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Qayyum A, Benzinou A, Saidani O, Alhayan F, Khan MA, Masood A, Mazher M. Assessment and classification of COVID-19 DNA sequence using pairwise features concatenation from multi-transformer and deep features with machine learning models. SLAS Technol 2024; 29:100147. [PMID: 38796034 DOI: 10.1016/j.slast.2024.100147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/31/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such a major viral outbreak demands early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. The emerging global infectious COVID-19 disease by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) presents critical threats to global public health and the economy since it was identified in late December 2019 in China. The virus has gone through various pathways of evolution. Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying deep learning and machine learning approaches. In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine and deep learning techniques have been used in recent years to complete this task with some success. The classification of DNA sequences is a key research area in bioinformatics as it enables researchers to conduct genomic analysis and detect possible diseases. In this paper, three state-of-the-art deep learning-based models are proposed using two DNA sequence conversion methods. We also proposed a novel multi-transformer deep learning model and pairwise features fusion technique for DNA sequence classification. Furthermore, deep features are extracted from the last layer of the multi-transformer and used in machine-learning models for DNA sequence classification. The k-mer and one-hot encoding sequence conversion techniques have been presented. The proposed multi-transformer achieved the highest performance in COVID DNA sequence classification. Automatic identification and classification of viruses are essential to avoid an outbreak like COVID-19. It also helps in detecting the effect of viruses and drug design.
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Affiliation(s)
- Abdul Qayyum
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | | | - Oumaima Saidani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia.
| | - Fatimah Alhayan
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi Arabia.
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Anum Masood
- Department of Physics, Norwegian University of Science and Technology, Trondheim NO-7491, Norway.
| | - Moona Mazher
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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Roy G, Prifti E, Belda E, Zucker JD. Deep learning methods in metagenomics: a review. Microb Genom 2024; 10. [PMID: 38630611 DOI: 10.1099/mgen.0.001231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
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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Affiliation(s)
- Gaspar Roy
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
| | - Edi Prifti
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| | - Eugeni Belda
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
| | - Jean-Daniel Zucker
- IRD, Sorbonne University, UMMISCO, 32 avenue Henry Varagnat, Bondy Cedex, France
- Sorbonne University, INSERM, Nutriomics, 91 bvd de l'hopital, 75013 Paris, France
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Zulfiqar M, Singh V, Steinbeck C, Sorokina M. Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities. Crit Rev Microbiol 2024:1-40. [PMID: 38270170 DOI: 10.1080/1040841x.2024.2306465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.
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Affiliation(s)
- Mahnoor Zulfiqar
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Vinay Singh
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, Jena, Germany
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University, Jena, Germany
- Data Science and Artificial Intelligence, Research and Development, Pharmaceuticals, Bayer, Berlin, Germany
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Shen W, Xiang H, Huang T, Tang H, Peng M, Cai D, Hu P, Ren H. KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping. Bioinformatics 2022; 39:6965021. [PMID: 36579886 PMCID: PMC9828150 DOI: 10.1093/bioinformatics/btac845] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 12/17/2022] [Accepted: 12/28/2022] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION The growing number of microbial reference genomes enables the improvement of metagenomic profiling accuracy but also imposes greater requirements on the indexing efficiency, database size and runtime of taxonomic profilers. Additionally, most profilers focus mainly on bacterial, archaeal and fungal populations, while less attention is paid to viral communities. RESULTS We present KMCP (K-mer-based Metagenomic Classification and Profiling), a novel k-mer-based metagenomic profiling tool that utilizes genome coverage information by splitting the reference genomes into chunks and stores k-mers in a modified and optimized Compact Bit-Sliced Signature Index for fast alignment-free sequence searching. KMCP combines k-mer similarity and genome coverage information to reduce the false positive rate of k-mer-based taxonomic classification and profiling methods. Benchmarking results based on simulated and real data demonstrate that KMCP, despite a longer running time than all other methods, not only allows the accurate taxonomic profiling of prokaryotic and viral populations but also provides more confident pathogen detection in clinical samples of low depth. AVAILABILITY AND IMPLEMENTATION The software is open-source under the MIT license and available at https://github.com/shenwei356/kmcp. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wei Shen
- To whom correspondence should be addressed. or or or
| | - Hongyan Xiang
- Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Department of Infectious Diseases, Institute for Viral Hepatitis, The Second Affiliated Hospital, Chongqing Medical University, Chongqing 400010, China
| | - Tianquan Huang
- Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Department of Infectious Diseases, Institute for Viral Hepatitis, The Second Affiliated Hospital, Chongqing Medical University, Chongqing 400010, China
| | - Hui Tang
- Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Department of Infectious Diseases, Institute for Viral Hepatitis, The Second Affiliated Hospital, Chongqing Medical University, Chongqing 400010, China
| | - Mingli Peng
- Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Department of Infectious Diseases, Institute for Viral Hepatitis, The Second Affiliated Hospital, Chongqing Medical University, Chongqing 400010, China
| | - Dachuan Cai
- Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Department of Infectious Diseases, Institute for Viral Hepatitis, The Second Affiliated Hospital, Chongqing Medical University, Chongqing 400010, China
| | - Peng Hu
- To whom correspondence should be addressed. or or or
| | - Hong Ren
- To whom correspondence should be addressed. or or or
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Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks. Proc Natl Acad Sci U S A 2022; 119:e2122636119. [PMID: 36018838 PMCID: PMC9436379 DOI: 10.1073/pnas.2122636119] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the database, since representative relatives must be present. Many genomic sequences cannot be classified at all or only with a high misclassification rate. Here we present BERTax, a deep neural network program based on natural language processing to precisely classify the superkingdom and phylum of DNA sequences taxonomically without the need for a known representative relative from a database. We show BERTax to be at least on par with the state-of-the-art approaches when taxonomically similar species are part of the training data. For novel organisms, however, BERTax clearly outperforms any existing approach. Finally, we show that BERTax can also be combined with database approaches to further increase the prediction quality in almost all cases. Since BERTax is not based on similar entries in databases, it allows precise taxonomic classification of a broader range of genomic sequences, thus increasing the overall information gain.
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Hsieh YP, Hung YM, Tsai MH, Lai LC, Chuang EY. 16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences. FRONTIERS IN BIOINFORMATICS 2022; 2:905489. [PMID: 36304264 PMCID: PMC9580931 DOI: 10.3389/fbinf.2022.905489] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/13/2022] [Indexed: 12/03/2022] Open
Abstract
Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers’ performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB.
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Affiliation(s)
- Yu-Peng Hsieh
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
| | - Yuan-Mao Hung
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Mong-Hsun Tsai
- Institute of Biotechnology, National Taiwan University, Taipei, Taiwan
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
| | - Liang-Chuan Lai
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, Taiwan
- *Correspondence: Eric Y. Chuang, ; Liang-Chuan Lai,
| | - Eric Y. Chuang
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
- College of Biomedical Engineering, China Medical University, Taichung, Taiwan
- *Correspondence: Eric Y. Chuang, ; Liang-Chuan Lai,
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Mushtaq S, Shafiq M, Ashraf T, Haider MS, Atta S, Almaary KS, Elshikh MS. Enumeration of citrus endophytic bacterial communities based on illumine metagenomics technique. PLoS One 2022; 17:e0263144. [PMID: 35417473 PMCID: PMC9007379 DOI: 10.1371/journal.pone.0263144] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/03/2022] [Indexed: 02/01/2023] Open
Abstract
Citrus is a valuable crop in Pakistan. It is rich in vitamin C, other nutrients and antioxidants. Huanglongbing (HLB) caused by a bacterium “Candidatus liberibacter asiaticus” (CLas), africanus and americanus has an influence on citrus production around the world. Beside HLB there exist several other bacterial species in citrus groves in Pakistan. The structure and diversity of bacterial species in various ecosystems can be quickly examined using NGS. This approach is considerably quicker and more precise than outdated methods. Healthy or citrus greening infected leaf samples of Grapefruit (Citrus paradisi), C. aurantifolia, and C. reticulata Blanco were used for diversity analysis. In this study high throughput, NGS technique was used to access the population of both cultivable and non-cultivable bacterial endophytes from citrus leaves, by using PCR amplicons of 16S rDNA sequences (V5–V7 regions) with Illumina Hi seq. As a result, a total number of 68,722 sequences were produced from the test samples. According to the NGS-based diversity classification, the most common genera of exploited bacterial endophytes were Proteobacteria, Firmicutes, Bacteroides, Cyanobacteria, and Actinobacteria. C. aurantifolia and C. paradisi showed almost equal diversity, whereas C. reticulata Blanco had a higher proportion of Proteobacteria and Cyanobacteria in their leaves. To determine alpha diversity (AD), additional data was analyzed using statistical indices such as Shannon, Chao1, and Simpson. According to the inverse Simpson diversity index, the abundance of the microbial population in six different citrus samples was 0.48, 0.567, and 0.163, respectively. The metagenomics of microbiota in plant tissues was successfully recorded by NGS technology, which can help us learn more about the interactions between plants and microbes. This research is the first step toward a better understanding of 16SrRNA-based metagenomics from citrus in Pakistan using Illumina (Hi seq) Technology.
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Affiliation(s)
- Sehrish Mushtaq
- Faculty of Agricultural Sciences, Department of Plant Pathology, University of the Punjab, Quaid-e-Azam Campus, Lahore, Pakistan
| | - Muhammad Shafiq
- Faculty of Agricultural Sciences, Department of Horticulture Sciences, University of the Punjab, Quaid-e-Azam Campus, Lahore, Pakistan
| | - Tehseen Ashraf
- Department of Horticulture Sciences University of Sargodha, Sargodha, Pakistan
| | - Muhammad Saleem Haider
- Faculty of Agricultural Sciences, Department of Plant Pathology, University of the Punjab, Quaid-e-Azam Campus, Lahore, Pakistan
| | - Sagheer Atta
- Department of Plant Protection, Faculty of Agricultural Sciences, Ghazi University Dera Ghazhi Khan, Punjab, Pakistan
- Molecular Plant Pathology Laboratory, USDA-ARS, Beltsville, MD, United States of America
- * E-mail:
| | - Khalid S. Almaary
- Department of Botany and Microbiology, College of Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Mohammad Soliman Elshikh
- Department of Botany and Microbiology, College of Sciences, King Saud University, Riyadh, Saudi Arabia
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Analysis of DNA Sequence Classification Using CNN and Hybrid Models. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:1835056. [PMID: 34306171 PMCID: PMC8285202 DOI: 10.1155/2021/1835056] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 06/25/2021] [Indexed: 12/23/2022]
Abstract
In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.
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Pacal I, Karaboga D. A robust real-time deep learning based automatic polyp detection system. Comput Biol Med 2021; 134:104519. [PMID: 34090014 DOI: 10.1016/j.compbiomed.2021.104519] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/19/2021] [Accepted: 05/20/2021] [Indexed: 01/16/2023]
Abstract
Colorectal cancer (CRC) is globally the third most common type of cancer. Colonoscopy is considered the gold standard in colorectal cancer screening and allows for the removal of polyps before they become cancerous. Computer-aided detection systems (CADs) have been developed to detect polyps. Unfortunately, these systems have limited sensitivity and specificity. In contrast, deep learning architectures provide better detection by extracting the different properties of polyps. However, the desired success has not yet been achieved in real-time polyp detection. Here, we propose a new structure for real-time polyp detection by scaling the YOLOv4 algorithm to overcome these obstacles. For this, we first replace the whole structure with Cross Stage Partial Networks (CSPNet), then substitute the Mish activation function for the Leaky ReLu activation function and also substituted the Distance Intersection over Union (DIoU) loss for the Complete Intersection over Union (CIoU) loss. We improved performance of YOLOv3 and YOLOv4 architectures using different structures such as ResNet, VGG, DarkNet53, and Transformers. To increase success of the proposed method, we utilized a variety of data augmentation approaches for preprocessing, an ensemble learning model, and NVIDIA TensorRT for post processing. In order to compare our study with other studies more objectively, we only employed public data sets and followed MICCAI Sub-Challenge on Automatic Polyp Detection in Colonoscopy. The proposed method differs from other methods with its real-time performance and state-of-the-art detection accuracy. The proposed method (without ensemble learning) achieved higher results than those found in the literature, precision: 91.62%, recall: 82.55%, F1-score: 86.85% on public ETIS-LARIB data set and precision: 96.04%, recall: 96.68%, F1-score: 96.36% on public CVC-ColonDB data set, respectively.
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Affiliation(s)
- Ishak Pacal
- Computer Engineering Department, Engineering Faculty, Igdir University, Igdir, Turkey.
| | - Dervis Karaboga
- Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey; Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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