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Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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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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Genomic DNA in Eukaryotes00:58

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Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
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Pantheon-DNA: Versatile encoding-decoding system with integrated adaptive NGS preprocessing algorithms for DNA data

Adriano Galindo Leal1, Thiago Yuji Aoyagi1, André Guilherme Costa-Martins1

  • 1Artificial Intelligence and Analytics Department, Institute for Technological Research, São Paulo, 05508-901, SP, Brazil.

Computational and Structural Biotechnology Journal
|January 16, 2026
PubMed
Summary
This summary is machine-generated.

Pantheon-DNA is a new pipeline for DNA data storage that handles large datasets efficiently. It achieves high data retrievability and uses novel methods to prevent errors during DNA sequence encoding and retrieval.

Keywords:
DNA data storageEncoding schemesSequencing data preprocessingSoftware engineering

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Data Storage

Background:

  • DNA data storage offers high density but faces scalability and error challenges.
  • Repetitive DNA sequences can cause molecular-level errors and complicate data processing.
  • Efficiently managing and retrieving large datasets from DNA is crucial for practical applications.

Purpose of the Study:

  • To introduce Pantheon-DNA, an end-to-end processing pipeline for DNA data storage.
  • To address scalability and efficiency challenges in DNA data storage systems.
  • To improve data retrievability and reliability in DNA-based storage.

Main Methods:

  • Developed a data arrangement scheme and randomization procedure to prevent problematic DNA sequence patterns.
  • Implemented a block data architecture for enhanced parallel processing and retrieval.
  • Utilized prior knowledge of encoded data structure to simplify preprocessing and reduce computational complexity.

Main Results:

  • Achieved ≥99.996% data retrievability at 10× coverage under both Low Error Rate (LER) and High Error Rate (HER) conditions.
  • Successfully encoded and decoded 1.59 MB of data containing multiple files in a synthesis and sequencing experiment.
  • Demonstrated reduced computational complexity and simplified clustering routines through the preprocessing pipeline.

Conclusions:

  • Pantheon-DNA effectively addresses scalability and efficiency in DNA data storage.
  • The proposed methods enhance data reliability and retrievability, validated by experimental results.
  • Future work will focus on improving error correction, especially for indel recovery, and optimizing preprocessing.