scholarly journals Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Tomas Hruz ◽  
Oliver Laule ◽  
Gabor Szabo ◽  
Frans Wessendorp ◽  
Stefan Bleuler ◽  
...  
Keyword(s):  
Data Storage ◽  
Regulatory Networks ◽  
Large Scale ◽  
Meta Analysis ◽  
Software Tool ◽  
Manual Curation ◽  
Discovery Research ◽  
Expression Of Genes ◽  
Computationally Intensive ◽  
Analysis System

The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis. In addition to improved querying features, Genevestigator V3 provides new tools to analyze the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into expression modules, and to model expression responses in the context of metabolic and regulatory networks. Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery research and hypothesis validation.

scholarly journals Meta-analysis and profiling of cardiac expression modules

2008 ◽  
Vol 35 (3) ◽  
pp. 305-315 ◽  
Author(s):  
Uri David Akavia ◽  
Dafna Benayahu
Keyword(s):  
Heart Failure ◽  
Large Scale ◽  
Meta Analysis ◽  
Gene Expression Omnibus ◽  
Biological Knowledge ◽  
Map Algorithm ◽  
Large Scale Analysis ◽  
Expression Of Genes ◽  
Prior Biological Knowledge ◽  
Clinical Conditions

Heart failure is a complex, complicated disease that is not yet fully understood. We used the Module Map algorithm to uncover groups of genes that have a similar pattern of expression under various conditions of heart stress. These groups of genes are called modules and may serve as computational predictions of biological pathways for the various clinical situations. The Module Map algorithm allows a large-scale analysis of genes expressed. We applied this algorithm to 700 different mouse experiments downloaded from the Gene Expression Omnibus database, which identified 884 modules. The analysis reconstructed partially known principles that play a role in governing the response of heart to stress, thus demonstrating the strength of the method. We have shown a role of genes related to the immune system in conditions of heart remodeling and failure. We have also shown changes in the expression of genes involved with energy metabolism and changes in the expression of contractile proteins of the heart following myocardial infarction. When focusing on another module we noted a new correlation between genes related to osteogenesis and heart failure, including Runx2 and Ahsg, whose role in heart failure was unknown so far. Despite a lack of prior biological knowledge, the Module Map algorithm has reconstructed known pathways, which demonstrates the strength of this new method for analyzing gene profiles related to clinical phenomenon. The method and the analysis presented are a new avenue to uncover the correlation of clinical conditions to the molecular level.

DIZZY: STOCHASTIC SIMULATION OF LARGE-SCALE GENETIC REGULATORY NETWORKS

2005 ◽  
Vol 03 (02) ◽  
pp. 415-436 ◽  
Author(s):  
STEPHEN RAMSEY ◽  
DAVID ORRELL ◽  
HAMID BOLOURI
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Software Tool ◽  
Genetic Regulatory Networks ◽  
Signaling Networks ◽  
Simulation Framework ◽  
Kinetic Rate ◽  
Rate Laws ◽  
Reaction Processes ◽  
Kinetics Of

We describe Dizzy, a software tool for stochastically and deterministically modeling the spatially homogeneous kinetics of integrated large-scale genetic, metabolic, and signaling networks. Notable features include a modular simulation framework, reusable modeling elements, complex kinetic rate laws, multi-step reaction processes, steady-state noise estimation, and spatial compartmentalization.

scholarly journals Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

2020 ◽  
Author(s):  
Bogdan Petre ◽  
Philip Kragel ◽  
Lauren Y. Atlas ◽  
Stephan Geuter ◽  
Marieke Jepma ◽  
...  
Keyword(s):  
Pain Intensity ◽  
Spatial Scale ◽  
Data Storage ◽  
Large Scale ◽  
Model Comparison ◽  
Spatial Scales ◽  
Meta Analysis ◽  
Individual Subject ◽  
System Models ◽  
Elementary Region

ABSTRACTInformation is coded in the brain at different scales for different phenomena: locally, distributed across regions and networks, and globally. For pain, the scale of representation is controversial. Although generally believed to be an integrated cognitive and sensory phenomenon implicating diverse brain systems, quantitative characterizations of which regions and networks are sufficient to represent pain are lacking. In this meta-analysis (or mega-analysis) using data from 289 participants across 10 studies, we use model comparison combined with multivariate predictive models to investigate the spatial scale and location of acute pain representation. We compare models based on (a) a single most pain-predictive module, either previously identified elementary regions or a single best large-scale cortical resting-state network module; (b) selected cortical-subcortical systems related to evoked pain in prior literature (‘multi-system models’); and (c) a model spanning the full brain. We estimate the accuracy of pain intensity predictions using cross validation (7 studies) and subsequently validate in three independent holdout studies. All spatial scales convey information about pain intensity, but distributed, multi-system models better characterize pain representations than any individual region or network (e.g. multisystem models explain >20% more of individual subject pain ratings than the best elementary region). Full brain models showed no predictive advantage over multi-system models. These findings quantify the extent that representation of evoked pain experience is distributed across multiple cortical and subcortical systems, show that pain representation is not circumscribed by any elementary region or conical network, and provide a blueprint for identifying the spatial scale of information in other domains.Significance StatementWe define modular, multisystem and global views of brain function, use multivariate fMRI decoding to characterize pain representations at each level, and provide evidence for a multisystem representation of evoked pain. We further show that local views necessarily exclude important components of pain representation, while a global full brain representation is superfluous, even though both are viable frameworks for representing pain. These findings quantitatively juxtapose and reconcile divergent conclusions from evoked pain studies within a generalized neuroscientific framework, and provide a blueprint for investigating representational architecture for diverse brain processes.Author NoteData storage supported by the University of Colorado Boulder “PetaLibrary”. Research funded by NIMH R01 MH076136, NIDA R01 DA046064 and NIDA R01 DA035484. Lauren Atlas is supported in part by funding from the Intramural Research Program of the National Center for Complementary and Integrative Health, National Institutes of Health (ZIA-AT000030). Marina Lopez-Sola is supported by a Serra Hunter fellow lecturer program. We would like to thank Dr. Christian Buchel for contributing data to this project, and Dr. Marta Čeko for comments and feedback on the manuscript.

scholarly journals Genetic links between endometriosis and cancers in women

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8135 ◽  
Author(s):  
Salma Begum Bhyan ◽  
Li Zhao ◽  
YongKiat Wee ◽  
Yining Liu ◽  
Min Zhao
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Relationships ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Expression Of Genes ◽  
Genetic Mechanisms

Endometriosis is a chronic disease occurring during the reproductive stage of women. Although there is only limited association between endometriosis and gynecological cancers with regard to clinical features, the molecular basis of the relationship between these diseases is unexplored. We conducted a systematic study by integrating literature-based evidence, gene expression and large-scale cancer genomics data in order to reveal any genetic relationships between endometriosis and cancers in women. We curated 984 endometriosis-related genes from 3270 PubMed articles and then conducted a meta-analysis of the two public gene expression profiles related to endometriosis which identified Differential Expression of Genes (DEGs). Following an overlapping analysis, we identified 39 key endometriosis-related genes common in both literature and DEG analysis. Finally, the functional analysis confirmed that all the 39 genes were associated with the vital processes of tumour formation and cancer progression and that two genes (PGR and ESR1) were common to four cancers of women. From network analysis, we identified a novel linker gene, C3AR1, which had not been implicated previously in endometriosis. The shared genetic mechanisms of endometriosis and cancers in women identified in this study provided possible new avenues of multiple disease management and treatments through early diagnosis.

scholarly journals Relationship between acoustic indices, length of recordings and processing time: a methodological test

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Edgar Cifuentes ◽  
Juliana Vélez Gómez ◽  
Simon J Butler
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Processing Time ◽  
Sample Length ◽  
Survey Period ◽  
Natural Landscapes ◽  
Computationally Intensive ◽  
Management And Conservation ◽  
Sampling Schedule

Ecoacoustic approaches have the potential to provide rapid biodiversity assessments and avoid costly fieldwork. Their use in biodiversity studies for improving management and conservation of natural landscapes has grown considerably in recent years. Standardised methods for sampling acoustic information that deliver reliable and consistent results within and between ecosystems are still lacking. Sampling frequency and duration are particularly important considerations because shorter, intermittent recordings mean recorder batteries last longer and data processing is less computationally intensive, but a smaller proportion of the available soundscape is sampled. Here, we compare acoustic indices and processing time for subsamples of increasing duration clipped from 94 one-hour recordings, to test how different acoustic indices behave, in order to identify the minimum sample length required. Our results suggest that short recordings distributed across the survey period accurately represent acoustic patterns, while optimizing data collection and processing. ACI and H are the most stable indices, showing an ideal sampling schedule of ten 1-minute samples in an hour. Although ADI, AEI and NDSI well represent acoustic patterns under the same sampling schedule, these are more robust under continuous recording formats. Such targeted subsampling could greatly reduce data storage and computational power requirements in large-scale and long-term projects.

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.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews &amp; Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

Association between ALDH2 Gene Polymorphism and Late-onset Alzheimer Disease: An Up-to-date Meta-analysis

2020 ◽  
Vol 17 (2) ◽  
pp. 105-111
Author(s):  
Haitao Liu ◽  
Wei Ge ◽  
Wei Chen ◽  
Xue Kong ◽  
Weiming Jian ◽  
...  
Keyword(s):  
Gene Polymorphism ◽  
Large Scale ◽  
Late Onset ◽  
Meta Analysis ◽  
Case Control ◽  
Cochrane Library ◽  
Positive Trend ◽  
Case Control Studies ◽  
China National Knowledge Infrastructure ◽  
Aldh2 Gene

Objectives: Previous case-control studies have focused on the relationship between ALDH2 gene polymorphism and late-onset Alzheimer's Disease (LOAD), but no definite unified conclusion has been reached. Therefore, the correlation between ALDH2 Glu504Lys polymorphism and LOAD remains controversial. To analyze the correlation between ALDH2 polymorphism and the risk of LOAD, we implemented this up-to-date meta-analysis to assess the probable association. Methods: Studies were searched through China National Knowledge Infrastructure (CNKI), VIP Database for Chinese Technical Periodicals, China Biology Medicine, PubMed, Cochrane Library, Clinical- Trials.gov, Embase, and MEDLINE from January 1, 1994 to December 31, 2018, without any restrictions on language and ethnicity. Results: Five studies of 1057 LOAD patients and 1136 healthy controls met our criteria for the analysis. Statistically, the ALDH2 GA/AA genotype was not linked with raising LOAD risk (odds ratio (OR) = 1.48, 95% confidence interval (CI) = 0.96-2.28, p = 0.07). In subgroup analysis, the phenomenon that men with ALDH2*2 had higher risk for LOAD (OR = 1.72, 95%CI = 1.10-2.67, p = 0.02) was observed. Conclusions: This study comprehends only five existing case-control studies and the result is negative. The positive trend might appear when the sample size is enlarged. In the future, more large-scale casecontrol or cohort studies should be done to enhance the association between ALDH2 polymorphism and AD or other neurodegenerative diseases.

scholarly journals Disentangling transcriptional responses in plant defense against arthropod herbivores

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alejandro Garcia ◽  
M. Estrella Santamaria ◽  
Isabel Diaz ◽  
Manuel Martinez
Keyword(s):  
Regulatory Networks ◽  
Pieris Rapae ◽  
Meta Analysis ◽  
Plant Response ◽  
Plant Responses ◽  
Transcriptional Responses ◽  
Crop Species ◽  
Physiological Processes ◽  
Meta Analyses ◽  
Plant Herbivore

AbstractThe success in the response of a plant to a pest depends on the regulatory networks that connect plant perception and plant response. Meta-analyses of transcriptomic responses are valuable tools to discover novel mechanisms in the plant/herbivore interplay. Considering the quantity and quality of available transcriptomic analyses, Arabidopsis thaliana was selected to test the ability of comprehensive meta-analyses to disentangle plant responses. The analysis of the transcriptomic data showed a general induction of biological processes commonly associated with the response to herbivory, like jasmonate signaling or glucosinolate biosynthesis. However, an uneven induction of many genes belonging to these biological categories was found, which was likely associated with the particularities of each specific Arabidopsis-herbivore interaction. A thorough analysis of the responses to the lepidopteran Pieris rapae and the spider mite Tetranychus urticae highlighted specificities in the perception and signaling pathways associated with the expression of receptors and transcription factors. This information was translated to a variable alteration of secondary metabolic pathways. In conclusion, transcriptomic meta-analysis has been revealed as a potent way to sort out relevant physiological processes in the plant response to herbivores. Translation of these transcriptomic-based analyses to crop species will permit a more appropriate design of biotechnological programs.

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