Diet and lifestyle have a strong influence on gut microbiota, which in turn has important implications on a variety of health-related aspects. Despite great advances in the field, it remains unclear to which extent the composition of the gut microbiota is modulated by the intake of animal derived products, compared to a vegetable based diet. Here the specific impact of vegan, vegetarian, and omnivore feeding type on the composition of gut microbiota of 101 adults was investigated among groups homogeneous for variables known to have a role in modulating gut microbial composition such as age, anthropometric variables, ethnicity, and geographic area. The results displayed a picture where the three different dietetic profiles could be well distinguished on the basis of participant’s dietetic regimen. Regarding the gut microbiota; vegetarians had a significantly greater richness compared to omnivorous. Moreover, counts of Bacteroidetes related operational taxonomic units (OTUs) were greater in vegans and vegetarians compared to omnivores. Interestingly considering the whole bacterial community composition the three cohorts were unexpectedly similar, which is probably due to their common intake in terms of nutrients rather than food, e.g., high fat content and reduced protein and carbohydrate intake. This finding suggests that fundamental nutritional choices such as vegan, vegetarian, or omnivore do influence the microbiota but do not allow to infer conclusions on gut microbial composition, and suggested the possibility for a preferential impact of other variables, probably related to the general life style on shaping human gut microbial community in spite of dietary influence. Consequently, research were individuals are categorized on the basis of their claimed feeding types is of limited use for scientific studies, since it appears to be oversimplified.

BackgroundIn the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data.ResultsIn this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim.ConclusionmetaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2882-6) contains supplementary material, which is available to authorized users.

Motivation Single cell RNA-seq (scRNA-seq) count data show many differences compared with bulk RNA-seq count data, making the application of many RNA-seq pre-processing/analysis methods not straightforward or even inappropriate. For this reason, the development of new methods for handling scRNA-seq count data is currently one of the most active research fields in bioinformatics. To help the development of such new methods, the availability of simulated data could play a pivotal role. However, only few scRNA-seq count data simulators are available, often showing poor or not demonstrated similarity with real data. Results In this article we present SPARSim, a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. We demonstrate that SPARSim allows to generate count data that resemble real data in terms of count intensity, variability and sparsity, performing comparably or better than one of the most used scRNA-seq simulator, Splat. In particular, SPARSim simulated count matrices well resemble the distribution of zeros across different expression intensities observed in real count data. Availability and implementation SPARSim R package is freely available at http://sysbiobig.dei.unipd.it/? q=SPARSim and at https://gitlab.com/sysbiobig/sparsim. Supplementary information Supplementary data are available at Bioinformatics online.