scholarly journals Omics Data Complementarity Underlines Functional Cross-Communication in Yeast

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Noël Malod-Dognin ◽  
Nataša Pržulj
Keyword(s):  
Functional Organization ◽  
Genetic Interaction ◽  
Molecular Data ◽  
Omics Data ◽  
Functional Communication ◽  
Data Types ◽  
Protein Protein Interaction ◽  
Exact Answer ◽  
A Cell ◽  
Computational Intractability

AbstractMapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker’s yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein–protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data.

scholarly journals Hierarchical Classification of Cancers of Unknown Primary Using Multi-Omics Data

2019 ◽  
Vol 18 ◽  
pp. 117693511987216 ◽  
Author(s):  
Elham Bavafaye Haghighi ◽  
Michael Knudsen ◽  
Britt Elmedal Laursen ◽  
Søren Besenbacher
Keyword(s):  
Metastatic Cancer ◽  
Hierarchical Classification ◽  
Molecular Data ◽  
Unknown Primary ◽  
Cancer Type ◽  
Omics Data ◽  
Data Types ◽  
Cancer Types ◽  
Hierarchical Classifier

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to locate the primary cancer. As standard treatments are based on the cancer type, such cases are hard to treat and have very poor prognosis. Using molecular data from the metastatic cancer to predict the primary site can make treatment choice easier and enable targeted therapy. In this article, we first examine the ability to predict cancer type using different types of omics data. Methylation data lead to slightly better prediction than gene expression and both these are superior to classification using somatic mutations. After using 3 data types independently, we notice some differences between the classes that tend to be misclassified, suggesting that integrating the data might improve accuracy. In light of the different levels of information provided by different omics types and to be able to handle missing data, we perform multi-omics classification by hierarchically combining the classifiers. The proposed hierarchical method first classifies based on the most informative type of omics data and then uses the other types of omics data to classify samples that did not get a high confidence classification in the first step. The resulting hierarchical classifier has higher accuracy than any of the single omics classifiers and thus proves that the combination of different data types is beneficial. Our results show that using multi-omics data can improve the classification of cancer types. We confirm this by testing our method on metastatic cancers from the MET500 dataset.

scholarly journals Comparison between instrumental variable and mediation-based approaches for reconstructing causal gene networks in yeast

2021 ◽  
Author(s):  
Adriaan-Alexander Ludl ◽  
Tom Michoel
Keyword(s):  
Instrumental Variable ◽  
Gene Networks ◽  
Omics Data ◽  
Causal Gene ◽  
Causal Networks ◽  
Transcriptomics Data ◽  
A Cell ◽  
Flow Of Information

Causal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data...

scholarly journals Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

scholarly journals A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

2021 ◽  
Vol 22 (6) ◽  
pp. 2822
Author(s):  
Efstathios Iason Vlachavas ◽  
Jonas Bohn ◽  
Frank Ückert ◽  
Sylvia Nürnberg
Keyword(s):  
Cancer Research ◽  
Clinical Information ◽  
Disease Diagnosis ◽  
Molecular Data ◽  
Molecular Networks ◽  
Biological Knowledge ◽  
Omics Data ◽  
Translational Cancer Research ◽  
Using Data ◽  
Biological Entities

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

scholarly journals Phylogenomic Discordance in the Eared Seals is best explained by Incomplete Lineage Sorting following Explosive Radiation in the Southern Hemisphere

2020 ◽  
Author(s):  
Fernando Lopes ◽  
Larissa R Oliveira ◽  
Amanda Kessler ◽  
Yago Beux ◽  
Enrique Crespo ◽  
...  
Keyword(s):  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Molecular Data ◽  
Species Tree ◽  
Gene Trees ◽  
Lineage Sorting ◽  
Data Types ◽  
High Coverage ◽  
Sea Lions ◽  
The Family

Abstract The phylogeny and systematics of fur seals and sea lions (Otariidae) have long been studied with diverse data types, including an increasing amount of molecular data. However, only a few phylogenetic relationships have reached acceptance because of strong gene-tree species tree discordance. Divergence times estimates in the group also vary largely between studies. These uncertainties impeded the understanding of the biogeographical history of the group, such as when and how trans-equatorial dispersal and subsequent speciation events occurred. Here we used high-coverage genome-wide sequencing for 14 of the 15 species of Otariidae to elucidate the phylogeny of the family and its bearing on the taxonomy and biogeographical history. Despite extreme topological discordance among gene trees, we found a fully supported species tree that agrees with the few well-accepted relationships and establishes monophyly of the genus Arctocephalus. Our data support a relatively recent trans-hemispheric dispersal at the base of a southern clade, which rapidly diversified into six major lineages between 3 to 2.5 Ma. Otaria diverged first, followed by Phocarctos and then four major lineages within Arctocephalus. However, we found Zalophus to be non-monophyletic, with California (Z. californianus) and Steller sea lions (Eumetopias jubatus) grouping closer than the Galapagos sea lion (Z. wollebaeki) with evidence for introgression between the two genera. Overall, the high degree of genealogical discordance was best explained by incomplete lineage sorting resulting from quasi-simultaneous speciation within the southern clade with introgresssion playing a subordinate role in explaining the incongruence among and within prior phylogenetic studies of the family.

scholarly journals A Cell-Based High-Throughput Assay for Gap Junction Communication Suitable for Assessing Connexin 43–Ezrin Interaction Disruptors Using IncuCyte ZOOM

2016 ◽  
Vol 22 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Aleksandra R. Dukic ◽  
David W. McClymont ◽  
Kjetil Taskén
Keyword(s):  
Gap Junction ◽  
High Throughput ◽  
Connexin 43 ◽  
General Mechanism ◽  
Protein Protein Interaction ◽  
Gap Junction Communication ◽  
Anchoring Protein ◽  
A Cell ◽  
Rat Liver Epithelial Cells ◽  
High Throughput Assay

Connexin 43 (Cx43), the predominant gap junction (GJ) protein, directly interacts with the A-kinase-anchoring protein (AKAP) Ezrin in human cytotrophoblasts and a rat liver epithelial cells (IAR20). The Cx43-Ezrin–protein kinase (PKA) complex facilitates Cx43 phosphorylation by PKA, which triggers GJ opening in cytotrophoblasts and IAR20 cells and may be a general mechanism regulating GJ intercellular communication (GJIC). Considering the importance of Cx43 GJs in health and disease, they are considered potential pharmaceutical targets. The Cx43-Ezrin interaction is a protein-protein interaction that opens possibilities for targeting with peptides and small molecules. For this reason, we developed a high-throughput cell-based assay in which GJIC can be assessed and new compounds characterized. We used two pools of IAR20 cells, calcein loaded and unloaded, that were mixed and allowed to attach. Next, GJIC was monitored over time using automated imaging via the IncuCyte imager. The assay was validated using known GJ inhibitors and anchoring peptide disruptors, and we further tested new peptides that interfered with the Cx43-Ezrin binding region and reduced GJIC. Although an AlphaScreen assay can be used to screen for Cx43-Ezrin interaction inhibitors, the cell-based assay described is an ideal secondary screen for promising small-molecule hits to help identify the most potent compounds.

scholarly journals Single-Tube Flow Cytometry Assay for the Detection of Mature Lymphoid Neoplasms in Paucicellular Samples

2016 ◽  
Vol 60 (4) ◽  
pp. 385-394
Author(s):  
Alessandra Stacchini ◽  
Anna Demurtas ◽  
Sabrina Aliberti ◽  
Antonella Barreca ◽  
Domenico Novero ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Hodgkin Lymphoma ◽  
Final Diagnosis ◽  
Molecular Data ◽  
Malignant Lymphomas ◽  
Flow Cytometry Assay ◽  
Single Tube ◽  
Fine Needle Aspirates ◽  
Non Hodgkin Lymphoma ◽  
A Cell

Objectives: Flow cytometry (FC) has become a useful support for cytomorphologic evaluation (CM) of fine-needle aspirates (FNA) and serous cavity effusions (SCE) in cases of suspected non-Hodgkin lymphoma (NHL). FC results may be hampered by the scarce viability and low cellularity of the specimens. Study Design: We developed a single-tube FC assay (STA) that included 10 antibodies cocktailed in 8-color labeling, a cell viability dye, and a logical gating strategy to detect NHL in hypocellular samples. The results were correlated with CM and confirmed by histologic or molecular data when available. Results: Using the STA, we detected B-type NHL in 31 out of 103 hypocellular samples (81 FNA and 22 SCE). Of these, 8 were not confirmed by CM and 2 were considered to be only suspicious. The FC-negative samples had a final diagnosis of benign/reactive process (42/72), carcinoma (27/72), or Hodgkin lymphoma (3/72). Conclusions: The STA approach allowed obtainment of maximum immunophenotyping data in specimens containing a low number of cells and a large amount of debris. The information obtained by STA can help cytomorphologists not only to recognize but also to exclude malignant lymphomas.

scholarly journals Evidence for GRN as part of a neuroinflammatory mechanism connecting common neurodegenerative diseases

2020 ◽  
Author(s):  
Mike A. Nalls ◽  
Cornelis Blauwendraat ◽  
Lana Sargent ◽  
Dan Vitale ◽  
Hampton Leonard ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Neurodegenerative Diseases ◽  
Genetic Risk ◽  
Association Studies ◽  
Molecular Data ◽  
Genome Wide Association Studies ◽  
Data Types ◽  
Genome Wide ◽  
Trait Locus ◽  
Lateral Sclerosis

SUMMARYBackgroundPrevious research using genome wide association studies (GWAS) has identified variants that may contribute to lifetime risk of multiple neurodegenerative diseases. However, whether there are common mechanisms that link neurodegenerative diseases is uncertain. Here, we focus on one gene, GRN, encoding progranulin, and the potential mechanistic interplay between genetic risk, gene expression in the brain and inflammation across multiple common neurodegenerative diseases.MethodsWe utilized GWAS, expression quantitative trait locus (eQTL) mapping and Bayesian colocalization analyses to evaluate potential causal and mechanistic inferences. We integrate various molecular data types from public resources to infer disease connectivity and shared mechanisms using a data driven process.FindingseQTL analyses combined with GWAS identified significant functional associations between increasing genetic risk in the GRN region and decreased expression of the gene in Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis. Additionally, colocalization analyses show a connection between blood based inflammatory biomarkers relating to platelets and GRN expression in the frontal cortex.InterpretationGRN expression mediates neuroinflammation function related to general neurodegeneration. This analysis suggests shared mechanisms for Parkinson’s, Alzheimer’s and amyotrophic lateral sclerosis.FundingNational Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J. Fox Foundation.

scholarly journals Subcellular Localization of Disease-Associated Proteins in Psoriasis

2020 ◽  
Vol 2 (1) ◽  
pp. 8
Keyword(s):  
Subcellular Localization ◽  
Metabolic Pathways ◽  
Inflammatory Disorder ◽  
Protein Protein Interaction ◽  
Extracellular Region ◽  
Associated Proteins ◽  
A Cell ◽  
Pathway Analyses ◽  
Cellular Compartments ◽  
Molecular Pathophysiology

Psoriasis is an autoimmune, persisting, inflammatory disorder that extremely affects the skin and joints of the system. In spite of the field under investigation across the globe roots toward the origin and the molecular pathophysiology of the disease, yet, the mechanism is vaguely presumed. The pathology has its basis in the underlying genes, the protein interactomes, and the metabolic pathways. Subcellular localization of the proteins (Sl) imparts geometrical details of proteins in a cell. In Sl, Proteins conjoin with suitable proteins to assemble into active complexes in signaling routes and metabolic pathways. Variations in the disease set of genes modify the production of gene outcomes as well alters the choosing steps of appropriate Sl, which interrupts the vital roles of the proteins. Proteins related to the disease are predominantly accumulated in typical Sl, which is why apt recognition of protein Sl guides to track down disease bound proteins and the interdependence between them. To do so, in the current investigation, the GOnet tool has been utilized to identify Sl of the proteins by the input of genes and by modeling and visualizing collaborative graphs in conjunction with GO terms and genes. The results obtained displays that the Psoriasis proteins have been localized in respective cellular compartments such as Golgi apparatus, cytoplasm, nucleolus, mitochondria, peroxisomes cytoskeleton, cytoplasm, endosomes, endoplasmic reticulum, extracellular region, nucleoplasm, cilium, vacuole, protein-containing complex, and nuclear chromosome. Further exploration of subcellular localization followed by protein-protein interaction and molecular pathway analyses may be the bedrock to a deeper insight towards disease development and molecular centered relations alongside multimorbidity interactions in Psoriasis.

scholarly journals Unsupervised discovery of phenotype-specific multi-omics networks

2019 ◽  
Vol 35 (21) ◽  
pp. 4336-4343 ◽  
Author(s):  
W Jenny Shi ◽  
Yonghua Zhuang ◽  
Pamela H Russell ◽  
Brian D Hobbs ◽  
Margaret M Parker ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Wide Spectrum ◽  
Supplementary Information ◽  
Chronic Obstructive ◽  
Phenotypic Traits ◽  
Omics Data ◽  
Data Types ◽  
Obstructive Pulmonary Disease ◽  
Quantitative Phenotype ◽  
Treatment Plans

Abstract Motivation Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits. Results We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA–mRNA networks. Through simulations, we demonstrate that SmCCNet has better overall prediction performance compared to popular gene expression network construction and integration approaches under realistic settings. Applying SmCCNet to studies on chronic obstructive pulmonary disease (COPD) and breast cancer, we found enrichment of known relevant pathways (e.g. the Cadherin pathway for COPD and the interferon-gamma signaling pathway for breast cancer) as well as less known omics features that may be important to the diseases. Although those applications focus on miRNA–mRNA co-expression networks, SmCCNet is applicable to a variety of omics and other data types. It can also be easily generalized to incorporate multiple quantitative phenotype simultaneously. The versatility of SmCCNet suggests great potential of the approach in many areas. Availability and implementation The SmCCNet algorithm is written in R, and is freely available on the web at https://cran.r-project.org/web/packages/SmCCNet/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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