DETIRE: A Hybrid Deep Learning Model for identifying Viral Sequences from Metagenomes

Yan, Miao; Liu, Fu; Hou, Tao; Liu, Qiaoliang; Dong, Tian; Liu, Yun

doi:10.1101/2021.11.19.469211

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…MetaMLP [89], for example, embeds k-mers with a small alphabet and partial matching, allowing for rapid functional profiling. DETIRE [90] uses methods close to those seen before, but by combining one-hot encoding with TF-IDF embedding of k-mers for virus detection. The structure of the data is also captured with a graph that links k-mers to their original sequences and their label (viral or not).…”

Section: Nlp-based Analysismentioning

confidence: 99%

Deep learning methods in metagenomics: a review

Roy,

Prifti,

Belda

et al. 2024

Microbial Genomics

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The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome’s key role in our health.

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Section: Nlp-based Analysismentioning

confidence: 99%

Deep learning methods in metagenomics: a review

Roy,

Prifti,

Belda

et al. 2024

Microbial Genomics

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Improving viral annotation with artificial intelligence

Flamholz,

Li,

Kelly

2024

mBio

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Viruses of bacteria, “phages,” are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environmental pressures. High-throughput sequencing approaches have begun to give us access to the diversity and range of phage populations in complex microbial community samples, and metagenomics is currently the primary tool with which we study phage populations. The study of phages by metagenomic sequencing, however, is fundamentally limited by viral diversity, which results in the vast majority of viral genomes and metagenome-annotated genomes lacking annotation. To harness bacteriophages for applications in human and environmental health and disease, we need new methods to organize and annotate viral sequence diversity. We recently demonstrated that methods that leverage self-supervised representation learning can supplement statistical sequence representations for remote viral protein homology detection in the ocean virome and propose that consideration of the functional content of viral sequences allows for the identification of similarity in otherwise sequence-diverse viruses and viral-like elements for biological discovery. In this review, we describe the potential and pitfalls of large language models for viral annotation. We describe the need for new approaches to annotate viral sequences in metagenomes, the fundamentals of what protein language models are and how one can use them for sequence annotation, the strengths and weaknesses of these models, and future directions toward developing better models for viral annotation more broadly.

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DETIRE: A Hybrid Deep Learning Model for identifying Viral Sequences from Metagenomes

Cited by 2 publications

References 36 publications

Deep learning methods in metagenomics: a review

Deep learning methods in metagenomics: a review

Improving viral annotation with artificial intelligence

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