Biomedical subjects

Matti Kankainen

Publications and source records attributed to Matti Kankainen.

5 recordsLinked to original sources

PubMed2006

Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana.

We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (http://ekhidna.biocenter.helsinki.fi/poxo/multigo). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.

Abscisic Acid↗

PubMed2006

POXO: a web-enabled tool series to discover transcription factor binding sites.

We present POXO, a comprehensive tool series to discover transcription factor binding sites from co-expressed genes (www.bioinfo.biocenter.helsinki.fi/poxo). POXO manages tasks such as functional evaluation and grouping of genes, sequence retrieval, pattern discovery and pattern verification. It also allows users to tailor analytical pipelines from these tools, with single mouse clicks. One typical pipeline of POXO begins by examining the biological functions that a set of co-expressed genes are involved in. In this examination, the functional coherence of the gene set is evaluated and representative functions are associated with the gene set. This examination can also be used to group genes into functionally similar subsets, if several biological processes are affected in the experiment. The next step in the pipeline is then to discover over-represented nucleotide patterns from the upstream sequences of the selected gene sets. This enables to investigate the possibility that the genes are co-regulated by common cis-elements. If over-represented patterns are found, similar ones can then be clustered together and be verified. The performance of POXO is demonstrated by analysing expression data from pathogen treated Arabidopsis thaliana. In this example, POXO detected activated gene sets and suggested transcription factors responsible for their regulation.

Arabidopsis↗

PubMed2005

POCO: discovery of regulatory patterns from promoters of oppositely expressed gene sets.

Functionally associated genes tend to be co-expressed, which indicates that they could also be co-regulated. Since co-regulation is usually governed by transcription factors via their specific binding elements, putative regulators can be identified from promoter sets of (co-expressed) genes by screening for over-represented nucleotide patterns. Here, we present a program, POCO, which discovers such over-represented patterns from either one or two promoter sets. Typical microarray experiments yield up- and down-regulated gene sets that may represent, for example, distinct defense pathways. Assuming that a functional transcription factor cannot simultaneously both up- and down-regulate the gene sets, its binding element should respectively be over- and under-represented in the corresponding promoter sets. This idea is implemented in POCO, which tests the hypothesis that the distributions of a pattern differ among three sets of promoters: up-regulated, down-regulated and randomly-chosen. In the program, pattern discovery is based on explicit enumeration of all possible patterns on the alphabet (A, C, G, T and N). The mean occurrences and SDs of the patterns are estimated using bootstrapping and their significance is assessed using ANOVA F-statistics, Tukey's honestly significantly difference test and P-values. The program is freely available at http://ekhidna.biocenter.helsinki.fi/poco.

Binding Sites↗

PubMed2004

POBO, transcription factor binding site verification with bootstrapping.

Transcription factors can either activate or repress target genes by binding onto short nucleotide sequence motifs in the promoter regions of these genes. Here, we present POBO, a promoter bootstrapping program, for gene expression data. POBO can be used to detect, compare and verify predetermined transcription factor binding site motifs in the promoters of one or two clusters of co-regulated genes. The program calculates the frequencies of the motif in the input promoter sets. A bootstrap analysis detects significantly over- or underrepresented motifs. The output of the program presents bootstrapped results in picture and text formats. The program was tested with published data from transgenic WRKY70 microarray experiments. Intriguingly, motifs recognized by the WRKY transcription factors of plant defense pathways are similarly enriched in both up- and downregulated clusters. POBO analysis suggests slightly modified hypothetical motifs that discriminate between up- and downregulated clusters. In conclusion, POBO allows easy, fast and accurate verification of putative regulatory motifs. The statistical tests implemented in POBO can be useful in eliminating false positives from the results of pattern discovery programs and increasing the reliability of true positives. POBO is freely available from http://ekhidna.biocenter.helsinki.fi:9801/pobo.

Binding Sites↗

PubMed2003

DANCER: a program for digital anatomical reconstruction of gene expression data.

A digital anatomy construction (DANCER) program was developed for gene expression data. DANCER can be used to reconstruct anatomical images from in situ hybridization images, microarray or other gene expression data. The program fills regions of a drawn figure with the corresponding values from a gene expression data set. The output of the program presents the expression levels of a particular gene in a particular region relative to other regions. The program was tested with values from experimental in situ hybridization autoradiographs and from a microarray experiment. Reconstruction of in situ hybridization data from adult rat brain made by DANCER corresponded well with the original autoradiograph. Reconstruction of microarray data from adult mouse brains provided images that reflect actual expression levels. This program should help to provide visualization and interpretation of data derived from gene expression experiments. DANCER may be freely downloaded.

Animals↗