Assigning confidence to structural annotations from mass spectra with COSMIC

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but these libraries are vastly incomplete; in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. As biological interpretation relies on accurate structure annotations, the ability to assign confidence to such annotations is a key outstanding problem. We introduce the COSMIC workflow that combines structure database generation, in silico annotation, and a confidence score consisting of kernel density p-value estimation and a Support Vector Machine with enforced directionality of features. In evaluation, COSMIC annotates a substantial number of hits at small false discovery rates, and outperforms spectral library search for this purpose. To demonstrate that COSMIC can annotate structures never reported before, we annotated twelve novel bile acid conjugates; nine structures were confirmed by manual evaluation and two structures using synthetic standards. Second, we annotated and manually evaluated 315 molecular structures in human samples currently absent from the Human Metabolome Database. Third, we applied COSMIC to 17,400 experimental runs and annotated 1,715 structures with high confidence that were absent from spectral libraries.

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High-confidence structural annotation of metabolites absent from spectral libraries

Nature Biotechnology ◽

10.1038/s41587-021-01045-9 ◽

2021 ◽

Author(s):

Martin A. Hoffmann ◽

Louis-Félix Nothias ◽

Marcus Ludwig ◽

Markus Fleischauer ◽

Emily C. Gentry ◽

...

Keyword(s):

In Silico ◽

Confidence Score ◽

Molecular Structures ◽

Support Vector ◽

High Confidence ◽

Structural Annotation ◽

False Discovery Rates ◽

Value Estimation ◽

Structure Annotation ◽

Spectral Libraries

AbstractUntargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.

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A Response Surface Methodology (RSM) Approach for Optimizing the Attenuation of Human IgE-Reactivity to β-Lactoglobulin (β-Lg) by Hydrostatic High Pressure Processing

Foods ◽

10.3390/foods10081741 ◽

2021 ◽

Vol 10 (8) ◽

pp. 1741

Author(s):

Xin Sun ◽

Jialing Vivien Chua ◽

Quynh Anh Le ◽

Francisco Trujillo ◽

Mi-Hwa Oh ◽

...

Keyword(s):

Response Surface Methodology ◽

Optimal Condition ◽

Response Surface ◽

High Pressure Processing ◽

Molecular Structures ◽

Holding Time ◽

P Value ◽

Molecular Change ◽

Ige Reactivity ◽

Β Lactoglobulin

The response surface methodology (RSM) and central composite design (CCD) technique were used to optimize the three key process parameters (i.e., pressure, temperature and holding time) of the high-hydrostatic-pressure (HHP) processing either standalone or combined with moderate thermal processing to modulate molecular structures of β-lactoglobulin (β-Lg) and α-lactalbumin (α-La) with reduced human IgE-reactivity. The RSM model derived for HHP-induced molecular changes of β-Lg determined immunochemically showed that temperature (temp), pressure (p2) and the interaction between temperature and time (t) had statistically significant effects (p < 0.05). The optimal condition defined as minimum (β-Lg specific) IgG-binding derived from the model was 505 MPa at 56 °C with a holding time of 102 min (R2 of 0.81 and p-value of 0.01). The validation carried at the optimal condition and its surrounding region showed that the model to be underestimating the β-Lg structure modification. The molecular change of β-Lg was directly correlated with HHP-induced dimerization in this study, which followed a quadratic equation. The β-Lg dimers also resulted in the undetectable human IgE-binding.

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In-silico bonding schemes to encode chemical bonds involving sharing of electrons in molecular structures

Journal of Molecular Graphics and Modelling ◽

10.1016/j.jmgm.2016.03.010 ◽

2016 ◽

Vol 66 ◽

pp. 67-75 ◽

Cited By ~ 3

Author(s):

Sankar Punnaivanam ◽

Jerome Pastal Raj Sathiadhas ◽

Vinoth Panneerselvam

Keyword(s):

In Silico ◽

Molecular Structures ◽

Chemical Bonds

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Two-Step Urban Water Index (TSUWI): A New Technique for High-Resolution Mapping of Urban Surface Water

Remote Sensing ◽

10.3390/rs10111704 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1704 ◽

Cited By ~ 2

Author(s):

Wei Wu ◽

Qiangzi Li ◽

Yuan Zhang ◽

Xin Du ◽

Hongyan Wang

Keyword(s):

Surface Water ◽

High Resolution ◽

Urban Surface ◽

Mapping Technique ◽

Support Vector ◽

Svm Classifier ◽

P Value ◽

Urban Water ◽

Water Index ◽

Water Mapping

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 2.28%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.

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The Expression of miR-513c and miR-3163 was Downregulated in Tumor Tissues Compared with Margin Tissues of Patients With Breast Cancer

10.21203/rs.3.rs-28926/v1 ◽

2020 ◽

Author(s):

Soheila Delgir ◽

Khandan Ilkhani ◽

Asma Safi ◽

Farhad Seif ◽

Milad Bastami ◽

...

Keyword(s):

Breast Cancer ◽

Family History ◽

In Silico ◽

In Silico Analysis ◽

Glutamine Metabolism ◽

Expression Level ◽

P Value ◽

Biological Processes ◽

Tumor Tissues ◽

Silico Analysis

Abstract Background Breast cancer (BC) is the most common invasive cancer with different subtypes that its metabolism is unique compared with normal cells. Glutamine is considered a critical nutrition for tumor cell growth and therefore, targeting glutamine metabolism, especially Glutaminase, which catalyzed the conversion of glutamine to glutamate can be beneficial to design anti-cancer agents. Recently, evidence has shown that miRNAs with short length and single strand properties play a significant role in regulating the genes related to glutamine metabolism and may control the development of cancer.Methods Since, in-silico analysis confirmed that miR-513c and miR-3163 might be involved in glutamine metabolism, the expression level of these two miRNAs was evaluated in eighty BC tissues and margin tissues. The data were analyzed to evaluate the correlation between expression level of these miRNAs and patient’s characteristics such as abortion history, family history, and age. Furthermore, in-silico analysis was applied to predict the potential biological processes and molecular pathways of miR-513c and miR-3163 based on its gene targets.Results In-silico studies revealed the top categories of biological processes and pathways that play a critical role in cancer development were target genes for miR-513c and miR-3163. The current study showed that miR-513c (P-value = 0.02062 and fold change= -2.3801) and miR-3163 (P-value = 0.02034 and fold change= -2.3792) were downregulated in tumor tissues compared to margin tissues. Furthermore, the subgroup studies did not show any substantial relationship between expression levels of these two miRNAs and factors such as age, family history cancer, and abortion.Conclusion Based on our data, miR-513c and miR-3163 may be offered as a potential diagnosis and therapeutic targets for patients with BC.

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In Silico Prediction of Indonesian Herbs Compounds as Covid-19 Supportive Therapy using Support Vector Machine

10.1109/ic2ie53219.2021.9649383 ◽

2021 ◽

Author(s):

M. Dendy Darma ◽

M. Reza Faisal ◽

Irwan Budiman ◽

Rudy Herteno ◽

Juliyatin Putri Utami ◽

...

Keyword(s):

Support Vector Machine ◽

In Silico ◽

Supportive Therapy ◽

Support Vector ◽

In Silico Prediction

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Volatile organic compound breath signatures of children with cystic fibrosis by real-time SESI-HRMS

ERJ Open Research ◽

10.1183/23120541.00171-2019 ◽

2020 ◽

Vol 6 (1) ◽

pp. 00171-2019 ◽

Cited By ~ 2

Author(s):

Ronja Weber ◽

Naemi Haas ◽

Astghik Baghdasaryan ◽

Tobias Bruderer ◽

Demet Inci ◽

...

Keyword(s):

Cystic Fibrosis ◽

High Resolution ◽

Organic Compound ◽

Real Time ◽

Volatile Organic Compound ◽

Lung Damage ◽

Support Vector ◽

P Value ◽

Large Set ◽

Volatile Organic

Early pulmonary infection and inflammation result in irreversible lung damage and are major contributors to cystic fibrosis (CF)-related morbidity. An easy to apply and noninvasive assessment for the timely detection of disease-associated complications would be of high value. We aimed to detect volatile organic compound (VOC) breath signatures of children with CF by real-time secondary electrospray ionisation high-resolution mass spectrometry (SESI-HRMS).A total of 101 children, aged 4–18 years (CF=52; healthy controls=49) and comparable for sex, body mass index and lung function were included in this prospective cross-sectional study. Exhaled air was analysed by a SESI-source linked to a high-resolution time-of-flight mass spectrometer. Mass spectra ranging from m/z 50 to 500 were recorded.Out of 3468 m/z features, 171 were significantly different in children with CF (false discovery rate adjusted p-value of 0.05). The predictive ability (CF versus healthy) was assessed by using a support-vector machine classifier and showed an average accuracy (repeated cross-validation) of 72.1% (sensitivity of 77.2% and specificity of 67.7%).This is the first study to assess entire breath profiles of children with SESI-HRMS and to extract sets of VOCs that are associated with CF. We have detected a large set of exhaled molecules that are potentially related to CF, indicating that the molecular breath of children with CF is diverse and informative.

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