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Novel Sequence Discovery by Subtractive Genomics
Published on: January 25, 2019
Efficient computation of absent words in genomic sequences.
Julia Herold1, Stefan Kurtz, Robert Giegerich
1Center of Biotechnology, Bielefeld University, Postfach 10 01 31, 33501 Bielefeld, Germany. jherold@cebitec.uni-bielefeld.de
BMC Bioinformatics
|March 28, 2008
Summary
A new algorithm efficiently computes absent words in genomes, identifying unique subsequences critical for comparative genomics and genetic engineering. This method is faster and requires less memory than previous approaches.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Genome research routinely analyzes sequence composition, including base composition and codon usage.
- Unique subsequences (words) are important markers for genome comparison, expression profiling, and genetic engineering.
- Absent words, or 'unwords', are shortest subsequences not found in a genome and have been a focus of recent studies.
Purpose of the Study:
- To develop a novel, efficient algorithm and software for computing absent words in genomic sequences.
- To provide a user-friendly tool that directly calculates absent words without requiring length estimation.
Main Methods:
- A new algorithm for absent word computation was developed, avoiding the need for suffix trees or suffix arrays.
- The software implementation is open-source and designed for efficiency and ease of use.
- Absent words were computed for various organisms, including human and mouse genomes, across a range of sizes.
Main Results:
- The new algorithm and software efficiently compute absent words.
- The method is more efficient and requires less memory compared to existing algorithms.
- Absent words were successfully computed for genomes ranging from 10^5 to 10^9 base pairs.
Conclusions:
- The developed algorithm computes absent words for the human genome in 10 minutes using minimal space (2.5 Mb).
- This advancement enables absent word analysis for large genomes and emerging pan- and meta-genomic datasets.
- The software provides a valuable tool for genomic sequence analysis and comparative studies.
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