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Soil fungal communities are an essential element in the terrestrial ecosystem, however their response to ongoing anthropogenic climate change is currently poorly understood. Fungi are one of the most abundant groups of microbes in soil, they are mainly responsible for the decomposition of organic matter (Baldrian et al., 2012; Buée et al., 2009). By binding carbon in soil, fungi thus maintain an important role in the global carbon cycle (Bardgett et al., 2008). Future climates are likely to influence the communities of belowground microbial organisms (Castro et al., 2010; Deacon et al., 2006). However, how these communities are affected in their diversity, composition, and function after environmental perturbation is insufficiently known.
Molecular techniques using high-throughput sequencing are presently revolutionizing the analysis of complex communities, such as soil fungi. High-throughput metabarcoding enables the recovery of DNA sequence data directly from environmental samples, and DNA sequences from entire communities present in these samples can be simultaneously recovered through massively parallel sequencing reactions (Bik et al., 2012; Taberlet et al., 2012b). This results in more accurate estimation of diversity and community composition and thus provides unprecedented insight into cryptic communities (Lindahl and Kuske, 2014). Yet, challenges associated with these novel techniques include the bioinformatic processing, and the ecological analyses of the large amount of sequence data generated. Most biologists without explicit training in bioinformatics spend a fair amount of time learning how to filter raw sequence data, and customize bioinformatics pipelines specific to their project. To improve the quality of data treatment, and decrease the time needed for the analyses, it is desirable to have bioinformatics pipelines that are easy to use, well explained to researchers not trained in bioinformatics, and adaptable to individual research needs...