Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
December 22nd, 2017
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This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis Tóxicos in Levadura (ATL) E3 ubiquitin ligases.
Overview
This article describes a procedure for the identification and characterization of a gene family in grapevine, specifically focusing on the Arabidopsis Tóxicos in Levadura (ATL) E3 ubiquitin ligases. The method aims to provide a comprehensive understanding of gene structure, protein motifs, chromosomal location, gene duplications, and expression patterns.
Key Study Components
Area of Science
- Plant Genetics
- Gene Characterization
- Ubiquitin Ligases
Background
- Understanding gene families is crucial for plant classification.
- Gene expression analysis aids in identifying family members.
- Characterization can reveal functional candidates for further studies.
- This method is applicable to any plant species with available genetic data.
Purpose of Study
- To achieve exhaustive characterization of a gene family.
- To define gene structure and associated protein motifs.
- To analyze gene duplications and expression patterns.
Methods Used
- Global genomic identification of gene family members.
- Transcriptomic analysis for gene expression profiling.
- Experimental steps for complete gene family characterization.
- Application of the procedure to various plant species.
Main Results
- Comprehensive list of experimental steps provided.
- Identification of interesting candidates for functional studies.
- Insights into gene family classification in plants.
- Demonstration of the procedure by Pietro Ariani.
Conclusions
- The procedure offers a systematic approach to gene family characterization.
- It enhances understanding of plant genetics and evolution.
- Valuable for future research in plant biology.
Frequently Asked Questions
What is the significance of E3 ubiquitin ligases in plants?
E3 ubiquitin ligases play a crucial role in regulating protein degradation and signaling pathways in plants.
Can this method be applied to other plant species?
Yes, the procedure can be applied to any plant species for which genetic data is available.
What are the main components analyzed in this procedure?
The main components include gene structure, protein motifs, chromosomal location, gene duplications, and expression patterns.
Who demonstrated the procedure in this study?
The procedure was demonstrated by Pietro Ariani from the laboratory.
How does this method contribute to plant classification?
It provides a comprehensive understanding of gene families, aiding in the classification of plants.
What advantages does this procedure offer?
It offers an exhaustive list of experimental steps for complete gene family characterization.
Executive Industry Relevance
Genome-wide identification and meta-analysis of gene families, such as ATL E3 ubiquitin ligases in grapevine, enable systematic target validation and mechanistic de-risking in plant biotechnology pipelines. This workflow supports predictive confidence in functional genomics, informing candidate prioritization for trait engineering and stress adaptation studies. The approach is broadly applicable to any plant species with genomic data, enhancing portfolio decision-making in agricultural R&D.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables comprehensive cataloging of gene family members for hypothesis-driven target selection.
- Supports mechanistic de-risking by clarifying gene structure, duplication, and conserved motifs.
- Facilitates functional annotation and prioritization of candidates for downstream validation.
Screening & Assay Development
- Provides standardized gene family datasets for assay development and screening workflows.
- Enables reproducible identification of expression patterns across tissues and conditions.
- Supports scalable analysis of gene function and response to biotic stressors.
Translational & Preclinical Research
- Aligns gene expression clusters with phenotypic outcomes relevant to stress adaptation.
- Enables continuity from discovery genomics to trait validation in preclinical plant models.
- Supports risk-adjusted advancement of gene candidates for functional studies.
Pipeline & Workflow Integration
This workflow integrates from early discovery through lead identification and preclinical trait validation in plant biotechnology pipelines.
- Discovery Biology: Supports genome-wide hypothesis testing and gene family classification for target validation.
- Screening: Delivers reproducible, quantitative gene expression profiles for candidate triage.
- Analytics: Provides hierarchical clustering and phylogenetic outputs to compare gene family members.
- Translational Research: Links gene expression modulation to stress response phenotypes in plants.
- Enterprise Reuse: Offers a reusable informatics workflow adaptable to any plant gene family with available genomic data.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in gene function and target selection.
- Operational Value: Standardizes gene family characterization and expression analysis across projects.
- Strategic Value: Improves go/no-go decisions for candidate advancement and resource allocation.
- Portfolio Impact: Enables risk-adjusted prioritization of gene targets for trait engineering and stress adaptation.
Implementation Considerations
- Requires expertise in bioinformatics, phylogenetics, and gene expression analysis.
- Depends on access to genomic databases, alignment tools, and clustering software.
- Necessitates standardized data processing and annotation protocols across teams.
- Adaptable to diverse plant species and gene families with available sequence data.
- Stringency in alignment and clustering parameters is critical for reliable outputs.
Biopharma FAQs
Why does null hypothesis testing matter for gene expression clustering?
Null hypothesis testing in hierarchical bi-clustered analysis ensures that observed gene expression clusters are statistically significant, supporting robust target validation and reducing false positives in candidate selection.
How does independent variable isolation fit in PSI-BLAST gene identification?
Isolating independent variables, such as specific sequence motifs, during PSI-BLAST iterations refines gene family member identification and minimizes inclusion of unrelated sequences, improving discovery pipeline accuracy.
What do quantitative dependent variable measurements enable in expression profiling?
Quantitative measurements of gene expression across tissues and conditions enable precise clustering, comparative analysis, and prioritization of gene family members for functional studies and trait engineering.
Why are replication requirements critical for cross-functional gene family analysis?
Replication in gene expression and phylogenetic analyses ensures reproducibility and reliability, facilitating cross-team collaboration and consistent interpretation of candidate gene function.
What statistical analysis capabilities are needed before implementing hierarchical clustering?
Robust statistical tools for normalization, clustering, and significance testing are required to validate gene expression patterns and support confident advancement of gene family candidates in R&D workflows.
Chapters in this video
0:05
Title
1:04
Identifying Putative Gene Family Members, Making Alignments, and Sequence Logos
3:33
Phylogenetic Analysis and Nomenclature
6:51
Tissue Expression Meta-analysis
8:27
Results: Identified ATL Gene Family Members in the Grapevine Genome
9:52
Conclusion
