Genome-wide association studies (GWAS) have become indispensable in human medicine and genomics, but very few have been carried out on bacteria. Here we introduce Scoary, an ultra-fast, easy-to-use, and widely applicable software tool that scores the components of the pan-genome for associations to observed phenotypic traits while accounting for population stratification, with minimal assumptions about evolutionary processes. We call our approach pan-GWAS to distinguish it from traditional, single nucleotide polymorphism (SNP)-based GWAS. Scoary is implemented in Python and is available under an open source GPLv3 license at https://github.com/AdmiralenOla/Scoary
 .

https://link.springer.com/content/pdf/10.1186/s13059-016-1108-8.pdf

Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary.

Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample

/pdf/a-review-of-methods-and-databases-for-metagenomic-3hnnkgfelu.pdf

A review of methods and databases for metagenomic classification and assembly.

Parasitic nematodes (roundworms) and platyhelminths (flatworms) cause debilitating chronic infections of humans and animals, decimate crop production and are a major impediment to socioeconomic development. Here we report the broadest comparative study to date of the genomes of parasitic and non-parasitic worms, involving 81. We have identified gene family births and hundreds of expanded gene families at key nodes in the phylogeny that are relevant to parasitism. Examples include gene families that modulate host immune responses, enable parasite migration though host tissues or allow the parasite to feed. We reveal extensive lineage-specific differences in core metabolism and protein families historically targeted for drug development. From an in silico screen, we have identified and prioritised new potential drug targets and compounds for testing. This comparative genomics resource provides a much needed boost for the research community to understand and combat parasitic worms.

/pdf/comparative-genomics-of-the-major-parasitic-worms-4kdqf5ty69.pdf

Comparative genomics of the major parasitic worms

The Epsilonproteobacteria is the fifth validly described class of the phylum Proteobacteria, known primarily for clinical relevance and for chemolithotrophy in various terrestrial and marine environments, including deep-sea hydrothermal vents. As 16S rRNA gene repositories have expanded and protein marker analysis become more common, the phylogenetic placement of this class has become less certain. A number of recent analyses of the bacterial tree of life using both 16S rRNA and concatenated marker gene analyses have failed to recover the Epsilonproteobacteria as monophyletic with all other classes of Proteobacteria. In order to address this issue, we investigated the phylogenetic placement of this class in the bacterial domain using 16S and 23S rRNA genes, as well as 120 single-copy marker proteins. Single- and concatenated-marker trees were created using a data set of 4,170 bacterial representatives, including 98 Epsilonproteobacteria. Phylogenies were inferred under a variety of tree building methods, with sequential jackknifing of outgroup phyla to ensure robustness of phylogenetic affiliations under differing combinations of bacterial genomes. Based on the assessment of nearly 300 phylogenetic tree topologies, we conclude that the continued inclusion of Epsilonproteobacteria within the Proteobacteria is not warranted, and that this group should be reassigned to a novel phylum for which we propose the name Epsilonbacteraeota (phyl. nov.). We further recommend the reclassification of the order Desulfurellales (Deltaproteobacteria) to a novel class within this phylum and a number of subordinate changes to ensure consistency with the genome-based phylogeny. Phylogenomic analysis of 658 genomes belonging to the newly proposed Epsilonbacteraeota suggests that the ancestor of this phylum was an autotrophic, motile, thermophilic chemolithotroph that likely assimilated nitrogen from ammonium taken up from the environment or generated from environmental nitrate and nitrite by employing a variety of functional redox modules. The emergence of chemoorganoheterotrophic lifestyles in several Epsilonbacteraeota families is the result of multiple independent losses of various ancestral chemolithoautotrophic pathways. Our proposed reclassification of this group resolves an important anomaly in bacterial systematics and ensures that the taxonomy of Proteobacteria remains robust, specifically as genome-based taxonomies become more common.

/pdf/comparative-genomic-analysis-of-the-class-2xjkq9qm96.pdf

Comparative Genomic Analysis of the Class Epsilonproteobacteria and Proposed Reclassification to Epsilonbacteraeota (phyl. nov.)

Author(s): Roux, Simon; Emerson, Joanne; Eloe-Fadrosh, Emiley; Sullivan, Matthew | Abstract: Background: . Viral metagenomics (viromics) is increasingly used to obtain uncultivated viral genomes, evaluate community diversity, and assess ecological hypotheses. While viromic experimental methods are relatively mature and widely accepted by the research community, robust bioinformatics standards remain to be established. Here we used in silico mock viral communities to evaluate the viromic sequence-to-ecological-inference pipeline, including ( i ) read pre-processing and metagenome assembly, ( ii ) thresholds applied to estimate viral relative abundances based on read mapping to assembled contigs, and ( iii ) normalization methods applied to the matrix of viral relative abundances for alpha and beta diversity estimates. Results: . Tools specifically designed for metagenomes, specifically metaSPAdes and MEGAHIT, were the most effective at assembling viromes. Read pre-processing, such as partitioning, had virtually no impact on assembly output, but may be useful when hardware is limited. Viral populations with 2–5x coverage typically assembled well, whereas lesser coverage led to fragmented assembly. Strain heterogeneity within populations hampered assembly, especially when strains were closely related (average nucleotide identity, or ANI ≥ 97%) and when the most abundant strain represented l 50% of the population. Viral community composition assessments based on read recruitment were generally accurate when the following thresholds for detection were applied: ( i ) ≥ 10kb contig lengths to define populations, ( ii ) coverage defined from reads mapping at ≥ 90% identity, and ( ii ) ≥ 75% of contig length with ≥ 1x coverage. Finally, although data are limited to the most abundant viruses in a community, alpha and beta diversity patterns were robustly estimated (±10%) when comparing samples of similar sequencing depth, but more divergent (up to 80%) when sequencing depth was uneven across the dataset. In the latter cases, the use of normalization methods specifically developed for metagenomes provided the best estimates. Conclusions: . These simulations provide benchmarks for selecting analysis cut-offs and establish that an optimized sample-to-ecological-inference viromics pipeline is robust for making ecological inferences from natural viral communities. Continued development to better accessing RNA, rare, and/or diverse viral populations and improved reference viral genome availability will alleviate many of viromics remaining limitations.

/pdf/benchmarking-viromics-an-in-silico-evaluation-of-metagenome-irap91ovi0.pdf

Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity.

The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely available under the BSD license at https://github.com/dib-lab/khmer/.

/pdf/the-khmer-software-package-enabling-efficient-nucleotide-3syqty6z0i.pdf

The khmer software package: enabling efficient nucleotide sequence analysis.

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The khmer software package: enabling efficient nucleotide sequence analysis.