Discovering sequence similarity by the algorithmic significance method
The minimal-length encoding approach is applied to define concept of sequence similarity. A sequence is defined to be similar to another sequence or to a set of keywords if it can be encoded in a small number of bits by taking advantage of common subwords. Minimal-length encoding of a sequence is computed in linear time, using a data compression algorithm that is based on a dynamic programming strategy and the directed acyclic word graph data structure. No assumptions about common word (``k-tuple``) length are made in advance, and common words of any length are considered. The newly proposed algorithmic significance method provides an exact upper bound on the probability that sequence similarity has occurred by chance, thus eliminating the need for any arbitrary choice of similarity thresholds. Preliminary experiments indicate that a small number of keywords can positively identify a DNA sequence, which is extremely relevant in the context of partial sequencing by hybridization.
- Research Organization:
- Argonne National Lab., IL (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-31109-ENG-38; FG03-91ER61152
- OSTI ID:
- 10165238
- Report Number(s):
- ANL/BIM/CP-78918; CONF-930745-2; ON: DE93015556
- Resource Relation:
- Conference: 1. international conference in intelligent systems for molecular biology,Washington, DC (United States),7-9 Jul 1993; Other Information: PBD: Feb 1993
- Country of Publication:
- United States
- Language:
- English
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