Motivation: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers. Results: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data. Availability: Source code written in R is available from Contact: hongyu.zhao@yale.edu Supplementary Information: Supplementary Data are available at
The functions of the plant body rely on interactions among distinct and nonequivalent cell types. The comparison of transcriptomes from different cell types should expose the transcriptional networks that underlie cellular attributes and contributions. Using laser microdissection and microarray profiling, we have produced a cell type transcriptome atlas that includes 40 cell types from rice (Oryza sativa) shoot, root and germinating seed at several developmental stages, providing patterns of cell specificity for individual genes and gene classes. Cell type comparisons uncovered previously unrecognized properties, including cell-specific promoter motifs and coexpressed cognate binding factor candidates, interaction partner candidates and hormone response centers. We inferred developmental regulatory hierarchies of gene expression in specific cell types by comparison of several stages within root, shoot and embryo.
matthew.holford@yale.edu; hongyu.zhao@yale.edu.
Motivation: Recent years have seen the development of a wide range of biomedical ontologies. Notable among these is Sequence Ontology (SO) which offers a rich hierarchy of terms and relationships that can be used to annotate genomic data. Well-designed formal ontologies allow data to be reasoned upon in a consistent and logically sound way and can lead to the discovery of new relationships. The Semantic Web Rules Language (SWRL) augments the capabilities of a reasoner by allowing the creation of conditional rules. To date, however, formal reasoning, especially the use of SWRL rules, has not been widely used in biomedicine.Results: We have built a knowledge base of human pseudogenes, extending the existing SO framework to incorporate additional attributes. In particular, we have defined the relationships between pseudogenes and segmental duplications. We then created a series of logical rules using SWRL to answer research questions and to annotate our pseudogenes appropriately. Finally, we were left with a knowledge base which could be queried to discover information about human pseudogene evolution.Availability: The fully populated knowledge base described in this document is available for download from http://ontology.pseudogene.org. A SPARQL endpoint from which to query the dataset is also available at this location.Contact: matthew.holford@yale.edu; mark.gerstein@yale.edu
Background: To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis.
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