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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Published on: September 25, 2021

ntEmbd: Deep learning embedding for nucleotide sequences.

Saber Hafezqorani^1,2, Ka Ming Nip^1,2, Inanc Birol^1,3

¹570 W 7 Ave, Michael Smith Genome Sciences Centre, BC Cancer, V5Z 4S6, Vancouver, BC, Canada.

Biorxiv : the Preprint Server for Biology

|May 15, 2024

Summary

This summary is machine-generated.

Deep learning tool ntEmbd creates nucleotide sequence embeddings for transcriptomic analysis. It effectively captures sequence features for RNA classification and adapter identification.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Deep learning excels in data analysis, offering powerful tools for transcriptomic studies.
Traditional methods struggle with long nucleotide sequences, hindering feature capture.
Effective nucleotide sequence embedding is crucial for advanced transcriptomic analysis.

Conclusions:

ntEmbd offers an advanced deep learning approach for nucleotide sequence embedding.
The tool enhances downstream transcriptomic analyses like RNA classification and adapter detection.
ntEmbd provides a valuable, freely available resource for the research community.