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LipidHome: a database of theoretical lipids optimized for high throughput mass spectrometry lipidomics.

Foster JM, Moreno P, Fabregat A, Hermjakob H, Steinbeck C, Apweiler R, Wakelam MJ, Vizcaíno JA - PLoS ONE (2013)

Bottom Line: While having a seasoned community of wet lab scientists, lipidomics lies significantly behind proteomics in the adoption of data standards and other core bioinformatics concepts.This work aims to reduce the gap by developing an equivalent resource to UniProt called 'LipidHome', providing theoretically generated lipid molecules and useful metadata.The web application encompasses a browser for viewing lipid records and a 'tools' section where an MS1 search engine is currently implemented.

View Article: PubMed Central - PubMed

Affiliation: EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom. jfoster@ebi.ac.uk

ABSTRACT
Protein sequence databases are the pillar upon which modern proteomics is supported, representing a stable reference space of predicted and validated proteins. One example of such resources is UniProt, enriched with both expertly curated and automatic annotations. Taken largely for granted, similar mature resources such as UniProt are not available yet in some other "omics" fields, lipidomics being one of them. While having a seasoned community of wet lab scientists, lipidomics lies significantly behind proteomics in the adoption of data standards and other core bioinformatics concepts. This work aims to reduce the gap by developing an equivalent resource to UniProt called 'LipidHome', providing theoretically generated lipid molecules and useful metadata. Using the 'FASTLipid' Java library, a database was populated with theoretical lipids, generated from a set of community agreed upon chemical bounds. In parallel, a web application was developed to present the information and provide computational access via a web service. Designed specifically to accommodate high throughput mass spectrometry based approaches, lipids are organised into a hierarchy that reflects the variety in the structural resolution of lipid identifications. Additionally, cross-references to other lipid related resources and papers that cite specific lipids were used to annotate lipid records. The web application encompasses a browser for viewing lipid records and a 'tools' section where an MS1 search engine is currently implemented. LipidHome can be accessed at http://www.ebi.ac.uk/apweiler-srv/lipidhome.

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The structural hierarchy of lipid records.a)The structural hierarchy of lipid records in the Lipid Maps Structural Database (LMSD). The Lipid Maps classification system organises all lipids into “Categories”, “Main Classes” and “Sub Classes”. The lipid records are stored at the “Geometric Isomer” level where the total number of carbons, total number of double bonds, the position of double bonds and the stereochemistry of double bonds are defined for each fatty acid. The transparent lipid identification hierarchy levels are not supported by the LMSD. b)The structural hierarchy of lipid records in the LipidHome database. Similar to the LIPID MAPS classification system, lipids are organised into “Categories”, “Main Classes” and “Sub Classes”. Lipid records are stored at four levels. Each level relates to a typical type of identification from a high throughput mass spectrometry experiment. From structurally undefined “Species” typically identified from a single precursor ion mass, to structurally resolved “Isomer” level identifications.
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pone-0061951-g001: The structural hierarchy of lipid records.a)The structural hierarchy of lipid records in the Lipid Maps Structural Database (LMSD). The Lipid Maps classification system organises all lipids into “Categories”, “Main Classes” and “Sub Classes”. The lipid records are stored at the “Geometric Isomer” level where the total number of carbons, total number of double bonds, the position of double bonds and the stereochemistry of double bonds are defined for each fatty acid. The transparent lipid identification hierarchy levels are not supported by the LMSD. b)The structural hierarchy of lipid records in the LipidHome database. Similar to the LIPID MAPS classification system, lipids are organised into “Categories”, “Main Classes” and “Sub Classes”. Lipid records are stored at four levels. Each level relates to a typical type of identification from a high throughput mass spectrometry experiment. From structurally undefined “Species” typically identified from a single precursor ion mass, to structurally resolved “Isomer” level identifications.

Mentions: A common problem in the field of lipidomics is the mismatch between the lipid identifications reported in the literature and the MS-based evidence available to back up those identifications. The existing reference systems for lipid identifications store lipids usually at a very high structural resolution. As a consequence, there is a disparity between the structural resolution of data that the large majority of experimentalists in the field are producing and the reference records stored in the existing lipid databases. For instance, the LMSD structural hierarchy (Figure 1a) shows that the lipids can be stored as “Geometric Isomers”, where not only double bond positions are defined on the component fatty acids, but also the stereochemistry of those double bonds. In addition, considering the billions of physically feasible lipid “Geometric Isomers”, the LMSD has an extremely limited coverage of the chemical space at the structural resolution they support. Another existing issue in lipid databases is their inability to distinguish between the experimentally validated and the theoretically possible lipids. Again, in the case of the LMSD, lipid product ion mass spectra have recently been made available for a number of lipids but this information is not so obvious and easy to access. As a consequence, it is often assumed that all records are experimentally validated.


LipidHome: a database of theoretical lipids optimized for high throughput mass spectrometry lipidomics.

Foster JM, Moreno P, Fabregat A, Hermjakob H, Steinbeck C, Apweiler R, Wakelam MJ, Vizcaíno JA - PLoS ONE (2013)

The structural hierarchy of lipid records.a)The structural hierarchy of lipid records in the Lipid Maps Structural Database (LMSD). The Lipid Maps classification system organises all lipids into “Categories”, “Main Classes” and “Sub Classes”. The lipid records are stored at the “Geometric Isomer” level where the total number of carbons, total number of double bonds, the position of double bonds and the stereochemistry of double bonds are defined for each fatty acid. The transparent lipid identification hierarchy levels are not supported by the LMSD. b)The structural hierarchy of lipid records in the LipidHome database. Similar to the LIPID MAPS classification system, lipids are organised into “Categories”, “Main Classes” and “Sub Classes”. Lipid records are stored at four levels. Each level relates to a typical type of identification from a high throughput mass spectrometry experiment. From structurally undefined “Species” typically identified from a single precursor ion mass, to structurally resolved “Isomer” level identifications.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3646891&req=5

pone-0061951-g001: The structural hierarchy of lipid records.a)The structural hierarchy of lipid records in the Lipid Maps Structural Database (LMSD). The Lipid Maps classification system organises all lipids into “Categories”, “Main Classes” and “Sub Classes”. The lipid records are stored at the “Geometric Isomer” level where the total number of carbons, total number of double bonds, the position of double bonds and the stereochemistry of double bonds are defined for each fatty acid. The transparent lipid identification hierarchy levels are not supported by the LMSD. b)The structural hierarchy of lipid records in the LipidHome database. Similar to the LIPID MAPS classification system, lipids are organised into “Categories”, “Main Classes” and “Sub Classes”. Lipid records are stored at four levels. Each level relates to a typical type of identification from a high throughput mass spectrometry experiment. From structurally undefined “Species” typically identified from a single precursor ion mass, to structurally resolved “Isomer” level identifications.
Mentions: A common problem in the field of lipidomics is the mismatch between the lipid identifications reported in the literature and the MS-based evidence available to back up those identifications. The existing reference systems for lipid identifications store lipids usually at a very high structural resolution. As a consequence, there is a disparity between the structural resolution of data that the large majority of experimentalists in the field are producing and the reference records stored in the existing lipid databases. For instance, the LMSD structural hierarchy (Figure 1a) shows that the lipids can be stored as “Geometric Isomers”, where not only double bond positions are defined on the component fatty acids, but also the stereochemistry of those double bonds. In addition, considering the billions of physically feasible lipid “Geometric Isomers”, the LMSD has an extremely limited coverage of the chemical space at the structural resolution they support. Another existing issue in lipid databases is their inability to distinguish between the experimentally validated and the theoretically possible lipids. Again, in the case of the LMSD, lipid product ion mass spectra have recently been made available for a number of lipids but this information is not so obvious and easy to access. As a consequence, it is often assumed that all records are experimentally validated.

Bottom Line: While having a seasoned community of wet lab scientists, lipidomics lies significantly behind proteomics in the adoption of data standards and other core bioinformatics concepts.This work aims to reduce the gap by developing an equivalent resource to UniProt called 'LipidHome', providing theoretically generated lipid molecules and useful metadata.The web application encompasses a browser for viewing lipid records and a 'tools' section where an MS1 search engine is currently implemented.

View Article: PubMed Central - PubMed

Affiliation: EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom. jfoster@ebi.ac.uk

ABSTRACT
Protein sequence databases are the pillar upon which modern proteomics is supported, representing a stable reference space of predicted and validated proteins. One example of such resources is UniProt, enriched with both expertly curated and automatic annotations. Taken largely for granted, similar mature resources such as UniProt are not available yet in some other "omics" fields, lipidomics being one of them. While having a seasoned community of wet lab scientists, lipidomics lies significantly behind proteomics in the adoption of data standards and other core bioinformatics concepts. This work aims to reduce the gap by developing an equivalent resource to UniProt called 'LipidHome', providing theoretically generated lipid molecules and useful metadata. Using the 'FASTLipid' Java library, a database was populated with theoretical lipids, generated from a set of community agreed upon chemical bounds. In parallel, a web application was developed to present the information and provide computational access via a web service. Designed specifically to accommodate high throughput mass spectrometry based approaches, lipids are organised into a hierarchy that reflects the variety in the structural resolution of lipid identifications. Additionally, cross-references to other lipid related resources and papers that cite specific lipids were used to annotate lipid records. The web application encompasses a browser for viewing lipid records and a 'tools' section where an MS1 search engine is currently implemented. LipidHome can be accessed at http://www.ebi.ac.uk/apweiler-srv/lipidhome.

Show MeSH
Related in: MedlinePlus