Evidence map›Paper›PMID 30774892›Full record

ArticleChemical science2019

Collision cross section compendium to annotate and predict multi-omic compound identities.

Jaqueline A Picache, Bailey S Rose, Andrzej Balinski, Katrina L Leaptrot, Stacy D Sherrod, Jody C May, John A McLean

Abstract read
In one paragraph

Article in Chemical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 136 papers.

0numbers the graph read from it
0cells of the map it votes in
136citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

136 citing papers in PubMed.

  1. Article
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  8. Mass Spectrometry-Based Spatial Imaging of the Cochlea.Journal of the American Society for Mass Spectrometry · 2026
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  19. Article
  20. Rapid Annotation Strategy forJournal of the American Society for Mass Spectrometry · 2025
    Article

76 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jaqueline A PicacheDepartment of Chemistry , Center for Innovative Technology , Vanderbilt Institute of Chemical Biology , Vanderbilt Institute for Integrative Biosystems Research and Education , Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , USA . Email: john.a.mclean@vanderbilt.edu.ORCID 0000-0002-1948-9243
Bailey S RoseDepartment of Chemistry , Center for Innovative Technology , Vanderbilt Institute of Chemical Biology , Vanderbilt Institute for Integrative Biosystems Research and Education , Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , USA . Email: john.a.mclean@vanderbilt.edu.ORCID 0000-0002-5900-7288
Andrzej BalinskiDepartment of Chemistry , Center for Innovative Technology , Vanderbilt Institute of Chemical Biology , Vanderbilt Institute for Integrative Biosystems Research and Education , Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , USA . Email: john.a.mclean@vanderbilt.edu.ORCID 0000-0001-5008-5338
Katrina L LeaptrotDepartment of Chemistry , Center for Innovative Technology , Vanderbilt Institute of Chemical Biology , Vanderbilt Institute for Integrative Biosystems Research and Education , Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , USA . Email: john.a.mclean@vanderbilt.edu.ORCID 0000-0002-9266-145X
Stacy D SherrodDepartment of Chemistry , Center for Innovative Technology , Vanderbilt Institute of Chemical Biology , Vanderbilt Institute for Integrative Biosystems Research and Education , Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , USA . Email: john.a.mclean@vanderbilt.edu.ORCID 0000-0002-2346-230X
Jody C MayDepartment of Chemistry , Center for Innovative Technology , Vanderbilt Institute of Chemical Biology , Vanderbilt Institute for Integrative Biosystems Research and Education , Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , USA . Email: john.a.mclean@vanderbilt.edu.ORCID 0000-0003-4871-5024
John A McLeanDepartment of Chemistry , Center for Innovative Technology , Vanderbilt Institute of Chemical Biology , Vanderbilt Institute for Integrative Biosystems Research and Education , Vanderbilt-Ingram Cancer Center , Vanderbilt University , Nashville , Tennessee 37235 , USA . Email: john.a.mclean@vanderbilt.edu.ORCID 0000-0001-8918-6419

Funding

Chemistry-Biology Interface Training GrantT32GM065086 · NIGMS · VANDERBILT UNIVERSITY · PI BACHMANN, BRIAN O, SULIKOWSKI, GARY ALLEN · 2002 to 2022
$7.0M
New Methodologies for Accelerating Natural Product DiscoveryR01GM092218 · NIGMS · VANDERBILT UNIVERSITY · PI BACHMANN, BRIAN O, MCLEAN, JOHN ALAN · 2010 to 2018
$2.8M
Ion mobility based informatics and visualization strategies in support of metabolomicsR03CA222452 · NCI · VANDERBILT UNIVERSITY · PI MCLEAN, JOHN ALAN · 2017 to 2017
$152k
NCI NIH HHS R03 CA222452NIGMS NIH HHS R01 GM092218NIGMS NIH HHS T32 GM065086
6 · The paper itself

Abstract

Ion mobility mass spectrometry (IM-MS) expands the analyte coverage of existing multi-omic workflows by providing an additional separation dimension as well as a parameter for characterization and identification of molecules - the collision cross section (CCS). This work presents a large, Unified CCS compendium of >3800 experimentally acquired CCS values obtained from traceable molecular standards and measured with drift tube ion mobility-mass spectrometers. An interactive visualization of this compendium along with data analytic tools have been made openly accessible. Represented in the compendium are 14 structurally-based chemical super classes, consisting of a total of 80 classes and 157 subclasses. Using this large data set, regression fitting and predictive statistics have been performed to describe mass-CCS correlations specific to each chemical ontology. These structural trends provide a rapid and effective filtering method in the traditional untargeted workflow for identification of unknown biochemical species. The utility of the approach is illustrated by an application to metabolites in human serum, quantified trends of which were used to assess the probability of an unknown compound belonging to a given class. CCS-based filtering narrowed the chemical search space by 60% while increasing the confidence in the remaining isomeric identifications from a single class, thus demonstrating the value of integrating predictive analyses into untargeted experiments to assist in identification workflows. The predictive abilities of this compendium will improve in specificity and expand to more chemical classes as additional data from the IM-MS community is contributed. Instructions for data submission to the compendium and criteria for inclusion are provided.

Identifiers

PMID30774892
PMCPMC6349024

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.