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CodSeqGen: A tool for generating synonymous coding sequences with desired GC-contents.

Abdulrakeeb M Al-Ssulami1, Aqil M Azmi2, Muhammad Hussain2

  • 1Department of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; Department of Computer Science, Faculty of Applied Sciences, Taiz University, Taiz, Yemen.

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Summary
This summary is machine-generated.

This study introduces CodSeqGen, an algorithm for creating synthetic DNA sequences that precisely match a desired amino acid sequence and GC-content, overcoming limitations of existing methods.

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GC-contentSequence analysisSynonymous coding sequence

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Regulatory elements control gene expression by binding transcription factors.
  • GC-content, a measure of nucleotide composition, influences regulatory element occurrence.
  • Existing methods for synthetic sequence generation have limitations in adhering to GC-content constraints.

Purpose of the Study:

  • To develop an algorithmic solution for generating synthetic coding sequences.
  • To ensure generated sequences precisely match a specified amino acid sequence and GC-content.

Main Methods:

  • The study presents an algorithmic solution named CodSeqGen.
  • CodSeqGen utilizes random selection for subsets and a backtracking approach.
  • It addresses limitations of multinomial and maximum entropy methods.

Main Results:

  • CodSeqGen successfully generates coding sequences that strictly adhere to a primary amino acid sequence.
  • The algorithm ensures the generated sequences meet the desired GC-content specification.
  • This provides an accurate method for synthetic sequence generation.

Conclusions:

  • CodSeqGen offers an effective solution for generating synthetic coding sequences with precise amino acid and GC-content control.
  • The tool overcomes the limitations of previous stochastic methods.
  • This advancement aids in understanding gene regulation and synthetic biology applications.