GenoretGenes
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| - | The GenoretGenes is a | + | The GenoretGenes is a Database and a Website builded around the [[Gscope]] project EVImm (and very soon EVIhs). |
It is developped and maintained by [[Laetitia Poidevin]] | It is developped and maintained by [[Laetitia Poidevin]] | ||
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It was used to select the 1500 genes for [[RetChip]] | It was used to select the 1500 genes for [[RetChip]] | ||
| - | == | + | ==Objectives== |
| - | + | Our main objectives are the development and deployment of a strategy for efficient computerised characterisation, analysis and exploitation of gene-linked data in the retinal tissue considered as a biological system. | |
| - | + | ||
| + | In the post-genomic era, these goals invoke various and distinct developments encompassing high-throughput data analysis from diverse biotechnological origins (transcriptomics, promotomics, proteomics, interactomics…) as well as the description and understanding of the sequence/structure/function/evolution relationships to perform pertinent phylogenetic inference from model organisms, rich in data, to human or non-model organisms. | ||
| + | |||
| + | At the biocomputing level, the deployment of an efficient system invokes numerous algorithmic developments ranging from high quality high-throughput raw data treatment, normalisation and clustering up to sequence and genomic data cleaning, cross-validation, analysis and exploitation. In addition, all these developments must be performed in the framework of a valid relational retrieval and storage system allowing rapid and automatised up-dating, interconnection and querying of complex information. | ||
| + | |||
| + | ==Major achievements== | ||
| + | # the identification and characterisation of target genes through the analysis of distinct transcriptomics experiments | ||
| + | # the creation of a unifying gene identity card relating very diverse information concerning retinal target genes | ||
| + | # the design of Retchip, the retinal-specific low-throughput micro-array | ||
| + | |||
| + | ==Link to RetinoBase== | ||
| + | Nevertheless, all these results have been obtained through a constant effort aiming at the creation of a unifying system for synchronized treatment and organisation of the data. Our major efforts concerning high-throughput data exploitation focused on the development of RetinoBase, the relational database specifically dedicated to retinal transcriptomics data. Regardless of the various relational schema that have been developed to perform rapid handling, integration and interconnection of retinal transcriptomics data from diverse experiments, microarray technologies or organisms, the major novelty is linked to the fact that RetinoBase is thinked up not only as a storage and querying database but also as an effective tool to improve future transcriptomics data analysis and exploitation. | ||
| + | |||
| + | This original approach implies that one single transcriptomics experiment integrated in RetinoBase is linked to the expert analysis as well as to the various results generated by classical or home-developed algorithms for raw data treatment, normalisation and clustering. This strategy aims at understanding the origins of the numerous inconsistencies and bottlenecks that impede efficient automated analysis of transcriptomics data and it allowed us to develop original algorithms, notably in the initial process of raw data treatment and noise reduction. In addition, by giving easy access and display to the distinct treatments and results, RetinoBase allows the biologist to visualize and understand the limits and inconsistencies inherent to the high-throughput analysis and facilitate the introduction of the biological expertise and knowledge in the analysis. This strategy proved to be valid by allowing the identification of retinal target genes obtained from transcriptomics experiments analysis that are currently tested and validated by biological partners. | ||
| + | |||
| + | ==Sequence/Structure/Function/Evolution Relationships== | ||
| + | Concerning the understanding of the sequence/structure/function/evolution relationships, our efforts have been focused on the gene annotation and on the establishment of an automated platform Retscope (Gscope applied to the Retina) dedicated to gene-related information handling, treatment and interconnection. Gene-related information represents very heterogeneous data encompassing genomic or gene features as well as sequence, 3D structure, function or taxonomical data. Regardless of the numerous protocols, original algorithms and web services developed for quality control, cleaning, validation and analysis of biological objects as diverse as completes genomes, promoters, mRNAs or ESTs, proteins, 3D structures or complexes…, the major point of the Retscope system is linked to its design allowing common organising schema and formats that are used to classify and interlink the data. This strategy allows automatic gene-related data treatment with updating and distribution of the retrieved or generated information in an original relational database. | ||
| + | |||
| + | ==Development of BIRD== | ||
| + | In the field of data integration, these recent developments have been complemented by the development and deployment of the first version of the BIRD system which represents a simplifying and unifying upper layer encompassing local as well as external data or databases. To date, our gene-related information system, and notably the RetScope system and the RetinoBase has been successfully implemented as instances in the BIRD system. This implies that any information related to the genome, protein, group of proteins, microarray or interactomics are easily accessible and querying through the BIRD system and the EVI-GENORET database. Clearly, in the future, BIRD will constitute our central system to further define and study the retinal gene networks. | ||
==Links== | ==Links== | ||
Visit the website [http://alnitak.u-strasbg.fr/GenoretGenes GenoretGenes] | Visit the website [http://alnitak.u-strasbg.fr/GenoretGenes GenoretGenes] | ||
Revision as of 13:20, 17 August 2007
The GenoretGenes is a Database and a Website builded around the Gscope project EVImm (and very soon EVIhs). It is developped and maintained by Laetitia Poidevin
It was used to select the 1500 genes for RetChip
Contents |
Objectives
Our main objectives are the development and deployment of a strategy for efficient computerised characterisation, analysis and exploitation of gene-linked data in the retinal tissue considered as a biological system.
In the post-genomic era, these goals invoke various and distinct developments encompassing high-throughput data analysis from diverse biotechnological origins (transcriptomics, promotomics, proteomics, interactomics…) as well as the description and understanding of the sequence/structure/function/evolution relationships to perform pertinent phylogenetic inference from model organisms, rich in data, to human or non-model organisms.
At the biocomputing level, the deployment of an efficient system invokes numerous algorithmic developments ranging from high quality high-throughput raw data treatment, normalisation and clustering up to sequence and genomic data cleaning, cross-validation, analysis and exploitation. In addition, all these developments must be performed in the framework of a valid relational retrieval and storage system allowing rapid and automatised up-dating, interconnection and querying of complex information.
Major achievements
- the identification and characterisation of target genes through the analysis of distinct transcriptomics experiments
- the creation of a unifying gene identity card relating very diverse information concerning retinal target genes
- the design of Retchip, the retinal-specific low-throughput micro-array
Link to RetinoBase
Nevertheless, all these results have been obtained through a constant effort aiming at the creation of a unifying system for synchronized treatment and organisation of the data. Our major efforts concerning high-throughput data exploitation focused on the development of RetinoBase, the relational database specifically dedicated to retinal transcriptomics data. Regardless of the various relational schema that have been developed to perform rapid handling, integration and interconnection of retinal transcriptomics data from diverse experiments, microarray technologies or organisms, the major novelty is linked to the fact that RetinoBase is thinked up not only as a storage and querying database but also as an effective tool to improve future transcriptomics data analysis and exploitation.
This original approach implies that one single transcriptomics experiment integrated in RetinoBase is linked to the expert analysis as well as to the various results generated by classical or home-developed algorithms for raw data treatment, normalisation and clustering. This strategy aims at understanding the origins of the numerous inconsistencies and bottlenecks that impede efficient automated analysis of transcriptomics data and it allowed us to develop original algorithms, notably in the initial process of raw data treatment and noise reduction. In addition, by giving easy access and display to the distinct treatments and results, RetinoBase allows the biologist to visualize and understand the limits and inconsistencies inherent to the high-throughput analysis and facilitate the introduction of the biological expertise and knowledge in the analysis. This strategy proved to be valid by allowing the identification of retinal target genes obtained from transcriptomics experiments analysis that are currently tested and validated by biological partners.
Sequence/Structure/Function/Evolution Relationships
Concerning the understanding of the sequence/structure/function/evolution relationships, our efforts have been focused on the gene annotation and on the establishment of an automated platform Retscope (Gscope applied to the Retina) dedicated to gene-related information handling, treatment and interconnection. Gene-related information represents very heterogeneous data encompassing genomic or gene features as well as sequence, 3D structure, function or taxonomical data. Regardless of the numerous protocols, original algorithms and web services developed for quality control, cleaning, validation and analysis of biological objects as diverse as completes genomes, promoters, mRNAs or ESTs, proteins, 3D structures or complexes…, the major point of the Retscope system is linked to its design allowing common organising schema and formats that are used to classify and interlink the data. This strategy allows automatic gene-related data treatment with updating and distribution of the retrieved or generated information in an original relational database.
Development of BIRD
In the field of data integration, these recent developments have been complemented by the development and deployment of the first version of the BIRD system which represents a simplifying and unifying upper layer encompassing local as well as external data or databases. To date, our gene-related information system, and notably the RetScope system and the RetinoBase has been successfully implemented as instances in the BIRD system. This implies that any information related to the genome, protein, group of proteins, microarray or interactomics are easily accessible and querying through the BIRD system and the EVI-GENORET database. Clearly, in the future, BIRD will constitute our central system to further define and study the retinal gene networks.
Links
Visit the website GenoretGenes
