Inter-dinucleotide distances in the human genome: an analysis of the whole-genome and protein-coding distributions

Bastos, Carlos A. C.; Afreixo, Vera; Pinho, Armando J.; Garcia, Sara P.; Rodrigues, João M. O. S.; Ferreira, Paulo J. S. G.

doi:10.1515/jib-2011-172

Cited by 13 publications

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“…With respect to IND‐distributions, a three‐samples period was also found in protein DNA coding for the relative error between observed IND‐ and expected geometric frequencies for each nucleotide (Afreixo et al ., ) and also for pairs of nucleotides (Bastos et al ., ). The work of Qi et al .…”

Section: Discussionmentioning

confidence: 97%

Binary Auto-Regressive Geometric Modelling in a DNA Context

Gouveia

Scotto

Weiß

et al. 2016

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Symbolic or categorical sequences occur in many contexts and can be characterized, for example, by integer‐valued intersymbol distances or binary‐valued indicator sequences. The analysis of these numerical sequences often sheds light on the properties of the original symbolic sequences. This work introduces new statistical tools for exploring auto‐correlation structure in the indicator sequences, for the specific case of deoxyribonucleic acid (DNA) sequences. It is known that the probability distribution of internucleotide distances of DNA sequences deviates significantly from the distribution obtained by assuming independent random placement (i.e. the geometric distribution) and that the deviations can be used either to discriminate between species or to build phylogenetic trees. To investigate the extent to which auto‐correlation structure explains these deviations, the 0–1 indicator sequence of each nucleotide (A, C, G and T) is endowed with a binary auto‐regressive (AR) model of optimum order. The corresponding binary AR geometric distribution is derived analytically and compared with the observed internucleotide distance distribution by appropriate goodness‐of‐fit testing. Results in 34 mitochondrial DNA sequences show that the hypothesis of equal observed/expected frequencies is seldom rejected when a binary AR model is considered instead of independence (76/136 versus 125/136 rejections at the 1% level), in spite of χ2‐testing tending to reject for large samples, regardless of how close observed/expected values are. Furthermore, binary AR structure also leads to a median discrepancy reduction of 90% for G, 80% for C, 60% for T and 30% for nucleotide A. Therefore, these models are useful to describe the dependences within a given nucleotide and encourage the development of a model‐based framework to compact internucleotide distance information and to understand DNA differences among species further.

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Section: Discussionmentioning

confidence: 97%

Binary Auto-Regressive Geometric Modelling in a DNA Context

Gouveia

Scotto

Weiß

et al. 2016

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…The internucleotide distance was also explored in the context of genome annotation by [11]. In [3], the inter-dinucleotide distance distribution was proposed and a comparison between all dinucleotide distributions in the human genome was performed. Note that in [3] overlapping dinucleotides were excluded from analysis, so that the expected distance distribution under an independent nucleotide model is a geometric distribution.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the inter-dinucleotide distance distribution was proposed and a comparison between all dinucleotide distributions in the human genome was performed. Note that in [3] overlapping dinucleotides were excluded from analysis, so that the expected distance distribution under an independent nucleotide model is a geometric distribution. Based on an inter-CpG distance, a CpG-island detection algorithm was proposed by [8], where a geometric distribution was used as a reference for comparison.…”

Section: Introductionmentioning

confidence: 99%

Detection of exceptional genomic words: a comparison between species

Tavares¹,

Rodrigues²,

Bastos³

et al. 2018

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In this study we explore the potentialities of the inter-word distances to detect exceptional genomic words (oligonucleotides) in several species, using whole-genome analysis. We confront the empirical results obtained from the complete genomes with the corresponding results obtained from the random background. We develop a procedure, based on some statistical properties of the global distance distributions in DNA sequences, to discriminate words with exceptional inter-word distance distribution and to identify distances with exceptional frequency of occurrence. We identify the statistically exceptional words in whole-genomes, i.e., words with unexpected inter-word distance distributions, and we suggest species signatures based on exceptional word profiles.

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“…Recent studies revealed that dinucleotide interdistances can be a powerful tool for detecting DNA properties [ 1 , 2 ], such as the identification of CpG islands [ 3 ] and the characterization of epigenomic regulation through methylation [ 4 , 5 ]. In a previous paper [ 4 ], we higlighted a peculiar feature of mammals CG dinucleotides: the tail of CG interdistance distributions showed an exponential decay, at difference with non CG’s which had a heavier tail more similar to a power law.…”

Section: Introductionmentioning

confidence: 99%

Statistical modelling of CG interdistance across multiple organisms

et al. 2018

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BackgroundStatistical approaches to genetic sequences have revealed helpful to gain deeper insight into biological and structural functionalities, using ideas coming from information theory and stochastic modelling of symbolic sequences. In particular, previous analyses on CG dinucleotide position along the genome allowed to highlight its epigenetic role in DNA methylation, showing a different distribution tail as compared to other dinucleotides.In this paper we extend the analysis to the whole CG distance distribution over a selected set of higher-order organisms. Then we apply the best fitting probability density function to a large range of organisms (>4400) of different complexity (from bacteria to mammals) and we characterize some emerging global features.ResultsWe find that the Gamma distribution is optimal for the selected subset as compared to a group of several distributions, chosen for their physical meaning or because recently used in literature for similar studies. The parameters of this distribution, when applied to our larger set of organisms, allows to highlight some biologically relavant features for the considered organism classes, that can be useful also for classification purposes.ConclusionsThe quantification of statistical properties of CG dinucleotide positioning along the genome is confirmed as a useful tool to characterize broad classes of organisms, spanning the whole range of biological complexity.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2303-2) contains supplementary material, which is available to authorized users.

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Inter-dinucleotide distances in the human genome: an analysis of the whole-genome and protein-coding distributions

Cited by 13 publications

References 0 publications

Binary Auto-Regressive Geometric Modelling in a DNA Context

Binary Auto-Regressive Geometric Modelling in a DNA Context

Detection of exceptional genomic words: a comparison between species

Statistical modelling of CG interdistance across multiple organisms

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