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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 Protocol for Computer-Based Protein Structure and Function Prediction
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An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences.

Muhammad Ishaq1, Asfandyar Khan1, Mazliham Mohd Su'ud2

  • 1Department of Computer Science and IT, Agriculture University Peshawar, Pakistan.

Computational and Mathematical Methods in Medicine
|February 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved dynamic programming algorithm for faster parallel multiple sequence alignment (MSA). It enhances task scheduling for complex protein structures, optimizing performance on CPU and GPU resources.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Algorithm Optimization

Background:

  • Multiple Sequence Alignment (MSA) is crucial for analyzing biological sequences.
  • Increasing complexity of protein structures necessitates efficient parallel processing.
  • Existing MSA methods struggle with computationally intensive tertiary protein structures.

Purpose of the Study:

  • To develop an optimized dynamic programming algorithm for parallel MSA.
  • To improve task scheduling for aligning complex tertiary protein structures.
  • To enhance the efficiency and speed of MSA on parallel systems.

Main Methods:

  • Utilized dynamic programming with improved task scheduling for parallel MSA.
  • Implemented a greedy strategy for search space reduction.
  • Employed recursive and iterative greedy approaches for performance enhancement.
  • Evaluated performance on heterogeneous resources (CPU/GPU).

Main Results:

  • The proposed algorithm significantly speeds up alignment processing for parallel MSA.
  • Demonstrated computational efficiency for protein-structured based superposition.
  • Achieved better results through computationally expensive recursive and iterative greedy methods.
  • Optimal scheduling schemes showed superior performance on both CPU and GPU.

Conclusions:

  • The improved dynamic programming algorithm offers efficient task scheduling for parallel MSA.
  • This approach is particularly beneficial for aligning complex tertiary protein structures.
  • The method enhances computational efficiency and performance on heterogeneous parallel systems.