Rob Knight

Bio: Rob Knight is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Microbiome & Gut flora. The author has an hindex of 201, co-authored 1061 publications receiving 253207 citations. Previous affiliations of Rob Knight include Anschutz Medical Campus & University of Sydney.

A semiparametric model for between-subject attributes: Applications to beta-diversity of microbiome data.

Abstract: The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain Operational Taxonomic Units (OTU) profiles for each subject. Due to the high-dimensionality and non-normality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based Beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model Beta-diversity as a response within a regression setting by utilizing the functional response models (FRM), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for Beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data. This article is protected by copyright. All rights reserved.

Feasibility of using alternative swabs and storage solutions for paired SARS-CoV-2 detection and microbiome analysis in the hospital environment

Abstract: Background: Determining the role of fomites in the transmission of SARS-CoV-2 is essential in the hospital setting and will likely be important outside of medical facilities as governments around the world make plans to ease COVID-19 public health restrictions and attempt to safely reopen economies. Expanding COVID-19 testing to include environmental surfaces would ideally be performed with inexpensive swabs that could be transported safely without concern of being a source of new infections. However, CDC-approved clinical-grade sampling supplies and techniques using a synthetic swab are expensive, potentially expose laboratory workers to viable virus and prohibit analysis of the microbiome due to the presence of antibiotics in viral transport media (VTM). To this end, we performed a series of experiments comparing the diagnostic yield using five consumer-grade swabs (including plastic and wood shafts and various head materials including cotton, synthetic, and foam) and one clinical grade swab for inhibition to RNA. For three of these swabs, we evaluated performance to detect SARS-CoV-2 in twenty intensive care unit (ICU) hospital rooms of patients with 16 COVID-19+. All swabs were placed in 95% ethanol and further evaluated in terms of RNase activity. SARS-CoV-2 was measured both directly from the swab and from the swab eluent. Results: Compared to samples collected in VTM, 95% ethanol demonstrated significant inhibition properties against RNases. When extracting directly from the swab head as opposed to the eluent, RNA recovery was approximately 2-4x higher from all six swab types tested as compared to the clinical standard of testing the eluent from a CDC-approved synthetic swab. The limit of detection (LoD) of SARs-CoV-2 from floor samples collected using the CGp or TMI swabs was similar or better than the CDC standard, further suggesting that swab type does not impact RNA recovery as measured by SARs-CoV-2. The LoD for TMI was between 0-362.5 viral particles while SYN and CGp were both between 725â€"1450 particles. Lastly microbiome analyses (16S rRNA) of paired samples (e.g., environment to host) collected using different swab types in triplicate indicated that microbial communities were not impacted by swab type but instead driven by the patient and sample type (floor or nasal). Conclusions: Compared to using a clinical-grade synthetic swab, detection of SARS-CoV-2 from environmental samples collected from ICU rooms of patients with COVID was similar using consumer grade swabs, stored in 95% ethanol. The yield was best from the swab head rather than the eluent and the low level of RNase activity in these samples makes it possible to perform concomitant microbiome analysis.

Enhanced computational methods for delineating microbial community composition

Abstract: Studies of microbial communities, including those found on and within humans and those found in both natural and engineered environments, have revealed the enormous levels of diversity contained within those communities The vast majority of this diversity cannot be observed using cultivation-based techniques However, advances in DNA sequencing technology have created the opportunity to survey microbial diversity in unprecedented detail, through direct sequencing of the small ribosomal subunit rRNA gene Modern datasets from a single study may contain hundreds of thousands to millions of 16S rRNA sequences, drawn from hundreds to thousands of biological samples Such sequences are obtained without the biases inherent in culture-dependent methods, and typically include many sequences representing undescribed and uncharacterized species The ability to obtain such extensive data relatively easily and inexpensively has revealed important constraints in our ability to detect patterns in these increasingly large and complex datasets, and to relate community composition to measures of human or health or environmental function To facilitate the analysis of sequence based community ecology surveys by researchers such as myself, I (in collaboration with others) developed a software tool entitled Quantitative Insights Into Microbial Ecology (QIIME) QIIME's extensive testing validates the analyses it performs, and its scalability guarantees its continued usefulness despite the trend towards studies of ever larger numbers of sequences and biological samples In addition, I embarked on investigations of the most appropriate methods for analyzing such data, and the quantity of DNA sequences and biological samples required I tested a large set of commonly used measures of microbial community resemblance for their efficacy on data typical of large-scale microbial ecology studies By applying the community resemblance measures to a combination of empirical results, as well as simulated results generated with a computational framework I designed, I was able to identify measures that are most useful, and the conditions under which they are most applicable The extent of sequencing required in community ecology studies in order to have confidence in the conclusions drawn from that data remains an open question, and is dependent on features of the particular communities often not known in advance, as well as the specific research goals Although researchers with finite budgets must always grapple with the tradeoff between more biological samples and deeper sequencing of fewer biological samples, I show that in many instances deeper sequencing, or obtaining larger numbers of sequences per sample, is of limited use, and a fixed sequencing budget is better applied to acquiring and sequencing more biological samples Lastly, I investigated the effects of incomplete sequencing of microbial communities, and the effects that incomplete data has on estimates of the resemblance (β diversity) of those communities To address the longstanding issue in microbial community ecology that comparisons of only limited samples of microbial communities are frequently biased estimates of the true resemblance of the full communities, I have developed a measure of community resemblance that is relatively unbiased when applied to incompletely sequenced microbial communities

Measuring Causal Specificity. Supplementary Online Materials

Abstract: Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well-defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the specificity of coding DNA and other factors in a simple model of the production of mRNA.

QIIME allows analysis of high-throughput community sequencing data.

Abstract: Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

Abstract: SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities

Abstract: mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.

Search and clustering orders of magnitude faster than BLAST

Abstract: Motivation: Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. Results: UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Availability: Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch Contact: [email protected] Supplementary information:Supplementary data are available at Bioinformatics online.