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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Published on: December 10, 2012

Redefining CpG islands using hidden Markov models.

Hao Wu1, Brian Caffo, Harris A Jaffee

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.

Biostatistics (Oxford, England)
|March 10, 2010
PubMed
Summary
This summary is machine-generated.

CpG islands (CGIs) are crucial epigenetic loci. This study introduces a new, flexible hidden Markov model approach for detecting CGIs, generating the first lists for invertebrates and improving overlap with epigenetic marks.

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Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Vertebrate DNA is depleted of CpG dinucleotides, with remaining CpGs often forming CpG islands (CGIs).
  • CGIs are critical epigenetic loci involved in development and disease.
  • Existing CGI detection methods, based on older definitions, need updating due to technological advancements.

Purpose of the Study:

  • To develop an extensible and flexible procedure for detecting CpG islands (CGIs) using hidden Markov models.
  • To adapt current CGI definitions to accommodate new, large-scale epigenetic data.
  • To generate novel CGI lists for species lacking them, particularly invertebrates.

Main Methods:

  • Utilized hidden Markov models (HMMs) for a probabilistic approach to CGI detection.
  • Developed an extensible procedure allowing for flexible CGI definition.
  • Applied the method to generate CGI lists for various species, including invertebrates.

Main Results:

  • Generated the first comprehensive CpG island lists for invertebrates.
  • The new method provides probability scores for CGI status, offering flexibility.
  • The generated CGI lists show substantially increased overlap with recently discovered epigenetic marks.

Conclusions:

  • The proposed hidden Markov model approach offers a flexible and extensible method for detecting CpG islands.
  • This method facilitates the creation of CGI lists for diverse species and enhances their utility in epigenetic research.
  • The generated CGI data and probability scores are publicly available for further research.