A Deep Learning Approach to DNA Sequence Classification

Rizzo, Riccardo; Fiannaca, Antonino; Rosa, Massimo La; Urso, Alfonso

doi:10.1007/978-3-319-44332-4_10

Cited by 62 publications

(37 citation statements)

References 24 publications

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“…The advantage of these methods are that they do not need pre-selected features to identify or classify DNA sequences. Deep Learning has been efficiently used for classification of DNA sequences, using one-hot label encoding and Convolution Neural Networks (CNN) 22,23 , albeit the examples in literature are featuring DNA sequences of length up to 500 bps, only.…”

Section: Introductionmentioning

confidence: 99%

Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

Lopez-Rincon

Tonda

Mendoza‐Maldonado

et al. 2020

Preprint

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One of the reasons for the fast spread of SARS-CoV-2 is the lack of accuracy in detection tools in the clinical field. Molecular techniques, such as quantitative real-time RT-PCR and nucleic acid sequencing methods, are widely used to identify pathogens. For this particular virus, however, they have an overall unsatisfying detection rate, due to its relatively recent emergence and still not completely understood features. In addition, SARS-CoV-2 is remarkably similar to other Coronaviruses, and it can present with other respiratory infections, making identification even harder. To tackle this issue, we propose an assisted detection test, combining molecular testing with deep learning. The proposed approach employs a state-of-the-art deep convolutional neural network, able to automatically create features starting from the genome sequence of the virus.Experiments on data from the Novel Coronavirus Resource (2019nCoVR) show that the proposed approach is able to correctly classify SARS-CoV-2, distinguishing it from other coronavirus strains, such as MERS-CoV, HCoV-NL63, HCoV-OC43, HCoV-229E, HCoV-HKU1, and SARS-CoV regardless of missing information and errors in sequencing (noise). From a dataset of 553 complete genome non-repeated sequences that vary from 1,260 to 31,029 bps in length, the proposed approach classifies the different coronaviruses with an average ac-: bioRxiv preprint curacy of 98.75% in a 10-fold cross-validation, identifying SARS-CoV-2 with an AUC of 98%, specificity of 0.9939 and sensitivity of 1.00 in a binary classification. Then, using the same basis, we classify SARS-CoV-2 from 384 complete viral genome sequences with human host, that contain the gene ORF1ab from the NCBI with a 10-fold accuracy of 98.17% , a specificity of 0.9797 and sensitivity of 1.00. Furthermore, an in-depth analysis of the results allow us to identify base pairs sequences that are unique to SARS-CoV-2 and do not appear in other virus strains, that could then be used as a base for designing new primers and examined by experts to extract further insights. These preliminary results seem encouraging enough to identify deep learning as a promising research venue to develop assisted detection tests for SARS-CoV-2. At this end the interaction between viromics and deep learning, will hopefully help to solve global infection problems. In addition, we offer our code and processed data to be used for diagnostic purposes by medical doctors, virologists and scientists involved in solving the SARS-CoV-2 pandemic. As more data become available we will update our system.

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Section: Introductionmentioning

confidence: 99%

Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

Lopez-Rincon

Tonda

Mendoza‐Maldonado

et al. 2020

Preprint

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“…If there is a match between both sequences, then the patient is confirmed positive. Otherwise, the patient is considered negative for Covid-19 (Bosco & Di Gangi, 2016;Rizzo et al, 2015;Zhang & Harmon, 2020;Chan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the representative attributes of the sequence can be combined with methods of artificial intelligence, especially machine learning. This makes possible to separate each analyzed sequence into a class (Covid-19 positive or Covid-19 negative, for example) (Bosco & Di Gangi, 2016;Rizzo et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the molecular diagnosis of Covid-19 by combining RT-PCR and a pseudo-convolutional machine learning approach to characterize virus DNA sequences

Gomes

Silva

Ferreira

et al. 2020

Preprint

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The proliferation of the SARS-Cov-2 virus to the whole world caused more than 250,000 deaths worldwide and over 4 million confirmed cases. The severity of Covid-19, the exponential rate at which the virus proliferates, and the rapid exhaustion of the public health resources are critical factors. The RT-PCR with virus DNA identification is still the benchmark Covid-19 diagnosis method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach, and represented by co-occurrence matrices. This technique analyzes the DNA sequences obtained by the RT-PCR method, eliminating sequence alignment.

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“…This approach is able to work with raw data and does not require the features to be extracted prior to processing [34] [35] [36]. It is still an abstract and complicated approach and the network architecture is still a black box to the scientists [37].…”

Section: Introductionmentioning

confidence: 99%

DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins

Shadab

Khan

Neezi

et al. 2019

Preprint

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DNA-Binding proteins (DBP) are associated with many cellular level functions which includes but not limited to body's defense mechanism and oxygen transportation.They bind DNAs and interact with them. In the past DBPs were identified using experimental lab based methods. However, in the recent years researchers are using supervised learning to identify DBPs solely from protein sequences. In this paper, we apply deep learning methods to identify DBPs. We have proposed two different deep learning based methods for identifying DBPs: DeepDBP-ANN and DeepDBP-CNN. DeepDBP-ANN uses a generated set of features trained on traditional neural network and DeepDBP-CNN uses a pre-learned embedding and Convolutional Neural Network. Both of our proposed methods were able to produce state-of-the-art results when tested on standard benchmark datasets.DeepDBP-ANN had a train accuracy of 99.02% and test accuracy of 82.80%.And DeepDBP-CNN though had train accuracy of 94.32%, it excelled at identifying test instances with 84.31% accuracy. All methods are available codes and methods are available for use at: https://github.com/antorkhan/DNABinding.

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A Deep Learning Approach to DNA Sequence Classification

Cited by 62 publications

References 24 publications

Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

Optimizing the molecular diagnosis of Covid-19 by combining RT-PCR and a pseudo-convolutional machine learning approach to characterize virus DNA sequences

DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins

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