Author
Lasse Buur Rasmussen
Bio: Lasse Buur Rasmussen is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Microbiome & Chemical similarity. The author has an hindex of 3, co-authored 4 publications receiving 4220 citations.
Topics: Microbiome, Chemical similarity
Papers
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Northern Arizona University1, National Institutes of Health2, University of Minnesota3, University of California, Davis4, Woods Hole Oceanographic Institution5, Massachusetts Institute of Technology6, University of Copenhagen7, University of Trento8, Chinese Academy of Sciences9, University of California, San Francisco10, University of Pennsylvania11, Pacific Northwest National Laboratory12, North Carolina State University13, University of California, San Diego14, Institute for Systems Biology15, Dalhousie University16, University of British Columbia17, Statens Serum Institut18, Anschutz Medical Campus19, University of Washington20, Michigan State University21, Stanford University22, Broad Institute23, Harvard University24, Australian National University25, University of Düsseldorf26, University of New South Wales27, Sookmyung Women's University28, San Diego State University29, Howard Hughes Medical Institute30, Cornell University31, Max Planck Society32, Colorado State University33, Google34, Syracuse University35, Webster University36, United States Department of Agriculture37, University of Arkansas for Medical Sciences38, Colorado School of Mines39, University of Southern Mississippi40, National Oceanic and Atmospheric Administration41, University of California, Merced42, Wageningen University and Research Centre43, University of Arizona44, Environment Agency45, University of Florida46, Merck & Co.47
TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.
Abstract: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. Partial support was also provided by the following: grants NIH U54CA143925 (J.G.C. and T.P.) and U54MD012388 (J.G.C. and T.P.); grants from the Alfred P. Sloan Foundation (J.G.C. and R.K.); ERCSTG project MetaPG (N.S.); the Strategic Priority Research Program of the Chinese Academy of Sciences QYZDB-SSW-SMC021 (Y.B.); the Australian National Health and Medical Research Council APP1085372 (G.A.H., J.G.C., Von Bing Yap and R.K.); the Natural Sciences and Engineering Research Council (NSERC) to D.L.G.; and the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University. All NCI coauthors were supported by the Intramural Research Program of the National Cancer Institute. S.M.G. and C. Diener were supported by the Washington Research Foundation Distinguished Investigator Award.
8,821 citations
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Northern Arizona University1, University of Minnesota2, Woods Hole Oceanographic Institution3, University of California, Davis4, Massachusetts Institute of Technology5, University of Copenhagen6, University of Trento7, Chinese Academy of Sciences8, University of California, San Francisco9, Children's Hospital of Philadelphia10, Pacific Northwest National Laboratory11, North Carolina State University12, University of Montana13, Dalhousie University14, University of British Columbia15, Shedd Aquarium16, University of Colorado Denver17, University of California, San Diego18, Michigan State University19, Stanford University20, Broad Institute21, Harvard University22, Australian National University23, University of Düsseldorf24, Sookmyung Women's University25, San Diego State University26, Howard Hughes Medical Institute27, Max Planck Society28, Cornell University29, University of Washington30, Colorado State University31, Google32, Syracuse University33, Webster University34, United States Department of Agriculture35, University of Arkansas for Medical Sciences36, Colorado School of Mines37, University of Southern Mississippi38, Atlantic Oceanographic and Meteorological Laboratory39, University of California, Merced40, Wageningen University and Research Centre41, University of Arizona42, Environment Agency43, University of Florida44, Merck & Co.45
TL;DR: QIIME 2 provides new features that will drive the next generation of microbiome research, including interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
Abstract: We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.
875 citations
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Northern Arizona University1, National Institutes of Health2, University of Minnesota3, University of California, Davis4, Woods Hole Oceanographic Institution5, Massachusetts Institute of Technology6, University of Copenhagen7, University of Trento8, Chinese Academy of Sciences9, University of California, San Francisco10, University of Pennsylvania11, Pacific Northwest National Laboratory12, North Carolina State University13, University of Montana14, Institute for Systems Biology15, Dalhousie University16, University of British Columbia17, Statens Serum Institut18, Anschutz Medical Campus19, University of Washington20, University of California, San Diego21, Michigan State University22, Stanford University23, Harvard University24, Broad Institute25, Australian National University26, University of Düsseldorf27, University of New South Wales28, Sookmyung Women's University29, San Diego State University30, Howard Hughes Medical Institute31, Cornell University32, Max Planck Society33, Colorado State University34, Google35, Syracuse University36, Webster University37, United States Department of Agriculture38, University of Arkansas for Medical Sciences39, Colorado School of Mines40, University of Southern Mississippi41, National Oceanic and Atmospheric Administration42, University of California, Merced43, Wageningen University and Research Centre44, University of Arizona45, Environment Agency46, University of Florida47, Merck & Co.48
TL;DR: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Abstract: In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.
301 citations
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TL;DR: A new implementation of the CSCS algorithm is provided that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications and it is shown that adding chemical information enriches existing methods.
Abstract: Motivation Tandem mass spectrometry (MS/MS) has the potential to substantially improve metabolomics by acquiring spectra of fragmented ions. These fragmentation spectra can be represented as a molecular network, by measuring cosine distances between them, thus identifying signals from the same or similar molecules. Metrics that enable comparison between pairs of samples based on their metabolite profiles are in great need. Taking inspiration from the successful phylogeny-aware beta-diversity measures used in microbiome research, integrating chemical similarity information about the features in addition to their abundances could lead to better insights when comparing metabolite profiles. Chemical Structural and Compositional Similarity (CSCS) is a recently published similarity metric comparing the full set of signals and their chemical similarity between two samples. Efficient, scalable and easily accessible implementations of this algorithm is currently lacking. Here, we present an easily accessible and scalable implementation of CSCS in both python and R, including a version not weighted by intensity information.
Results We provide a new implementation of the CSCS algorithm that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications. We also show that adding chemical information enriches existing methods. Furthermore, the R implementation includes functions for exporting molecular networks directly from the mass spectral molecular networking platform GNPS for ease of use for downstream applications.
Contact brejnrod{at}sund.ku.dk
Availability [github.com/askerdb/rCSCS][1], [github.com/askerdb/pyCSCS][2]
[1]: http://github.com/askerdb/rCSCS
[2]: http://github.com/askerdb/pyCSCS
3 citations
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TL;DR: Some notable features of IQ-TREE version 2 are described and the key advantages over other software are highlighted.
Abstract: IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
4,337 citations
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TL;DR: This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data, a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces.
Abstract: MicrobiomeAnalyst is an easy-to-use, web-based platform for comprehensive analysis of common data outputs generated from current microbiome studies. It enables researchers and clinicians with little or no bioinformatics training to explore a wide variety of well-established methods for microbiome data processing, statistical analysis, functional profiling and comparison with public datasets or known microbial signatures. MicrobiomeAnalyst currently contains four modules: Marker-gene Data Profiling (MDP), Shotgun Data Profiling (SDP), Projection with Public Data (PPD), and Taxon Set Enrichment Analysis (TSEA). This protocol will first introduce the MDP module by providing a step-wise description of how to prepare, process and normalize data; perform community profiling; identify important features; and conduct correlation and classification analysis. We will then demonstrate how to perform predictive functional profiling and introduce several unique features of the SDP module for functional analysis. The last two sections will describe the key steps involved in using the PPD and TSEA modules for meta-analysis and visual exploration of the results. In summary, MicrobiomeAnalyst offers a one-stop shop that enables microbiome researchers to thoroughly explore their preprocessed microbiome data via intuitive web interfaces. The complete protocol can be executed in ~70 min. This protocol details MicrobiomeAnalyst, a user-friendly, web-based platform for comprehensive statistical, functional, and meta-analysis of microbiome data.
823 citations
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Graz University of Technology1, Université Paris-Saclay2, University of Waterloo3, Guizhou University4, European Food Information Council5, Institut national de la recherche agronomique6, Agricultural University of Athens7, University of Minnesota8, University of Minho9, University of Vienna10, Agriculture and Agri-Food Canada11, Rothamsted Research12, Pacific Northwest National Laboratory13, Austrian Institute of Technology14, CABI15, Tallinn University of Technology16, Wageningen University and Research Centre17, Pondicherry University18, State University of Campinas19, University of Sydney20, Teagasc21
TL;DR: A definition of microbiome is proposed based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings.
Abstract: The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term “microbiome.” Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role.
733 citations
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TL;DR: Treatment with FMT was associated with favorable changes in immune cell infiltrates and gene expression profiles in both the gut lamina propria and the tumor microenvironment, which have implications for modulating the gut microbiota in cancer treatment.
Abstract: The gut microbiome has been shown to influence the response of tumors to anti-PD-1 (programmed cell death-1) immunotherapy in preclinical mouse models and observational patient cohorts. However, modulation of gut microbiota in cancer patients has not been investigated in clinical trials. In this study, we performed a phase 1 clinical trial to assess the safety and feasibility of fecal microbiota transplantation (FMT) and reinduction of anti-PD-1 immunotherapy in 10 patients with anti-PD-1-refractory metastatic melanoma. We observed clinical responses in three patients, including two partial responses and one complete response. Notably, treatment with FMT was associated with favorable changes in immune cell infiltrates and gene expression profiles in both the gut lamina propria and the tumor microenvironment. These early findings have implications for modulating the gut microbiota in cancer treatment.
609 citations
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TL;DR: It is proposed that the gut microbiota regulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD.
598 citations