2018
DOI: 10.1002/jez.b.22829
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From gene list to gene network: Recognizing functional connections that regulate behavioral traits

Abstract: The study of social breeding systems is often gene focused, and the field of insect sociobiology has been successful at assimilating tools and techniques from molecular biology. One common output from sociogenomic studies is a gene list. Gene lists are readily generated from microarray, RNA sequencing, or other molecular screens that typically aim to prioritize genes based on the differences in their expression. Gene lists, however, are often unsatisfying because the information they provide is simply tabular … Show more

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Cited by 4 publications
(3 citation statements)
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“…We conducted gene network analysis to identify co-expressed networks of genes underlying ovary size differences among individuals. This analysis may provide additional insight into functional connections between sets of genes underlying our phenotypes of interest that may not be captured during standard differential expression analysis (Faragalla et al, 2018). To do this, we used the WGCNA (weighted gene co-expression network analysis) package in R (Langfelder & Horvath, 2008) to identify modules of genes showing co-expression.…”
Section: Methodsmentioning
confidence: 99%
“…We conducted gene network analysis to identify co-expressed networks of genes underlying ovary size differences among individuals. This analysis may provide additional insight into functional connections between sets of genes underlying our phenotypes of interest that may not be captured during standard differential expression analysis (Faragalla et al, 2018). To do this, we used the WGCNA (weighted gene co-expression network analysis) package in R (Langfelder & Horvath, 2008) to identify modules of genes showing co-expression.…”
Section: Methodsmentioning
confidence: 99%
“…We conducted gene network analysis to identify co-expressed networks of genes underlying ovary size differences among individuals. This analysis may provide additional insight into functional connections between sets of genes underlying phenotypes of interest that may not be captured during standard differential expression analysis (Faragalla et al 2018). To do this, we used the WGCNA (weighted gene co-expression network analysis) package in R (Langfelder and Horvath 2008) to identify modules of genes showing co-expression.…”
Section: Gene Network Analysismentioning
confidence: 99%
“…We also targeted a TF related to Broad that did not exhibit a strong signature of transcriptional regulatory plasticity (see Results), to complement and contrast with Broad: Fushi tarazu transcription factor 1 (Ftz-F1). Ftz-F1 is a downstream target of Broad in highly conserved endocrine cascades that are critical to insect development (Bonneton and Laudet, 2012) and, like Broad, it is predicted to be a key regulator of several behaviors in honey bees (Ament et al, 2012a;Chandrasekaran et al, 2011;Faragalla et al, 2018). Additionally, like Broad, Ftz-F1's putative targets are enriched for gene ontology terms related to neuronal plasticity, cognition and learning and memory (Chandrasekaran et al, 2011).…”
Section: Tfs Chosen For Manipulationmentioning
confidence: 99%