Data analysis methods for defining biomarkers from omics data

Li, Chao; Gao, Zhenbo; Su, Benzhe; Xu, Guowang; Lin, Xiaohui

doi:10.1007/s00216-021-03813-7

Cited by 23 publications

(12 citation statements)

References 119 publications

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“…This is also reflected by the low diagnostic sensitivity of single biomarkers like blood glucose and HbA 1c in the context of IGT detection. Secondly, combinational markers are much more robust than a single marker or a pattern of only a few biomarkers ( 72 , 73 ). Thirdly, the metabolites described here can be analyzed in a single LC-MS run.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes

Zhang

Hoene

et al. 2022

Front. Endocrinol.

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Aims/HypothesisLarge-scale prediabetes screening is still a challenge since fasting blood glucose and HbA1c as the long-standing, recommended analytes have only moderate diagnostic sensitivity, and the practicability of the oral glucose tolerance test for population-based strategies is limited. To tackle this issue and to identify reliable diagnostic patterns, we developed an innovative metabolomics-based strategy deviating from common concepts by employing urine instead of blood samples, searching for sex-specific biomarkers, and focusing on modified metabolites.MethodsNon-targeted, modification group-assisted metabolomics by liquid chromatography–mass spectrometry (LC-MS) was applied to second morning urine samples of 340 individuals from a prediabetes cohort. Normal (n = 208) and impaired glucose-tolerant (IGT; n = 132) individuals, matched for age and BMI, were randomly divided in discovery and validation cohorts. ReliefF, a feature selection algorithm, was used to extract sex-specific diagnostic patterns of modified metabolites for the detection of IGT. The diagnostic performance was compared with conventional screening parameters fasting plasma glucose (FPG), HbA1c, and fasting insulin.ResultsFemale- and male-specific diagnostic patterns were identified in urine. Only three biomarkers were identical in both. The patterns showed better AUC and diagnostic sensitivity for prediabetes screening of IGT than FPG, HbA1c, insulin, or a combination of FPG and HbA1c. The AUC of the male-specific pattern in the validation cohort was 0.889 with a diagnostic sensitivity of 92.6% and increased to an AUC of 0.977 in combination with HbA1c. In comparison, the AUCs of FPG, HbA1c, and insulin alone reached 0.573, 0.668, and 0.571, respectively. Validation of the diagnostic pattern of female subjects showed an AUC of 0.722, which still exceeded the AUCs of FPG, HbA1c, and insulin (0.595, 0.604, and 0.634, respectively). Modified metabolites in the urinary patterns include advanced glycation end products (pentosidine-glucuronide and glutamyl-lysine-sulfate) and microbiota-associated compounds (indoxyl sulfate and dihydroxyphenyl-gamma-valerolactone-glucuronide).Conclusions/InterpretationOur results demonstrate that the sex-specific search for diagnostic metabolite biomarkers can be superior to common metabolomics strategies. The diagnostic performance for IGT detection was significantly better than routinely applied blood parameters. Together with recently developed fully automatic LC-MS systems, this opens up future perspectives for the application of sex-specific diagnostic patterns for prediabetes screening in urine.

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Section: Discussionmentioning

confidence: 99%

Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes

Zhang

Hoene

et al. 2022

Front. Endocrinol.

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“…Data handling is a vital component of analyzing raw data from omics experiments for their correct biological interpretation. Data handling must address issues related to data filtering, imputation, transformation, normalization, quality control, and scaling (Li et al, 2022). Several algorithms and pipelines are now available for the analysis of various omics data, including transcriptomics (Figure 3), proteomics (Figure 4), and metagenomics (Figure 4).…”

Section: Analysis Of Omics Datamentioning

confidence: 99%

Applications of Omics Technology for Livestock Selection and Improvement

et al. 2022

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Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals’ actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

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“…Finally, the available tools can be categorized on the basis of the study objectives, e.g. referenced hereafter as description, selection and prediction [ 17 – 20 ]. In addition to the various approaches of data integration, it should also be recognized that data preprocessing and preliminary tests are essential for a successful implementation of data integration.…”

Section: Introductionmentioning

confidence: 99%

Genomic data integration tutorial, a plant case study

Mardoc,

Sow,

Déjean

et al. 2024

BMC Genomics

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Background The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts. Results To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration. Conclusion The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar, and allowing the selection of master drivers in genomic data variation and interplay.

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Data analysis methods for defining biomarkers from omics data

Cited by 23 publications

References 119 publications

Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes

Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes

Applications of Omics Technology for Livestock Selection and Improvement

Genomic data integration tutorial, a plant case study

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