Abstract
The overrepresented approach (ORA) is the most widely-accepted method for functional analysis of microarray datasets. The ORA is computationally-efficient and robust; however, it suffers from the inability of comparing results from multiple gene lists particularly with time-course experiments or those involving multiple treatments. To overcome such limitation a novel method termed Dynamic Impact Approach (DIA) is proposed. The DIA provides an estimate of the biological impact of the experimental conditions and the direction of the impact. The impact is obtained by combining the proportion of differentially expressed genes (DEG) with the log2 mean fold change and mean –log P-value of genes associated with the biological term. The direction of the impact is calculated as the difference of the impact of up-regulated DEG and down-regulated DEG associated with the biological term. The DIA was validated using microarray data from a time-course experiment of bovine mammary gland across the lactation cycle. Several annotation databases were analyzed with DIA and compared to the same analysis performed by the ORA. The DIA highlighted that during lactation both BTA6 and BTA14 were the most impacted chromosomes; among Uniprot tissues those related with lactating mammary gland were the most positively-impacted; within KEGG pathways ‘Galactose metabolism’ and several metabolism categories related to lipid synthesis were among the most impacted and induced; within Gene Ontology “lactose biosynthesis” among Biological processes and “Lactose synthase activity” and “Stearoyl-CoA 9-desaturase activity” among Molecular processes were the most impacted and induced. With the exception of the terms ‘Milk’, ‘Milk protein’ and ‘Mammary gland’ among Uniprot tissues and SP_PIR_Keyword, the use of ORA failed to capture as significantly-enriched (i.e., biologically relevant) any term known to be associated with lactating mammary gland. Results indicate the DIA is a biologically-sound approach for analysis of time-course experiments. This tool represents an alternative to ORA for functional analysis.
Highlights
The gold standard for the functional analysis of highthroughput datasets is the enrichment analysis, called overrepresented approach or ORA [1]
When the proportion of genes associated with a particular biological term is higher than what is is the case with experiments including multiple treatments or time points [1]
There was significant enrichment of terms related to translation at –15 vs. –30d and terms related to Overall, our analysis indicated that the Dynamic Impact Approach (DIA) is a reliable method to uncover known biological functions affected during lactation in bovine mammary (e.g., BTA6 and 14 among chromosomes, all mammary related UP_terms during lactation, and lactose synthesis in other databases) and appears to have outperformed the ORA
Summary
The gold standard for the functional analysis of highthroughput datasets is the enrichment analysis, called overrepresented approach or ORA [1]. Time-course experiments lend themselves to use of microarray analysis (or any high-throughput technique) because the dynamic nature of the changing transcriptome can be captured. This approach allows for the study of adaptations of the tissue to the environment in ‘‘real-time’’; one can infer the biological adaptations of the tissue using the transcriptome. The inability of ORA to capture the dynamic changes in functions inferred by the transcriptome limits its use in highthroughput time-course experiments. There is an urgent need to develop a new approach to functional analysis of microarray datasets from time-course experiments. The DIA was validated using microarray data from a large time-course experiment of bovine mammary tissue during an entire lactation cycle. Validation was further performed by comparing the results of DIA vs. the results from the ORA
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