MG-RAST version 4-lessons learned from a decade of low-budget ultra-high-throughput metagenome analysis.

As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1-3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community's data analysis tasks.

The MG-RAST API explorer: an on-ramp for RESTful query composition.

BACKGROUND: The MG-RAST API provides search capabilities and delivers organism and function data as well as raw or annotated sequence data via the web interface and its RESTful API. For casual users, however, RESTful APIs are hard to learn and work with. RESULTS: We created the graphical MG-RAST API explorer to help researchers more easily build and export API queries; understand the data abstractions and indices available in MG-RAST; and use the results presented in-browser for exploration, development, and debugging. CONCLUSIONS: The API explorer lowers the barrier to entry for occasional or first-time MG-RAST API users.

The MG-RAST metagenomics database and portal in 2015.

MG-RAST (http://metagenomics.anl.gov) is an open-submission data portal for processing, analyzing, sharing and disseminating metagenomic datasets. The system currently hosts over 200,000 datasets and is continuously updated. The volume of submissions has increased 4-fold over the past 24 months, now averaging 4 terabasepairs per month. In addition to several new features, we report changes to the analysis workflow and the technologies used to scale the pipeline up to the required throughput levels. To show possible uses for the data from MG-RAST, we present several examples integrating data and analyses from MG-RAST into popular third-party analysis tools or sequence alignment tools.

A RESTful API for accessing microbial community data for MG-RAST.

Metagenomic sequencing has produced significant amounts of data in recent years. For example, as of summer 2013, MG-RAST has been used to annotate over 110,000 data sets totaling over 43 Terabases. With metagenomic sequencing finding even wider adoption in the scientific community, the existing web-based analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and analysis, such as comparative analysis between multiple data sets. Moreover, although the system provides many analysis tools, it is not comprehensive. By opening MG-RAST up via a web services API (application programmers interface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use of third-party analysis tools with MG-RAST data. This RESTful API makes all data and data objects created by the MG-RAST pipeline accessible as JSON objects. As part of the DOE Systems Biology Knowledgebase project (KBase, http://kbase.us) we have implemented a web services API for MG-RAST. This API complements the existing MG-RAST web interface and constitutes the basis of KBase's microbial community capabilities. In addition, the API exposes a comprehensive collection of data to programmers. This API, which uses a RESTful (Representational State Transfer) implementation, is compatible with most programming environments and should be easy to use for end users and third parties. It provides comprehensive access to sequence data, quality control results, annotations, and many other data types. Where feasible, we have used standards to expose data and metadata. Code examples are provided in a number of languages both to show the versatility of the API and to provide a starting point for users. We present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the utility of the MG-RAST service.

Betting on Illusory Patterns: Probability Matching in Habitual Gamblers.

Why do people gamble? A large body of research suggests that cognitive distortions play an important role in pathological gambling. Many of these distortions are specific cases of a more general misperception of randomness, specifically of an illusory perception of patterns in random sequences. In this article, we provide further evidence for the assumption that gamblers are particularly prone to perceiving illusory patterns. In particular, we compared habitual gamblers to a matched sample of community members with regard to how much they exhibit the choice anomaly 'probability matching'. Probability matching describes the tendency to match response proportions to outcome probabilities when predicting binary outcomes. It leads to a lower expected accuracy than the maximizing strategy of predicting the most likely event on each trial. Previous research has shown that an illusory perception of patterns in random sequences fuels probability matching. So does impulsivity, which is also reported to be higher in gamblers. We therefore hypothesized that gamblers will exhibit more probability matching than non-gamblers, which was confirmed in a controlled laboratory experiment. Additionally, gamblers scored much lower than community members on the cognitive reflection task, which indicates higher impulsivity. This difference could account for the difference in probability matching between the samples. These results suggest that gamblers are more willing to bet impulsively on perceived illusory patterns.

The complete genome sequence for putative H2- and S-oxidizer Candidatus Sulfuricurvum sp., assembled de novo from an aquifer-derived metagenome.

We reconstructed the complete 2.4 Mb-long genome of a previously uncultivated epsilonproteobacterium, Candidatus Sulfuricurvum sp. RIFRC-1, via assembly of short-read shotgun metagenomic data using a complexity reduction approach. Genome-based comparisons indicate the bacterium is a novel species within the Sulfuricurvum genus, which contains one cultivated representative, S. kujiense. Divergence between the species appears due in part to extensive genomic rearrangements, gene loss and chromosomal versus plasmid encoding of certain (respiratory) genes by RIFRC-1. Deoxyribonucleic acid for the genome was obtained from terrestrial aquifer sediment, in which RIFRC-1 comprised ∼ 47% of the bacterial community. Genomic evidence suggests RIFRC-1 is a chemolithoautotrophic diazotroph capable of deriving energy for growth by microaerobic or nitrate-/nitric oxide-dependent oxidation of S°, sulfide or sulfite or H2oxidation. Carbon may be fixed via the reductive tricarboxylic acid cycle. Consistent with these physiological attributes, the local aquifer was microoxic with small concentrations of available nitrate, small but elevated concentrations of reduced sulfur and NH(4)(+) /NH3-limited. Additionally, various mechanisms for heavy metal and metalloid tolerance and virulence point to a lifestyle well-adapted for metal(loid)-rich environments and a shared evolutionary past with pathogenic Epsilonproteobacteria. Results expand upon recent findings highlighting the potential importance of sulfur and hydrogen metabolism in the terrestrial subsurface.

Seleno groups control the energy-level alignment between conjugated organic molecules and metals.

The charge injection from metallic electrodes into hole transporting layers of organic devices often suffers from deviations from vacuum-level alignment at the interface. Even for weakly interacting cases, Pauli repulsion causes an interface dipole between the metal and conjugated organic molecules (COMs) (so called "push-back" or "cushion" effect), which leads notoriously to an increase of the hole injection barrier. On the other hand, for chalcogenol self assembled monolayers (SAMs) on metal surfaces, chemisorption via the formation of chalcogen-metal bonds is commonly observed. In these cases, the energy-level alignment is governed by chalcogen-derived interface states in the vicinity of the metal Fermi-level. In this work, we present X-ray and ultraviolet photoelectron spectroscopy data that demonstrate that the interfacial energy-level alignment mechanism found for chalcogenol SAMs also applies to seleno-functionalized COMs. This can be exploited to mitigate the push-back effect at metal contacts, notably also when COMs with low ionization energies are employed, permitting exceedingly low hole injection barriers, as shown here for the interfaces of tetraseleno-tetracene with Au(111), Ag(111), and Cu(111).

A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE.

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as "noise" or "error") within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms.

Short-read reading-frame predictors are not created equal: sequence error causes loss of signal.

BACKGROUND: Gene prediction algorithms (or gene callers) are an essential tool for analyzing shotgun nucleic acid sequence data. Gene prediction is a ubiquitous step in sequence analysis pipelines; it reduces the volume of data by identifying the most likely reading frame for a fragment, permitting the out-of-frame translations to be ignored. In this study we evaluate five widely used ab initio gene-calling algorithms-FragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal-for accuracy on short (75-1000 bp) fragments containing sequence error from previously published artificial data and "real" metagenomic datasets. RESULTS: While gene prediction tools have similar accuracies predicting genes on error-free fragments, in the presence of sequencing errors considerable differences between tools become evident. For error-containing short reads, FragGeneScan finds more prokaryotic coding regions than does MetaGeneAnnotator, MetaGeneMark, Orphelia, or Prodigal. This improved detection of genes in error-containing fragments, however, comes at the cost of much lower (50%) specificity and overprediction of genes in noncoding regions. CONCLUSIONS: Ab initio gene callers offer a significant reduction in the computational burden of annotating individual nucleic acid reads and are used in many metagenomic annotation systems. For predicting reading frames on raw reads, we find the hidden Markov model approach in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are better suited for higher-quality sequences such as assembled contigs.

The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools.

BACKGROUND: Computing of sequence similarity results is becoming a limiting factor in metagenome analysis. Sequence similarity search results encoded in an open, exchangeable format have the potential to limit the needs for computational reanalysis of these data sets. A prerequisite for sharing of similarity results is a common reference. DESCRIPTION: We introduce a mechanism for automatically maintaining a comprehensive, non-redundant protein database and for creating a quarterly release of this resource. In addition, we present tools for translating similarity searches into many annotation namespaces, e.g. KEGG or NCBI's GenBank. CONCLUSIONS: The data and tools we present allow the creation of multiple result sets using a single computation, permitting computational results to be shared between groups for large sequence data sets.

Connecting genotype to phenotype in the era of high-throughput sequencing.

BACKGROUND: The development of next generation sequencing technology is rapidly changing the face of the genome annotation and analysis field. One of the primary uses for genome sequence data is to improve our understanding and prediction of phenotypes for microbes and microbial communities, but the technologies for predicting phenotypes must keep pace with the new sequences emerging. SCOPE OF REVIEW: This review presents an integrated view of the methods and technologies used in the inference of phenotypes for microbes and microbial communities based on genomic and metagenomic data. Given the breadth of this topic, we place special focus on the resources available within the SEED Project. We discuss the two steps involved in connecting genotype to phenotype: sequence annotation, and phenotype inference, and we highlight the challenges in each of these steps when dealing with both single genome and metagenome data. MAJOR CONCLUSIONS: This integrated view of the genotype-to-phenotype problem highlights the importance of a controlled ontology in the annotation of genomic data, as this benefits subsequent phenotype inference and metagenome annotation. We also note the importance of expanding the set of reference genomes to improve the annotation of all sequence data, and we highlight metagenome assembly as a potential new source for complete genomes. Finally, we find that phenotype inference, particularly from metabolic models, generates predictions that can be validated and reconciled to improve annotations. GENERAL SIGNIFICANCE: This review presents the first look at the challenges and opportunities associated with the inference of phenotype from genotype during the next generation sequencing revolution. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.

Red death in Caenorhabditis elegans caused by Pseudomonas aeruginosa PAO1.

During host injury, Pseudomonas aeruginosa can be cued to express a lethal phenotype within the intestinal tract reservoir-a hostile, nutrient scarce environment depleted of inorganic phosphate. Here we determined if phosphate depletion activates a lethal phenotype in P. aeruginosa during intestinal colonization. To test this, we allowed Caenorhabditis elegans to feed on lawns of P. aeruginosa PAO1 grown on high and low phosphate media. Phosphate depletion caused PAO1 to kill 60% of nematodes whereas no worms died on high phosphate media. Unexpectedly, intense redness was observed in digestive tubes of worms before death. Using a combination of transcriptome analyses, mutants, and reporter constructs, we identified 3 global virulence systems that were involved in the "red death" response of P. aeruginosa during phosphate depletion; they included phosphate signaling (PhoB), the MvfR-PQS pathway of quorum sensing, and the pyoverdin iron acquisition system. Activation of all 3 systems was required to form a red colored PQS+Fe(3+) complex which conferred a lethal phenotype in this model. When pyoverdin production was inhibited in P. aeruginosa by providing excess iron, red death was attenuated in C. elegans and mortality was decreased in mice intestinally inoculated with P. aeruginosa. Introduction of the red colored PQS+Fe(3+) complex into the digestive tube of C. elegans or mouse intestine caused mortality associated with epithelial disruption and apoptosis. In summary, red death in C. elegans reveals a triangulated response between PhoB, MvfR-PQS, and pyoverdin in response to phosphate depletion that activates a lethal phenotype in P. aeruginosa.

Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG.

BACKGROUND: Metagenomics is the study of microbial organisms using sequencing applied directly to environmental samples. Technological advances in next-generation sequencing methods are fueling a rapid increase in the number and scope of metagenome projects. While metagenomics provides information on the gene content, metatranscriptomics aims at understanding gene expression patterns in microbial communities. The initial computational analysis of a metagenome or metatranscriptome addresses three questions: (1) Who is out there? (2) What are they doing? and (3) How do different datasets compare? There is a need for new computational tools to answer these questions. In 2007, the program MEGAN (MEtaGenome ANalyzer) was released, as a standalone interactive tool for analyzing the taxonomic content of a single metagenome dataset. The program has subsequently been extended to support comparative analyses of multiple datasets. RESULTS: The focus of this paper is to report on new features of MEGAN that allow the functional analysis of multiple metagenomes (and metatranscriptomes) based on the SEED hierarchy and KEGG pathways. We have compared our results with the MG-RAST service for different datasets. CONCLUSIONS: The MEGAN program now allows the interactive analysis and comparison of the taxonomical and functional content of multiple datasets. As a stand-alone tool, MEGAN provides an alternative to web portals for scientists that have concerns about uploading their unpublished data to a website.

Synergistic use of plant-prokaryote comparative genomics for functional annotations.

BACKGROUND: Identifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these 'unknown' proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations. RESULTS: Among Arabidopsis genes, we focused on those (2,325 in total) that (i) are unique or belong to families with no more than three members, (ii) occur in prokaryotes, and (iii) have unknown or poorly known functions. Computer-assisted selection of promising targets for deeper analysis was based on homology-independent characteristics associated in the SEED database with the prokaryotic members of each family. In-depth comparative genomic analysis was performed for 360 top candidate families. From this pool, 78 families were connected to general areas of metabolism and, of these families, specific functional predictions were made for 41. Twenty-one predicted functions have been experimentally tested or are currently under investigation by our group in at least one prokaryotic organism (nine of them have been validated, four invalidated, and eight are in progress). Ten additional predictions have been independently validated by other groups. Discovering the function of very widespread but hitherto enigmatic proteins such as the YrdC or YgfZ families illustrates the power of our approach. CONCLUSIONS: Our approach correctly predicted functions for 19 uncharacterized protein families from plants and prokaryotes; none of these functions had previously been correctly predicted by computational methods. The resulting annotations could be propagated with confidence to over six thousand homologous proteins encoded in over 900 bacterial, archaeal, and eukaryotic genomes currently available in public databases.

Past and present environments: the evolution of decision making.

The human mind is filled with evolved decision mechanisms designed to meet adaptively important goals. In this article we lay out a framework for studying those mechanisms from the perspective of evolutionary psychology, emphasizing the importance of multiple influences of the environment on shaping the decision strategies and their operation. These strategies often take the form of simple heuristics constructed from building blocks that draw on evolved capacities, all of which fit to particular information structures in the environment. We illustrate these ideas with two examples of heuristics used in important adaptive domains: deciding when to leave a resource patch, and predicting when a sequence of events will stop or continue.

Fishing for the right words: decision rules for human foraging behavior in internal search tasks.

Animals depleting one patch of resources must decide when to leave and switch to a fresh patch. Foraging theory has predicted various decision mechanisms; which is best depends on environmental variation in patch quality. Previously we tested whether these mechanisms underlie human decision making when foraging for external resources; here we test whether humans behave similarly in a cognitive task seeking internally generated solutions. Subjects searched for meaningful words made from random letter sequences, and as their success rate declined, they could opt to switch to a fresh sequence. As in the external foraging context, time since the previous success and the interval preceding it had a major influence on when subjects switched. Subjects also used the commonness of sequence letters as a proximal cue to patch quality that influenced when to switch. Contrary to optimality predictions, switching decisions were independent of whether sequences differed little or widely in quality.

Investigating the ability of the microbial model Cunninghamella elegans for the metabolism of synthetic tryptamines.

Tryptamines can occur naturally in plants, mushrooms, microbes, and amphibians. Synthetic tryptamines are sold as new psychoactive substances (NPS) because of their hallucinogenic effects. When it comes to NPS, metabolism studies are of crucial importance, due to the lack of pharmacological and toxicological data. Different approaches can be taken to study in vitro and in vivo metabolism of xenobiotica. The zygomycete fungus Cunninghamella elegans (C. elegans) can be used as a microbial model for the study of drug metabolism. The current study investigated the biotransformation of four naturally occurring and synthetic tryptamines [N,N-Dimethyltryptamine (DMT), 4-hydroxy-N-methyl-N-ethyltryptamine (4-HO-MET), N,N-di allyl-5-methoxy tryptamine (5-MeO-DALT) and 5-methoxy-N-methyl-N-isoporpoyltryptamine (5-MeO-MiPT)] in C. elegans after incubation for 72 hours. Metabolites were identified using liquid chromatography-high resolution-tandem mass spectrometry (LC-HR-MS/MS) with a quadrupole time-of-flight (QqTOF) instrument. Results were compared to already published data on these substances. C. elegans was capable of producing all major biotransformation steps: hydroxylation, N-oxide formation, carboxylation, deamination, and demethylation. On average 63% of phase I metabolites found in the literature could also be detected in C. elegans. Additionally, metabolites specific for C. elegans were identified. Therefore, C. elegans is a suitable complementary model to other in vitro or in vivo methods to study the metabolism of naturally occurring or synthetic tryptamines.

The RAST Server: rapid annotations using subsystems technology.

BACKGROUND: The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. DESCRIPTION: We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12-24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service. CONCLUSION: By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.

In vitro phase I metabolism of three phenethylamines 25D-NBOMe, 25E-NBOMe and 25N-NBOMe using microsomal and microbial models.

Numerous 2,5-dimethoxy-N-benzylphenethylamines (NBOMe), carrying a variety of lipophilic substituents at the 4-position, are potent agonists at 5-hydroxytryptamine (5HT2A ) receptors and show hallucinogenic effects. The present study investigated the metabolism of 25D-NBOMe, 25E-NBOMe, and 25N-NBOMe using the microsomal model of pooled human liver microsomes (pHLM) and the microbial model of the fungi Cunninghamella elegans (C. elegans). Identification of metabolites was performed using liquid chromatography-high resolution-tandem mass spectrometry (LC-HR-MS/MS) with a quadrupole time-of-flight (QqToF) instrument. In total, 36 25D-NBOMe phase I metabolites, 26 25E-NBOMe phase I metabolites and 24 25N-NBOMe phase I metabolites were detected and identified in pHLM. Furthermore, 14 metabolites of 25D-NBOMe, 11 25E-NBOMe metabolites, and nine 25N-NBOMe metabolites could be found in C. elegans. The main biotransformation steps observed were oxidative deamination, oxidative N-dealkylation also in combination with hydroxylation, oxidative O-demethylation possibly combined with hydroxylation, oxidation of secondary alcohols, mono- and dihydroxylation, oxidation of primary alcohols, and carboxylation of primary alcohols. Additionally, oxidative di-O-demethylation for 25E-NBOMe and reduction of the aromatic nitro group and N-acetylation of the primary aromatic amine for 25N-NBOMe took place. The resulting 25N-NBOMe metabolites were unique for NBOMe compounds. For all NBOMes investigated, the corresponding 2,5-dimethoxyphenethylamine (2C-X) metabolite was detected. This study reports for the first time 25X-NBOMe N-oxide metabolites and hydroxylamine metabolites, which were identified for 25D-NBOMe and 25N-NBOMe and all three investigated NBOMes, respectively. C. elegans was capable of generating all main biotransformation steps observed in pHLM and might therefore be an interesting model for further studies of new psychoactive substances (NPS) metabolism.

SAMPI: protein identification with mass spectra alignments.

BACKGROUND: Mass spectrometry based peptide mass fingerprints (PMFs) offer a fast, efficient, and robust method for protein identification. A protein is digested (usually by trypsin) and its mass spectrum is compared to simulated spectra for protein sequences in a database. However, existing tools for analyzing PMFs often suffer from missing or heuristic analysis of the significance of search results and insufficient handling of missing and additional peaks. RESULTS: We present an unified framework for analyzing Peptide Mass Fingerprints that offers a number of advantages over existing methods: First, comparison of mass spectra is based on a scoring function that can be custom-designed for certain applications and explicitly takes missing and additional peaks into account. The method is able to simulate almost every additive scoring scheme. Second, we present an efficient deterministic method for assessing the significance of a protein hit, independent of the underlying scoring function and sequence database. We prove the applicability of our approach using biological mass spectrometry data and compare our results to the standard software Mascot. CONCLUSION: The proposed framework for analyzing Peptide Mass Fingerprints shows performance comparable to Mascot on small peak lists. Introducing more noise peaks, we are able to keep identification rates at a similar level by using the flexibility introduced by scoring schemes.