Aug 7, 2016

Longitudinal network theory approaches identify crucial factors affecting sporulation efficiency in yeast

BioRxiv : the Preprint Server for Biology
Camellia SarkarSarika Jalan

Abstract

Integrating network theory approaches over longitudinal genome-wide gene expression data is a robust approach to understand the molecular underpinnings of a dynamic biological process. Here, we performed a network-based investigation of longitudinal gene expression changes during sporulation of a yeast strain, SK1. Using global network attributes, viz. clustering coefficient, degree distribution of a node, degree-degree mixing of the connected nodes and disassortativity, we observed dynamic changes in these parameters indicating a highly connected network with inter-module crosstalk. Analysis of local attributes, such as clustering coefficient, hierarchy, betweenness centrality and Granovetter's weak ties showed that there was an inherent hierarchy under regulatory control that was determined by specific nodes. Biological annotation of these nodes indicated the role of specifically linked pairs of genes in meiosis. These genes act as crucial regulators of sporulation in the highly sporulating SK1 strain. An independent analysis of these network properties in a less efficient sporulating strain helped to understand the heterogeneity of network profiles. We show that comparison of network properties has the potential to identify ...Continue Reading

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Sporulation
Genome
Genes
Yeasts
Kcnn1 protein, mouse
Cross Reactions
Gene Expression
Evaluation
Meiosis
Vibrio sp. Sk1

About this Paper

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.

Related Papers

Molecular & General Genetics : MGG
C BreschR Egel
Social Networks
Martin G Everett, Thomas W Valente
Journal of Bioinformatics and Computational Biology
Omar Odibat, Chandan K Reddy
© 2020 Meta ULC. All rights reserved