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Open AccessProceedings Article

A Highly Parallel Finite State Automaton Processor for Biological Pattern Matching.

Glen Herrmannsfeldt
- pp 58-72
TLDR
This paper describes here a parallel implementation of a hardware Deterministic Finite State Automaton processor that can rapidly search a large database for approximately matching strings, as a lter for more detailed processing later.
Abstract
Finite State Automata are useful for string searching problems mostly because they are fast. For very large problems, a software implementation will not be fast enough. I describe here a parallel implementation of a hardware Deterministic Finite State Automaton processor. It can rapidly search a large database for approximately matching strings, as a lter for more detailed processing later. As the most important parts, large Random Access Memory chips, are continually getting cheaper, it should be possible and a ordable to make large arrays of such processors.

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Citations
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Duplications and Pseudo-Duplications

TL;DR: In this paper, the authors consider three variants of duplication operations, namely, duplication, pseudo-duplication and reverse duplication, and give the necessary and sufficient number of states that a non-deterministic finite automaton needs to recognize duplications on a string.
Dissertation

Flexible finite automata-based algorithms for detecting microsatellites in DNA

TL;DR: This dissertation is the development of a data-analytical and theoretical algorithm to contribute to the analysis of DNA, and in particular, to detect microsatellites through the implementation of finite automata.
References
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Book

Introduction to Automata Theory, Languages, and Computation

TL;DR: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity, appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
Journal ArticleDOI

A general method applicable to the search for similarities in the amino acid sequence of two proteins

TL;DR: A computer adaptable method for finding similarities in the amino acid sequences of two proteins has been developed and it is possible to determine whether significant homology exists between the proteins to trace their possible evolutionary development.
Journal ArticleDOI

An improved algorithm for matching biological sequences

TL;DR: The algorithm of Waterman et al. (1976) for matching biological sequences was modified under some limitations to be accomplished in essentially MN steps, instead of the M 2 N steps necessary in the original algorithm.
Book

Theory of computation

Derick Wood
TL;DR: This chapter discusses models for Finite Automata Regular Expressions Context-Free Grammars Pushdown Automata Turing Machines Functions, Relations, and Translations, and properties of these models.
Proceedings ArticleDOI

A systolic array processor for biological information signal processing

TL;DR: The comparison algorithm is described, the chip and system designs are outlined, and Estimated performance of the BISP system is compared with several different computer architectures.