A Comprehensive Database and Analysis Framework To Incorporate Multiscale Data Types and Enable Integrated Analysis of Bioactive Polyphenols

Gene expression profiling has provided insights into different cancer types and revealed tissue-specific expression signatures. Alterations in microRNA expression contribute to the pathogenesis of many types of human diseases. Few studies have integrated all levels of gene expression, miRNA and methylation to uncover correlations between these data types. We performed an integrated profiling to discover instances of miRNAs associated with a gene expression and DNA methylation signature across multiple cancer types. Using data from The Cancer Genome Atlas (TCGA), we revealed a concordant gene expression and methylation signature associated with the microRNA hsa-miR-142 across the same samples. In all cancer types examined, we found a signature of co-expression of a gene set R and methylated sites M, which correlate positively (M+) or negatively (M–) with the expression of hsa-miR-142. The set R consistently contains many genes, such as TRAF3IP3, NCKAP1L, CD53, LAPTM5, PTPRC, EVI2B, DOCK2, LCP2, CYBB and FYB. The signature is preserved across glioblastoma, ovarian, breast, colon, kidney, lung, uterine and rectum cancer. There is 28% overlap of methylation sites in M between glioblastoma (GBM) and ovarian cancer. There is 60% overlap of genes in R between GBM and ovarian ( P = 1.3e−-11). Most of the genes in R are known to be expressed in lymphocytes and haematopoietic stem cells, while M reflects membrane proteins involved in cell-cell adhesion functions. We speculate that the hsa-miR-142 associated signature may signal haematopoietic-specific processes and an accumulation of methylation events triggering a progressive loss of cell-cell adhesion. We also observed that GBM samples belonging to the proneural subtype tend to have underexpressed hsa-miR-142 and R genes, hypomethylated M+ and hypermethylated M–, while the mesenchymal samples have the opposite profile.

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RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data

Briefings in Bioinformatics ◽

10.1093/bib/bbz142 ◽

2019 ◽

Vol 21 (6) ◽

pp. 2167-2174 ◽

Cited By ~ 1

Author(s):

Qun Dong ◽

Feng Li ◽

Yanjun Xu ◽

Jing Xiao ◽

Yingqi Xu ◽

...

Keyword(s):

Large Scale ◽

Drug Sensitivity ◽

Integrated Analysis ◽

Omics Data ◽

Next Generation Sequencing Technology ◽

Rna Molecules ◽

Comprehensive Database ◽

Number Variation ◽

Association Data ◽

Search Facility

Abstract Drug sensitivity has always been at the core of individualized cancer chemotherapy. However, we have been overwhelmed by large-scale pharmacogenomic data in the era of next-generation sequencing technology, which makes it increasingly challenging for researchers, especially those without bioinformatic experience, to perform data integration, exploration and analysis. To bridge this gap, we developed RNAactDrug, a comprehensive database of RNAs associated with drug sensitivity from multi-omics data, which allows users to explore drug sensitivity and RNA molecule associations directly. It provides association data between drug sensitivity and RNA molecules including mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) at four molecular levels (expression, copy number variation, mutation and methylation) from integrated analysis of three large-scale pharmacogenomic databases (GDSC, CellMiner and CCLE). RNAactDrug currently stores more than 4 924 200 associations of RNA molecules and drug sensitivity at four molecular levels covering more than 19 770 mRNAs, 11 119 lncRNAs, 438 miRNAs and 4155 drugs. A user-friendly interface enriched with various browsing sections augmented with advance search facility for querying the database is offered for users retrieving. RNAactDrug provides a comprehensive resource for RNA molecules acting in drug sensitivity, and it could be used to prioritize drug sensitivity–related RNA molecules, further promoting the identification of clinically actionable biomarkers in drug sensitivity and drug development more cost-efficiently by making this knowledge accessible to both basic researchers and clinical practitioners. Database URL: http://bio-bigdata.hrbmu.edu.cn/RNAactDrug.

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Feature Selection using a Random Forests Classifier for the Integrated Analysis of Multiple Data Types

2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology ◽

10.1109/cibcb.2006.330987 ◽

2006 ◽

Cited By ~ 25

Author(s):

David M. Reif ◽

Alison A. Motsinger ◽

Brett A. McKinney ◽

James E. Crowe ◽

Jason H. Moore

Keyword(s):

Feature Selection ◽

Random Forests ◽

Integrated Analysis ◽

Data Types ◽

Multiple Data

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Integrated analysis of multimodal single-cell data

10.1101/2020.10.12.335331 ◽

2020 ◽

Cited By ~ 3

Author(s):

Yuhan Hao ◽

Stephanie Hao ◽

Erica Andersen-Nissen ◽

William M. Mauck ◽

Shiwei Zheng ◽

...

Keyword(s):

Single Cell ◽

Nearest Neighbor ◽

White Blood Cells ◽

Integrative Analysis ◽

Integrated Analysis ◽

Data Types ◽

Multiple Modalities ◽

Multimodal Analysis ◽

Multiple Data ◽

Definition Of

AbstractThe simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity.AvailabilityInstallation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

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A generic multivariate framework for the integration of microbiome longitudinal studies with other data types

10.1101/585802 ◽

2019 ◽

Cited By ~ 2

Author(s):

Antoine Bodein ◽

Olivier Chapleur ◽

Arnaud Droit ◽

Kim-Anh Lê Cao

Keyword(s):

Microbial Communities ◽

Time Course ◽

Molecular Mechanisms ◽

Smoothing Splines ◽

Individual Variability ◽

Integrated Analysis ◽

Data Types ◽

Phenotypic Data ◽

Different Types ◽

Derived Data

AbstractSimultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g. sparsity, compositional).We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial communities data, and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modelling, to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

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Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer’s risk factors among 1,111 cohort participants

10.1101/436923 ◽

2018 ◽

Author(s):

Burcu F. Darst ◽

Qiongshi Lu ◽

Sterling C. Johnson ◽

Corinne D. Engelman

Keyword(s):

Risk Factors ◽

Complex Traits ◽

Multiple Testing ◽

Disease Risk ◽

Plasma Metabolites ◽

Integrated Analysis ◽

Data Types ◽

Integrated Network ◽

Genome Wide ◽

Underlying Mechanisms

AbstractAlthough Alzheimer’s disease (AD) is highly heritable, genetic variants known to be associated with AD only explain a small proportion of its heritability. Genetic factors may only convey disease risk in individuals with certain environmental exposures, suggesting that a multi-omics approach could reveal underlying mechanisms contributing to complex traits, such as AD. We developed an integrated network to investigate relationships between metabolomics, genomics, and AD risk factors using Wisconsin Registry for Alzheimer’s Prevention participants. Analyses included 1,111 non-Hispanic Caucasian participants with whole blood expression for 11,376 genes (imputed from dense genome-wide genotyping), 1,097 fasting plasma metabolites, and 17 AD risk factors. A subset of 155 individuals also had 364 fasting cerebral spinal fluid (CSF) metabolites. After adjusting each of these 12,854 variables for potential confounders, we developed an undirected graphical network, representing all significant pairwise correlations upon adjusting for multiple testing. There were many instances of genes being indirectly linked to AD risk factors through metabolites, suggesting that genes may influence AD risk through particular metabolites. Follow-up analyses suggested that glycine mediates the relationship between CPS1 and measures of cardiovascular and diabetes risk, including body mass index, waist-hip ratio, inflammation, and insulin resistance. Further, 38 CSF metabolites explained more than 60% of the variance of CSF levels of tau, a detrimental protein that accumulates in the brain of AD patients and is necessary for its diagnosis. These results further our understanding of underlying mechanisms contributing to AD risk while demonstrating the utility of generating and integrating multiple omics data types.

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