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Discarding duplicate ditags in LongSAGE analysis may introduce significant error
Jeppe Emmersen1, Anna M Heidenblut, Annabeth Laursen Høgh
1Department of Biotechnology, Chemistry and Environmental Engineering, Aalborg University, Aalborg, Denmark. je@bio.aau.dk <je@bio.aau.dk>
BMC Bioinformatics
|March 16, 2007
Summary
Removing duplicate ditags in Serial Analysis of Gene Expression (SAGE) is often unnecessary and introduces significant errors in LongSAGE analysis. New algorithms can identify true artifact ditags for accurate gene expression profiling.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- Serial Analysis of Gene Expression (SAGE) involves removing duplicate ditags, presumed artifacts, during analysis.
- This removal process can eliminate naturally occurring duplicate ditags, leading to measurement errors.
Purpose of the Study:
- To investigate the necessity of removing all duplicate ditags in SAGE and LongSAGE analysis.
- To develop and apply an algorithm for analyzing ditag populations and identifying artifact ditags.
Main Methods:
- Development of a novel algorithm to analyze the differential occurrence of SAGE tags within ditag combinations.
- Application of the algorithm to a pancreatic acinar cell LongSAGE library and ten additional LongSAGE libraries.
- Comparative analysis of error introduced by duplicate ditag removal in SAGE versus LongSAGE.
Main Results:
- Analysis revealed no general amplification bias justifying the removal of all duplicate ditags in the studied LongSAGE libraries.
- Removal of duplicate ditags introduced insignificant errors in SAGE but up to 3-fold errors in LongSAGE.
- The developed algorithm successfully identified artifact ditags originating from nucleotide variations and vector contamination.
Conclusions:
- The routine removal of all duplicate ditags is unfounded for the analyzed datasets and introduces substantial errors, particularly in LongSAGE.
- This practice may lead to similar errors in other existing LongSAGE datasets.
- Ditag population analysis is crucial for identifying and managing artifact tags, ensuring more accurate gene expression data.
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