Temporal graphical models for cross-species gene regulatory network discovery

Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causality relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L(1) penalty to uncover the regulatory network structure for different species. The algorithm provides a framework for sharing information across species via hidden component graphs and is able to incorporate domain knowledge across species easily. We demonstrate our method on two synthetic datasets and apply it to discover causal graphs from innate immune response data.     

The emerging role for rat models in gene discovery

Rat models have been used for many decades to study physiological and pathophysiological mechanisms. Prior to the release of the rat genome and new technologies for targeting gene manipulation, the rat had been the underdog in the genomics era, despite the abundance of physiological data compared to the mouse. The overarching goal of biomedical research is to improve health and advance medical science. Translating human disease gene discovery and validation in the rat, through the use of emerging technologies and integrated tools and databases, is providing power to understand the genetics, environmental influences, and biology of disease. In this review we briefly outline the rat models, bioinformatics tools, and technologies that are changing the landscape of translational research. The strategies used to translate disease traits to genes to function, and, ultimately, to improve human health is discussed. Finally, our perspective on how rat models will continue to positively impact biomedical research is provided.     

False discovery rate, sensitivity and sample size for microarray studies

MOTIVATION: In microarray data studies most researchers are keenly aware of the potentially high rate of false positives and the need to control it. One key statistical shift is the move away from the well-known P-value to false discovery rate (FDR). Less discussion perhaps has been spent on the sensitivity or the associated false negative rate (FNR). The purpose of this paper is to explain in simple ways why the shift from P-value to FDR for statistical assessment of microarray data is necessary, to elucidate the determining factors of FDR and, for a two-sample comparative study, to discuss its control via sample size at the design stage. RESULTS: We use a mixture model, involving differentially expressed (DE) and non-DE genes, that captures the most common problem of finding DE genes. Factors determining FDR are (1) the proportion of truly differentially expressed genes, (2) the distribution of the true differences, (3) measurement variability and (4) sample size. Many current small microarray studies are plagued with large FDR, but controlling FDR alone can lead to unacceptably large FNR. In evaluating a design of a microarray study, sensitivity or FNR curves should be computed routinely together with FDR curves. Under certain assumptions, the FDR and FNR curves coincide, thus simplifying the choice of sample size for controlling the FDR and FNR jointly.     

False discovery rate control in two-stage designs

ABSTRACT: BACKGROUND: For gene expression or gene association studies with a large number of hypotheses the number of measurements per marker in a conventional single-stage design is often low due to limited resources. Two-stage designs have been proposed where in a first stage promising hypotheses are identified and further investigated in the second stage with larger sample sizes. For two types of two-stage designs proposed in the literature we derive multiple testing procedures controlling the False Discovery Rate (FDR) demonstrating FDR control by simulations: designs where a fixed number of top-ranked hypotheses are selected and designs where the selection in the interim analysis is based on an FDR threshold. In contrast to earlier approaches which use only the second-stage data in the hypothesis tests (pilot approach), the proposed testing procedures are based on the pooled data from both stages (integrated approach). Results: For both selection rules the multiple testing procedures control the FDR in the considered simulation scenarios. This holds for the case of independent observations across hypotheses as well as for certain correlation structures. Additionally, we show that in scenarios with small effect sizes the testing procedures based on the pooled data from both stages can give a considerable improvement in power compared to tests based on the second-stage data only. Conclusion: The proposed hypothesis tests provide a tool for FDR control for the considered two-stage designs. Comparing the integrated approaches for both selection rules with the corresponding pilot approaches showed an advantage of the integrated approach in many simulation scenarios.     

False discovery rate in linkage and association genome screens for complex disorders

We explore the implications of the false discovery rate (FDR) controlling procedure in disease gene mapping. With the aid of simulations, we show how, under models commonly used, the simple step-down procedure introduced by Benjamini and Hochberg controls the FDR for the dependent tests on which linkage and association genome screens are based. This adaptive multiple comparison procedure may offer an important tool for mapping susceptibility genes for complex diseases.     

False discovery rate estimation for cross-linked peptides identified by mass spectrometry

The mass spectrometric identification of chemically cross-linked peptides (CXMS) specifies spatial restraints of protein complexes; these values complement data obtained from common structure-determination techniques. Generic methods for determining false discovery rates of cross-linked peptide assignments are currently lacking, thus making data sets from CXMS studies inherently incomparable. Here we describe an automated target-decoy strategy and the software tool xProphet, which solve this problem for large multicomponent protein complexes.     

Genome-wide approaches for cancer gene discovery

One of the central aims of cancer research is to identify and characterize cancer-causing alterations in cancer genomes. In recent years, unprecedented advances in genome-wide sequencing, functional genomics technologies for RNA interference screens and methods for evaluating three-dimensional chromatin organization in vivo have resulted in important discoveries regarding human cancer. The cancer-causing genes identified from these new genome-wide technologies have also provided opportunities for effective and personalized cancer therapy. In this review, we describe some of the most recent technologies for cancer gene discovery. We also provide specific examples in which these technologies have proven remarkably successful in uncovering important cancer-causing alterations.     

False discovery rates and related statistical concepts in mass spectrometry-based proteomics

Development of statistical methods for assessing the significance of peptide assignments to tandem mass spectra obtained using database searching remains an important problem. In the past several years, several different approaches have emerged, including the concept of expectation values, target-decoy strategy, and the probability mixture modeling approach of PeptideProphet. In this work, we provide a background on statistical significance analysis in the field of mass spectrometry-based proteomics, and present our perspective on the current and future developments in this area.     

Genomics tools for QTL analysis and gene discovery

In recent years, several new genomics resources and tools have become available that will greatly assist quantitative trait locus (QTL) mapping and cloning of the corresponding genes. Genome sequences, tens of thousands of molecular markers, microarrays, and knock-out collections are being applied to QTL mapping, facilitating the use of natural accessions for gene discovery.     

DIG--a system for gene annotation and functional discovery

SUMMARY: We describe a database and information discovery system named DIG (Duke Integrated Genomics) designed to facilitate the process of gene annotation and the discovery of functional context. The DIG system collects and organizes gene annotation and functional information, and includes tools that support an understanding of genes in a functional context by providing a framework for integrating and visualizing gene expression, protein interaction and literature-based interaction networks.     

Validation and discovery from computational biology models

Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analysis software is reliable and that techniques exist for automating the analysis of the vast amounts of data which such simulation environments generate. A rigorous approach to the development of complex modelling software is needed. Such a framework is presented here together with techniques for the automated analysis of such models and a process for the automatic discovery of biological phenomena from large simulation data sets. Illustrations are taken from a major systems biology research project involving the in vitro investigation, modelling and simulation of epithelial tissue.     

Gene expression monitoring for gene discovery in models of peripheral and central nervous system differentiation, regeneration, and trauma

Gene expression monitoring using gene expression microarrays represents an extremely powerful technology for gene discovery in a variety of systems. We describe the results of seven experiments using Incyte GEM technology to compile a proprietary portfolio of data concerning differential gene expression in six different models of neuronal differentiation and regeneration, and recovery from injury or disease. Our first two experiments cataloged genes significantly up- or down-regulated during two phases of the retinoic acid-induced differentiation of the embryonal carcinoma line Ntera-2. To identify genes involved in neuronal regeneration we performed three GEM experiments, which included changes in gene expression in rat dorsal root ganglia during the healing of experimentally injured sciatic nerve, in regenerating neonatal opossum spinal cord, and during lipopolysaccharide stimulation of primary cultures of rat Schwann cells. Finally we have monitored genes involved in the recovery phase of the inflammatory disease of the rat spinal cord, experimental allergic encephalomyelitis, as well as those responsible for protection from oxidative stress in a glutamate-resistant rat hippocampal cell line. Analysis of the results of the approximately 70,000 data points collected is presented.     

Phosphate/pyrophosphate and MV-related proteins in mineralisation: discoveries from mouse models

During the process of matrix vesicle (MV)-mediated initiation of mineralisation, chondrocytes and osteoblasts mineralise the extracellular matrix by promoting the seeding of basic calcium phosphate crystals of hydroxyapatite (HA) along the collagen fibrils. This orchestrated process is carefully regulated by the balanced action of propagators and inhibitors of calcification. The primary antagonistic regulators of extracellular matrix mineralisation are phosphate (Pi) and inorganic pyrophosphate (PPi). Studies in mouse models and in humans have established critical roles for Pi/PPi homeostasis in biomineralisation. In this review, we present the regulators of Pi/PPi, as derived from animal models, and discuss their clinical relevance to physiological and pathological mineralisation.     

Breast cancer gene discovery

Many important advances have been made in the past decade in understanding breast cancer at the molecular level, and two important high-penetrance breast cancer genes--BRCA1 and BRCA2--have been identified. However, germline mutations in these two genes are responsible for only a minority (approximately 5%) of all breast carcinomas, and the genes responsible for the majority of breast cancer cases remain to be identified. There is evidence that there are additional high-to-moderate-penetrance breast cancer susceptibility genes but, given the high degree of molecular heterogeneity in breast carcinomas, it is likely that each of these genes is responsible for only a subset of cases. There are also many candidate low-penetrance breast cancer genes and many more are likely to be identified. In addition to germline, and somatic, sequence alterations, epigenetic changes in many genes are likely to play an important role in the pathobiology of breast cancer. Recently developed genomic technologies and the completion of the human genome sequence provide us with powerful tools to identify novel candidate breast cancer genes that could play an important role in breast tumourigenesis.     

Pooled library tissue tags for EST-based gene discovery

MOTIVATION: In gene discovery projects based on EST sequencing, effective post-sequencing identification methods are important in determining tissue sources of ESTs within pooled cDNA libraries. In the past, such identification efforts have been characterized by higher than necessary failure rates due to the presence of errors within the subsequence containing the oligo tag intended to define the tissue source for each EST. RESULTS: A large-scale EST-based gene discovery program at The University of Iowa has led to the creation of a unique software method named UITagCreator usable in the creation of large sets of synthetic tissue identification tags. The identification tags provide error detection and correction capability and, in conjunction with automated annotation software, result in a substantial improvement in the accurate identification of the tissue source in the presence of sequencing and base-calling errors. These identification rates are favorable, relative to past paradigms. AVAILABILITY: The UITagCreator source code and installation instructions, along with detection software usable in concert with created tag sets, is freely available at http://genome.uiowa.edu/pubsoft/software.html CONTACT: tomc@eng.uiowa.edu     

Second-generation sequencing for gene discovery in the Brassicaceae

The Brassicaceae contains the most diverse collection of agriculturally important crop species of all plant families. Yet, this is one of the few families that do not form functional symbiotic associations with mycorrhizal fungi in the soil for improved nutrient acquisition. The genes involved in this symbiosis were more recently recruited by legumes for symbiotic association with nitrogen-fixing rhizobia bacteria. This study applied second-generation sequencing (SGS) and analysis tools to discover that two such genes, NSP1 (Nodulation Signalling Pathway 1) and NSP2, remain conserved in diverse members of the Brassicaceae despite the absence of these symbioses. We demonstrate the utility of SGS data for the discovery of putative gene homologs and their analysis in complex polyploid crop genomes with little prior sequence information. Furthermore, we show how this data can be applied to enhance downstream reverse genetics analyses. We hypothesize that Brassica NSP genes may function in the root in other plant-microbe interaction pathways that were recruited for mycorrhizal and rhizobial symbioses during evolution.