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Genomics02:02

Genomics

37.4K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Related Experiment Video

Updated: Sep 11, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Tokenization and deep learning architectures in genomics: A comprehensive review.

Conrad Testagrose1, Christina Boucher1

  • 1Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States.

Computational and Structural Biotechnology Journal
|August 18, 2025
PubMed
Summary

Advancements in DNA sequencing generate vast genomic data, necessitating better computational tools. Current deep learning tokenization methods for genomics are often inefficient or biologically inaccurate, requiring improved techniques for effective data analysis.

Keywords:
DNA sequencingDeep learningGenomicsLarge language modelsTokenization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Modern DNA sequencing technologies have led to an exponential increase in genomic data.
  • There is a growing demand for computational tools to analyze this data for applications like antimicrobial resistance and gene annotation.

Purpose of the Study:

  • To survey current and foundational literature on deep learning architectures and tokenization techniques in genomics.
  • To identify limitations in existing methods and suggest future directions for modeling DNA sequences.

Main Methods:

  • Literature review of deep learning architectures and tokenization techniques in genomics.
  • Analysis of the efficacy and biological relevance of current tokenization strategies.
  • Comparison of sequence representation methods and their impact on scalability.

Main Results:

  • Existing tokenization methods often struggle to capture or model underlying motifs in DNA sequences effectively.
  • Many current tokenization approaches are either computationally inefficient, misrepresent biological motifs, or are adapted from Natural Language Processing (NLP) without sufficient biological consideration.
  • Deep learning models are becoming more efficient, but tokenization remains a bottleneck.

Conclusions:

  • Significant research is needed to develop efficient and biologically relevant tokenization techniques for genomic data.
  • Future deep learning models in genomics should prioritize advanced tokenization strategies that accurately capture the information within DNA sequences.
  • Improved tokenization is crucial for unlocking the full potential of genomic data analysis.