Co-inoculation with novel nodule-inhabiting bacteria reduces the benefits of legume-rhizobium symbiosis

The ecologically and economically vital symbiosis between nitrogen-fixing rhizobia and leguminous plants is often thought of as a bi-partite interaction, yet studies increasingly show the prevalence of non-rhizobial endophytes (NREs) that occupy nodules alongside rhizobia. Yet, what impact these NREs have on plant or rhizobium fitness remains unclear. Here, we investigated four NRE strains found to naturally co-occupy nodules of the legume Medicago truncatula alongside Sinorhizobium meliloti in native soils. Our objectives were to (1) examine the direct and indirect effects of NREs on M. truncatula and S. meliloti fitness, and (2) determine whether NREs can re-colonize root and nodule tissues upon reinoculation. We identified one NRE strain (522) as a novel Paenibacillus species, another strain (717A) as a novel Bacillus species, and the other two (702A and 733B) as novel Pseudomonas species. Additionally, we found that two NREs (Bacillus 717A and Pseudomonas 733B) reduced the fitness benefits obtained from symbiosis for both partners, while the other two (522, 702A) had little effect. Lastly, we found that NREs were able to co-infect host tissues alongside S. meliloti. This study demonstrates that variation of NREs present in natural populations must be considered to better understand legume-rhizobium dynamics in soil communities.

msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

Micro-DeMix: A mixture beta-multinomial model for investigating the fecal microbiome compositions

Extensive research has uncovered the involvement of the human gut microbiome in various facets of human health, including metabolism, nutrition, physiology, and immune function. Researchers often study fecal microbiota as a proxy for understanding the gut microbiome. However, it has been demonstrated that this approach may not suffice to yield a comprehensive understanding of the entire gut microbial community. Emerging research is revealing the heterogeneity of the gut microbiome across different gastrointestinal (GI) locations in both composition and functions. While spatial metagenomics approach has been developed to address these variations in mice, limitations arise when applying it to human-subject research, primarily due to its invasive nature. With these restrictions, we introduce Micro-DeMix, a mixture beta-multinomial model that decomposes the fecal microbiome at compositional level to understand the heterogeneity of the gut microbiome across various GI locations and extract meaningful insights about the biodiversity of the gut microbiome. Moreover, Micro-DeMix facilitates the discovery of differentially abundant microbes between GI regions through a hypothesis testing framework. We utilize the Inflammatory Bowel Disease (IBD) data from the NIH Integrative Human Microbiome Project to demonstrate the effectiveness and efficiency of the proposed Micro-DeMix.

Viromes vs. mixed community metagenomes: choice of method dictates interpretation of viral community ecology

Background

Viruses, the majority of which are uncultivated, are among the most abundant biological entities on Earth. From altering microbial physiology to driving community dynamics, viruses are fundamental members of microbiomes. While the number of studies leveraging viral metagenomics (viromics) for studying uncultivated viruses is growing, standards for viromics research are lacking. Viromics can utilize computational discovery of viruses from total metagenomes of all community members (hereafter metagenomes) or use physical separation of virus-specific fractions (hereafter viromes). However, differences in the recovery and interpretation of viruses from metagenomes and viromes obtained from the same samples remain understudied.

Results

Here, we compare viral communities from paired viromes and metagenomes obtained from 60 diverse samples across human gut, soil, freshwater, and marine ecosystems. Overall, viral communities obtained from viromes were more abundant and species rich than those obtained from metagenomes, although there were some exceptions. Despite this, metagenomes still contained many viral genomes not detected in viromes. We also found notable differences in the predicted lytic state of viruses detected in viromes vs metagenomes at the time of sequencing. Other forms of variation observed include genome presence/absence, genome quality, and encoded protein content between viromes and metagenomes, but the magnitude of these differences varied by environment.

Conclusions

Overall, our results show that the choice of method can lead to differing interpretations of viral community ecology. We suggest that the choice of whether to target a metagenome or virome to study viral communities should be dependent on the environmental context and ecological questions being asked. However, our overall recommendation to researchers investigating viral ecology and evolution is to pair both approaches to maximize their respective benefits.

Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency

Background

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Interestingly, many microbiome studies have performed both sequencing experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, highlighting the potential for an integrative analysis to improve power of testing these signatures. However, differential experimental biases, partially overlapping samples, and differential library sizes pose tremendous challenges when combining the two datasets. Currently, researchers either discard one dataset entirely or use different datasets for different objectives.

Methods

In this article, we introduce the first method of this kind, named Com-2seq, that combines the two sequencing datasets for testing differential abundance at the genus and community levels while overcoming these difficulties. The new method is based on our LOCOM model (Hu et al., 2022), which employs logistic regression for testing taxon differential abundance while remaining robust to experimental bias. To benchmark the performance of Com-2seq, we introduce two ad hoc approaches: applying LOCOM to pooled taxa count data and combining LOCOM p-values from analyzing each dataset separately.

Results

Our simulation studies indicate that Com-2seq substantially improves statistical efficiency over analysis of either dataset alone and works better than the two ad hoc approaches. An application of Com-2seq to two real microbiome studies uncovered scientifically plausible findings that would have been missed by analyzing individual datasets.

Conclusions

Com-2seq performs integrative analysis of 16S and metagenomic sequencing data, which improves statistical efficiency and has the potential to accelerate the search of microbial communities and taxa that are involved in human health and diseases.

Effect of bovine respiratory disease on the respiratory microbiome: a meta-analysis

Background

Bovine respiratory disease (BRD) is the most devastating disease affecting beef and dairy cattle producers in North America. An emerging area of interest is the respiratory microbiome's relationship with BRD. However, results regarding the effect of BRD on respiratory microbiome diversity are conflicting.

Results

To examine the effect of BRD on the alpha diversity of the respiratory microbiome, a meta-analysis analyzing the relationship between the standardized mean difference (SMD) of three alpha diversity metrics (Shannon's Diversity Index (Shannon), Chao1, and Observed features (OTUs, ASVs, species, and reads) and BRD was conducted. Our multi-level model found no difference in Chao1 and Observed features SMDs between calves with BRD and controls. The Shannon SMD was significantly greater in controls compared to that in calves with BRD. Furthermore, we re-analyzed 16S amplicon sequencing data from four previously published datasets to investigate BRD's effect on individual taxa abundances. Additionally, based on Bray Curtis and Jaccard distances, health status, sampling location, and dataset were all significant sources of variation. Using a consensus approach based on RandomForest, DESeq2, and ANCOM-BC2, we identified three differentially abundant amplicon sequence variants (ASVs) within the nasal cavity, ASV5_Mycoplasma, ASV19_Corynebacterium, and ASV37_Ruminococcaceae. However, no ASVs were differentially abundant in the other sampling locations. Moreover, based on SECOM analysis, ASV37_Ruminococcaceae had a negative relationship with ASV1_Mycoplasma_hyorhinis, ASV5_Mycoplasma, and ASV4_Mannheimia. ASV19_Corynebacterium had negative relationships with ASV1_Mycoplasma_hyorhinis, ASV4_Mannheimia, ASV54_Mycoplasma, ASV7_Mycoplasma, and ASV8_Pasteurella.

Conclusions

Our results confirm a relationship between bovine respiratory disease and respiratory microbiome diversity and composition, which provide additional insight into microbial community dynamics during BRD development. Furthermore, as sampling location and sample processing (dataset) can also affect results, consideration should be taken when comparing results across studies.

A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.

Statistical challenges in longitudinal microbiome data analysis

The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.

Exposure to Antibacterial Chemicals Is Associated With Altered Composition of Oral Microbiome

Antimicrobial chemicals are used as preservatives in cosmetics, pharmaceuticals, and food to prevent the growth of bacteria and fungi in the products. Unintentional exposure in humans to such chemicals is well documented, but whether they also interfere with human oral microbiome composition is largely unexplored. In this study, we explored whether the oral bacterial composition is affected by exposure to antibacterial and environmental chemicals. Gingival fluid, urine, and interview data were collected from 477 adults (18–47 years) from the RHINESSA study in Bergen, Norway. Urine biomarkers of triclosan, triclocarban, parabens, benzophenone-3, bisphenols, and 2,4- and 2,5-dichlorophenols (DCPs) were quantified (by mass spectrometry). Microbiome analysis was based on 16S amplicon sequencing. Diversity and differential abundance analyses were performed to identify how microbial communities may change when comparing groups of different chemical exposure. We identified that high urine levels (>75th percentile) of propyl parabens were associated with a lower abundance of bacteria genera TM7 [G-3], Helicobacter, Megasphaera, Mitsuokella, Tannerella, Propionibacteriaceae [G-2], and Dermabacter, as compared with low propylparaben levels (<25^th percentile). High exposure to ethylparaben was associated with a higher abundance of Paracoccus. High urine levels of bisphenol A were associated with a lower abundance of Streptococcus and exposure to another environmental chemical, 2,4-DCP, was associated with a lower abundance of Treponema, Fretibacterium, and Bacteroidales [G-2]. High exposure to antibacterial and environmental chemicals was associated with an altered composition of gingiva bacteria; mostly commensal bacteria in the oral cavity. Our results highlight a need for a better understanding of how antimicrobial chemical exposure influences the human microbiome.

A 4-Week Diet Low or High in Advanced Glycation Endproducts Has Limited Impact on Gut Microbial Composition in Abdominally Obese Individuals: The deAGEing Trial

Dietary advanced glycation endproducts (AGEs), abundantly present in Westernized diets, are linked to negative health outcomes, but their impact on the gut microbiota has not yet been well investigated in humans. We investigated the effects of a 4-week isocaloric and macronutrient-matched diet low or high in AGEs on the gut microbial composition of 70 abdominally obese individuals in a double-blind parallel-design randomized controlled trial (NCT03866343). Additionally, we investigated the cross-sectional associations between the habitual intake of dietary dicarbonyls, reactive precursors to AGEs, and the gut microbial composition, as assessed by 16S rRNA amplicon-based sequencing. Despite a marked percentage difference in AGE intake, we observed no differences in microbial richness and the general community structure. Only the Anaerostipes spp. had a relative abundance >0.5% and showed differential abundance (0.5 versus 1.11%; p = 0.028, after low- or high-AGE diet, respectively). While the habitual intake of dicarbonyls was not associated with microbial richness or a general community structure, the intake of 3-deoxyglucosone was especially associated with an abundance of several genera. Thus, a 4-week diet low or high in AGEs has a limited impact on the gut microbial composition of abdominally obese humans, paralleling its previously observed limited biological consequences. The effects of dietary dicarbonyls on the gut microbiota composition deserve further investigation.

A Review on Differential Abundance Analysis Methods for Mass Spectrometry-Based Metabolomic Data

This review presents an overview of the statistical methods on differential abundance (DA) analysis for mass spectrometry (MS)-based metabolomic data. MS has been widely used for metabolomic abundance profiling in biological samples. The high-throughput data produced by MS often contain a large fraction of zero values caused by the absence of certain metabolites and the technical detection limits of MS. Various statistical methods have been developed to characterize the zero-inflated metabolomic data and perform DA analysis, ranging from simple tests to more complex models including parametric, semi-parametric, and non-parametric approaches. In this article, we discuss and compare DA analysis methods regarding their assumptions and statistical modeling techniques.

Ulcerative Colitis Seems to Imply Oral Microbiome Dysbiosis

Ulcerative colitis (UC) is a recurrent pathology of complex etiology that has been occasionally associated with oral lesions, but the overall composition of the oral microbiome in UC patients and its role in the pathogenesis of the disease are still poorly understood. In this study, the oral microbiome of UC patients and healthy individuals was compared to ascertain the possible changes in the oral microbial communities associated with UC. For this, the salivary microbiota of 10 patients diagnosed with an active phase of UC and 11 healthy controls was analyzed by 16S rRNA gene sequencing (trial ref. ISRCTN39987). Metataxonomic analysis revealed a decrease in the alpha diversity and an imbalance in the relative proportions of some key members of the oral core microbiome in UC patients. Additionally, Staphylococcus members and four differential species or phylotypes were only present in UC patients, not being detected in healthy subjects. This study provides a global snapshot of the existence of oral dysbiosis associated with UC, and the possible presence of potential oral biomarkers.

Effect of water management on microbial diversity and composition in an Italian rice field system

Traditional rice cultivation consumes up to 2500 L of water per kg yield and new strategies such as the ‘Alternate Wetting and Drying’ (AWD) might be promising water-saving alternatives. However, they might have large impacts on the soil microbiology. In this study, we compared the bacterial and archaeal communities in experimental field plots, cultivated under continuously flooding (CF) and AWD management, by high-throughput sequencing of the 16S rRNA gene. We analysed alpha and beta diversity in bulk soil and on plant roots, in plots cultivated with two different rice cultivars. The strongest difference was found between soil and root communities. Beside others, the anaerobic methanotroph Methanoperedens was abundant in soil, however, we detected a considerable number of ANME-2a-2b on plant roots. Furthermore, root communities were significantly affected by the water management: Differential abundance analysis revealed the enrichment of aerobic and potentially plant-growth-promoting bacteria under AWD treatment, such as Sphingomonadaceae and Rhizobiaceae (both Alphaproteobacteria), and Bacteroidetes families. Microorganisms with an overall anaerobic lifestyle, such as various Delta- and Epsilonproteobacteria, and Firmicutes were depleted. Our study indicates that the bulk soil communities seem overall well adapted and more resistant to changes in the water treatment, whereas the root microbiota seems more vulnerable.

Dynamics of the Salivary Microbiome During Different Phases of Crohn's Disease

Crohn's disease is a chronic disorder that typically affects the gastrointestinal tract. The increased incidence in the recent years, especially in Asian countries, prompts for performing studies and gain newer insights into the etiology and pathogenesis of the disease. Among other causative factors, gut microbiome and its cross-talk with the salivary microbiome is a known factor that has a plausible role in the pathogenesis of Crohn's disease. The gut microbiome has been extensively studied, however, the salivary microbiome and its dynamics during different phases of this disease remain understudied. In this study, we obtained saliva samples from the patients during active and remission phases of the disease and compared them with control samples and highlighted the differences in taxonomic as well as predicted functional pathways among them. Our results indicated that the α and β diversities were significantly lower during the active phase in contrast with remission phase and healthy samples. In general, Firmicutes were most abundant among the three sample groups, followed by Bacteroidetes and Proteobacteria. Genus level distribution highlighted Streptococcus, Neisseria, Prevotella, Haemophilus, and Veillonella as the five most abundant taxa. Differential abundance analysis of the three sample groups identified significant enrichment of 30 bacterial taxa in the active phase that included g_Prevotella, f_Prevotellaceae, and p_Bacteroidetes. Furthermore, remission phase and control also exhibited significant enrichment of 24 and 22 bacterial taxa, respectively. Eleven differentially abundant pathways were also identified, four were significantly enriched in healthy controls whereas other seven were significantly enriched in active phase of the disease. Several important pathways, such as ribosome biogenesis and Energy metabolism were depleted in the active phase. Our study has highlighted several taxa and functional categories that could be implicated with the onset of Crohn's disease and thus have the potential to serve as biomarkers of the active disease. However, these findings require further validation through functional studies in the future.

Eatomics: Shiny Exploration of Quantitative Proteomics Data

Quantitative proteomics data are becoming increasingly more available, and as a consequence are being analyzed and interpreted by a larger group of users. However, many of these users have less programming experience. Furthermore, experimental designs and setups are getting more complicated, especially when tissue biopsies are analyzed. Luckily, the proteomics community has already established some best practices on how to conduct quality control, differential abundance analysis and enrichment analysis. However, an easy-to-use application that wraps together all steps for the exploration and flexible analysis of quantitative proteomics data is not yet available. For Eatomics, we utilize the R Shiny framework to implement carefully chosen parts of established analysis workflows to (i) make them accessible in a user-friendly way, (ii) add a multitude of interactive exploration possibilities, and (iii) develop a unique experimental design setup module, which interactively translates a given research hypothesis into a differential abundance and enrichment analysis formula. In this, we aim to fulfill the needs of a growing group of inexperienced quantitative proteomics data analysts. Eatomics may be tested with demo data directly online via https://we.analyzegenomes.com/now/eatomics/ or with the user's own data by installation from the Github repository at https://github.com/Millchmaedchen/Eatomics.

Core and Differentially Abundant Bacterial Taxa in the Rhizosphere of Field Grown Brassica napus Genotypes: Implications for Canola Breeding

Modifying the rhizosphere microbiome through targeted plant breeding is key to harnessing positive plant-microbial interrelationships in cropping agroecosystems. Here, we examine the composition of rhizosphere bacterial communities of diverse Brassica napus genotypes to identify: (1) taxa that preferentially associate with genotypes, (2) core bacterial microbiota associated with B. napus, (3) heritable alpha diversity measures at flowering and whole growing season, and (4) correlation between microbial and plant genetic distance among canola genotypes at different growth stages. Our aim is to identify and describe signature microbiota with potential positive benefits that could be integrated in B. napus breeding and management strategies. Rhizosphere soils of 16 diverse genotypes sampled weekly over a 10-week period at single location as well as at three time points at two additional locations were analyzed using 16S rRNA gene amplicon sequencing. The B. napus rhizosphere microbiome was characterized by diverse bacterial communities with 32 named bacterial phyla. The most abundant phyla were Proteobacteria, Actinobacteria, and Acidobacteria. Overall microbial and plant genetic distances were highly correlated (R = 0.65). Alpha diversity heritability estimates were between 0.16 and 0.41 when evaluated across growth stage and between 0.24 and 0.59 at flowering. Compared with a reference B. napus genotype, a total of 81 genera were significantly more abundant and 71 were significantly less abundant in at least one B. napus genotype out of the total 558 bacterial genera. Most differentially abundant genera were Proteobacteria and Actinobacteria followed by Bacteroidetes and Firmicutes. Here, we also show that B. napus genotypes select an overall core bacterial microbiome with growth-stage-related patterns as to how taxa joined the core membership. In addition, we report that sets of B. napus core taxa were consistent across our three sites and 2 years. Both differential abundance and core analysis implicate numerous bacteria that have been reported to have beneficial effects on plant growth including disease suppression, antifungal properties, and plant growth promotion. Using a multi-site year, temporally intensive field sampling approach, we showed that small plant genetic differences cause predictable changes in canola microbiome and are potential target for direct and indirect selection within breeding programs.

Effects of Agricultural Management on Rhizosphere Microbial Structure and Function in Processing Tomato Plants

Agricultural management practices affect bulk soil microbial communities and the functions they carry out, but it remains unclear how these effects extend to the rhizosphere in different agroecosystem contexts. Given close linkages between rhizosphere processes and plant nutrition and productivity, understanding how management practices impact this critical zone is of great importance to optimize plant-soil interactions for agricultural sustainability. A comparison of six paired conventional-organic processing tomato farms was conducted to investigate relationships between management, soil physicochemical parameters, and rhizosphere microbial community composition and functions. Organically managed fields were higher in soil total N and NO₃-N, total and labile C, plant Ca, S, and Cu, and other essential nutrients, while soil pH was higher in conventionally managed fields. Differential abundance, indicator species, and random forest analyses of rhizosphere communities revealed compositional differences between organic and conventional systems and identified management-specific microbial taxa. Phylogeny-based trait prediction showed that these differences translated into more abundant pathogenesis-related gene functions in conventional systems. Structural equation modeling revealed a greater effect of soil biological communities than physicochemical parameters on plant outcomes. These results highlight the importance of rhizosphere-specific studies, as plant selection likely interacts with management in regulating microbial communities and functions that impact agricultural productivity.IMPORTANCE Agriculture relies, in part, on close linkages between plants and the microorganisms that live in association with plant roots. These rhizosphere bacteria and fungi are distinct from microbial communities found in the rest of the soil and are even more important to plant nutrient uptake and health. Evidence from field studies shows that agricultural management practices such as fertilization and tillage shape microbial communities in bulk soil, but little is known about how these practices affect the rhizosphere. We investigated how agricultural management affects plant-soil-microbe interactions by comparing soil physical and chemical properties, plant nutrients, and rhizosphere microbial communities from paired fields under organic and conventional management. Our results show that human management effects extend even to microorganisms living in close association with plant roots and highlight the importance of these bacteria and fungi to crop nutrition and productivity.

MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences.  While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results.

Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs.  Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files.

Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. 

MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data

We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible, and its R implementation is time-consuming for data sets containing a large number of peptides (e.g., >100 000). The new tool built in the C++ language is not only faster in computation time but also equipped with a flexible semiparametric model that handles skewed ratio distributions better. We have also developed a Perseus plugin for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential abundance analysis using mixture modeling. This approach is especially useful when the label-free data have many missing peptide intensity data points.

Discrete False-Discovery Rate Improves Identification of Differentially Abundant Microbes

DS-FDR can achieve higher statistical power to detect significant findings in sparse and noisy microbiome data compared to the commonly used Benjamini-Hochberg procedure and other FDR-controlling procedures.

Differential abundance testing is a critical task in microbiome studies that is complicated by the sparsity of data matrices. Here we adapt for microbiome studies a solution from the field of gene expression analysis to produce a new method, discrete false-discovery rate (DS-FDR), that greatly improves the power to detect differential taxa by exploiting the discreteness of the data. Additionally, DS-FDR is relatively robust to the number of noninformative features, and thus removes the problem of filtering taxonomy tables by an arbitrary abundance threshold. We show by using a combination of simulations and reanalysis of nine real-world microbiome data sets that this new method outperforms existing methods at the differential abundance testing task, producing a false-discovery rate that is up to threefold more accurate, and halves the number of samples required to find a given difference (thus increasing the efficiency of microbiome experiments considerably). We therefore expect DS-FDR to be widely applied in microbiome studies.

IMPORTANCE DS-FDR can achieve higher statistical power to detect significant findings in sparse and noisy microbiome data compared to the commonly used Benjamini-Hochberg procedure and other FDR-controlling procedures.

ABRF Proteome Informatics Research Group (iPRG) 2015 Study: Detection of Differentially Abundant Proteins in Label-Free Quantitative LC-MS/MS Experiments

Detection of differentially abundant proteins in label-free quantitative shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments requires a series of computational steps that identify and quantify LC-MS features. It also requires statistical analyses that distinguish systematic changes in abundance between conditions from artifacts of biological and technical variation. The 2015 study of the Proteome Informatics Research Group (iPRG) of the Association of Biomolecular Resource Facilities (ABRF) aimed to evaluate the effects of the statistical analysis on the accuracy of the results. The study used LC-tandem mass spectra acquired from a controlled mixture, and made the data available to anonymous volunteer participants. The participants used methods of their choice to detect differentially abundant proteins, estimate the associated fold changes, and characterize the uncertainty of the results. The study found that multiple strategies (including the use of spectral counts versus peak intensities, and various software tools) could lead to accurate results, and that the performance was primarily determined by the analysts' expertise. This manuscript summarizes the outcome of the study, and provides representative examples of good computational and statistical practice. The data set generated as part of this study is publicly available.

Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis

BACKGROUND

Experimental designs that take advantage of high-throughput sequencing to generate datasets include RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), sequencing of 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar and are composed of counts of sequencing reads mapped to a large number of features in each sample. Despite this underlying similarity, the data analysis methods used for these experimental designs are all different, and do not translate across experiments. Alternative methods have been developed in the physical and geological sciences that treat similar data as compositions. Compositional data analysis methods transform the data to relative abundances with the result that the analyses are more robust and reproducible.

RESULTS

Data from an in vitro selective growth experiment, an RNA-seq experiment and the Human Microbiome Project 16S rRNA gene abundance dataset were examined by ALDEx2, a compositional data analysis tool that uses Bayesian methods to infer technical and statistical error. The ALDEx2 approach is shown to be suitable for all three types of data: it correctly identifies both the direction and differential abundance of features in the differential growth experiment, it identifies a substantially similar set of differentially expressed genes in the RNA-seq dataset as the leading tools and it identifies as differential the taxa that distinguish the tongue dorsum and buccal mucosa in the Human Microbiome Project dataset. The design of ALDEx2 reduces the number of false positive identifications that result from datasets composed of many features in few samples.

CONCLUSION

Statistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.