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Recently, interest in untargeted metabolomics has become prevalent in the general scientific community among an increasing number of investigators. The majority of these investigators, however, do not have the bioinformatic expertise that has been required to process metabolomic data by using command-line driven software programs. Here we introduce a novel platform to process untargeted metabolomic data that uses an intuitive graphical interface and does not require installation or technicaldoi:10.1021/ac300698c pmid:22533540 pmcid:PMC3703953 fatcat:buqcpq5x7vcshfhxavq2ptlfd4
more »... ertise. This platform, called XCMS Online, is a web-based version of the widely used XCMS software that allows users to easily upload and process liquid chromatography/mass spectrometry data with only a few mouse clicks. XCMS Online provides a solution for the complete untargeted metabolomic workflow including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Results can be browsed online in an interactive, customizable table showing statistics, chromatograms, and putative METLIN identities for each metabolite. Additionally, all results and images can be downloaded as zip files for offline analysis and publication. XCMS Online
The essential ones include the increase of raw data upload speed (Rinehart et al. 2014) , biochemical pathway mapping of feature clusters, automated metabolite identification through MS/MS matching against ...doi:10.1007/s11306-014-0759-2 pmid:26195918 pmcid:PMC4505375 fatcat:beceflpjr5hgtec32ybachvaqq
Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoingdoi:10.1038/nprot.2017.151 pmid:29494574 pmcid:PMC5937130 fatcat:ojwld7dmhvcgdfci5pqb6hx7yu
more »... gical processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LCLC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter Reprints and permissions information is available online at http://www.nature.com/reprints/index.html. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias. With mass spectrometry-based methods, global metabolomic data sets typically comprise thousands of peaks, each of which is associated with a mass-to-charge ratio, retention time, fold change, p-value, and relative intensity. Although several visualization schemes have been used for metabolomic data, most commonly used representations exclude important data dimensions and thereforedoi:10.1021/ac3029745 pmid:23206250 pmcid:PMC3716252 fatcat:yvlni5jxonbxjnzsziryjhpyoa
more »... it interpretation of global data sets. Given that metabolite identification through tandem mass spectrometry data acquisition is a time-limiting step of the untargeted metabolomic workflow, simultaneous visualization of these parameters from large sets of data could facilitate compound identification and data interpretation. Here, we present such a visualization scheme of global metabolomic data using a so-called "cloud plot" to represent multidimensional data from septic mice. While much attention has been dedicated to lipid compounds as potential biomarkers for sepsis, the cloud plot shows that alterations in hydrophilic metabolites may provide an early signature of the disease prior to the onset of clinical symptoms. The cloud plot is an effective representation of global mass spectrometry-based metabolomic data, and we describe how to extract it as standard output from our XCMS metabolomic software.
Motivation: Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. Results: With the aim ofdoi:10.1093/bioinformatics/btv475 pmid:26275895 pmcid:PMC4836397 fatcat:7fck6fegnrcjhli7bjjvtzh3ty
more »... ving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloudbased data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. Availability and implementation. METLIN can be accessed by logging on to: https://
for assistance with 34 sample collection, and Alexander Shiklomanov for additional discharge data for the Kolyma 35 River. Discussions with John Gibson and Greg Fiske improved our understanding of 36 groundwater hydrology and watershed vegetation data, respectively, and are highly appreciated. 37 38 39 40 41 42 43 44 45 46 47 48 Abstract 49 The biomarker composition of dissolved organic carbon (DOC) of the six largest Arctic rivers 50 was studied between 2003 and 2007 as part of the PARTNERSdoi:10.1016/j.gca.2012.07.015 fatcat:i6q4nnqi2ffg5fuavappwoiyce
more »... ject. Samples were collected 51 over seasonal cycles relatively close to the river mouths. Here we report the lignin phenol and p-52 hydroxybenzene composition of Arctic river DOC in order to identify major sources of carbon. 53 Arctic river DOC represents an important carbon conduit linking the large pools of organic 54 carbon in the Arctic/Subarctic watersheds to the Arctic Ocean. Most of the annual lignin 55 discharge (>75%) occurs during the two month of spring freshet with extremely high lignin 56 concentrations and a lignin phenol composition indicative of fresh vegetation from boreal 57 forests. The three large Siberian rivers, Lena, Yenisei, and Ob, which also have the highest 58 proportion of forests within their watersheds, contribute about 90% of the total lignin discharge 59 to the Arctic Ocean. The composition of river DOC is also characterized by elevated levels of p-60 hydroxybenzenes, particularly during the low flow season, which indicates a larger contribution 61 from mosses and peat bogs. The lignin composition was strongly related to the average 14 C-age 62 of DOC supporting the abundance of young, boreal-vegetation-derived leachates during spring 63 flood, and older, soil-, peat-, and wetland-derived DOC during groundwater dominated low flow 64 conditions, particularly in the Ob and Yukon Rivers. We observed significant differences in 65 DOC concentration and composition between the rivers over the seasonal cycles with the 66 Mackenzie River being the most unique, the Lena River being similar to the Yenisei, and the 67 Yukon being most similar to the Ob. The observed relationship between the lignin phenol 68 composition and watershed characteristics suggests that DOC discharge from these rivers could 69 increase in a warmer climate under otherwise undisturbed conditions. 70 71 Mackenzie) cover more than 10x10 6 km 2 of surface area (larger than Canada) including extended 74 boreal forests, tundra, and wetlands. Approximately 76% of the combined watershed area is 75 located in Eurasia (Zhulidov et al., 1997). Within these large watersheds lies an immense carbon 76 reservoir, including biomass organic carbon in vegetation, soil organic carbon, and methane 77 hydrates. A large portion of the soil organic carbon is trapped in permafrost soils with ~54% of 78 this designated as continuous permafrost (Tarnocai et al., 2009). Among these large carbon 79 pools, soil organic carbon is quantitatively the most important with 1400-1850 PgC, followed by 80 60-70 Pg biomass carbon, and 2-65 PgC as land-based methane hydrates (Tarnocai et al., 2009). 81 The soil organic carbon in these watersheds represents roughly 50% of the global soil organic 82 matter with 67% of it located in the Eurasian watersheds (Tarnocai et al., 2009). Biomass carbon 83 in Arctic watersheds represents roughly 10-20% of the global vegetation carbon with about 73% 84 of the high latitude vegetation carbon located in Eurasia (McGuire et al., 2009, 2010). The size 85 of these carbon pools triggered the interest of researchers studying the global carbon cycle and 86 its response to climate change. The Arctic has experienced a larger increase of mean annual air 87 temperature (MAAT) over the last few decades (IPCC 2007) relative to the global average along 88 with a shift in the total flow and distribution of flow in high latitude rivers (Peterson et al., 2002; 89 Walvoord and Striegl, 2007). Temperature and moisture are key parameters governing the fate of 90 organic matter by influencing vegetation, permafrost stability, peat formation and 91 decomposition, and the frequency of forest fires. The transfer of carbon from high latitude 92 watersheds to the Arctic Ocean and the atmosphere will be partitioned between gaseous forms 93 (CO 2 and CH 4 ) and dissolved and particulate carbon in the rivers. Recent estimates for these 94 5 fluxes indicate that the large Arctic watersheds are currently net sinks for CO 2 (200-400 Tgyr -1 ; 95 McGuire et al., 2009), net sources for CH 4 (33-46 TgCyr -1 ; McGuire et al., 2009), and deliver 96 between 25 and 36 TgCyr -1 in the form of dissolved organic carbon (DOC) to the Arctic Ocean 97
Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e. the exposome, and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queriesdoi:10.1101/145722 fatcat:qya23pxeu5dxbj74s6gd2oyf7e
more »... nd cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700,000 chemical structures to now include more than 950,000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis and further, cognitive computing provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the outstanding methodological challenges current exposome research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.
An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneouslydoi:10.1021/ac5025649 pmid:25496351 pmcid:PMC4303330 fatcat:vz6x7wko65aqpmwlpmd3zfrnsi
more »... tain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.
The speed and throughput of analytical platforms has been a driving force in recent years in the "omics" technologies and while great strides have been accomplished in both chromatography and mass spectrometry, data analysis times have not benefited at the same pace. Even though personal computers have become more powerful, data transfer times still represent a bottleneck in data processing because of the increasingly complex data files and studies with a greater number of samples. To meet thedoi:10.1021/acs.analchem.6b03890 pmid:27983788 pmcid:PMC5244434 fatcat:dxf5lwhinjdkpdcmtksvdv3vey
more »... emand of analyzing hundreds to thousands of samples within a given experiment, we have developed a data streaming platform, XCMS Stream, which capitalizes on the acquisition time to compress and stream recently acquired data files to data processing servers, mimicking just-in-time production strategies from the manufacturing industry. The utility of this XCMS Online-based technology is demonstrated here in the analysis of T cell metabolism and other large-scale metabolomic studies. A large scale example on a 1000 sample data set demonstrated a 10 000-fold time savings, reducing data analysis time from days to minutes. Further, XCMS Stream has the capability to increase the efficiency of downstream biochemical dependent data acquisition (BDDA) analysis by initiating data conversion and data processing on subsets of data acquired, expanding its application beyond data transfer to smart preliminary data decision-making prior to full acquisition.
XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, "control" versus "disease" experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statisticaldoi:10.1021/ac500734c pmid:24934772 pmcid:PMC4215863 fatcat:f6tcpjgrrjdxzp4r6wxamxxjci
more »... utput and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.
Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, whichdoi:10.1021/acs.analchem.6b02676 pmid:27560777 pmcid:PMC5054939 fatcat:ftlu5jdhkfbs7laonzhjowijje
more »... the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and hierarchical cluster analysis), and database searching for metabolite identification. These apps, available on Apple iOS and Google Android operating systems, allow for the migration of metabolomic research onto mobile devices for better accessibility beyond direct instrument operation. The utility of XCMS Mobile and METLIN Mobile functionalities was developed and is demonstrated here through the metabolomic LC-MS analyses of stem cells, colon cancer, aging, and bacterial metabolism.
Rumbaugh, Duane M., ed. Language Learn- ing by a Chimpanzee: The Lana Project. Communication and Behavior: An Inter- disciplinary Series, Duane M. Rumbaugh, ed. ... New York: Holt, Rinehart and Winston, 1977. iv + 508 pp. n.p. (paper). ISBN 0-03-018396-0. ...
National Union Catalog
Title. 21-1948 Library of Congress BD161.WS Copyright 1889: 4065 (2001, Wilson, William Duane. ... New York, Toronto, Farrar and Rinehart, inc. ;°1941, Sp. 1, 3-808 p. 21™. 1. Title. ...
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