Differences in genomic GC content skew species abundances in microbial communities. Here, the authors develop GuaCAMOLE, a computational method that quantifies this GC bias from metagenomic sequencing data and corrects abundances accordingly, showing its application increases abundances of some pathogenic bacteria up to twofold in human gut microbiomes.

AbstractMotivation. Over the past years, substantial numbers of microbial species’ genomes have been deposited outside of conventional INSDC databases.Resu

Multiple genetic codes developed during the evolution of eukaryotes and bacteria, yet no alternative genetic code is known for archaea. We used proteomics to confirm our prediction that certain archae...

Abstract. The classification of living systems presents significant challenges due to the prevalence of gene transfer between genomes. Traditional taxonomi

By developing a strategy of metagenomic coassembly and prioritizing divergent marker gene sequences, Bin Chicken efficiently recovers more than 77,000 microbial genomes with high novel diversity, ther...

Marker genes used in microbial phylogenomics are limited to fixed gene sets selected from complete genomes. TMarSel is a flexible yet robust method for selecting any number of markers from genomes or ...

It is commonly thought that bacterial species borders tend to be fuzzy, due to frequent exchange of DNA. Here, Diop et al. quantify the patterns of gene flow between core genomes across 50 major bacte...

Abstract. Multi-omics analyses have significantly advanced the understanding of complex marine microbial communities and their interactions. Despite notabl

FUGAsseM predicts protein function in microbiomes using coexpression patterns from metatranscriptomes and diverse community-wide data.

Phages are typically viewed as very rapidly evolving biological entities. Little is known, however, about whether and how phages can establish long-term genetic stability. We addressed this eco-evolut...

As the ecological and evolutionary importance of symbiotic interactions between protists (microbial eukaryotes) and prokaryotes (bacteria and archaea) is better appreciated, keeping an overview of th...

The availability of large genome datasets has changed the microbiology research landscape. Analyzing such data requires computationally demanding analyses, and new approaches have come from different data analysis philosophies. Machine learning and statistical inference have overlapping knowledge discovery aims and approaches. However, machine learning focuses on optimizing prediction, whereas statistical inference focuses on understanding the processes relating variables. In this review, we outline the different aspirations, precepts, and resulting methodologies, with examples from microbial genomics. Emphasizing complementarity, we argue that the combination and synthesis of machine learning and statistics has potential for pathogen research in the big data era.