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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Sustainable Agricultural Systems Laboratory » Research » Publications at this Location » Publication #400634
Research Project: Soil, Crop, and Manure Biochemistry and Molecular Ecology: Bridging Knowledge Gaps in Microbiome Response to Management and Climate Change

Location: Sustainable Agricultural Systems Laboratory

Title: When and why adjust environmental sequence relative abundances using Q-PCR

Author
item DIETRITCH, EPP SCHMIDT - University Of Maryland
item YARWOOD, STEPHANIE - University Of Maryland
item Maul, Jude

Submitted to: Microbial Ecology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/18/2023
Publication Date: 8/10/2023
Citation: Dietritch, E., Yarwood, S.A., Maul, J.E. 2023. When and why adjust environmental sequence relative abundances using Q-PCR. Microbial Ecology. https://doi.org/10.1007/s00248-023-02273-z.
DOI: https://doi.org/10.1007/s00248-023-02273-z

Interpretive Summary: High throughput, multiplexed-amplicon sequencing has become a core tool for understanding microbiomes. As researchers have widely adopted sequencing many open-source analysis pipelines have been developed to compare microbiomes based on the relative abundance of assigned sequence variants (ASVs). These data analysis pipelines can distinguish one community from another but lack reliable information about either the true abundance of taxa or the covariance of taxa along ecological gradients. This report describes the creation of an app, Qseq, that preforms statistical analysis and synthesis of multiplexed-amplicon sequencing raw data and then outputs formatted data for use in complementary downstream algorithms commonly used among the microbial ecology research community. The app is freely available and will be useful to any researcher conducting bioinformatic analysis of microbial multiplexed-amplicon sequencing. Although the app was developed using data derived from agroecosystems it is likely applicable to other fields of study including human health and industrial quality control and monitoring.

Technical Abstract: Recently, multiplexed-amplicon sequencing has been combined with quantitative PCR (qPCR), to generate “Quantitative Sequencing” (QSeq) data with the goal to more accurately estimate the true abundance of taxa. No systematic evaluation has been done to determine under what conditions (and for what inferences) analysis using QSeq offers an advantage over compositional analysis. The core assumption of compositional analysis is that count abundance in sequence data is confounded by sequencing depth, and therefore cannot convey direct information about the abundance of taxa. Log-ratio transformation mitigates some of the confounding effects of sequencing depth, but has the disadvantage that it gives only relative information and therefore in most cases cannot be used to infer changes in the true abundance of taxa in the environment. The central assumption of QSeq is that sequence data can provide direct information about the abundance of taxa if it is appropriately normalized using an independent measure of the total abundance of taxa in each sample. The QSeq transformation is done by first transforming the sequence count data into relative abundance, then scaling the counts to the total abundance of each sample. We show that QSeq is primarily useful for taxon-specific analysis; QSeq provided the greatest advantage when both an overlying gradient of total abundance existed, and when the effects of environment (differential abundance) and other taxa (i.e. covariance) was being evaluated. We show that most environmental datasets are likely to benefit from QSeq if taxon-specific questions are being asked.