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Author

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.
Topics: Microbiome, Gut flora, Medicine, Metagenomics, Biology


Papers
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Book ChapterDOI
09 Jun 2005
TL;DR: This work performed virtual experiments in RNA folding on computational grids composed of fast supercomputers, in order to estimate the smallest pool of random RNA molecules that would contain enough catalytic motifs for starting a primitive metabolism.
Abstract: Due to ever-increasing data sizes and the high computational complexity of many algorithms, there is a natural drive towards applying parallel and distributed computing to bioinformatics problems. Grid computing techniques can provide flexible, portable and scalable software solutions for parallel bioinformatics. Here we describe the TaskSpaces software framework for grid computing. TaskSpaces is characterized by two major design choices: decentralization, provided by an underlying tuple space concept, and platform independence, provided by implementation in Java. We discuss advantages and disadvantages of this approach, and demonstrate seamless performance on an ad-hoc grid composed of a wide variety of hardware for a real-life parallel bioinformatics problem. Specifically, we performed virtual experiments in RNA folding on computational grids composed of fast supercomputers, in order to estimate the smallest pool of random RNA molecules that would contain enough catalytic motifs for starting a primitive metabolism. These experiments may establish one of the missing links in the chain of events that led to the origin of life. — Note: To appear as a Chapter in the textbook Parallel Computing in Bioinformatics and Computational Biology, A. Zomaya, editor, John Wiley and Sons, 2005.

5 citations

01 Jan 2019
TL;DR: In this paper, the authors showed that exposure to a farm-like house dust microbiome is associated with protection against asthma development in children living in urban environments, and they were able to reproduce these findings in a study among rural German children and showed that children living living in German non-farm homes with an indoor microbiota more similar to Finnish farm homes have decreased asthma risk.
Abstract: Asthma prevalence has increased in epidemic proportions with urbanization, but growing up on traditional farms offers protection even today1. The asthma-protective effect of farms appears to be associated with rich home dust microbiota2,3, which could be used to model a health-promoting indoor microbiome. Here we show by modeling differences in house dust microbiota composition between farm and non-farm homes of Finnish birth cohorts4 that in children who grow up in non-farm homes, asthma risk decreases as the similarity of their home bacterial microbiota composition to that of farm homes increases. The protective microbiota had a low abundance of Streptococcaceae relative to outdoor-associated bacterial taxa. The protective effect was independent of richness and total bacterial load and was associated with reduced proinflammatory cytokine responses against bacterial cell wall components ex vivo. We were able to reproduce these findings in a study among rural German children2 and showed that children living in German non-farm homes with an indoor microbiota more similar to Finnish farm homes have decreased asthma risk. The indoor dust microbiota composition appears to be a definable, reproducible predictor of asthma risk and a potential modifiable target for asthma prevention. Exposure to a farm-like house dust microbiome is associated with protection against asthma development in children living in urban environments.

5 citations

Journal ArticleDOI
03 May 2019-Science
TL;DR: Discussion pertained to all types of data, many or most of which do not include any personal information or do not involve individuals or humans, and the public release of data may offer more value for the public and more widely distributed benefits of science.
Abstract: Nicol et al. make insightful comments on issues related to data sharing and ethics, regulations, and imbalance of power. We largely share their concerns, and we did indeed mention some of them briefly in our Policy Forum. Our discussion pertained to all types of data, many or most of which do not include any personal information or do not involve individuals or humans in any way. For example, environmental genomics data generated through public funding should become available shortly after generation and should enjoy unrestricted usage. As we acknowledged in the Policy Forum, when it comes to personal data, issues of privacy, confidentiality, and informed consent need to be considered carefully and the best arrangements may vary on a case-by-case basis. It makes sense to anticipate these issues before data collection begins and to aim for research designs and informed consent forms that allow maximal, prompt, open use of valuable data. We agree with Nicol et al. that public release of data may affect distribution of burdens and benefits across and within populations, and this is something that should be closely monitored. However, we think that usually more openness would diminish the inadvertent concentration of informational power in the hands of select for-profit enterprises that may wish to hoard data for their own advantage. Conversely, the public release of data may offer more value for the public and more widely distributed benefits of science. The list of author affiliations is available at [www.sciencemag.org/content/363/6425/350/suppl/DC1][1]. [1]: http://www.sciencemag.org/content/363/6425/350/suppl/DC1

5 citations

Journal ArticleDOI
TL;DR: A Coherence-based spectro-spatial filter that allows for reconstructing stimulus features from brain signal’s features and takes into account frequency, phase, and spatial distribution of brain features, hence avoiding the need to predefine specific frequency bands of interest or phase relationships between stimulus and brain responses manually.
Abstract: The traditional approach in neuroscience relies on encoding models where brain responses are related to different stimuli in order to establish dependencies. In decoding tasks, on the contrary, brain responses are used to predict the stimuli, and traditionally, the signals are assumed stationary within trials, which is rarely the case for natural stimuli. We hypothesize that a decoding model assuming each experimental trial as a realization of a random process more likely reflects the statistical properties of the undergoing process compared to the assumption of stationarity. Here, we propose a Coherence-based spectro-spatial filter that allows for reconstructing stimulus features from brain signal's features. The proposed method extracts common patterns between features of the brain signals and the stimuli that produced them. These patterns, originating from different recording electrodes are combined, forming a spatial filter that produces a unified prediction of the presented stimulus. This approach takes into account frequency, phase, and spatial distribution of brain features, hence avoiding the need to predefine specific frequency bands of interest or phase relationships between stimulus and brain responses manually. Furthermore, the model does not require the tuning of hyper-parameters, reducing significantly the computational load attached to it. Using three different cognitive tasks (motor movements, speech perception, and speech production), we show that the proposed method consistently improves stimulus feature predictions in terms of correlation (group averages of 0.74 for motor movements, 0.84 for speech perception, and 0.74 for speech production) in comparison with other methods based on regularized multivariate regression, probabilistic graphical models and artificial neural networks. Furthermore, the model parameters revealed those anatomical regions and spectral components that were discriminant in the different cognitive tasks. This novel method does not only provide a useful tool to address fundamental neuroscience questions, but could also be applied to neuroprosthetics.

5 citations

Journal ArticleDOI
28 Jul 2020
TL;DR: While a few species were associated with previously cultivated isolates from the International Space Station and MIR spacecraft, the majority of the microbial ecology of the submerged analog habitat differs greatly from that of previously studied analog habitats.
Abstract: Microbial contamination during long-term confinements of space exploration presents potential risks for both crew members and spacecraft life support systems. A novel swab kit was used to sample various surfaces from a submerged, closed, analog habitat to characterize the microbial populations. Samples were collected from various locations across the habitat which were constructed from various surface materials (linoleum, dry wall, particle board, glass, and metal), and microbial populations were examined by culture, quantitative PCR (qPCR), microbiome 16S rRNA gene sequencing, and shotgun metagenomics. Propidium monoazide (PMA)-treated samples identified the viable/intact microbial population of the habitat. The cultivable microbial population ranged from below the detection limit to 106 CFU/sample, and their identity was characterized using Sanger sequencing. Both 16S rRNA amplicon and shotgun sequencing were used to characterize the microbial dynamics, community profiles, and functional attributes (metabolism, virulence, and antimicrobial resistance). The 16S rRNA amplicon sequencing revealed abundance of viable (after PMA treatment) Actinobacteria (Brevibacterium, Nesternkonia, Mycobacterium, Pseudonocardia, and Corynebacterium), Firmicutes (Virgibacillus, Staphylococcus, and Oceanobacillus), and Proteobacteria (especially Acinetobacter) on linoleum, dry wall, and particle board (LDP) surfaces, while members of Firmicutes (Leuconostocaceae) and Proteobacteria (Enterobacteriaceae) were high on the glass/metal surfaces. Nonmetric multidimensional scaling determined from both 16S rRNA and metagenomic analyses revealed differential microbial species on LDP surfaces and glass/metal surfaces. The shotgun metagenomic sequencing of samples after PMA treatment showed bacterial predominance of viable Brevibacterium (53.6%), Brachybacterium (7.8%), Pseudonocardia (9.9%), Mycobacterium (3.7%), and Staphylococcus (2.1%), while fungal analyses revealed Aspergillus and Penicillium dominance.IMPORTANCE This study provides the first assessment of monitoring cultivable and viable microorganisms on surfaces within a submerged, closed, analog habitat. The results of the analyses presented herein suggest that the surface material plays a role in microbial community structure, as the microbial populations differed between LDP and metal/glass surfaces. The metal/glass surfaces had less-complex community, lower bioburden, and more closely resembled the controls. These results indicated that material choice is crucial when building closed habitats, even if they are simply analogs. Finally, while a few species were associated with previously cultivated isolates from the International Space Station and MIR spacecraft, the majority of the microbial ecology of the submerged analog habitat differs greatly from that of previously studied analog habitats.

5 citations


Cited by
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Journal ArticleDOI
TL;DR: An overview of the analysis pipeline and links to raw data and processed output from the runs with and without denoising are provided.
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.

28,911 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组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�ly.
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。

18,940 citations

Journal ArticleDOI
TL;DR: 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.
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.

18,256 citations

Journal ArticleDOI
TL;DR: M mothur is used as a case study to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments.
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.

17,350 citations

Journal ArticleDOI
TL;DR: UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters and 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.
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.

17,301 citations