Autism spectrum disorders (ASD) represent a group of developmental disabilities with a strong genetic basis. The laboratory mouse is increasingly used as a model organism for ASD, and MGI, the Mouse Genome Informatics resource, is the primary model organism database for the laboratory mouse. MGI uses the Mammalian Phenotype (MP) ontology to describe mouse models of human diseases. Using bioinformatics tools including Phenologs, MouseNET, and the Ontological Discovery Environment, we tested data associated with MP terms to characterize new gene-phenotype associations related to ASD. Our integrative analysis using these tools identified numerous mouse genotypes that are likely to have previously uncharacterized autistic-like phenotypes. The genes implicated in these mouse models had considerable overlap with a set of over 300 genes recently associated with ASD due to small, rare copy number variation (Pinto D. et al, 2010). Prediction and characterization of autistic mutant mouse alleles assists researchers in studying the complex nature of ASD and provides a generalizable approach to candidate gene prioritization.
Autism spectrum disorders; phenotype ontology; mouse disease models
The laboratory mouse is the premier animal model for studying human biology because all life stages can be accessed experimentally, a completely sequenced reference genome is publicly available and there exists a myriad of genomic tools for comparative and experimental research. In the current era of genome scale, data-driven biomedical research, the integration of genetic, genomic and biological data are essential for realizing the full potential of the mouse as an experimental model. The Mouse Genome Database (MGD; http://www.informatics.jax.org), the community model organism database for the laboratory mouse, is designed to facilitate the use of the laboratory mouse as a model system for understanding human biology and disease. To achieve this goal, MGD integrates genetic and genomic data related to the functional and phenotypic characterization of mouse genes and alleles and serves as a comprehensive catalog for mouse models of human disease. Recent enhancements to MGD include the addition of human ortholog details to mouse Gene Detail pages, the inclusion of microRNA knockouts to MGD’s catalog of alleles and phenotypes, the addition of video clips to phenotype images, providing access to genotype and phenotype data associated with quantitative trait loci (QTL) and improvements to the layout and display of Gene Ontology annotations.
Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.
Tissue specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. We propose an effective strategy to model tissue-specific functional relationship networks in the laboratory mouse. We integrated large scale genomics datasets as well as low-throughput tissue-specific expression profiles to estimate the probability that two proteins are co-functioning in the tissue under study. These networks can accurately reflect the diversity of protein functions across different organs and tissue compartments. By computationally exploring the tissue-specific networks, we can accurately predict novel phenotype-related gene candidates. We experimentally confirmed a top candidate gene, Mybl1, to affect several male fertility phenotypes, predicted based on male-reproductive system-specific networks and we predicted candidates related to a rare genetic disease ataxia, which are supported by experimental and literature evidence. The above results demonstrate the power of modeling tissue-specific dynamics of co-functionality through computational approaches.
In 2007, the International Knockout Mouse Consortium (IKMC) made the ambitious promise to generate mutations in virtually every protein-coding gene of the mouse genome in a concerted worldwide action. Now, 5 years later, the IKMC members have developed high-throughput gene trapping and, in particular, gene-targeting pipelines and generated more than 17,400 mutant murine embryonic stem (ES) cell clones and more than 1,700 mutant mouse strains, most of them conditional. A common IKMC web portal (www.knockoutmouse.org) has been established, allowing easy access to this unparalleled biological resource. The IKMC materials considerably enhance functional gene annotation of the mammalian genome and will have a major impact on future biomedical research.
The Mouse Phenome Project was launched a decade ago to complement mouse genome sequencing efforts by promoting new phenotyping initiatives under standardized conditions and collecting the data in a central public database, the Mouse Phenome Database (MPD; http://phenome.jax.org). MPD houses a wealth of strain characteristics data to facilitate the use of the laboratory mouse in translational research for human health and disease, helping alleviate problems involving experimentation in humans that cannot be done practically or ethically. Data sets are voluntarily contributed by researchers from a variety of institutions and settings, or in some cases, retrieved by MPD staff from public sources. MPD maintains a growing collection of standardized reference data that assists investigators in selecting mouse strains for research applications; houses treatment/control data for drug studies and other interventions; offers a standardized platform for discovering genotype–phenotype relationships; and provides tools for hypothesis testing. MPD improvements and updates since our last NAR report are presented, including the addition of new tools and features to facilitate navigation and data mining as well as the acquisition of new data (phenotypic, genotypic and gene expression).
The Mouse Genome Database (MGD, http://www.informatics.jax.org) is the international community resource for integrated genetic, genomic and biological data about the laboratory mouse. Data in MGD are obtained through loads from major data providers and experimental consortia, electronic submissions from laboratories and from the biomedical literature. MGD maintains a comprehensive, unified, non-redundant catalog of mouse genome features generated by distilling gene predictions from NCBI, Ensembl and VEGA. MGD serves as the authoritative source for the nomenclature of mouse genes, mutations, alleles and strains. MGD is the primary source for evidence-supported functional annotations for mouse genes and gene products using the Gene Ontology (GO). MGD provides full annotation of phenotypes and human disease associations for mouse models (genotypes) using terms from the Mammalian Phenotype Ontology and disease names from the Online Mendelian Inheritance in Man (OMIM) resource. MGD is freely accessible online through our website, where users can browse and search interactively, access data in bulk using Batch Query or BioMart, download data files or use our web services Application Programming Interface (API). Improvements to MGD include expanded genome feature classifications, inclusion of new mutant allele sets and phenotype associations and extensions of GO to include new relationships and a new stream of annotations via phylogenetic-based approaches.
Representing species-specific proteins and protein complexes in ontologies that are both human- and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes.
We describe here how the PRO Consortium is meeting the challenge of representing species-specific protein complexes, how protein complex representation in PRO supports annotation of protein complexes and comparative biology, and how PRO is being integrated into existing community bioinformatics resources. The PRO resource is accessible at http://pir.georgetown.edu/pro/.
PRO is a unique database resource for species-specific protein complexes. PRO facilitates robust annotation of variations in composition and function contexts for protein complexes within and between species.
The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources; and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.
The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.
The Mouse Genome Database (MGD) is the community model organism database for the laboratory mouse and the authoritative source for phenotype and functional annotations of mouse genes. MGD includes a complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) resource. MGD contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology. Major improvements to the Mouse Genome Database include comprehensive update of genetic maps, implementation of new classification terms for genome features, development of a recombinase (cre) portal and inclusion of all alleles generated by the International Knockout Mouse Consortium (IKMC).
The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results of protein research. PRO (http://pir.georgetown.edu/pro) is part of the Open Biomedical Ontology Foundry.
The International Knockout Mouse Consortium (IKMC) aims to mutate all protein-coding genes in the mouse using a combination of gene targeting and gene trapping in mouse embryonic stem (ES) cells and to make the generated resources readily available to the research community. The IKMC database and web portal (www.knockoutmouse.org) serves as the central public web site for IKMC data and facilitates the coordination and prioritization of work within the consortium. Researchers can access up-to-date information on IKMC knockout vectors, ES cells and mice for specific genes, and follow links to the respective repositories from which corresponding IKMC products can be ordered. Researchers can also use the web site to nominate genes for targeting, or to indicate that targeting of a gene should receive high priority. The IKMC database provides data to, and features extensive interconnections with, other community databases.
General transcription factor (TFII-I) is a multi-functional transcription factor encoded by the Gtf2i gene, that has been demonstrated to regulate transcription of genes critical for development. Because of the broad range of genes regulated by TFII-I as well as its potential role in a significant neuro-developmental disorder, developing a comprehensive expression profile is critical to the study of this transcription factor. We sought to define the timing and pattern of expression of TFII-I in post-implantation embryos at a time during which many putative TFII-I target genes are expressed.
Antibodies to the N-terminus of TFII-I were used to probe embryonic mouse sections. TFII-I protein is widely expressed in the developing embryo. TFII-I is expressed throughout the period from E8-E16. However, within this period there are striking shifts in localization from cytoplasmic predominant to nuclear. TFII-I expression varies in both a spatial and temporal fashion. There is extensive expression in neural precursors at E8. This expression persists at later stages. TFII-I is expressed in developing lung, heart and gut structures. There is no evidence of isoform specific expression. Available data regarding expression patterns at both an RNA and protein level throughout development are also comprehensively reviewed.
Our immunohistochemical studies of the temporal and spatial expression patterns of TFII-I in mouse embryonic sections are consistent with the hypothesis that hemizygous deletion of GTF2I in individuals with Williams-Beuren Syndrome contributes to the distinct cognitive and physiological symptoms associated with the disorder.
The Jackson Laboratory Colony Management System (JCMS) is a software application for managing data and information related to research mouse colonies, associated biospecimens, and experimental protocols. JCMS runs directly on computers that run one of the PC Windows® operating systems, but can be accessed via web browser interfaces from any computer running a Windows, Macintosh®, or Linux® operating system. JCMS can be configured for a single user or multiple users in small- to medium-size work groups. The target audience for JCMS includes laboratory technicians, animal colony managers, and principal investigators. The application provides operational support for colony management and experimental workflows, sample and data tracking through transaction-based data entry forms, and date-driven work reports. Flexible query forms allow researchers to retrieve database records based on user-defined criteria. Recent advances in handheld computers with integrated barcode readers, middleware technologies, web browsers, and wireless networks add to the utility of JCMS by allowing real-time access to the database from any networked computer.
The Mouse Genome Database (MGD) is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) database resource and serves as the primary community model organism database for the laboratory mouse. MGD is the authoritative source for mouse gene, allele and strain nomenclature and for phenotype and functional annotations of mouse genes. MGD contains comprehensive data and information related to mouse genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data for MGD are obtained from diverse sources including manual curation of the biomedical literature and direct contributions from individual investigator’s laboratories and major informatics resource centers, such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology and the Mammalian Phenotype Ontology. Recent improvements in MGD described here includes integration of mouse gene trap allele and sequence data, integration of gene targeting information from the International Knockout Mouse Consortium, deployment of an MGI Biomart, and enhancements to our batch query capability for customized data access and retrieval.
MouseCyc is a database of curated metabolic pathways for the laboratory mouse.
Linking biochemical genetic data to the reference genome for the laboratory mouse is important for comparative physiology and for developing mouse models of human biology and disease. We describe here a new database of curated metabolic pathways for the laboratory mouse called MouseCyc . MouseCyc has been integrated with genetic and genomic data for the laboratory mouse available from the Mouse Genome Informatics database and with pathway data from other organisms, including human.
The laboratory mouse has long been an important tool in the study of the biology and genetics of human cancer. With the advent of genetic engineering techniques, DNA microarray analyses, tissue arrays, and other large-scale, high-throughput data generating methods, the amount of data available for mouse models of cancer is growing exponentially. Tools to integrate, locate and visualize these data are crucial to aid researchers in their investigations. The Mouse Tumor Biology database (http://tumor.informatics.jax.org) seeks to address that need.
A finished clone-based assembly of the mouse genome reveals extensive recent sequence duplication during recent evolution and rodent-specific expansion of certain gene families. Newly assembled duplications contain protein-coding genes that are mostly involved in reproductive function.
The mouse (Mus musculus) is the premier animal model for understanding human disease and development. Here we show that a comprehensive understanding of mouse biology is only possible with the availability of a finished, high-quality genome assembly. The finished clone-based assembly of the mouse strain C57BL/6J reported here has over 175,000 fewer gaps and over 139 Mb more of novel sequence, compared with the earlier MGSCv3 draft genome assembly. In a comprehensive analysis of this revised genome sequence, we are now able to define 20,210 protein-coding genes, over a thousand more than predicted in the human genome (19,042 genes). In addition, we identified 439 long, non–protein-coding RNAs with evidence for transcribed orthologs in human. We analyzed the complex and repetitive landscape of 267 Mb of sequence that was missing or misassembled in the previously published assembly, and we provide insights into the reasons for its resistance to sequencing and assembly by whole-genome shotgun approaches. Duplicated regions within newly assembled sequence tend to be of more recent ancestry than duplicates in the published draft, correcting our initial understanding of recent evolution on the mouse lineage. These duplicates appear to be largely composed of sequence regions containing transposable elements and duplicated protein-coding genes; of these, some may be fixed in the mouse population, but at least 40% of segmentally duplicated sequences are copy number variable even among laboratory mouse strains. Mouse lineage-specific regions contain 3,767 genes drawn mainly from rapidly-changing gene families associated with reproductive functions. The finished mouse genome assembly, therefore, greatly improves our understanding of rodent-specific biology and allows the delineation of ancestral biological functions that are shared with human from derived functions that are not.
The availability of an accurate genome sequence provides the bedrock upon which modern biomedical research is based. Here we describe a high-quality assembly, Build 36, of the mouse genome. This assembly was put together by aligning overlapping individual clones representing parts of the genome, and it provides a more complete picture than previous assemblies, because it adds much rodent-specific sequence that was previously unavailable. The addition of these sequences provides insight into both the genomic architecture and the gene complement of the mouse. In particular, it highlights recent gene duplications and the expansion of certain gene families during rodent evolution. An improved understanding of the mouse genome and thus mouse biology will enhance the utility of the mouse as a model for human disease.
The Mouse Phenome Database (MPD; http://www.jax.org/phenome) is an open source, web-based repository of phenotypic and genotypic data on commonly used and genetically diverse inbred strains of mice and their derivatives. MPD is also a facility for query, analysis and in silico hypothesis testing. Currently MPD contains about 1400 phenotypic measurements contributed by research teams worldwide, including phenotypes relevant to human health such as cancer susceptibility, aging, obesity, susceptibility to infectious diseases, atherosclerosis, blood disorders and neurosensory disorders. Electronic access to centralized strain data enables investigators to select optimal strains for many systems-based research applications, including physiological studies, drug and toxicology testing, modeling disease processes and complex trait analysis. The ability to select strains for specific research applications by accessing existing phenotype data can bypass the need to (re)characterize strains, precluding major investments of time and resources. This functionality, in turn, accelerates research and leverages existing community resources. Since our last NAR reporting in 2007, MPD has added more community-contributed data covering more phenotypic domains and implemented several new tools and features, including a new interactive Tool Demo available through the MPD homepage (quick link: http://phenome.jax.org/phenome/trytools).
The Mouse Genome Database (MGD, http://www.informatics.jax.org/), integrates genetic, genomic and phenotypic information about the laboratory mouse, a primary animal model for studying human biology and disease. Information in MGD is obtained from diverse sources, including the scientific literature and external databases, such as EntrezGene, UniProt and GenBank. In addition to its extensive collection of phenotypic allele information for mouse genes that is curated from the published biomedical literature and researcher submission, MGI includes a comprehensive representation of mouse genes including sequence, functional (GO) and comparative information. MGD provides a data mining platform that enables the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. MGI can be accessed by a variety of methods including web-based search forms, a genome sequence browser and downloadable database reports. Programmatic access is available using web services. Recent improvements in MGD described here include the unified mouse gene catalog for NCBI Build 37 of the reference genome assembly, and improved representation of mouse mutants and phenotypes.
Establishing a functional network is invaluable to our understanding of gene function, pathways, and systems-level properties of an organism and can be a powerful resource in directing targeted experiments. In this study, we present a functional network for the laboratory mouse based on a Bayesian integration of diverse genetic and functional genomic data. The resulting network includes probabilistic functional linkages among 20,581 protein-coding genes. We show that this network can accurately predict novel functional assignments and network components and present experimental evidence for predictions related to Nanog homeobox (Nanog), a critical gene in mouse embryonic stem cell pluripotency. An analysis of the global topology of the mouse functional network reveals multiple biologically relevant systems-level features of the mouse proteome. Specifically, we identify the clustering coefficient as a critical characteristic of central modulators that affect diverse pathways as well as genes associated with different phenotype traits and diseases. In addition, a cross-species comparison of functional interactomes on a genomic scale revealed distinct functional characteristics of conserved neighborhoods as compared to subnetworks specific to higher organisms. Thus, our global functional network for the laboratory mouse provides the community with a key resource for discovering protein functions and novel pathway components as well as a tool for exploring systems-level topological and evolutionary features of cellular interactomes. To facilitate exploration of this network by the biomedical research community, we illustrate its application in function and disease gene discovery through an interactive, Web-based, publicly available interface at http://mouseNET.princeton.edu.
Functionally related proteins interact in diverse ways to carry out biological processes, and each protein often participates in multiple pathways. Proteins are therefore organized into a complex network through which different functions of the cell are carried out. An accurate description of such a network is invaluable to our understanding of both the system-level features of a cell and those of an individual biological process. In this study, we used a probabilistic model to combine information from diverse genome-scale studies as well as individual investigations to generate a global functional network for mouse. Our analysis of the global topology of this network reveals biologically relevant systems-level characteristics of the mouse proteome, including conservation of functional neighborhoods and network features characteristic of known disease genes and key transcriptional regulators. We have made this network publicly available for search and dynamic exploration by researchers in the community. Our Web interface enables users to easily generate hypotheses regarding potential functional roles of uncharacterized proteins, investigate possible links between their proteins of interest and disease, and identify new players in specific biological processes.
A systematic analysis of the relationship between the neoplastic and developmental transcriptome provides an outline of global trends in cancer gene expression.
In recent years, the molecular underpinnings of the long-observed resemblance between neoplastic and immature tissue have begun to emerge. Genome-wide transcriptional profiling has revealed similar gene expression signatures in several tumor types and early developmental stages of their tissue of origin. However, it remains unclear whether such a relationship is a universal feature of malignancy, whether heterogeneities exist in the developmental component of different tumor types and to which degree the resemblance between cancer and development is a tissue-specific phenomenon.
We defined a developmental landscape by summarizing the main features of ten developmental time courses and projected gene expression from a variety of human tumor types onto this landscape. This comparison demonstrates a clear imprint of developmental gene expression in a wide range of tumors and with respect to different, even non-cognate developmental backgrounds. Our analysis reveals three classes of cancers with developmentally distinct transcriptional patterns. We characterize the biological processes dominating these classes and validate the class distinction with respect to a new time series of murine embryonic lung development. Finally, we identify a set of genes that are upregulated in most cancers and we show that this signature is active in early development.
This systematic and quantitative overview of the relationship between the neoplastic and developmental transcriptome spanning dozens of tissues provides a reliable outline of global trends in cancer gene expression, reveals potentially clinically relevant differences in the gene expression of different cancer types and represents a reference framework for interpretation of smaller-scale functional studies.
The Mouse Genome Database, (MGD, http://www.informatics.jax.org/), integrates genetic, genomic and phenotypic information about the laboratory mouse, a primary animal model for studying human biology and disease. MGD data content includes comprehensive characterization of genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data within MGD are obtained from diverse sources including manual curation of the biomedical literature, direct contributions from individual investigator's laboratories and major informatics resource centers such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development of data and semantic standards such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology. MGD provides a data-mining platform that enables the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the association of gene trap data with mouse genes and a new batch query capability for customized data access and retrieval.
Specific mammalian genes functionally and dynamically associate together within the nucleus. Yet, how an array of many genes along the chromosome sequence can be spatially organized and folded together is unknown. We investigated the 3D structure of a well-annotated, highly conserved 4.3-Mb region on mouse chromosome 14 that contains four clusters of genes separated by gene “deserts.” In nuclei, this region forms multiple, nonrandom “higher order” structures. These structures are based on the gene distribution pattern in primary sequence and are marked by preferential associations among multiple gene clusters. Associating gene clusters represent expressed chromatin, but their aggregation is not simply dependent on ongoing transcription. In chromosomes with aggregated gene clusters, gene deserts preferentially align with the nuclear periphery, providing evidence for chromosomal region architecture by specific associations with functional nuclear domains. Together, these data suggest dynamic, probabilistic 3D folding states for a contiguous megabase-scale chromosomal region, supporting the diverse activities of multiple genes and their conserved primary sequence organization.
The Mouse Phenome Database (MPD; ) is a repository of phenotypic and genotypic data on commonly used and genetically diverse inbred strains of mice. Strain characteristics data are contributed by members of the scientific community. Electronic access to centralized strain data enables biomedical researchers to choose appropriate strains for many systems-based research applications, including physiological studies, drug and toxicology testing and modeling disease processes. MPD provides a community data repository and a platform for data analysis and in silico hypothesis testing. The laboratory mouse is a premier genetic model for understanding human biology and pathology; MPD facilitates research that uses the mouse to identify and determine the function of genes participating in normal and disease pathways.