Dataset Information

Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing.

ABSTRACT: Recent advances in molecular approaches and DNA sequencing have greatly progressed the field of ecology and allowed for the study of complex communities in unprecedented detail. Next generation sequencing (NGS) can reveal powerful insights into the diversity, composition, and dynamics of cryptic organisms, but results may be sensitive to a number of technical factors, including molecular practices used to generate amplicons, sequencing technology, and data processing. Despite the popularity of some techniques over others, explicit tests of the relative benefits they convey in molecular ecology studies remain scarce. Here we tested the effects of PCR replication, sequencing depth, and sequencing platform on ecological inference drawn from environmental samples of soil fungi. We sequenced replicates of three soil samples taken from pine biomes in North America represented by pools of either one, two, four, eight, or sixteen PCR replicates with both 454 pyrosequencing and Illumina MiSeq. Increasing the number of pooled PCR replicates had no detectable effect on measures of ?- and ?-diversity. Pseudo-?-diversity - which we define as dissimilarity between re-sequenced replicates of the same sample - decreased markedly with increasing sampling depth. The total richness recovered with Illumina was significantly higher than with 454, but measures of ?- and ?-diversity between a larger set of fungal samples sequenced on both platforms were highly correlated. Our results suggest that molecular ecology studies will benefit more from investing in robust sequencing technologies than from replicating PCRs. This study also demonstrates the potential for continuous integration of older datasets with newer technology.

SUBMITTER: Smith DP

PROVIDER: S-EPMC3938664 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing.

Smith Dylan P DP Peay Kabir G KG

PloS one 20140228 2

Recent advances in molecular approaches and DNA sequencing have greatly progressed the field of ecology and allowed for the study of complex communities in unprecedented detail. Next generation sequencing (NGS) can reveal powerful insights into the diversity, composition, and dynamics of cryptic organisms, but results may be sensitive to a number of technical factors, including molecular practices used to generate amplicons, sequencing technology, and data processing. Despite the popularity of s ...[more]

PMID: 24587293

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Project description:Motivation:Inferring population structure is important for both population genetics and genetic epidemiology. Principal components analysis (PCA) has been effective in ascertaining population structure with array genotype data but can be difficult to use with sequencing data, especially when low depth leads to uncertainty in called genotypes. Because PCA is sensitive to differences in variability, PCA using sequencing data can result in components that correspond to differences in sequencing quality (read depth and error rate), rather than differences in population structure. We demonstrate that even existing methods for PCA specifically designed for sequencing data can still yield biased conclusions when used with data having sequencing properties that are systematically different across different groups of samples (i.e. sequencing groups). This situation can arise in population genetics when combining sequencing data from different studies, or in genetic epidemiology when using historical controls such as samples from the 1000 Genomes Project. Results:To allow inference on population structure using PCA in these situations, we provide an approach that is based on using sequencing reads directly without calling genotypes. Our approach is to adjust the data from different sequencing groups to have the same read depth and error rate so that PCA does not generate spurious components representing sequencing quality. To accomplish this, we have developed a subsampling procedure to match the depth distributions in different sequencing groups, and a read-flipping procedure to match the error rates. We average over subsamples and read flips to minimize loss of information. We demonstrate the utility of our approach using two datasets from 1000 Genomes, and further evaluate it using simulation studies. Availability and implementation:TASER-PC software is publicly available at http://web1.sph.emory.edu/users/yhu30/software.html. Contact:yijuan.hu@emory.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

Dataset Information

Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing.

Publications

Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing.

Similar Datasets

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