One major limitation of microbial community marker gene sequencing is that it does not provide direct information on the functional composition of sampled communities. Here, we present PICRUSt2, which expands the capabilities of the original PICRUSt method to predict approximate functional potential of a community based on marker gene sequencing profiles. This updated method and implementation includes several improvements over the previous algorithm: an expanded database of gene families and reference genomes, a new approach now compatible with any OTU-picking or denoising algorithm, novel phenotype predictions, and novel fungal reference databases that enable predictions from 18S rRNA gene and internal transcribed spacer amplicon data. Upon evaluation, PICRUSt2 was more accurate than PICRUSt1 and other current approaches and also more flexible to allow the addition of custom reference databases. Last, we demonstrate the utility of PICRUSt2 by identifying potential disease-associated microbial functional signatures based on 16S rRNA gene sequencing of ileal biopsies collected from a cohort of human subjects with inflammatory bowel disease. PICRUSt2 is freely available at: https://github.com/picrust/picrust2.

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PICRUSt2: An improved and extensible approach for metagenome inference

One major limitation of microbial community marker gene sequencing is that it does not provide direct information on the functional composition of sampled communities. Here, we present PICRUSt2 (https://github.com/picrust/picrust2), which expands the capabilities of the original PICRUSt method1 to predict the functional potential of a community based on marker gene sequencing profiles. This updated method and implementation includes several improvements over the previous algorithm: an expanded database of gene families and reference genomes, a new approach now compatible with any OTU-picking or denoising algorithm, and novel phenotype predictions. Upon evaluation, PICRUSt2 was more accurate than PICRUSt1 and other current approaches overall. PICRUSt2 is also now more flexible and allows the addition of custom reference databases. We highlight these improvements and also important caveats regarding the use of predicted metagenomes, which are related to the inherent challenges of analyzing metagenome data in general.

/pdf/picrust2-an-improved-and-customizable-approach-for-2g2i6l8srj.pdf

PICRUSt2: An improved and customizable approach for metagenome inference

Microbial networks are an increasingly popular tool to investigate microbial community structure, as they integrate multiple types of information and may represent systems-level behaviour. Interpreting these networks is not straightforward, and the biological implications of network properties are unclear. Analysis of microbial networks allows researchers to predict hub species and species interactions. Additionally, such analyses can help identify alternative community states and niches. Here, we review factors that can result in spurious predictions and address emergent properties that may be meaningful in the context of the microbiome. We also give an overview of studies that analyse microbial networks to identify new hypotheses. Moreover, we show in a simulation how network properties are affected by tool choice and environmental factors. For example, hub species are not consistent across tools, and environmental heterogeneity induces modularity. We highlight the need for robust microbial network inference and suggest strategies to infer networks more reliably.

From hairballs to hypotheses-biological insights from microbial networks.

Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.

/pdf/a-practical-guide-to-amplicon-and-metagenomic-analysis-of-3b4dhcga92.pdf

A practical guide to amplicon and metagenomic analysis of microbiome data

Soil salinization is a growing environmental problem caused by both natural and human activities. Excessive salinity in soil suppresses growth, decreases species diversity, and alters the community composition of plants; however, the effect of salinity on soil microbial communities is poorly understood. Here, we characterize the soil microbial community along a natural salinity gradient in Gurbantunggut Desert, Northwestern China. Microbial diversity linearly decreased with increases in salinity, and community dissimilarity significantly increased with salinity differences. Soil salinity showed a strong effect on microbial community dissimilarity, even after controlling for the effects of spatial distance and other environmental variables. Microbial phylotypes (n = 270) belonging to Halobacteria, Nitriliruptoria, [Rhodothermi], Gammaproteobacteria, and Alphaproteobacteria showed a high-salinity niche preference. Out of nine potential phenotypes predicted by BugBase, oxygen-related phenotypes showed a significant relationship with salinity content. To explore the community assembly processes, we used null models of within-community (nearest-taxon index [NTI]) and between-community (βNTI) phylogenetic composition. NTI showed a significantly negative relationship with salinity, suggesting that the microbial community was less phylogenetically clustered in more-saline soils. βNTI, the between-community analogue of NTI, showed that deterministic processes have overtaken stochastic processes across all sites, suggesting the importance of environmental filtering in microbial community assembly. Taken together, these results suggest the importance of salinity in soil microbial community composition and assembly processes in a desert ecosystem. IMPORTANCE Belowground microorganisms are indispensable components for nutrient cycling in desert ecosystems, and understanding how they respond to increased salinity is essential for managing and ameliorating salinization. Our sequence-based data revealed that microbial diversity decreased with increasing salinity, and certain salt-tolerant phylotypes and phenotypes showed a positive relationship with salinity. Using a null modeling approach to estimate microbial community assembly processes along a salinity gradient, we found that salinity imposed a strong selection pressure on the microbial community, which resulted in a dominance of deterministic processes. Studying microbial diversity and community assembly processes along salinity gradients is essential in understanding the fundamental ecological processes in desert ecosystems affected by salinization.

Salinity Is a Key Determinant for Soil Microbial Communities in a Desert Ecosystem.

Shotgun metagenomics and marker gene amplicon sequencing can be used to directly measure or predict the functional repertoire of the microbiota en masse, but current methods do not readily estimate the functional capability of individual microorganisms. Here we present BugBase, an algorithm that predicts organism-level coverage of functional pathways as well as biologically interpretable phenotypes such as oxygen tolerance, Gram staining, and pathogenic potential, within complex microbiomes using either whole-genome shotgun or marker gene sequencing data. We find the organism-level pathway coverage of BugBase predictions to be statistically higher powered than current bag-of-genes approaches for discerning functional changes in both host-associated and environmental microbiomes.

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Larson J

Papers

BugBase predicts organism-level microbiome phenotypes