Tutorial: assessing metagenomics software with the CAMI benchmarking toolkit.

Computational methods are key in microbiome research, and obtaining a quantitative and unbiased performance estimate is important for method developers and applied researchers. For meaningful comparisons between methods, to identify best practices and common use cases, and to reduce overhead in benchmarking, it is necessary to have standardized datasets, procedures and metrics for evaluation. In this tutorial, we describe emerging standards in computational meta-omics benchmarking derived and agreed upon by a larger community of researchers. Specifically, we outline recent efforts by the Critical Assessment of Metagenome Interpretation (CAMI) initiative, which supplies method developers and applied researchers with exhaustive quantitative data about software performance in realistic scenarios and organizes community-driven benchmarking challenges. We explain the most relevant evaluation metrics for assessing metagenome assembly, binning and profiling results, and provide step-by-step instructions on how to generate them. The instructions use simulated mouse gut metagenome data released in preparation for the second round of CAMI challenges and showcase the use of a repository of tool results for CAMI datasets. This tutorial will serve as a reference for the community and facilitate informative and reproducible benchmarking in microbiome research.

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we sequenced the genomes and transcriptomes of 414 drug-resistant clinical Pseudomonas aeruginosa isolates. By training machine learning classifiers on information about the presence or absence of genes, their sequence variation, and expression profiles, we generated predictive models and identified biomarkers of resistance to four commonly administered antimicrobial drugs. Using these data types alone or in combination resulted in high (0.8-0.9) or very high (> 0.9) sensitivity and predictive values. For all drugs except for ciprofloxacin, gene expression information improved diagnostic performance. Our results pave the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers. The implementation of a molecular susceptibility test system in routine microbiology diagnostics holds promise to provide earlier and more detailed information on antibiotic resistance profiles of bacterial pathogens and thus could change how physicians treat bacterial infections.

CAMITAX: Taxon labels for microbial genomes.

BACKGROUND: The number of microbial genome sequences is increasing exponentially, especially thanks to recent advances in recovering complete or near-complete genomes from metagenomes and single cells. Assigning reliable taxon labels to genomes is key and often a prerequisite for downstream analyses. FINDINGS: We introduce CAMITAX, a scalable and reproducible workflow for the taxonomic labelling of microbial genomes recovered from isolates, single cells, and metagenomes. CAMITAX combines genome distance-, 16S ribosomal RNA gene-, and gene homology-based taxonomic assignments with phylogenetic placement. It uses Nextflow to orchestrate reference databases and software containers and thus combines ease of installation and use with computational reproducibility. We evaluated the method on several hundred metagenome-assembled genomes with high-quality taxonomic annotations from the TARA Oceans project, and we show that the ensemble classification method in CAMITAX improved on all individual methods across tested ranks. CONCLUSIONS: While we initially developed CAMITAX to aid the Critical Assessment of Metagenome Interpretation (CAMI) initiative, it evolved into a comprehensive software package to reliably assign taxon labels to microbial genomes. CAMITAX is available under Apache License 2.0 at https://github.com/CAMI-challenge/CAMITAX.

Assessing taxonomic metagenome profilers with OPAL.

The explosive growth in taxonomic metagenome profiling methods over the past years has created a need for systematic comparisons using relevant performance criteria. The Open-community Profiling Assessment tooL (OPAL) implements commonly used performance metrics, including those of the first challenge of the initiative for the Critical Assessment of Metagenome Interpretation (CAMI), together with convenient visualizations. In addition, we perform in-depth performance comparisons with seven profilers on datasets of CAMI and the Human Microbiome Project. OPAL is freely available at https://github.com/CAMI-challenge/OPAL .

CAMISIM: simulating metagenomes and microbial communities.

BACKGROUND: Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. RESULTS: We describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series, and differential abundance studies, includes real and simulated strain-level diversity, and generates second- and third-generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes, we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT, and metaSPAdes, on several thousand small data sets generated with CAMISIM. CONCLUSIONS: CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with standards of truth for method evaluation. All data sets and the software are freely available at https://github.com/CAMI-challenge/CAMISIM.

Assembly of the Lactuca sativa, L. cv. Tizian draft genome sequence reveals differences within major resistance complex 1 as compared to the cv. Salinas reference genome.

Lettuce (Lactuca sativa, L.) is an important annual plant of the family Asteraceae (Compositae). The commercial lettuce cultivar Tizian has been used in various scientific studies investigating the interaction of the plant with phytopathogens or biological control agents. Here, we present the de novo draft genome sequencing and gene prediction for this specific cultivar derived from transcriptome sequence data. The assembled scaffolds amount to a size of 2.22 Gb. Based on RNAseq data, 31,112 transcript isoforms were identified. Functional predictions for these transcripts were determined within the GenDBE annotation platform. Comparison with the cv. Salinas reference genome revealed a high degree of sequence similarity on genome and transcriptome levels, with an average amino acid identity of 99%. Furthermore, it was observed that two large regions are either missing or are highly divergent within the cv. Tizian genome compared to cv. Salinas. One of these regions covers the major resistance complex 1 region of cv. Salinas. The cv. Tizian draft genome sequence provides a valuable resource for future functional and transcriptome analyses focused on this lettuce cultivar.

Targeted in situ metatranscriptomics for selected taxa from mesophilic and thermophilic biogas plants.

Biogas production is performed anaerobically by complex microbial communities with key species driving the process. Hence, analyses of their in situ activities are crucial to understand the process. In a previous study, metagenome sequencing and subsequent genome binning for different production-scale biogas plants (BGPs) resulted in four genome bins of special interest, assigned to the phyla Thermotogae, Fusobacteria, Spirochaetes and Cloacimonetes, respectively, that were genetically analysed. In this study, metatranscriptome sequencing of the same BGP samples was conducted, enabling in situ transcriptional activity determination of these genome bins. For this, mapping of metatranscriptome reads on genome bin sequences was performed providing transcripts per million (TPM) values for each gene. This approach revealed an active sugar-based metabolism of the Thermotogae and Spirochaetes bins and an active amino acid-based metabolism of the Fusobacteria and Cloacimonetes bins. The data also hint at syntrophic associations of the four corresponding species with methanogenic Archaea.

Investigation of different nitrogen reduction routes and their key microbial players in wood chip-driven denitrification beds.

Field denitrification beds containing polymeric plant material are increasingly used to eliminate nitrate from agricultural drainage water. They mirror a number of anoxic ecosystems. However, knowledge of the microbial composition, the interaction of microbial species, and the carbon degradation processes within these denitrification systems is sparse. This study revealed several new aspects of the carbon and nitrogen cycle, and these findings can be correlated with the dynamics of the microbial community composition and the activity of key species. Members of the order Pseudomonadales seem to be important players in denitrification at low nitrate concentrations, while a switch to higher nitrate concentrations seems to select for members of the orders Rhodocyclales and Rhizobiales. We observed that high nitrate loading rates lead to an unpredictable transition of the community's activity from denitrification to dissimilatory reduction of nitrate to ammonium (DNRA). This transition is mirrored by an increase in transcripts of the nitrite reductase gene nrfAH and the increase correlates with the activity of members of the order Ignavibacteriales. Denitrification reactors sustained the development of an archaeal community consisting of members of the Bathyarchaeota and methanogens belonging to the Euryarchaeota. Unexpectedly, the activity of the methanogens positively correlated with the nitrate loading rates.

Genomics and prevalence of bacterial and archaeal isolates from biogas-producing microbiomes.

BACKGROUND: To elucidate biogas microbial communities and processes, the application of high-throughput DNA analysis approaches is becoming increasingly important. Unfortunately, generated data can only partialy be interpreted rudimentary since databases lack reference sequences. RESULTS: Novel cellulolytic, hydrolytic, and acidogenic/acetogenic Bacteria as well as methanogenic Archaea originating from different anaerobic digestion communities were analyzed on the genomic level to assess their role in biomass decomposition and biogas production. Some of the analyzed bacterial strains were recently described as new species and even genera, namely Herbinix hemicellulosilytica T3/55T, Herbinix luporum SD1DT, Clostridium bornimense M2/40T, Proteiniphilum saccharofermentans M3/6T, Fermentimonas caenicola ING2-E5BT, and Petrimonas mucosa ING2-E5AT. High-throughput genome sequencing of 22 anaerobic digestion isolates enabled functional genome interpretation, metabolic reconstruction, and prediction of microbial traits regarding their abilities to utilize complex bio-polymers and to perform specific fermentation pathways. To determine the prevalence of the isolates included in this study in different biogas systems, corresponding metagenome fragment mappings were done. Methanoculleus bourgensis was found to be abundant in three mesophilic biogas plants studied and slightly less abundant in a thermophilic biogas plant, whereas Defluviitoga tunisiensis was only prominent in the thermophilic system. Moreover, several of the analyzed species were clearly detectable in the mesophilic biogas plants, but appeared to be only moderately abundant. Among the species for which genome sequence information was publicly available prior to this study, only the species Amphibacillus xylanus, Clostridium clariflavum, and Lactobacillus acidophilus are of importance for the biogas microbiomes analyzed, but did not reach the level of abundance as determined for M. bourgensis and D. tunisiensis. CONCLUSIONS: Isolation of key anaerobic digestion microorganisms and their functional interpretation was achieved by application of elaborated cultivation techniques and subsequent genome analyses. New isolates and their genome information extend the repository covering anaerobic digestion community members.

Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software.

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

Characterisation of a stable laboratory co-culture of acidophilic nanoorganisms.

This study describes the laboratory cultivation of ARMAN (Archaeal Richmond Mine Acidophilic Nanoorganisms). After 2.5 years of successive transfers in an anoxic medium containing ferric sulfate as an electron acceptor, a consortium was attained that is comprised of two members of the order Thermoplasmatales, a member of a proposed ARMAN group, as well as a fungus. The 16S rRNA identity of one archaeon is only 91.6% compared to the most closely related isolate Thermogymnomonas acidicola. Hence, this organism is the first member of a new genus. The enrichment culture is dominated by this microorganism and the ARMAN. The third archaeon in the community seems to be present in minor quantities and has a 100% 16S rRNA identity to the recently isolated Cuniculiplasma divulgatum. The enriched ARMAN species is most probably incapable of sugar metabolism because the key genes for sugar catabolism and anabolism could not be identified in the metagenome. Metatranscriptomic analysis suggests that the TCA cycle funneled with amino acids is the main metabolic pathway used by the archaea of the community. Microscopic analysis revealed that growth of the ARMAN is supported by the formation of cell aggregates. These might enable feeding of the ARMAN by or on other community members.

Metagenomics and CAZyme Discovery.

Microorganisms play a primary role in regulating biogeochemical cycles and are a valuable source of enzymes that have biotechnological applications, such as carbohydrate-active enzymes (CAZymes). However, the inability to culture the majority of microorganisms that exist in natural ecosystems using common culture-dependent techniques restricts access to potentially novel cellulolytic bacteria and beneficial enzymes. The development of molecular-based culture-independent methods such as metagenomics enables researchers to study microbial communities directly from environmental samples, and presents a platform from which enzymes of interest can be sourced. We outline key methodological stages that are required as well as describe specific protocols that are currently used for metagenomic projects dedicated to CAZyme discovery.

Unraveling the microbiome of a thermophilic biogas plant by metagenome and metatranscriptome analysis complemented by characterization of bacterial and archaeal isolates.

BACKGROUND: One of the most promising technologies to sustainably produce energy and to mitigate greenhouse gas emissions from combustion of fossil energy carriers is the anaerobic digestion and biomethanation of organic raw material and waste towards biogas by highly diverse microbial consortia. In this context, the microbial systems ecology of thermophilic industrial-scale biogas plants is poorly understood. RESULTS: The microbial community structure of an exemplary thermophilic biogas plant was analyzed by a comprehensive approach comprising the analysis of the microbial metagenome and metatranscriptome complemented by the cultivation of hydrolytic and acido-/acetogenic Bacteria as well as methanogenic Archaea. Analysis of metagenome-derived 16S rRNA gene sequences revealed that the bacterial genera Defluviitoga (5.5 %), Halocella (3.5 %), Clostridium sensu stricto (1.9 %), Clostridium cluster III (1.5 %), and Tepidimicrobium (0.7 %) were most abundant. Among the Archaea, Methanoculleus (2.8 %) and Methanothermobacter (0.8 %) were predominant. As revealed by a metatranscriptomic 16S rRNA analysis, Defluviitoga (9.2 %), Clostridium cluster III (4.8 %), and Tepidanaerobacter (1.1 %) as well as Methanoculleus (5.7 %) mainly contributed to these sequence tags indicating their metabolic activity, whereas Hallocella (1.8 %), Tepidimicrobium (0.5 %), and Methanothermobacter (<0.1 %) were transcriptionally less active. By applying 11 different cultivation strategies, 52 taxonomically different microbial isolates representing the classes Clostridia, Bacilli, Thermotogae, Methanomicrobia and Methanobacteria were obtained. Genome analyses of isolates support the finding that, besides Clostridium thermocellum and Clostridium stercorarium, Defluviitoga tunisiensis participated in the hydrolysis of hemicellulose producing ethanol, acetate, and H2/CO2. The latter three metabolites are substrates for hydrogentrophic and acetoclastic archaeal methanogenesis. CONCLUSIONS: Obtained results showed that high abundance of microorganisms as deduced from metagenome analysis does not necessarily indicate high transcriptional or metabolic activity, and vice versa. Additionally, it appeared that the microbiome of the investigated thermophilic biogas plant comprised a huge number of up to now unknown and insufficiently characterized species.

Identification and genome reconstruction of abundant distinct taxa in microbiomes from one thermophilic and three mesophilic production-scale biogas plants.

BACKGROUND: Biofuel production from conversion of biomass is indispensable in the portfolio of renewable energies. Complex microbial communities are involved in the anaerobic digestion process of plant material, agricultural residual products and food wastes. Analysis of the genetic potential and microbiology of communities degrading biomass to biofuels is considered to be the key to develop process optimisation strategies. Hence, due to the still incomplete taxonomic and functional characterisation of corresponding communities, new and unknown species are of special interest. RESULTS: Three mesophilic and one thermophilic production-scale biogas plants (BGPs) were taxonomically profiled using high-throughput 16S rRNA gene amplicon sequencing. All BGPs shared a core microbiome with the thermophilic BGP featuring the lowest diversity. However, the phyla Cloacimonetes and Spirochaetes were unique to BGPs 2 and 3, Fusobacteria were only found in BGP3 and members of the phylum Thermotogae were present only in the thermophilic BGP4. Taxonomic analyses revealed that these distinctive taxa mostly represent so far unknown species. The only exception is the dominant Thermotogae OTU featuring 16S rRNA gene sequence identity to Defluviitoga tunisiensis L3, a sequenced and characterised strain. To further investigate the genetic potential of the biogas communities, corresponding metagenomes were sequenced in a deepness of 347.5 Gbp in total. A combined assembly comprised 80.3 % of all reads and resulted in the prediction of 1.59 million genes on assembled contigs. Genome binning yielded genome bins comprising the prevalent distinctive phyla Cloacimonetes, Spirochaetes, Fusobacteria and Thermotogae. Comparative genome analyses between the most dominant Thermotogae bin and the very closely related Defluviitoga tunisiensis L3 genome originating from the same BGP revealed high genetic similarity. This finding confirmed applicability and reliability of the binning approach. The four highly covered genome bins of the other three distinct phyla showed low or very low genetic similarities to their closest phylogenetic relatives, and therefore indicated their novelty. CONCLUSIONS: In this study, the 16S rRNA gene sequencing approach and a combined metagenome assembly and binning approach were used for the first time on different production-scale biogas plants and revealed insights into the genetic potential and functional role of so far unknown species.

An integrated metagenome and -proteome analysis of the microbial community residing in a biogas production plant.

To study the metaproteome of a biogas-producing microbial community, fermentation samples were taken from an agricultural biogas plant for microbial cell and protein extraction and corresponding metagenome analyses. Based on metagenome sequence data, taxonomic community profiling was performed to elucidate the composition of bacterial and archaeal sub-communities. The community's cytosolic metaproteome was represented in a 2D-PAGE approach. Metaproteome databases for protein identification were compiled based on the assembled metagenome sequence dataset for the biogas plant analyzed and non-corresponding biogas metagenomes. Protein identification results revealed that the corresponding biogas protein database facilitated the highest identification rate followed by other biogas-specific databases, whereas common public databases yielded insufficient identification rates. Proteins of the biogas microbiome identified as highly abundant were assigned to the pathways involved in methanogenesis, transport and carbon metabolism. Moreover, the integrated metagenome/-proteome approach enabled the examination of genetic-context information for genes encoding identified proteins by studying neighboring genes on the corresponding contig. Exemplarily, this approach led to the identification of a Methanoculleus sp. contig encoding 16 methanogenesis-related gene products, three of which were also detected as abundant proteins within the community's metaproteome. Thus, metagenome contigs provide additional information on the genetic environment of identified abundant proteins.

Genomic characterization of Defluviitoga tunisiensis L3, a key hydrolytic bacterium in a thermophilic biogas plant and its abundance as determined by metagenome fragment recruitment.

The genome sequence of Defluviitoga tunisiensis L3 originating from a thermophilic biogas-production plant was established and recently published as Genome Announcement by our group. The circular chromosome of D. tunisiensis L3 has a size of 2,053,097bp and a mean GC content of 31.38%. To analyze the D. tunisiensis L3 genome sequence in more detail, a phylogenetic analysis of completely sequenced Thermotogae strains based on shared core genes was performed. It appeared that Petrotoga mobilis DSM 10674(T), originally isolated from a North Sea oil-production well, is the closest relative of D. tunisiensis L3. Comparative genome analyses of P. mobilis DSM 10674(T) and D. tunisiensis L3 showed moderate similarities regarding occurrence of orthologous genes. Both genomes share a common set of 1351 core genes. Reconstruction of metabolic pathways important for the biogas production process revealed that the D. tunisiensis L3 genome encodes a large set of genes predicted to facilitate utilization of a variety of complex polysaccharides including cellulose, chitin and xylan. Ethanol, acetate, hydrogen (H2) and carbon dioxide (CO2) were found as possible end-products of the fermentation process. The latter three metabolites are considered to represent substrates for methanogenic Archaea, the key organisms in the final step of the anaerobic digestion process. To determine the degree of relatedness between D. tunisiensis L3 and dominant biogas community members within the thermophilic biogas-production plant, metagenome sequences obtained from the corresponding microbial community were mapped onto the L3 genome sequence. This fragment recruitment revealed that the D. tunisiensis L3 genome is almost completely covered with metagenome sequences featuring high matching accuracy. This result indicates that strains highly related or even identical to the reference strain D. tunisiensis L3 play a dominant role within the community of the thermophilic biogas-production plant.

MeCorS: Metagenome-enabled error correction of single cell sequencing reads.

UNLABELLED: We present a new tool, MeCorS, to correct chimeric reads and sequencing errors in Illumina data generated from single amplified genomes (SAGs). It uses sequence information derived from accompanying metagenome sequencing to accurately correct errors in SAG reads, even from ultra-low coverage regions. In evaluations on real data, we show that MeCorS outperforms BayesHammer, the most widely used state-of-the-art approach. MeCorS performs particularly well in correcting chimeric reads, which greatly improves both accuracy and contiguity of de novo SAG assemblies. AVAILABILITY AND IMPLEMENTATION: https://github.com/metagenomics/MeCorS CONTACT: abremges@cebitec.uni-bielefeld.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Finished genome sequence and methylome of the cyanide-degrading Pseudomonas pseudoalcaligenes strain CECT5344 as resolved by single-molecule real-time sequencing.

Pseudomonas pseudoalcaligenes CECT5344 tolerates cyanide and is also able to utilize cyanide and cyano-derivatives as a nitrogen source under alkaline conditions. The strain is considered as candidate for bioremediation of habitats contaminated with cyanide-containing liquid wastes. Information on the genome sequence of the strain CECT5344 became available previously. The P. pseudoalcaligenes CECT5344 genome was now resequenced by applying the single molecule, real-time (SMRT(®)) sequencing technique developed by Pacific Biosciences. The complete and finished genome sequence of the strain consists of a 4,696,984 bp chromosome featuring a GC-content of 62.34%. Comparative analyses between the new and previous versions of the P. pseudoalcaligenes CECT5344 genome sequence revealed additional regions in the new sequence that were missed in the older version. These additional regions mostly represent mobile genetic elements. Moreover, five additional genes predicted to play a role in sulfoxide reduction are present in the newly established genome sequence. The P. pseudoalcaligenes CECT5344 genome sequence is highly related to the genome sequences of different Pseudomonas mendocina strains. Approximately, 70% of all genes are shared between P. pseudoalcaligenes and P. mendocina. In contrast to P. mendocina, putative pathogenicity genes were not identified in the P. pseudoalcaligenes CECT5344 genome. P. pseudoalcaligenes CECT5344 possesses unique genes for nitrilases and mercury resistance proteins that are of importance for survival in habitats contaminated with cyano- and mercury compounds. As an additional feature of the SMRT sequencing technology, the methylome of P. pseudoalcaligenes was established. Six sequence motifs featuring methylated adenine residues (m6A) were identified in the genome. The genome encodes several methyltransferases, some of which may be considered for methylation of the m6A motifs identified. The complete genome sequence of the strain CECT5344 now provides the basis for exploitation of genetic features for biotechnological purposes.