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Final Probe Regions. Complete listing of all regions used in alignments to generate probes. (XLSX 1446Â kb)

Description of online comparison settings. (XLSX 34 kb)

1,000 name-strings randomly selected from GNI and used to determine Accuracy, Precision and Recall data (Table 1). (TXT 32 kb)

XRF Analysis: Archive halves of sections from Core MD01-2444 were analysed using an Avaatech XRF core scanner at the University of Cambridge. The core surface was carefully scraped cleaned and covered with a 4-mm thin SPEXCertiPrep Ultralene foil to avoid contamination and minimize desiccation. Each section was measured using a current of 0.2mA at three different voltages: 10 kV, 30 kV using a thin lead filter, and 50 kV using a copper filter. XRF data were collected every 2.5 mm. The length and width of the irradiated surface was 2.5 and 12 mm, respectively, with a count time of 40s. The record was generated by D.A. Hodell and S. Crowhurst. Age model: The age model was developed by aligning the abrupt transitions of d18O record of G. bulloides in core MD01-2444 to warming and cooling events in the NGRIP d18Oice record, presented on: (i) the GICC05 timescale [Svensson, A. et al. A 60,000 year Greenland stratigraphic ice core chronology. Clim. Past 4, 47–57 (2008)]; and (ii) the WD2014: modified timescale, whereby NGRIP GICC05 ice ages 31.2–67.2 ka are multiplied by 1.0063. [Buizert, C. et al. The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference. Clim. Past 11, 153-173 (2015)].

Abstract Objective The body of DNA sequence data lacking taxonomically informative sequence headers is rapidly growing in user and public databases (e.g. sequences lacking identification and contaminants). In the context of systematics studies, sorting such sequence data for taxonomic curation and/or molecular diversity characterization (e.g. crypticism) often requires the building of exploratory phylogenetic trees with reference taxa. The subsequent step of segregating DNA sequences of interest based on observed topological relationships can represent a challenging task, especially for large datasets. Results We have written TREE2FASTA, a Perl script that enables and expedites the sorting of FASTA-formatted sequence data from exploratory phylogenetic trees. TREE2FASTA takes advantage of the interactive, rapid point-and-click color selection and/or annotations of tree leaves in the popular Java tree-viewer FigTree to segregate groups of FASTA sequences of interest to separate files. TREE2FASTA allows for both simple and nested segregation designs to facilitate the simultaneous preparation of multiple data sets that may overlap in sequence content.

Benthic foraminifera oxygen isotopes: The benthic isotope record was generated by the late N.J. Shackleton. Measurements were carried out in the Godwin Laboratory for Palaeoclimate Research (University of Cambridge), using a VG PRISM mass spectrometer. Where possible two or three separate analyses of different benthic species were made in each sample; a correction factor was applied according to the species and the average of all the corrected values at each level is shown in the figures. The following species were analysed, and adjusted as indicated: Cibicidoides sp.: +0.51; Uvigerina peregrina and similar specimens: 0.0; Globobulimina affinis: -0.4; Cibicidoides wuellerstorfi, +0.64; Globocassidulina sp.: -0.1; Hoeglundina elegans: -0.7. These adjustments are optimized for this particular core in accordance with the long-standing convention by which Uvigerina peregrina is assumed to deposit oxygen in isotopic equilibrium. Results are reported with reference to the international standard VPDB and the precision is better than ±0.08‰ for d18O. Age model: The age model was developed by aligning the abrupt transitions of d18O record of G. bulloides in core MD01-2444 to warming and cooling events in the NGRIP d18Oice record, presented on: (i) the GICC05 timescale [Svensson, A. et al. A 60,000 year Greenland stratigraphic ice core chronology. Clim. Past 4, 47–57 (2008)]; and (ii) the WD2014: modified timescale, whereby NGRIP GICC05 ice ages 31.2–67.2 ka are multiplied by 1.0063. [Buizert, C. et al. The WAIS Divide deep ice core WD2014 chronology – Part 1: Methane synchronization (68–31 ka BP) and the gas age–ice age difference. Clim. Past 11, 153-173 (2015)].

Abstract Background Comparative data from non-human primates provide insight into the processes that shaped the evolution of the human gut microbiome and highlight microbiome traits that differentiate humans from other primates. Here, in an effort to improve our understanding of the human microbiome, we compare gut microbiome composition and functional potential in 14 populations of humans from ten nations and 18 species of wild, non-human primates. Results Contrary to expectations from host phylogenetics, we find that human gut microbiome composition and functional potential are more similar to those of cercopithecines, a subfamily of Old World monkey, particularly baboons, than to those of African apes. Additionally, our data reveal more inter-individual variation in gut microbiome functional potential within the human species than across other primate species, suggesting that the human gut microbiome may exhibit more plasticity in response to environmental variation compared to that of other primates. Conclusions Given similarities of ancestral human habitats and dietary strategies to those of baboons, these findings suggest that convergent ecologies shaped the gut microbiomes of both humans and cercopithecines, perhaps through environmental exposure to microbes, diet, and/or associated physiological adaptations. Increased inter-individual variation in the human microbiome may be associated with human dietary diversity or the ability of humans to inhabit novel environments. Overall, these findings show that diet, ecology, and physiological adaptations are more important than host-microbe co-diversification in shaping the human microbiome, providing a key foundation for comparative analyses of the role of the microbiome in human biology and health.

Abstract Background The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. Results In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. Conclusions Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.