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.

Leveling up citizen science.

Abstract: Since 2008, the Foldit team has recruited online participants to predict protein 3D structure via a 3D puzzle game distributed as a standalone program for personal computers1. This project helped refine a retroviral protease structure2 and later discovered novel protein folds3. Two years later, Phylo4 pushed the gamification concept to reach a broader public by crowdsourcing comparative genomics tasks via tile-matching puzzle games. Other successful projects include web-based games for RNA structural modeling (for example, EteRNA5) and neuron segmentation (for example, EyeWire6). The efficiency and effectiveness of SDGs is necessarily determined by the number of contributors and volume of data collected, regardless of citizen-science strategy. Games like Foldit take advantage of large groups of participants for identifying skilled users and promising solutions to a hard problem. In contrast, Phylo and related SDGs aim to harness the wisdom of the crowd by detecting collective behaviors of the gamers while solving small instances of the problem, and using this knowledge to build a global solution. Having sustainable and large crowds of participants is necessary for opening this technology as a service to the whole scientific community7. Popular SDGs represent the best option for solving on-demand problems for many groups of independent scientists. Although few such platforms have yet to be deployed, this approach constitutes a natural and promising evolution of SDGs that is exemplified by recent initiatives like OpenVaccine or OpenCRISPR from the EteRNA project. Recruiting large groups of volunteers should also serve long-term objectives and not be confined to solving a specific problem without a larger context. Projects like ENCODE8 or the Earth Microbiome Project9 involve hundreds of scientists worldwide and require assembly and coordination of many diverse areas of expertise. At all stages of the research pipeline and at various degrees of involvement, collection, curation and analysis of the data need the supervision of human experts. But as the size and complexity of the project teams expand, so too does the cost of human intervention and logistics, which may not be sustainable. To expand the boundaries of the system, we need to refactor the organization of large scientific projects. The most successful citizen science initiatives have reported several hundreds of thousands of volunteers, and eventually more than a million if we aggregate statistics from distinct projects using the same infrastructure over years of operation. Notably, following a pattern commonly observed in user participation, the majority of the contribution is completed by the participants during the first days or weeks following the launch of the project. To prevent this drop-off, projects like Stall Catchers or EyeWire organize various events and competitions to renew participant interest and maintain engagement. Foldit and EteRNA also regularly unveil new challenges and datasets designed to support research in different areas, such as COVID, tuberculosis or synthetic biology. Nevertheless, in the best case, the volume of work generated reaches a couple of hundreds of hours of work per day10. Although such a number is useful, it hardly meets the demand of large-scale science projects. Therefore, despite undeniable successes not only in scientific impact but also in science outreach to the general public, the vision of online citizen science initiatives is still out of range of current methodologies. A model based on distributing scientific tasks through a web page or mobile application managed by academic researchers appears to have reached its limits. Even when a project benefits from extensive media coverage, the core of its audience remains confined to participants already naturally interested in the scientific subjects, which is unfortunately only a small fraction of the population. Fundamentally, the goal of citizen science is to organize science at population scale. The internet has been a key technology for gathering crowds of participants worldwide in a short time, and the development of SDGs has been equally important for enabling citizen scientists to participate in the resolution of complex problems. But the next frontier is to bring SDGs to the general public. In 2016, Massively Multiplayer Online Science11 (MMOS) proposed an original solution to this problem. The first implementation was a collaboration with the video game company CCP to integrate citizen science activities in the science fiction massively multiplayer online role-playing game Eve Online. In less than a year, Project Discovery enrolled more than 300,000 online gamers to classify fluorescence microscopy images for the Human Protein Atlas12. Remarkably, engagement was also very high, with an average of 100 tasks solved per participant. Overall, these statistics represent a considerable improvement over those of previous projects. The rationale for the approach taken by MMOS is both simple and efficient; instead of trying to attract users to yet another third-party website, bring citizen science to the virtual universes that users already inhabit. This strategy is even more powerful given that gamers are already expert at developing problem-solving skills. With the introduction of Project Discovery, citizen science entered the AAA video game scene. (AAA is an informal classification used for video games produced and distributed by a mid-sized or major publisher, typically having a high development and marketing budget.) But another crucial step is needed to make this approach broadly accepted by both researchers and game developers. Eve Online is a very special game. The player base is more mature and attuned to science than other player communities. The game is also slower paced and more complex, making it an almost too perfect match for citizen-science activities. The deployment of SDGs within fast-paced games with a wide and...

A Microbiome-Based Index for Assessing Skin Health and Treatment Effects for Atopic Dermatitis in Children.

Abstract: A quantitative and objective indicator for skin health via the microbiome is of great interest for personalized skin care, but differences among skin sites and across human populations can make this goal challenging. A three-city (two Chinese and one American) comparison of skin microbiota from atopic dermatitis (AD) and healthy pediatric cohorts revealed that, although city has the greatest effect size (the skin microbiome can predict the originated city with near 100% accuracy), a microbial index of skin health (MiSH) based on 25 bacterial genera can diagnose AD with 83 to ∼95% accuracy within each city and 86.4% accuracy across cities (area under the concentration-time curve [AUC], 0.90). Moreover, nonlesional skin sites across the bodies of AD-active children (which include shank, arm, popliteal fossa, elbow, antecubital fossa, knee, neck, and axilla) harbor a distinct but lesional state-like microbiome that features relative enrichment of Staphylococcus aureus over healthy individuals, confirming the extension of microbiome dysbiosis across body surface in AD patients. Intriguingly, pretreatment MiSH classifies children with identical AD clinical symptoms into two host types with distinct microbial diversity and treatment effects of corticosteroid therapy. These findings suggest that MiSH has the potential to diagnose AD, assess risk-prone state of skin, and predict treatment response in children across human populations.IMPORTANCE MiSH, which is based on the skin microbiome, can quantitatively assess pediatric skin health across cohorts from distinct countries over large geographic distances. Moreover, the index can identify a risk-prone skin state and compare treatment effect in children, suggesting applications in diagnosis and patient stratification.

Links between gut microbiome composition and fatty liver disease in a large population sample

Abstract: Fatty liver disease is the most common liver disease in the world. Its connection with the gut microbiome has been known for at least 80 years, but this association remains mostly unstudied in the general population because of underdiagnosis and small sample sizes. To address this knowledge gap, we studied the link between the Fatty Liver Index (FLI), a well-established proxy for fatty liver disease, and gut microbiome composition in a representative, ethnically homogeneous population sample of 6,269 Finnish participants. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between individuals with a high FLI (≥ 60, indicates likely liver steatosis) and low FLI (

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.