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Related Experiment Video

Updated: Jan 27, 2026

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Low-dimensional representation of genomic sequences.

Richard C Tillquist¹, Manuel E Lladser²

¹Department of Computer Science, The University of Colorado, Boulder, CO, 80309-0526, USA.

Journal of Mathematical Biology

|April 1, 2019

Summary

This summary is machine-generated.

We introduce multilateration, a novel graph-based method for embedding biological sequences into Euclidean space. This approach efficiently handles long genomic sequences, overcoming limitations of existing methods like Multidimensional Scaling (MDS) and Node2Vec.

Keywords:

Feature extraction Graph embeddings Hamming graph Metric dimension Reads Resolving set

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Last Updated: Jan 27, 2026

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

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Published on: March 15, 2019

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

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Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Area of Science:

Bioinformatics
Computational Biology
Data Mining

Background:

Data analysis and mining often require data embedding in Euclidean space.
Conventional methods like binary and k-mer vectors can be too high-dimensional for biological sequence data.
Existing techniques like Multidimensional Scaling (MDS) and Node2Vec struggle with large datasets and new data points.

Purpose of the Study:

To develop an efficient and scalable method for embedding biological sequence data.
To address the limitations of existing embedding techniques for high-throughput sequencing data.
To create low-dimensional embeddings for arbitrary length sequences using graph theory.

Main Methods:

Amended the graph-theoretic notion of "metric dimension" to "multilateration."
Specialized multilateration to Hamming graphs for biological sequence representation.
Applied Multidimensional Scaling (MDS), Node2Vec, and multilateration for classifying DNA 20-mers.

Main Results:

Multilateration provides an efficient means of mapping long genomic sequences.
The proposed method generates low-dimensional embeddings for sequences of arbitrary length.
Multilateration embeddings offer comparable performance to MDS and Node2Vec in classification tasks.

Conclusions:

Multilateration is a scalable and efficient technique for embedding biological sequences.
This method overcomes the scalability issues associated with MDS and Node2Vec for long sequences.
Multilateration offers a promising approach for analyzing large-scale genomic data.