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ExprAlign--the identification of ESTs in non-model species by alignment of cDNA microarray expression profiles
Weizhong Li1, Andrew Y Gracey, Luciane Vieira Mello
1Centre for Genome Research, School of Biological Sciences, University of Liverpool, Crown Street, Liverpool, L69 7ZB, UK. w.li@ebi.ac.uk
BMC Genomics
|November 27, 2009
Summary
Analyzing gene expression profiles using microarray data helps identify unknown gene sequences in common carp. This co-expression landscape approach successfully assigned identities to hundreds of previously unidentified expressed sequence tags (ESTs).
Area of Science:
- Genomics and Bioinformatics
- Aquaculture and Environmental Science
Background:
- Identifying expressed sequence tags (ESTs) in non-model organisms like common carp is challenging due to duplicated genomes and phylogenetic distance from model species.
- A significant number of common carp ESTs remained unidentified using traditional BLAST sequence alignment methods.
Purpose of the Study:
- To develop and apply a novel method for identifying unknown common carp ESTs using gene expression profiles.
- To leverage large-scale microarray data to infer gene identities based on co-expression patterns.
Main Methods:
- Utilized expression profiles from approximately 700 cDNA microarrays covering 7 major tissues and multiple environmental stressors.
- Constructed a co-expression landscape using Pearson's correlation coefficient to identify clusters of highly correlated genes ('mountains').
- Assessed gene identities by analyzing patterns within these co-expression clusters.
Main Results:
- Successfully suggested identities for 522 out of 2701 previously unknown carp EST sequences.
- Identified common carp genes and isoforms missed by standard BLAST sequence alignment alone.
- Demonstrated that using data from multiple tissues and treatments significantly improved identification precision.
Conclusions:
- Analysis of co-expression landscapes is a sensitive method for identifying unknown cDNAs from EST projects.
- This technique can detect subtle expression changes, differentiating genes with similar BLAST identities.
- The approach benefits from large-scale microarray data and the use of multiple experimental treatments.
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