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Southern Blot

Southern Blot

Agarose gel electrophoresis is very useful in separating DNA fragments by size. Running a DNA ladder containing fragments of the known length alongside the sample helps determine the approximate length of the sample DNA fragments. However, additional steps are needed to verify the sequence identity of the sample DNA fragments.
Denatured DNA fragments must be transferred onto a carrier membrane from the gel to make it accessible to a probe - a small ssDNA fragment complementary to the target DNA...

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Related Experiment Video

Updated: Feb 26, 2026

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

An artificial neural network system to identify alleles in reference electropherograms.

Duncan Taylor¹, Ash Harrison², David Powers²

¹Forensic Science South Australia, 21 Divett Place, Adelaide, SA 5000, Australia; Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia.

Forensic Science International. Genetics

|July 21, 2017

Summary

Forensic DNA analysis uses artificial neural networks to automatically identify and classify peaks in electropherograms, reducing subjective interpretation and saving time in criminal casework. This automated approach aids in distinguishing true DNA profiles from technical artifacts.

Keywords:

Allele detection Artificial neural network Electropherogram Gel reading

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Area of Science:

Forensic Science
Bioinformatics
Computational Biology

Background:

Electropherograms are crucial in forensic DNA profiling for criminal casework.
Manual interpretation of electropherograms is time-consuming and subject to analyst variability.
Distinguishing true allelic DNA data from artefactual signals is a key challenge.

Purpose of the Study:

To develop and evaluate algorithms and artificial neural networks for automated peak identification and classification in electropherograms.
To improve the objectivity and efficiency of DNA profile analysis.
To extend previous work on using artificial neural networks for electropherogram interpretation.

Main Methods:

Development of algorithms for peak detection on electropherograms.
Implementation of artificial neural networks for classifying peaks as allelic or artefactual.
Testing the system on multiple forensic DNA profiles.
Comparison of the developed system with a commercial DNA profile reading system.

Main Results:

The developed algorithms and artificial neural networks successfully identified and classified peaks in electropherograms.
The system demonstrated effective differentiation between allelic peaks and artefacts.
Performance comparison showed competitive results against a leading commercial system.

Conclusions:

Automated analysis using artificial neural networks offers a promising solution for objective and efficient electropherogram interpretation in forensic laboratories.
This approach has the potential to standardize DNA profile analysis and reduce interpretation errors.
Further development and validation could lead to widespread adoption in criminal casework.