UCLA

ABSTRACT

The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis, cilium assembly and function, lipid and starch metabolism and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot , to easily assess co-expression and anti-correlation between genes from manually-curated and community-generated gene lists. We extracted 400 high-confidence cilia-related genes at the intersection of multiple co-expressed lists, illustrating the power of our simple method. Surprisingly, Chlamydomonas experiments did not cluster according to an obvious pattern, suggesting an underappreciated variable during sample collection. One possible source of variation may stem from the strong clustering of nuclear genes as a function of their diurnal phase, even in samples collected in constant conditions, indicating substantial residual synchronization in batch cultures. We provide a step-by-step guide into the analysis of co-expression across Chlamydomonas transcriptome datasets to help foster gene function discovery.

One-sentence summary

we reveal co-expression potential between Chlamydomonas genes and describe strong synchronization of cells grown in batch cultures as a possible source of underappreciated variation.