Compareads: comparing huge metagenomic experiments

Assessing Engraftment Following Fecal Microbiota Transplant

Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for cancer treatment, and restoring microbiomes impacted by cancer treatment. Quantifying the extent of engraftment following an FMT is important in determining if a recipient didn't respond because the engrafted microbiome didn't produce the desired outcomes (a successful FMT, but negative treatment outcome), or the microbiome didn't engraft (an unsuccessful FMT and negative treatment outcome). The lack of a consistent methodology for quantifying FMT engraftment extent hinders the assessment of FMT success and its relation to clinical outcomes, and presents challenges for comparing FMT results and protocols across studies. Here we review 46 studies of FMT in humans and model organisms and group their approaches for assessing the extent to which an FMT engrafts into three criteria: 1) Chimeric Asymmetric Community Coalescence investigates microbiome shifts following FMT engraftment using methods such as alpha diversity comparisons, beta diversity comparisons, and microbiome source tracking. 2) Donated Microbiome Indicator Features tracks donated microbiome features (e.g., amplicon sequence variants or species of interest) as a signal of engraftment with methods such as differential abundance testing based on the current sample collection, or tracking changes in feature abundances that have been previously identified (e.g., from FMT or disease-relevant literature). 3) Temporal Stability examines how resistant post-FMT recipient's microbiomes are to reverting back to their baseline microbiome. Individually, these criteria each highlight a critical aspect of microbiome engraftment; investigated together, however, they provide a clearer assessment of microbiome engraftment. We discuss the pros and cons of each of these criteria, providing illustrative examples of their application. We also introduce key terminology and recommendations on how FMT studies can be analyzed for rigorous engraftment extent assessment.

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.

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.

Kmasker plants - a tool for assessing complex sequence space in plant species

Many plant genomes display high levels of repetitive sequences. The assembly of these complex genomes using short high-throughput sequence reads is still a challenging task. Underestimation or disregard of repeat complexity in these datasets can easily misguide downstream analysis. Detection of repetitive regions by k-mer counting methods has proved to be reliable. Easy-to-use applications utilizing k-mer counting are in high demand, especially in the domain of plants. We present Kmasker plants, a tool that uses k-mer count information as an assistant throughout the analytical workflow of genome data that is provided as a command-line and web-based solution. Beside its core competence to screen and mask repetitive sequences, we have integrated features that enable comparative studies between different cultivars or closely related species and methods that estimate target specificity of guide RNAs for application of site-directed mutagenesis using Cas9 endonuclease. In addition, we have set up a web service for Kmasker plants that maintains pre-computed indices for 10 of the economically most important cultivated plants. Source code for Kmasker plants has been made publically available at https://github.com/tschmutzer/kmasker. The web service is accessible at https://kmasker.ipk-gatersleben.de.

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.

Differences in the fungal communities nursed by two genetic groups of the alpine cushion plant, Silene acaulis

Foundation plants shape the composition of local biotic communities and abiotic environments, but the impact of a plant's intraspecific variations on these processes is poorly understood. We examined these links in the alpine cushion moss campion (Silene acaulis) on two neighboring mountain ranges in the French Alps. Genotyping of cushion plants revealed two genetic clusters matching known subspecies. The exscapa subspecies was found on both limestone and granite, while the longiscapa one was only found on limestone. Even on similar limestone bedrock, cushion soils from the two S. acaulis subspecies deeply differed in their impact on soil abiotic conditions. They further strikingly differed from each other and from the surrounding bare soils in fungal community composition. Plant genotype variations accounted for a large part of the fungal composition variability in cushion soils, even when considering geography or soil chemistry, and particularly for the dominant molecular operational taxonomic units (MOTUs). Both saprophytic and biotrophic fungal taxa were related to the MOTUs recurrently associated with a single plant genetic cluster. Moreover, the putative phytopathogens were abundant, and within the same genus (Cladosporium) or species (Pyrenopeziza brassicae), MOTUs showing specificity for each plant subspecies were found. Our study highlights the combined influences of bedrock and plant genotype on fungal recruitment into cushion soils and suggests the coexistence of two mechanisms, an indirect selection resulting from the colonization of an engineered soil by free-living saprobes and a direct selection resulting from direct plant-fungi interactions.

A Content-Based Retrieval Framework for Whole Metagenome Sequencing Samples

Finding similarities and differences between metagenomic samples within large repositories has been rather a significant issue for researchers. Over the recent years, content-based retrieval has been suggested by various studies from different perspectives. In this study, a content-based retrieval framework for identifying relevant metagenomic samples is developed. The framework consists of feature extraction, selection methods and similarity measures for whole metagenome sequencing samples. Performance of the developed framework was evaluated on given samples. A ground truth was used to evaluate the system performance such that if the system retrieves patients with the same disease, -called positive samples-, they are labeled as relevant samples otherwise irrelevant. The experimental results show that relevant experiments can be detected by using different fingerprinting approaches. We observed that Latent Semantic Analysis (LSA) Method is a promising fingerprinting approach for representing metagenomic samples and finding relevance among them. Source codes and executable files are available at www.baskent.edu.tr/∼hogul/WMS_retrieval.rar.

GePMI: A statistical model for personal intestinal microbiome identification

Human gut microbiomes consist of a large number of microbial genomes, which vary by diet and health conditions and from individual to individual. In the present work, we asked whether such variation or similarity could be measured and, if so, whether the results could be used for personal microbiome identification (PMI). To address this question, we herein propose a method to estimate the significance of similarity among human gut metagenomic samples based on reference-free, long k-mer features. Using these features, we find that pairwise similarities between the metagenomes of any two individuals obey a beta distribution and that a p value derived accordingly well characterizes whether two samples are from the same individual or not. We develop a computational framework called GePMI (Generating inter-individual similarity distribution for Personal Microbiome Identification) and apply it to several human gut metagenomic datasets (>300 individuals and >600 samples in total). From the results of GePMI, most of the human gut microbiomes can be identified (auROC = 0.9470, auPRC = 0.8702). Even after antibiotic treatment or fecal microbiota transplantation, the individual k-mer signature still maintains a certain specificity.

A framework for space-efficient read clustering in metagenomic samples

Background

A metagenomic sample is a set of DNA fragments, randomly extracted from multiple cells in an environment, belonging to distinct, often unknown species. Unsupervised metagenomic clustering aims at partitioning a metagenomic sample into sets that approximate taxonomic units, without using reference genomes. Since samples are large and steadily growing, space-efficient clustering algorithms are strongly needed.

Results

We design and implement a space-efficient algorithmic framework that solves a number of core primitives in unsupervised metagenomic clustering using just the bidirectional Burrows-Wheeler index and a union-find data structure on the set of reads. When run on a sample of total length n, with m reads of maximum length ℓ each, on an alphabet of total size σ, our algorithms take O(n(t+logσ)) time and just 2n+o(n)+O(max{ℓσlogn,K logm}) bits of space in addition to the index and to the union-find data structure, where K is a measure of the redundancy of the sample and t is the query time of the union-find data structure.

Conclusions

Our experimental results show that our algorithms are practical, they can exploit multiple cores by a parallel traversal of the suffix-link tree, and they are competitive both in space and in time with the state of the art.

GUTSS: An Alignment-Free Sequence Comparison Method for Use in Human Intestinal Microbiome and Fecal Microbiota Transplantation Analysis

BACKGROUND

Comparative analysis of gut microbiomes in clinical studies of human diseases typically rely on identification and quantification of species or genes. In addition to exploring specific functional characteristics of the microbiome and potential significance of species diversity or expansion, microbiome similarity is also calculated to study change in response to therapies directed at altering the microbiome. Established ecological measures of similarity can be constructed from species abundances, however methods for calculating these commonly used ecological measures of similarity directly from whole genome shotgun (WGS) metagenomic sequence are lacking.

RESULTS

We present an alignment-free method for calculating similarity of WGS metagenomic sequences that is analogous to the Bray-Curtis index for species, implemented by the General Utility for Testing Sequence Similarity (GUTSS) software application. This method was applied to intestinal microbiomes of healthy young children to measure developmental changes toward an adult microbiome during the first 3 years of life. We also calculate similarity of donor and recipient microbiomes to measure establishment, or engraftment, of donor microbiota in fecal microbiota transplantation (FMT) studies focused on mild to moderate Crohn's disease. We show how a relative index of similarity to donor can be calculated as a measure of change in a patient's microbiome toward that of the donor in response to FMT.

CONCLUSION

Because clinical efficacy of the transplant procedure cannot be fully evaluated without analysis methods to quantify actual FMT engraftment, we developed a method for detecting change in the gut microbiome that is independent of species identification and database bias, sensitive to changes in relative abundance of the microbial constituents, and can be formulated as an index for correlating engraftment success with clinical measures of disease. More generally, this method may be applied to clinical evaluation of human microbiomes and provide potential diagnostic determination of individuals who may be candidates for specific therapies directed at alteration of the microbiome.

Mash: fast genome and metagenome distance estimation using MinHash

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (https://github.com/marbl/mash).

Dynamics of Viral Abundance and Diversity in a Sphagnum-Dominated Peatland: Temporal Fluctuations Prevail Over Habitat

Viruses impact microbial activity and carbon cycling in various environments, but their diversity and ecological importance in Sphagnum-peatlands are unknown. Abundances of viral particles and prokaryotes were monitored bi-monthly at a fen and a bog at two different layers of the peat surface. Viral particle abundance ranged from 1.7 x 10⁶ to 5.6 x 10⁸ particles mL^-1, and did not differ between fen and bog but showed seasonal fluctuations. These fluctuations were positively correlated with prokaryote abundance and dissolved organic carbon, and negatively correlated with water-table height and dissolved oxygen. Using shotgun metagenomics we observed a shift in viral diversity between winter/spring and summer/autumn, indicating a seasonal succession of viral communities, mainly driven by weather-related environmental changes. Based on the seasonal asynchrony between viral and microbial diversity, we hypothesize a seasonal shift in the active microbial communities associated with a shift from lysogenic to lytic lifestyles. Our results suggest that temporal variations of environmental conditions rather than current habitat differences control the dynamics of virus-host interactions in Sphagnum-dominated peatlands.

GenomeTester4: a toolkit for performing basic set operations - union, intersection and complement on k-mer lists

BACKGROUND

K-mer-based methods of genome analysis have attracted great interest because they do not require genome assembly and can be performed directly on sequencing reads. Many analysis tasks require one to compare k-mer lists from different sequences to find words that are either unique to a specific sequence or common to many sequences. However, no stand-alone k-mer analysis tool currently allows one to perform these algebraic set operations.

FINDINGS

We have developed the GenomeTester4 toolkit, which contains a novel tool GListCompare for performing union, intersection and complement (difference) set operations on k-mer lists. We provide examples of how these general operations can be combined to solve a variety of biological analysis tasks.

CONCLUSIONS

GenomeTester4 can be used to simplify k-mer list manipulation for many biological analysis tasks.

Subtractive assembly for comparative metagenomics, and its application to type 2 diabetes metagenomes

Comparative metagenomics remains challenging due to the size and complexity of metagenomic datasets. Here we introduce subtractive assembly, a de novo assembly approach for comparative metagenomics that directly assembles only the differential reads that distinguish between two groups of metagenomes. Using simulated datasets, we show it improves both the efficiency of the assembly and the assembly quality of the differential genomes and genes. Further, its application to type 2 diabetes (T2D) metagenomic datasets reveals clear signatures of the T2D gut microbiome, revealing new phylogenetic and functional features of the gut microbial communities associated with T2D.

Ocean plankton. Environmental characteristics of Agulhas rings affect interocean plankton transport

Agulhas rings provide the principal route for ocean waters to circulate from the Indo-Pacific to the Atlantic basin. Their influence on global ocean circulation is well known, but their role in plankton transport is largely unexplored. We show that, although the coarse taxonomic structure of plankton communities is continuous across the Agulhas choke point, South Atlantic plankton diversity is altered compared with Indian Ocean source populations. Modeling and in situ sampling of a young Agulhas ring indicate that strong vertical mixing drives complex nitrogen cycling, shaping community metabolism and biogeochemical signatures as the ring and associated plankton transit westward. The peculiar local environment inside Agulhas rings may provide a selective mechanism contributing to the limited dispersal of Indian Ocean plankton populations into the Atlantic.

Low Level Engraftment and Improvement following a Single Colonoscopic Administration of Fecal Microbiota to Patients with Ulcerative Colitis

Objective

Fecal microbiota transplantation (FMT) is an investigational treatment for diseases thought to involve alterations in the intestinal microbiota including ulcerative colitis (UC). Case reports have described therapeutic benefit of FMT in patients with UC, possibly due to changes in the microbiota. We measured the degree to which the transplanted microbiota engraft following FMT in patients with UC using a donor similarity index (DSI).

Methods

Seven patients with mild to moderate UC (UC disease activity index scores 3–10) received a single colonoscopic administration of FMT. Metagenomic sequence data from stool were analyzed using an alignment-free comparison tool, to measure the DSI, and a phylogenetic analysis tool, to characterize taxonomic changes. Clinical, endoscopic, histologic, and fecal calprotectin outcome measures were also collected.

Results

One of 5 patients from whom sequencing data were available achieved the primary endpoint of 50% donor similarity at week 4; an additional 2 patients achieved 40% donor similarity. One patient with 40% donor similarity achieved clinical and histologic remission 1 month after FMT. However, these were lost by 2−3 months, and loss correlated with a decrease in DSI. The remaining patients did not demonstrate clinical response or remission. Histology scores improved in all but 1 patient. No patients remained in remission at 3 months after FMT.

Conclusions

Following a single colonoscopic fecal transplant, a DSI of 40-50% is achieved in about two-thirds of recipients. This level of engraftment correlated with a temporary clinical improvement in only 1/5 patients. Larger sample sizes could further validate this method for measuring engraftment, and changes in transplant frequency or method might improve microbiota engraftment and efficacy.

Trial Registration

ClinicalTrials.gov NCT01742754

Fecal microbial transplant effect on clinical outcomes and fecal microbiome in active Crohn's disease

BACKGROUND

Crohn's disease (CD) is a chronic idiopathic inflammatory intestinal disorder associated with fecal dysbiosis. Fecal microbial transplant (FMT) is a potential therapeutic option for individuals with CD based on the hypothesis that changing the fecal dysbiosis could promote less intestinal inflammation.

METHODS

Nine patients, aged 12 to 19 years, with mild-to-moderate symptoms defined by Pediatric Crohn's Disease Activity Index (PCDAI of 10-29) were enrolled into a prospective open-label study of FMT in CD (FDA IND 14942). Patients received FMT by nasogastric tube with follow-up evaluations at 2, 6, and 12 weeks. PCDAI, C-reactive protein, and fecal calprotectin were evaluated at each study visit.

RESULTS

All reported adverse events were graded as mild except for 1 individual who reported moderate abdominal pain after FMT. All adverse events were self-limiting. Metagenomic evaluation of stool microbiome indicated evidence of FMT engraftment in 7 of 9 patients. The mean PCDAI score improved with patients having a baseline of 19.7 ± 7.2, with improvement at 2 weeks to 6.4 ± 6.6 and at 6 weeks to 8.6 ± 4.9. Based on PCDAI, 7 of 9 patients were in remission at 2 weeks and 5 of 9 patients who did not receive additional medical therapy were in remission at 6 and 12 weeks. No or modest improvement was seen in patients who did not engraft or whose microbiome was most similar to their donor.

CONCLUSIONS

This is the first study to demonstrate that FMT for CD may be a possible therapeutic option for CD. Further prospective studies are required to fully assess the safety and efficacy of the FMT in patients with CD.

Fecal microbial transplant effect on clinical outcomes and fecal microbiome in active Crohn's disease

BACKGROUND

Crohn's disease (CD) is a chronic idiopathic inflammatory intestinal disorder associated with fecal dysbiosis. Fecal microbial transplant (FMT) is a potential therapeutic option for individuals with CD based on the hypothesis that changing the fecal dysbiosis could promote less intestinal inflammation.

METHODS

Nine patients, aged 12 to 19 years, with mild-to-moderate symptoms defined by Pediatric Crohn's Disease Activity Index (PCDAI of 10-29) were enrolled into a prospective open-label study of FMT in CD (FDA IND 14942). Patients received FMT by nasogastric tube with follow-up evaluations at 2, 6, and 12 weeks. PCDAI, C-reactive protein, and fecal calprotectin were evaluated at each study visit.

RESULTS

All reported adverse events were graded as mild except for 1 individual who reported moderate abdominal pain after FMT. All adverse events were self-limiting. Metagenomic evaluation of stool microbiome indicated evidence of FMT engraftment in 7 of 9 patients. The mean PCDAI score improved with patients having a baseline of 19.7 ± 7.2, with improvement at 2 weeks to 6.4 ± 6.6 and at 6 weeks to 8.6 ± 4.9. Based on PCDAI, 7 of 9 patients were in remission at 2 weeks and 5 of 9 patients who did not receive additional medical therapy were in remission at 6 and 12 weeks. No or modest improvement was seen in patients who did not engraft or whose microbiome was most similar to their donor.

CONCLUSIONS

This is the first study to demonstrate that FMT for CD may be a possible therapeutic option for CD. Further prospective studies are required to fully assess the safety and efficacy of the FMT in patients with CD.

Whole-genome re-sequencing of non-model organisms: lessons from unmapped reads

Unmapped reads are often discarded from the analysis of whole-genome re-sequencing, but new biological information and insights can be uncovered through their analysis. In this paper, we investigate unmapped reads from the re-sequencing data of 33 pea aphid genomes from individuals specialized on different host plants. The unmapped reads for each individual were retrieved following mapping to the Acyrthosiphon pisum reference genome and its mitochondrial and symbiont genomes. These sets of unmapped reads were then cross-compared, revealing that a significant number of these unmapped sequences were conserved across individuals. Interestingly, sequences were most commonly shared between individuals adapted to the same host plant, suggesting that these sequences may contribute to the divergence between host plant specialized biotypes. Analysis of the contigs obtained from assembling the unmapped reads pooled by biotype allowed us to recover some divergent genomic regions previously excluded from analysis and to discover putative novel sequences of A. pisum and its symbionts. In conclusion, this study emphasizes the interest of the unmapped component of re-sequencing data sets and the potential loss of important information. We here propose strategies to aid the capture and interpretation of this information.

Impact of fire on active layer and permafrost microbial communities and metagenomes in an upland Alaskan boreal forest

Permafrost soils are large reservoirs of potentially labile carbon (C). Understanding the dynamics of C release from these soils requires us to account for the impact of wildfires, which are increasing in frequency as the climate changes. Boreal wildfires contribute to global emission of greenhouse gases (GHG-CO2, CH4 and N2O) and indirectly result in the thawing of near-surface permafrost. In this study, we aimed to define the impact of fire on soil microbial communities and metabolic potential for GHG fluxes in samples collected up to 1 m depth from an upland black spruce forest near Nome Creek, Alaska. We measured geochemistry, GHG fluxes, potential soil enzyme activities and microbial community structure via 16SrRNA gene and metagenome sequencing. We found that soil moisture, C content and the potential for respiration were reduced by fire, as were microbial community diversity and metabolic potential. There were shifts in dominance of several microbial community members, including a higher abundance of candidate phylum AD3 after fire. The metagenome data showed that fire had a pervasive impact on genes involved in carbohydrate metabolism, methanogenesis and the nitrogen cycle. Although fire resulted in an immediate release of CO2 from surface soils, our results suggest that the potential for emission of GHG was ultimately reduced at all soil depths over the longer term. Because of the size of the permafrost C reservoir, these results are crucial for understanding whether fire produces a positive or negative feedback loop contributing to the global C cycle.

Exploring neighborhoods in the metagenome universe

The variety of metagenomes in current databases provides a rapidly growing source of information for comparative studies. However, the quantity and quality of supplementary metadata is still lagging behind. It is therefore important to be able to identify related metagenomes by means of the available sequence data alone. We have studied efficient sequence-based methods for large-scale identification of similar metagenomes within a database retrieval context. In a broad comparison of different profiling methods we found that vector-based distance measures are well-suitable for the detection of metagenomic neighbors. Our evaluation on more than 1700 publicly available metagenomes indicates that for a query metagenome from a particular habitat on average nine out of ten nearest neighbors represent the same habitat category independent of the utilized profiling method or distance measure. While for well-defined labels a neighborhood accuracy of 100% can be achieved, in general the neighbor detection is severely affected by a natural overlap of manually annotated categories. In addition, we present results of a novel visualization method that is able to reflect the similarity of metagenomes in a 2D scatter plot. The visualization method shows a similarly high accuracy in the reduced space as compared with the high-dimensional profile space. Our study suggests that for inspection of metagenome neighborhoods the profiling methods and distance measures can be chosen to provide a convenient interpretation of results in terms of the underlying features. Furthermore, supplementary metadata of metagenome samples in the future needs to comply with readily available ontologies for fine-grained and standardized annotation. To make profile-based k-nearest-neighbor search and the 2D-visualization of the metagenome universe available to the research community, we included the proposed methods in our CoMet-Universe server for comparative metagenome analysis.

Exploration and retrieval of whole-metagenome sequencing samples

MOTIVATION

Over the recent years, the field of whole-metagenome shotgun sequencing has witnessed significant growth owing to the high-throughput sequencing technologies that allow sequencing genomic samples cheaper, faster and with better coverage than before. This technical advancement has initiated the trend of sequencing multiple samples in different conditions or environments to explore the similarities and dissimilarities of the microbial communities. Examples include the human microbiome project and various studies of the human intestinal tract. With the availability of ever larger databases of such measurements, finding samples similar to a given query sample is becoming a central operation.

RESULTS

In this article, we develop a content-based exploration and retrieval method for whole-metagenome sequencing samples. We apply a distributed string mining framework to efficiently extract all informative sequence k-mers from a pool of metagenomic samples and use them to measure the dissimilarity between two samples. We evaluate the performance of the proposed approach on two human gut metagenome datasets as well as human microbiome project metagenomic samples. We observe significant enrichment for diseased gut samples in results of queries with another diseased sample and high accuracy in discriminating between different body sites even though the method is unsupervised.

AVAILABILITY AND IMPLEMENTATION

A software implementation of the DSM framework is available at https://github.com/HIITMetagenomics/dsm-framework.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Advances in genome studies in plants and animals

The area of plant and animal genomics covers the entire suite of issues in biology because it aims to determine the structure and function of genetic material. Although specific issues define research advances at an organism level, it is evident that many of the fundamental features of genome structure and the translation of encoded information to function share common ground. The Plant and Animal Genome (PAG) conference held in San Diego (California), in January each year provides an overview across all organisms at the genome level, and often it is evident that investments in the human area provide leadership, applications, and discoveries for researchers studying other organisms. This mini-review utilizes the plenary lectures as a basis for summarizing the trends in the genome-level studies of organisms, and the lectures include presentations by Ewan Birney (EBI, UK), Eric Green (NIH, USA), John Butler (NIST, USA), Elaine Mardis (Washington, USA), Caroline Dean (John Innes Centre, UK), Trudy Mackay (NC State University, USA), Sue Wessler (UC Riverside, USA), and Patrick Wincker (Genoscope, France). The work reviewed is based on published papers. Where unpublished information is cited, permission to include the information in this manuscript was obtained from the presenters.