scholarly journals “A novel paradigm for optimal mass feature peak picking in large scale LC-MS datasets using the ‘isopair’: isoLock, autoCredential and anovAlign”

2021 ◽  
Author(s):  
Allen Hubbard ◽  
Louis Connelly ◽  
Shrikaar Kambhampati ◽  
Brad Evans ◽  
Ivan Baxter
Keyword(s):  
Retention Time ◽  
Large Scale ◽  
Time Windows ◽  
Untargeted Metabolomics ◽  
Liquid Chromatography Mass Spectrometry ◽  
Computational Tools ◽  
Mass Spectrometers ◽  
New Era ◽  
Clinical Dataset ◽  
Carbon Labeling

AbstractUntargeted metabolomics enables direct quantification of metabolites without apriori knowledge of their identity. Liquid chromatography mass spectrometry (LC-MS), a popular method to implement untargeted metabolomics, identifies metabolites via combined mass/charge (m/z) and retention time as mass features. Improvements in the sensitivity of mass spectrometers has increased the complexity of data produced, leading to computational obstacles. One outstanding challenge is calling metabolite mass feature peaks rapidly and accurately in large LC-MS datasets (dozens to thousands of samples) in the presence of measurement and other noise. While existing algorithms are useful, they have limitations that become pronounced at scale and lead to false positive metabolite predictions as well as signal dropouts. To overcome some of these shortcomings, biochemists have developed hybrid computational and carbon labeling techniques, such as credentialing. Credentialing can validate metabolite signals, but is laborious and its applicability is limited. We have developed a suite of three computational tools to overcome the challenges of unreliable algorithms and inefficient validation protocols: isolock, autoCredential and anovAlign. Isolock uses isopairs, or metabolite-istopologue pairs, to calculate and correct for mass drift noise across LC-MS runs. autoCredential leverages statistical features of LC-MS data to amplify naturally present 13C isotopologues and validate metabolites through isopairs. This obviates the need to artificially introduce carbon labeling. anovAlign, an anova-derived algorithm, is used to align retention time windows across samples to accurately delineate retention time windows for mass features. Using a large published clinical dataset as well as a plant dataset with biological replicates across time, genotype and treatment, we demonstrate that this suite of tools is more sensitive and reproducible than both an open source metabolomics pipelines, XCMS, and the commercial software progenesis QI. This software suite opens a new era for enhanced accuracy and increased throughput for untargeted metabolomics.

scholarly journals MetaboAnalystR 2.0: From Raw Spectra to Biological Insights

Metabolites ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 57 ◽  
Author(s):  
Jasmine Chong ◽  
Mai Yamamoto ◽  
Jianguo Xia
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
High Performance ◽  
Large Scale ◽  
Liquid Chromatography Mass Spectrometry ◽  
Spectral Processing ◽  
Functional Interpretation ◽  
Metabolomics Data ◽  
Clinical Dataset ◽  
Peak Annotation

Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.

Computer Analyses in Preventive Health Research

1967 ◽  
Vol 06 (01) ◽  
pp. 8-14 ◽  
Author(s):  
M. F. Collen
Keyword(s):  
Medical Research ◽  
Preventive Medicine ◽  
Health Research ◽  
Large Scale ◽  
Preventive Health ◽  
New Era ◽  
Large Numbers ◽  
Normal Test ◽  
Computer Analyses

The utilization of an automated multitest laboratory as a data acquisition center and of a computer for trie data processing and analysis permits large scale preventive medical research previously not feasible. Normal test values are easily generated for the particular population studied. Long-term epidemiological research on large numbers of persons becomes practical. It is our belief that the advent of automation and computers has introduced a new era of preventive medicine.

Microcomputers in Political Science

1983 ◽  
Vol 38 ◽  
pp. 1-9
Author(s):  
Herbert F. Weisberg
Keyword(s):  
Data Analysis ◽  
Political Science ◽  
Large Scale ◽  
Turnaround Time ◽  
General Purpose ◽  
Batch Mode ◽  
New Era ◽  
Large Scale Data ◽  
The Social ◽  
Frequency Counts

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.

scholarly journals A non-targeted LC–MS metabolic profiling of pregnancy: longitudinal evidence from healthy and pre-eclamptic pregnancies

Metabolomics ◽  
2021 ◽  
Vol 17 (2) ◽  
Author(s):  
Tiina Jääskeläinen ◽  
◽  
Olli Kärkkäinen ◽  
Jenna Jokkala ◽  
Anton Klåvus ◽  
...  
Keyword(s):  
Large Scale ◽  
Scale Effects ◽  
Late Pregnancy ◽  
Metabolite Profile ◽  
Adverse Outcomes ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Serum Samples ◽  
Healthy Women ◽  
Maternal Metabolism

Abstract Introduction Maternal metabolism changes substantially during pregnancy. However, few studies have used metabolomics technologies to characterize changes across gestation. Objectives and methods We applied liquid chromatography–mass spectrometry (LC–MS) based non-targeted metabolomics to determine whether the metabolic profile of serum differs throughout the pregnancy between pre-eclamptic and healthy women in the FINNPEC (Finnish Genetics of Preeclampsia Consortium) Study. Serum samples were available from early and late pregnancy. Results Progression of pregnancy had large-scale effects to the serum metabolite profile. Altogether 50 identified metabolites increased and 49 metabolites decreased when samples of early pregnancy were compared to samples of late pregnancy. The metabolic signatures of pregnancy were largely shared in pre-eclamptic and healthy women, only urea, monoacylglyceride 18:1 and glycerophosphocholine were identified to be increased in the pre-eclamptic women when compared to healthy controls. Conclusions Our study highlights the need of large-scale longitudinal metabolomic studies in non-complicated pregnancies before more detailed understanding of metabolism in adverse outcomes could be provided. Our findings are one of the first steps for a broader metabolic understanding of the physiological changes caused by pregnancy per se.

scholarly journals Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

2021 ◽  
Author(s):  
Mehdi A. Beniddir ◽  
Kyo Bin Kang ◽  
Grégory Genta-Jouve ◽  
Florian Huber ◽  
Simon Rogers ◽  
...  
Keyword(s):  
Natural Product ◽  
Large Scale ◽  
Scale Analysis ◽  
Computational Tools ◽  
Natural Product Discovery ◽  
Large Scale Analysis ◽  
Metabolite Annotation

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.

scholarly journals The SAMI Galaxy Survey: the third and final data release

2021 ◽  
Author(s):  
Scott M Croom ◽  
Matt S Owers ◽  
Nicholas Scott ◽  
Henry Poetrodjojo ◽  
Brent Groves ◽  
...  
Keyword(s):  
Large Scale ◽  
Resolving Power ◽  
Stellar Kinematics ◽  
New Era ◽  
Optical Wavelength ◽  
Astronomical Optics ◽  
Data Release ◽  
Gas Ionization ◽  
Release Data ◽  
First Time

Abstract We have entered a new era where integral-field spectroscopic surveys of galaxies are sufficiently large to adequately sample large-scale structure over a cosmologically significant volume. This was the primary design goal of the SAMI Galaxy Survey. Here, in Data Release 3 (DR3), we release data for the full sample of 3068 unique galaxies observed. This includes the SAMI cluster sample of 888 unique galaxies for the first time. For each galaxy, there are two primary spectral cubes covering the blue (370–570 nm) and red (630–740 nm) optical wavelength ranges at spectral resolving power of R = 1808 and 4304 respectively. For each primary cube, we also provide three spatially binned spectral cubes and a set of standardized aperture spectra. For each galaxy, we include complete 2D maps from parameterized fitting to the emission-line and absorption-line spectral data. These maps provide information on the gas ionization and kinematics, stellar kinematics and populations, and more. All data are available online through Australian Astronomical Optics (AAO) Data Central.

scholarly journals The Impact of Gaia and LSST on Binaries and Exoplanets

2011 ◽  
Vol 7 (S282) ◽  
pp. 33-40
Author(s):  
L. Eyer ◽  
P. Dubath ◽  
N. Mowlavi ◽  
P. North ◽  
A. Triaud ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Large Scale ◽  
Binary Systems ◽  
New Era ◽  
The Subject ◽  
The Impact

AbstractTwo upcoming large scale surveys, the ESA Gaia and LSST projects, will bring a new era in astronomy. The number of binary systems that will be observed and detected by these projects is enormous, estimations range from millions for Gaia to several tens of millions for LSST. We review some tools that should be developed and also what can be gained from these missions on the subject of binaries and exoplanets from the astrometry, photometry, radial velocity and their alert systems.

scholarly journals The daunting polygenicity of mental illness: making a new map

2018 ◽  
Vol 373 (1742) ◽  
pp. 20170031 ◽  
Author(s):  
Steven E. Hyman
Keyword(s):  
Mental Health ◽  
Large Scale ◽  
Computational Models ◽  
Model Organisms ◽  
Human Biology ◽  
Genetic Studies ◽  
Computational Tools ◽  
Experimental Systems ◽  
Terra Incognita ◽  
Phenotypic Complexity

An epochal opportunity to elucidate the pathogenic mechanisms of psychiatric disorders has emerged from advances in genomic technology, new computational tools and the growth of international consortia committed to data sharing. The resulting large-scale, unbiased genetic studies have begun to yield new biological insights and with them the hope that a half century of stasis in psychiatric therapeutics will come to an end. Yet a sobering picture is coming into view; it reveals daunting genetic and phenotypic complexity portending enormous challenges for neurobiology. Successful exploitation of results from genetics will require eschewal of long-successful reductionist approaches to investigation of gene function, a commitment to supplanting much research now conducted in model organisms with human biology, and development of new experimental systems and computational models to analyse polygenic causal influences. In short, psychiatric neuroscience must develop a new scientific map to guide investigation through a polygenic terra incognita . This article is part of a discussion meeting issue ‘Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists’.

scholarly journals TADOSS: computational estimation of tandem domain swap stability

2018 ◽  
Vol 35 (14) ◽  
pp. 2507-2508 ◽  
Author(s):  
Aleix Lafita ◽  
Pengfei Tian ◽  
Robert B Best ◽  
Alex Bateman
Keyword(s):  
Large Scale ◽  
Domain Swapping ◽  
Coarse Grained ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Computational Tools ◽  
Domain Swap ◽  
Computational Estimation ◽  
High Propensity ◽  
The Stability

Abstract Summary Proteins with highly similar tandem domains have shown an increased propensity for misfolding and aggregation. Several molecular explanations have been put forward, such as swapping of adjacent domains, but there is a lack of computational tools to systematically analyze them. We present the TAndem DOmain Swap Stability predictor (TADOSS), a method to computationally estimate the stability of tandem domain-swapped conformations from the structures of single domains, based on previous coarse-grained simulation studies. The tool is able to discriminate domains susceptible to domain swapping and to identify structural regions with high propensity to form hinge loops. TADOSS is a scalable method and suitable for large scale analyses. Availability and implementation Source code and documentation are freely available under an MIT license on GitHub at https://github.com/lafita/tadoss. Supplementary information Supplementary data are available at Bioinformatics online.

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