SUMMARY: In this article we present Simple Exploration of Ecological Data (Seed), a data exploration tool for microbial communities. Seed is written in R using the Shiny library. This provides access to powerful R-based functions and libraries through a simple user interface. Seed allows users to explore ecological datasets using principal coordinate analyses, scatter plots, bar plots, hierarchal clustering and heatmaps. AVAILABILITY AND IMPLEMENTATION: Seed is open source and available at https://github.com/danlbek/Seed. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: We present a novel method and corresponding application, MetAmp, to combine amplicon data from multiple genomic markers into Operational Taxonomic Units (OTUs) for microbial community analysis, calibrating the markers using data from known microbial genomes. When amplicons for multiple markers such as the 16S rRNA gene hypervariable regions are available, MetAmp improves the accuracy of OTU-based methods for characterizing bacterial composition and community structure. MetAmp works best with at least three markers, and is applicable to non-bacterial analyses and to non 16S markers. Our application and testing have been limited to 16S analysis of microbial communities. RESULTS: We clustered standard test sequences derived from the Human Microbiome Mock Community test sets and compared MetAmp and other tools with respect to their ability to recover OTUs for these benchmark bacterial communities. MetAmp compared favorably to QIIME, UPARSE and Mothur using amplicons from one, two, and three markers. AVAILABILITY AND IMPLEMENTATION: MetAmp is available at http://izhbannikov.github.io/MetAmp/.

In this paper, we successfully apply GEFeS (Genetic & Evolutionary Feature Selection) to identify the key features in the human vaginal microbiome and in patient meta-data that are associated with bacterial vaginosis (BV). The vaginal microbiome is the community of bacteria found in a patient, and meta-data include behavioral practices and demographic information. Bacterial vaginosis is a disease that afflicts nearly one third of all women, but the current diagnostics are crude at best. We describe two types of classifies for BV diagnosis, and show that each is associated with one of two treatments. Our results show that the classifiers associated with the 'Treat Any Symptom' version have better performances that the classifier associated with the 'Treat Based on N-Score Value'. Our long term objective is to develop a more accurate and objective diagnosis and treatment of BV.

This article reviews recent advances in 'microbiome studies': molecular, statistical and graphical techniques to explore and quantify how microbial organisms affect our environments and ourselves given recent increases in sequencing technology. Microbiome studies are moving beyond mere inventories of specific ecosystems to quantifications of community diversity and descriptions of their ecological function. We review the last 24 months of progress in this sort of research, and anticipate where the next 2 years will take us. We hope that bioinformaticians will find this a helpful springboard for new collaborations with microbiologists.

MOTIVATION: The massive data produced by next-generation sequencing require advanced statistical tools. We address estimating the total diversity or species richness in a population. To date, only relatively simple methods have been implemented in available software. There is a need for software employing modern, computationally intensive statistical analyses including error, goodness-of-fit and robustness assessments. RESULTS: We present CatchAll, a fast, easy-to-use, platform-independent program that computes maximum likelihood estimates for finite-mixture models, weighted linear regression-based analyses and coverage-based non-parametric methods, along with outlier diagnostics. Given sample 'frequency count' data, CatchAll computes 12 different diversity estimates and applies a model-selection algorithm. CatchAll also derives discounted diversity estimates to adjust for possibly uncertain low-frequency counts. It is accompanied by an Excel-based graphics program. AVAILABILITY: Free executable downloads for Linux, Windows and Mac OS, with manual and source code, at www.northeastern.edu/catchall. CONTACT: [email protected].

UNLABELLED: Microbial communities have an important role in natural ecosystems and have an impact on animal and human health. Intuitive graphic and analytical tools that can facilitate the study of these communities are in short supply. This article introduces Microbial Community Analysis GUI, a graphical user interface (GUI) for the R-programming language (R Development Core Team, 2010). With this application, researchers can input aligned and clustered sequence data to create custom abundance tables and perform analyses specific to their needs. This GUI provides a flexible modular platform, expandable to include other statistical tools for microbial community analysis in the future. AVAILABILITY: The mcaGUI package and source are freely available as part of Bionconductor at http://www.bioconductor.org/packages/release/bioc/html/mcaGUI.html

SUMMARY: OTUbase is an R package designed to facilitate the analysis of operational taxonomic unit (OTU) data and sequence classification (taxonomic) data. Currently there are programs that will cluster sequence data into OTUs and/or classify sequence data into known taxonomies. However, there is a need for software that can take the summarized output of these programs and organize it into easily accessed and manipulated formats. OTUbase provides this structure and organization within R, to allow researchers to easily manipulate the data with the rich library of R packages currently available for additional analysis. AVAILABILITY: OTUbase is an R package available through Bioconductor. It can be found at http://www.bioconductor.org/packages/release/bioc/html/OTUbase.html.

In some cases, evolutionary algorithms represent individuals as typical binary trees with n leaves and n-1 internal nodes. When designing a crossover operator for a particular representation and application, it is desirable to quantify the operator’s destructiveness in order to estimate its effectiveness at using building blocks. For the case of binary tree representations, we present a novel approach for empirically estimating the destructiveness of any crossover operator by computing and summarizing the distribution of Robinson-Foulds distances from the parent to the entire neighborhood of possible children. We demonstrate the approach by quantifying the destructiveness of a popular tree-based crossover operator as applied to the problem of phylogenetic inferencing. We discuss the benefits and limitations of the destructiveness metric.

SUMMARY: Clearcut is an open source implementation for the relaxed neighbor joining (RNJ) algorithm. While traditional neighbor joining (NJ) remains a popular method for distance-based phylogenetic tree reconstruction, it suffers from a O(N(3)) time complexity, where N represents the number of taxa in the input. Due to this steep asymptotic time complexity, NJ cannot reasonably handle very large datasets. In contrast, RNJ realizes a typical-case time complexity on the order of N(2)logN without any significant qualitative difference in output. RNJ is particularly useful when inferring a very large tree or a large number of trees. In addition, RNJ retains the desirable property that it will always reconstruct the true tree given a matrix of additive pairwise distances. Clearcut implements RNJ as a C program, which takes either a set of aligned sequences or a pre-computed distance matrix as input and produces a phylogenetic tree. Alternatively, Clearcut can reconstruct phylogenies using an extremely fast standard NJ implementation. AVAILABILITY: Clearcut source code is available for download at: http://bioinformatics.hungry.com/clearcut

We have performed several experiments to study the possible use of chaos in simulated evolution. Chaos is often associated with dynamic situations in which there is feedback, hence there is speculation in the literature that chaos is a factor in natural evolution. We chose the iterated prisoner's dilemma problem as a test case, since it is a dynamic environment with feedback. To further illustrate the benefits of employing chaos in genetic algorithms, data derived from a genetic data clustering algorithm under development at the Idaho National Engineering and Environmental Laboratory is also presented. To perform an initial assessment of the use of chaos, we used the logistic function, a simple equation involving chaos, as the basis of a special mutation operator, which we call /spl lambda/ mutation. The behavior of the logistic function is well known and comprises three characteristic ranges of operation: convergent, bifurcating, and chaotic. We hypothesize that the chaotic regime will aid exploration, and the convergent range will aid exploitation. The bifurcating range is likely neutral, and hence an insignificant factor. Our results confirm these expectations.

Parsimony pressure, the explicit penalization of larger programs, has been increasingly used as a means of controlling code growth in genetic programming. However, in many cases parsimony pressure degrades the performance of the genetic program. In this paper we show that poor average results with parsimony pressure are a result of 'failed' populations that overshadow the results of populations that incorporate parsimony pressure successfully. Additionally, we show that the effect of parsimony pressure can be measured by calculating the relationship between program size and performance within the population. This measure can be used as a partial indicator of success or failure for individual populations.