RNN-VirSeeker: A Deep Learning Method for Identification of Short Viral Sequences From Metagenomes

Liu, Fu; Yan, Miao; Liu, Yun; Hou, Tao

doi:10.1109/tcbb.2020.3044575

Cited by 26 publications

(15 citation statements)

References 37 publications

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“…Tools such as the machine learning algorithms RNN-VirSeeker and VirFinder, along with cloud-based platforms like Serratus, have been innovatively designed to pinpoint viral sequences within metagenomic data, markedly enhancing the efficiency and precision of virus detection. [129][130][131] Despite the remarkable advancements in the construction of infectious clones for insect viruses, significant challenges persist. These challenges include the assembly or preservation of large genomic fragments in prokaryotic cells, such as Escherichia coli or Agrobacterium.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, advancements in bioinformatics have greatly enriched the domain of virus identification. Tools such as the machine learning algorithms RNN‐VirSeeker and VirFinder, along with cloud‐based platforms like Serratus, have been innovatively designed to pinpoint viral sequences within metagenomic data, markedly enhancing the efficiency and precision of virus detection 129–131 …”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging insect viruses and genetic manipulation for sustainable agricultural pest control

Sun,

Fu,

et al. 2023

Pest Management Science

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Insect viruses have long been recognized for their potential as biological control agents in managing agricultural pests. Despite their promise, challenges such as host specificity, virulence, and limited viral resources have hindered their widespread application. The need for efficient discovery and utilization of insect viruses is paramount for sustainable agricultural practices. With the introduction of high‐throughput sequencing (HTS) technologies, the discovery and identification of new insect viruses have been significantly accelerated, enriching the available resources for pest management. Furthermore, advancements in reverse genetics tools have enabled the development of multifunctional viral expression vectors, enhancing the specificity and efficacy of insect viruses against targeted pests. This review provides a comprehensive overview of the methodologies for insect virus identification using HTS and the potential of genetically modified insect viruses in pest control. Genetically engineered insect viruses offer a promising avenue for targeted and efficient pest management. While HTS technologies have revolutionized the discovery of insect viruses, challenges remain. Addressing these challenges and harnessing the potential of these technologies will be pivotal in shaping sustainable agricultural management strategies in the future.This article is protected by copyright. All rights reserved.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Leveraging insect viruses and genetic manipulation for sustainable agricultural pest control

Sun,

Fu,

et al. 2023

Pest Management Science

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“…An alternative is to use one-dimensional (1D) convolution, which has linear complexity with respect to k , to represent kmers. Naturally, convolutional networks have been well adopted in the literature, − but convolutional networks struggle to grasp extended sequence dependencies vital for DNA-related tasks. Conversely, Transformers employ a neural network design that incorporates self-attention, enabling them to understand relationships across any length of a sequence .…”

Section: Introductionmentioning

confidence: 99%

Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions

He,

Gao,

Sabnis

et al. 2023

ACS Synth. Biol.

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Much work has been done to apply machine learning and deep learning to genomics tasks, but these applications usually require extensive domain knowledge, and the resulting models provide very limited interpretability. Here, we present the Nucleic Transformer, a conceptually simple but effective and interpretable model architecture that excels in the classification of DNA sequences. The Nucleic Transformer employs self-attention and convolutions on nucleic acid sequences, leveraging two prominent deep learning strategies commonly used in computer vision and natural language analysis. We demonstrate that the Nucleic Transformer can be trained without much domain knowledge to achieve high performance in Escherichia coli promoter classification, viral genome identification, enhancer classification, and chromatin profile predictions.

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“…Long-short term memory (LSTM) network and convolutional neural network (CNN) are the most commonly used models. For example, ViraMiner [21], VirNet [22] and RNN-VirSeeker [23] utilize a single LSTM network to learn the interconnections between each part in a one-hot encoded sequence; DeepVirFinder [24], PPR-Meta [25] and CHEER [26] establish a single CNN to extract high-level features from one-hot encoded sequences automatically before a set of dense layers and a softmax layer for classification. However, the following two issues hamper the performance of deep learning models for the recognition of short viral sequences: 1) the single deep learning architecture suffers from failing to extract enough features to represent sequences; 2) the one-hot encoding strategy omits the relationship between two parts of a sequence because of its orthogonal property [27].…”

Section: Introductionmentioning

confidence: 99%

“…Universally, a confusion matrix is calculated to evaluate the performance of a classifier according to four statistics: True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN)[23]. TP are examples correctly labeled as positives; FP refer to negative examples incorrectly labeled as positive; TN correspond to negatives correctly labeled as negative; and FN refer to positive examples incorrectly labeled as negative.…”

mentioning

confidence: 99%

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

Yan

Liu

Hou

et al. 2021

Preprint

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A metagenome contains all DNA sequences from an environmental sample, including viruses, bacteria, fungi, actinomycetes and so on. Since viruses are of huge abundance and have caused vast mortality and morbidity to human society in history as a kind of major pathogens, detecting viruses from metagenomes plays a crucial role in analysing the viral component of samples and is the very first step for clinical diagnosis. However, detecting viral fragments directly from the metagenomes is still a tough issue because of the existence of huge number of short sequences. In this paper, a hybrid Deep lEarning model for idenTifying vIral sequences fRom mEtagenomes (DETIRE), is proposed to solve the problem. Firstly, the graph-based nucleotide sequence embedding strategy is utilized to enrich the expression of DNA sequences by training an embedding matrix. Then the spatial and sequential features are extracted by trained CNN and BiLSTM networks respectively to improve the feature expression of short sequences. Finally, the two set of features are weighted combined for the final decision. Trained by 220,000 sequences of 500bp subsampled from the Virus and Host RefSeq genomes, DETIRE identifies more short viral sequences (<1,000bp) than three latest methods, DeepVirFinder, PPR-Meta and CHEER. DETIRE is freely available at https://github.com/crazyinter/DETIRE.

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RNN-VirSeeker: A Deep Learning Method for Identification of Short Viral Sequences From Metagenomes

Cited by 26 publications

References 37 publications

Leveraging insect viruses and genetic manipulation for sustainable agricultural pest control

Leveraging insect viruses and genetic manipulation for sustainable agricultural pest control

Nucleic Transformer: Classifying DNA Sequences with Self-Attention and Convolutions

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

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