Compression of Annotated Nucleotide Sequences

This paper introduces an algorithm for the lossless compression of DNA files which contain annotation text besides the nucleotide sequence. First, a grammar is specifically designed to capture the regularities of the annotation text. A revertible transformation uses the grammar rules in order to equ...

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Veröffentlicht in:IEEE/ACM transactions on computational biology and bioinformatics Jg. 4; H. 3; S. 447 - 457
Hauptverfasser: Korodi, G., Tabus, I.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.07.2007
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
4 [Data]: Coding and Information Theory | Data compaction and compression
Algorithms
Annotation
Base Sequence
Bioinformatics
Biological cells
Compression
Data Compression - methods
Databases, Genetic
Decision making
DNA
DNA - chemistry
DNA - classification
DNA - genetics
Documentation - methods
F.4 [Theory of Computation]: Mathematical Logic and Formal Languages | Formal languages
Feature extraction
Formal Grammars
G.3 [Mathematics of Computing]: Probability and Statistics | Markov processes
Genomics
Humans
J.3 [Computer Applications]: Life and Medical Sciences | Biology and genetics
Molecular Sequence Data
Natural Language Processing
Nucleotide sequences
Organisms
Planets
Sequence Analysis, DNA - methods
Sequences
ISSN:1545-5963, 1557-9964
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Beschreibung
Zusammenfassung:This paper introduces an algorithm for the lossless compression of DNA files which contain annotation text besides the nucleotide sequence. First, a grammar is specifically designed to capture the regularities of the annotation text. A revertible transformation uses the grammar rules in order to equivalently represent the original file as a collection of parsed segments and a sequence of decisions made by the grammar parser. This decomposition enables the efficient use of state-of-the-art encoders for processing the parsed segments. The output size of the decision-making process of the grammar is optimized by extending the states to account for high-order Markovian dependencies. The practical implementation of the algorithm achieves a significant improvement when compared to the general-purpose methods currently used for DNA files.
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ISSN:1545-5963
1557-9964
DOI:10.1109/tcbb.2007.1017