基于基因本体论的生物信息个人数据库平台

论述了一个基于基因本体论(geneontology)的生物信息个人数据库平台BIO.该数据库平台根据自身研究的需要,用基因本体论中的相关的规范术语来对基因序列信息进行注释,从而可以让用户从基因本体论的角度对生物信息序列进行查询.由于因特网上生物数据库中大量的关于基因序列信息的术语不统一、不规范,存在大量的信息冗余,用此方式可最大限度地精确所要查找的结果.文中详细论述了该数据库平台的研究背景、查询功能以及维护.     

Genome comparison using Gene Ontology (GO) with statistical testing

Background  

Automated comparison of complete sets of genes encoded in two genomes can provide insight on the genetic basis of differences in biological traits between species. Gene ontology (GO) is used as a common vocabulary to annotate genes for comparison. Current approaches calculate the fold of unweighted or weighted differences between two species at the high-level GO functional categories. However, to ensure the reliability of the differences detected, it is important to evaluate their statistical significance. It is also useful to search for differences at all levels of GO.     

Gene flow in complex landscapes: testing multiple hypotheses with causal modeling

Predicting population-level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears (Ursus americanus), sampled across a 3,000-km(2) study area in northern Idaho, with 110 landscape-resistance hypotheses. Genetic similarities were based on the pairwise percentage dissimilarity among all individuals based on nine microsatellite loci (average expected heterozygosity=0.79). Landscape-resistance hypotheses describe a range of potential relationships between movement cost and land cover, slope, elevation, roads, Euclidean distance, and a putative movement barrier. These hypotheses were divided into seven organizational models in which the influences of barriers, distance, and landscape features were statistically separated using partial Mantel tests. Only one of the competing organizational models was fully supported: patterns of genetic structure are primarily related to landscape gradients of land cover and elevation. The alternative landscape models, isolation by barriers and isolation by distance, are not supported. In this black bear population, gene flow is facilitated by contiguous forest cover at middle elevations.     

Ethical issues raised by genetic testing with oligonucleotide microarrays

Because genes and alterations within them determine the identity, characteristics, and inheritance of every individual, the application of genetic science to humans has long been surrounded by apprehension, controversy, and real or perceived potential for abuse. Crude eugenics practices of the past now find a theoretical rebirth and transformation through the use of modern molecular genetic technologies for mutation detection, predictive and prenatal diagnosis, and, ultimately, gene replacement. The advent of oligonucleotide microarray analysis, in which hundreds or thousands of genes and mutations can be tested in parallel, offers tremendous promise for more accurate, sensitive, and efficient genetic testing. At the same time, however, this powerful technology dramatically increases the number and scope of ethical concerns accompanying each individual test request. This article considers the evolution and implications of these concerns, from the initial ordering of a microarray test by the physician to such issues as informed consent, privacy, confidentiality, clinical utility, discrimination, stigmatization, ethnic and population impact, and reimbursement.     

GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

Background  

Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets.     

The Neural/Immune Gene Ontology: clipping the Gene Ontology for neurological and immunological systems

Background  

The Gene Ontology (GO) is used to describe genes and gene products from many organisms. When used for functional annotation of microarray data, GO is often slimmed by editing so that only higher level terms remain. This practice is designed to improve the summarizing of experimental results by grouping high level terms and the statistical power of GO term enrichment analysis.     

Gene Function Prediction Based on the Gene Ontology Hierarchical Structure

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.     

Measuring the Evolution of Ontology Complexity: The Gene Ontology Case Study

Ontologies support automatic sharing, combination and analysis of life sciences data. They undergo regular curation and enrichment. We studied the impact of an ontology evolution on its structural complexity. As a case study we used the sixty monthly releases between January 2008 and December 2012 of the Gene Ontology and its three independent branches, i.e. biological processes (BP), cellular components (CC) and molecular functions (MF). For each case, we measured complexity by computing metrics related to the size, the nodes connectivity and the hierarchical structure.The number of classes and relations increased monotonously for each branch, with different growth rates. BP and CC had similar connectivity, superior to that of MF. Connectivity increased monotonously for BP, decreased for CC and remained stable for MF, with a marked increase for the three branches in November and December 2012. Hierarchy-related measures showed that CC and MF had similar proportions of leaves, average depths and average heights. BP had a lower proportion of leaves, and a higher average depth and average height. For BP and MF, the late 2012 increase of connectivity resulted in an increase of the average depth and average height and a decrease of the proportion of leaves, indicating that a major enrichment effort of the intermediate-level hierarchy occurred.The variation of the number of classes and relations in an ontology does not provide enough information about the evolution of its complexity. However, connectivity and hierarchy-related metrics revealed different patterns of values as well as of evolution for the three branches of the Gene Ontology. CC was similar to BP in terms of connectivity, and similar to MF in terms of hierarchy. Overall, BP complexity increased, CC was refined with the addition of leaves providing a finer level of annotations but decreasing slightly its complexity, and MF complexity remained stable.     

GoFish finds genes with combinations of Gene Ontology attributes

SUMMARY: GoFish is a Java application that allows users to search for gene products with particular gene ontology (GO) attributes, or combinations of attributes. GoFish ranks gene products by the degree to which they satisfy a Boolean query. Four organisms are currently supported: Saccaromyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and M.musculus.     

Gene expression microarrays and the integration of biological knowledge

Large-scale parallel measurement of whole-genome RNA expression is now possible with high-density arrays of cDNA or oligonucleotides. Using this technology efficiently will require the integration of other sources of biological information, such as gene identity, biomedical literature and biochemical pathway for a given gene. Such integration is essential to understand the cellular program of gene expression and the molecular physiology of an organism. Advances in microarray technology, and the expected rapid rise in microarray data will lead to new insight into fundamental biological problems such as the prediction of gene function from expression profiles and the identification of potential drug targets from biologically active compounds.     

Combining multiple microarrays in the presence of controlling variables

MOTIVATION: Microarray technology enables the monitoring of expression levels for thousands of genes simultaneously. When the magnitude of the experiment increases, it becomes common to use the same type of microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for the differences. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. The analysis of variance (ANOVA) model has been commonly used to detect differentially expressed genes after accounting for the sources of variation commonly observed in the microarray experiment. RESULTS: We extended the usual ANOVA model to account for an additional variability resulting from many confounding variables such as the effect of different hospitals. The proposed model is a two-stage ANOVA model. The first stage is the adjustment for the effects of no interests. The second stage is the detection of differentially expressed genes among the experimental groups using the residuals obtained from the first stage. Based on these residuals, we propose a permutation test to detect the differentially expressed genes. The proposed model is illustrated using the data from 133 microarrays collected at three different hospitals. The proposed approach is more flexible to use, and it is easier to accommodate the individual covariates in this model than using the meta-analysis approach. AVAILABILITY: A set of programs written in R will be electronically sent upon request.     

Data exploration tools for the Gene Ontology database

MOTIVATION: To improve the ability of biologists (both researchers and students) to ask biologically interesting questions of the Gene Ontology (GO) database and to explore the ontologies by seeing large portions of the ontology graphs in context, along with details of individual terms in the ontologies. RESULTS: GoGet and GoView are two new tools built as part of an extensible web application system based on Java 2 Enterprise Edition technology. GoGet has a user interface that enables users to ask biologically interesting questions, such as (1) What are the DNA binding proteins involved in DNA repair, but not in DNA replication? and (2) Of the terms containing the word triphosphatase, which have associated gene products from mouse, but not fruit fly? The results of such queries can be viewed in a collapsed tabular format that eases the burden of getting through large tables of data. GoView enables users to explore the large directed acyclic graph structure of the ontologies in the GO database. The two tools are coordinated, so that results from queries in GoGet can be visualized in GoView in the ontology in which they appear, and explorations started from GoView can request details of gene product associations to appear in a result table in GoGet. AVAILABILITY: Free access to the GoGet query tool and free download of the GoView ontology viewer are provided to all users at http://db.math.macalester.edu/goproject. In addition, source code for the GoView tool is also available from this site, along with a user manual for both tools.     

Automatic annotation of protein motif function with Gene Ontology terms

Background  

Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, amuch needed and importanttask is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base.