De-Bruijn graph partitioning for scalable and accurate DNA storage processing.

Area of Science:

• Bioinformatics
• Data Science
• Molecular Engineering

Background:

• DNA-based data storage offers high-density, long-term archiving potential.
• Accurate sequence reconstruction is vital for error correction in DNA storage systems.
• Efficient and scalable data processing is necessary for managing large DNA sequencing datasets.

Purpose of the Study:

• To develop a novel, fast, and accurate method for processing DNA sequencing data.
• To enable efficient reconstruction of encoded sequences for DNA data storage applications.
• To create a scalable solution for handling large volumes of sequencing data.

Main Methods:

• Introduced a de-Bruijn graph partitioning approach for sequence processing.
• Developed the ConCluD software implemented in C++.
• Optimized the method for multi-core server environments.

Main Results:

• The method achieves high precision and recall on synthetic and real datasets.
• ConCluD processed 89 million reads (10 GB fasta file) in under a minute on a 32-core server.
• The approach is independent of sequencing technology and prior encoding knowledge.

Conclusions:

• The novel de-Bruijn graph partitioning method provides efficient and accurate DNA sequencing data processing.
• ConCluD software is a scalable solution for large-scale DNA data storage applications.
• This advancement supports the practical implementation of DNA-based data archiving.