scholarly journals LipidLynxX: a data transfer hub to support integration of large scale lipidomics datasets

2020 ◽  
Author(s):  
Zhixu Ni ◽  
Maria Fedorova
Keyword(s):  
Systems Biology ◽  
Data Integration ◽  
High Throughput ◽  
Large Scale ◽  
Data Transfer ◽  
Structural Information ◽  
Meta Analysis ◽  
Health And Disease ◽  
Different Levels ◽  
Cross Match

AbstractModern high throughput lipidomics provides large-scale datasets reporting hundreds of lipid molecular species. However, cross-laboratory comparison, meta-analysis, and systems biology integration of in-house generated and published datasets remain challenging due to a high diversity of used lipid annotation systems, different levels of reported structural information, and shortage in links to data integration resources. To support lipidomics data integration and interoperability of experimental lipidomics with data integration tools, we developed LipidLynxX serving as a hub facilitating data flow from high-throughput lipidomics analysis to systems biology data integration. LipidLynxX provides the possibility to convert, cross-match, and link various lipid annotations to the tools supporting lipid ontology, pathway, and network analysis aiming systems-wide integration and functional annotation of lipidome dynamics in health and disease. LipidLynxX is a flexible, customizable open-access tool freely available for download at https://github.com/SysMedOs/LipidLynxX.

scholarly journals ENIGMA and Global Neuroscience: A Decade of Large-Scale Studies of the Brain in Health and Disease across more than 40 Countries

2019 ◽  
Author(s):  
Paul Thompson ◽  
Neda Jahanshad ◽  
Christopher R. K. Ching ◽  
Lauren Salminen ◽  
Sophia I Thomopoulos ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
22Q11.2 Deletion Syndrome ◽  
Autism Spectrum ◽  
Deletion Syndrome ◽  
Sex And Gender ◽  
Post Traumatic Stress ◽  
Global Alliance ◽  
Compulsive Disorder ◽  
Health And Disease

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1,400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive and psychosocial factors.

scholarly journals CSAO-1. Interrogative Biology: Unraveling insights into causal disease drivers by use of a dynamic systems biology and Bayesian AI to identify the intersect of disease and healthy signatures

2021 ◽  
Vol 3 (Supplement_2) ◽  
pp. ii1-ii1
Author(s):  
Niven Narain ◽  
Michael Kiebish ◽  
Vivek Vishnudas ◽  
Vladimir Tolstikov ◽  
Gregory Miller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systems Biology ◽  
Data Analytics ◽  
Large Scale ◽  
Model Systems ◽  
Dynamic Monitoring ◽  
Treatment Modalities ◽  
Data Sets ◽  
Health And Disease ◽  
Corrective Response

Abstract The past decade has been witness to an explosive proliferation of data analytics modalities, all seeking to unravel insight into large-scale data sets. Machine learning and AI methodologies now occupy a central role in analyses of data sets that range in nature from genomics, “omics”, clinical, real-world evidence, and demographic data. Despite advances in data analytics/machine learning, access to complex population level clinical and related datasets, translating information into actionable guidance in human health and disease remains a challenge. Interrogative Biology, a systems biology/AI platform generates an unbiased, data-informed network for identifying targets (disease drivers) and biomarkers for disease interception at the point of transition to dysregulation, preceding clinical phenotype. The data topology is enabled by a systematic acquisition and interrogation of longitudinal bio-samples of clinically annotated human matrices (e.g. blood, urine, saliva, tissues) subjected to comprehensive multi-omic (genomic, proteomics, lipidomics and metabolomics) profiling over time. The molecular profiles are integrated with clinical health information using Bayesian artificial intelligence analytics, bAIcis, to generate causal network maps of overall health. Differentials between “health” and “disease” network maps identifies drivers (targets and biomarkers) of disease and are rapidly validated in orthogonal wet-lab disease specific perturbed model systems. Target information imputed into the bAIcis framework can define therapeutic strategies including identification of existing drugs and bio-actives for corrective response. Using a combination of clinic based sampling and dried blood spot analysis for longitudinal dynamic monitoring of markers of health-disease status provides opportunity for proactive clinical management and intervention for corrective response in advance of major deterioration of health status. Taken together, the approach herein allows for health surveillance based on in-depth biological profiling of alterations in the patient narrative to guide treatment modalities and strategies in a longitudinal and dynamic manner to identify, track, intercept, and arrest human disease.

scholarly journals A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3582
Author(s):  
Sierra N. Young
Keyword(s):  
High Throughput ◽  
Data Transfer ◽  
Meta Analysis ◽  
Human Machine Interaction ◽  
System Complexity ◽  
Potential Barriers ◽  
Processing Pipeline ◽  
Future Improvement ◽  
High Throughput Phenotyping ◽  
Machine Interaction

This paper presents a framework for the evaluation of system complexity and utility and the identification of bottlenecks in the deployment of field-based, high-throughput phenotyping (FB-HTP) systems. Although the capabilities of technology used for high-throughput phenotyping has improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems. To identify areas for future improvement in developing turnkey FB-HTP solutions, a framework for evaluating their complexity and utility was developed and applied to total of 10 case studies to highlight potential barriers in their development and adoption. The framework performs system factorization and rates the complexity and utility of subsystem factors, as well as each FB-HTP system as a whole, and provides data related to the trends and relationships within the complexity and utility factors. This work suggests that additional research and development are needed focused around the following areas: (i) data handling and management, specifically data transfer from the field to the data processing pipeline, (ii) improved human-machine interaction to facilitate usability across multiple users, and (iii) design standardization of the factors common across all FB-HTP systems to limit the competing drivers of system complexity and utility. This framework can be used to evaluate both previously developed and future proposed systems to approximate the overall system complexity and identify areas for improvement prior to implementation.

scholarly journals Sleep Modelling across Physiological Levels

2019 ◽  
Vol 1 (1) ◽  
pp. 166-184 ◽  
Author(s):  
Svetlana Postnova
Keyword(s):  
Systems Biology ◽  
Circadian Rhythms ◽  
Glial Cells ◽  
Large Scale ◽  
Integrated Systems ◽  
Brain Dynamics ◽  
Sleep Regulation ◽  
Underlying Mechanisms ◽  
Different Levels ◽  
Reductionist Approach

Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms—the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.

scholarly journals Whole-genome approaches for large-scale gene identification and expression analysis in mammalian preimplantation embryos

2005 ◽  
Vol 17 (2) ◽  
pp. 37 ◽  
Author(s):  
James Adjaye
Keyword(s):  
Systems Biology ◽  
Molecular Biology ◽  
Regenerative Medicine ◽  
High Throughput ◽  
Molecular Basis ◽  
Large Scale ◽  
Transcriptional Control ◽  
Preimplantation Embryos ◽  
Gene Identification ◽  
Whole Genome

The elucidation, unravelling and understanding of the molecular basis of transcriptional control during preimplantion development is of utmost importance if we are to intervene and eliminate or reduce abnormalities associated with growth, disease and infertility by applying assisted reproduction. Importantly, these studies should enhance our knowledge of basic reproductive biology and its application to regenerative medicine and livestock production. A major obstacle impeding progress in these areas is the ability to successfully generate molecular portraits of preimplantation embryos from their minute amounts of RNA. The present review describes the various approaches whereby classical embryology fuses with molecular biology, high-throughput genomics and systems biology to address and solve questions related to early development in mammals.

scholarly journals Plant Proteomic Research 4.0: Frontiers in Stress Resilience

2021 ◽  
Vol 22 (24) ◽  
pp. 13362
Author(s):  
Sixue Chen ◽  
Setsuko Komatsu
Keyword(s):  
Systems Biology ◽  
High Throughput ◽  
Large Scale ◽  
Stress Resilience ◽  
Omics Technologies ◽  
Proteomic Research

Large-scale high-throughput multi-omics technologies are indispensable components of systems biology in terms of discovering and defining parts of the system [...]

scholarly journals High-Throughput Large-Scale Targeted Proteomics Assays for Quantifying Pathway Proteins in Pseudomonas putida KT2440

2020 ◽  
Vol 8 ◽  
Author(s):  
Yuqian Gao ◽  
Thomas L. Fillmore ◽  
Nathalie Munoz ◽  
Gayle J. Bentley ◽  
Christopher W. Johnson ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Systems Biology ◽  
Pseudomonas Putida ◽  
High Throughput ◽  
High Speed ◽  
Large Scale ◽  
Protein Quantification ◽  
Selected Reaction Monitoring ◽  
Targeted Proteomics ◽  
Pseudomonas Putida Kt2440

Targeted proteomics is a mass spectrometry-based protein quantification technique with high sensitivity, accuracy, and reproducibility. As a key component in the multi-omics toolbox of systems biology, targeted liquid chromatography-selected reaction monitoring (LC-SRM) measurements are critical for enzyme and pathway identification and design in metabolic engineering. To fulfill the increasing need for analyzing large sample sets with faster turnaround time in systems biology, high-throughput LC-SRM is greatly needed. Even though nanoflow LC-SRM has better sensitivity, it lacks the speed offered by microflow LC-SRM. Recent advancements in mass spectrometry instrumentation significantly enhance the scan speed and sensitivity of LC-SRM, thereby creating opportunities for applying the high speed of microflow LC-SRM without losing peptide multiplexing power or sacrificing sensitivity. Here, we studied the performance of microflow LC-SRM relative to nanoflow LC-SRM by monitoring 339 peptides representing 132 enzymes in Pseudomonas putida KT2440 grown on various carbon sources. The results from the two LC-SRM platforms are highly correlated. In addition, the response curve study of 248 peptides demonstrates that microflow LC-SRM has comparable sensitivity for the majority of detected peptides and better mass spectrometry signal and chromatography stability than nanoflow LC-SRM.

scholarly journals An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

Retrovirology ◽  
2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Sayed-Hamidreza Mozhgani ◽  
Mehran Piran ◽  
Mohadeseh Zarei-Ghobadi ◽  
Mohieddin Jafari ◽  
Seyed-Mohammad Jazayeri ◽  
...  
Keyword(s):  
Data Integration ◽  
High Throughput ◽  
Meta Analysis ◽  
Spastic Paraparesis ◽  
Tropical Spastic Paraparesis ◽  
Hub Genes ◽  
Down Regulation ◽  
Major Genes ◽  
High Throughput Data ◽  
Network Analyses

Abstract Background Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. Results High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein–protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). Conclusions High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.

scholarly journals Tissue-specific transcriptional biomarkers in medicinal plants: Application of large-scale meta-analysis and computational systems biology

Gene ◽  
2019 ◽  
Vol 691 ◽  
pp. 114-124 ◽  
Author(s):  
Ahmad Tahmasebi ◽  
Esmaeil Ebrahimie ◽  
Hassan Pakniyat ◽  
Mansour Ebrahimi ◽  
Manijeh Mohammadi-Dehcheshmeh
Keyword(s):  
Systems Biology ◽  
Medicinal Plants ◽  
Large Scale ◽  
Meta Analysis ◽  
Computational Systems Biology ◽  
Tissue Specific ◽  
Computational Systems ◽  
Transcriptional Biomarkers

Relationship between total plasma homocysteine and the risk of aneurysms – a meta-analysis

VASA ◽  
2020 ◽  
pp. 1-6
Author(s):  
Hanji Zhang ◽  
Dexin Yin ◽  
Yue Zhao ◽  
Yezhou Li ◽  
Dejiang Yao ◽  
...  
Keyword(s):  
Large Scale ◽  
Meta Analysis ◽  
Single Species ◽  
Total Plasma ◽  
Control Groups ◽  
Randomized Controlled ◽  
Randomized Controlled Studies ◽  
The Difference ◽  
The Relationship ◽  
Healthy Participants

Summary: Our meta-analysis focused on the relationship between homocysteine (Hcy) level and the incidence of aneurysms and looked at the relationship between smoking, hypertension and aneurysms. A systematic literature search of Pubmed, Web of Science, and Embase databases (up to March 31, 2020) resulted in the identification of 19 studies, including 2,629 aneurysm patients and 6,497 healthy participants. Combined analysis of the included studies showed that number of smoking, hypertension and hyperhomocysteinemia (HHcy) in aneurysm patients was higher than that in the control groups, and the total plasma Hcy level in aneurysm patients was also higher. These findings suggest that smoking, hypertension and HHcy may be risk factors for the development and progression of aneurysms. Although the heterogeneity of meta-analysis was significant, it was found that the heterogeneity might come from the difference between race and disease species through subgroup analysis. Large-scale randomized controlled studies of single species and single disease species are needed in the future to supplement the accuracy of the results.

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