Description: Marine microorganisms adapt to their habitat by structural modification of their membrane lipids. This concept is the basis of numerous molecular proxies used for paleoenvironmental reconstruction. Archaeal tetraether lipids from ubiquitous marine planktonic archaea are particularly abundant, well preserved in the sedimentary record and utilized in several molecular proxies. We here introduce the direct, extraction-free analysis of these compounds in intact sediment core sections using laser desorption ionization (LDI) coupled to Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). LDI FTICR-MS can detect the target lipids in single sub-mm sized spots on sediment sections, equivalent to a sample mass in the nanogram range, and could thus pave the way for biomarker-based reconstruction of past environments and ecosystems at subannual to decadal resolution. We demonstrate that ratios of selected archaeal tetraethers acquired by LDI FTICR-MS are highly correlated with values obtained by conventional LC/MS protocols. The ratio of the major archaeal lipids, caldarchaeol and crenarchaeol, analyzed in a 6.2-cm intact section of Mediterranean sapropel S1 at 250-µm resolution (~4-year temporal resolution), provides an unprecedented view of the fine-scale patchiness of sedimentary biomarker distributions and the processes involved in proxy signal formation. Temporal variations of this lipid ratio indicate a strong influence of the 200-yr de Vries solar cycle on reconstructed sea surface temperatures with possible amplitudes of several degrees, and suggest signal amplification by a complex interplay of ecological and hydrological factors. Laser-based biomarker analysis of geological samples has the potential to revolutionize molecular stratigraphic studies of paleoenvironments.

Description: Background The proportion of conserved DNA sequences with no clear function is steadily growing in bioinformatics databases. Studies of sequence and structural homology have indicated that many uncharacterized protein domain sequences are variants of functionally described domains. If these variants promote an organism's ecological fitness, they are likely to be conserved in the genome of its progeny and the population at large. The genetic composition of microbial communities in their native ecosystems is accessible through metagenomics. We hypothesize the co-variation of protein domain sequences across metagenomes from similar ecosystems will provide insights into their potential roles and aid further investigation. Methodology/Principal findings We calculated the correlation of Pfam protein domain sequences across the Global Ocean Sampling metagenome collection, employing conservative detection and correlation thresholds to limit results to well-supported hits and associations. We then examined intercorrelations between domains of unknown function (DUFs) and domains involved in known metabolic pathways using network visualization and cluster-detection tools. We used a cautious “guilty-by-association” approach, referencing knowledge-level resources to identify and discuss associations that offer insight into DUF function. We observed numerous DUFs associated to photobiologically active domains and prevalent in the Cyanobacteria. Other clusters included DUFs associated with DNA maintenance and repair, inorganic nutrient metabolism, and sodium-translocating transport domains. We also observed a number of clusters reflecting known metabolic associations and cases that predicted functional reclassification of DUFs. Conclusion/Significance Critically examining domain covariation across metagenomic datasets can grant new perspectives on the roles and associations of DUFs in an ecological setting. Targeted attempts at DUF characterization in the laboratory or in silico may draw from these insights and opportunities to discover new associations and corroborate existing ones will arise as more large-scale metagenomic datasets emerge.

Description: Here we present a standard developed by the Genomic Standards Consortium (GSC) for reporting marker gene sequences—the minimum information about a marker gene sequence (MIMARKS). We also introduce a system for describing the environment from which a biological sample originates. The ‘environmental packages’ apply to any genome sequence of known origin and can be used in combination with MIMARKS and other GSC checklists. Finally, to establish a unified standard for describing sequence data and to provide a single point of entry for the scientific community to access and learn about GSC checklists, we present the minimum information about any (x) sequence (MIxS). Adoption of MIxS will enhance our ability to analyze natural genetic diversity documented by massive DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere.

Description: The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.

Description: Oligotyping is a novel, supervised computational method that classifies closely related sequences into “oligotypes” (OTs) based on subtle nucleotide variation (Eren et al., 2013). Its application to microbial datasets has helped reveal ecological patterns which are often hidden by the way sequence data are currently clustered to define operational taxonomic units (OTUs). Here, we implemented the OT entropy decomposition procedure and its unsupervised version, Minimal Entropy Decomposition (MED; Eren et al., 2014c), in the statistical programming language and environment, R. The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework. In addition, two complementary approaches are implemented: (1) An analytical method (the broken stick model) is proposed to help identify OTs of low abundance that could be generated by chance alone and (2) a one-pass profiling (OP) method, to efficiently identify those OTUs whose subsequent oligotyping would be most promising to be undertaken. These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible. The package and procedures are illustrated by several tutorials and examples.

Description: “If names be not correct, language is not in accordance with the truth of things. If language be not in accordance with the truth of things, affairs cannot be carried on to success.” - Confucius, Analects, Book XIII, Chapter 3, verses 4-7, translated by James Legge Two workshops (hereafter described as “workshops”) were held in 2012, which brought together domain experts from genomic and biodiversity informatics, information modeling and biology, to clarify concepts and terms at the intersection of these domains. These workshops grew out of efforts sponsored by the NSF funded Resource Coordination Network (RCN) project for GSC [1] (RCN4GSC, hosted at UCSD, with John Wooley as PI) to reconcile terms from the Darwin Core (DwC) [2] vocabulary and with those in the MIxS family of checklists (Minimum Information about Any Type of Sequence) [3]. The original RCN4GSC meetings were able to align many terms between DwC and MIxS, finding both common and complementary terms. However, deciding exactly what constitutes the concept of a sample, a specimen, and an occurrence [4] to satisfy the needs of all use cases proved difficult, especially given the wide variety of sampling strategies employed within and between communities. Further, participants in the initial RCN4GSC workshops needed additional guidance on how to relate these entities to processes that act upon them and the environments in which organisms live. These issues provided the motivation for the workshops described below. The two workshops drew largely from experiences of the Basic Formal Ontology (BFO) [5] and were led by Barry Smith, State University of New York at Buffalo. We chose to interact with Smith based on his successful interactions with the GSC in developing the Environment Ontology (EnvO) [6] and also, on the ability of BFO to unite previously disconnected ontologies in the medical domain [7]. The first workshop addressed term definitions in biodiversity informatics, working within the BFO framework, while the second workshop developed a prototype Bio-Collections Ontology, dealing with samples and processes acting on samples. Concurrent with these workshops were two ongoing efforts involving data acquisition, visualization, and analysis that rely on a solid conceptual understanding of samples, specimens, and occurrences. These implementations are included in this report to show practical applications of term clarification. Finally, this report provides a discussion of some of the next steps discussed during the workshops.

Description: The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community.

Description: Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature Biotechnology 29 (2011): 415-420, doi:10.1038/nbt.1823.

Description: Here we present a standard developed by the Genomic Standards Consortium (GSC) to describe marker gene sequences—the minimum information about a marker gene sequence (MIMARKS). We also introduce a system for describing the environment from which a biological sample originates. The “environmental packages” apply to any sequence whose origin is known and can therefore be used in combination with MIMARKS or other GSC checklists. Finally, to establish a unified standard for describing sequence data and to provide a single point of entry for the scientific community to access and learn about GSC checklists, we establish the minimum information about any (x) sequence (MIxS). Adoption of MIxS will enhance our ability to analyze natural genetic diversity across the Tree of Life as it is currently being documented by massive DNA sequencing efforts from myriad ecosystems in our ever-changing biosphere.

Description: See Supplementary Note