Text compression
A new challenge for compression algorithms: genetic sequences
Information Processing and Management: an International Journal - Special issue: data compression
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Solving the String Statistics Problem in Time O(n log n)
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
A Corpus for the Evaluation of Lossless Compression Algorithms
DCC '97 Proceedings of the Conference on Data Compression
Data Compression Using Long Common Strings
DCC '99 Proceedings of the Conference on Data Compression
Compression of Biological Sequences by Greedy Off-Line Textual Substitution
DCC '00 Proceedings of the Conference on Data Compression
Application of Lempel--Ziv factorization to the approximation of grammar-based compression
Theoretical Computer Science
Replacing suffix trees with enhanced suffix arrays
Journal of Discrete Algorithms - SPIRE 2002
The unsupervised learning of natural language structure
The unsupervised learning of natural language structure
Choosing word occurrences for the smallest grammar problem
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
Grammar-based codes: a new class of universal lossless source codes
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
An effective heuristic for the smallest grammar problem
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Motivated by the inference of the structure of genomic sequences, we address here the smallest grammar problem. In previous work, we introduced a new perspective on this problem, splitting the task into two different optimization problems: choosing which words will be considered constituents of the final grammar and finding a minimal parsing with these constituents. Here we focus on making these ideas applicable on large sequences. First, we improve the complexity of existing algorithms by using the concept of maximal repeats when choosing which substrings will be the constituents of the grammar. Then, we improve the size of the grammars by cautiously adding a minimal parsing optimization step. Together, these approaches enable us to propose new practical algorithms that return smaller grammars (up to 10%) in approximately the same amount of time than their competitors on a classical set of genomic sequences and on whole genomes of model organisms.