Sentence examples for categorisation of genes from inspiring English sources

The Gene Ontology (GO) project [ 60] was used to aid categorisation of genes.

Categorisation of genes affected in leaves and roots of the glu1-2 mutant presented above showed a high number of genes related to stimulus and stress responses.

According to a strict interpretation of our categorisation of genes, on the basis of the 10-10 BLAST cut-off score, the Pomatoceros FMRFamide and mollusc genes represent lophotrochozoan-specific genes, and are counted as such in our classification here.

In addition to noting gross trends of TCS organisation, categorisation of TCS genes allowed analysis of previously unexplored aspects of TCS gene organisation from a multi-genomic perspective.

Therefore, functional categorisation of these genes may provide clues to the understanding of physiological and molecular mechanisms involved in Cd stress response.

A functional categorisation of this gene list resulted in overrepresented functional categories such as "Heavy metal binding (Cu, Fe, Zn)" and "Lipid, fatty acid and isoprenoid metabolism".

We have made available the annotated R. culicivorax genome, with functional categorisations of predicted genes and proteins and annotation features, in a dedicated genome browser at http://romanomermis.nematod.es.es

To assist with the categorisation of co-expressed genes and partitioned modules according to their potential function or processes, we assessed the modules for enrichment primarily for GO terms in R (http://www.r-project.org) using the 'gProfileR' package [ 37] to interface g:profiler (http://gprofiler.at.mt.ut.ee/gprofiler/).ee/gprofiler/

GO term annotations were used to perform a functional categorisation of the resulting list of genes generated in the class comparison analysis.

Although our data are limited by the fact that this library was constructed from pooled samples collected at various growth stages, a functional categorisation of ESTs with known gene matches underlines general differences in the expression profiles of different tissues.

This model is implemented in a binary decision tree, where each node represents a linear regression model over a subset of genes and each non-leaf node represents a categorisation step (e.g. associated with H2A.Z (bivalent) or not).