A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models.

PLoS One. 2016;11(10):e0163353

Authors: Yang MQ, Elnitski L

Abstract
Ovarian carcinomas can be aggressive with a high mortality rate (e.g., high-grade serous ovarian carcinomas, or HGSOCs), or indolent with much better long-term outcomes (e.g., low-malignant-potential, or LMP, serous ovarian carcinomas). By comparing LMP and HGSOC tumors, we can gain insight into the mechanisms underlying malignant progression in ovarian cancer. However, previous studies of the two subtypes have been focused on gene expression analysis. Here, we applied a systems biology approach, integrating gene expression profiles derived from two independent data sets containing both LMP and HGSOC tumors with protein-protein interaction data. Genes and related networks implicated by both data sets involved both known and novel disease mechanisms and highlighted the different roles of BRCA1 and CREBBP in the two tumor types. In addition, the incorporation of somatic mutation data revealed that amplification of PAK4 is associated with poor survival in patients with HGSOC. Thus, perturbations in protein interaction networks demonstrate differential trafficking of network information between malignant and benign ovarian cancers. The novel network-based molecular signatures identified here may be used to identify new targets for intervention and to improve the treatment of invasive ovarian cancer as well as early diagnosis.

PMID: 27788148 [PubMed - in process]

Age- and Sex-Dependent Changes of Intra-Articular Cortical and Trabecular Bone Structure and the Effects of Rheumatoid Arthritis.

J Bone Miner Res. 2016 Oct 27;:

Authors: Simon D, Kleyer A, Stemmler F, Simon C, Berlin A, Hueber AJ, Haschka J, Renner N, Figueiredo C, Neuhuber W, Buder T, Englbrecht M, Rech J, Engelke K K, Schett G

Abstract
The objective of this cross-sectional study was to define normal sex and age-dependent values of intra-articular bone mass and microstructures in the metacarpal heads of healthy individuals by high-resolution peripheral quantitative computed tomography (HR-pQCT) and test the effect of rheumatoid arthritis (RA) on these parameters. Human cadaveric metacarpal heads were used to exactly define intra-articular bone. Healthy individuals of different sex and age categories and RA patients with similar age- and sex-distribution received HR-pQCT scans of the second metacarpal head and the radius. Total, cortical and trabecular bone densities as well as microstructural parameters were compared between (i) the different ages and sexes in healthy individuals, (ii) between metacarpal heads and the radius and (iii) between healthy individuals and RA patients. The cadaveric study allowed exact definition of the intra-articular (=intra-capsular) bone margins. These data were applied in measuring intra-articular and radial bone parameters in 214 women and men (108 healthy individuals, 106 RA patients). Correlations between intra-articular and radial bone parameters were good (r = 0.51 to 0.62, p < 0.001). In contrast to radial bone, intra-articular bone remained stable until 60 years (between 297 and 312 mg HA/cm(3) ), but decreased significantly (p < 0.001) in women thereafter (237.5 ± 44.3) with loss of both cortical and trabecular bone. Similarly, RA patients showed significant (p < 0.001) loss of intra-articular total (263.0 ± 44.8), trabecular (171.2 ± 35.6) and cortical bone (610.2 ± 62.0) compared to sex and age -adjusted controls. Standard sex- and age-dependent values for physiological intra-articular bone were defined. Postmenopausal state and RA led to significant decrease of intra-articular bone. This article is protected by copyright. All rights reserved.

PMID: 27787923 [PubMed - as supplied by publisher]

Evaluation of molecular brain changes associated with environmental stress in rodent models compared to human major depressive disorder: a proteomic systems approach.

World J Biol Psychiatry. 2016 Oct 27;:1-31

Authors: Cox DA, Gottschalk MG, Stelzhammer V, Wesseling H, Cooper JD, Bahn S

Abstract
OBJECTIVES: Rodent models of major depressive disorder (MDD) are indispensable when screening for novel treatments, but assessing their translational relevance with human brain pathology has proved difficult.
METHODS: Using a novel systems approach, proteomics data obtained from post-mortem MDD anterior prefrontal cortex tissue (n =12) and matched controls (n = 23) were compared with equivalent data from three commonly used preclinical models exposed to environmental stressors (chronic mild stress, prenatal stress, social defeat). Functional pathophysiological features associated with depression-like behaviour were identified in these models through enrichment of protein-protein interaction networks. A cross-species comparison evaluated which model(s) represent human MDD pathology most closely.
RESULTS: Seven functional domains associated with MDD and represented across at least two models such as "carbohydrate metabolism and cellular respiration" were identified. Through statistical evaluation using kernel-based machine learning techniques, the social defeat model was found to represent MDD brain changes most closely for four of the seven domains.
CONCLUSIONS: This is the first study to apply a method for directly evaluating the relevance of the molecular pathology of multiple animal models to human MDD on the functional level. The methodology and findings outlined here could help to overcome translational obstacles of preclinical psychiatric research.

PMID: 27784204 [PubMed - as supplied by publisher]

High Performance Enzyme Kinetics of Turnover, Activation and Inhibition for Translational Drug Discovery.

Expert Opin Drug Discov. 2016 Oct 27;

Authors: Zhang R, Wong K

Abstract
INTRODUCTION: Enzymes are the macromolecular catalysts of many living processes and represent a sizable proportion of all druggable biological targets. Enzymology has been practiced just over a century during which much progress has been made in both the identification of new enzymes and the development of novel methodologies for enzyme kinetics. Areas covered: This review aims to address several key practical aspects in enzyme kinetics in reference to translational drug discovery research. The authors first define what constitutes a high performance enzyme kinetic assay. The authors then review the best practices for turnover, activation and inhibition kinetics to derive critical parameters guiding drug discovery. Notably, the authors recommend global progress curve analysis of dose/time dependence employing an integrated Michaelis-Menten equation and global curve fitting of dose/dose dependence. Expert opinion: The authors believe that in vivo enzyme and substrate abundance and their dynamics, binding modality, drug binding kinetics and enzyme's position in metabolic networks should be assessed to gauge the translational impact on drug efficacy and safety. Integrating these factors in a systems biology and systems pharmacology model should facilitate translational drug discovery.

PMID: 27784173 [PubMed - as supplied by publisher]

Highly Multiplexed Quantitative Mass Spectrometry Analysis of Ubiquitylomes.

Cell Syst. 2016 Oct 26;3(4):395-403.e4

Authors: Rose CM, Isasa M, Ordureau A, Prado MA, Beausoleil SA, Jedrychowski MP, Finley DJ, Harper JW, Gygi SP

Abstract
System-wide quantitative analysis of ubiquitylomes has proven to be a valuable tool for elucidating targets and mechanisms of the ubiquitin-driven signaling systems, as well as gaining insights into neurodegenerative diseases and cancer. Current mass spectrometry methods for ubiquitylome detection require large amounts of starting material and rely on stochastic data collection to increase replicate analyses. We describe a method compatible with cell line and tissue samples for large-scale quantification of 5,000-9,000 ubiquitylation forms across ten samples simultaneously. Using this method, we reveal site-specific ubiquitylation in mammalian brain and liver tissues, as well as in cancer cells undergoing proteasome inhibition. To demonstrate the power of the approach for signal-dependent ubiquitylation, we examined protein and ubiquitylation dynamics for mitochondria undergoing PARKIN- and PINK1-dependent mitophagy. This analysis revealed the largest collection of PARKIN- and PINK1-dependent ubiquitylation targets to date in a single experiment, and it also revealed a subset of proteins recruited to the mitochondria during mitophagy.

PMID: 27667366 [PubMed - in process]

12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/10/27

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

How do precision medicine and system biology response to human body's complex adaptability?

Chin J Integr Med. 2016 Oct 24;

Authors: Yuan B

Abstract
In the field of life sciences, although system biology and "precision medicine" introduce some complex scientifific methods and techniques, it is still based on the "analysis-reconstruction" of reductionist theory as a whole. Adaptability of complex system increase system behaviour uncertainty as well as the difficulties of precise identifification and control. It also put systems biology research into trouble. To grasp the behaviour and characteristics of organism fundamentally, systems biology has to abandon the "analysis-reconstruction" concept. In accordance with the guidelines of complexity science, systems biology should build organism model from holistic level, just like the Chinese medicine did in dealing with human body and disease. When we study the living body from the holistic level, we will fifind the adaptability of complex system is not the obstacle that increases the diffificulty of problem solving. It is the "exceptional", "right-hand man" that helping us to deal with the complexity of life more effectively.

PMID: 27778262 [PubMed - as supplied by publisher]

A Computational Systems Biology Approach for Identifying Candidate Drugs for Repositioning for Cardiovascular Disease.

Interdiscip Sci. 2016 Oct 24;

Authors: Yu AZ, Ramsey SA

Abstract
We report an in silico method to screen for receptors or pathways that could be targeted to elicit beneficial transcriptional changes in a cellular model of a disease of interest. In our method, we integrate: (1) a dataset of transcriptome responses of a cell line to a panel of drugs; (2) two sets of genes for the disease; and (3) mappings between drugs and the receptors or pathways that they target. We carried out a gene set enrichment analysis (GSEA) test for each of the two gene sets against a list of genes ordered by fold-change in response to a drug in a relevant cell line (HL60), with the overall score for a drug being the difference of the two enrichment scores. Next, we applied GSEA for drug targets based on drugs that have been ranked by their differential enrichment scores. The method ranks drugs by the degree of anti-correlation of their gene-level transcriptional effects on the cell line with the genes in the disease gene sets. We applied the method to data from (1) CMap 2.0; (2) gene sets from two transcriptome profiling studies of atherosclerosis; and (3) a combined dataset of drug/target information. Our analysis recapitulated known targets related to CVD (e.g., PPARγ; HMG-CoA reductase, HDACs) and novel targets (e.g., amine oxidase A, δ-opioid receptor). We conclude that combining disease-associated gene sets, drug-transcriptome-responses datasets and drug-target annotations can potentially be useful as a screening tool for diseases that lack an accepted cellular model for in vitro screening.

PMID: 27778232 [PubMed - as supplied by publisher]

Systems biology analysis reveals role of MDM2 in diabetic nephropathy.

JCI Insight. 2016 Oct 20;1(17):e87877

Authors: Saito R, Rocanin-Arjo A, You YH, Darshi M, Van Espen B, Miyamoto S, Pham J, Pu M, Romoli S, Natarajan L, Ju W, Kretzler M, Nelson R, Ono K, Thomasova D, Mulay SR, Ideker T, D'Agati V, Beyret E, Belmonte JC, Anders HJ, Sharma K

Abstract
To derive new insights in diabetic complications, we integrated publicly available human protein-protein interaction (PPI) networks with global metabolic networks using metabolomic data from patients with diabetic nephropathy. We focused on the participating proteins in the network that were computationally predicted to connect the urine metabolites. MDM2 had the highest significant number of PPI connections. As validation, significant downregulation of MDM2 gene expression was found in both glomerular and tubulointerstitial compartments of kidney biopsy tissue from 2 independent cohorts of patients with diabetic nephropathy. In diabetic mice, chemical inhibition of MDM2 with Nutlin-3a led to reduction in the number of podocytes, increased blood urea nitrogen, and increased mortality. Addition of Nutlin-3a decreased WT1(+) cells in embryonic kidneys. Both podocyte- and tubule-specific MDM2-knockout mice exhibited severe glomerular and tubular dysfunction, respectively. Interestingly, the only 2 metabolites that were reduced in both podocyte and tubule-specific MDM2-knockout mice were 3-methylcrotonylglycine and uracil, both of which were also reduced in human diabetic kidney disease. Thus, our bioinformatics tool combined with multi-omics studies identified an important functional role for MDM2 in glomeruli and tubules of the diabetic nephropathic kidney and links MDM2 to a reduction in 2 key metabolite biomarkers.

PMID: 27777973 [PubMed - in process]

Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE.

Elife. 2015 Dec 23;4:

Authors: Riley TR, Lazarovici A, Mann RS, Bussemaker HJ

Abstract
Transcription factors are crucial regulators of gene expression. Accurate quantitative definition of their intrinsic DNA binding preferences is critical to understanding their biological function. High-throughput in vitro technology has recently been used to deeply probe the DNA binding specificity of hundreds of eukaryotic transcription factors, yet algorithms for analyzing such data have not yet fully matured. Here, we present a general framework (FeatureREDUCE) for building sequence-to-affinity models based on a biophysically interpretable and extensible model of protein-DNA interaction that can account for dependencies between nucleotides within the binding interface or multiple modes of binding. When training on protein binding microarray (PBM) data, we use robust regression and modeling of technology-specific biases to infer specificity models of unprecedented accuracy and precision. We provide quantitative validation of our results by comparing to gold-standard data when available.

PMID: 26701911 [PubMed - indexed for MEDLINE]

The evolution of adhesiveness as a social adaptation.

Elife. 2015 Nov 27;4:

Authors: Garcia T, Doulcier G, De Monte S

Abstract
Cellular adhesion is a key ingredient to sustain collective functions of microbial aggregates. Here, we investigate the evolutionary origins of adhesion and the emergence of groups of genealogically unrelated cells with a game-theoretical model. The considered adhesiveness trait is costly, continuous and affects both group formation and group-derived benefits. The formalism of adaptive dynamics reveals two evolutionary stable strategies, at each extreme on the axis of adhesiveness. We show that cohesive groups can evolve by small mutational steps, provided the population is already endowed with a minimum adhesiveness level. Assortment between more adhesive types, and in particular differential propensities to leave a fraction of individuals ungrouped at the end of the aggregation process, can compensate for the cost of increased adhesiveness. We also discuss the change in the social nature of more adhesive mutations along evolutionary trajectories, and find that altruism arises before directly beneficial behavior, despite being the most challenging form of cooperation.

PMID: 26613415 [PubMed - indexed for MEDLINE]

Minimal genome: Worthwhile or worthless efforts toward being smaller?

Biotechnol J. 2016 Feb;11(2):199-211

Authors: Choe D, Cho S, Kim SC, Cho BK

Abstract
Microbial cells are versatile hosts for the production of value-added products due to the well-established background knowledge, various genetic tools, and ease of manipulation. Despite those advantages, efficiency of newly incorporated synthetic pathways in microbial cells is frequently limited by innate metabolism, product toxicity, and growth-mediated genetic instability. To overcome those obstacles, a minimal genome harboring only the essential set of genes was proposed, which is a fascinating concept with potential for use as a platform strain. Here, we review the currently available artificial reduced genomes and discuss the prospects for extending use of the genome-reduced strains as programmable chasses. The genome-reduced strains generally showed comparable growth to and higher productivity than their ancestral strains. In Escherichia coli, about 300 genes are estimated as the minimal number of genes under laboratory conditions. However, recent advances revealed that there are non-essential components in essential genes, suggesting that the design principle of minimal genomes should be reconstructed. Current technology is not efficient enough to reduce large amount of interspaced genomic regions or to synthesize the genome. Furthermore, construction of minimal genome frequently has failed due to lack of genomic information. Technological breakthroughs and intense systematic studies on genomes remain tasks.

PMID: 26356135 [PubMed - indexed for MEDLINE]

Integration of 'omics' data in aging research: from biomarkers to systems biology.

Aging Cell. 2015 Dec;14(6):933-44

Authors: Zierer J, Menni C, Kastenmüller G, Spector TD

Abstract
Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

PMID: 26331998 [PubMed - indexed for MEDLINE]

11 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/10/25

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

A computational interactome and functional annotation for the human proteome.

Elife. 2016 Oct 22;5:

Authors: Garzón JI, Deng L, Murray D, Shapira S, Petrey D, Honig B

Abstract
We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein's function. We provide annotations for most human proteins, including many annotated as having unknown function.

PMID: 27770567 [PubMed - as supplied by publisher]

Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures-Systems biology-based interpretation using genome-scale metabolic flux balance model and multivariate data analysis.

Biotechnol Prog. 2016 Sep;32(5):1163-1173

Authors: Reimonn TM, Park SY, Agarabi CD, Brorson KA, Yoon S

Abstract
Genome-scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745-753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome-scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163-1173, 2016.

PMID: 27452371 [PubMed - in process]

Human Environmental Disease Network: A computational model to assess toxicology of contaminants.

ALTEX. 2016 Oct 21;:

Authors: Taboureau O, Audouze K

Abstract
During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants for diverse human disorders. However, the relationships between diseases based on chemical exposure have been rarely studied by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration on systems biology and chemical toxicology using chemical contaminants information and their disease relationships from the reported TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships allowing including some degrees of significance in the disease-disease associations. Such network can be used to identify uncharacterized connections between diseases. Examples are discussed with type 2 diabetes (T2D). Additionally, this computational model allows to confirm already know chemical-disease links (e.g. bisphenol A and behavioral disorders) and also to reveal unexpected associations between chemicals and diseases (e.g. chlordane and olfactory alteration), thus predicting which chemicals may be risk factors to human health. With the proposed human EDN model, it is possible to explore common biological mechanism between two diseases through chemical exposure helping us to gain insight into disease etiology and comorbidity. Such computational approach is an alternative to animal testing supporting the 3R concept.

PMID: 27768803 [PubMed - as supplied by publisher]

Data for the qualitative modeling of the osmotic stress response to NaCl in Escherichia coli.

Data Brief. 2016 Dec;9:606-612

Authors: Ropers D, Métris A

Abstract
Qualitative modeling approaches allow to provide a coarse-grained description of the functioning of cellular networks when experimental data are scarce and heterogeneous. We translate the primary literature data on the response of Escherichia coli to hyperosmotic stress caused by NaCl addition into a piecewise linear (PL) model. We provide a data file of the qualitative model, which can be used for simulation of changes of protein concentrations and of DNA coiling during the physiological response of the bacterium to the stress. The qualitative model predictions are directly comparable to the available experimental data. This data is related to the research article entitled "Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens" (Metris et al., 2016) [1].

PMID: 27766288 [PubMed - in process]

Attractor landscape analysis of colorectal tumorigenesis and its reversion.

BMC Syst Biol. 2016 Oct 20;10(1):96

Authors: Cho SH, Park SM, Lee HS, Lee HY, Cho KH

Abstract
BACKGROUND: Colorectal cancer arises from the accumulation of genetic mutations that induce dysfunction of intracellular signaling. However, the underlying mechanism of colorectal tumorigenesis driven by genetic mutations remains yet to be elucidated.
RESULTS: To investigate colorectal tumorigenesis at a system-level, we have reconstructed a large-scale Boolean network model of the human signaling network by integrating previous experimental results on canonical signaling pathways related to proliferation, metastasis, and apoptosis. Throughout an extensive simulation analysis of the attractor landscape of the signaling network model, we found that the attractor landscape changes its shape by expanding the basin of attractors for abnormal proliferation and metastasis along with the accumulation of driver mutations. A further hypothetical study shows that restoration of a normal phenotype might be possible by reversely controlling the attractor landscape. Interestingly, the targets of approved anti-cancer drugs were highly enriched in the identified molecular targets for the reverse control.
CONCLUSIONS: Our results show that the dynamical analysis of a signaling network based on attractor landscape is useful in acquiring a system-level understanding of tumorigenesis and developing a new therapeutic strategy.

PMID: 27765040 [PubMed - in process]

New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data.

J Comput Biol. 2016 Sep 28;

Authors: Gogoshin G, Boerwinkle E, Rodin AS

Abstract
Bayesian network (BN) reconstruction is a prototypical systems biology data analysis approach that has been successfully used to reverse engineer and model networks reflecting different layers of biological organization (ranging from genetic to epigenetic to cellular pathway to metabolomic). It is especially relevant in the context of modern (ongoing and prospective) studies that generate heterogeneous high-throughput omics datasets. However, there are both theoretical and practical obstacles to the seamless application of BN modeling to such big data, including computational inefficiency of optimal BN structure search algorithms, ambiguity in data discretization, mixing data types, imputation and validation, and, in general, limited scalability in both reconstruction and visualization of BNs. To overcome these and other obstacles, we present BNOmics, an improved algorithm and software toolkit for inferring and analyzing BNs from omics datasets. BNOmics aims at comprehensive systems biology-type data exploration, including both generating new biological hypothesis and testing and validating the existing ones. Novel aspects of the algorithm center around increasing scalability and applicability to varying data types (with different explicit and implicit distributional assumptions) within the same analysis framework. An output and visualization interface to widely available graph-rendering software is also included. Three diverse applications are detailed. BNOmics was originally developed in the context of genetic epidemiology data and is being continuously optimized to keep pace with the ever-increasing inflow of available large-scale omics datasets. As such, the software scalability and usability on the less than exotic computer hardware are a priority, as well as the applicability of the algorithm and software to the heterogeneous datasets containing many data types-single-nucleotide polymorphisms and other genetic/epigenetic/transcriptome variables, metabolite levels, epidemiological variables, endpoints, and phenotypes, etc.

PMID: 27681505 [PubMed - as supplied by publisher]