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

Clair, Gérémy; Reehl, Sarah; Stratton, Kelly G.; Monroe, Matthew E.; Tfaily, Malak M.; Ansong, Charles; Kyle, Jennifer

doi:10.1093/bioinformatics/btz250

Cited by 42 publications

(49 citation statements)

References 6 publications

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“…We have recently developed an R package called Rodin (https://github.com/PNNL-Comp-Mass-Spec/Rodin), to perform structural “lipid ontology” (LO) enrichment analysis. A web interface, Lipid Mini-On, was developed for non-R users (https://omicstools.pnnl.gov/shiny/lipid-mini-on/) (62). Briefly, this tool creates automatically LO bins based on the lipids naming and their inferred structure, and then it performs enrichment analysis using enrichment statistics to compare a query list to a Universe (Fisher exact test, EASE score, binomial test, or hypergeometric tests).…”

Section: Methodsmentioning

confidence: 99%

Multi-omics Signature of Candida auris , an Emerging and Multidrug-Resistant Pathogen

et al. 2019

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Candida auris is a recently described pathogenic fungus that is causing invasive outbreaks on all continents. The fungus is of high concern given the numbers of multidrug-resistant strains that have been isolated in distinct sites across the globe. The fact that its diagnosis is still problematic suggests that the spreading of the pathogen remains underestimated. Notably, the molecular mechanisms of virulence and antifungal resistance employed by this new species are largely unknown. In the present work, we compared two clinical isolates of C. auris with distinct drug susceptibility profiles and a Candida albicans reference strain using a multi-omics approach. Our results show that, despite the distinct drug resistance profile, both C. auris isolates appear to be very similar, albeit with a few notable differences. However, compared to C. albicans both C. auris isolates have major differences regarding their carbon utilization and downstream lipid and protein content, suggesting a multifactorial mechanism of drug resistance. The molecular profile displayed by C. auris helps to explain the antifungal resistance and virulence phenotypes of this new emerging pathogen. IMPORTANCE Candida auris was first described in Japan in 2009 and has now been the cause of significant outbreaks across the globe. The high number of isolates that are resistant to one or more antifungals, as well as the high mortality rates from patients with bloodstream infections, has attracted the attention of the medical mycology, infectious disease, and public health communities to this pathogenic fungus. In the current work, we performed a broad multi-omics approach on two clinical isolates isolated in New York, the most affected area in the United States and found that the omic profile of C. auris differs significantly from C. albicans. In addition to our insights into C. auris carbon utilization and lipid and protein content, we believe that the availability of these data will enhance our ability to combat this rapidly emerging pathogenic yeast.

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Section: Methodsmentioning

confidence: 99%

Multi-omics Signature of Candida auris , an Emerging and Multidrug-Resistant Pathogen

et al. 2019

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“…Similar features can be found in other freely available software suites. Lipid Mini-On 27 is graphical user interface based tool for enrichment analysis of lipidomic data. The underlying rodin R package can recognize LipidMaps-compliant lipid identifiers, and parse chain information.…”

Section: Methodsmentioning

confidence: 99%

liputils: a Python module to manage individual fatty acid moieties from complex lipids

Manzini

Busnelli

Colombo

et al. 2020

Sci Rep

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Lipidomic analyses address the problem of characterizing the lipid components of given cells, tissues and organisms by means of chromatographic separations coupled to high-resolution, tandem mass spectrometry analyses. A number of software tools have been developed to help in the daunting task of mass spectrometry signal processing and cleaning, peak analysis and compound identification, and a typical finished lipidomic dataset contains hundreds to thousands of individual molecular lipid species. To provide researchers without a specific technical expertise in mass spectrometry the possibility of broadening the exploration of lipidomic datasets, we have developed liputils, a Python module that specializes in the extraction of fatty acid moieties from individual molecular lipids. there is no prerequisite data format, as liputils extracts residues from RefMet-compliant textual identifiers and from annotations of other commercially available services. We provide three examples of realworld data processing with liputils, as well as a detailed protocol on how to readily process an existing dataset that can be followed with basic informatics skills. Lipidomics is the analysis of the large-scale identification and quantification of individual lipid species, which relies-at its finest-on the analytical technology of chromatographic separation coupled to mass spectrometry (MS) and dedicated software 1,2. Lipidomics is among the most juvenile-omic technologies, yet recent advances in MS, lipid biochemistry and software pipelines allow for high-throughput, unbiased analysis of diverse biomolecules. A typical workflow of a high-resolution, high-throughput lipidomics involves MS-based studies of the structure, composition, and quantity of lipids in biological systems-typically organs, cells, and bodily fluids. Analytes are then identified and quantified with software tools that can process MS data in an automated or semi-automated manner 3,4. Most of the tools that are available on dedicated portals, such as LIPID MAPS 5 , are designed to tackle the problems such as fragments recognition, lipids reconstruction from fragment analysis and artefactual signals removal 4. As of beginning of 2020, ~ 3,000 papers featuring "lipidomics" and "mass spectrometry" could be retrieved from Web of Science, showing an ever growing trend since 2003 6. A modern lipidomic analysis can contain hundreds of individual molecular lipid species, of the 43,645 annotated in databases such as LIPID MAPS, as of March 2020 5. Lipids-generically defined as water-insoluble molecules, or molecules soluble in organic solvents-have been grouped into eight categories: fatty acyls (9,374), glycerolipids (7,608), glycerophospholipids (9,918), sphingolipids (4,438), sterol lipids (2,828), prenol lipids (1,353), saccharolipids (1,316) and polyketides (6,810), further divided into sub-categories 7. In living organisms, they are used as energy reserve 8 , structural components of biological membranes, transporters, lipoprotein components 9,10 , signalling molecules 1...

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“…Following the fast progress in lipidomics over the last two decades, new tools are needed to get more insights into this field. An R-based, open-source tool called Lipid Mining and Ontology (Lipid Mini-On) was later developed, which can split individual lipids into multiple ontology classes with Lipid Ontology terms (similar to Gene Ontology) based on LIPID MAPS classification as well as other molecular characteristics for the enrichment analysis of lipids currently absent from databases [130]. LION, a new and freely accessible lipid ontology, is able to assign more than 50,000 lipid species to various biophysical, chemical, and cell biological processes [131].…”

Section: The Need For High-confidence Lipid Identification and Quantimentioning

confidence: 99%

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

et al. 2020

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Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

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

Cited by 42 publications

References 6 publications

Multi-omics Signature of Candida auris , an Emerging and Multidrug-Resistant Pathogen

Multi-omics Signature of Candida auris , an Emerging and Multidrug-Resistant Pathogen

liputils: a Python module to manage individual fatty acid moieties from complex lipids

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

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