ReviewComputational and structural biotechnology journal2014
Investigating genomic structure using changept: A Bayesian segmentation model.
Review in Computational and structural biotechnology journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Detection and identification of cis-regulatory elements using change-point and classification algorithms.BMC genomics · 2022Article
- A Bayesian model based computational analysis of the relationship between bisulfite accessible single-stranded DNA in chromatin and somatic hypermutation of immunoglobulin genes.PLoS computational biology · 2021Article
- Genome-wide identification of conserved intronic non-coding sequences using a Bayesian segmentation approach.BMC genomics · 2017Article
- Discovery of putative small non-coding RNAs from the obligate intracellular bacterium Wolbachia pipientis.PloS one · 2015Article
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Authors and funding
2 authors.
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Abstract
Genomes are composed of a wide variety of elements with distinct roles and characteristics. Some of these elements are well-characterised functional components such as protein-coding exons. Other elements play regulatory or structural roles, encode functional non-protein-coding RNAs, or perform some other function yet to be characterised. Still others may have no functional importance, though they may nevertheless be of interest to biologists. One technique for investigating the composition of genomes is to segment sequences into compositionally homogenous blocks. This technique, known as 'sequence segmentation' or 'change-point analysis', is used to identify patterns of variation across genomes such as GC-rich and GC-poor regions, coding and non-coding regions, slowly evolving and rapidly evolving regions and many other types of variation. In this mini-review we outline many of the genome segmentation methods currently available and then focus on a Bayesian DNA segmentation algorithm, with examples of its various applications.
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