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DNA as a Genetic Template

DNA as a Genetic Template

Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...

DNA as a Genetic Template

DNA as a Genetic Template

Genome Annotation and Assembly

Genome Annotation and Assembly

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.

Sanger Sequencing

Sanger Sequencing

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...

The DNA Helix

The DNA Helix

Deoxyribonucleic acid, or DNA, is the genetic material responsible for passing traits from generation to generation in all organisms and most viruses. DNA is composed of two strands of nucleotides that wind around each other to form a spring-like structure called a double helix. However, the double helix is not perfectly symmetrical. Instead, there are regularly occurring grooves in the structure. The major groove occurs where the sugar-phosphate backbones are relatively far apart. This space...

The DNA Helix

The DNA Helix

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Related Experiment Video

Updated: Jan 15, 2026

Analyzing and Building Nucleic Acid Structures with 3DNA

Analyzing and Building Nucleic Acid Structures with 3DNA

Published on: April 26, 2013

gReLU: a comprehensive framework for DNA sequence modeling and design.

Avantika Lal¹, Laura Gunsalus¹, Surag Nair¹

¹Biology Research | AI Development, gRED Computational Sciences, Genentech, South San Francisco, CA, USA.

|October 15, 2025

Summary

This summary is machine-generated.

Deep learning for DNA sequence analysis is powerful but challenging. gReLU is a new software framework simplifying these advanced modeling pipelines for better insights and applications.

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Related Experiment Videos

Last Updated: Jan 15, 2026

Analyzing and Building Nucleic Acid Structures with 3DNA

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Published on: April 26, 2013

Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles

Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles

Published on: May 8, 2015

Design and Synthesis of a Reconfigurable DNA Accordion Rack

Design and Synthesis of a Reconfigurable DNA Accordion Rack

Published on: August 15, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Deep learning models analyze DNA sequences for regulatory activity prediction.
Challenges include model interpretability, interoperability, and correct implementation.
Existing tools lack comprehensive functionality for advanced DNA sequence modeling.

Purpose of the Study:

Introduce gReLU, a unified software framework for DNA sequence modeling.
Enable advanced pipelines for preprocessing, modeling, evaluation, and interpretation.
Facilitate variant effect prediction and synthetic DNA design.

Main Methods:

Developed gReLU as a comprehensive software framework.
Integrated data preprocessing, modeling, and evaluation modules.
Included tools for model interpretation, variant effect prediction, and DNA design.

Main Results:

gReLU provides an interoperable platform for diverse deep learning tasks on DNA.
The framework streamlines complex sequence modeling workflows.
Enables accurate prediction of cell-type-specific regulatory activity and genetic variant impact.

Conclusions:

gReLU addresses key challenges in deep learning for DNA sequence analysis.
Offers a robust and user-friendly solution for researchers.
Accelerates discovery in genomics and synthetic biology.