ATTED-II

ATTED-II provides gene coexpression information for nine plant species by integrating public RNA sequencing (RNAseq) and microarray expression datasets to enable condition-independent coexpression analysis that mitigates sampling bias.

Key Features:

• Supported species: Provides coexpression data for nine plant species.
• Input data types: Integrates publicly available RNA sequencing (RNAseq) and microarray expression datasets.
• Version 11 coexpression algorithm: Implements a coexpression calculation in version 11 that employs principal component analysis (PCA) and ensemble calculation techniques.
• Principal component filtering: Reduces noise by excluding principal components with low contribution rates.
• Ensemble calculation: Uses ensemble calculation to derive coexpression measures from processed components.
• Sample contribution balancing: Balances discrepancies in sample contributions across different conditions to address sampling bias.
• Species-representative integration: Produces integrated coexpression information representative of each species.
• Z-score standardization: Standardizes coexpression data as z-scores to enable interspecies comparisons and integration with other datasets.
• Condition-independent analysis: Facilitates coexpression analysis that considers diverse experimental settings rather than single conditions.

Scientific Applications:

• Interspecies comparative analysis: Enables comparison of gene coexpression patterns across multiple plant species using standardized z-scores.
• Integrated analysis of heterogeneous datasets: Supports integration of RNAseq and microarray data for combined coexpression studies.
• Plant genomics: Supports investigations into gene relationships and networks in plant genomics.
• Functional genomics: Aids functional genomics studies by providing coexpression evidence across diverse conditions and species.

Methodology:

ATTED-II v11 integrates public RNAseq and microarray expression datasets, applies principal component analysis with exclusion of low-contribution components, performs ensemble calculations to compute coexpression, balances sample contributions across conditions, and standardizes coexpression values as z-scores.