English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper demonstrates the utility of organized numerical representations ofgenes in research involving flat string gene formats (i.e., FASTA/FASTQ5).FASTA/FASTQ files have several current limitations, such as their large filesizes, slow processing speeds for mapping and alignment, and contextualdependencies. These challenges significantly hinder investigations and tasksthat involve finding similar sequences. The solution lies in transformingsequences into an alternative representation that facilitates easier clusteringinto similar groups compared to the raw sequences themselves. By assigning aunique vector embedding to each short sequence, it is possible to moreefficiently cluster and improve upon compression performance for the stringrepresentations of cDNA libraries. Furthermore, through learning alternativecoordinate vector embeddings based on the contexts of codon triplets, we candemonstrate clustering based on amino acid properties. Finally, using thissequence embedding method to encode barcodes and cDNA sequences, we can improvethe time complexity of the similarity search by coupling vector embeddings withan algorithm that determines the proximity of vectors in Euclidean space; thisallows us to perform sequence similarity searches in a quicker and more modularfashion.

Vector Embeddings by Sequence Similarity and Context for Improved  Compression, Similarity Search, Clustering, Organization, and Manipulation of  cDNA Libraries