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Spaceflight is rigorous and dangerous environment which can negatively affect astronauts’ health and the entire mission. Recently studies showed that the human gut microbiota were enormously affected by spaceflight. The 60 days of 6 degrees head down bed rest(HDBR) experiment provided us with an opportunity to trace the change of gut microbiota under simulated microgravity. The gut microbiota of volunteers was analyzed and characterized by 16S rRNA gene sequencing and metagenomic sequencing. Our results showed that the composition and function of the volunteers’ gut microbiota were markedly affected by 60 days of 6 degrees HDBR. We further confirmed the species and diversity fluctuations. Resistance and virulence genes in the gut microbiota were also affected by 60 days of 6 degrees HDBR, but the species attributions remained stable. The human gut microbiota affected by 60 days of 6 degrees HDBR which was partially consistent with the effect of spaceflight, implying that HDBR can be used as the spaceflight simulation model in terms of gut microbiota.

Microbiome samples with low microbial biomass or severe DNA degradation remain challenging for amplicon-based or whole-metagenome sequencing approaches. Here, we introduce 2bRAD-M, a highly reduced and cost-effective strategy which only sequences ~ 1% of metagenome and can simultaneously produce species-level bacterial, archaeal, and fungal profiles. 2bRAD-M can accurately generate species-level taxonomic profiles for otherwise hard-to-sequence samples with merely 1 pg of total DNA, high host DNA contamination, or severely fragmented DNA from degraded samples. Tests of 2bRAD-M on various stool, skin, environmental, and clinical FFPE samples suggest a successful reconstruction of comprehensive, high-resolution microbial profiles. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02576-9.

A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile., Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (

Background Due to their much lower costs in experiment and computation than metagenomic whole-genome sequencing (WGS), 16S rRNA gene amplicons have been widely used for predicting the functional profiles of microbiome, via software tools such as PICRUSt 2. However, due to the potential PCR bias and gene profile variation among phylogenetically related genomes, functional profiles predicted from 16S amplicons may deviate from WGS-derived ones, resulting in misleading results. Results Here we present Meta-Apo, which greatly reduces or even eliminates such deviation, thus deduces much more consistent diversity patterns between the two approaches. Tests of Meta-Apo on > 5000 16S-rRNA amplicon human microbiome samples from 4 body sites showed the deviation between the two strategies is significantly reduced by using only 15 WGS-amplicon training sample pairs. Moreover, Meta-Apo enables cross-platform functional comparison between WGS and amplicon samples, thus greatly improve 16S-based microbiome diagnosis, e.g. accuracy of gingivitis diagnosis via 16S-derived functional profiles was elevated from 65 to 95% by WGS-based classification. Therefore, with the low cost of 16S-amplicon sequencing, Meta-Apo can produce a reliable, high-resolution view of microbiome function equivalent to that offered by shotgun WGS. Conclusions This suggests that large-scale, function-oriented microbiome sequencing projects can probably benefit from the lower cost of 16S-amplicon strategy, without sacrificing the precision in functional reconstruction that otherwise requires WGS. An optimized C++ implementation of Meta-Apo is available on GitHub (https://github.com/qibebt-bioinfo/meta-apo) under a GNU GPL license. It takes the functional profiles of a few paired WGS:16S-amplicon samples as training, and outputs the calibrated functional profiles for the much larger number of 16S-amplicon samples.

Additional file 1: Figure S1. Comparison of the dominated functional profiles annotated by KEGG BRITE hierarchical level 2 classification. Figure S2. Comparison of the dominated functional profiles annotated by KEGG BRITE hierarchical level 1 classification. Figure S3. Meta-Apo significantly reduces the derivation of functional profile between amplicon and WGS datasets from Dataset 2. Figure S4. Functional beta diversity of the 295 WGS-amplicon sample pairs of Dataset 2. Figure S5. The 2655 WGS samples and the 5350 amplicon samples from Dataset 3 have consistent overall taxonomical patterns at the Genus level. Figure S6. Functional beta diversity of the 2045 WGS samples and the 2186 V1-V3 region amplicon samples from Dataset 4. Figure S7. Calibration of skin amplicons using different habitat models. Figure S8. Calibration of amplicons for disease detection using status-specific models. Figure S9. Calibration of amplicons using training samples that produced under inconsistent experiment protocols.

Functional beta-diversity analysis on numerous microbiomes interprets the linkages between metabolic functions and their meta-data. To evaluate the microbiome beta-diversity, widely used distance metrices only count overlapped gene families but omit their inherent relationships, resulting in erroneous distances due to the sparsity of high-dimensional function profiles. Here we propose Hierarchical Meta-Storms (HMS) to tackle such problem. HMS contains two core components: (i) a dissimilarity algorithm that comprehensively measures functional distances among microbiomes using multi-level metabolic hierarchy and (ii) a fast Principal Co-ordinates Analysis (PCoA) implementation that deduces the beta-diversity pattern optimized by parallel computing. Results showed HMS can detect the variations of microbial functions in upper-level metabolic pathways, however, always missed by other methods. In addition, HMS accomplished the pairwise distance matrix and PCoA for 20 000 microbiomes in 3.9 h on a single computing node, which was 23 times faster and 80% less RAM consumption compared to existing methods, enabling the in-depth data mining among microbiomes on a high resolution. HMS takes microbiome functional profiles as input, produces their pairwise distance matrix and PCoA coordinates. Availability and implementation It is coded in C/C++ with parallel computing and released in two alternative forms: a standalone software (https://github.com/qdu-bioinfo/hierarchical-meta-storms) and an equivalent R package (https://github.com/qdu-bioinfo/hrms). Supplementary information Supplementary data are available at Bioinformatics Advances online.

Microbiome samples with low microbial biomass or severe DNA degradation remain challenging for amplicon-based (e.g., 16S/18S-rRNA) or whole-metagenome sequencing (WMS) approaches. Here, we introduce 2bRAD-M, a highly reduced and cost-effective metagenome-sequencing strategy which only sequences ~1% of metagenome and can simultaneously produce species-level bacterial, archaeal, and fungal profiles for low-biomass and highly degraded samples. For mock communities, 2bRAD-M can accurately generate species-level taxonomic profiles for otherwise hard-to-sequence samples with (i) low biomass of merely 1 pg of total DNA, (ii) high host DNA contamination (99%), and (iii) severely fragmented DNA (50-bp) from degraded samples. Tests of 2bRAD-M on stool, skin and environment-surface samples deliver successful reconstruction of comprehensive, high-resolution microbial profiles with agreement across 16S-rRNA, WMS and existing literature. In addition, it enables microbial profiling in formalin-fixed paraffin-embedded (FFPE) cervical tissue samples which were recalcitrant to conventional approaches due to the low amount and heavy degradation of microbial DNA, and discriminated healthy tissue, pre-invasive cancer and invasive cancer via species-level microbial profiles with 91.1% accuracy. Therefore, 2bRAD-M greatly expands the reach of microbiome sequencing.

Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here, we propose as a solution a search-based microbiome transition network. By traversing a composition-similarity-based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiomes at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes. IMPORTANCE It remains unclear whether and how compositional changes at the "community to community" level among microbiomes are linked to the origin and evolution of global microbiome diversity. Here we propose a microbiome transition model and a network-based analysis framework to describe and simulate the variation and dispersal of the global microbial beta-diversity across multiple habitats. The traversal of a transition network with 177,022 samples shows the inherent homology of microbiome at the global scale. Then a global roadmap of microbiome dispersal derived from the network tracks the potential paths of formulating and propagating microbiome diversity. Such search-based microbiome network provides a readily expanded reference for tracing the origin and evolution of existing or new microbiomes at the global scale.

Most adults experience episodes of gingivitis, which can progress to the irreversible, chronic state of periodontitis. However the mechanistic roles of plaque in gingivitis onset and progression to periodontitis remain elusive. Here, we integrated the longitudinal multi-omics data from plaque metagenome, metabolome and salivary cytokines in 40 adults who transit from naturally-occurring gingivitis (NG), to healthy gingivae (baseline) and then to experimental gingivitis (EG). During EG, rapid and consistent alterations in plaque microbiota, metabolites and salivary cytokines emerged as early as 24-72 hours after pause of oral hygiene, defining an asymptomatic ‘sub-optimal health’ (SoH) stage. SoH also features a steep and synergetic decrease of plaque-derived betaine and Rothia spp., suggesting an anti-gum-inflammation mechanism by health-promoting microbial residents. Global, cross-cohort meta-analysis revealed a high Microbiome-based Periodontitis Index at SoH state, due to its convergent taxonomical and functional profiles towards those of periodontitis. In contrast, caries SoH features a microbial signature very distinct from caries. Thus SoH is a universal state of polymicrobial inflammations with disease-specific features, which is key to maintaining a disease-preventive plaque.

During the past decade, tremendous amount of microbiome sequencing data has been generated to study on the dynamic associations between microbial profiles and environments. How to precisely and efficiently decipher large-scale of microbiome data and furtherly take advantages from it has become one of the most essential bottlenecks for microbiome research at present. In this mini-review, we focus on the three key steps of analyzing cross-study microbiome datasets, including microbiome profiling, data integrating and data mining. By introducing the current bioinformatics approaches and discussing their limitations, we prospect the opportunities in development of computational methods for the three steps, and propose the promising solutions to multi-omics data analysis for comprehensive understanding and rapid investigation of microbiome from different angles, which could potentially promote the data-driven research by providing a broader view of the “microbiome data space”.

Here, we present a search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares them to databases of samples from patients. This approach enables the identification of microbiome states associated with disease even in the presence of different cohorts, multiple sequencing platforms, or significant contamination., Microbiome-based disease classification depends on well-validated disease-specific models or a priori organismal markers. These are lacking for many diseases. Here, we present an alternative, search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares these to databases of samples from patients. Our strategy’s precision, sensitivity, and speed outperform model-based approaches. In addition, it is more robust to platform heterogeneity and to contamination in 16S rRNA gene amplicon data sets. This search-based strategy shows promise as an important first step in microbiome big-data-based diagnosis. IMPORTANCE Here, we present a search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares them to databases of samples from patients. This approach enables the identification of microbiome states associated with disease even in the presence of different cohorts, multiple sequencing platforms, or significant contamination.

Motivation An accurate and reliable distance (or dissimilarity) among shotgun metagenomes is fundamental to deducing the beta-diversity of microbiomes. To compute the distance at the species level, current methods either ignore the evolutionary relationship among species or fail to account for unclassified organisms that cannot be mapped to definite tip nodes in the phylogenic tree, thus can produce erroneous beta-diversity pattern. Results To solve these problems, we propose the Dynamic Meta-Storms (DMS) algorithm to enable the comprehensive comparison of metagenomes on the species level with both taxonomy and phylogeny profiles. It compares the identified species of metagenomes with phylogeny, and then dynamically places the unclassified species to the virtual nodes of the phylogeny tree via their higher-level taxonomy information. Its high speed and low memory consumption enable pairwise comparison of 100 000 metagenomes (synthesized from 3688 bacteria) within 6.4 h on a single computing node. Availability and implementation An optimized implementation of DMS is available on GitHub (https://github.com/qibebt-bioinfo/dynamic-meta-storms) under a GNU GPL license. It takes the species-level profiles of metagenomes as input, and generates their pairwise distance matrix. The bacterial species-level phylogeny tree and taxonomy information of MetaPhlAn2 have been integrated into this implementation, while customized tree and taxonomy are also supported. Supplementary information Supplementary data are available at Bioinformatics online.

MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification., A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations. IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification.

To quantitatively measure the beta diversities between microbiomes, Microbiome Search Engine (MSE) (1) calculates phylogeny similarity using operational taxonomy unit (OTU) profiles; for both query and database samples, all 16S rRNA gene sequences are mapped to the Greengenes database (version 13-8) (2) for reference-based OTU picking with a 97% cutoff. Thus, in MSE, the comparison between query and database samples is approximately at the species level (3), although the actual taxonomic resolution varies according to taxon, due to differences in the evolutionary rates of the 16S rRNAs. Moreover, in MSE, both the relative abundance (with 16S rRNA gene copy number normalization [4]) and the phylogenetic structures of OTUs are utilized for similarity calculation (as in UniFrac [5, 6]), yet the speed is optimized by nonrecursive computing to enable real-time responses (7). By comparing the query sample (i.e., dust from university dormitories) provided by Sun …

Background RNA-Seq has become one of the most widely used applications based on next-generation sequencing technology. However, raw RNA-Seq data may have quality issues, which can significantly distort analytical results and lead to erroneous conclusions. Therefore, the raw data must be subjected to vigorous quality control (QC) procedures before downstream analysis. Currently, an accurate and complete QC of RNA-Seq data requires of a suite of different QC tools used consecutively, which is inefficient in terms of usability, running time, file usage, and interpretability of the results. Results We developed a comprehensive, fast and easy-to-use QC pipeline for RNA-Seq data, RNA-QC-Chain, which involves three steps: (1) sequencing-quality assessment and trimming; (2) internal (ribosomal RNAs) and external (reads from foreign species) contamination filtering; (3) alignment statistics reporting (such as read number, alignment coverage, sequencing depth and pair-end read mapping information). This package was developed based on our previously reported tool for general QC of next-generation sequencing (NGS) data called QC-Chain, with extensions specifically designed for RNA-Seq data. It has several features that are not available yet in other QC tools for RNA-Seq data, such as RNA sequence trimming, automatic rRNA detection and automatic contaminating species identification. The three QC steps can run either sequentially or independently, enabling RNA-QC-Chain as a comprehensive package with high flexibility and usability. Moreover, parallel computing and optimizations are embedded in most of the QC procedures, providing a superior efficiency. The performance of RNA-QC-Chain has been evaluated with different types of datasets, including an in-house sequencing data, a semi-simulated data, and two real datasets downloaded from public database. Comparisons of RNA-QC-Chain with other QC tools have manifested its superiorities in both function versatility and processing speed. Conclusions We present here a tool, RNA-QC-Chain, which can be used to comprehensively resolve the quality control processes of RNA-Seq data effectively and efficiently.

With the current fast accumulation of microbial community samples and related metagenomic sequencing data, data integration and analysis system is urgently needed for in-depth analysis of large number of metagenomic samples (also referred to as “microbial communities”) of interest. Although several existing databases have collected a large number of metagenomic samples, they mostly serve as data repositories with crude annotations, and offer limited functionality for analysis. Moreover, the few available tools for comparative analysis in the literature could only support the comparison of a few pre-defined set of metagenomic samples. To facilitate comprehensive comparative analysis on large amount of diverse microbial community samples, we have designed a Meta-Mesh system for a variety of analyses including quantitative analysis of similarities among microbial communities and computation of the correlation between the meta-information of these samples. We have used Meta-Mesh for systematically and efficiently analyses on diverse sets of human associate-habitat microbial community samples. Results have shown that Meta-Mesh could serve well as an efficient data analysis platform for discovery of clusters, biomarker and other valuable biological information from a large pool of human microbial samples.

The number of metagenomes is increasing rapidly. However, current methods for metagenomic analysis are limited by their capability for in-depth data mining among a large number of microbiome each of which carries a complex community structure. Moreover, the complexity of configuring and operating computational pipeline also hinders efficient data processing for the end users. In this work we introduce Parallel-META 3, a comprehensive and fully automatic computational toolkit for rapid data mining among metagenomic datasets, with advanced features including 16S rRNA extraction for shotgun sequences, 16S rRNA copy number calibration, 16S rRNA based functional prediction, diversity statistics, bio-marker selection, interaction network construction, vector-graph-based visualization and parallel computing. Application of Parallel-META 3 on 5,337 samples with 1,117,555,208 sequences from diverse studies and platforms showed it could produce similar results as QIIME and PICRUSt with much faster speed and lower memory usage, which demonstrates its ability to unravel the taxonomical and functional dynamics patterns across large datasets and elucidate ecological links between microbiome and the environment. Parallel-META 3 is implemented in C/C++ and R, and integrated into an executive package for rapid installation and easy access under Linux and Mac OS X. Both binary and source code packages are available at http://bioinfo.single-cell.cn/parallel-meta.html.

Nannochloropsis spp. are a group of oleaginous microalgae that harbor an expanded array of lipid-synthesis related genes, yet how they are transcriptionally regulated remains unknown. Here a phylogenomic approach was employed to identify and functionally annotate the transcriptional factors (TFs) and TF binding-sites (TFBSs) in N. oceanica IMET1. Among 36 microalgae and higher plants genomes, a two-fold reduction in the number of TF families plus a seven-fold decrease of average family-size in Nannochloropsis, Rhodophyta and Chlorophyta were observed. The degree of similarity in TF-family profiles is indicative of the phylogenetic relationship among the species, suggesting co-evolution of TF-family profiles and species. Furthermore, comparative analysis of six Nannochloropsis genomes revealed 68 “most-conserved” TFBS motifs, with 11 of which predicted to be related to lipid accumulation or photosynthesis. Mapping the IMET1 TFs and TFBS motifs to the reference plant TF-“TFBS motif” relationships in TRANSFAC enabled the prediction of 78 TF-“TFBS motif” interaction pairs, which consisted of 34 TFs (with 11 TFs potentially involved in the TAG biosynthesis pathway), 30 TFBS motifs and 2,368 regulatory connections between TFs and target genes. Our results form the basis of further experiments to validate and engineer the regulatory network of Nannochloropsis spp. for enhanced biofuel production.

Motivation: The number of microbial community samples is increasing with exponential speed. Data-mining among microbial community samples could facilitate the discovery of valuable biological information that is still hidden in the massive data. However, current methods for the comparison among microbial communities are limited by their ability to process large amount of samples each with complex community structure. Summary: We have developed an optimized GPU-based software, GPU-Meta-Storms, to efficiently measure the quantitative phylogenetic similarity among massive amount of microbial community samples. Our results have shown that GPU-Meta-Storms would be able to compute the pair-wise similarity scores for 10 240 samples within 20 min, which gained a speed-up of >17 000 times compared with single-core CPU, and >2600 times compared with 16-core CPU. Therefore, the high-performance of GPU-Meta-Storms could facilitate in-depth data mining among massive microbial community samples, and make the real-time analysis and monitoring of temporal or conditional changes for microbial communities possible. Availability and implementation: GPU-Meta-Storms is implemented by CUDA (Compute Unified Device Architecture) and C++. Source code is available at http://www.computationalbioenergy.org/meta-storms.html. Contact: ningkang@qibebt.ac.cn