Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)

Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)

Abstract Background 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,...

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Bibliographic Details
Main Authors: Claus Vogl, Mariia Karapetiants, Burçin Yıldırım, Hrönn Kjartansdóttir, Carolin Kosiol, Juraj Bergman, Michal Majka, Lynette Caitlin Mikula
Format: Article
Language:English
Published: BMC 2024-04-01
Series:BMC Bioinformatics
Subjects:
Genome segmentation algorithm
Hidden Markov Model
Ordered hidden states (convex emission densities)
Markov Chain Monte Carlo sampler
Autocorrelation
Variation in GC proportion
Online Access:https://doi.org/10.1186/s12859-024-05751-4

Internet

https://doi.org/10.1186/s12859-024-05751-4

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