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  • Kyle, Jennifer E.Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA (author)

Interpreting the lipidome : bioinformatic approaches to embrace the complexity

  • Article/chapterEnglish2021

Publisher, publication year, extent ...

  • 2021-06-06
  • Springer-Verlag New York,2021
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:oru-92172
  • https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-92172URI
  • https://doi.org/10.1007/s11306-021-01802-6DOI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:for swepub-publicationtype

Notes

  • Funding Agencies:Pacific Northwest National Laboratory (PNNL)  United States Department of Energy (DOE)DE-AC05-76RLO1830Federal Ministry of Education & Research (BMBF) 
  • BACKGROUND: Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites.AIM OF REVIEW: To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome.KEY SCIENTIFIC CONCEPTS OF REVIEW: Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

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  • Aimo, LucilaSwiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland (author)
  • Bridge, Alan J.Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland (author)
  • Clair, GeremyBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA (author)
  • Fedorova, MariaInstitute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Center for Biotechnology and Biomedicine, Universität Leipzig, Leipzig, Germany (author)
  • Helms, J. BerndDepartment of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands (author)
  • Molenaar, Martijn R.Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany (author)
  • Ni, ZhixuInstitute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Center for Biotechnology and Biomedicine, Universität Leipzig, Leipzig, Germany (author)
  • Oresic, Matej,1967-Örebro universitet,Institutionen för medicinska vetenskaper,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland(Swepub:oru)moc (author)
  • Slenter, DeniseDepartment of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands (author)
  • Willighagen, EgonDepartment of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands (author)
  • Webb-Robertson, Bobbie-Jo M.Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA (author)
  • Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USASwiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland (creator_code:org_t)

Related titles

  • In:Metabolomics: Springer-Verlag New York17:61573-38821573-3890

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