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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
Correction of sequence-based artifacts in serial analysis of gene expression
Viatcheslav R Akmaev1, Clarence J Wang
1Genzyme Corporation, Framingham, MA 01701-9322, USA. slava.akmaev@genzyme.com
Bioinformatics (Oxford, England)
|February 12, 2004
Summary
Serial Analysis of Gene Expression (SAGE) errors can be corrected using the SAGEScreen algorithm. This method improves the accuracy of transcript tag data, aiding in gene discovery and transcriptome analysis.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Serial Analysis of Gene Expression (SAGE) is a powerful tool for global gene expression profiling.
- Long SAGE enhances transcript tag generation for transcriptome analysis and gene discovery.
- Sequencing errors in SAGE and Long SAGE data can impact downstream analyses.
Purpose of the Study:
- To develop and evaluate an efficient algorithm for correcting tag errors in SAGE data.
- To provide accurate error rate estimates for SAGE and Long SAGE datasets.
Main Methods:
- Developed SAGEScreen, a multi-step algorithm for processing ditags and correcting sequencing errors.
- Estimated empirical error rates using highly abundant tags and statistical testing.
- Applied SAGEScreen to Long SAGE libraries and simulated tag collections.
Main Results:
- SAGEScreen corrects 78% of recoverable tag errors in simulated datasets.
- The algorithm effectively reduces the occurrence of singleton tags.
- Error rate estimates incorporate Polymerase chain reaction and sequencing contributions.
Conclusions:
- SAGEScreen is an effective tool for correcting sequencing artifacts in SAGE data.
- Accurate SAGE data enhances the reliability of transcriptome analysis and gene discovery.
- The SAGEScreen software is available for academic use.
