scholarly journals From ArrayExpress to BioStudies

2020 ◽  
Vol 49 (D1) ◽  
pp. D1502-D1506
Author(s):  
Ugis Sarkans ◽  
Anja Füllgrabe ◽  
Ahmed Ali ◽  
Awais Athar ◽  
Ehsan Behrangi ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Microarray Data ◽  
Archival Data ◽  
Central Concept ◽  
Multimodal Data ◽  
Online Tool ◽  
Technical Aspects ◽  
Data Infrastructure ◽  
European Nucleotide Archive ◽  
Programmatic Access

Abstract ArrayExpress (https://www.ebi.ac.uk/arrayexpress) is an archive of functional genomics data at EMBL-EBI, established in 2002, initially as an archive for publication-related microarray data and was later extended to accept sequencing-based data. Over the last decade an increasing share of biological experiments involve multiple technologies assaying different biological modalities, such as epigenetics, and RNA and protein expression, and thus the BioStudies database (https://www.ebi.ac.uk/biostudies) was established to deal with such multimodal data. Its central concept is a study, which typically is associated with a publication. BioStudies stores metadata describing the study, provides links to the relevant databases, such as European Nucleotide Archive (ENA), as well as hosts the types of data for which specialized databases do not exist. With BioStudies now fully functional, we are able to further harmonize the archival data infrastructure at EMBL-EBI, and ArrayExpress is being migrated to BioStudies. In future, all functional genomics data will be archived at BioStudies. The process will be seamless for the users, who will continue to submit data using the online tool Annotare and will be able to query and download data largely in the same manner as before. Nevertheless, some technical aspects, particularly programmatic access, will change. This update guides the users through these changes.

scholarly journals Morphants: A New Systematic Vertebrate Functional Genomics Approach

Yeast ◽  
2000 ◽  
Vol 1 (4) ◽  
pp. 302-306 ◽  
Author(s):  
Stephen C. Ekker
Keyword(s):  
Functional Genomics ◽  
Danio Rerio ◽  
Unknown Function ◽  
Gene Inactivation ◽  
Vertebrate Genome ◽  
Technical Aspects ◽  
Knock Down ◽  
Genome Wide

The vertebrate genome contains a predicted 50 000 100 000 genes, many of unknown function. The recent development of morpholino-based gene knock-down technology in zebrafish has opened the door to the genome-wide assignment of function based on sequence in a model vertebrate. This review describes technical aspects of morpholino use for functional genomics applications, including the potential for multigene targeting and known methodological limitations. The result of successful gene inactivation by this agent is proposed to yield embryos with a ‘morphant’ phenotypic designation. The establishment of a morphant database opens the door to true functional genomics using the vertebrate,Danio rerio.

scholarly journals A nutrigenomics database – integrated repository for publications and associated microarray data in nutrigenomics research

2005 ◽  
Vol 94 (4) ◽  
pp. 493-495 ◽  
Author(s):  
Kenji Saito ◽  
Soichi Arai ◽  
Hisanori Kato
Keyword(s):  
Microarray Data ◽  
Microarray Experiment ◽  
Data Sets ◽  
Expression Data ◽  
Web Based ◽  
Data Infrastructure ◽  
Microarray Expression Data ◽  
Efficient Data ◽  
Microarray Expression ◽  
Support Research

In the current situation where microarray data in the field of nutritional genomics (nutrigenomics) are accumulating rapidly, there is imminent need for an efficient data infrastructure to support research workflow. We have established a web-based, integrated database of the publications and microarray expression data in the field of nutrigenomics. The registered data include links to external databases such as PubMed of the National Center for Biotechnology Information and public microarray databases that contain Minimum Information About a Microarray Experiment-compliant microarray expression data. Using this database, all data sets created will be effectively utilized and shared with other researchers. This database is built on an open-source database system and is freely accessible via the World Wide Web (http://a-yo5.ch.a.u-tokyo.ac.jp/index.phtml</url).

scholarly journals FGDP: functional genomics data pipeline for automated, multiple microarray data analyses

2004 ◽  
Vol 20 (2) ◽  
pp. 282-283 ◽  
Author(s):  
J. D. Grant ◽  
L. A. Somers ◽  
Y. Zhang ◽  
F. J. Manion ◽  
G. Bidaut ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Microarray Data ◽  
Data Analyses ◽  
Data Pipeline

scholarly journals Morphants: A New Systematic Vertebrate Functional Genomics Approach

Yeast ◽  
2000 ◽  
Vol 1 (4) ◽  
pp. 302-306
Author(s):  
Stephen C. Ekker
Keyword(s):  
Functional Genomics ◽  
Danio Rerio ◽  
Unknown Function ◽  
Gene Inactivation ◽  
Vertebrate Genome ◽  
Technical Aspects ◽  
Knock Down ◽  
Genome Wide

The vertebrate genome contains a predicted 50 000 100 000 genes, many of unknown function. The recent development of morpholino-based gene knock-down technology in zebrafish has opened the door to the genome-wide assignment of function based on sequence in a model vertebrate. This review describes technical aspects of morpholino use for functional genomics applications, including the potential for multigene targeting and known methodological limitations. The result of successful gene inactivation by this agent is proposed to yield embryos with a ‘morphant’ phenotypic designation. The establishment of a morphant database opens the door to true functional genomics using the vertebrate, Danio rerio.

scholarly journals Ranking, selecting, and prioritising genes with desirability functions

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1444 ◽  
Author(s):  
Stanley E. Lazic
Keyword(s):  
Breast Cancer ◽  
Functional Genomics ◽  
Differentially Expressed Genes ◽  
Microarray Data ◽  
Selection Criteria ◽  
Ad Hoc ◽  
Data Set ◽  
Desirability Functions ◽  
Sharp Thresholds

In functional genomics experiments, researchers often select genes to follow-up or validate from a long list of differentially expressed genes. Typically, sharp thresholds are used to bin genes into groups such as significant/non-significant or fold change above/below a cut-off value, andad hoccriteria are also used such as favouring well-known genes. Binning, however, is inefficient and does not take the uncertainty of the measurements into account. Furthermore,p-values, fold-changes, and other outcomes are treated as equally important, and relevant genes may be overlooked with such an approach. Desirability functions are proposed as a way to integrate multiple selection criteria for ranking, selecting, and prioritising genes. These functions map any variable to a continuous 0–1 scale, where one is maximally desirable and zero is unacceptable. Multiple selection criteria are then combined to provide an overall desirability that is used to rank genes. In addition top-values and fold-changes, further experimental results and information contained in databases can be easily included as criteria. The approach is demonstrated with a breast cancer microarray data set. The functions and an example data set can be found in the desiR package on CRAN (https://cran.r-project.org/web/packages/desiR/) and the development version is available on GitHub (https://github.com/stanlazic/desiR).

scholarly journals Muon: multimodal omics analysis framework

2021 ◽  
Author(s):  
Danila Bredikhin ◽  
Ilia Kats ◽  
Oliver Stegle
Keyword(s):  
Data Structure ◽  
Easy Access ◽  
Omics Data ◽  
Analysis Framework ◽  
Multimodal Data ◽  
Data Infrastructure ◽  
Omics Technologies ◽  
Data Standard ◽  
Basic Biology ◽  
Rich Data

Advances in multi-omics technologies have led to an explosion of multimodal datasets to address questions ranging from basic biology to translation. While these rich data provide major opportunities for discovery, they also come with data management and analysis challenges, thus motivating the development of tailored computational solutions to deal with multi-omics data. Here, we present a data standard and an analysis framework for multi-omics - MUON - designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible data structure, supporting an arbitrary number of omics layers. The MUON data structure is interoperable with existing community standards for single omics, and it provides easy access to both data from individual omics as well as multimodal dataviews. Building on this data infrastructure, MUON enables a versatile range of analyses, from data preprocessing, the construction of multi-omics containers to flexible multi-omics alignment.

Analysis of Collaborative Meetings in Developing Data Mining Models

2005 ◽  
Vol 49 (5) ◽  
pp. 686-689
Author(s):  
Jiao Ma ◽  
Colin G. Drury
Keyword(s):  
Data Mining ◽  
Longitudinal Study ◽  
Microarray Data ◽  
Current Understanding ◽  
Design Approach ◽  
Field Observations ◽  
Technical Aspects ◽  
Collaborative Groups ◽  
Group Meetings

Observations of group meetings were used to help our understanding of the Data Mining (DM) process, which can take a year to complete. Over a course of three months, we followed two collaborative groups and observed their weekly meetings, where they devised DM models and explored new ways to analyze and present microarray data. The study furthered the current understanding of the DM activities by revealing its socio-technical aspects, and directed a promising design approach for a more efficient and effective DM system. Field observations of collaborative meetings disclosed that a longitudinal study is, in fact, appropriate and necessary to further understand the DM process and the system.

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