Strategies to Explore Functional Genomics Data Sets in NCBI’s GEO Database

ABSTRACT We introduce COnTORT (COmprehensive Transcriptomic ORganizational Tool), a publicly available program that retrieves all available gene expression data and associated metadata for an organism from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. The data are compiled into text files that can be used for downstream bioinformatic applications.

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Predicting Cancer Prognosis Using Functional Genomics Data Sets

Cancer Informatics ◽

10.4137/cin.s14064 ◽

2014 ◽

Vol 13s5 ◽

pp. CIN.S14064 ◽

Cited By ~ 3

Author(s):

Jishnu Das ◽

Kaitlyn M. Gayvert ◽

Haiyuan Yu

Keyword(s):

Functional Genomics ◽

Cancer Prognosis ◽

Data Sets ◽

Molecular Signatures ◽

Future Directions ◽

Computational Tools ◽

Human Cancers ◽

Wide Range ◽

Prognostic Outcome ◽

Mechanistic Basis

Elucidating the molecular basis of human cancers is an extremely complex and challenging task. A wide variety of computational tools and experimental techniques have been used to address different aspects of this characterization. One major hurdle faced by both clinicians and researchers has been to pinpoint the mechanistic basis underlying a wide range of prognostic outcomes for the same type of cancer. Here, we provide an overview of various computational methods that have leveraged different functional genomics data sets to identify molecular signatures that can be used to predict prognostic outcome for various human cancers. Furthermore, we outline challenges that remain and future directions that may be explored to address them.

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Open Polar

Ravnetrykk ◽

10.7557/15.5505 ◽

2020 ◽

Author(s):

Tamer Abu-Alam

Keyword(s):

Open Access ◽

Research Data ◽

The Arctic ◽

Data Sets ◽

Polar Regions ◽

Dublin Core ◽

Standard Data ◽

Advanced Search ◽

Definition Of ◽

Geo Database

Data from the Polar Regions are of critical importance to modern research and decision makers. Regardless of their disciplinary and institutional affiliations, researchers rely heavily on the comparison of existing data with new data sets to assess changes that are taking effect. However, in a recent survey of 113 major polar data providers, we found that an estimated 60% of the existing polar research data is unfindable through common search engines and can only be accessed through institutional webpages. This raises an awareness sign of the need of the scientific community to harvest different metadata related to the Polar Regions and collect it in a homogenous, seamless database and making this database available to researchers, students and publics through one search platform. This contribution describes the progress in an ongoing project, Open Polar, started in 2019 at UiT The Arctic University of Norway. The project aims to collect metadata about all the open-access research data, articles and other scholarly documents related to the Polar Regions in a homogenous and seamless database. During the first six months of the project, the beta version of the user-interface was established, with a search by map and an advanced search function. An extensive geo-database that includes thousands of polar locations and their geographic information was collected from different sources. The geo-database together with a list of keywords (i.e. on sources, indigenous peoples, languages and other polar-related keywords) will be used in the filtration process. A Reference Board was formed, and the first board meeting took place in April 2020. The geographic definition of “Polar Regions” was defined in order to include most of the current geographic definitions of “Arctic”. The project is still facing some challenges that include for example integration with non-standard data sources who do not use Dublin Core Metadata schema, or are not harvestable through the Open Access Initiative’s standard protocol for harvesting (OAI-PMH).

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Hub Targets Analysis of miRNA-mRNA-TF Network in Diabetic Cardiomyopathy

10.21203/rs.3.rs-1013451/v1 ◽

2021 ◽

Author(s):

Bincheng Ren ◽

Kaini He ◽

Miao Yuan ◽

Yu Wang ◽

Yuanyuan Tie ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Diabetic Cardiomyopathy ◽

Bioinformatics Analysis ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Vital Role ◽

Data Sets ◽

Go Enrichment ◽

Geo Database

Abstract Background: The pathogenic mechanism and development of the diabetic cardiomyopathy(DCM) has been generally explained, and it is clear that the microRNAs(miRNAs), mRNAs and transcription factors(TFs) participate in the process of the DCM disease. Yet, the hub targets of the disease progression are not clear.Methods: To figure out the problem, we downloaded data sets from the Gene Expression Omnibus(GEO) database (GSE44179 and GSE4745). The targeted mRNAs of miRNAs were downloaded from TargetScan, miRBD and microT-CDS database. Gene Ontology (GO) enrichment of miRNAs and mRNAs were analysed in DAVID.R studio software was used to visualize the results of screened targets and GO enrichment. Cytoscape software was used to visualize the miRNA-mRNA-TF interaction network and calculate the hub targets. Results: We filtered eight miRNAs, nine mRNAs and ten transcription factors(TFs) by bioinformatics analysis, and constructed a miRNA-mRNA-TF network. The top ten degrees of nodes in the network are rno-miR-7a, Hnf4a, rno-miR-17, rno-miR-21, rno-miR-122, rno-miR-200c, Med1, Mlxipl, SP1 and rno-miR-34a, which were closely related to the process of DCM. Conclusion: This study revealed that rno-miR-7a, Hnf4a, rno-miR-17and rno-miR-21 may play vital role in the progress of diabetic cardiomyopathy.

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Associative Clustering for Exploring Dependencies between Functional Genomics Data Sets

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2005.32 ◽

2005 ◽

Vol 2 (3) ◽

pp. 203-216 ◽

Cited By ~ 10

Author(s):

S. Kaski ◽

J. Nikkila ◽

J. Sinkkonen ◽

L. Lahti ◽

J.E.A. Knuuttila ◽

...

Keyword(s):

Functional Genomics ◽

Data Sets

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NCBI GEO: archive for functional genomics data sets—update

Nucleic Acids Research ◽

10.1093/nar/gks1193 ◽

2012 ◽

Vol 41 (D1) ◽

pp. D991-D995 ◽

Cited By ~ 2845

Author(s):

Tanya Barrett ◽

Stephen E. Wilhite ◽

Pierre Ledoux ◽

Carlos Evangelista ◽

Irene F. Kim ◽

...

Keyword(s):

Functional Genomics ◽

Data Sets

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Integrate analysis and identification for different expression genes in chondrogenesis

10.21203/rs.2.20142/v1 ◽

2020 ◽

Author(s):

Keda Liu ◽

Nanjue Cao ◽

Yuhe Zhu ◽

Wei Wang

Keyword(s):

Interaction Network ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Data Sets ◽

Protein Protein Interaction ◽

Related Data ◽

Integrate Analysis ◽

And Function ◽

Microarray Datasets ◽

Geo Database

Abstract Background: The intricate mechanisms of articular chondrogenesis are largely unknown. Gradually, with the help of high-throughput platforms, microarrays have become an important and useful method to testify hub genes in desease. Today, advanced bioinformatic analysis of available microarray data can provide more reliable and accurate screening results by duplicating related data sets. Results: Microarray datasets GSE9451 and GSE104113 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were performed, and function enrichment analyses were demonstrated. The protein-protein interaction network (PPI) was constructed and the module analysis was performed by using STRING and Cytoscape. Quantitative PCR was used to confirm the results of bioinformatics analysis. Conclusion: Compared to individual studies, this study can provide extra reliable and accurate screening results by duplicating relevant records. Additional molecular experiments are required to confirm the discovery of candidate genes identified by chondrogenesis. S100A4 is predicted to integrate with miR-325-3p to promote osteogenesis.

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Applications of Functional Genomics for Drug Discovery

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555220902092 ◽

2020 ◽

Vol 25 (8) ◽

pp. 823-842 ◽

Cited By ~ 1

Author(s):

Ami Kabadi ◽

Eoin McDonnell ◽

Christopher L. Frank ◽

Lauren Drowley

Keyword(s):

Functional Genomics ◽

Drug Targets ◽

Disease Modeling ◽

Association Studies ◽

Data Sets ◽

Genome Wide Association Studies ◽

Functional Genomic ◽

Causative Gene ◽

Functional Changes ◽

Epigenome Editing

Many diseases, such as diabetes, autoimmune diseases, cancer, and neurological disorders, are caused by a dysregulation of a complex interplay of genes. Genome-wide association studies have identified thousands of disease-linked polymorphisms in the human population. However, detailing the causative gene expression or functional changes underlying those associations has been elusive in many cases. Functional genomics is an emerging field of research that aims to deconvolute the link between genotype and phenotype by making use of large -omic data sets and next-generation gene and epigenome editing tools to perturb genes of interest. Here we review how functional genomic tools can be used to better understand the biological interplay between genes, improve disease modeling, and identify novel drug targets. Incorporation of functional genomic capabilities into conventional drug development pipelines is predicted to expedite the development of first-in-class therapeutics.

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Integrating functional genomics data

Biochemical Society Transactions ◽

10.1042/bst0311484 ◽

2003 ◽

Vol 31 (6) ◽

pp. 1484-1487 ◽

Cited By ~ 9

Author(s):

P. Kemmeren ◽

F.C.P. Holstege

Keyword(s):

Data Quality ◽

Functional Genomics ◽

High Throughput ◽

Functional Annotation ◽

Data Sets ◽

Functional Annotations ◽

High Throughput Data

Functional annotation of fully sequenced genomes is still a major issue. High-throughput data sets could be used to provide more and better functional annotations. However differences in data quality need to be taken into account. For this purpose these high-throughput data sets need to be integrated so that the data quality can be assessed, hypotheses can be prioritized and existing annotations can be improved and extended.

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