Information theory applications for biological sequence analysis

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.

A Tutorial for Information Theory in Neuroscience

“…Information theory is currently used in genetics (Vinga, 2013; Ignac et al, 2014; Smouse et al, 2015), but studies could be performed linking genetics and neuroscience. For instance, it is possible to examine how neural activity, neural responses to stimuli, or animal behavior relate to genetic information by examining model animals with certain genetic differences.…”

What Kind of Information is Brain Information?

“…Information theory is a branch of applied mathematics, and has a staggering range of interesting applications. For example, it is used to determine how hugely complex genomic data sets can be represented most efficiently, in order to simplify computation (Vinga, 2013). Set cases like this one aside.…”

Dynamical complexity of short and noisy time series

“…), continue to be widely used as measures of dynamical complexity in several applications. It is used in biomedical applications [2], for eg., as a pattern classification tool in heart rate variability analysis [3]; to measure structural and dynamical complexity of networks [4] and communication complexity [5]; for biological sequence analysis in bioinformatics [6,7]; in econometric/financial time series analysis [8,9,10]; and not to miss out on the various entropic forms in physics [11]. This is by no means an exhaustive list, but only serves as indicative of the diverse domains in which Shannon entropy is applied.…”