programmes of transcriptional activation and chromatin remodelling. The highly packaged paternal chromatin delivered by the sperm is decondensed and acquires a number of specific epigenetic marks, but markedly remains devoid of those usually associated with constitutive heterochromatin. During this period the maternal chromatin remains relatively stable except for marks associated with transcription and/or replication such as arginine methylation and H3/H4 acetylation. The embryo then undergoes a series of mitotic divisions without significant additional growth but differentiation, resulting in the formation of a blastocyst containing distinct cell types. The chromatin remodelling events during these stages are likely to be important in establishing the nuclear foundations required for later triggers of differentiation. Overall, we summarize three important points during these earliest reprogramming events: (i) relatively stable maternal chromatin after fertilization, (ii) rapid acquisition of specific histone marks by the paternal chromatin during the hours that follow fertilization and (iii) rapid remodelling of constitutive heterochromatic marks and modifications in the core of the nucleosome from the first mitotic division. These features are likely to be required for the creation of a chromatin environment compatible with cellular reprogramming and plasticity.

Eukaryotic chromatin can be highly dynamic and can continuously exchange between an open transcriptionally active conformation and a compacted silenced one. Post-translational modifications of histones have a pivotal role in regulating chromatin states, thus influencing all chromatin dependent processes. Methylation is currently one of the best characterized histone modification and occurs on arginine and lysine residues. Histone methylation can regulate other modifications (e.g. acetylation, phosphorylation and ubiquitination) in order to define a precise functional chromatin environment. In this review we focus on histone methylation and demethylation, as well as on the enzymes responsible for setting these marks. In particular we are describing novel concepts on the interdependence of histone modifications marks and discussing the molecular mechanisms governing this cross-talks.

Next generation sequencing has brought epigenomic studies to the forefront of current research. The power of massively parallel sequencing coupled to innovative molecular and computational techniques has allowed researchers to profile the epigenome at resolutions that were unimaginable only a few years ago. With early proof of concept studies published, the field is now moving into the next phase where the importance of method standardization and rigorous quality control are becoming paramount. In this review we will describe methodologies that have been developed to profile the epigenome using next generation sequencing platforms. We will discuss these in terms of library preparation, sequence platforms and analysis techniques.

As time goes by, it becomes more and more apparent that the puzzles of life involve more and more molecular pieces that fit together in increasingly complex ways. Genomics and Proteomics technologies nowadays, produce reliable and quantitative data that could potentially reveal all the molecular pieces of a particular puzzle. However, this is akin to the opening of Pandora's box; and we are now facing the problem of integrating this vast amount of data with its incredible complexity into some coherent whole. With the aid of engineering methods designed to build and analyze computerized man-made systems, a new emerging field called 'Executable Biology' aims to create computer programmes that put together the pieces in ways that allows capturing their dynamicity and ultimately elucidating how molecular function generates cellular function. This review aspires to highlight the main features characterizing these kinds of executable models and what makes them uniquely qualified to reason about and analyze biological networks.

Nature is replete with examples of diverse cell types, tissues and body plans, forming very different creatures from genomes with similar gene complements. However, while the genes and the structures of proteins they encode can be highly conserved, the production of those proteins in specific cell types and at specific developmental time points might differ considerably between species. A full understanding of the factors that orchestrate gene expression will be essential to fully understand evolutionary variety. Transcription factor (TF) proteins, which form gene regulatory networks (GRNs) to act in cooperative or competitive partnerships to regulate gene expression, are key components of these unique regulatory programs. Although many TFs are conserved in structure and function, certain classes of TFs display extensive levels of species diversity. In this review, we highlight families of TFs that have expanded through gene duplication events to create species-unique repertoires in different evolutionary lineages. We discuss how the hierarchical structures of GRNs allow for flexible small to large-scale phenotypic changes. We survey evidence that explains how newly evolved TFs may be integrated into an existing GRN and how molecular changes in TFs might impact the GRNs. Finally, we review examples of traits that evolved due to lineage-specific TFs and species differences in GRNs.

In the last decade or so, advances in genome-scale technologies have allowed systematic and detailed analysis of gene function. The experimental accessibility of budding yeast makes it a test-bed for technology development and application of new functional genomic tools and resources that pave the way for comparable efforts in higher eukaryotes. In this article, we review advances in reporter screening technology to discover trans-acting regulators of promoters (or cis-elements) of interest in the context of a novel functional genomics approach called Reporter Synthetic Genetic Array (R-SGA) analysis. We anticipate that this methodology will enable researchers to collect quantitative data on hundreds of gene expression pathways in an effort to better understand transcriptional regulatory networks.

RNA interference (RNAi) is a powerful method to unravel the role of a given gene in eukaryotic cells. The development of high throughput assay platforms such as fluorescence plate readers and high throughput microscopy has allowed the design of genome wide RNAi screens to systemically discern members of regulatory networks around various cellular processes. Here we summarize the different strategies employed in RNAi screens to reveal regulators of transcriptional networks. We focus our discussion in experimental approaches designed to uncover regulatory interactions modulating transcription factor activity.

Proper development and functioning of an organism depends on precise spatial and temporal expression of all its genes. These coordinated expression-patterns are maintained primarily through the process of transcriptional regulation. Transcriptional regulation is mediated by proteins binding to regulatory elements on the DNA in a combinatorial manner, where particular combinations of transcription factor binding sites establish specific regulatory codes. In this review, we survey experimental and computational approaches geared towards the identification of proximal and distal gene regulatory elements in the genomes of complex eukaryotes. Available approaches that decipher the genetic structure and function of regulatory elements by exploiting various sources of information like gene expression data, chromatin structure, DNA-binding specificities of transcription factors, cooperativity of transcription factors, etc. are highlighted. We also discuss the relevance of regulatory elements in the context of human health through examples of mutations in some of these regions having serious implications in misregulation of genes and being strongly associated with human disorders.

Quantitative targeted proteomics has recently taken front stage in the proteomics community. Centered on multiple reaction monitoring ^ mass spectrometry (MRM^MS) methodologies, quantitative targeted proteomics is being used in the verification of global proteomics data, the discovery of lower abundance proteins, protein post-translational modifications, discrimination of select highly homologous protein isoforms and as the final step in biomarker discovery. An older methodology utilized with small molecule analysis, the proteomics community is making great technological strides to develop MRM^MS as the next method to address previously challenging issues in global proteomics experimentation, namely dynamic range, identification of post-translational modifications, sensitivity and selectivity of measurement which will undoubtedly further biomedical knowledge.This brief review will provide a general introduction of MRM^MS and highlight its novel application for targeted quantitative proteomic experimentations.

The rapid rise and application of proteomic technologies has resulted in an exponential increase in the number of proteins that have been discovered and presented as 'potential' biomarkers for specific diseases. Unfortunately, the number of biomarkers approved for use by the Food and Drug Administration has not risen in likewise manner. While there are a number of reasons for this discrepancy, this glut of 'potential' biomarkers also indicates the need for validation methods to confirm or refute their utility in clinical diagnostics. For this reason, the emphasis on developing methods to target and measure the absolute quantity of specific proteins and peptides in complex proteomic samples has grown.

Glycosylation plays fundamental roles in controlling various biological processes. Therefore, glycosylation analysis has become an important target for proteomic research and has great potential for clinical applications. With the continuous development and refinement of glycoprotein isolation methods, increasing attention has been directed to the quantitative and comparative aspects. This review describes the mass spectrometry (MS)-based techniques for the comparative analysis of glycoproteins and their applications to answer a wide range of interesting biological questions.

In standard survival analysis, it is generally assumed that every individual will experience someday the event of interest. However, this is not always the case, as some individuals may not be susceptible to this event. Also, in medical studies, it is frequent that patients come to scheduled interviews and that the time to the event is only known to occur between two visits. That is, the data are interval‐censored with a cure fraction. Variable selection in such a setting is of outstanding interest. Covariates impacting the survival are not necessarily the same as those impacting the probability to experience the event. The objective of this paper is to develop a parametric but flexible statistical model to analyze data that are interval‐censored and include a fraction of cured individuals when the number of potential covariates may be large. We use the parametric mixture cure model with an accelerated failure time regression model for the survival, along with the extended generalized gamma for the error term. To overcome the issue of non‐stable and non‐continuous variable selection procedures, we extend the adaptive LASSO to our model. By means of simulation studies, we show good performance of our method and discuss the behavior of estimates with varying cure and censoring proportion. Lastly, our proposed method is illustrated with a real dataset studying the time until conversion to mild cognitive impairment, a possible precursor of Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

In this paper we hypothesize that education is associated with a higher efficiency of health investment, yet that this efficiency advantage is solely driven by intelligence. We operationalize efficiency of health investment as the probability of dying conditional on a certain hospital diagnosis, and estimate a multistate structural equation model with three states: (i) healthy, (ii) ill (in hospital), and (iii) death. We use data from a Dutch cohort born around 1940 that links intelligence tests at age 12 to later-life hospitalization and mortality records. The results suggest that higher Intelligence induces the higher educated to be more efficient users of health investment - intelligent individuals have a clear survival advantage for most hospital diagnoses - yet for unanticipated health shocks and diseases that require complex treatments such as COPD, education still plays a role.

Laboratory mice often exhibit wide differences in susceptibility when infected experimentally with viruses. Based on such observations, experiments have been designed to investigate the determinism of these differences at the molecular level, and a few genes that play a major role in the innate mechanisms of defence of the species toward viral aggressions have been characterised. For example, the extraordinary resistance of SJL mice to experimental infections with hepatitis virus strain A59 is the consequence of a structural alteration of a cell adhesion molecule which normally binds to the spikes of the virus, allowing its entry into the cells. If the virus cannot bind to the molecule, or if the molecule is absent, epithelial cells of the intestine and liver are not infected and mice are resistant. In the same way, most — not to say all — laboratory strains of mice are susceptible to infections with orthomyxoviruses or flaviviruses because essential molecules, the synthesis of which is normally triggered by interferon, are defective in these mice. Wild mice, by contrast — probably because they are constantly exposed to natural infections — are resistant. Finally, some mouse strains resist experimental infections by the mouse cytomegalovirus 1 (MCMV-1) because, once infected, these mice synthesise a molecule at the surface of infected cells which allows immediate recognition and killing by natural killer (NK) cells. With the exuberant development of mouse genetics and the constant generation of new mutant alleles, it is likely that many more genes with an impact on the phenotype of resistance or susceptibility will be identified in the forthcoming years. These genes are probably numerous, however, and many of them presumably interact with each other and/or have additive effects. This might slow down progress in our understanding of the innate mechanism of defence.

The validity and practical utility of observational medical research depends critically on good study design, excellent data quality, appropriate statistical methods and accurate interpretation of results. Statistical methodology has seen substantial development in recent times. Unfortunately, many of these methodological developments are ignored in practice. Consequently, design and analysis of observational studies often exhibit serious weaknesses. The lack of guidance on vital practical issues discourages many applied researchers from using more sophisticated and possibly more appropriate methods when analyzing observational studies. Furthermore, many analyses are conducted by researchers with a relatively weak statistical background and limited experience in using statistical methodology and software. Consequently, even 'standard' analyses reported in the medical literature are often flawed, casting doubt on their results and conclusions. An efficient way to help researchers to keep up with recent methodological developments is to develop guidance documents that are spread to the research community at large. These observations led to the initiation of the strengthening analytical thinking for observational studies (STRATOS) initiative, a large collaboration of experts in many different areas of biostatistical research. The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies. The guidance is intended for applied statisticians and other data analysts with varying levels of statistical education, experience and interests. In this article, we introduce the STRATOS initiative and its main aims, present the need for guidance documents and outline the planned approach and progress so far. We encourage other biostatisticians to become involved.

One widely held view of prenatal screening (PNS) is that its foremost aim is, or should be, to enable reproductive choice; this is the Pure Choice view. The article critiques this position by comparing it with an alternative: Public Health Pluralism. It is argued that there are good reasons to prefer the latter, including the following. (1) Public Health Pluralism does not, as is often supposed, render PNS more vulnerable to eugenics-objections. (2) The Pure Choice view, if followed through to its logical conclusions, may have unpalatable implications, such as extending choice well beyond health screening. (3) Any sensible version of Public Health Pluralism will be capable of taking on board the moral seriousness of abortion and will advocate, where practicable, alternative means of reducing the prevalence of disease and disability. (4) Public Health Pluralism is at least as well-equipped as the Pure Choice model to deal with autonomy and consent issues.

Determining the Antarctic contribution to sea-level rise from observational data is a complex problem. The number of physical processes involved (such as ice dynamics and surface climate) exceeds the number of observables, some of which have very poor spatial definition. This has led, in general, to solutions that utilise strong prior assumptions or physically based deterministic models to simplify the problem. Here, we present a new approach for estimating the Antarctic contribution, which only incorporates descriptive aspects of the physically based models in the analysis and in a statistical manner. By combining physical insights with modern spatial statistical modelling techniques, we are able to provide probability distributions on all processes deemed to play a role in both the observed data and the contribution to sea-level rise. Specifically, we use stochastic partial differential equations and their relation to geostatistical fields to capture our physical understanding and employ a Gaussian Markov random field approach for efficient computation. The method, an instantiation of Bayesian hierarchical modelling, naturally incorporates uncertainty in order to reveal credible intervals on all estimated quantities. The estimated sea-level rise contribution using this approach corroborates those found using a statistically independent method. © 2013 The Authors. Environmetrics Published by John Wiley & Sons, Ltd.

Recent advances in high-throughput biotechnologies have led to the rapid growing research interest in reverse engineering of biomolecular systems (REBMS). 'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling, can be used to simulate emergent system properties. Consequently, both data- and design-driven approaches applied to -omic data may lead to novel insights in reverse engineering biological systems that could not be expected before using low-throughput platforms. However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top-down and bottom-up approaches for systems modeling and (iii) to validate system models experimentally. In addition to reviewing progress made by the community and opportunities encountered in addressing these challenges, we explore the emerging field of synthetic biology, which is an exciting approach to validate and analyze theoretical system models directly through experimental synthesis, i.e. analysis-by-synthesis. The ultimate goal is to address the present and future challenges in reverse engineering biomolecular systems (REBMS) using integrated workflow of data mining, systems modeling and synthetic biology.

Methylation of histone H3 at lysine 4 (H3K4) is a conserved feature of active chromatin catalyzed by methyltransferases of the SET1-family (SET1A, SET1B, MLL1, MLL2, MLL3 and MLL4 in humans). These enzymes participate in diverse gene regulatory networks with a multitude of known biological functions, including direct involvement in several human disease states. Unlike most lysine methyltransferases, SET1-family enzymes are only fully active in the context of a multi-subunit complex, which includes a protein module comprised of WDR5, RbBP5, ASH2L and DPY-30 (WRAD). These proteins bind in close proximity to the catalytic SET domain of SET1-family enzymes and stimulate H3K4 methyltransferase activity. The mechanism by which WRAD promotes catalysis involves elements of allosteric control and possibly the utilization of a second H3K4 methyltransferase active site present within WRAD itself. WRAD components also engage in physical interactions that recruit SET1-family proteins to target sites on chromatin. Here, the known molecular mechanisms through which WRAD enables the function of SET1-related enzymes will be reviewed.

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field-the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome. © The Author 2012. Published by Oxford University Press.

The goal of the Gene Ontology (GO) project is to provide a uniform way to describe the functions of gene products from organisms across all kingdoms of life and thereby enable analysis of genomic data. Protein annotations are either based on experiments or predicted from protein sequences. Since most sequences have not been experimentally characterized, most available annotations need to be based on predictions. To make as accurate inferences as possible, the GO Consortium's Reference Genome Project is using an explicit evolutionary framework to infer annotations of proteins from a broad set of genomes from experimental annotations in a semi-automated manner. Most components in the pipeline, such as selection of sequences, building multiple sequence alignments and phylogenetic trees, retrieving experimental annotations and depositing inferred annotations, are fully automated. However, the most crucial step in our pipeline relies on software-assisted curation by an expert biologist. This curation tool, Phylogenetic Annotation and INference Tool (PAINT) helps curators to infer annotations among members of a protein family. PAINT allows curators to make precise assertions as to when functions were gained and lost during evolution and record the evidence (e.g. experimentally supported GO annotations and phylogenetic information including orthology) for those assertions. In this article, we describe how we use PAINT to infer protein function in a phylogenetic context with emphasis on its strengths, limitations and guidelines. We also discuss specific examples showing how PAINT annotations compare with those generated by other highly used homology-based methods.

Morpholino oligonucleotides (MOs) are an effective, gene-specific antisense knockdown technology used in many model systems. Here we describe the application of MOs in zebrafish (Danio rerio )f orin vivo functional characterization of gene activity. We summarize our screening experience beginning with gene target selection. We then discuss screening parameter considerations and data and database management. Finally, we emphasize the importance of off-target effect management and thorough downstream phenotypic validation. We discuss current morpholino limitations, including reduced stability when stored in aqueous solution. Advances in MO technology now provide a measure of spatiotemporal control over MO activity, presenting the opportunity for incorporating more finely tuned analyses into MO-based screening. Therefore, with careful management, MOs remain a valuable tool for discovery screening as well as individual gene knockdown analysis.

Meta-analyses that simultaneously compare multiple treatments (usually referred to as network meta-analyses or mixed treatment comparisons) are becoming increasingly common. An important component of a network meta-analysis is an assessment of the extent to which different sources of evidence are compatible, both substantively and statistically. A simple indirect comparison may be confounded if the studies involving one of the treatments of interest are fundamentally different from the studies involving the other treatment of interest. Here, we discuss methods for addressing inconsistency of evidence from comparative studies of different treatments. We define and review basic concepts of heterogeneity and inconsistency, and attempt to introduce a distinction between ‘loop inconsistency’ and ‘design inconsistency’. We then propose that the notion of design-by-treatment interaction provides a useful general framework for investigating inconsistency. In particular, using design-by-treatment interactions successfully addresses complications that arise from the presence of multi-arm trials in an evidence network. We show how the inconsistency model proposed by Lu and Ades is a restricted version of our full design-by-treatment interaction model and that there may be several distinct Lu–Ades models for any particular data set. We introduce novel graphical methods for depicting networks of evidence, clearly depicting multi-arm trials and illustrating where there is potential for inconsistency to arise. We apply various inconsistency models to data from trials of different comparisons among four smoking cessation interventions and show that models seeking to address loop inconsistency alone can run into problems. Copyright © 2012 John Wiley & Sons, Ltd.

Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple ‘tree-like’ mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.

A recent study examined the stability of rankings from random forests using two variable importance measures (mean decrease accuracy (MDA) and mean decrease Gini (MDG)) and concluded that rankings based on the MDG were more robust than MDA. However, studies examining data-specific characteristics on ranking stability have been few. Rankings based on the MDG measure showed sensitivity to within-predictor correlation and differences in category frequencies, even when the number of categories was held constant, and thus may produce spurious results. The MDA measure was robust to these data characteristics. Further, under strong within-predictor correlation, MDG rankings were less stable than those using MDA.

Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrell’s concordance index. For high-dimensional data applications, however, computing these measures as re-substitution statistics on the same data used for model development results in highly biased estimates. Most developments in methodology for survival risk modeling with high-dimensional data have utilized separate test data sets for model evaluation. Cross-validation has sometimes been used for optimization of tuning parameters. In many applications, however, the data available are too limited for effective division into training and test sets and consequently authors have often either reported re-substitution statistics or analyzed their data using binary classification methods in order to utilize familiar cross-validation. In this article we have tried to indicate how to utilize cross-validation for the evaluation of survival risk models; specifically how to compute cross-validated estimates of survival distributions for predicted risk groups and how to compute cross-validated time-dependent ROC curves. We have also discussed evaluation of the statistical significance of a survival risk model and evaluation of whether high-dimensional genomic data adds predictive accuracy to a model based on standard covariates alone.

Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and proteomic data. Cross-validation methods are often used to obtain estimates of classification accuracy, but both simulations and case studies suggest that, when inappropriate methods are used, bias may ensue. Bias can be bypassed and generalizability can be tested by external (independent) validation. We evaluated 35 studies that have reported on external validation of a molecular classifier. We extracted information on study design and methodological features, and compared the performance of molecular classifiers in internal cross-validation versus external validation for 28 studies where both had been performed. We demonstrate that the majority of studies pursued cross-validation practices that are likely to overestimate classifier performance. Most studies were markedly underpowered to detect a 20% decrease in sensitivity or specificity between internal cross-validation and external validation [median power was 36% (IQR, 21-61%) and 29% (IQR, 15-65%), respectively]. The median reported classification performance for sensitivity and specificity was 94% and 98%, respectively, in cross-validation and 88% and 81% for independent validation. The relative diagnostic odds ratio was 3.26 (95% CI 2.04-5.21) for cross-validation versus independent validation. Finally, we reviewed all studies (n = 758) which cited those in our study sample, and identified only one instance of additional subsequent independent validation of these classifiers. In conclusion, these results document that many cross-validation practices employed in the literature are potentially biased and genuine progress in this field will require adoption of routine external validation of molecular classifiers, preferably in much larger studies than in current practice.

Enhancers, silencer and insulators are DNA elements that play central roles in regulation of the genome that are crucial for development and differentiation. In metazoans, these elements are often separated from target genes by distances that can reach 100 Kb. How regulation can be accomplished over long distances has long been intriguing. Current data indicate that although the mechanisms by which these diverse regulatory elements affect gene transcription may vary, an underlying feature is the establishment of close contacts or chromatin loops. With the generalization of this principle, new questions emerge, such as how the close contacts are formed and stabilized and, importantly, how they contribute to the regulation of transcriptional output at target genes. This review will concentrate on examples where a functional role and a mechanistic understanding has been explored for loops formed between genes and their regulatory elements or among the elements themselves.

Chromatin modifications at both histones and DNA are critical for regulating gene expression. Mis-regulation of such epigenetic marks can lead to pathological states; indeed, cancer affecting the hematopoietic system is frequently linked to epigenetic abnormalities. Here, we discuss the different types of modifications and their general impact on transcription, as well as the polycomb group of proteins, which effect transcriptional repression and are often mis-regulated. Further, we discuss how chromosomal translocations leading to fusion proteins can aberrantly regulate gene transcription through chromatin modifications within the hematopoietic system. PML-RARa, AML1-ETO and MLL-fusions are examples of fusion proteins that mis-regulate epigenetic modifications (either directly or indirectly), which can lead to acute myeloblastic leukemia (AML). An in-depth understanding of the mechanisms behind the mis-regulation of epigenetic modifications that lead to the development and progression of AMLs could be critical for designing effective treatments.

The eukaryotic cell nucleus displays a high degree of spatial organization, with discrete functional subcompartments that provide microenvironments where specialized processes take place. Concordantly, the genome also adopts defined conformations that, in part, enable specific genomic regions to interface with these functional centers. Yet the roles of many subcompartments and the genomic regions that contact them have not been explored fully. More fundamentally, it is not entirely clear how genome organization impacts function, and vice versa. The past decade has witnessed the development of a new breed of methods that are capable of assessing the spatial organization of the genome. These stand to further our understanding of the relationship between genome structure and function, and potentially assign function to various nuclear subcompartments. Here, we review the principal techniques used for analyzing genomic interactions, the functional insights they have afforded and discuss the outlook for future advances in nuclear structure and function dynamics.

Advanced research requires intensive interaction among a multitude of actors, often possessing different expertise and usually working at a distance from each other. The field of collaborative research aims to establish suitable models and technologies to properly support these interactions. In this article, we first present the reasons for an interest of Bioinformatics in this context by also suggesting some research domains that could benefit from collaborative research. We then review the principles and some of the most relevant applications of social networking, with a special attention to networks supporting scientific collaboration, by also highlighting some critical issues, such as identification of users and standardization of formats. We then introduce some systems for collaborative document creation, including wiki systems and tools for ontology development, and review some of the most interesting biological wikis. We also review the principles of Collaborative Development Environments for software and show some examples in Bioinformatics. Finally, we present the principles and some examples of Learning Management Systems. In conclusion, we try to devise some of the goals to be achieved in the short term for the exploitation of these technologies.

International audience; The mechanisms regulating the coordinate activation of tens of thousands of replication origins in multicellular organisms remain poorly explored. Recent advances in genomics have provided valuable information about the sites at which DNA replication is initiated and the selection mechanisms of specific sites in both yeast and vertebrates. Studies in yeast have advanced to the point that it is now possible to develop convincing models for origin selection. A general model has emerged, but yeast data have also revealed an unsuspected diversity of strategies for origin positioning. We focus here on the ways in which chromatin structure may affect the formation of pre-replication complexes, a prerequisite for origin activation. We also discuss the need to exercise caution when trying to extrapolate yeast models directly to more complex vertebrate genomes.

Phylogenomic databases provide orthology predictions for species with fully sequenced genomes. Although the goal seems well-defined, the content of these databases differs greatly. Seven ortholog databases (Ensembl Compara, eggNOG, HOGENOM, InParanoid, OMA, OrthoDB, Panther) were compared on the basis of reference trees. For three well-conserved protein families, we observed a generally high specificity of orthology assignments for these databases. We show that differences in the completeness of predicted gene relationships and in the phylogenetic information are, for the great majority, not due to the methods used, but to differences in the underlying database concepts. According to our metrics, none of the databases provides a fully correct and comprehensive protein classification. Our results provide a framework for meaningful and systematic comparisons of phylogenomic databases. In the future, a sustainable set of ‘Gold standard’ phylogenetic trees could provide a robust method for phylogenomic databases to assess their current quality status, measure changes following new database releases and diagnose improvements subsequent to an upgrade of the analysis procedure., Briefings in Bioinformatics, 12 (5), ISSN:1467-5463, ISSN:1477-4054

Recent development of deep sequencing technologies has facilitated de novo genome sequencing projects, now conducted even by individual laboratories. However, this will yield more and more genome sequences that are not well assembled, and will hinder thorough annotation when no closely related reference genome is available. One of the challenging issues is the identification of protein-coding sequences split into multiple unassembled genomic segments, which can confound orthology assignment and various laboratory experiments requiring the identification of individual genes. In this study, using the genome of a cartilaginous fish, Callorhinchus milii, as test case, we performed gene prediction using a model specifically trained for this genome. We implemented an algorithm, designated ESPRIT, to identify possible linkages between multiple protein-coding portions derived from a single genomic locus split into multiple unassembled genomic segments. We developed a validation framework based on an artificially fragmented human genome, improvements between early and recent mouse genome assemblies, comparison with experimentally validated sequences from GenBank, and phylogenetic analyses. Our strategy provided insights into practical solutions for efficient annotation of only partially sequenced (low-coverage) genomes. To our knowledge, our study is the first formulation of a method to link unassembled genomic segments based on proteomes of relatively distantly related species as references., Briefings in Bioinformatics, 12 (5), ISSN:1467-5463, ISSN:1477-4054

The epigenome plays the pivotal role as interface between genome and environment. True genome-wide assessments of epigenetic marks, such as DNA methylation (methylomes) or chromatin modifications (chromatinomes), are now possible, either through high-throughput arrays or increasingly by second-generation DNA sequencing methods. The ability to collect these data at this level of resolution enables us to begin to be able to propose detailed questions, and interrogate this information, with regards to changes that occur due to development, lineage and tissue-specificity, and significantly those caused by environmental influence, such as ageing, stress, diet, hormones or toxins. Common complex traits are under variable levels of genetic influence and additionally epigenetic effect. The detection of pathological epigenetic alterations will reveal additional insights into their aetiology and how possible environmental modulation of this mechanism may occur. Due to the reversibility of these marks, the potential for sequence-specific targeted therapeutics exists. This review surveys recent epigenomic advances and their current and prospective application to the study of common diseases.

Protein kinase phosphorylation is central to the regulation and control of protein and cellular function. Over the past decade, the development of many high-throughput approaches has revolutionized the understanding of protein phosphorylation and allowed rapid and unbiased surveys of phosphoproteins and phosphorylation events. In addition to this technological advancement, there have also been computational improvements; recent studies on network models of protein phosphorylation have provided many insights into the cellular processes and pathways regulated by phosphorylation. This article gives an overview of experimental and computational techniques for identifying and analyzing protein phosphorylation on a systems level.

While initiation of transcription has attracted the most attention in the field of gene regulation, it has become clear that additional stages in the gene expression cascade including post-transcriptional events are under equally exquisite control. The seminal discovery that short RNAs (microRNA, small interfering RNA, Piwi-interacting RNA), play important roles in repressing gene expression has spurred a rush of new interest in post-transcriptional gene silencing mechanisms. The development of affinity tags and high-resolution tandem mass spectrometry (MS/MS) has greatly simplified the analysis of proteins that regulate gene expression. Further, the use of DNA microarrays and ‘second generation’ nucleic acid sequencing (‘deep sequencing’) technologies has facilitated the identification of their regulatory targets. These technological advancements mark a significant step towards a comprehensive understanding of gene regulatory networks. The purpose of this review is to highlight several recent reports that illustrate the value of affinity-purification (immunoprecipitation) followed by mass spectrometric protein analysis and nucleic acid analysis by deep sequencing (AP-MS/Seq) to examine mRNA after it has been transcribed. The ability to identify the direct nucleic acid targets of post-transcriptional gene regulatory machines is a critical first step towards understanding the contribution of post-transcriptional pathways on gene expression.

Our understanding of the genetic mechanisms of organismal aging has advanced dramatically during the past two decades. With the development of large-scale RNAi screens, the last few years saw the remarkable identifications of hundreds of new longevity genes in the roundworm Caenorhabditis elegans. The various RNAi screens revealed many biological pathways previously unknown to be related to aging. In this review, we focus on findings from the recent large-scale RNAi longevity screens, and discuss insights they have provided into the complex biological process of aging and considerations of the RNAi technology will continue to have on the future development of the aging field.

Differential gene expression plays a critical role in the development and physiology of multicellular organisms. At a ‘systems level’ (e.g. at the level of a tissue, organ or whole organism), this process can be studied using gene regulatory network (GRN) models that capture physical and regulatory interactions between genes and their regulators. In the past years, significant progress has been made toward the mapping of GRNs using a variety of experimental and computational approaches. Here, we will discuss gene-centered approaches that we employed to characterize GRNs and describe insights that we have obtained into the global design principles of gene regulation in complex metazoan systems.

Non-coding RNA (ncRNA) transcripts are RNA molecules that do not code for proteins, but elicit function by other mechanisms. The vast majority of RNA produced in a cell is non-coding ribosomal RNA, produced from relatively few loci, however more recently complementary DNA (cDNA) cloning, tag sequencing, and genome tiling array studies suggest that ncRNAs also account for the majority of RNA species produced by a cell. ncRNA based regulation has been referred to as a ‘hidden layer’ of signals or ‘dark matter’ that control gene expression in cellular processes by poorly described mechanisms. These terms have appeared as ncRNAs until recently have been ignored by expression profiling and cDNA annotation projects and their mode of action is diverse (e.g. influencing chromatin structure and epigenetics, translational silencing, transcriptional silencing). Here, we highlight recent functional genomics strategies toward identifying and assigning function to ncRNA transcription.