A global survey of prokaryotic genomes reveals the eco-evolutionary pressures driving horizontal gene transfer.

Horizontal gene transfer, the exchange of genetic material through means other than reproduction, is a fundamental force in prokaryotic genome evolution. Genomic persistence of horizontally transferred genes has been shown to be influenced by both ecological and evolutionary factors. However, there is limited availability of ecological information about species other than the habitats from which they were isolated, which has prevented a deeper exploration of ecological contributions to horizontal gene transfer.

Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments.

A knowledge-based grouping of genes into pathways or functional units is essential for describing and understanding cellular complexity. However, it is not always clear a priori how and at what level of specificity functionally interconnected genes should be partitioned into pathways, for a given application. Here, we assess and compare nine existing and two conceptually novel functional classification systems, with respect to their discovery power and generality in gene set enrichment testing.

High precision measurement of the Sm beta decay by means of a metallic magnetic calorimeter.

The beta decay of Sm was measured by means of a metallic magnetic calorimeter. The measurement and subsequent analysis yielded a beta spectrum with an outstanding high-energy resolution of about 70 eV (FWHM) at 22 keV and a very low energy threshold well below 400 eV. The spectrum exhibited unexpectedly elevated beta emission probabilities at very low energy that we have not been able to reproduce in our theoretical study.

Rapid Inference of Direct Interactions in Large-Scale Ecological Networks from Heterogeneous Microbial Sequencing Data.

The availability of large-scale metagenomic sequencing data can facilitate the understanding of microbial ecosystems in unprecedented detail. However, current computational methods for predicting ecological interactions are hampered by insufficient statistical resolution and limited computational scalability. They also do not integrate metadata, which can reduce the interpretability of predicted ecological patterns.

MAPseq: highly efficient k-mer search with confidence estimates, for rRNA sequence analysis.

Motivation: Ribosomal RNA profiling has become crucial to studying microbial communities, but meaningful taxonomic analysis and inter-comparison of such data are still hampered by technical limitations, between-study design variability and inconsistencies between taxonomies used.

Results: Here we present MAPseq, a framework for reference-based rRNA sequence analysis that is up to 30% more accurate (F½ score) and up to one hundred times faster than existing solutions, providing in a single run multiple taxonomy classifications and hierarchical operational taxonomic unit mappings, for rRNA sequences in both amplicon and shotgun sequencing strategies, and for datasets of virtually any size.

Availability And Implementation: Source code and binaries are freely available at https://github.

Suppression of the Escherichia coli dnaA46 mutation by changes in the activities of the pyruvate-acetate node links DNA replication regulation to central carbon metabolism.

To ensure faithful transmission of genetic material to progeny cells, DNA replication is tightly regulated, mainly at the initiation step. Escherichia coli cells regulate the frequency of initiation according to growth conditions. Results of the classical, as well as the latest studies, suggest that the DNA replication in E.

A family of interaction-adjusted indices of community similarity.

Interactions between taxa are essential drivers of ecological community structure and dynamics, but they are not taken into account by traditional indices of β diversity. In this study, we propose a novel family of indices that quantify community similarity in the context of taxa interaction networks. Using publicly available datasets, we assessed the performance of two specific indices that are Taxa INteraction-Adjusted (TINA, based on taxa co-occurrence networks), and Phylogenetic INteraction-Adjusted (PINA, based on phylogenetic similarities).

Limits to robustness and reproducibility in the demarcation of operational taxonomic units.

The demarcation of operational taxonomic units (OTUs) from complex sequence data sets is a key step in contemporary studies of microbial ecology. However, as biologically motivated 'optimal' OTU-binning algorithms remain elusive, many conceptually distinct approaches continue to be used. Using a global data set of 887 870 bacterial 16S rRNA gene sequences, we objectively quantified biases introduced by several widely employed sequence clustering algorithms.

Ecological consistency of SSU rRNA-based operational taxonomic units at a global scale.

Operational Taxonomic Units (OTUs), usually defined as clusters of similar 16S/18S rRNA sequences, are the most widely used basic diversity units in large-scale characterizations of microbial communities. However, it remains unclear how well the various proposed OTU clustering algorithms approximate 'true' microbial taxa. Here, we explore the ecological consistency of OTUs--based on the assumption that, like true microbial taxa, they should show measurable habitat preferences (niche conservatism).

HPC-CLUST: distributed hierarchical clustering for large sets of nucleotide sequences.

Motivation: Nucleotide sequence data are being produced at an ever increasing rate. Clustering such sequences by similarity is often an essential first step in their analysis-intended to reduce redundancy, define gene families or suggest taxonomic units. Exact clustering algorithms, such as hierarchical clustering, scale relatively poorly in terms of run time and memory usage, yet they are desirable because heuristic shortcuts taken during clustering might have unintended consequences in later analysis steps.

Genotype networks, innovation, and robustness in sulfur metabolism.

Background: A metabolism is a complex network of chemical reactions. This network synthesizes multiple small precursor molecules of biomass from chemicals that occur in the environment. The metabolic network of any one organism is encoded by a metabolic genotype, defined as the set of enzyme-coding genes whose products catalyze the network's reactions.

Genotype networks in metabolic reaction spaces.

Background: A metabolic genotype comprises all chemical reactions an organism can catalyze via enzymes encoded in its genome. A genotype is viable in a given environment if it is capable of producing all biomass components the organism needs to survive and reproduce. Previous work has focused on the properties of individual genotypes while little is known about how genome-scale metabolic networks with a given function can vary in their reaction content.

Evolutionary plasticity and innovations in complex metabolic reaction networks.

Genome-scale metabolic networks are highly robust to the elimination of enzyme-coding genes. Their structure can evolve rapidly through mutations that eliminate such genes and through horizontal gene transfer that adds new enzyme-coding genes. Using flux balance analysis we study a vast space of metabolic network genotypes and their relationship to metabolic phenotypes, the ability to sustain life in an environment defined by an available spectrum of carbon sources.