Features of the dataset

This dataset was selected for our exercise on NGS Data Carpentry for several reasons, including:

• Simple, but iconic NGS-problem: Examine a population where we want to characterize changes in sequence a priori
• Dataset publicly available - in this case through the NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra)
• Small file sizes - while several of related files may still be hundreds of MBs, overall we will be able to get through more quickly than if we worked with a larger eukaryotic genome

Introduction to the dataset

Temperatures and precipitations in the Arctic are expected to increase dramatically over the next century, yet little is known about how microbial communities and their underlying metabolic processes will be affected by these environmental changes. To address this knowledge gap, we adopted a unique space-for-time design to analyse sediments sampled from Lake Hazen, NU Canada. Over the past decade, in this world’s largest high Arctic lake by volume, climate change has enhanced glacial melt, resulting in increased annual runoff from the watershed. We exploit the spatial heterogeneity created by varying runoff regimes, conducting metagenomic analyses of lake sediments along these spatial gradients to study how a transition from low to high runoff, used as a proxy for environmental change, affects microbial community structure and functional potential. We sampled sediment and soil sites that capture different runoff regimes from high (H), low (L), and negligible (C). Here we show that increasing runoff leads to a decrease in taxonomic and functional diversity Colby et al 2019.

References

Colby GA, Ruuskanen MO, St. Pierre KA, et al. Climate change negatively impacts dominant microbes in the sediments of a High Arctic lake. bioRxiv; 2019. DOI: 10.1101/705178. Paper Data on NCBI SRA: https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=PRJNA556841
Data on EMBL-EBI ENA: https://www.ebi.ac.uk/ena/data/view/PRJNA556841