Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution?

Abstract

Molecular ecology has moved beyond the use of a relatively small number of markers, often noncoding, and it is now possible to use whole-genome measures of gene expression with microarrays and RNAseq (i.e. transcriptomics) to capture molecular response to environmental challenges. While transcriptome studies are shedding light on the mechanistic basis of traits as complex as personality or physiological response to catastrophic events, these approaches are still challenging because of the required technical expertise, difficulties with analysis and cost. Still, we found that in the last 10 years, 575 studies used microarrays or RNAseq in ecology. These studies broadly address three questions that reflect the progression of the field: (i) How much variation in gene expression is there and how is it structured? (ii) How do environmental stimuli affect gene expression? (iii) How does gene expression affect phenotype? We discuss technical aspects of RNAseq and microarray technology, and a framework that leverages the advantages of both. Further, we highlight future directions of research, particularly related to moving beyond correlation and the development of additional annotation resources. Measuring gene expression across an array of taxa in ecological settings promises to enrich our understanding of ecology and genome function.

Genetic and epigenetic changes during the invasion of a cosmopolitan species (Phragmites australis)

“…Among all identified epigenetic mechanisms, including histone modifications, DNA methylation, and small noncoding RNAs, DNA methylation is relatively stable with transgenerational heritability that can be independent of heritable genes (Bird, 2007; Eichten, Schmitz, & Springer, 2014), with the result that DNA methylation has attracted the most attention in epigenetic studies in ecology and evolution (Alvarez, Schrey, & Richards, 2015). The pattern of DNA methylation can affect ecologically important phenotypes and plasticity (Herrera & Bazaga, 2013; Nicotra et al., 2015; Zhang et al., 2013) and may play a significant role in adaptation to various habitat conditions (Foust et al., 2016; Richards et al., 2012; Schulz et al., 2014).…”

Current Knowledge and Recent Advances in Marine Dinoflagellate Transcriptomic Research

“…Model organisms have major advantages over a non-model organism due to the availability of vast genomic resources to aid transcriptomics data analysis including gene annotations and prediction of gene interactions [125]. Annotation of non-model organisms like dinoflagellates based on the annotations from their closest model relatives and several limitations have been identified in this approach [125,126].…”

RNA-Seq as an Emerging Tool for Marine Dinoflagellate Transcriptome Analysis: Process and Challenges

“…This practice is often desirable when the main interest of the study is to gain vast transcriptomics data from the same individual without concerning the expression level (e.g., comparison of toxic and non-toxic dinoflagellates) as the transcriptome expression level is subjected to non-linear distortion during data normalization [34,88]. Another limitation is the fact that the information regarding the biological variation cannot be traced back and differently expressed genes from individual RNA samples cannot be detected [89]. It is crucial to check the quality of individual RNA samples before pooling the RNA as the quality of transcriptomics data will be affected by a contaminated or degraded sample [86].…”