Information Capacity of Symbol Sequences

Sadovsky, Michael G.

doi:10.1023/a:1014230928565

Cited by 6 publications

(9 citation statements)

References 4 publications

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“…(4-6) could easily be extended for the case of derivation of a dictionary e W ðqÞ from W(l), where 2 B l \ q -1. Equation 6 looks like S q ¼ ðq À l þ 1ÞS l À S q À ðq À lÞS q À l and S q ¼ qS 1 À S q for this case; see details in Sadovsky (2002aSadovsky ( , b, 2003Sadovsky ( , 2006. The Eqs.…”

Section: Information Capacity Of a Dictionarymentioning

confidence: 98%

“…Information capacity measured through the calculation of mutual entropy reveals numerous biologically valuable effects in nucleotide sequences (see Sadovsky 2002aSadovsky , b, 2003Sadovsky , 2006. Meanwhile, a study of the structure of frequency dictionary of a genetic entity reveals more peculiarities and biological issues standing behind; here, we investigate a problem of codon usage bias determination (see Codon usage bias), that is a classical problem of molecular biology and genetics.…”

Section: Some More Applicationsmentioning

confidence: 99%

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Genes, information and sense: complexity and knowledge retrieval

2008

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Information capacity of nucleotide sequences measures the unexpectedness of a continuation of a given string of nucleotides, thus having a sound relation to a variety of biological issues. A continuation is defined in a way maximizing the entropy of the ensemble of such continuations. The capacity is defined as a mutual entropy of real frequency dictionary of a sequence with respect to the one bearing the most expected continuations; it does not depend on the length of strings contained in a dictionary. Various genomes exhibit a multi-minima pattern of the dependence of information capacity on the string length, thus reflecting an order within a sequence. The strings with significant deviation of an expected frequency from the real one are the words of increased information value. Such words exhibit a non-random distribution alongside a sequence, thus making it possible to retrieve the correlation between a structure, and a function encoded within a sequence.

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Section: Information Capacity Of a Dictionarymentioning

confidence: 98%

Section: Some More Applicationsmentioning

confidence: 99%

Genes, information and sense: complexity and knowledge retrieval

2008

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“…Here we follow the second approach that is completely similar to that one used to study the statistical properties of nucleotide sequences [5][6][7][8][9][10][11].…”

Section: Study Of the Real Distribution Of Start Points Of Reads Along A Genomementioning

confidence: 99%

“…where n ω is the number of copies of a word ω, and N is the length of a sequence; to make the definition (2) feasible, one must connect a sequence into a ring, see details in [5][6][7][8][9][10][11]. Such closure results in appearance of q − 1 phantom words in a dictionary, while we neglect them.…”

Section: Simulation Of Start Points Of Reads: Theoretical Backgroundmentioning

confidence: 99%

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Reads in NGS Are Distributed over a Sequence Very Inhomogeneously

Sadovsky

Kobets

Khodos

et al. 2019

Bioinformatics and Biomedical Engineering

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Distribution of read starts over a sequences genetic entity is studied. Key question was whether the starts are distributed uniformly and homogeneously along a sequence, or there exist some spots of the increased local density of the starts. To answer the question, 15 bacterial genomes have been studied. It was found that some genomes exhibit extremely far distribution pattern, from an homogeneity, while others show lower level of the inhomogeneity. The inhomogeneity level was determined through the Kullback-Leibler distance between the real string distribution, and that one bearing the most probable continuations of the shorter strings.

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Data Loss Reparation Due to Indeterminate Fine-Grained Parallel Computation

Gorbunova

Kondratenko²,

Sadovsky

2003

Lecture Notes in Computer Science

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The new method of a gap recovery in symbol sequences is presented. A covering is combined from the suitable reasonably short strings of the parts of a sequence available for an observation. Two criteria are introduced to choose the best covering. It must yield the maximum of entropy of a frequency dictionary developed over the sequence obtained due to the recovery, if an overlapping combined from the copies of strings from the available parts of the sequence exists. The second criterion identifies the best covering in case when one has to use any string to cover the gap; here the best covering must yield the minimum of specific entropy of the frequency dictionary developed over the available parts of the sequence against that one developed over the entire sequence obtained due to the recovery. Kirdin kinetic machine that is the ideal fine-grained structureless computer has been used to resolve the problem of the reconstruction of a gap in symbol sequence.

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Information Capacity of Symbol Sequences

Cited by 6 publications

References 4 publications

Genes, information and sense: complexity and knowledge retrieval

Genes, information and sense: complexity and knowledge retrieval

Reads in NGS Are Distributed over a Sequence Very Inhomogeneously

Data Loss Reparation Due to Indeterminate Fine-Grained Parallel Computation

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