The simple fool's guide to population genomics via RNA‐Seq: an introduction to high‐throughput sequencing data analysis

Abstract

High-throughput sequencing technologies are currently revolutionizing the field of biology and medicine, yet bioinformatic challenges in analysing very large data sets have slowed the adoption of these technologies by the community of population biologists. We introduce the 'Simple Fool's Guide to Population Genomics via RNA-seq' (SFG), a document intended to serve as an easy-to-follow protocol, walking a user through one example of high-throughput sequencing data analysis of nonmodel organisms. It is by no means an exhaustive protocol, but rather serves as an introduction to the bioinformatic methods used in population genomics, enabling a user to gain familiarity with basic analysis steps. The SFG consists of two parts. This document summarizes the steps needed and lays out the basic themes for each and a simple approach to follow. The second document is the full SFG, publicly available at http://sfg.stanford.edu, that includes detailed protocols for data processing and analysis, along with a repository of custom-made scripts and sample files. Steps included in the SFG range from tissue collection to de novo assembly, blast annotation, alignment, gene expression, functional enrichment, SNP detection, principal components and F(ST) outlier analyses. Although the technical aspects of population genomics are changing very quickly, our hope is that this document will help population biologists with little to no background in high-throughput sequencing and bioinformatics to more quickly adopt these new techniques.

Population Genomics of Marine Fishes: Next-Generation Prospects and Challenges

“…It is likely that this level of resolution will be needed to obtain a detailed understanding of patterns of genomic variationfor instance to understand the importance of structural variation for local adaptation (Lawniczak et al, 2010;Jones et al, 2012). Less resolution obtained through sequencing restriction-enzyme-digested genomes (Davey et al, 2011) or transcriptomes (De Wit et al, 2012) will be highly informative for more general assessments of genomic architectures and patterns of variation. These data could provide important information about the number and size of genomic regions showing signatures of adaptive population divergence.…”

Regulation of gene expression is associated with tolerance of the Arctic copepod Calanus glacialis to CO2‐acidified sea water

“…Sequenced reads were quality‐checked and used for de novo transcriptome assembly and quantification of gene expression following the pipeline in De Wit, Pespeni, and Ladner (2012) and the associated open‐access scripts (https://github.com/DeWitP/SFG; downloaded on 13 January 2016). In short, low‐quality sequenced bases (Phred score < 20) were removed using the Fastx‐toolkit (0.0.13), as were adapter sequences.…”

“…As a proxy of gene expression, the counts of uniquely mapped reads mapping to each contig were compiled for each sample using a custom script from De Wit et al. (2012) with a mapping quality threshold of 20 and a read length threshold of 20 bp.…”

Avian transcriptomics: opportunities and challenges

“…By sequencing a subset of the genome, the sequencing power can instead be focused on a small region of interest and more individuals can then be included for the same costs. As the transcriptome represents only the functional subset of the genome, the complexity is further reduced; thus there is great potential to study population genomics or phylogenomics using transcriptomic data (De Wit et al 2012). One experimental difficulty is to obtain non-degraded RNA from wild birds (see "Preservation methods").…”

“…The remaining sequences are then either aligned to a reference genome or transcriptome (also called 'mapping'), or in the absence of a reference genome assembled de novo Vijay et al 2013). For those interested in sequence diversity or genetic marker discovery, the assembled transcripts can then be used for further analysis (De Wit et al 2012). In comparative transcriptomic studies the quantitative expression levels of the genes are used to infer differential expression between groups of interest.…”