Hardware Accelerator for the Multifractal Analysis of DNA Sequences

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Multifractal analysis quantifies genetic variability and non-linear stability in human genome sequences.
• This analysis is crucial for understanding genetic diseases linked to chromosomal abnormalities.
• Current methods involve time-consuming processing of large DNA datasets.

Purpose of the Study:

• To design and implement an efficient application-specific processor for multifractal analysis of DNA sequences.
• To accelerate the calculation of the generalized dimension spectrum for genomic data.
• To overcome the computational bottlenecks of traditional software-based approaches.

Main Methods:

• Developed a hardware-oriented algorithm for calculating the generalized dimension spectrum.
• Designed and implemented a dedicated multifractal processor on a low-cost System-on-Chip Field-Programmable Gate Array (SoC-FPGA).
• Validated the processor by analyzing a complete human genome sequence.

Main Results:

• The multifractal processor achieved a 2.6x speedup compared to a 20-core workstation software implementation.
• The hardware implementation demonstrated high accuracy with an average error of 0.0003 percent.
• Successfully processed a complete human genome, confirming the efficiency and reliability of the designed system.

Conclusions:

• The developed multifractal processor significantly enhances the efficiency of genomic sequence analysis.
• This hardware acceleration provides a valuable tool for studying genetic variability and disease-related genomic particularities.
• The approach offers a practical solution for large-scale genomic data processing in bioinformatics research.