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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...
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Gene sequence analysis model construction based on k-mer statistics.

Dongjie Gao1

  • 1School of Mathematics and Statistics, Heze University, Heze, China.

Plos One
|September 12, 2024
PubMed
Summary

A new k-mer statistics model improves gene sequence alignment efficiency for complex DNA structures. This method enhances analysis accuracy and demonstrates significant application value in biotechnology.

Area of Science:

  • Biotechnology
  • Bioinformatics
  • Computational Biology

Background:

  • Gene sequencing technologies are advancing, leading to increasingly complex gene sequences.
  • Traditional sequence alignment methods struggle with the complexity of modern gene sequence analysis.
  • Efficient analysis of complex gene sequences is crucial for biotechnological advancements.

Purpose of the Study:

  • To develop an efficient gene sequence alignment analysis model for complex gene sequences.
  • To improve the accuracy and speed of gene sequence analysis using k-mer statistics.
  • To design and implement an application system for the proposed sequence alignment model.

Main Methods:

  • Utilized the D2 series method of k-mer statistics to build a gene sequence alignment model.

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  • Developed a strategy to segment sequences into subsequences of varying lengths based on foreground sequence structure.
  • Determined statistical results by identifying the maximum dissimilarity within alignment results of selected subsequences.
  • Designed a dedicated application system to implement the sequence alignment analysis model.

    • Main Results:

    • The statistical power of the model showed direct proportionality to sequence coverage and cutting length.
    • Statistical power was inversely proportional to the K value and module length.
    • The application system demonstrated efficient performance with a maximum storage capacity of 71 GB and disk capacity of 135 GB.
    • The system achieved a running time of less than 2.0 seconds for sequence alignment analysis.

    Conclusions:

    • The proposed k-mer statistic sequence alignment model offers a robust solution for analyzing complex gene sequences.
    • The developed application system effectively supports the model, providing practical utility in gene alignment analysis.
    • This approach holds considerable value for advancing gene sequence analysis in biotechnology and related fields.