Deepdefense: annotation of immune systems in prokaryotes using deep learning.

Autor:	Hauns S; Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg 79110, Germany., Alkhnbashi OS; Center for Applied and Translational Genomics (CATG), Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Healthcare City, Al Razi St. P.O 505055, Dubai, United Arab Emirates.; College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Healthcare City, Al Razi St. 505055, Dubai, United Arab Emirates., Backofen R; Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg 79110, Germany.; Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg 79104, Germany.
Jazyk:	angličtina
Zdroj:	GigaScience [Gigascience] 2024 Jan 02; Vol. 13.
DOI:	10.1093/gigascience/giae062
Abstrakt:	Background: Due to a constant evolutionary arms race, archaea and bacteria have evolved an abundance and diversity of immune responses to protect themselves against phages. Since the discovery and application of CRISPR-Cas adaptive immune systems, numerous novel candidates for immune systems have been identified. Previous approaches to identifying these new immune systems rely on hidden Markov model (HMM)-based homolog searches or use labor-intensive and costly wet-lab experiments. To aid in finding and classifying immune systems genomes, we use machine learning to classify already known immune system proteins and discover potential candidates in the genome. Neural networks have shown promising results in classifying and predicting protein functionality in recent years. However, these methods often operate under the closed-world assumption, where it is presumed that all potential outcomes or classes are already known and included in the training dataset. This assumption does not always hold true in real-world scenarios, such as in genomics, where new samples can emerge that were not previously accounted for in the training phase. Results: In this work, we explore neural networks for immune protein classification, deal with different methods for rejecting unrelated proteins in a genome-wide search, and establish a benchmark. Then, we optimize our approach for accuracy. Based on this, we develop an algorithm called Deepdefense to predict immune cassette classes based on a genome. This design facilitates the differentiation between immune system-related and unrelated proteins by analyzing variations in model-predicted confidence values, aiding in the identification of both known and potentially novel immune system proteins. Finally, we test our approach for detecting immune systems in the genome against an HMM-based method. Conclusions: Deepdefense can automatically detect genes and define cassette annotations and classifications using 2 model classifications. This is achieved by creating an optimized deep learning model to annotate immune systems, in combination with calibration methods, and a second model to enable the scanning of an entire genome. (© The Author(s) 2024. Published by Oxford University Press on behalf of GigaScience.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu