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Efficient discovery of conserved patterns using a pattern graph

¹Department of Informatics, University of Bergen, Norway. inge@ii.uib.no

Computer Applications in the Biosciences : CABIOS

|November 21, 1997

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A new Pratt program algorithm improves conserved pattern discovery in protein sequences. This enhanced search strategy is faster and integrates better with other bioinformatics tools.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Previous algorithms for conserved pattern discovery in unaligned protein sequences were implemented in the Pratt program.
The original Pratt program allowed users to define pattern classes but could be inefficient for complex analyses.

Purpose of the Study:

To develop a more efficient algorithm for discovering conserved patterns in protein sequence sets.
To improve the speed and applicability of the Pratt program for bioinformatics analyses.

Main Methods:

A novel searching strategy was developed and implemented in a new version of the Pratt program.
The new strategy incorporates branch-and-bound search and heuristics to accelerate pattern discovery.
The approach facilitates integration with multiple sequence alignment and database search tools.

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Main Results:

The enhanced Pratt program demonstrates improved efficiency in identifying conserved protein sequence patterns.
The new search strategy offers better integration capabilities with existing bioinformatics workflows.
The use of branch-and-bound search and heuristics significantly speeds up the pattern discovery process.

Conclusions:

The revised Pratt program provides a more efficient and versatile tool for analyzing protein sequence conservation.
This advancement aids in understanding protein function and evolution through pattern identification.