Sentence examples for broad view of gene from inspiring English sources

GO slim allows a broad view of gene ontologies for use in genome scale analysis.

Microarray analysis (MA) of host-pathogen interactions can provide a broad view of gene expression during infection.

We present a broad view of gene expression in non-blood-fed male and female tissues, focusing particularly on gene families related to neuronal function and chemosensation.

A broad view of gene expression is provided by >4-fold coverage from nine distinct libraries (~300,000 SAGE tags) representing key developmental transitions in primary parasite populations and in laboratory strains representing the three canonical genotypes.

This comprehensive SAGE database offers a broad view of gene expression in primary as well as laboratory adapted parasite populations and defines fundamental changes in mRNA pools that will serve as a comparative base for future functional genomic studies.

All significantly overrepresented and underrepresented GO-Slim Biological Processes and PANTHER Protein Classes for each analysis are listed in File S1 and File S2. Results from the three analyses were similar, adding confidence to the signal and providing a broad view of gene expression during each developmental stage of M. genalis.

Therefore, our current work provides a broader view of gene expression profiles in different soybean lines in the presence or absence of SCN.

Our analysis also emphasizes that microarray datasets can be successfully combined to create a broader view of gene expression changes during development.

Classification by gene ontologies and cluster analysis by k-means of over and under expressed genes provided a broader view of gene expression and modulation in the soybean cotyledons during this process and more specifically during the cotyledonary functional transition from nutrient storage to photosynthesis.

To create a broader view of gene expression changes during development, we combined our dataset from the GD19 - PND67 male C57BL/6 mice with a dataset from CD-1 mice at GD11.5, GD12.5, GD13.5, GD14.5 and GD16.5 in which 10-week-old females were used as controls ([ 16]; GDS2577).

In addition, we employed a combinatory approach using bioinformatics, serum cytokine screens and microarray to get a broad view of the genes and pathways that may be targeted by these age-associated serum miRNAs.