The unifrac significance test is sensitive to tree topology

Long et al. (BMC Bioinformatics 2014, 15(1):278) describe a “discrepancy” in using UniFrac to assess statistical significance of community differences. Specifically, they find that weighted UniFrac results differ between input trees where (a) replicate sequences each have their own tip, or (b) all replicates are assigned to one tip with an associated count. We argue that these are two distinct cases that differ in the probability distribution on which the statistical test is based, because of the differences in tree topology. Further study is needed to understand which randomization procedure best detects different aspects of community dissimilarities

WGSUniFrac: Applying UniFrac Metric to Whole Genome Shotgun Data

The UniFrac metric has proven useful in revealing diversity across metagenomic communities. Due to the phylogeny-based nature of this measurement, UniFrac has historically only been applied to 16S rRNA data. Simultaneously, Whole Genome Shotgun (WGS) metagenomics has been increasingly widely employed and proven to provide more information than 16S data, but a UniFrac-like diversity metric suitable for WGS data has not previously been developed. The main obstacle for UniFrac to be applied directly to WGS data is the absence of phylogenetic distances in the taxonomic relationship derived from WGS data. In this study, we demonstrate a method to overcome this intrinsic difference and compute the UniFrac metric on WGS data by assigning branch lengths to the taxonomic tree obtained from input taxonomic profiles. We conduct a series of experiments to demonstrate that this WGSUniFrac method is comparably robust to traditional 16S UniFrac and is not highly sensitive to branch lengths assignments, be they data-derived or model-prescribed

Characterization of shifts of koala (Phascolarctos cinereus) intestinal microbial communities associated with antibiotic treatment.

Koalas (Phascolarctos cinereus) are arboreal marsupials native to Australia that eat a specialized diet of almost exclusively eucalyptus leaves. Microbes in koala intestines are known to break down otherwise toxic compounds, such as tannins, in eucalyptus leaves. Infections by Chlamydia, obligate intracellular bacterial pathogens, are highly prevalent in koala populations. If animals with Chlamydia infections are received by wildlife hospitals, a range of antibiotics can be used to treat them. However, previous studies suggested that koalas can suffer adverse side effects during antibiotic treatment. This study aimed to use 16S rRNA gene sequences derived from koala feces to characterize the intestinal microbiome of koalas throughout antibiotic treatment and identify specific taxa associated with koala health after treatment. Although differences in the alpha diversity were observed in the intestinal flora between treated and untreated koalas and between koalas treated with different antibiotics, these differences were not statistically significant. The alpha diversity of microbial communities from koalas that lived through antibiotic treatment versus those who did not was significantly greater, however. Beta diversity analysis largely confirmed the latter observation, revealing that the overall communities were different between koalas on antibiotics that died versus those that survived or never received antibiotics. Using both machine learning and OTU (operational taxonomic unit) co-occurrence network analyses, we found that OTUs that are very closely related to Lonepinella koalarum, a known tannin degrader found by culture-based methods to be present in koala intestines, was correlated with a koala's health status. This is the first study to characterize the time course of effects of antibiotics on koala intestinal microbiomes. Our results suggest it may be useful to pursue alternative treatments for Chlamydia infections without the use of antibiotics or the development of Chlamydia-specific antimicrobial compounds that do not broadly affect microbial communities

Impacts of agricultural disturbance on communities of selected soil fungi (Agaricomycetes)

The objective of this study was to use phylogeny-based and community-based analyses to compare the community composition of Agaricomycetes among four different agricultural treatments at the Kellogg Biological Station Long Term Ecological Research (KBS LTER) site. A phylogenetic tree that included 591 ribosomal DNA sequences previously obtained from KBS LTER documented the composition of Agaricomycete communities in each treatment. Sequences from KBS LTER were placed into 472 OTUs (putatively species-level operational taxonomic units defined by 99% or greater sequence similarity) and these were dominated by the Agaricales (with 330 OTUs), Cantharellales (39 OTUs), Hymenochaetales (29 OTUs), and Polyporales (23 OTUs). Multivariate statistical analyses incorporating phylogenetic information showed never tilled successional grasslands to be the most phylogenetically distinct treatment. The trend that phylotype and clade diversity decreased with increasing disturbance by tillage was consistent with results from previous individual studies emphasizing the importance of protecting remnant untilled grassland habitats

The GDR : a novel approach to detect large-scale genomic sequence patterns

Utvikling av ny sekvenseringsteknologi de to siste tiårene har tillatt dypere dykk ned i de biomolekylære aspektene ved menneskets oppskrift. Hel-genom data fra flere hundre tusen mennesker er allerede tilgjengelig, men hvordan den økende mengden informasjon kan settes sammen til meningsfull funksjonell tolkning er komplisert og krever nye metoder. MikroRNA - mRNA interaksjoner utgjør et enormt genreguleringsnettverk som er vanskelig å predikere, selv for dagens beste maskinlæringsalgoritmer(1). Disse ikke-kodende elementene er involvert i omtrent alle cellulære prosesser i mennesket, primært via delvis komplementær baseparing mellom mikroRNA og mRNA, men det er mye vi ikke forstår av dette nettverkets betydning i vår biologi (2-4). Nye metoder er nødvendige for å kunne utforske genetisk variasjon i dette nettverket, som kan gi nye innblikk i hvordan genene våre reguleres. Her presenteres «The Group Diversity Ratio» (GDR) som en ny målenhet til å møte denne utfordringen. GDR kan kvantifisere evolusjonær struktur av variasjon i store mengder genomisk sekvensdata, med et resultat som kan statistisk valideres. Metoden baserer seg på å måle gruppe-struktur i et distanse-basert fylogenetisk tre av sekvensdata, for forhåndsdefinerte grupper av «blader» i treet. Gruppene representerer en egenskap som kan relateres til sekvensdataen, og det undersøkes til hvilken grad det finnes en sammenheng mellom de to. Metoden kan primært brukes til å raskt skaffe overblikk over store mengder genomisk sekvensdata, som kan gi verdifulle innblikk til videre etterforskning. For å teste metoden ble GDR brukt til å identifisere variasjon assosiert med etniske populasjoner i 3’UTR data fra «The 1000 Genomes Project» (1KGP). 1KGP var det første store prosjektet som adresserte den etniske skjevheten som nå finnes i genom-databaser, og som utgjør en god grunn til å utforske etnisk genetisk variasjon (5). I tillegg til identifikasjon av mer enn 1000 3’UTR sekvenser som inneholder signifikant etnisitet-spesifikk variasjon, viser dette studiet GDR-metodens høye potensial til å undersøke genetisk variasjon i stor skala.The emergence of new sequencing technologies over the past two decades has enabled us to dive deeper into the biomolecular aspect of the human recipe. Entire genomes from several hundred thousand people are already accessible, but how to interpretate the connections between the blueprints and the phenotypes are complicated, even for the best developed machine learning algorithms. Prediction of the microRNA-mRNA targeting network is a classic example, which is involved with gene regulation of all living cell processes. These non-coding features make up complex networks of interactions, where microRNAs primarily target 3’UTRs through partial complementary base-pairing. Thus, the challenge to investigate patterns in such large-scaled genomic sequence data requires new approaches. The Group Diversity Ratio (GDR) metric is presented here as a novel approach to aid in this challenge. The GDR quantifies genome-wide structure in large-scale sequence data with a statistically testable result. Patterns are measured for a group feature that may be related to variation in sequence samples, based on phylogenetic distance estimations. It opens opportunities to quickly gain insights into genomic regions of interests and used to guide further research. To demonstrate the use of the GDR metric, ethnicity-associated variation patterns in more than 1000 human 3’UTRs was identified with the GDR. The study set was from 1000 Genomes project, which was the first major effort to address the problem of ethnic bias in genetic studies and contained more than 2500 whole-genome sequences from 26 ethnic lineages. In addition to detecting significantly distinct 3’UTR elements for ethnic populations, the key finding of this study was the high potentials of the GDR to facilitate more high-throughput characterization of genomic sequence data.M-BIA

Phylogenetic Analysis Suggests That Habitat Filtering Is Structuring Marine Bacterial Communities Across the Globe

The phylogenetic structure and community composition were analysed in an existing data set of marine bacterioplankton communities to elucidate the evolutionary and ecological processes dictating the assembly. The communities were sampled from coastal waters at nine locations distributed worldwide and were examined through the use of comprehensive clone libraries of 16S ribosomal RNA genes. The analyses show that the local communities are phylogenetically different from each other and that a majority of them are phylogenetically clustered, i.e. the species (operational taxonomic units) were more related to each other than expected by chance. Accordingly, the local communities were assembled non-randomly from the global pool of available bacterioplankton. Further, the phylogenetic structures of the communities were related to the water temperature at the locations. In agreement with similar studies, including both macroorganisms and bacteria, these results suggest that marine bacterial communities are structured by “habitat filtering”, i.e. through non-random colonization and invasion determined by environmental characteristics. Different bacterial types seem to have different ecological niches that dictate their survival in different habitats. Other eco-evolutionary processes that may contribute to the observed phylogenetic patterns are discussed. The results also imply a mapping between phenotype and phylogenetic relatedness which facilitates the use of community phylogenetic structure analysis to infer ecological and evolutionary assembly processes

Variance adjusted weighted UniFrac: a powerful beta diversity measure for comparing communities based on phylogeny

<p>Abstract</p> <p>Background</p> <p>Beta diversity, which involves the assessment of differences between communities, is an important problem in ecological studies. Many statistical methods have been developed to quantify beta diversity, and among them, UniFrac and weighted-UniFrac (W-UniFrac) are widely used. The W-UniFrac is a weighted sum of branch lengths in a phylogenetic tree of the sequences from the communities. However, W-UniFrac does not consider the variation of the weights under random sampling resulting in less power detecting the differences between communities.</p> <p>Results</p> <p>We develop a new statistic termed variance adjusted weighted UniFrac (VAW-UniFrac) to compare two communities based on the phylogenetic relationships of the individuals. The VAW-UniFrac is used to test if the two communities are different. To test the power of VAW-UniFrac, we first ran a series of simulations which revealed that it always outperforms W-UniFrac, as well as UniFrac when the individuals are not uniformly distributed. Next, all three methods were applied to analyze three large 16S rRNA sequence collections, including human skin bacteria, mouse gut microbial communities, microbial communities from hypersaline soil and sediments, and a tropical forest census data. Both simulations and applications to real data show that VAW-UniFrac can satisfactorily measure differences between communities, considering not only the species composition but also abundance information.</p> <p>Conclusions</p> <p>VAW-UniFrac can recover biological insights that cannot be revealed by other beta diversity measures, and it provides a novel alternative for comparing communities.</p

Phylobetadiversity among Forest Types in the Brazilian Atlantic Forest Complex

Phylobetadiversity is defined as the phylogenetic resemblance between communities or biomes. Analyzing phylobetadiversity patterns among different vegetation physiognomies within a single biome is crucial to understand the historical affinities between them. Based on the widely accepted idea that different forest physiognomies within the Southern Brazilian Atlantic Forest constitute different facies of a single biome, we hypothesize that more recent phylogenetic nodes should drive phylobetadiversity gradients between the different forest types within the Atlantic Forest, as the phylogenetic divergence among those forest types is biogeographically recent. We compiled information from 206 checklists describing the occurrence of shrub/tree species across three different forest physiognomies within the Southern Brazilian Atlantic Forest (Dense, Mixed and Seasonal forests). We analyzed intra-site phylogenetic structure (phylogenetic diversity, net relatedness index and nearest taxon index) and phylobetadiversity between plots located at different forest types, using five different methods differing in sensitivity to either basal or terminal nodes (phylogenetic fuzzy weighting, COMDIST, COMDISTNT, UniFrac and Rao’s H). Mixed forests showed higher phylogenetic diversity and overdispersion than the other forest types. Furthermore, all forest types differed from each other in relation phylobetadiversity patterns, particularly when phylobetadiversity methods more sensitive to terminal nodes were employed. Mixed forests tended to show higher phylogenetic differentiation to Dense and Seasonal forests than these latter from each other. The higher phylogenetic diversity and phylobetadiversity levels found in Mixed forests when compared to the others likely result from the biogeographical origin of several taxa occurring in these forests. On one hand, Mixed forests shelter several temperate taxa, like the conifers Araucaria and Podocarpus. On the other hand, tropical groups, like Myrtaceae, are also very representative of this forest type. We point out to the need of more attention to Mixed forests as a conservation target within the Brazilian Atlantic Forest given their high phylogenetic uniqueness