Computational Frameworks for Studying Biological Pathways in Sequenced Plants Species

Closing gaps in our current knowledge about biological pathways is a fundamental challenge. The development of novel computational methods along with high throughput experimental data carries the promise to help in the challenge. Comparative transcriptomics is a common approach in functional gene discovery efforts. It allows for finding conserved co-expression patterns between orthologous genes in closely related plant species, suggesting that these genes potentially share similar function and regulation. Existing co-expression tools are limited to data from model systems, which greatly limit their utility. Moreover, in addition, none of the existing pipelines allow plant researchers to make use of their own unpublished gene expression data for performing a comparative co-expression analysis and generate multi-species co-expression networks. We introduce here two tools for gene discovery:

1) CoExpNetViz (Tzfadia et al., 2016) is a computational tool that uses a set of query or ‘bait’ genes as an input and a minimum of one pre-processed gene expression dataset.

2) MORPH (MOdule guided Ranking of candidate PatHway genes) (Tzfadia et al., 2012, Amar et al., 2015) is an algorithm, for revealing unknown genes in biological pathways. The method receives as input a set of known genes from the target pathway, a collection of expression profiles, and interaction and metabolic networks. Using machine-learning techniques, MORPH selects the best combination of data and analysis method and outputs a ranking of candidate genes predicted to belong to the target pathway.

Both MORPH and CoExpNetViz could be adapted and applied for any sequenced species.