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High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage.

ABSTRACT: We developed Growth Rate InDex (GRiD) for estimating in situ growth rates of ultra-low coverage (>0.2×) and de novo-assembled metagenomes. Applying GRiD to human and environmental metagenomic datasets to demonstrate its versatility, we uncovered new associations with previously uncharacterized bacteria whose growth rates were associated with several disease characteristics or environmental interactions. In addition, with GRiD-MG (metagenomic), a high-throughput implementation of GRiD, we estimated growth dynamics of 1756 bacteria species from a healthy skin metagenomic dataset and identified a new Staphylococcus-Corynebacterium antagonism likely mediated by antimicrobial production in the skin. GRiD-MG significantly increases the ability to extract growth rate inferences from complex metagenomic data with minimal input from the user.

SUBMITTER: Emiola A

PROVIDER: S-EPMC6251912 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage.

Emiola Akintunde A Oh Julia J

Nature communications 20181123 1

We developed Growth Rate InDex (GRiD) for estimating in situ growth rates of ultra-low coverage (>0.2×) and de novo-assembled metagenomes. Applying GRiD to human and environmental metagenomic datasets to demonstrate its versatility, we uncovered new associations with previously uncharacterized bacteria whose growth rates were associated with several disease characteristics or environmental interactions. In addition, with GRiD-MG (metagenomic), a high-throughput implementation of GRiD, we estimat ...[more]

PMID: 30470746

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Project description:BackgroundIdentification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data.Materials and methodsWe have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure.ResultsHere, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes.ConclusionWe show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.

Project description:Annotating Low Abundance and Transient RNAs in Yeast using Tiling Microarrays and Ultra High-throughput Sequencing Reveals New Transcripts that are not Conserved Across Closely Related Yeast Species A complete description of the transcriptome of an organism is crucial for a comprehensive understanding of how it functions, how its transcriptional networks are controlled, and may provide insights into the organism’s evolution. Despite the status of Saccharomyces cerevisiae as arguably the most well studied model eukaryote, we still do not have a full catalog or understanding of all its genes. In order to interrogate the transcriptome of S. cerevisiae for low abundance or rapidly turned over transcripts, we deleted elements of the RNA degradation machinery with the goal of preferentially increasing the relative abundance of such transcripts. We then used high-resolution tiling microarrays and ultra high-throughput sequencing (UHTS) to identify, map and validate unannotated transcripts that are more abundant in the RNA degradation mutants relative to wild-type cells. We identified 365 currently unannotated transcripts, the majority presumably representing low abundance or short-lived RNAs, of which 185 are previously unknown and unique to this study. It is likely that many of these are cryptic unstable transcripts (CUTs), which are rapidly degraded and whose function(s) within the cell are still unclear, while others may be novel functional transcripts. Of the 185 transcripts we identified as novel to our study, greater than 80 percent come from regions of the genome that have lower conservation scores amongst closely related yeast species than 85 percent of the verified ORFs in S. cerevisiae. Such regions of the genome have typically been less well studied, and by definition transcripts from these regions will distinguish S. cerevisiae from these closely related species. Keywords: Saccharomyces cerevisiae, transcriptome, RNA-Seq, RNA degradation, XRN1, RRP6, LSM1, PAT1, Salt Shock, NaCl Four samples, a wild-type reference and three mutants (1 containing 4 deletions and the other two containing 3 each), were subjected to high salt shock, and total RNA was harvested. PolyA RNA was purified, and libraries were generated for the Solexa platform. Each library was run on 4 lanes of a Solexa flow cell.

Dataset Information

High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage.

Publications

High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage.

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