The minimum information about a genome sequence (MIGS) specification

Field, Dawn; Gray, Tanya; Morrison, Norman; Selengut, Jeremy D.; Sterk, Peter J.; Tatusova, Tatiana; Thomson, Nicholas R.; Allen, Michael J.; Angiuoli, Samuel V.; Ashburner, Michael; Axelrod, Nelson; Baldauf, Sandra L.; Ballard, S.; Boore, Jeffrey L.; Cochrane, Guy; Cole, James R.; Dawyndt, Peter; Vos, Paul De; dePamphilis, Claude W.; Edwards, Robert A.; Faruque, Nadeem; Feldman, Robert A.; Gilbert, Jack A.; Gilna, Paul; Glöckner, Frank Oliver; Goldstein, Philip; Guralnick, Robert; Haft, Dan H.; Hancock, David G.; Hermjakob, Henning; Hertz‐Fowler, Christiane; Hugenholtz, Philip; Joint, Ian; Kagan, Leonid; Kane, Matthew D.; Kennedy, Jessie; Kowalchuk, George A.; Kottmann, Renzo; Kolker, Eugene; Kravitz, Saul; Kyrpides, Nikos C.; Leebens‐Mack, Jim; Lewis, Suzanna; Li, Kelvin; Lister, Allyson L.; Lord, Phillip; Maltsev, Natalia; Markowitz, Victor; Martiny, Jennifer B. H.; Methé, Barbara A.; Karsch-Mizrachi, Ilene; Moxon, Richard; Nelson, Karen E.; Parkhill, Julian; Proctor, Lita M.; White, Owen; Sansone, Susanna-Assunta; Spiers, Andrew J.; Stevens, Robert; Swift, Paul; Taylor, Chris; Tateno, Yoshio; Tett, Adrian; Turner, Sarah L.; Ussery, David W.; Vaughan, R. W.; Ward, Naomi; Whetzel, Trish; Gil, Ingio San; Wilson, Gareth A.; Wipat, Anil

doi:10.1038/nbt1360

Cited by 1,067 publications

(555 citation statements)

References 29 publications

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“…This could range from the exact geographical location (for example, longitude, latitude, depth, height, time or date) to biochemical habitat measurements (for example, pH, levels of oxygen, phosphate or nitrate, and salinity) to patient information (for example, gender, age and disease or nutritional state) [29,80]. The 'Minimum Information about a Metagenome Sequence' specification should allow this information to be captured [81].…”

Section: Detailed Sampling-methodsology Description and Meta-data Recomentioning

confidence: 99%

Molecular eco-systems biology: towards an understanding of community function

Raes¹,

Bork²

2008

Nat Rev Microbiol

351

274

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| Systems-biology approaches, which are driven by genome sequencing and high-throughput functional genomics data, are revolutionizing single-cell-organism biology. With the advent of various high-throughput techniques that aim to characterize complete microbial ecosystems (metagenomics, meta-transcriptomics and meta-metabolomics), we propose that the time is ripe to consider molecular systems biology at the ecosystem level (eco-systems biology). Here, we discuss the necessary data types that are required to unite molecular microbiology and ecology to develop an understanding of community function and discuss the potential shortcomings of these approaches.Over the past decade, the advent of robotics has enabled a paradigm shift in molecular biology: a change of emphasis from reductionistic approaches and 'singleprotein' studies to global investigations of increasingly more complex systems of molecules and their interrelationships. These 'systems approaches' are used to investigate processes as a whole and enable models to be built to predict the behaviour of a system in response to various external cues, disturbances or modifications of its composition [1]. After ground-breaking work on the properties of small networks that consisted of a few genes, the wiring of complete cells and microbial organisms is now being investigated and modelled [2,3]. However, as free-living organisms constantly interact with each other and the environment, systems biologists are already looking towards the next big challengeunravelling the complexity of complete ecosystems.A microbial ecosystem can be defined as a system that consists of all the microorganisms that live in a certain area or niche and that function together in the context of the other biotic (plants and animals) and abiotic (temperature, chemical composition and structure of the surroundings) factors of that niche. Communities range from being simple (for example, one-or two-speciesdominated bioreactors and biofilms that are growing on ore-mine effluents or medical implants) to complex (for example, symbiotic human gut flora, plant rhizospheres, soil communities and ocean dwelling or even airborne microorganisms, such as those present in clouds). The complexity of the interactions in ecosystems depends on the number of species and the population structure, variation in food and energy supply and the geography of the habitat [4]. Eco-systems biology seeks to understand, as a whole, the immensely complex set of molecular processes and interactions that contribute to ecosystem functioning -the total sum of ecosystem-level processes, such as matter, nutrient and energy cycling [5]. This understanding should ultimately lead to predictive modelling of ecosystems, allowing the in silico investigation of ecosystem properties. Important issues that could be addressed by an ecosystems approach include estimating the relative importance of ecosystem members in ecosystem functioning and productivity, the effect of nutrient availability on species composition or the resil...

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Section: Detailed Sampling-methodsology Description and Meta-data Recomentioning

confidence: 99%

Molecular eco-systems biology: towards an understanding of community function

Raes¹,

Bork²

2008

Nat Rev Microbiol

351

274

View full text Add to dashboard Cite

show abstract

“…As stated by other authors, it becomes crucial to gain a consensus rationale for measuring and reporting some basic environmental variables in microbial surveys (Robertson et al, 2005, Field et al, 2008. In addition, our approach contained a stochastic component inherent to any environmental study (Sloan et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Global ecological patterns in uncultured Archaea

2009

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We have applied a global analytical approach to uncultured Archaea that for the first time reveals well-defined community patterns along broad environmental gradients and habitat types. Phylogenetic patterns and the environmental factors governing the creation and maintenance of these patterns were analyzed for c. 2000 archaeal 16S rRNA gene sequences from 67 globally distributed studies. The sequences were dereplicated at 97% identity, grouped into seven habitat types, and analyzed with both Unifrac (to explore shared phylogenetic history) and multivariate regression tree (that considers the relative abundance of the lineages or taxa) approaches. Both phylogenetic and taxon-based approaches showed salinity and not temperature as one of the principal driving forces at the global scale. Hydrothermal vents and planktonic freshwater habitats emerged as the largest reservoirs of archaeal diversity and consequently are promising environments for the discovery of new archaeal lineages. Conversely, soils were more phylogenetically clustered and archaeal diversity was the result of a high number of closely related phylotypes rather than different lineages. Applying the ecological concept of 'indicator species', we detected up to 13 indicator archaeal lineages for the seven habitats prospected. Some of these lineages (that is, hypersaline MSBL1, marine sediment FCG1 and freshwater plSA1), for which ecological importance has remained unseen to date, deserve further attention as they represent potential key archaeal groups in terms of distribution and ecological processes. Hydrothermal vents held the highest number of indicator lineages, suggesting it would be the earliest habitat colonized by Archaea. Overall, our approach provided ecological support for the often arbitrary nomenclature within uncultured Archaea, as well as phylogeographical clues on key ecological and evolutionary aspects of archaeal biology.

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“…5). To make full use of these genomes, their quality must be reported in reference databases along with other essential genome information (Field et al 2008). A qualitative vocabulary for discussing genomes of varying quality was proposed by Chain et al (2009); here, we supplement this effort by proposing a vocabulary based on a quantitative threshold that augments existing schemes for describing draft genome quality (Table 3).…”

Section: Proposed Genome Quality Classification Schemementioning

confidence: 99%

CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

et al. 2015

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Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of "marker" genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene-and genome-centric analyses of microbial communities.

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The minimum information about a genome sequence (MIGS) specification

Cited by 1,067 publications

References 29 publications

Molecular eco-systems biology: towards an understanding of community function

Molecular eco-systems biology: towards an understanding of community function

Global ecological patterns in uncultured Archaea

CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

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