Signed distance correlation (SiDCo): an online implementation of distance correlation and partial distance correlation for data-driven network analysis

MOTIVATION

There is a need for easily accessible implementations that measure the strength of both linear and non-linear relationships between metabolites in biological systems as an approach for data-driven network development. While multiple tools implement linear Pearson and Spearman methods, there are no such tools that assess distance correlation.

RESULTS

We present here SIgned Distance COrrelation (SiDCo). SiDCo is a GUI-platform for calculation of distance correlation in omics data, measuring linear and non-linear dependences between variables, as well as correlation between vectors of different lengths, e.g., different sample sizes. By combining the sign of the overall trend from Pearson's correlation with distance correlation values, we further provide a novel signed distance correlation of particular use in metabolomic and lipidomic analyses. Distance correlations can be selected as one-to-one or one-to-all correlations, showing relationships between each feature and all other features one at a time or in combination. Additionally, we implement partial distance correlation, calculated using the Gaussian Graphical model approach adapted to distance covariance. Our platform provides an easy-to-use software implementation that can be applied to the investigation of any dataset.

AVAILABILITY

The SiDCo software application is freely available at https://complimet.ca/sidco.

SUPPLEMENTARY INFORMATION

Supplementary help pages are provided at https://complimet.ca/sidco. Supplementary Material shows an example of an application of SiDCo in metabolomics.

METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance

MOTIVATION

Class imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms.

RESULTS

METAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the user's choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN) or Random Over-Sampling Examples (ROSE). To present the effect of over-sampling on the data META-BOA further displays both principal component analysis and t-distributed stochastic neighbor embedding visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their further analyses.

AVAILABILITY AND IMPLEMENTATION

META-BOA is available at https://complimet.ca/meta-boa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Dementia with Lewy bodies post-mortem brains reveal differentially methylated CpG sites with biomarker potential

Dementia with Lewy bodies (DLB) is a common form of dementia with known genetic and environmental interactions. However, the underlying epigenetic mechanisms which reflect these gene-environment interactions are poorly studied. Herein, we measure genome-wide DNA methylation profiles of post-mortem brain tissue (Broadmann area 7) from 15 pathologically confirmed DLB brains and compare them with 16 cognitively normal controls using Illumina MethylationEPIC arrays. We identify 17 significantly differentially methylated CpGs (DMCs) and 17 differentially methylated regions (DMRs) between the groups. The DMCs are mainly located at the CpG islands, promoter and first exon regions. Genes associated with the DMCs are linked to "Parkinson's disease" and "metabolic pathway", as well as the diseases of "severe intellectual disability" and "mood disorders". Overall, our study highlights previously unreported DMCs offering insights into DLB pathogenesis with the possibility that some of these could be used as biomarkers of DLB in the future.

Analysis and Simulation of Glioblastoma Cell Lines-Derived Extracellular Vesicles Metabolome

Glioblastoma (GBM) is one of the most aggressive cancers of the central nervous system. Despite current advances in non-invasive imaging and the advent of novel therapeutic modalities, patient survival remains very low. There is a critical need for the development of effective biomarkers for GBM diagnosis and therapeutic monitoring. Extracellular vesicles (EVs) produced by GBM tumors have been shown to play an important role in cellular communication and modulation of the tumor microenvironment. As GBM-derived EVs contain specific "molecular signatures" of their parental cells and are able to transmigrate across the blood-brain barrier into biofluids such as the blood and cerebrospinal fluid (CSF), they are considered as a valuable source of potential diagnostic biomarkers. Given the relatively harsh extracellular environment of blood and CSF, EVs have to endure and adapt to different conditions. The ability of EVs to adjust and function depends on their lipid bilayer, metabolic content and enzymes and transport proteins. The knowledge of EVs metabolic characteristics and adaptability is essential for their utilization as diagnostic and therapeutic tools. The main aim of this study was to determine the metabolome of small EVs or exosomes derived from different GBM cells and compare to the metabolic profile of their parental cells using NMR spectroscopy. In addition, a possible flux of metabolic processes in GBM-derived EVs was simulated using constraint-based modeling from published proteomics information. Our results showed a clear difference between the metabolic profiles of GBM cells, EVs and media. Machine learning analysis of EV metabolomics, as well as flux simulation, supports the notion of active metabolism within EVs, including enzymatic reactions and the transfer of metabolites through the EV membrane. These results are discussed in the context of novel GBM diagnostics and therapeutic monitoring.

Small head-to-tail macrocyclic α-peptoids

A convenient and efficient methodology for the head-to-tail macrocyclization of small 3-mer, 4-mer, and 5-mer α-peptoid acids (9-, 12-, and 15-atom N-substituted glycine oligomers) is described. The cyclic trimer has a ccc amide sequence in the crystal structure, whereas the tetramer has ctct and the pentamer has ttccc stereochemistry. NMR analysis reveals rigid structures in solution. These synthetic macrocycles may prove useful in medicinal and materials applications.

Dihydropyrimidinones containing boronic acids

The addition of formylphenylboronic acid derivatives to urea and ethyl acetoacetate proceeds in the absence of an additional Lewis acid catalyst to give the corresponding dihydropyrimidinones (Biginelli products) in good yields. Novel boron-containing dihydropyrimidinones have been investigated for their ability to act as anticancer agents against the breast cancer cell line MCF7.Key words: anticancer, Biginelli compounds, boronic acids, breast cancer, dihydropyrimidinones.