Lipid Mini-On: Mining and ontology tool for enrichment analysis of lipidomic data

Lipid Mini-On: Mining and ontology tool for enrichment analysis of lipidomic data Abstract Summary Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g., LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user's database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g., lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g, human lung endothelial cells) and environmental (e.g., peat soil) samples. Availability Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/). Supplementary information Supplementary data are available at Bioinformatics online. This content is only available as a PDF. © The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

Lipid Mini-On: Mining and ontology tool for enrichment analysis of lipidomic data

Lipid Mini-On: Mining and ontology tool for enrichment analysis of lipidomic data

Summary: Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g., LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user’s database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g., lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g, human lung endothelial cells) and environmental (e.g., peat soil) samples. Availability: Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/). Contact: geremy.clair@pnnl.gov; charles.ansong@pnnl.gov; jennifer.kyle@pnnl.gov Supplementary Information: Supplementary data are available at Bioinformatics online. (Sud, et al., 2007) and other molecular characteristics (e.g. chain length and number of double bonds). As such, it can perform enrichment 1 Introduction analysis on any lipid based on the LipidMaps annotation scheme (e.g., The examination of lipidomics data for pattern recognition and PC(16:0/18:1)). This approach enables the enrichment of classes of biological interpretation remains highly manual. In addition, researchers lipids currently absent from databases or those yet to be discovered. with limited background in lipidomics (e.g.,...
Loading next page...
 
/lp/oxford-university-press/lipid-mini-on-mining-and-ontology-tool-for-enrichment-analysis-of-0qcY7yp2fk
Publisher
Oxford University Press
Copyright
© The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
ISSN
1367-4803
eISSN
1460-2059
D.O.I.
10.1093/bioinformatics/btz250
Publisher site
See Article on Publisher Site

Abstract

Abstract Summary Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g., LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user's database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g., lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g, human lung endothelial cells) and environmental (e.g., peat soil) samples. Availability Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/). Supplementary information Supplementary data are available at Bioinformatics online. This content is only available as a PDF. © The Author(s) (2019). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

BioinformaticsOxford University Press

Published: Oct 10, 17

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off