The massive growth of transcriptomics beyond clinical biology and into the fields of ecology and evolution has been fueled by RNA sequencing (RNA-seq). RNA-seq promised unprecedented sensitivity for sensing the expression differences among rare transcripts, splice variants, and microRNAs, then quickly surpassing microarrays as the high-throughput method for studying differential expression in non-model species.
Detecting Ecological Variations using RNA Sequencing
Biologists have rapidly adopted RNA-seq to resolve major concerns in ecology and evolution that whole-transcriptome analysis is feasible in almost any organism currently. RNA-seq allows researchers to study the evolutionary forces shaping gene expression at the whole-transcriptome level, whereas microarrays suffer from strain- or species-specific probe biases.
Ribosome allows researchers to examine expression differences underlying interindividual or interpopulation variation within ecological contexts. This can be applied to other ecologically important traits, such as disease resistance and mating behavior, as well as identify genes with potential adaptive significance in changing environments.
Understanding the Mechanisms of Phenotypic and Behavioral Plasticity using RNA Sequencing
RNA-seq is a critical technology that has aided in comprehending molecular mechanisms of phenotypic and behavioral plasticity in wild populations using integrative biology. For example, incorporating RNA-seq DE analysis with quantitative PCR (qPCR) and transcription factor binding site information allowed researchers to identify gene regulatory networks underlying the development of alternative jaw phenotypes in cichlid fish.
Measurement of gene expression in wild populations and natural settings remains challenging. Nevertheless, in natural settings, non-model species gene expression measurements are subject to high biological and technical variability. Regardless of the technology used, transcription is a stochastic process, and biological variability must be factored into experimental design. The probability of properly rejecting the null hypothesis in the context of RNA-seq equals the probability of accurately identifying a gene or transcript differentially expressed between conditions, which builds the power of any statistical significance test. Biological replication is just as crucial for RNA-seq as it was for microarrays when determining the statistical significance of DE tests. For data sets indicating cell lines or inbred strains, ecological and evolutionary studies often necessitate larger sample sizes to accomplish the same power as their clinical counterparts, despite more limited budgets.
Ongoing Challenges of Applying RNA Sequencing in Ecology
In ecology and evolution, RNA-seq holds promises for whole-transcriptome gene expression analysis, but there are still obstacles to overcome. Following significant reductions in the cost of sequencing and major progress in sequencing technologies’ effectiveness, RNA-seq has become increasingly popular in published research. However, this has not yet been interpreted into clear advancements in experimental design for biological replication. Hypothesis-driven RNA-seq necessitates a meticulous experimental design that considers desired power in the context of the study's objectives. Since gene expression is sensitive to environmental stimuli, field-based RNA-seq experiments on wild populations can help generate hypotheses, but drawing firm conclusions about the processes underlying observed expression differences can be difficult without repeated replications.
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