Alain B. Tchagang

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm

BackgroundNowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space.ResultsWe developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples.ConclusionsOur analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.

GOAL: A software tool for assessing biological significance of genes groups

BackgroundModern high throughput experimental techniques such as DNA microarrays often result in large lists of genes. Computational biology tools such as clustering are then used to group together genes based on their similarity in expression profiles. Genes in each group are probably functionally related. The functional relevance among the genes in each group is usually characterized by utilizing available biological knowledge in public databases such as Gene Ontology (GO), KEGG pathways, association between a transcription factor (TF) and its target genes, and/or gene networks.ResultsWe developed GOAL: G ene O ntology A naL yzer, a software tool specifically designed for the functional evaluation of gene groups. GOAL implements and supports efficient and statistically rigorous functional interpretations of gene groups through its integration with available GO, TF-gene association data, and association with KEGG pathways. In order to facilitate more specific functional characterization of a gene group, we implement three GO-tree search strategies rather than one as in most existing GO analysis tools. Furthermore, GOAL offers flexibility in deployment. It can be used as a standalone tool, a plug-in to other computational biology tools, or a web server application.ConclusionWe developed a functional evaluation software tool, GOAL, to perform functional characterization of a gene group. GOAL offers three GO-tree search strategies and combines its strength in function integration, portability and visualization, and its flexibility in deployment. Furthermore, GOAL can be used to evaluate and compare gene groups as the output from computational biology tools such as clustering algorithms.

Extracting biologically significant patterns from short time series gene expression data

BackgroundTime series gene expression data analysis is used widely to study the dynamics of various cell processes. Most of the time series data available today consist of few time points only, thus making the application of standard clustering techniques difficult.ResultsWe developed two new algorithms that are capable of extracting biological patterns from short time point series gene expression data. The two algorithms, ASTRO and MiMeSR, are inspired by the rank order preserving framework and the minimum mean squared residue approach, respectively. However, ASTRO and MiMeSR differ from previous approaches in that they take advantage of the relatively few number of time points in order to reduce the problem from NP-hard to linear. Tested on well-defined short time expression data, we found that our approaches are robust to noise, as well as to random patterns, and that they can correctly detect the temporal expression profile of relevant functional categories. Evaluation of our methods was performed using Gene Ontology (GO) annotations and chromatin immunoprecipitation (ChIP-chip) data.ConclusionOur approaches generally outperform both standard clustering algorithms and algorithms designed specifically for clustering of short time series gene expression data. Both algorithms are available at http://www.benoslab.pitt.edu/astro/.

Enrichment of Triticum aestivum gene annotations using ortholog cliques and gene ontologies in other plants

BackgroundWhile the gargantuan multi-nation effort of sequencing T. aestivum gets close to completion, the annotation process for the vast number of wheat genes and proteins is in its infancy. Previous experimental studies carried out on model plant organisms such as A. thaliana and O. sativa provide a plethora of gene annotations that can be used as potential starting points for wheat gene annotations, proven that solid cross-species gene-to-gene and protein-to-protein correspondences are provided.ResultsDNA and protein sequences and corresponding annotations for T. aestivum and 9 other plant species were collected from Ensembl Plants release 22 and curated. Cliques of predicted 1-to-1 orthologs were identified and an annotation enrichment model was defined based on existing gene-GO term associations and phylogenetic relationships among wheat and 9 other plant species. A total of 13 cliques of size 10 were identified, which represent putative functionally equivalent genes and proteins in the 10 plant species. Eighty-five new and more specific GO terms were associated with wheat genes in the 13 cliques of size 10, which represent a 65% increase compared with the previously 130 known GO terms. Similar expression patterns for 4 genes from Arabidopsis, barley, maize and rice in cliques of size 10 provide experimental evidence to support our model. Overall, based on clique size equal or larger than 3, our model enriched the existing gene-GO term associations for 7,838 (8%) wheat genes, of which 2,139 had no previous annotation.ConclusionsOur novel comparative genomics approach enriches existing T. aestivum gene annotations based on cliques of predicted 1-to-1 orthologs, phylogenetic relationships and existing gene ontologies from 9 other plant species.

Biclustering of DNA microarray data: Theory, evaluation, and applications

In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. Mathematical aspects of the models are highlighted, and applications to solve biological problems are discussed. Panayiotis V. Benos University of Pittsburgh, USA

Towards a temporal modeling of the genetic network controlling Systemic Acquired Resistance in Arabidopsis thaliana

We studied defense mechanism of the Arabidopsis thaliana subjected to Salicylic Acid (SA) treatment for 0, 1, and 8 hours using a broader application of the frequent itemset approach. Four genotypes of the plant were used in this study, Columbia wild type, mutant npr1-3, double mutant tga1 tga4 and triple mutant tga2 tga5 tga6. We defined the major patterns of transcription regulation governing pathogen defense mechanism, thereby creating a model of the Systemic Acquired Resistance (SAR) at three time points. The temporal model describes the relationships among the regulators and defines groups of genes that are subject to similar regulation. The results obtained offered a first glimpse into the temporal pattern of the transcription regulatory network during SAR in Arabidopsis thaliana. We found that most of the genes that responded to SA challenge are in fact dependent on one or more of the NPR1 and TGA factors tested in this study.

Bioinformatics identification of new targets for improving low temperature stress tolerance in spring and winter wheat

BackgroundPhenotypic studies in Triticeae have shown that low temperature-induced protective mechanisms are developmentally regulated and involve dynamic acclimation processes. Understanding these mechanisms is important for breeding cold-resistant wheat cultivars. In this study, we combined three computational techniques for the analysis of gene expression data from spring and winter wheat cultivars subjected to low temperature treatments. Our main objective was to construct a comprehensive network of cold response transcriptional events in wheat, and to identify novel cold tolerance candidate genes in wheat.ResultsWe assigned novel cold stress-related roles to 35 wheat genes, uncovered novel transcription (TF)-gene interactions, and identified 127 genes representing known and novel candidate targets associated with cold tolerance in wheat. Our results also show that delays in terms of activation or repression of the same genes across wheat cultivars play key roles in phenotypic differences among winter and spring wheat cultivars, and adaptation to low temperature stress, cold shock and cold acclimation.ConclusionsUsing three computational approaches, we identified novel putative cold-response genes and TF-gene interactions. These results provide new insights into the complex mechanisms regulating the expression of cold-responsive genes in wheat.

Digitization of trait representation in microarray data analysis of wheat infected by fusarium graminearum

Fusarium head blight (FHB) limits wheat yield and compromises grain quality. We investigated differentially expressed genes after FHB challenge. FHB-susceptible and -resistant common wheat (Triticum aestivum) cultivars were challenged with the toxigenic fungus Fusarium graminearum and gene expression was analyzed using 61K Affymetrix wheat microarrays. We digitized trait specificity in the susceptible and resistant lines with and without the infection in order to facilitate subsequent data mining. We discovered various patterns of differential gene expression between susceptible and resistant lines in response to the infection. We performed association network analysis among genes in clusters significantly correlated with one or more quantitative trait loci known to contribute to Fusarium resistance. We found 11 interconnected hub genes responsive to FHB infection and significantly correlated with wheat resistance to FHB, among which two are predicted to encode a polygalacturonase-inhibiting protein (PGIP1).

Subspace Clustering of DNA Microarray Data: Theory, Evaluation, and Applications

Identification of biological significant subspace clusters biclusters and triclusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Several methods and applications of subspace clustering biclustering and triclustering in DNA microarray data analysis have been developed in recent years. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. This review discusses and compares these methods, highlights their mathematical principles, and provides insight into the applications to solve biological problems.

A generic model of transcriptional regulatory networks: Application to plants under abiotic stress

Understanding the relationships between transcription factors (TFs) and genes in plants under abiotic stress responses, tolerance and adaptation to adverse environments is very important in developing resilient crop varieties. While experimental methods to characterize stress responsive TFs and their targets are highly accurate, identification and characterization of the role of a given gene in a given stress response event are often laborious and time consuming. Computational approaches, on the other hand, offer a platform to identify new knowledge by integrating high throughput omics data and mathematical methods/models. In this research, we have developed a generic linear model of transcriptional regulatory networks (TRNs) and a companion algorithm to identify and to characterize stress responsive genes and their roles in a given stress response event. The proposed methodology was applied to plants, by using Arabidopsis thaliana as an example, under abiotic stress. Well known interactions were inferred as well as putative novel ones that may play important roles in plants under abiotic stress conditions as confirmed by statistical and literature evidences.