1. Transcriptomics experiment selection algorithm

2. A novel GO-based  approach for gene functional analyses  [Scroll down]

3. Microarray data analyses [Scroll down]

4. My publications [Scroll down]

 

Transcriptomics Experiment Selection Algorithm

My research interest is in an area of bioinformatics called as gene functional analyses. Although the genomes of hundreds of organisms have been sequenced, the functions of these genes are largely unknown. Traditional techniques for understanding gene function are highly resource intensive and will never match the pace of the sequencing projects. In the past decade, efforts for elucidating gene function have gained new impetus with the emergence of large scale transcriptomics and protein-protein interaction experiments. These datasets are mined to identify groups of genes sharing similar features, which implies that they may share similar functions – this principle has often been called Guilt-By-Association (GBA). Amongst the various high-throughput data types available, transcriptional profiling is currently the most abundant.

 

The GBA principle is generally implemented using clustering techniques which are able to group genes with similar expression profile using a suitable measure of similarity. Recently, graph theoretical techniques have also been used (this is done after building a graph of gene similarity where the nodes are genes and edges between the nodes are weighted by similarity score). However, the focus of my research has been on understanding the effects of the datasets used in these analyses. Generally, large collections of data are pooled from hundreds of experiments and similarities between genes are then analysed using the pooled data. I believe that this may in fact mask many functional relationships between genes and I have developed a technique for identifying experiments that are beneficial to the analyses. A paper on this topic has been submitted for review and a webpage with additional information regarding the technique can be found here.

 

 

A novel GO-based  approach for gene functional analyses

In gene functional analyses, often large lists or groups of genes are identified as responsive to a given treatment or experimental condition. It is then necessary to summarize the functions of genes in the group. Traditionally, this summarization is achieved by performing an over-representation analysis of the GO annotation of the genes. This involves performing a hyper-geometric test to test whether certain GO annotations are found significantly more often than by chance.

We are currently developing a method which summarizes the functions of the genes in a completely novel way. The technique over-rides the need for identifying groups of genes and overcomes many of the limitations of traditional GO over-representation analyses techniques.

 

 

Microarray data analyses

I routinely work with pre-processing and data analyses of microarrays starting from raw data. I have worked with microarrays from a wide variety of technologies such as Affymetrix, CATMA, Custom 2-colour arrays and Agilent. I routinely support other projects with microarray data analyses.

 

 

My Publications:

 

Bhat P, Yang H, Bogre L, Devoto A and Paccanaro A., Computational selection of transcriptomics experiments improves Guilt-by-Association analyses (2011), submitted.

 

Hatzimasoura E, Doczi R, Ditengou F, Bhat P, Magyar Z, Helfer A., Menke F, Hirt H, , Lopez, E, Paccanaro A, Palme K, Bogre L., Meristem outgrowth is repressed by a stress activated MAPK kinase pathway through regulation of auxin transport (2011), manuscript under preparation.

 

Yang H, Bhat P, Shanahan H, & Paccanaro A., A maximal eigenvalue for detecting process representative genes by intergrating data from multiple sources (2008), NIPS Workshop on learning from multiple sources, Vancouver, Canada.