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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...
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...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
Per-Unit Sequence Models01:26

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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A Practical Guide to Phylogenetics for Nonexperts
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Statistical alignment with a sequence evolution model allowing rate heterogeneity along the sequence.

Ana Arribas-Gil1, Dirk Metzler, Jean-Louis Plouhinec

  • 1Departamento de Estadística, Universidad Carlos III de Madrid, Getafe, Spain. aarribas@est-econ.uc3m.es

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 2, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel stochastic sequence evolution model that identifies conserved DNA regions and estimates mutation rates. The model effectively segments sequences into slow and fast evolutionary zones, improving alignment accuracy.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Homologous sequences exhibit varying evolutionary rates, complicating accurate alignment and mutation rate estimation.
  • Identifying conserved regions within DNA sequences is crucial for understanding evolutionary processes and functional importance.

Purpose of the Study:

  • To develop a stochastic sequence evolution model that accounts for sequence heterogeneity by distinguishing between slow and fast evolving regions.
  • To detect boundaries between these regions and estimate mutation rates and alignments for homologous sequences.
  • To enable efficient statistical alignment algorithms through a pair hidden Markov model structure.

Main Methods:

  • A stochastic sequence evolution model incorporating fragment insertion/deletion in fast regions and differential substitution rates in both fast and slow regions.
  • Development of two complementary estimation methods: a Gibbs sampler for Bayesian estimation and a stochastic Expectation-Maximization (EM) algorithm for maximum likelihood estimation.
  • Implementation of a computationally efficient partial alignment sampler to improve algorithm performance and demonstrate convergence.

Main Results:

  • The proposed algorithms provide consistent estimates for mutation rates.
  • The model generates plausible sequence alignments and accurate segmentation of sequences into distinct evolutionary rate regions.
  • Successful application and validation on both simulated and real biological data.

Conclusions:

  • The developed stochastic model effectively captures sequence heterogeneity, leading to improved alignment and mutation rate estimation.
  • The combination of the model and estimation algorithms offers a robust approach for analyzing homologous sequences.
  • This work advances the field of sequence analysis by providing tools for better understanding DNA sequence evolution.