RNA-Seq Analysis (RNAA02) – Applications for Evolutionary Biology Only 3 places left! https://prstats.org/course/rna-seq-analysis-rnaa02/?utm_source=chatgpt.com Delivered by Dr. Frances Turner, a bioinformatician at the University of Edinburgh with extensive experience supporting life-science researchers in the analysis and interpretation of high-throughput sequencing data. Her expertise includes transcriptomics, genomics, metagenomics, data quality, reproducible bioinformatics workflows, and downstream biological interpretation. Learn how to analyse bulk RNA-Seq data, from experimental design and raw sequencing data through quality control, alignment, gene-expression quantification, differential expression, visualisation, and functional analysis. RNA-Seq has become a powerful tool in evolutionary biology, allowing researchers to investigate how changes in gene expression contribute to adaptation, phenotypic variation, evolutionary responses, and divergence among populations and species. Transcriptomic approaches can reveal the molecular mechanisms underlying responses to environmental pressures, identify genes associated with adaptive traits, and help connect genomic variation with phenotype. This course provides comprehensive, hands-on training in RNA-Seq analysis, with methods highly applicable to evolutionary biology, evolutionary ecology, and comparative genomics. What you'll gain: A strong understanding of RNA-Seq experimental design Practical experience with raw sequencing data and quality control Skills in sequence alignment and post-alignment quality assessment Understanding of gene-expression quantification and its potential pitfalls Practical experience performing differential expression analysis with DESeq2 Skills in PCA for investigating patterns of transcriptomic variation Experience analysing complex experimental designs, covariates, and continuous variables Skills in visualising differential expression using volcano plots, MA plots, and other approaches Understanding of functional analysis of differentially expressed genes using FGSEA Course format: 4-day live, instructor-led online course Hands-on practical exercises throughout Real RNA-Seq analysis workflows Interactive discussions and opportunities to discuss participants' own datasets All code, datasets, and presentation materials provided Sessions recorded with 30 days of recording access and post-course email support Who is this course for? Evolutionary biologists and evolutionary ecologists Population and evolutionary geneticists Evolutionary genomic researchers Researchers studying adaptation and phenotypic variation Researchers working with transcriptomic and high-throughput sequencing datasets PhD students and quantitative life scientists Participants should have basic experience with R, RStudio, and Linux, together with a basic understanding of transcriptomics and molecular biology. Why take this course? Understanding evolutionary change increasingly requires researchers to investigate not only variation in DNA sequence, but also how genes are expressed and regulated. RNA-Seq provides a powerful framework for examining how gene expression varies between populations, species, environments, treatments, and phenotypes. Transcriptomic approaches can be used to investigate adaptation, phenotypic plasticity, environmental responses, population divergence, evolutionary development, host–pathogen interactions, and the molecular basis of complex traits. Integrating RNA-Seq with genomic and ecological data can therefore provide important insights into the mechanisms connecting genotype, phenotype, and evolutionary processes. This course equips you with the practical skills needed to take RNA-Seq data from initial quality assessment through to differential expression and functional interpretation. Whether you're investigating adaptation, population divergence, environmental responses, comparative transcriptomics, or genotype–phenotype relationships, you'll gain a reproducible analytical workflow that can be applied directly to evolutionary research. Course dates 21–24 September 2026 18:00–21:30 UK time 14 hours | Live online | £350 Questions? Email: oliver@prstats.org (to subscribe/unsubscribe the EvolDir send mail to evoldir@evoldir.net)