DOI: 10.5061/dryad.fxpnvx0r3
title: Data from: Pitfalls and pointers: an accessible guide to marker gene amplicon sequencing in ecological applications

description: Next Generation Sequencing (NGS) is a powerful tool that has been rapidly
 adopted by many ecologists studying microbial communities. Despite the
 exciting demonstration of NGS technology as a tool for ecological
 research, cryptic pitfalls inherent to its use can obscure correct
 interpretation of NGS data. Here, we provide an accessible overview of a
 NGS process that uses marker gene amplicon sequences (MGAS) that will
 allow scientists, particularly community ecologists, to make appropriate
 methodological choices and understand limits on inference about community
 composition and diversity that can be drawn from MGAS data. We describe
 the MGAS pipeline, focusing specifically on cryptic sources of variation
 that have received less emphasis in the ecological literature, but which
 may substantially impact inference about microbial community diversity and
 composition. By simulating communities from published microbiome data, we
 demonstrate how these sources of variation can generate inaccurate or
 misleading patterns. We specifically highlight sample dilution without
 researcher awareness and lane-to-lane variability, two cryptic sources of
 variation arising during the MGAS pipeline. These sources of variation
 affect estimates of species presence and relative abundance, particularly
 for species with moderate to low abundances. Each of these sources of bias
 can lead to errors in the estimation of both absolute and relative
 abundance within, and turnover among, microbial communities. Awareness and
 understanding of what happens and, specifically, why it happens during
 MGAS generation is key to generating a strong data set and building a
 robust community matrix. Requesting sample dilution information from the
 sequencing center, including technical replicates across sequencing lanes,
 and understanding how sampling intensity and community taxa distribution
 patterns shape the measurement of community richness, evenness, and
 diversity are critical for drawing correct ecological inferences using
 MGAS data.
description: Comparisons within and
 across communities commonly involve measures of diversity.
 <b> </b>With the awareness that diluting samples or placing
 samples on different lanes can exclude rare taxa and misrepresent species
 abundance and presence, building and working with the community matrix
 generated by MGAS data raises downstream concerns.
 <b> </b>Here we demonstrate how the community matrix can
 affect measures of diversity depending on the types of communities (skew
 of their rank-abundance distributions) and sampling effort (i.e.,
 <i>depth</i> of sequencing).  Using published MGAS data, we
 evaluate the bias arising from sampling communities with different
 evenness (i.e., skew in rank-abundance distribution) at different
 intensities. Utilizing ITS1 sequence data generated with Illumina MiSeq
 that represent foliar fungal endophyte community samples from about 120
 prairie grass plants across 4 sites in the north central United States
 (Seabloom et al. 2019), we fit rank-abundance distributions to the
 OTU's using the vegan package (Oksanen et al., 2008) in R version
 3.5.2 (R Core Team, 2013).  We then simulated communities with a skew to
 match the empirical data (Fig. 3, center column), doubling the skew
 parameter (left column) or halving the skew (right column) to generate
 three versions of skew that are centered around empirical data.  These
 examples (Fig. 3) represent typical communities ranging from high
 evenness, such as dispersed plant litter (Albright & Martiny,
 2018), to very low evenness (highly skewed), such as the gut microbiome of
 human infants (Pannaraj et al., 2017).  All simulated communities
 contained 2,753 species, which was identified as the number of species in
 the empirical data. We simulated communities with
 multinomial distributions of 2,753 categories (representing the number of
 unique species from the empirical data), with probabilities of each
 category drawn from the three rank abundance distributions.  For each skew
 scenario, we simulated communities of 1 million, 13 million, or 26 million
 OTUs, to represent a range of microbial community sizes identified across
 natural systems and in mock communities (from aphids (Jousselin et al.,
 2016), plants (Seabloom et al., 2019), and mock communities (Bokulich et
 al., 2013), respectively).  Then we sub-sampled each simulated ‘parent’
 community by randomly drawing 100, 200, 500, 1000, 2500, 5000, 7500, or
 10000 individuals (representing potential sample dilution and the depth of
 sequencing).  For both the simulated parent communities (horizontal dashed
 lines in Fig. 3) and the sub-sampled communities (points with SE), we
 calculated richness, abundance-rarefied richness (rarified to 1,000
 individuals)  and common measures of alpha diversity including Shannon’s
 diversity index (Shannon, 1948) and inverse Simpson’s diversity (Simpson,
 1949), also known as the Effective Number of Species based on the
 Probability of Interspecific Encounter
 (<i>ENS<sub>PIE</sub>)</i> (Chase &
 Knight, 2013).  We also calculated the compositional similarity between
 the sample and the parent (full) community using abundance-based
 Bray-Curtis (Bray & Curtis, 1957) and incidence-based Jaccard
 (Jaccard, 1901) similarities.  The simulations were replicated 100x, 20x,
 and 10x for the communities of 1 million, 13 million, and 26 million
 individuals, respectively.