ReviewBriefings in bioinformatics2014
Information theory applications for biological sequence analysis.
Review in Briefings in bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Enlarging viral mutation estimation: a view from the distribution of mutation rates.Microbiology (Reading, England) · 2026Article
- From Energy Deprivation Stress to Catalytic Adaptation of Reductive Dehalogenase TmrA for 1,1-DCA Dechlorination: An Experimental and in Silico Study.Microbial biotechnology · 2026Article
- Energy entropy vector: a novel approach for efficient microbial genomic sequence analysis and classification.Briefings in bioinformatics · 2025Article
- Highly Conserved Genetic Factors RegulatingJournal of microbiology and biotechnology · 2025Article
- Advancements in Information-Theoretic Methods for Data Analytics.Entropy (Basel, Switzerland) · 2025Article
- Conservation of regulatory elements with highly diverged sequences across large evolutionary distances.Nature genetics · 2025Article
- Information Theory, Living Systems, and Communication Engineering.Entropy (Basel, Switzerland) · 2024Review
- CGRWDL: alignment-free phylogeny reconstruction method for viruses based on chaos game representation weighted by dynamical language model.Frontiers in microbiology · 2024Article
- Navigating the archaeal frontier: insights and projections from bioinformatic pipelines.Frontiers in microbiology · 2024Article
- Optimal entropic properties of SARS-CoV-2 RNA sequences.Royal Society open science · 2024Article
- Application of Feature Definition and Quantification in Biological Sequence Analysis.Current genomics · 2023Article
- Article
- An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator.PLoS computational biology · 2023Article
- Bioinformatics tools for the sequence complexity estimates.Biophysical reviews · 2023Review
- Predicting chromosomal compartments directly from the nucleotide sequence with DNA-DDA.Briefings in bioinformatics · 2023Article
- Genomic Signature in Evolutionary Biology: A Review.Biology · 2023Review
- Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy.Entropy (Basel, Switzerland) · 2022Article
- Progress in and Opportunities for Applying Information Theory to Computational Biology and Bioinformatics.Entropy (Basel, Switzerland) · 2022Article
- Disentangling single-cell omics representation with a power spectral density-based feature extraction.Nucleic acids research · 2022Article
- Subjective Information and Survival in a Simulated Biological System.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. 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. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.
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