Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns
Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time. ABSTRACT High-throughput sequencing of 16S ribosomal RNA gene amplicons has facilitated understanding of complex microbial communities, but the inherent noise in PCR and DNA sequencing limits differentiation of closely related bacteria. Although many scientific questions can be addressed with broad taxonomic profiles, clinical, food safety, and some ecological applications require higher specificity. Here we introduce a novel sub-operational-taxonomic-unit (sOTU) approach, Deblur, that uses error profiles to obtain putative error-free sequences from Illumina MiSeq and HiSeq sequencing platforms. Deblur substantially reduces computational demands relative to similar sOTU methods and does so with similar or better sensitivity and specificity. Using simulations, mock mixtures, and real data sets, we detected closely related bacterial sequences with single nucleotide differences while removing false positives and maintaining stability in detection, suggesting that Deblur is limited only by read length and diversity within the amplicon sequences. Because Deblur operates on a per-sample level, it scales to modern data sets and meta-analyses. To highlight Deblur’s ability to integrate data sets, we include an interactive exploration of its application to multiple distinct sequencing rounds of the American Gut Project. Deblur is open source under the Berkeley Software Distribution (BSD) license, easily installable, and downloadable from https://github.com/biocore/deblur . IMPORTANCE Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.
World incidence of AKI: a meta-analysis.
BACKGROUND AND OBJECTIVES The burden of AKI around the globe has not been systematically examined. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS A systematic review (2004-2012) of large cohort studies was conducted to estimate the world incidence of AKI and its stages of severity and associated mortality, and to describe geographic variations according to countries, regions, and their economies. AKI definitions were reclassified according to the Kidney Disease Improving Global Outcomes (KDIGO) staging system. Random-effects model meta-analyses and meta-regressions were used to generate summary estimates and explore sources of heterogeneity. RESULTS There were 312 studies identified (n=49,147,878) , primarily in hospital settings. Most studies originated from North America, Northern Europe, and Eastern Asia, from high-income countries, and from nations that spent ≥5% of the gross domestic product on total health expenditure. Among the 154 studies (n=3,585,911) that adopted a KDIGO-equivalent AKI definition, the pooled incidence rates of AKI were 21.6% in adults (95% confidence interval [95% CI], 19.3 to 24.1) and 33.7% in children (95% CI, 26.9 to 41.3). The pooled AKI-associated mortality rates were 23.9% in adults (95% CI, 22.1 to 25.7) and 13.8% in children (95% CI, 8.8 to 21.0). The AKI-associated mortality rate declined over time, and was inversely related to income of countries and percentage of gross domestic product spent on total health expenditure. CONCLUSIONS Using the KDIGO definition, 1 in 5 adults and 1 in 3 children worldwide experience AKI during a hospital episode of care. This analysis provides a platform to raise awareness of AKI with the public, government officials, and health care professionals.
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