SUMMARY: High-throughput sequencing is a powerful technique for addressing biological questions. Grabseqs streamlines access to publicly available metagenomic data by providing a single, easy-to-use interface to download data and metadata from multiple repositories, including the Sequence Read Archive, the Metagenomics Rapid Annotation through Subsystems Technology server and iMicrobe. Users can download data and metadata in a standardized format from any number of samples or projects from a given repository with a single grabseqs command. AVAILABILITY AND IMPLEMENTATION: Grabseqs is an open-source tool implemented in Python and licensed under the MIT license. The source code is freely available at https://github.com/louiejtaylor/grabseqs, the Python Package Index and Anaconda Cloud repository. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Population genomic analyses are often hindered by difficulties in obtaining sufficient numbers of genomes for analysis by DNA sequencing. Selective whole-genome amplification (SWGA) provides an efficient approach to amplify microbial genomes from complex backgrounds for sequence acquisition. However, the process of designing sets of primers for this method has many degrees of freedom and would benefit from an automated process to evaluate the vast number of potential primer sets. RESULTS: Here, we present swga , a program that identifies primer sets for SWGA and evaluates them for efficiency and selectivity. We used swga to design and test primer sets for the selective amplification of Wolbachia pipientis genomic DNA from infected Drosophila melanogaster and Mycobacterium tuberculosis from human blood. We identify primer sets that successfully amplify each against their backgrounds and describe a general method for using swga for arbitrary targets. In addition, we describe characteristics of primer sets that correlate with successful amplification, and present guidelines for implementation of SWGA to detect new targets. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are freely available on https://www.github.com/eclarke/swga . The program is implemented in Python and C and licensed under the GNU Public License. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA. RESULTS: We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (ω2). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study.

MOTIVATION: Gene therapy with retroviral vectors can induce adverse effects when those vectors integrate in sensitive genomic regions. Retroviral vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of integration sites and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of false discovery rates providing statistical methods for comparing retroviral vectors. RESULTS: A scan statistic for comparing two vectors using multiple window widths is proposed with software to detect clustering differentials and compute false discovery rates. Application to several sets of experimentally determined HIV integration sites demonstrates the software. Simulated datasets of various sizes and signal strengths are used to determine the power to discover clusters and evaluate a convenient lower bound. This provides a toolkit for planning evaluations of new gene therapy vectors. AVAILABILITY AND IMPLEMENTATION: The geneRxCluster R package containing a simple tutorial and usage hints is available from http://www.bioconductor.org.

MOTIVATION: The relative abundance of retroviral insertions in a host genome is important in understanding the persistence and pathogenesis of both natural retroviral infections and retroviral gene therapy vectors. It could be estimated from a sample of cells if only the host genomic sites of retroviral insertions could be directly counted. When host genomic DNA is randomly broken via sonication and then amplified, amplicons of varying lengths are produced. The number of unique lengths of amplicons of an insertion site tends to increase according to its abundance, providing a basis for estimating relative abundance. However, as abundance increases amplicons of the same length arise by chance leading to a non-linear relation between the number of unique lengths and relative abundance. The difficulty in calibrating this relation is compounded by sample-specific variations in the relative frequencies of clones of each length. RESULTS: A likelihood function is proposed for the discrete lengths observed in each of a collection of insertion sites and is maximized with a hybrid expectation-maximization algorithm. Patient data illustrate the method and simulations show that relative abundance can be estimated with little bias, but that variation in highly abundant sites can be large. In replicated patient samples, variation exceeds what the model implies-requiring adjustment as in Efron (2004) or using jackknife standard errors. Consequently, it is advantageous to collect replicate samples to strengthen inferences about relative abundance.

MOTIVATION: The human microbiome plays an important role in human disease and health. Identification of factors that affect the microbiome composition can provide insights into disease mechanism as well as suggest ways to modulate the microbiome composition for therapeutical purposes. Distance-based statistical tests have been applied to test the association of microbiome composition with environmental or biological covariates. The unweighted and weighted UniFrac distances are the most widely used distance measures. However, these two measures assign too much weight either to rare lineages or to most abundant lineages, which can lead to loss of power when the important composition change occurs in moderately abundant lineages. RESULTS: We develop generalized UniFrac distances that extend the weighted and unweighted UniFrac distances for detecting a much wider range of biologically relevant changes. We evaluate the use of generalized UniFrac distances in associating microbiome composition with environmental covariates using extensive Monte Carlo simulations. Our results show that tests using the unweighted and weighted UniFrac distances are less powerful in detecting abundance change in moderately abundant lineages. In contrast, the generalized UniFrac distance is most powerful in detecting such changes, yet it retains nearly all its power for detecting rare and highly abundant lineages. The generalized UniFrac distance also has an overall better power than the joint use of unweighted/weighted UniFrac distances. Application to two real microbiome datasets has demonstrated gains in power in testing the associations between human microbiome and diet intakes and habitual smoking. AVAILABILITY: http://cran.r-project.org/web/packages/GUniFrac

MOTIVATION: The Nearest Alignment Space Termination (NAST) tool is commonly used in sequence-based microbial ecology community analysis, but due to the limited portability of the original implementation, it has not been as widely adopted as possible. Python Nearest Alignment Space Termination (PyNAST) is a complete reimplementation of NAST, which includes three convenient interfaces: a Mac OS X GUI, a command-line interface and a simple application programming interface (API). RESULTS: The availability of PyNAST will make the popular NAST algorithm more portable and thereby applicable to datasets orders of magnitude larger by allowing users to install PyNAST on their own hardware. Additionally because users can align to arbitrary template alignments, a feature not available via the original NAST web interface, the NAST algorithm will be readily applicable to novel tasks outside of microbial community analysis. AVAILABILITY: PyNAST is available at http://pynast.sourceforge.net.

DNA sequences from retroviruses, retrotransposons, DNA transposons, and parvoviruses can all become integrated into the human genome. Accumulation of such sequences accounts for at least 40% of our genome today. These integrating elements are also of interest as gene-delivery vectors for human gene therapy. Here we present a comprehensive bioinformatic analysis of integration targeting by HIV, MLV, ASLV, SFV, L1, SB, and AAV. We used a mathematical method which allowed annotation of each base pair in the human genome for its likelihood of hosting an integration event by each type of element, taking advantage of more than 200 types of genomic annotation. This bioinformatic resource documents a wealth of new associations between genomic features and integration targeting. The study also revealed that the length of genomic intervals analyzed strongly affected the conclusions drawn--thus, answering the question "What genomic features affect integration?" requires carefully specifying the length scale of interest.