MOTIVATION: Metagenome-assembled genomes (MAGs) have substantially extended our understanding of microbial functionality. However, 16S rRNA genes, which are commonly used in phylogenetic analysis and environmental surveys, are often missing from MAGs. Here, we developed MarkerMAG, a pipeline that links 16S rRNA genes to MAGs using paired-end sequencing reads. RESULTS: Assessment of MarkerMAG on three benchmarking metagenomic datasets with various degrees of complexity shows substantial increases in the number of MAGs with 16S rRNA genes and a 100% assignment accuracy. MarkerMAG also estimates the copy number of 16S rRNA genes in MAGs with high accuracy. Assessments on three real metagenomic datasets demonstrate 1.1- to 14.2-fold increases in the number of MAGs with 16S rRNA genes. We also show that MarkerMAG-improved MAGs increase the accuracy of functional prediction from 16S rRNA gene amplicon data. MarkerMAG is helpful in connecting information in MAG databases with those in 16S rRNA databases and surveys and hence contributes to our increasing understanding of microbial diversity, function and phylogeny. AVAILABILITY AND IMPLEMENTATION: MarkerMAG is implemented in Python3 and freely available at https://github.com/songweizhi/MarkerMAG. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Microbial genomes have recently been reconstructed from metagenomic datasets using binning approaches. Inconsistent binning results are however often observed between different binning programs, likely due to the different algorithms or statistical models used. We present Binning_refiner, a pipeline that merges the results of different binning programs. Our results demonstrated that Binning_refiner can significantly reduce the contamination level of genome bins and increase the total size of contamination-free and 'good-quality' genome bins. Binning_refiner is thus an useful tool to improve the quality of genome bins derived from metagenomic data. AVAILABILITY AND IMPLEMENTATION: Binning_refiner is implemented in Python3 and is freely available at: https://github.com/songweizhi/Binning_refiner . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: SHAP (simple high-throughput annotation pipeline) is a lightweight and scalable sequence annotation pipeline capable of supporting research efforts that generate or utilize large volumes of DNA sequence data. The software provides Grid capable analysis, relational storage and Web-based full-text searching of annotation results. Implemented in Java, SHAP recognizes the limited resources of many smaller research groups. AVAILABILITY: Source code is freely available under GPLv3 at https://sourceforge.net/projects/shap. CONTACT: [email protected]; [email protected].

Background Pseudomonas aeruginosa infection is the leading cause of death for Cystic Fibrosis patients. Antibiotic resistance is rife, possibly due to high colonising population diversity. Our lab has replicated phenotypic diversification in a P. aeruginosa PAO1 biofilm model of lung infection. To reveal underlying genetic variants, we deep-sequenced PAO1 biofilm samples. Our analysis demonstrates several techniques for extracting meaningful biological information from error-prone sequencing data.