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Erschienen in:
Unveiling the Robustness of Machine Learning Models in Classifying COVID-19 Spike Sequences Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Graph-Based Motif Discovery in Mimotope Profiles of Serum Antibody Repertoire

verfasst von : Hossein Saghaian, Pavel Skums, Yurij Ionov, Alex Zelikovsky

Erschienen in: Bioinformatics Research and Applications

Verlag: Springer Nature Singapore

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Abstract

Phage display technique has a multitude of applications such as epitope mapping, organ targeting, therapeutic antibody engineering and vaccine design. One area of particular importance is the detection of cancers in early stages, where the discovery of binding motifs and epitopes is critical. While several techniques exist to characterize phages, Next Generation Sequencing (NGS) stands out for its ability to provide detailed insights into antibody binding sites on antigens. However, when dealing with NGS data, identifying regulatory motifs poses significant challenges. Existing methods often lack scalability for large datasets, rely on prior knowledge about the number of motifs, and exhibit low accuracy. In this paper, we present a novel approach for identifying regulatory motifs in NGS data. Our method leverages results from graph theory to overcome the limitations of existing techniques.

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Vorheriges Kapitel USTAR: Improved Compression of k-mer Sets with Counters Using de Bruijn Graphs
Nächstes Kapitel Sequence-Based Nanobody-Antigen Binding Prediction
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Metadaten
Titel
Graph-Based Motif Discovery in Mimotope Profiles of Serum Antibody Repertoire
verfasst von
Hossein Saghaian
Pavel Skums
Yurij Ionov
Alex Zelikovsky
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
Bioinformatics Research and Applications

Print ISBN: 978-981-9970-73-5

Electronic ISBN: 978-981-9970-74-2

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-981-99-7074-2_17

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