Types Of Genomic Data Research Articles

Recent additions and improvements to the Onto-Tools

The Onto-Tools suite is composed of an annotation database and six seamlessly integrated, web-accessible data mining tools: Onto-Express, Onto-Compare, Onto-Design, Onto-Translate, Onto-Miner and Pathway-Express. The Onto-Tools database has been expanded to include various types of data from 12 new databases. Our database now integrates different types of genomic data from 19 sequence, gene, protein and annotation databases. Additionally, our database is also expanded to include complete Gene Ontology (GO) annotations. Using the enhanced database and GO annotations, Onto-Express now allows functional profiling for 24 organisms and supports 17 different types of input IDs. Onto-Translate is also enhanced to fully utilize the capabilities of the new Onto-Tools database with an ultimate goal of providing the users with a non-redundant and complete mapping from any type of identification system to any other type. Currently, Onto-Translate allows arbitrary mappings between 29 types of IDs. Pathway-Express is a new tool that helps the users find the most interesting pathways for their input list of genes. Onto-Tools are freely available at .

Putting microarrays in a context: Integrated analysis of diverse biological data

In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.

Integration of Genomic Data in Electronic Health Records

In this paper we give an overview about the challenge the postgenomic era poses on biomedical informaticists. The occurrence of new (genomic) data types necessitates new data models, new viewing metaphors and methods to deal with the disclosure of genomic data. We discuss integration issues when inferring phenotype and genotype data. Another challenge is to find the right phenotype to genotype data in order to get appropriate case numbers for sound clinical genotype-phenotype inference studies. Genomic data could be integrated in an Electronic Health Record (EHR) in several ways. We describe patient-centered and pointer-based integration strategies and the corresponding data types and data models. The inference mechanisms for the interpretation of row data contain different agents. We describe vertical, horizontal and temporal agents. We have to deal with several new data types, not being standardized for EHR integration. Genomic data tends to be more structured than phenotype data. Beyond the development of new data models, vertical, horizontal and temporal agents have to be developed in order to link genotype and phenotype. As the genomic EHR will contain very sensitive data, confidentiality and privacy concerns have to be addressed. Given the necessity to capture both environment and genomic state of a patient and their interaction, clinical information systems have to be redesigned. While genotyping seems to be automatable easily, this is not the case for clinical information. More integration work on terminologies and ontologies has to be done.

Protein network inference from multiple genomic data: a supervised approach

An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology. This paper presents a new method to infer protein networks from multiple types of genomic data. Based on a variant of kernel canonical correlation analysis, its originality is in the formalization of the protein network inference problem as a supervised learning problem, and in the integration of heterogeneous genomic data within this framework. We present promising results on the prediction of the protein network for the yeast Saccharomyces cerevisiae from four types of widely available data: gene expressions, protein interactions measured by yeast two-hybrid systems, protein localizations in the cell and protein phylogenetic profiles. The method is shown to outperform other unsupervised protein network inference methods. We finally conduct a comprehensive prediction of the protein network for all proteins of the yeast, which enables us to propose protein candidates for missing enzymes in a biosynthesis pathway. Softwares are available upon request.

Comparative genomics for reliable protein-function prediction from genomic data

Genomic data provide invaluable, yet unreliable information about protein function. However, if the overlap in information among various genomic datasets is taken into account, one observes an increase in the reliability of the protein-function predictions that can be made. Recently published approaches achieved this either by comparing the same type of data from multiple species (horizontal comparative genomics) or by using subtle, Bayesian methods to compare different types of genomic data from a single species (vertical comparative genomics). In this article, we discuss these methods, illustrating horizontal comparative genomics by comparing yeast two-hybrid (Y2H) data from Saccharomyces cerevisiae with Y2H data from Drosophila melanogaster, and illustrating vertical comparative genomics by comparing RNA expression data with proteomic data from Plasmodium falciparum.

Genomic data visualization on the Web.

Many types of genomic data can be represented in matrix format, with rows corresponding to genes and columns corresponding to gene features. The heat map is a popular technique for visualizing such data, plotting the data on a two-dimensional grid and using a color scale to represent the magnitude of each matrix entry. Prism is a Web-based software tool for generating annotated heat map visualizations of genome-wide data quickly. The tool provides a selection of genome-specific annotation catalogs as well as a catalog upload capability. The heat maps generated are clickable, allowing the user to drill down to examine specific matrix entries, and gene annotations are linked to relevant genomic databases. http://noble.gs.washington.edu/prism

Information assessment on predicting protein-protein interactions

BackgroundIdentifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.ResultsOur assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.ConclusionsIn the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.

Functional exploration of the C. elegans genome using DNA microarrays.

Global changes in gene expression underlie developmental processes such as organogenesis, embryogenesis and aging in Caenorhabditis elegans. Recently developed methods allow gene expression profiles to be determined selectively for individual tissues and cell types. Results from both whole-animal and tissue-specific expression profiling have provided an unprecedented view into genome organization and gene function. Integration of these results with other types of functional genomics data gathered from RNA-mediated interference and yeast two-hybrid analyses will allow rapid identification and exploration of the complex functional gene networks that govern C. elegans development.

Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters.

Genome sequencing has led to the discovery of tens of thousands of potential new genes. Six years after the sequencing of the well-studied yeast Saccharomyces cerevisiae and the discovery that its genome encodes approximately 6,000 predicted proteins, more than 2,000 have not yet been characterized experimentally, and determining their functions seems far from a trivial task. One crucial constraint is the generation of useful hypotheses about protein function. Using a new approach to interpret microarray data, we assign likely cellular functions with confidence values to these new yeast proteins. We perform extensive genome-wide validations of our predictions and offer visualization methods for exploration of the large numbers of functional predictions. We identify potential new members of many existing functional categories including 285 candidate proteins involved in transcription, processing and transport of non-coding RNA molecules. We present experimental validation confirming the involvement of several of these proteins in ribosomal RNA processing. Our methodology can be applied to a variety of genomics data types and organisms.

Protein Interaction Verification and Functional Annotation by Integrated Analysis of Genome-Scale Data

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.

Learning gene functional classifications from multiple data types.

In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. In addition, we describe feature scaling methods for further exploiting prior knowledge of heterogeneity by giving each data type different weights.

Tree Reconstruction from Multi-State Characters

In evolutionary biology, a character is a function χ from a set X of present-day species into a finite set of states. Suppose the species in X have evolved according to a bifurcating tree T. Biologists would like to use characters to infer this tree. Assume that χ is the result of an evolutionary process on T that has not involved reverse or parallel transitions; such characters are called homoplasy-free. In this case, χ provides direct combinatorial information about the underlying evolutionary tree T for X. We consider the question of how many homoplasy-free characters are required so that T can be correctly reconstructed. We first establish lower bounds showing that, when the number of states is bounded, the number of homoplasy-free characters required to reconstruct T grows (at least) linearly with the size of X. In contrast, our main result shows that, when the state space is sufficiently large, every bifurcating tree can be uniquely determined by just five homoplasy-free characters. We briefly describe the relevance of this result for some new types of genomic data, and for the amalgamation of evolutionary trees.

Analytical approaches to genomic evolution

We model the non-local mechanisms of genomic evolution and propose methods for studying the evolutionary divergence of species based on these models. Mechanisms include the movement of segments of genomes within a single chromosome (transpositions), the reciprocal translocation of segments between two chromosomes, and the inversion of segments. Each of these is studied in the context of a different type of genomic data. We introduce the theory of phylogenetic invariants for evolutionary inference based on very long macromolecular sequences.