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Journal ArticleDOI

Estimating the Entropy of DNA Sequences

Armin O. Schmitt, +1 more
- 07 Oct 1997 - 
- Vol. 188, Iss: 3, pp 369-377
TLDR
It seems as if DNA sequences possess much more freedom in the combination of the symbols of their alphabet than written language or computer source codes.
About
This article is published in Journal of Theoretical Biology.The article was published on 1997-10-07. It has received 151 citations till now. The article focuses on the topics: Maximum entropy probability distribution & Min entropy.

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Journal ArticleDOI

Information theory in molecular biology

TL;DR: The physics of information in the context of molecular biology and genomics is introduced and applications to molecular sequence and structure analysis are reviewed, and new tools in the characterization of resistance mutations, and in drug design are introduced.
Proceedings ArticleDOI

Towards a theory of semantic communication

TL;DR: A model-theoretical approach for semantic data compression and reliable semantic communication is investigated and it is shown that Shannon's source and channel coding theorems have semantic counterparts.
Journal ArticleDOI

IVT-seq reveals extreme bias in RNA sequencing

TL;DR: It is found rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation, which suggest exon-level expression analysis may be inadvisable, and the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq is shown.
Journal ArticleDOI

Information content of protein sequences.

TL;DR: The results confirm the idea that protein sequences can be regarded as slightly edited random strings and discuss secondary structure and low-complexity regions as causes of the redundancy observed.
Journal ArticleDOI

Information theory applications for biological sequence analysis

TL;DR: This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles.
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