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Related Concept Videos

Genome Copying Errors02:46

Genome Copying Errors

DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
DNA-only Transposons02:57

DNA-only Transposons

DNA-only transposons are called autonomous transposons since they code for the enzyme transposase that is required for the transposition mechanism. Insertion of transposons can alter gene functions in multiple ways. They can mutate the gene, alter gene expression by introducing a novel promoter or insulator sequence, introduce new splice sites, and change the mRNA transcripts produced, or remodel chromatin structure.
The donor site from where the transposon is excised is either degraded or...

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

A continuous-index hidden Markov jump process for modeling DNA copy number data.

Susann Stjernqvist1, Tobias Rydén

  • 1Centre for Mathematical Sciences, Lund University, Box 118, 22100 Lund, Sweden. susann.stjernqvist@matstat.lu.se

Biostatistics (Oxford, England)
|July 25, 2009
PubMed
Summary

This study introduces a novel statistical model for analyzing DNA copy number variations using array comparative genomic hybridization (aCGH). The model effectively captures complex aCGH data features and offers robust outlier detection for improved genomic analysis.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Area of Science:

  • Genomics
  • Statistical Modeling
  • Bioinformatics

Background:

  • Array comparative genomic hybridization (aCGH) is crucial for measuring DNA copy numbers in human cells.
  • Existing methods may not fully capture complexities like uneven probe length, spacing, and overlap in aCGH data.

Purpose of the Study:

  • To develop an advanced statistical model for analyzing aCGH data.
  • To address limitations of current methods by incorporating probe characteristics and complex genomic events.

Main Methods:

  • A latent continuous-index Markov jump process model was devised.
  • Bayesian inference and Markov chain Monte Carlo (MCMC) methods were employed for parameter estimation.
  • Comparison of models with normal versus t-distributed noise was conducted.

Main Results:

  • The proposed model effectively captures features of aCGH data, including probe characteristics.
  • The model accommodates continuous states for normal copy number (2), amplifications, and deletions.
  • A t-distributed noise model demonstrated superior robustness to outliers compared to a normal distribution model.

Conclusions:

  • The developed statistical model provides a powerful framework for aCGH data analysis.
  • The Bayesian approach with MCMC is suitable for parameter estimation and process analysis.
  • The t-distributed noise model enhances the reliability of aCGH analyses by handling outliers effectively.