Habitat-Lite: A GSC Case Study Based on Free Text Terms for Environmental Metadata

Hirschman, Lynette; Clark, Cheryl; Cohen, K. Bretonnel; Mardis, Scott; Luciano, Joanne S.; Kottmann, Renzo; Cole, James R.; Markowitz, Victor; Kyrpides, Nikos C.; Morrison, Norman; Schriml, Lynn M.; Field, Dawn

doi:10.1089/omi.2008.0016

Cited by 38 publications

(36 citation statements)

References 16 publications

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“…Each bacterial assemblage was assigned into 11 habitat groups by a priori classification (a classification made before experimentation) (Table 1), using two-level habitat definitions from the Genomic Standards Consortium's "Habitat-Lite" version 0.4 ontology (14). Detailed sample descriptions are provided in Table S1.…”

Section: Resultsmentioning

confidence: 99%

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

Sul

Cole

Jesus

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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High-throughput sequencing of 16S rRNA genes has increased our understanding of microbial community structure, but now even higher-throughput methods to the Illumina scale allow the creation of much larger datasets with more samples and orders-ofmagnitude more sequences that swamp current analytic methods. We developed a method capable of handling these larger datasets on the basis of assignment of sequences into an existing taxonomy using a supervised learning approach (taxonomy-supervised analysis). We compared this method with a commonly used clustering approach based on sequence similarity (taxonomy-unsupervised analysis). We sampled 211 different bacterial communities from various habitats and obtained w1.3 million 16S rRNA sequences spanning the V4 hypervariable region by pyrosequencing. Both methodologies gave similar ecological conclusions in that β-diversity measures calculated by using these two types of matrices were significantly correlated to each other, as were the ordination configurations and hierarchical clustering dendrograms. In addition, our taxonomy-supervised analyses were also highly correlated with phylogenetic methods, such as UniFrac. The taxonomy-supervised analysis has the advantages that it is not limited by the exhaustive computation required for the alignment and clustering necessary for the taxonomy-unsupervised analysis, is more tolerant of sequencing errors, and allows comparisons when sequences are from different regions of the 16S rRNA gene. With the tremendous expansion in 16S rRNA data acquisition underway, the taxonomy-supervised approach offers the potential to provide more rapid and extensive community comparisons across habitats and samples.taxonomy bin | operational taxonomic unit T he increasing abundance of 16S rRNA gene sequences stimulated by reduced sequencing costs and greatly expanded parallel capacities is providing a more encompassing view of microbial communities (1). Although the short read lengths provided by the current technologies make it more challenging to assign sequences to bacterial taxonomy, the depth and replication provided are powerful advantages (2-4).Information on bacterial community structure can be compiled in a matrix where different communities are represented as rows and "species" as columns, i.e., a community-by-species matrix. When describing bacterial community relationships based on 16S rRNA gene sequences, each sequence is allocated to a species, usually termed an operational taxonomic unit (OTU), by alignment-based clustering at a specified nucleotide distance, often at a 97% identity. This community-by-OTU matrix, which is based exclusively on nucleotide distances among 16S rRNA gene sequences, has bacterial communities as rows with OTU as columns. This community-by-OTU matrix can be used to measure dissimilarities between bacterial communities (β-diversity) either by presence/absence or abundance data. These dissimilarities combined in a distance matrix can be used for bacterial community comparisons by ordination and clustering m...

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Section: Resultsmentioning

confidence: 99%

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

Sul

Cole

Jesus

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…By 2008, well over 10 million genes from over 150 distinct habitats have been sequenced. Raw habitat and sequence data were collected from the Genomes Online database (43), and habitats were classified into the categories mentioned above using the Habitat-Lite terms of Environment Ontology (32). When an organism was reported to have multiple habitats, the primary one was used.…”

Section: Methodsmentioning

confidence: 99%

Discovering Functional Novelty in Metagenomes: Examples from Light-Mediated Processes

et al. 2009

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The emerging coverage of diverse habitats by metagenomic shotgun data opens new avenues of discovering functional novelty using computational tools. Here, we apply three different concepts for predicting novel functions within light-mediated microbial pathways in five diverse environments. Using phylogenetic approaches, we discovered two novel deep-branching subfamilies of photolyases (involved in light-mediated repair) distributed abundantly in high-UV environments. Using neighborhood approaches, we were able to assign seven novel functional partners in luciferase synthesis, nitrogen metabolism, and quorum sensing to BLUF domain-containing proteins (involved in light sensing). Finally, by domain analysis, for RcaE proteins (involved in chromatic adaptation), we predict 16 novel domain architectures that indicate novel functionalities in habitats with little or no light. Quantification of protein abundance in the various environments supports our findings that bacteria utilize light for sensing, repair, and adaptation far more widely than previously thought. While the discoveries illustrate the opportunities in function discovery, we also discuss the immense conceptual and practical challenges that come along with this new type of data.One of the central questions in biology, starting from the time of Charles Darwin, has been the extent and distribution of biological diversity (68). The recent sequencing of several hundred bacterial and archaeal genomes and metagenomes, along with the discovery of large-scale lateral gene transfer (10) and recombination (25) in bacterial evolution, has not only renewed interest in the question of diversity but also confounded it. The sequencing projects reveal that contrary to previous estimates, it is microbes that account for the vast majority of diversity in phenotype and genotype on earth (44, 47). Underlying this dazzling diversity in species and habitat is molecular diversity. Indeed, we are just beginning to scratch the surface of this molecular diversity (50). Even though our understanding of how the living world functions at the molecular level is far from complete, the discovery of novel molecules has important applications to medicine, agriculture, industry, and environmental conservation and remediation.But how are we to discover functional novelty in the exponentially increasing amounts of sequenced genes and habitats (Fig.

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“…35 . ' isolation_source ' fi elds are further mapped to an environment ontology (EnvO) 36 (for example, ' agricultural soil ' and ' Rocky Mountain alpine soil ' are mapped to the term ' soil ' ). Overall, 2,662 samples are mapped to 183 EnvO categories, termed ' niches ' .…”

Section: ⇔ >mentioning

confidence: 99%

“…To identify ecologically associated species, we examined the distribution pattern of the 80 OTU-mapped models across the 59 niches 36 . Th e probability that two species co-occur together at a rate higher than chance expectation was determined by calculating a cumulative hypergeometric P-value.…”

Section: Romentioning

confidence: 99%

Competitive and cooperative metabolic interactions in bacterial communities

et al. 2011

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Revealing the ecological principles that shape communities is a major challenge of the postgenomic era. To date, a systematic approach for describing inter-species interactions has been lacking. Here we independently predict the competitive and cooperative potential between 6,903 bacterial pairs derived from a collection of 118 species ' metabolic models. We chart an intricate association between competition and cooperation indicating that the cooperative potential is maximized at moderate levels of resource overlap. Utilizing ecological data from 2,801 samples, we explore the associations between bacterial interactions and coexistence patterns. The high level of competition observed between species with mutual-exclusive distribution patterns supports the role of competition in community assembly. Cooperative interactions are typically unidirectional with no obvious benefi t to the giver. However, within their natural communities, bacteria typically form close cooperative loops resulting in indirect benefi t to all species involved. These fi ndings are important for the future design of consortia optimized towards bioremediation and bio-production applications.

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Habitat-Lite: A GSC Case Study Based on Free Text Terms for Environmental Metadata

Cited by 38 publications

References 16 publications

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

Discovering Functional Novelty in Metagenomes: Examples from Light-Mediated Processes

Competitive and cooperative metabolic interactions in bacterial communities

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