PosMed (Positional Medline): prioritizing genes with an artificial neural network comprising medical documents to accelerate positional cloning

Gain-of-function mutations in a member of the Src family kinases cause autoinflammatory bone disease in mice and humans

Chronic recurrent multifocal osteomyelitis (CRMO) is an autoinflammatory bone disease that presents with bone destruction occurring primarily in children. In a mouse ENU mutagenesis screen, the Ali18 strain was isolated because of spontaneous inflammation in the joints and bones. Sequencing candidate genes in the Ali18 critical region identified a missense mutation in the C-terminal regulatory region of the Src family kinase (SFK) member, Fgr. Genome editing revealed Fgr dependency of the inflammatory phenotype in Ali18 mice. Further, whole exome sequencing in our CRMO cohort identified two patients with missense mutations in FGR. In vitro functional assays confirm altered protein function. This work identifies FGR as a CRMO susceptibility gene and suggests that targeting SFKs may be useful in its treatment.

Autoinflammatory syndromes are characterized by dysregulation of the innate immune response with subsequent episodes of acute spontaneous inflammation. Chronic recurrent multifocal osteomyelitis (CRMO) is an autoinflammatory bone disorder that presents with bone pain and localized swelling. Ali18 mice, isolated from a mutagenesis screen, exhibit a spontaneous inflammatory paw phenotype that includes sterile osteomyelitis and systemic reduced bone mineral density. To elucidate the molecular basis of the disease, positional cloning of the causative gene for Ali18 was attempted. Using a candidate gene approach, a missense mutation in the C-terminal region of Fgr, a member of Src family tyrosine kinases (SFKs), was identified. For functional confirmation, additional mutations at the N terminus of Fgr were introduced in Ali18 mice by CRISPR/Cas9-mediated genome editing. N-terminal deleterious mutations of Fgr abolished the inflammatory phenotype in Ali18 mice, but in-frame and missense mutations in the same region continue to exhibit the phenotype. The fact that Fgr null mutant mice are morphologically normal suggests that the inflammation in this model depends on Fgr products. Furthermore, the levels of C-terminal negative regulatory phosphorylation of Fgr^Ali18 are distinctly reduced compared with that of wild-type Fgr. In addition, whole-exome sequencing of 99 CRMO patients including 88 trios (proband and parents) identified 13 patients with heterozygous coding sequence variants in FGR, including two missense mutant proteins that affect kinase activity. Our results strongly indicate that gain-of-function mutations in Fgr are involved in sterile osteomyelitis, and thus targeting SFKs using specific inhibitors may allow for efficient treatment of the disease.

Scuba: scalable kernel-based gene prioritization

BACKGROUND

The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability.

RESULTS

We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods.

CONCLUSIONS

Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

Hyperactivation of JAK1 tyrosine kinase induces stepwise, progressive pruritic dermatitis

Skin homeostasis is maintained by the continuous proliferation and differentiation of epidermal cells. The skin forms a strong but flexible barrier against microorganisms as well as physical and chemical insults; however, the physiological mechanisms that maintain this barrier are not fully understood. Here, we have described a mutant mouse that spontaneously develops pruritic dermatitis as the result of an initial defect in skin homeostasis that is followed by induction of a Th2-biased immune response. These mice harbor a mutation that results in a single aa substitution in the JAK1 tyrosine kinase that results in hyperactivation, thereby leading to skin serine protease overexpression and disruption of skin barrier function. Accordingly, treatment with an ointment to maintain normal skin barrier function protected mutant mice from dermatitis onset. Pharmacological inhibition of JAK1 also delayed disease onset. Together, these findings indicate that JAK1-mediated signaling cascades in skin regulate the expression of proteases associated with the maintenance of skin barrier function and demonstrate that perturbation of these pathways can lead to the development of spontaneous pruritic dermatitis.

A Rb1 promoter variant with reduced activity contributes to osteosarcoma susceptibility in irradiated mice

BACKGROUND

Syndromic forms of osteosarcoma (OS) account for less than 10% of all recorded cases of this malignancy. An individual OS predisposition is also possible by the inheritance of low penetrance alleles of tumor susceptibility genes, usually without evidence of a syndromic condition. Genetic variants involved in such a non-syndromic form of tumor predisposition are difficult to identify, given the low incidence of osteosarcoma cases and the genetic heterogeneity of patients. We recently mapped a major OS susceptibility QTL to mouse chromosome 14 by comparing alpha-radiation induced osteosarcoma in mouse strains which differ in their tumor susceptibility.

METHODS

Tumor-specific allelic losses in murine osteosacoma were mapped along chromosome 14 using microsatellite markers and SNP allelotyping. Candidate gene search in the mapped interval was refined using PosMed data mining and mRNA expression analysis in normal osteoblasts. A strain-specific promoter variant in Rb1 was tested for its influence on mRNA expression using reporter assay.

RESULTS

A common Rb1 allele derived from the BALB/cHeNhg strain was identified as the major determinant of radiation-induced OS risk at this locus. Increased OS-risk is linked with a hexanucleotide deletion in the promoter region which is predicted to change WT1 and SP1 transcription factor-binding sites. Both in-vitro reporter and in-vivo expression assays confirmed an approx. 1.5 fold reduced gene expression by this promoter variant. Concordantly, the 50% reduction in Rb1 expression in mice bearing a conditional hemizygous Rb1 deletion causes a significant rise of OS incidence following alpha-irradiation.

CONCLUSION

This is the first experimental demonstration of a functional and genetic link between reduced Rb1 expression from a common promoter variant and increased tumor risk after radiation exposure. We propose that a reduced Rb1 expression by common variants in regulatory regions can modify the risk for a malignant transformation of bone cells after radiation exposure.

Integrative literature and data mining to rank disease candidate genes

While the genomics-derived discoveries promise benefits to basic research and health care, the speed and affordability of sequencing following recent technological advances has further aggravated the data deluge. Seamless integration of the ever-increasing clinical, genomic, and experimental data and efficient mining for knowledge extraction, delivering actionable insight and generating testable hypotheses are therefore critical for the needs of biomedical research. For instance, high-throughput techniques are frequently applied to detect disease candidate genes. Experimental validation of these candidates however is both time-consuming and expensive. Hence, several computational approaches based on literature and data mining have been developed to identify the most promising candidates for follow-up studies. Based on "guilt by association" principle, most of these methods use prior knowledge about a disease of interest to discover and rank novel candidate genes. In this chapter, we provide a brief overview of recent advances made in literature- and data-mining-based approaches for candidate gene prioritization. As a case study, we focus on a Web-based computational approach that uses integrated heterogeneous data sources including gene-literature associations for ranking disease candidate genes and explain how to run typical queries using this system.

Candidate gene identification for systemic lupus erythematosus using network centrality measures and gene ontology

Systemic lupus erythematosus (SLE) commonly accredited as “the great imitator” is a highly complex disease involving multiple gene susceptibility with non-specific symptoms. Many experimental and computational approaches have been used to investigate the disease related candidate genes. But the limited knowledge of gene function and disease correlation and also lack of complete functional details about the majority of genes in susceptible locus, encumbrances the identification of SLE related candidate genes. In this paper, we have studied the human immunome network (undirected) using various graph theoretical centrality measures integrated with the gene ontology terms to predict the new candidate genes. As a result, we have identified 8 candidate genes, which may act as potential targets for SLE disease. We have also carried out the same analysis by replacing the human immunome network with human immunome signaling network (directed) and as an outcome we have obtained 5 candidate genes as potential targets for SLE disease. From the comparison study, we have found these two approaches are complementary in nature.

Eeyore: a novel mouse model of hereditary deafness

Animal models that recapitulate human disease are proving to be an invaluable tool in the identification of novel disease-associated genes. These models can improve our understanding of the complex genetic mechanisms involved in disease and provide a basis to guide therapeutic strategies to combat these conditions. We have identified a novel mouse model of non-syndromic sensorineural hearing loss with linkage to a region on chromosome 18. Eeyore mutant mice have early onset progressive hearing impairment and show abnormal structure of the sensory epithelium from as early as 4 weeks of age. Ultrastructural and histological analyses show irregular hair cell structure and degeneration of the sensory hair bundles in the cochlea. The identification of new genes involved in hearing is central to understanding the complex genetic pathways involved in the hearing process and the loci at which these pathways are interrupted in people with a genetic hearing loss. We therefore discuss possible candidate genes within the linkage region identified in eeyore that may underlie the deafness phenotype in these mice. Eeyore provides a new model of hereditary sensorineural deafness and will be an important tool in the search for novel deafness genes.

Progress in detecting genetic alterations and their association with human disease

The completion of the Human Genome Project provided a reference sequence to which researchers could compare sequences from individual patients in the hope of identifying disease-causing mutations. However, this still necessitated candidate gene testing or a very limited screen of multiple genes using Sanger sequencing. With the advent of high-throughput Sanger sequencing, it became possible to screen hundreds of patients for alterations in hundreds of genes. This process was time consuming and limited to a few locations/institutions that had the space to house tens of sequencing equipment. The development of next generation sequencing revolutionized the process. It is now feasible to sequence the entire exome of multiple individuals in about 10 days. However, this meant that a massive amount of data needed to be filtered to identify the relevant alteration. This is presently the rate-limiting step in providing a convincing association between a genetic alteration and a human disorder.

PosMed: Ranking genes and bioresources based on Semantic Web Association Study

Positional MEDLINE (PosMed; http://biolod.org/PosMed) is a powerful Semantic Web Association Study engine that ranks biomedical resources such as genes, metabolites, diseases and drugs, based on the statistical significance of associations between user-specified phenotypic keywords and resources connected directly or inferentially through a Semantic Web of biological databases such as MEDLINE, OMIM, pathways, co-expressions, molecular interactions and ontology terms. Since 2005, PosMed has long been used for in silico positional cloning studies to infer candidate disease-responsible genes existing within chromosomal intervals. PosMed is redesigned as a workbench to discover possible functional interpretations for numerous genetic variants found from exome sequencing of human disease samples. We also show that the association search engine enhances the value of mouse bioresources because most knockout mouse resources have no phenotypic annotation, but can be associated inferentially to phenotypes via genes and biomedical documents. For this purpose, we established text-mining rules to the biomedical documents by careful human curation work, and created a huge amount of correct linking between genes and documents. PosMed associates any phenotypic keyword to mouse resources with 20 public databases and four original data sets as of May 2013.

An unbiased evaluation of gene prioritization tools

MOTIVATION

Gene prioritization aims at identifying the most promising candidate genes among a large pool of candidates-so as to maximize the yield and biological relevance of further downstream validation experiments and functional studies. During the past few years, several gene prioritization tools have been defined, and some of them have been implemented and made available through freely available web tools. In this study, we aim at comparing the predictive performance of eight publicly available prioritization tools on novel data. We have performed an analysis in which 42 recently reported disease-gene associations from literature are used to benchmark these tools before the underlying databases are updated.

RESULTS

Cross-validation on retrospective data provides performance estimate likely to be overoptimistic because some of the data sources are contaminated with knowledge from disease-gene association. Our approach mimics a novel discovery more closely and thus provides more realistic performance estimates. There are, however, marked differences, and tools that rely on more advanced data integration schemes appear more powerful.

CONTACT

yves.moreau@esat.kuleuven.be

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

GPSy: a cross-species gene prioritization system for conserved biological processes--application in male gamete development

We present gene prioritization system (GPSy), a cross-species gene prioritization system that facilitates the arduous but critical task of prioritizing genes for follow-up functional analyses. GPSy’s modular design with regard to species, data sets and scoring strategies enables users to formulate queries in a highly flexible manner. Currently, the system encompasses 20 topics related to conserved biological processes including male gamete development discussed in this article. The web server-based tool is freely available at http://gpsy.genouest.org.

GPEC: a Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection

Finding genes associated with a disease is an important issue in the biomedical area and many gene prioritization methods have been proposed for this goal. Among these, network-based approaches are recently proposed and outperformed functional annotation-based ones. Here, we introduce a novel Cytoscape plug-in, GPEC, to help identify putative genes likely to be associated with specific diseases or pathways. In the plug-in, gene prioritization is performed through a random walk with restart algorithm, a state-of-the art network-based method, along with a gene/protein relationship network. The plug-in also allows users efficiently collect biomedical evidence for highly ranked candidate genes. A set of known genes, candidate genes and a gene/protein relationship network can be provided in a flexible way.

The genetic control of avascular area in mouse oxygen-induced retinopathy

PURPOSE

The C57BL/6ByJ and BALB/cByJ inbred strains of mice are, respectively, susceptible and resistant to oxygen-induced retinopathy (OIR). The purpose of this work was to investigate the genetic control of the retinal avascular area in mouse OIR using a mapping cross.

METHODS

The central retinal avascular area was measured on postnatal day 16 (P16) in C57BL/6ByJ, BALB/cByJ, 101 (C57BL/6ByJ x BALB/cByJ)F₂, and 116 (BALB/cByJ x C57BL/6ByJ)F₂ mice that had been subjected to the OIR protocol. A genome-wide scan was performed of selected albino and non-albino mice to determine quantitative trait loci associated with weight and avascular area.

RESULTS

C57BL/6ByJ mice had significantly larger avascular areas than BALB/cByJ ones. Albino mice of the F₂ generation had smaller avascular areas than the non-albino mice. Genotyping was performed at 856 informative single nucleotide polymorphisms approximately evenly distributed across the genome from each of 85 selected F₂ mice. Weight, sex, and the paternal grandmother were found to act as additive covariates associated with the avascular area on P16; mapping analyses that used a model incorporating these covariates found a quantitative trait locus on chromosome 7 related to avascular area. Mapping analyses that used a model that did not incorporate covariates found a quantitative trait locus on chromosome 9 related to avascular area. A quantitative trait locus for bodyweight on P16 was mapped to chromosome 5.

CONCLUSIONS

The retinal avascular area in the mouse OIR model is under genetic control. Revascularization in OIR is related to the weight, strain of paternal grandmother, sex, and albinism. Our data support the existence of a quantitative trait locus on chromosome 5 that influences weight after exposure to hyperoxia, as well as quantitative trait loci on chromosomes 7 and 9 that modify susceptibility to OIR.

Development and function of invariant natural killer T cells producing T(h)2- and T(h)17-cytokines

Four distinct subsets of invariant natural killer T (NKT) cells are shown to differentiate in the thymus, then migrate to peripheral tissues where they retain their phenotypic and functional characteristics.

There is heterogeneity in invariant natural killer T (iNKT) cells based on the expression of CD4 and the IL-17 receptor B (IL-17RB), a receptor for IL-25 which is a key factor in T_H2 immunity. However, the development pathway and precise function of these iNKT cell subtypes remain unknown. IL-17RB⁺iNKT cells are present in the thymic CD44^+/− NK1.1⁻ population and develop normally even in the absence of IL-15, which is required for maturation and homeostasis of IL-17RB⁻iNKT cells producing IFN-γ. These results suggest that iNKT cells contain at least two subtypes, IL-17RB⁺ and IL-17RB⁻ subsets. The IL-17RB⁺iNKT subtypes can be further divided into two subtypes on the basis of CD4 expression both in the thymus and in the periphery. CD4⁺ IL-17RB⁺iNKT cells produce T_H2 (IL-13), T_H9 (IL-9 and IL-10), and T_H17 (IL-17A and IL-22) cytokines in response to IL-25 in an E4BP4-dependent fashion, whereas CD4⁻ IL-17RB⁺iNKT cells are a retinoic acid receptor-related orphan receptor (ROR)γt⁺ subset producing T_H17 cytokines upon stimulation with IL-23 in an E4BP4-independent fashion. These IL-17RB⁺iNKT cell subtypes are abundantly present in the lung in the steady state and mediate the pathogenesis in virus-induced airway hyperreactivity (AHR). In this study we demonstrated that the IL-17RB⁺iNKT cell subsets develop distinct from classical iNKT cell developmental stages in the thymus and play important roles in the pathogenesis of airway diseases.

T cells are a diverse group of immune cells involved in cell-mediated acquired immunity. One subset of T cells is the innate-like invariant natural killer T (iNKT) cells that recognize glycolipid ligands on target cells instead of peptides. We know that functionally distinct subtypes of iNKT cells are involved in specific pathologies, yet their development, phenotypes, and functions are not well understood. Here, we determine the relationship between various mouse iNKT cell subsets, identify reliable molecular markers for these subsets, and show that these contribute to their functional differences. We identify four iNKT cell subsets that we show arise via different developmental pathways and exhibit different cytokine profiles. Importantly, we show that these subsets can be isolated from the thymus (the organ of all T cells), as well as from peripheral tissues such as spleen, liver, lung, and lymph nodes. Contrary to the general understanding that iNKT cells mature after their exit from the thymus and their migration into peripheral tissues, we conclude that distinct phenotypic and functional iNKT cell subsets can be distinguished in the thymus by virtue of the presence or absence of the cytokine receptor IL-17RB and another cell surface molecule called CD4, and these subsets then migrate to peripheral tissues where they retain their phenotypic and functional characteristics. Regarding functional significance, we show that those iNKT cell subsets that lead to airway hyper-responsiveness to respiratory viruses are different to those that lead to allergen-induced airway hyperreactivity, which will enable researchers to focus on specific subsets as potential targets for therapeutic intervention.

Bioinformatics for personal genome interpretation

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.

pubmed2ensembl: a resource for mining the biological literature on genes

BACKGROUND

The last two decades have witnessed a dramatic acceleration in the production of genomic sequence information and publication of biomedical articles. Despite the fact that genome sequence data and publications are two of the most heavily relied-upon sources of information for many biologists, very little effort has been made to systematically integrate data from genomic sequences directly with the biological literature. For a limited number of model organisms dedicated teams manually curate publications about genes; however for species with no such dedicated staff many thousands of articles are never mapped to genes or genomic regions.

METHODOLOGY/PRINCIPAL FINDINGS

To overcome the lack of integration between genomic data and biological literature, we have developed pubmed2ensembl (http://www.pubmed2ensembl.org), an extension to the BioMart system that links over 2,000,000 articles in PubMed to nearly 150,000 genes in Ensembl from 50 species. We use several sources of curated (e.g., Entrez Gene) and automatically generated (e.g., gene names extracted through text-mining on MEDLINE records) sources of gene-publication links, allowing users to filter and combine different data sources to suit their individual needs for information extraction and biological discovery. In addition to extending the Ensembl BioMart database to include published information on genes, we also implemented a scripting language for automated BioMart construction and a novel BioMart interface that allows text-based queries to be performed against PubMed and PubMed Central documents in conjunction with constraints on genomic features. Finally, we illustrate the potential of pubmed2ensembl through typical use cases that involve integrated queries across the biomedical literature and genomic data.

CONCLUSION/SIGNIFICANCE

By allowing biologists to find the relevant literature on specific genomic regions or sets of functionally related genes more easily, pubmed2ensembl offers a much-needed genome informatics inspired solution to accessing the ever-increasing biomedical literature.

PINTA: a web server for network-based gene prioritization from expression data

PINTA (available at http://www.esat.kuleuven.be/pinta/; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes based on the differential expression of their neighborhood in a genome-wide protein–protein interaction network. Our strategy is meant for biological and medical researchers aiming at identifying novel disease genes using disease specific expression data. PINTA supports both candidate gene prioritization (starting from a user defined set of candidate genes) as well as genome-wide gene prioritization and is available for five species (human, mouse, rat, worm and yeast). As input data, PINTA only requires disease specific expression data, whereas various platforms (e.g. Affymetrix) are supported. As a result, PINTA computes a gene ranking and presents the results as a table that can easily be browsed and downloaded by the user.

Intersubspecific subcongenic mouse strain analysis reveals closely linked QTLs with opposite effects on body weight

A previous genome-wide QTL study revealed many QTLs affecting postnatal body weight and growth in an intersubspecific backcross mouse population between the C57BL/6J (B6) strain and wild Mus musculus castaneus mice captured in the Philippines. Subsequently, several closely linked QTLs for body composition traits were revealed in an F(2) intercross population between B6 and B6.Cg-Pbwg1, a congenic strain on the B6 genetic background carrying the growth QTL Pbwg1 on proximal chromosome 2. However, no QTL affecting body weight has been duplicated in the F(2) population, except for mapping an overdominant QTL that causes heterosis of body weight. In this study, we developed 17 intersubspecific subcongenic strains with overlapping and nonoverlapping castaneus regions from the B6.Cg-Pbwg1 congenic strain in order to search for and genetically dissect QTLs affecting body weight into distinct closely linked loci. Phenotypic comparisons of several developed subcongenic strains with the B6 strain revealed that two closely linked but distinct QTLs that regulate body weight, named Pbwg1.11 and Pbwg1.12, are located on an 8.9-Mb region between D2Mit270 and D2Mit472 and on the next 3.6-Mb region between D2Mit205 and D2Mit182, respectively. Further analyses using F(2) segregating populations obtained from intercrosses between B6 and each of the two selected subcongenic strains confirmed the presence of these two body weight QTLs. Pbwg1.11 had an additive effect on body weight at 6, 10, and 13 weeks of age, and its castaneus allele decreased it. In contrast, the castaneus allele at Pbwg1.12 acted in a dominant fashion and surprisingly increased body weight at 6, 10, and 13 weeks of age despite the body weight of wild castaneus mice being 60% of that of B6 mice. These findings illustrate the complex genetic nature of body weight regulation and support the importance of subcongenic mouse analysis to dissect closely linked loci.

Annotating genes and genomes with DNA sequences extracted from biomedical articles

Motivation: Increasing rates of publication and DNA sequencing make the problem of finding relevant articles for a particular gene or genomic region more challenging than ever. Existing text-mining approaches focus on finding gene names or identifiers in English text. These are often not unique and do not identify the exact genomic location of a study.

Results: Here, we report the results of a novel text-mining approach that extracts DNA sequences from biomedical articles and automatically maps them to genomic databases. We find that ∼20% of open access articles in PubMed central (PMC) have extractable DNA sequences that can be accurately mapped to the correct gene (91%) and genome (96%). We illustrate the utility of data extracted by text2genome from more than 150 000 PMC articles for the interpretation of ChIP-seq data and the design of quantitative reverse transcriptase (RT)-PCR experiments.

Conclusion: Our approach links articles to genes and organisms without relying on gene names or identifiers. It also produces genome annotation tracks of the biomedical literature, thereby allowing researchers to use the power of modern genome browsers to access and analyze publications in the context of genomic data.

Availability and implementation: Source code is available under a BSD license from http://sourceforge.net/projects/text2genome/ and results can be browsed and downloaded at http://text2genome.org.

Contact:maximilianh@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

ARTADE2DB: improved statistical inferences for Arabidopsis gene functions and structure predictions by dynamic structure-based dynamic expression (DSDE) analyses

Recent advances in technologies for observing high-resolution genomic activities, such as whole-genome tiling arrays and high-throughput sequencers, provide detailed information for understanding genome functions. However, the functions of 50% of known Arabidopsis thaliana genes remain unknown or are annotated only on the basis of static analyses such as protein motifs or similarities. In this paper, we describe dynamic structure-based dynamic expression (DSDE) analysis, which sequentially predicts both structural and functional features of transcripts. We show that DSDE analysis inferred gene functions 12% more precisely than static structure-based dynamic expression (SSDE) analysis or conventional co-expression analysis based on previously determined gene structures of A. thaliana. This result suggests that more precise structural information than the fixed conventional annotated structures is crucial for co-expression analysis in systems biology of transcriptional regulation and dynamics. Our DSDE method, ARabidopsis Tiling-Array-based Detection of Exons version 2 and over-representation analysis (ARTADE2-ORA), precisely predicts each gene structure by combining two statistical analyses: a probe-wise co-expression analysis of multiple transcriptome measurements and a Markov model analysis of genome sequences. ARTADE2-ORA successfully identified the true functions of about 90% of functionally annotated genes, inferred the functions of 98% of functionally unknown genes and predicted 1,489 new gene structures and functions. We developed a database ARTADE2DB that integrates not only the information predicted by ARTADE2-ORA but also annotations and other functional information, such as phenotypes and literature citations, and is expected to contribute to the study of the functional genomics of A. thaliana. URL: http://artade.org.

Web tools for the prioritization of candidate disease genes

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be expedited by prioritizing the candidates prior to testing. Over the last decade, a large number of computational methods and tools have been developed to assist the clinical geneticist in prioritizing candidate disease genes. In this chapter, we give an overview of computational tools that can be used for this purpose, all of which are freely available over the web.

Random mutagenesis of the mouse genome: a strategy for discovering gene function and the molecular basis of disease

Mutagenesis of mice with N-ethyl-N-nitrosourea (ENU) is a phenotype-driven approach to unravel gene function and discover new biological pathways. Phenotype-driven approaches have the advantage of making no assumptions about the function of genes and their products and have been successfully applied to the discovery of novel gene-phenotype relationships in many physiological systems. ENU mutagenesis of mice is used in many large-scale and more focused projects to generate and identify novel mouse models for the study of gene functions and human disease. This review examines the strategies and tools used in ENU mutagenesis screens to efficiently generate and identify functional mutations.

The RIKEN integrated database of mammals

The RIKEN integrated database of mammals (http://scinets.org/db/mammal) is the official undertaking to integrate its mammalian databases produced from multiple large-scale programs that have been promoted by the institute. The database integrates not only RIKEN's original databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists' Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information.

Outcome of array CGH analysis for 255 subjects with intellectual disability and search for candidate genes using bioinformatics

Array CGH enables the detection of pathogenic copy number variants (CNVs) in 5-15% of individuals with intellectual disability (ID), making it a promising tool for uncovering ID candidate genes. However, most CNVs encompass multiple genes, making it difficult to identify key disease gene(s) underlying ID etiology. Using array CGH we identified 47 previously unreported unique CNVs in 45/255 probands. We prioritized ID candidate genes using five bioinformatic gene prioritization web tools. Gene priority lists were created by comparing integral genes from each CNV from our ID cohort with sets of training genes specific either to ID or randomly selected. Our findings suggest that different training sets alter gene prioritization only moderately; however, only the ID gene training set resulted in significant enrichment of genes with nervous system function (19%) in prioritized versus non-prioritized genes from the same de novo CNVs (7%, p < 0.05). This enrichment further increased to 31% when the five web tools were used in concert and included genes within mitogen-activated protein kinase (MAPK) and neuroactive ligand-receptor interaction pathways. Gene prioritization web tools enrich for genes with relevant function in ID and more readily facilitate the selection of ID candidate genes for functional studies, particularly for large CNVs.

Coexpression landscape in ATTED-II: usage of gene list and gene network for various types of pathways

Gene coexpression analyses are a powerful method to predict the function of genes and/or to identify genes that are functionally related to query genes. The basic idea of gene coexpression analyses is that genes with similar functions should have similar expression patterns under many different conditions. This approach is now widely used by many experimental researchers, especially in the field of plant biology. In this review, we will summarize recent successful examples obtained by using our gene coexpression database, ATTED-II. Specifically, the examples will describe the identification of new genes, such as the subunits of a complex protein, the enzymes in a metabolic pathway and transporters. In addition, we will discuss the discovery of a new intercellular signaling factor and new regulatory relationships between transcription factors and their target genes. In ATTED-II, we provide two basic views of gene coexpression, a gene list view and a gene network view, which can be used as guide gene approach and narrow-down approach, respectively. In addition, we will discuss the coexpression effectiveness for various types of gene sets.

Exploring Candidate Genes for Epilepsy by Computational Disease-Gene Identification Strategy

Epilepsy is a complex disease with a strong genetic component. So far, studies have focused on experimental validation or genome-wide linkage scans for epilepsy susceptibility genes in multiple populations. We have used four bioinformatic tools (SNPs3D, PROSPECTR and SUSPECTS, GenWanderer, PosMed) to analyze 16 susceptibility loci selected from a literature search. Pathways and regulatory network analyses were performed using the Ingenuity Pathways Analysis (IPA) software. We identified a subset of 48 candidate epilepsy susceptibility genes. Five significant canonical pathways, in four typical networks, were identified: GABA receptor signaling, interleukin-6 (IL-6) signaling, G-protein coupled receptor signaling, type 2 diabetes mellitus signaling and airway inflammation in asthma. We concluded that online analytical tools provide a powerful way to reveal candidate genes which can greatly reduce experimental time. Our study contributes to further experimental tests for epilepsy susceptibility genes.

A novel gene essential for the development of single positive thymocytes

A critical step during intrathymic T-cell development is the transition of CD4(+) CD8(+) double-positive (DP) cells to the major histocompatibility complex class I (MHC-I)-restricted CD4(-) CD8(+) and MHC-II-restricted CD4(+) CD8(-) single-positive (SP) cell stage. Here, we identify a novel gene that is essential for this process. Through the T-cell phenotype-based screening of N-ethyl-N-nitrosourea (ENU)-induced mutant mice, we established a mouse line in which numbers of CD4 and CD8 SP thymocytes as well as peripheral CD4 and CD8 T cells were dramatically reduced. Using linkage analysis and DNA sequencing, we identified a missense point mutation in a gene, E430004N04Rik (also known as themis), that does not belong to any known gene family. This orphan gene is expressed specifically in DP and SP thymocytes and peripheral T cells, whereas in mutant thymocytes the levels of protein encoded by this gene were drastically reduced. We generated E430004N04Rik-deficient mice, and their phenotype was virtually identical to that of the ENU mutant mice, thereby confirming that this gene is essential for the development of SP thymocytes.

PosMed-plus: an intelligent search engine that inferentially integrates cross-species information resources for molecular breeding of plants

Molecular breeding of crops is an efficient way to upgrade plant functions useful to mankind. A key step is forward genetics or positional cloning to identify the genes that confer useful functions. In order to accelerate the whole research process, we have developed an integrated database system powered by an intelligent data-retrieval engine termed PosMed-plus (Positional Medline for plant upgrading science), allowing us to prioritize highly promising candidate genes in a given chromosomal interval(s) of Arabidopsis thaliana and rice, Oryza sativa. By inferentially integrating cross-species information resources including genomes, transcriptomes, proteomes, localizomes, phenomes and literature, the system compares a user's query, such as phenotypic or functional keywords, with the literature associated with the relevant genes located within the interval. By utilizing orthologous and paralogous correspondences, PosMed-plus efficiently integrates cross-species information to facilitate the ranking of rice candidate genes based on evidence from other model species such as Arabidopsis. PosMed-plus is a plant science version of the PosMed system widely used by mammalian researchers, and provides both a powerful integrative search function and a rich integrative display of the integrated databases. PosMed-plus is the first cross-species integrated database that inferentially prioritizes candidate genes for forward genetics approaches in plant science, and will be expanded for wider use in plant upgrading in many species.

OmicBrowse: a Flash-based high-performance graphics interface for genomic resources

OmicBrowse is a genome browser designed as a scalable system for maintaining numerous genome annotation datasets. It is an open source tool capable of regulating multiple user data access to each dataset to allow multiple users to have their own integrative view of both their unpublished and published datasets, so that the maintenance costs related to supplying each collaborator exclusively with their own private data are significantly reduced. OmicBrowse supports DAS1 imports and exports of annotations to Internet site servers worldwide. We also provide a data-download named OmicDownload server that interactively selects datasets and filters the data on the selected datasets. Our OmicBrowse server has been freely available at http://omicspace.riken.jp/ since its launch in 2003. The OmicBrowse source code is downloadable from http://sourceforge.net/projects/omicbrowse/.