Research Article
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Year 2020, Volume: 62 Issue: 1, 100 - 114, 30.06.2020
https://doi.org/10.33769/aupse.673996

Abstract

References

  • Kim, M., Rai, N., Zorraquino, V., Tagkopoulos I., Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli, Nat Commun. 7 (2016), 13090-96.
  • Wilson, B.J., Nicholls, S.G., The Human Genome Project, and recent advances in personalized genomics, Risk Manag Healthc Policy, 8 (2015), 9-20.
  • Du, F., Zou, Y., Hu, Q., Zhang, H., Ye, D., Comparative transcriptomic analysis reveals molecular processes involved in pileus morphogenesis in Pleurotus eryngii under different light conditions, Genomics, 2019.
  • Ahmed, F., Kumar, G., Soliman, F.M., Adly, M.A., Soliman, H.A.M., El-Matbouli, M., et al., Proteomics for understanding pathogenesis, immune modulation and host pathogen interactions in aquaculture, Comp Biochem Physiol Part D Genomics Proteomics, 32 (2019), 100625.
  • Jia, H., Wang L, Li, J., Sun, P., Lu, M., Hu J., Comparative metabolomics analysis reveals different metabolic responses to drought in tolerant and susceptible poplar species, Physiol Plant, 2019.
  • Lydic, TA, Goo, Y.H., Lipidomics unveils the complexity of the lipidome in metabolic diseases, Clin Transl Med., 7 (2018), 4-17.
  • Rupasinghe, T.W., Lipidomics: extraction protocols for biological matrices. Methods Mol Biol. 1055 (2013) 71-80.
  • Hu T, Zhang, JL., Mass-spectrometry-based lipidomics. J Sep Sci. 41 (2018) 351-72.
  • Hsu FF. Mass spectrometry-based shotgun lipidomics-a critical review from the technical point of view, Anal Bioanal Chem., 410 (2018), 6387-409.
  • Loizides-Mangold, U., On the future of mass-spectrometry-based lipidomics, FEBS J., 280 (2013), 2817-29.
  • Kyle JE, Crowell KL, Casey CP, Fujimoto GM, Kim S, Dautel SE, et al. LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data. Bioinformatics. 33 (2017) 1744-6.
  • Zhou Z, Shen X, Chen X, Tu J, Xiong X, Zhu ZJ. LipidIMMS Analyzer: integrating multi-dimensional information to support lipid identification in ion mobility-mass spectrometry based lipidomics. Bioinformatics. 35 (2019) 698-700.
  • Zhou Z, Tu J, Xiong X, Shen X, Zhu ZJ. LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility-Mass Spectrometry-Based Lipidomics. Anal Chem. 89 (2017) 9559-66.
  • Yeo HC, Chen S, Ho YS, Lee DY. An LC-MS-based lipidomics pre-processing framework underpins rapid hypothesis generation towards CHO systems biotechnology. Metabolomics. 14 (2018) 98.
  • Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 12 (2015) 523-6.
  • Tsugawa H, Ikeda K, Tanaka W, Senoo Y, Arita M, Arita M. Comprehensive identification of sphingolipid species by in silico retention time and tandem mass spectral library. J Cheminform. (2017) 19.
  • Klatt S, Brammananth R, O'Callaghan S, Kouremenos KA, Tull D, Crellin PK, et al. Identification of novel lipid modifications and intermembrane dynamics in Corynebacterium glutamicum using high-resolution mass spectrometry, J Lipid Res. 59 (2018) 59 1190-204.
  • Pluskal T, Castillo S, Villar-Briones A, Oresic M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data, BMC Bioinformatics, 11 (2010) 395.

EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS

Year 2020, Volume: 62 Issue: 1, 100 - 114, 30.06.2020
https://doi.org/10.33769/aupse.673996

Abstract

Lipidomics covers analysis of all lipid species in an organism. Lipid metabolism is one of the key factors to understand cellular processes at molecular level. Lipidomics has been used to find diagnostic and prognostic biomarkers in clinical sample (plasma, serum, urine, tissue). Today mass spectroscopy based approach dominates lipidomics and several computational platforms have been developed to process raw mass spectra data. However, there is no routine procedure for data processing in lipidomics. In present work, two different bioinformatics platforms, which are MS-DIAL and MZmine2, was compared for lipidomics analysis of plasma sample. Peak detection, identification and quantification parameters were investigated to understand advantages and disadvantages. In peak detection process, it was observed that MZmine2 detected more peak than MS-DIAL at same threshold level. In identification process, Lipidmaps database was used for identification. MZmine2 identifies more lipid than MS-DIAL. Semi-quantification is very important to find differentially expressed lipid species and biomarkers in clinical studies. MS-DIAL and MZmine2 calculated normalized peak intensities and results were compared to understand reproducibility. Average relative standard deviation of all peaks was calculated and results showed that MS-DIAL gives more reproducible results than MZmine2. In conclusion, MZmine2 and MS-DIAL could be used in clinical lipidomics studies.

References

  • Kim, M., Rai, N., Zorraquino, V., Tagkopoulos I., Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli, Nat Commun. 7 (2016), 13090-96.
  • Wilson, B.J., Nicholls, S.G., The Human Genome Project, and recent advances in personalized genomics, Risk Manag Healthc Policy, 8 (2015), 9-20.
  • Du, F., Zou, Y., Hu, Q., Zhang, H., Ye, D., Comparative transcriptomic analysis reveals molecular processes involved in pileus morphogenesis in Pleurotus eryngii under different light conditions, Genomics, 2019.
  • Ahmed, F., Kumar, G., Soliman, F.M., Adly, M.A., Soliman, H.A.M., El-Matbouli, M., et al., Proteomics for understanding pathogenesis, immune modulation and host pathogen interactions in aquaculture, Comp Biochem Physiol Part D Genomics Proteomics, 32 (2019), 100625.
  • Jia, H., Wang L, Li, J., Sun, P., Lu, M., Hu J., Comparative metabolomics analysis reveals different metabolic responses to drought in tolerant and susceptible poplar species, Physiol Plant, 2019.
  • Lydic, TA, Goo, Y.H., Lipidomics unveils the complexity of the lipidome in metabolic diseases, Clin Transl Med., 7 (2018), 4-17.
  • Rupasinghe, T.W., Lipidomics: extraction protocols for biological matrices. Methods Mol Biol. 1055 (2013) 71-80.
  • Hu T, Zhang, JL., Mass-spectrometry-based lipidomics. J Sep Sci. 41 (2018) 351-72.
  • Hsu FF. Mass spectrometry-based shotgun lipidomics-a critical review from the technical point of view, Anal Bioanal Chem., 410 (2018), 6387-409.
  • Loizides-Mangold, U., On the future of mass-spectrometry-based lipidomics, FEBS J., 280 (2013), 2817-29.
  • Kyle JE, Crowell KL, Casey CP, Fujimoto GM, Kim S, Dautel SE, et al. LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data. Bioinformatics. 33 (2017) 1744-6.
  • Zhou Z, Shen X, Chen X, Tu J, Xiong X, Zhu ZJ. LipidIMMS Analyzer: integrating multi-dimensional information to support lipid identification in ion mobility-mass spectrometry based lipidomics. Bioinformatics. 35 (2019) 698-700.
  • Zhou Z, Tu J, Xiong X, Shen X, Zhu ZJ. LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility-Mass Spectrometry-Based Lipidomics. Anal Chem. 89 (2017) 9559-66.
  • Yeo HC, Chen S, Ho YS, Lee DY. An LC-MS-based lipidomics pre-processing framework underpins rapid hypothesis generation towards CHO systems biotechnology. Metabolomics. 14 (2018) 98.
  • Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 12 (2015) 523-6.
  • Tsugawa H, Ikeda K, Tanaka W, Senoo Y, Arita M, Arita M. Comprehensive identification of sphingolipid species by in silico retention time and tandem mass spectral library. J Cheminform. (2017) 19.
  • Klatt S, Brammananth R, O'Callaghan S, Kouremenos KA, Tull D, Crellin PK, et al. Identification of novel lipid modifications and intermembrane dynamics in Corynebacterium glutamicum using high-resolution mass spectrometry, J Lipid Res. 59 (2018) 59 1190-204.
  • Pluskal T, Castillo S, Villar-Briones A, Oresic M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data, BMC Bioinformatics, 11 (2010) 395.
There are 18 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Engin Koçak 0000-0002-1076-1300

Publication Date June 30, 2020
Submission Date January 13, 2020
Acceptance Date May 7, 2020
Published in Issue Year 2020 Volume: 62 Issue: 1

Cite

APA Koçak, E. (2020). EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 62(1), 100-114. https://doi.org/10.33769/aupse.673996
AMA Koçak E. EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. June 2020;62(1):100-114. doi:10.33769/aupse.673996
Chicago Koçak, Engin. “EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62, no. 1 (June 2020): 100-114. https://doi.org/10.33769/aupse.673996.
EndNote Koçak E (June 1, 2020) EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62 1 100–114.
IEEE E. Koçak, “EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 62, no. 1, pp. 100–114, 2020, doi: 10.33769/aupse.673996.
ISNAD Koçak, Engin. “EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 62/1 (June 2020), 100-114. https://doi.org/10.33769/aupse.673996.
JAMA Koçak E. EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2020;62:100–114.
MLA Koçak, Engin. “EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 62, no. 1, 2020, pp. 100-14, doi:10.33769/aupse.673996.
Vancouver Koçak E. EVALUATION OF MS-DIAL AND MZMINE2 SOFTWARES FOR CLINICAL LIPIDOMICS ANALYSIS. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2020;62(1):100-14.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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