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Related Concept Videos

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

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Related Experiment Video

Updated: Jul 1, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Time-dependent ARMA modeling of genomic sequences.

Jerzy S Zielinski1, Nidhal Bouaynaya, Dan Schonfeld

  • 1Department of Systems Engineering, University of Arkansas at Little Rock, Little Rock, AR, USA. jszielinski@ualr.edu

BMC Bioinformatics
|September 20, 2008
PubMed
Summary

Genomic sequences are non-stationary, requiring new analysis methods. A time-dependent autoregressive moving average (TD-ARMA) model offers a stable approach, revealing coding DNA is "whiter" than non-coding DNA.

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Published on: January 19, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Genomic sequences exhibit non-stationary statistical behavior, challenging traditional time series analysis.
  • Existing methods like power spectrum analysis are limited to stationary data.
  • Time-varying spectral methods are unstable and sensitive to minor data perturbations.

Purpose of the Study:

  • To develop a stable time series tool for analyzing non-stationary genomic sequences.
  • To model genomic sequences using a time-dependent autoregressive moving average (TD-ARMA) process.
  • To investigate statistical properties and differentiate coding from non-coding DNA regions.

Main Methods:

  • Proposed a time-dependent autoregressive moving average (TD-ARMA) model for non-stationary genomic sequences.
  • Utilized a series expansion of time-varying coefficients to form a Yule-Walker-type system.
  • Employed a recursive least-squares algorithm for estimating time-dependent coefficients.
  • Defined a quantitative measure of randomness to assess deviation from white noise.

Main Results:

  • The TD-ARMA model provides a stable platform for analyzing genomic sequence correlations.
  • Both coding and non-coding regions are non-random, but coding sequences are "whiter" (more random).
  • A higher index of randomness was observed in coding sequences compared to non-coding sequences.

Conclusions:

  • The TD-ARMA model offers a stable time series tool for non-stationary genomic sequence analysis.
  • An index of randomness derived from TD-ARMA coefficients reveals subtle statistical differences between coding and non-coding DNA.
  • Coding DNA is statistically "whiter" than non-coding DNA, contradicting previous stationary analysis conclusions.