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Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)
University of Veterinary Medicine Vienna / Vienna Graduate School of Population Genetics
Lynette Caitlin Mikula University of St Andrews
Michal Majka Erste Group Bank AG
Juraj Bergman Aarhus University
University of St Andrews
Hrönn Kjartansdóttir University of Veterinary Medicine Vienna
Mariia Karapetiants University of Veterinary Medicine Vienna
University of Veterinary Medicine Vienna / Vienna Graduate School of Population Genetics / Uppsala University
BMC
Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred.We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations.Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.
Englisch
2024
Dieses Werk bzw. dieser Inhalt steht unter einer
CC BY 4.0 - Creative Commons Namensnennung 4.0 International Lizenz.
CC BY 4.0 International
http://creativecommons.org/licenses/by/4.0/
Animals; Humans; Mice; Markov Chains; Genome; Genomics; Base Composition; Probability; Algorithms