MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data

RNA-Seq Virus Fraction in Lake Baikal and Treated Wastewaters

In this study, we analyzed the transcriptomes of RNA and DNA viruses from the oligotrophic water of Lake Baikal and the effluent from wastewater treatment plants (WWTPs) discharged into the lake from the towns of Severobaikalsk and Slyudyanka located on the lake shores. Given the uniqueness and importance of Lake Baikal, the issues of biodiversity conservation and the monitoring of potential virological hazards to hydrobionts and humans are important. Wastewater treatment plants discharge treated effluent directly into the lake. In this context, the identification and monitoring of allochthonous microorganisms entering the lake play an important role. Using high-throughput sequencing methods, we found that dsDNA-containing viruses of the class Caudoviricetes were the most abundant in all samples, while Leviviricetes (ssRNA(+) viruses) dominated the treated water samples. RNA viruses of the families Nodaviridae, Tombusviridae, Dicitroviridae, Picobirnaviridae, Botourmiaviridae, Marnaviridae, Solemoviridae, and Endornavirida were found in the pelagic zone of three lake basins. Complete or nearly complete genomes of RNA viruses belonging to such families as Dicistroviridae, Marnaviridae, Blumeviridae, Virgaviridae, Solspiviridae, Nodaviridae, and Fiersviridae and the unassigned genus Chimpavirus, as well as unclassified picorna-like viruses, were identified. In general, the data of sanitary/microbiological and genetic analyses showed that WWTPs inadequately purify the discharged water, but, at the same time, we did not observe viruses pathogenic to humans in the pelagic zone of the lake.

Comparison of k-mer-based de novo comparative metagenomic tools and approaches

Aim: Comparative metagenomic analysis requires measuring a pairwise similarity between metagenomes in the dataset. Reference-based methods that compute a beta-diversity distance between two metagenomes are highly dependent on the quality and completeness of the reference database, and their application on less studied microbiota can be challenging. On the other hand, de-novo comparative metagenomic methods only rely on the sequence composition of metagenomes to compare datasets. While each one of these approaches has its strengths and limitations, their comparison is currently limited. Methods: We developed sets of simulated short-reads metagenomes to (1) compare k-mer-based and taxonomy-based distances and evaluate the impact of technical and biological variables on these metrics and (2) evaluate the effect of k-mer sketching and filtering. We used a real-world metagenomic dataset to provide an overview of the currently available tools for de novo metagenomic comparative analysis. Results: Using simulated metagenomes of known composition and controlled error rate, we showed that k-mer-based distance metrics were well correlated to the taxonomic distance metric for quantitative Beta-diversity metrics, but the correlation was low for presence/absence distances. The community complexity in terms of taxa richness and the sequencing depth significantly affected the quality of the k-mer-based distances, while the impact of low amounts of sequence contamination and sequencing error was limited. Finally, we benchmarked currently available de-novo comparative metagenomic tools and compared their output on two datasets of fecal metagenomes and showed that most k-mer-based tools were able to recapitulate the data structure observed using taxonomic approaches. Conclusion: This study expands our understanding of the strength and limitations of k-mer-based de novo comparative metagenomic approaches and aims to provide concrete guidelines for researchers interested in applying these approaches to their metagenomic datasets.

Spatio-temporal changes in endosymbiont diversity and composition in the African cassava whitefly, Bemisia tabaci SSA1

Sap-sucking insects, including whiteflies, are amongst the most devastating and widely distributed organisms on the planet. They are often highly invasive and endosymbiont communities within these insects help them adapt to new or changing environments. Bemisia tabaci (Gennadius; Hemiptera: Aleyrodidae) whitefly species are vectors of more than 500 known plant-viruses and harbour highly diverse endosymbionts communities. To date, however, whitefly–endosymbiont interactions, community structure and their spatio-temporal changes are still poorly understood. In this study, we investigated the spatio-temporal changes in the composition and diversity of bacterial endosymbionts in the agricultural crop pest whitefly species, Bemisia tabaci sub-Saharan Africa 1-subgroup 1 and 2 (SSA1-SG1 and SSA1-SG2). 16S rRNA amplicon sequencing analysis was carried out to characterise endosymbiont compositionsin field-collected SSA1 (SSA1-SG1 and SSA1-SG2) populations infesting cassava in Uganda in 1997 and 2017. We detected Portiera, Arsenophonus, Wolbachia, Hamiltonella and Hemipteriphilus, with Arsenophonus and Wolbachia infections being predominant. Hemipteriphilus and Hamiltonella frequencies were very low and were detected in seven and two samples, respectively. Bacterial diversity based on three independent parameters including Simpson index, number of haplotypes and Bray–Curtis dissimilarity matrix was significantly higher in 1997 than in 2017. This period also coincided with the advent of super-abundant cassava-whitefly populations on cassava crops in Uganda. We discuss how endosymbionts may influence the biology and behaviour of whiteflies leading to population explosions.

Seasonal microbial dynamics in the ocean inferred from assembled and unassembled data: a view on the unknown biosphere

In environmental metagenomic experiments, a very high proportion of the microbial sequencing data (> 70%) remains largely unexploited because rare and closely related genomes are missed in short-read assemblies. The identity and the potential metabolisms of a large fraction of natural microbial communities thus remain inaccessible to researchers. The purpose of this study was to explore the genomic content of unassembled metagenomic data and test their level of novelty. We used data from a three-year microbial metagenomic time series of the NW Mediterranean Sea, and conducted reference-free and database-guided analysis. The results revealed a significant genomic difference between the assembled and unassembled reads. The unassembled reads had a lower mean identity against public databases, and fewer metabolic pathways could be reconstructed. In addition, the unassembled fraction presented a clear temporal pattern, unlike the assembled ones, and a specific community composition that was similar to the rare communities defined by metabarcoding using the 16S rRNA gene. The rare gene pool was characterised by keystone bacterial taxa, and the presence of viruses, suggesting that viral lysis could maintain some taxa in a state of rarity. Our study demonstrates that unassembled metagenomic data can provide important information on the structure and functioning of microbial communities.

Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review

Big data in health care is a fast-growing field and a new paradigm that is transforming case-based studies to large-scale, data-driven research. As big data is dependent on the advancement of new data standards, technology, and relevant research, the future development of big data applications holds foreseeable promise in the modern day health care revolution. Enormously large, rapidly growing collections of biomedical omics-data (genomics, proteomics, transcriptomics, metabolomics, glycomics, etc.) and clinical data create major challenges and opportunities for their analysis and interpretation and open new computational gateways to address these issues. The design of new robust algorithms that are most suitable to properly analyze this big data by taking into account individual variability in genes has enabled the creation of precision (personalized) medicine. We reviewed and highlighted the significance of big data analytics for personalized medicine and health care by focusing mostly on machine learning perspectives on personalized medicine, genomic data models with respect to personalized medicine, the application of data mining algorithms for personalized medicine as well as the challenges we are facing right now in big data analytics.

Applications of de Bruijn graphs in microbiome research

High-throughput sequencing has become an increasingly central component of microbiome research. The development of de Bruijn graph-based methods for assembling high-throughput sequencing data has been an important part of the broader adoption of sequencing as part of biological studies. Recent advances in the construction and representation of de Bruijn graphs have led to new approaches that utilize the de Bruijn graph data structure to aid in different biological analyses. One type of application of these methods has been in alternative approaches to the assembly of sequencing data like gene-targeted assembly, where only gene sequences are assembled out of larger metagenomes, and differential assembly, where sequences that are differentially present between two samples are assembled. de Bruijn graphs have also been applied for comparative genomics where they can be used to represent large sets of multiple genomes or metagenomes where structural features in the graphs can be used to identify variants, indels, and homologous regions in sequences. These de Bruijn graph-based representations of sequencing data have even begun to be applied to whole sequencing databases for large-scale searches and experiment discovery. de Bruijn graphs have played a central role in how high-throughput sequencing data is worked with, and the rapid development of new tools that rely on these data structures suggests that they will continue to play an important role in biology in the future.

KOMB: K-core based de novo characterization of copy number variation in microbiomes

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Minimal-moderate variation of human oral virome and microbiome in IgA deficiency

Immunoglobulin A (IgA) is the dominant antibody found in our mucosal secretions and has long been recognized to play an important role in protecting our epithelium from pathogens. Recently, IgA has been shown to be involved in gut homeostatic regulation by 'recognizing' and shaping our commensal microbes. Paradoxically, yet selective IgA-deficiency is often described as asymptomatic and there is a paucity of studies only focused on the mice and human gut microbiome context fully ignoring other niches of our body and our commensal viruses. Here, we used as a model the human oral cavity and employed a holistic view and studied the impact of IgA deficiency and also common variable IgA and IgM immunodeficiencies (CVID), on both the human virome and microbiome. Unexpectedly, metagenomic and experimental data in human IgA deficiency and CVID indicate minimal-moderate changes in microbiome and virome composition compared to healthy control group and point out to a rather functional, resilient oral commensal viruses and microbes. However, a significant depletion (two fold) of bacterial cells (p-value < 0.01) and viruses was observed in IgA-deficiency. Our results demonstrate that, within the limits of our cohort, IgA role is not critical for maintaining a rather functional salivary microbiome and suggest that IgA is not a major influence on the composition of abundant commensal microbes.

Metagenome SNP calling via read-colored de Bruijn graphs

MOTIVATION

Metagenomics refers to the study of complex samples containing of genetic contents of multiple individual organisms and, thus, has been used to elucidate the microbiome and resistome of a complex sample. The microbiome refers to all microbial organisms in a sample, and the resistome refers to all of the antimicrobial resistance (AMR) genes in pathogenic and non-pathogenic bacteria. Single-nucleotide polymorphisms (SNPs) can be effectively used to 'fingerprint' specific organisms and genes within the microbiome and resistome and trace their movement across various samples. However, to effectively use these SNPs for this traceability, a scalable and accurate metagenomics SNP caller is needed. Moreover, such an SNP caller should not be reliant on reference genomes since 95% of microbial species is unculturable, making the determination of a reference genome extremely challenging. In this article, we address this need.

RESULTS

We present LueVari, a reference-free SNP caller based on the read-colored de Bruijn graph, an extension of the traditional de Bruijn graph that allows repeated regions longer than the k-mer length and shorter than the read length to be identified unambiguously. LueVari is able to identify SNPs in both AMR genes and chromosomal DNA from shotgun metagenomics data with reliable sensitivity (between 91% and 99%) and precision (between 71% and 99%) as the performance of competing methods varies widely. Furthermore, we show that LueVari constructs sequences containing the variation, which span up to 97.8% of genes in datasets, which can be helpful in detecting distinct AMR genes in large metagenomic datasets.

AVAILABILITY AND IMPLEMENTATION

Code and datasets are publicly available at https://github.com/baharpan/cosmo/tree/LueVari.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparison of microbiome samples: methods and computational challenges

The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed in recent years. Here, we review three key challenges in the comparison of such data sets: species identification and quantification, the efficient computation of distances between metagenomic samples and the identification of metagenomic features associated with a phenotype such as disease status. We present current solutions for such challenges, considering both reference-based methods relying on a database of reference genomes and reference-free methods working directly on all sequencing reads from the samples.

Cationic Dendrimer G2-S16 Inhibits Herpes Simplex Type 2 Infection and Protects Mice Vaginal Microbiome

The G2-S16 polyanionic carbosilane dendrimer is a promising microbicide that inhibits HSV-2 infection in vitro and in vivo in mice models. This G2-S16 dendrimer inhibits HSV-2 infection even in the presence of semen. Murine models, such as BALB/c female mice, are generally used to characterize host-pathogen interactions within the vaginal tract. However, the composition of endogenous vaginal flora remains largely undefined with modern microbiome analyses. It is important to note that the G2-S16 dendrimer does not change healthy mouse vaginal microbiome where Pseudomonas (10.2–79.1%) and Janthinobacterium (0.7–13%) are the more abundant genera. The HSV-2 vaginally infected female mice showed a significant microbiome alteration because an increase of Staphylococcus (up to 98.8%) and Escherichia (30.76%) levels were observed becoming these bacteria the predominant genera. BALB/c female mice vaginally-treated with the G2-S16 dendrimer and infected with the HSV-2 maintained a healthy vaginal microbiome similar to uninfected female mice. Summarizing, the G2-S16 polyanionic carbosilane dendrimer inhibits the HSV-2 infection in the presence of semen and prevents the alteration of mice female vaginal microbiome.

Reconstructing phylogenetic relationships based on repeat sequence similarities

A recent phylogenetic method based on genome-wide abundance of different repeat types proved to be useful in reconstructing the evolutionary history of several plant and animal groups. Here, we demonstrate that an alternative information source from the repeatome can also be employed to infer phylogenetic relationships among taxa. Specifically, this novel approach makes use of the repeat sequence similarity matrices obtained from the comparative clustering analyses of RepeatExplorer 2, which are subsequently transformed to between-taxa distance matrices. These pairwise matrices are used to construct neighbour-joining trees for each of the top most-abundant clusters and they are finally summarized in a consensus network. This methodology was tested on three groups of angiosperms and one group of insects, resulting in congruent evolutionary hypotheses compared to more standard systematic analyses based on commonly used DNA markers. We propose that the combined application of these phylogenetic approaches based on repeat abundances and repeat sequence similarities could be helpful to understand mechanisms governing genome and repeatome evolution.

Computational profiling of the gut-brain axis: microflora dysbiosis insights to neurological disorders

Almost 2500 years after Hippocrates' observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora's compositional diversity regulates via its genes (the microbiome) the host's health and provides preliminary insights into disease progression and regulation. The microbiome's involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host's phenotype but also in neurological investigations, substantiating the aptly named gut-brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome-gut-brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host.

Libra: scalable k-mer-based tool for massive all-vs-all metagenome comparisons

Background

Shotgun metagenomics provides powerful insights into microbial community biodiversity and function. Yet, inferences from metagenomic studies are often limited by dataset size and complexity and are restricted by the availability and completeness of existing databases. De novo comparative metagenomics enables the comparison of metagenomes based on their total genetic content.

Results

We developed a tool called Libra that performs an all-vs-all comparison of metagenomes for precise clustering based on their k-mer content. Libra uses a scalable Hadoop framework for massive metagenome comparisons, Cosine Similarity for calculating the distance using sequence composition and abundance while normalizing for sequencing depth, and a web-based implementation in iMicrobe (http://imicrobe.us) that uses the CyVerse advanced cyberinfrastructure to promote broad use of the tool by the scientific community.

Conclusions

A comparison of Libra to equivalent tools using both simulated and real metagenomic datasets, ranging from 80 million to 4.2 billion reads, reveals that methods commonly implemented to reduce compute time for large datasets, such as data reduction, read count normalization, and presence/absence distance metrics, greatly diminish the resolution of large-scale comparative analyses. In contrast, Libra uses all of the reads to calculate k-mer abundance in a Hadoop architecture that can scale to any size dataset to enable global-scale analyses and link microbial signatures to biological processes.

Global genomic similarity and core genome sequence diversity of the Streptococcus genus as a toolkit to identify closely related bacterial species in complex environments

Background

The Streptococcus genus is relevant to both public health and food safety because of its ability to cause pathogenic infections. It is well-represented (>100 genomes) in publicly available databases. Streptococci are ubiquitous, with multiple sources of isolation, from human pathogens to dairy products. The Streptococcus genus has traditionally been classified by morphology, serum types, the 16S ribosomal RNA (rRNA) gene, and multi-locus sequence types subject to in-depth comparative genomic analysis.

Methods

Core and pan-genomes described the genomic diversity of 108 strains belonging to 16 Streptococcus species. The core genome nucleotide diversity was calculated and compared to phylogenomic distances within the genus Streptococcus. The core genome was also used as a resource to recruit metagenomic fragment reads from streptococci dominated environments. A conventional 16S rRNA gene phylogeny reconstruction was used as a reference to compare the resulting dendrograms of average nucleotide identity (ANI) and genome similarity score (GSS) dendrograms.

Results

The core genome, in this work, consists of 404 proteins that are shared by all 108 Streptococcus. The average identity of the pairwise compared core proteins decreases proportionally to GSS lower scores, across species. The GSS dendrogram recovers most of the clades in the 16S rRNA gene phylogeny while distinguishing between 16S polytomies (unresolved nodes). The GSS is a distance metric that can reflect evolutionary history comparing orthologous proteins. Additionally, GSS resulted in the most useful metric for genus and species comparisons, where ANI metrics failed due to false positives when comparing different species.

Discussion

Understanding of genomic variability and species relatedness is the goal of tools like GSS, which makes use of the maximum pairwise shared orthologous sequences for its calculation. It allows for long evolutionary distances (above species) to be included because of the use of amino acid alignment scores, rather than nucleotides, and normalizing by positive matches. Newly sequenced species and strains could be easily placed into GSS dendrograms to infer overall genomic relatedness. The GSS is not restricted to ubiquitous conservancy of gene features; thus, it reflects the mosaic-structure and dynamism of gene acquisition and loss in bacterial genomes.

A strong link between marine microbial community composition and function challenges the idea of functional redundancy

Marine microbes have tremendous diversity, but a fundamental question remains unanswered: why are there so many microbial species in the sea? The idea of functional redundancy for microbial communities has long been assumed, so that the high level of richness is often explained by the presence of different taxa that are able to conduct the exact same set of metabolic processes and that can readily replace each other. Here, we refute the hypothesis of functional redundancy for marine microbial communities by showing that a shift in the community composition altered the overall functional attributes of communities across different temporal and spatial scales. Our metagenomic monitoring of a coastal northwestern Mediterranean site also revealed that diverse microbial communities harbor a high diversity of potential proteins. Working with all information given by the metagenomes (all reads) rather than relying only on known genes (annotated orthologous genes) was essential for revealing the similarity between taxonomic and functional community compositions. Our finding does not exclude the possibility for a partial redundancy where organisms that share some specific function can coexist when they differ in other ecological requirements. It demonstrates, however, that marine microbial diversity reflects a tremendous diversity of microbial metabolism and highlights the genetic potential yet to be discovered in an ocean of microbes.

Genome-resolved metagenomics of sugarcane vinasse bacteria

BACKGROUND

The production of 1 L of ethanol from sugarcane generates up to 12 L of vinasse, which is a liquid waste containing an as-yet uncharacterized microbial assemblage. Most vinasse is destined for use as a fertilizer on the sugarcane fields because of the high organic and K content; however, increased N₂O emissions have been observed when vinasse is co-applied with inorganic N fertilizers. Here we aimed to characterize the microbial assemblage of vinasse to determine the gene potential of vinasse microbes for contributing to negative environmental effects during fertirrigation and/or to the obstruction of bioethanol fermentation.

RESULTS

We measured chemical characteristics and extracted total DNA from six vinasse batches taken over 1.5 years from a bioethanol and sugar mill in Sao Paulo State. The vinasse microbial assemblage was characterized by low alpha diversity with 5-15 species across the six vinasses. The core genus was Lactobacillus. The top six represented bacterial genera across the samples were Lactobacillus, Megasphaera and Mitsuokella (Phylum Firmicutes, 35-97% of sample reads); Arcobacter and Alcaligenes (Phylum Proteobacteria, 0-40%); Dysgonomonas (Phylum Bacteroidetes, 0-53%); and Bifidobacterium (Phylum Actinobacteria, 0-18%). Potential genes for denitrification but not nitrification were identified in the vinasse metagenomes, with putative nirK and nosZ genes the most represented. Binning resulted in 38 large bins with between 36.0 and 99.3% completeness, and five small mobile element bins. Of the large bins, 53% could be classified at the phylum level as Firmicutes, 15% as Proteobacteria, 13% as unknown phyla, 13% as Bacteroidetes and 6% as Actinobacteria. The large bins spanned a range of potential denitrifiers; moreover, the genetic repertoires of all the large bins included the presence of genes involved in acetate, CO₂, ethanol, H₂O₂, and lactose metabolism; for many of the large bins, genes related to the metabolism of mannitol, xylose, butyric acid, cellulose, sucrose, "3-hydroxy" fatty acids and antibiotic resistance were present based on the annotations. In total, 21 vinasse bacterial draft genomes were submitted to the genome repository.

CONCLUSIONS

Identification of the gene repertoires of vinasse bacteria and assemblages supported the idea that organic carbon and nitrogen present in vinasse together with microbiological variation of vinasse might lead to varying patterns of N₂O emissions during fertirrigation. Furthermore, we uncovered draft genomes of novel strains of known bioethanol contaminants, as well as draft genomes unknown at the phylum level. This study will aid efforts to improve bioethanol production efficiency and sugarcane agriculture sustainability.

Links of gut microbiota composition with alcohol dependence syndrome and alcoholic liver disease

BACKGROUND

Alcohol abuse has deleterious effects on human health by disrupting the functions of many organs and systems. Gut microbiota has been implicated in the pathogenesis of alcohol-related liver diseases, with its composition manifesting expressed dysbiosis in patients suffering from alcoholic dependence. Due to its inherent plasticity, gut microbiota is an important target for prevention and treatment of these diseases. Identification of the impact of alcohol abuse with associated psychiatric symptoms on the gut community structure is confounded by the liver dysfunction. In order to differentiate the effects of these two factors, we conducted a comparative "shotgun" metagenomic survey of 99 patients with the alcohol dependence syndrome represented by two cohorts-with and without liver cirrhosis. The taxonomic and functional composition of the gut microbiota was subjected to a multifactor analysis including comparison with the external control group.

RESULTS

Alcoholic dependence and liver cirrhosis were associated with profound shifts in gut community structures and metabolic potential across the patients. The specific effects on species-level community composition were remarkably different between cohorts with and without liver cirrhosis. In both cases, the commensal microbiota was found to be depleted. Alcoholic dependence was inversely associated with the levels of butyrate-producing species from the Clostridiales order, while the cirrhosis-with multiple members of the Bacteroidales order. The opportunist pathogens linked to alcoholic dependence included pro-inflammatory Enterobacteriaceae, while the hallmarks of cirrhosis included an increase of oral microbes in the gut and more frequent occurrence of abnormal community structures. Interestingly, each of the two factors was associated with the expressed enrichment in many Bifidobacterium and Lactobacillus-but the exact set of the species was different between alcoholic dependence and liver cirrhosis. At the level of functional potential, the patients showed different patterns of increase in functions related to alcohol metabolism and virulence factors, as well as pathways related to inflammation.

CONCLUSIONS

Multiple shifts in the community structure and metabolic potential suggest strong negative influence of alcohol dependence and associated liver dysfunction on gut microbiota. The identified differences in patterns of impact between these two factors are important for planning of personalized treatment and prevention of these pathologies via microbiota modulation. Particularly, the expansion of Bifidobacterium and Lactobacillus suggests that probiotic interventions for patients with alcohol-related disorders using representatives of the same taxa should be considered with caution. Taxonomic and functional analysis shows an increased propensity of the gut microbiota to synthesis of the toxic acetaldehyde, suggesting higher risk of colorectal cancer and other pathologies in alcoholics.

Alignment-free sequence comparison: benefits, applications, and tools

Alignment-free sequence analyses have been applied to problems ranging from whole-genome phylogeny to the classification of protein families, identification of horizontally transferred genes, and detection of recombined sequences. The strength of these methods makes them particularly useful for next-generation sequencing data processing and analysis. However, many researchers are unclear about how these methods work, how they compare to alignment-based methods, and what their potential is for use for their research. We address these questions and provide a guide to the currently available alignment-free sequence analysis tools.

Insights into study design and statistical analyses in translational microbiome studies

Research questions in translational microbiome studies are substantially more complex than their counterparts in basic science. Robust study designs with appropriate statistical analysis frameworks are pivotal to the success of these translational studies. This review considers how study designs can account for heterogeneous phenotypes by adopting representative sampling schemes for recruiting the study population and making careful choices about the control population. Advantages and limitations of 16S profiling and whole-genome sequencing, the two primary techniques for measuring the microbiome, are discussed followed by an overview of bioinformatic processing of high-throughput sequencing data from these measurements. Practical insights into the downstream statistical analyses including data processing and integration, variable transformations, and data exploration are provided. The merits of regularization and ensemble modeling for analyzing microbiome data are discussed along with a recommendation for selecting modeling approaches based on data-driven simulations and objective evaluation. The review builds on several recent discussions of study design issues in microbiome research but with a stronger emphasis on the downstream and often-ignored aspects of statistical analyses that are crucial for bridging the gap between basic science and translation.