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Uritskiy, G. V.

Publications and source records attributed to Uritskiy, G. V..

2 recordsLinked to original sources

Regulatory non-coding small RNAs are diverse and abundant in an extremophilic microbial community

Regulatory small RNAs (sRNAs) represent a major class of regulatory molecules that play large-scale and essential roles in many cellular processes across all domains of life. Microbial sRNAs have been primarily investigated in a few model organisms and little is known about the dynamics of sRNA synthesis in natural environments, and the roles of these short transcripts at the community level. Analyzing the metatranscriptome of a model extremophilic community inhabiting halite nodules (salt rocks) from the Atacama Desert with SnapT - a new sRNA annotation pipeline - we discovered hundreds of intergenic (itsRNAs) and antisense (asRNAs) sRNAs. The halite sRNAs were taxonomically diverse with the majority expressed by members of the Halobacteria. We found asRNAs with expression levels negatively correlated with that of their putative overlapping target, suggesting a potential gene regulatory mechanism. A number of itsRNAs were conserved and significantly differentially expressed (FDR<5%) between 2 sampling time points allowing for stable secondary structure modeling and target prediction. This work demonstrates that metatranscriptomic field experiments link environmental variation with changes in RNA pools and have the potential to provide new insights into environmental sensing and responses in natural microbial communities through non-coding RNA mediated gene regulation.

microbiology↗

MetaWRAP - a flexible pipeline for genome-resolved metagenomic data analysis

BackgroundThe study of microbiomes using whole-metagenome shotgun sequencing enables the analysis of uncultivated microbial populations that may have important roles in their environments. Extracting individual draft genomes (bins) facilitates metagenomic analysis at the single genome level. Software and pipelines for such analysis have become diverse and sophisticated, resulting in a significant burden for biologists to access and use them. Furthermore, while bin extraction algorithms are rapidly improving, there is still a lack of tools for their evaluation and visualization.\n\nResultsTo address these challenges, we present metaWRAP, a modular pipeline software for shotgun metagenomic data analysis. MetaWRAP deploys state-of-the-art software to handle metagenomic data processing starting from raw sequencing reads and ending in metagenomic bins and their analysis. MetaWRAP is flexible enough to give investigators control over the analysis, while still being easy-to-install and easy-to-use. It includes hybrid algorithms that leverage the strengths of a variety of software to extract and refine high-quality bins from metagenomic data through bin consolidation and reassembly. MetaWRAPs hybrid bin extraction algorithm outperforms individual binning approaches and other bin consolidation programs in both synthetic and real datasets. Finally, metaWRAP comes with numerous modules for the analysis of metagenomic bins, including taxonomy assignment, abundance estimation, functional annotation, and visualization.\n\nConclusionsMetaWRAP is an easy-to-use modular pipeline that automates the core tasks in metagenomic analysis, while contributing significant improvements to the extraction and interpretation of high-quality metagenomic bins. The bin refinement and reassembly modules of metaWRAP consistently outperform other binning approaches. Each module of metaWRAP is also a standalone component, making it a flexible and versatile tool for tackling metagenomic shotgun sequencing data. MetaWRAP is open-source software available at https://github.com/bxlab/metaWRAP.

microbiology↗