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Journal articles on the topic 'DNA Sequence Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Pandey, Subhash Chandra, and Saket Kumar Singh. "DNA sequence based data classification technique." CSI Transactions on ICT 3, no. 1 (2015): 59–69. http://dx.doi.org/10.1007/s40012-015-0072-x.

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WANG, JASON T. L., STEVE ROZEN, BRUCE A. SHAPIRO, DENNIS SHASHA, ZHIYUAN WANG, and MAISHENG YIN. "New Techniques for DNA Sequence Classification." Journal of Computational Biology 6, no. 2 (1999): 209–18. http://dx.doi.org/10.1089/cmb.1999.6.209.

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Nguyen, Ngoc Giang, Vu Anh Tran, Duc Luu Ngo, et al. "DNA Sequence Classification by Convolutional Neural Network." Journal of Biomedical Science and Engineering 09, no. 05 (2016): 280–86. http://dx.doi.org/10.4236/jbise.2016.95021.

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Lee, Dong Wook, and Kwee-Bo Sim. "Negative Selection Algorithm for DNA Sequence Classification." International Journal of Fuzzy Logic and Intelligent Systems 4, no. 2 (2004): 231–35. http://dx.doi.org/10.5391/ijfis.2004.4.2.231.

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Biedrzycki, Rafał, and Jarosław Arabas. "KIS: An automated attribute induction method for classification of DNA sequences." International Journal of Applied Mathematics and Computer Science 22, no. 3 (2012): 711–21. http://dx.doi.org/10.2478/v10006-012-0053-2.

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Abstract This paper presents an application of methods from the machine learning domain to solving the task of DNA sequence recognition. We present an algorithm that learns to recognize groups of DNA sequences sharing common features such as sequence functionality. We demonstrate application of the algorithm to find splice sites, i.e., to properly detect donor and acceptor sequences. We compare the results with those of reference methods that have been designed and tuned to detect splice sites. We also show how to use the algorithm to find a human readable model of the IRE (Iron-Responsive Ele

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6

Zheng, Zhiming, and Ya Wang. "DNA binding proteins: outline of functional classification." BioMolecular Concepts 2, no. 4 (2011): 293–303. http://dx.doi.org/10.1515/bmc.2011.023.

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AbstractDNA-binding proteins composed of DNA-binding domains directly affect genomic functions, mainly by performing transcription, DNA replication or DNA repair. Here, we briefly describe the DNA-binding proteins according to these three major functions. Transcription factors that usually bind to specific sequences of DNA could be classified based on their sequence similarity and the structure of the DNA-binding domains, such as basic, zinc-coordinating, helix-turn-helix domains, etc. Most DNA replication factors do not need a specific sequence of DNA, but instead mainly depend on a DNA struc

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Gunasekaran, Hemalatha, K. Ramalakshmi, A. Rex Macedo Arokiaraj, S. Deepa Kanmani, Chandran Venkatesan, and C. Suresh Gnana Dhas. "Analysis of DNA Sequence Classification Using CNN and Hybrid Models." Computational and Mathematical Methods in Medicine 2021 (July 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/1835056.

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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,

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Valiunas, Deividas, Rasa Jomantiene та Robert Edward Davis. "Evaluation of the DNA-dependent RNA polymerase β-subunit gene (rpoB) for phytoplasma classification and phylogeny". International Journal of Systematic and Evolutionary Microbiology 63, Pt_10 (2013): 3904–14. http://dx.doi.org/10.1099/ijs.0.051912-0.

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Phytoplasmas are classified into 16Sr groups and subgroups and ‘Candidatus Phytoplasma ’ species, largely or entirely based on analysis of 16S rRNA gene sequences. Yet, distinctions among closely related ‘Ca. Phytoplasma ’ species and strains based on 16S rRNA genes alone have limitations imposed by the high degree of rRNA nucleotide sequence conservation across diverse phytoplasma lineages and by the presence in a phytoplasma genome of two, sometimes sequence-heterogeneous, copies of the 16S rRNA gene. Since the DNA-dependent RNA polymerase (DpRp) β-subunit gene (rpoB) exists as a single copy

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Marrero, Glorimar, Kevin L. Schneider, Daniel M. Jenkins, and Anne M. Alvarez. "Phylogeny and classification of Dickeya based on multilocus sequence analysis." International Journal of Systematic and Evolutionary Microbiology 63, Pt_9 (2013): 3524–39. http://dx.doi.org/10.1099/ijs.0.046490-0.

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Bacterial heart rot of pineapple reported in Hawaii in 2003 and reoccurring in 2006 was caused by an undetermined species of Dickeya . Classification of the bacterial strains isolated from infected pineapple to one of the recognized Dickeya species and their phylogenetic relationships with Dickeya were determined by a multilocus sequence analysis (MLSA), based on the partial gene sequences of dnaA, dnaJ, dnaX, gyrB and recN. Individual and concatenated gene phylogenies revealed that the strains form a clade with reference Dickeya sp. isolated from pineapple in Malaysia and are closely related

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Sohsah, Gihad N., Ali Reza Ibrahimzada, Huzeyfe Ayaz, and Ali Cakmak. "Scalable classification of organisms into a taxonomy using hierarchical supervised learners." Journal of Bioinformatics and Computational Biology 18, no. 05 (2020): 2050026. http://dx.doi.org/10.1142/s0219720020500262.

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Accurately identifying organisms based on their partially available genetic material is an important task to explore the phylogenetic diversity in an environment. Specific fragments in the DNA sequence of a living organism have been defined as DNA barcodes and can be used as markers to identify species efficiently and effectively. The existing DNA barcode-based classification approaches suffer from three major issues: (i) most of them assume that the classification is done within a given taxonomic class and/or input sequences are pre-aligned, (ii) highly performing classifiers, such as SVM, ca

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Paço, Ana, Renata Freitas, and Ana Vieira-da-Silva. "Conversion of DNA Sequences: From a Transposable Element to a Tandem Repeat or to a Gene." Genes 10, no. 12 (2019): 1014. http://dx.doi.org/10.3390/genes10121014.

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Eukaryotic genomes are rich in repetitive DNA sequences grouped in two classes regarding their genomic organization: tandem repeats and dispersed repeats. In tandem repeats, copies of a short DNA sequence are positioned one after another within the genome, while in dispersed repeats, these copies are randomly distributed. In this review we provide evidence that both tandem and dispersed repeats can have a similar organization, which leads us to suggest an update to their classification based on the sequence features, concretely regarding the presence or absence of retrotransposons/transposon s

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WU, CATHY H., HSI-LIEN CHEN, and SHENG-CHIH CHEN. "GENE CLASSIFICATION ARTIFICIAL NEURAL SYSTEM." International Journal on Artificial Intelligence Tools 04, no. 04 (1995): 501–10. http://dx.doi.org/10.1142/s0218213095000255.

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A gene classification artificial neural system has been developed for rapid annotation of the molecular sequencing data being generated by the Human Genome Project. Three neural networks have been implemented, one full-scale system to classify protein sequences according to PIR (Protein Identification Resources) superfamilies, one system to classify ribosomal RNA sequences into RDP (Ribosomal Database Project) phylogenetic classes, and one pilot system to classify proteins according to Blocks motifs. The sequence encoding schema involved an n-gram hashing method to convert molecular sequences

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Wang, Li-Ting, Fwu-Ling Lee, Chun-Ju Tai, and Hiroaki Kasai. "Comparison of gyrB gene sequences, 16S rRNA gene sequences and DNA–DNA hybridization in the Bacillus subtilis group." International Journal of Systematic and Evolutionary Microbiology 57, no. 8 (2007): 1846–50. http://dx.doi.org/10.1099/ijs.0.64685-0.

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The Bacillus subtilis group comprises eight closely related species that are indistinguishable from one another by 16S rRNA gene sequence analysis. Therefore, the gyrB gene, which encodes the subunit B protein of DNA gyrase, was selected as an alternative phylogenetic marker. To determine whether gyrB gene sequence analysis could be used for phylogenetic analysis and species identification of members of the B. subtilis group, the congruence of gyrB grouping with both 16S rRNA gene sequencing and DNA–DNA hybridization data was evaluated. Ranges of gyrB nucleotide and translated amino acid seque

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Mohamed, Marghny, Abeer A. Al-Mehdhar, Mohamed Bamatraf, and Moheb R. Girgis. "Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification." Intelligent Information Management 05, no. 01 (2013): 25–33. http://dx.doi.org/10.4236/iim.2013.51004.

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Bhargavi, K. "Classification of DNA Sequence Using Soft Computing Techniques: A Survey." Indian Journal of Science and Technology 9, no. 1 (2016): 1–7. http://dx.doi.org/10.17485/ijst/2016/v9i47/89343.

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Ashlock, Wendy, and Suprakash Datta. "Evolved Features for DNA Sequence Classification and Their Fitness Landscapes." IEEE Transactions on Evolutionary Computation 17, no. 2 (2013): 185–97. http://dx.doi.org/10.1109/tevc.2012.2207120.

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RAY, KUMAR S., and MANDRITA MONDAL. "CLASSIFICATION OF SODAR DATA BY DNA COMPUTING." New Mathematics and Natural Computation 07, no. 03 (2011): 413–32. http://dx.doi.org/10.1142/s1793005711002074.

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In this paper, we propose a wet lab algorithm for classification of SODAR data by DNA computing. The concept of DNA computing is essentially exploited to generate the classifier algorithm in the wet lab. The classifier is based on a new concept of similarity-based fuzzy reasoning suitable for wet lab implementation. This new concept of similarity-based fuzzy reasoning is different from conventional approach to fuzzy reasoning based on similarity measure and also replaces the logical aspect of classical fuzzy reasoning by DNA chemistry. Thus, we add a new dimension to the existing forms of fuzz

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Nightingale, Kendra. "Listeria monocytogenes: Knowledge Gained Through DNA Sequence-Based Subtyping, Implications, and Future Considerations." Journal of AOAC INTERNATIONAL 93, no. 4 (2010): 1275–86. http://dx.doi.org/10.1093/jaoac/93.4.1275.

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Abstract The purpose of subtyping is to differentiate bacterial isolates beyond the classification of species or subspecies. Subtyping methods can be grouped into two broad categories based on the cellular components targeted: (1) phenotypic subtyping methods that differentiate isolates by the enzymes, proteins, or other metabolites expressed by the cell, and (2) molecular subtyping methods that discriminate isolates based on interrogation of nucleic acid sequences. The two major types of molecular subtyping methods include band-based methods based on fragment pattern data or DNA fingerprints,

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Jin, Lina, Jiong Yu, Xiaoqian Yuan, and Xusheng Du. "Fish Classification Using DNA Barcode Sequences through Deep Learning Method." Symmetry 13, no. 9 (2021): 1599. http://dx.doi.org/10.3390/sym13091599.

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Fish is one of the most extensive distributed organisms in the world. Fish taxonomy is an important component of biodiversity and the basis of fishery resources management. The DNA barcode based on a short sequence fragment is a valuable molecular tool for fish classification. However, the high dimensionality of DNA barcode sequences and the limitation of the number of fish species make it difficult to reasonably analyze the DNA sequences and correctly classify fish from different families. In this paper, we propose a novel deep learning method that fuses Elastic Net-Stacked Autoencoder (EN-SA

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Najam, Maleeha, Raihan Ur Rasool, Hafiz Farooq Ahmad, Usman Ashraf, and Asad Waqar Malik. "Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters." BioMed Research International 2019 (April 14, 2019): 1–9. http://dx.doi.org/10.1155/2019/7074387.

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Storing and processing of large DNA sequences has always been a major problem due to increasing volume of DNA sequence data. However, a number of solutions have been proposed but they require significant computation and memory. Therefore, an efficient storage and pattern matching solution is required for DNA sequencing data. Bloom filters (BFs) represent an efficient data structure, which is mostly used in the domain of bioinformatics for classification of DNA sequences. In this paper, we explore more dimensions where BFs can be used other than classification. A proposed solution is based on M

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Anshori, Mochammad, Wayan Firdaus Mahmudy, and Ahmad Afif Supianto. "Classification Tuberculosis DNA using LDA-SVM." Journal of Information Technology and Computer Science 4, no. 3 (2019): 233. http://dx.doi.org/10.25126/jitecs.201943113.

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Tuberculosis is a disease caused by the mycobacterium tuberculosis virus. Tuberculosis is very dangerous and it is included in the top 10 causes of the death in the world. In its detection, errors often occur because it is similar to other diffuse lungs. The challenge is how to better detect using DNA sequence data from mycobacterium tuberculosis. Therefore, preprocessing data is necessary. Preprocessing method is used for feature extraction, it is k-Mer which is then processed again with TF-IDF. The use of dimensional reduction is needed because the data is very large. The used method is LDA.

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Zhao, Zhengqiao, Stephen Woloszynek, Felix Agbavor, Joshua Chang Mell, Bahrad A. Sokhansanj, and Gail L. Rosen. "Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network." PLOS Computational Biology 17, no. 9 (2021): e1009345. http://dx.doi.org/10.1371/journal.pcbi.1009345.

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Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach

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23

Ray, Chhanda, and Ankita Sasmal. "A Genome based Detection and Classification of Coronavirus Infection." International Journal of Engineering and Computer Science 9, no. 08 (2020): 25148–55. http://dx.doi.org/10.18535/ijecs/v9i08.4522.

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The Coronavirus (COVID-19) infection has become a global threat in recent time. Many researchers have been dedicated to control COVID-19 pandemic. In this paper, an effective method is presented for detection and classification of COVID-19 infection based on genome sequences. First, the COVID-19 infection is detected based on the induction of changes in the DNA microarray gene expression pattern of the host during and after infection and comparing it with DNA sequences of Coronavirus (SARS-CoV-2). In order to analyse DNA microarray gene expression data, a bi-directional string matching algorit

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Davis, Robert E., and Ellen L. Dally. "Revised Subgroup Classification of Group 16SrV Phytoplasmas and Placement of Flavescence Dorée-Associated Phytoplasmas in Two Distinct Subgroups." Plant Disease 85, no. 7 (2001): 790–97. http://dx.doi.org/10.1094/pdis.2001.85.7.790.

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The subgroup classification of phytoplasmas in 16S rRNA group 16SrV (elm yellows phytoplasma group) was revised and extended on the basis of enzymatic restriction fragment length polymorphism (RFLP) analysis of ribosomal (r) DNA and analysis of putative restriction sites in nucleotide sequences. A 1.85 kbp fragment of the rRNA operon from flavescence dorée (FD) phytoplasma strain FD70 from France was amplified and cloned, and its nucleotide sequence determined (GenBank acc. no. AF176319). Placement of FD70 in subgroup V-C was verified by analysis of amplified DNA and of the cloned sequence. He

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Mendizabal-Ruiz, Gerardo, Israel Román-Godínez, Sulema Torres-Ramos, Ricardo A. Salido-Ruiz, Hugo Vélez-Pérez, and J. Alejandro Morales. "Genomic signal processing for DNA sequence clustering." PeerJ 6 (January 24, 2018): e4264. http://dx.doi.org/10.7717/peerj.4264.

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Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and anal

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Guzmán-Dávalos, Laura, Gregory M. Mueller, Joaquín Cifuentes, Andrew N. Miller, and Anne Santerre. "Traditional infrageneric classification ofGymnopilusis not supported by ribosomal DNA sequence data." Mycologia 95, no. 6 (2003): 1204–14. http://dx.doi.org/10.1080/15572536.2004.11833028.

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JI, YUNHENG, PETER W. FRITSCH, HENG LI, TIAOJIANG XIAO, and ZHEKUN ZHOU. "Phylogeny and Classification of Paris (Melanthiaceae) Inferred from DNA Sequence Data." Annals of Botany 98, no. 1 (2006): 245–56. http://dx.doi.org/10.1093/aob/mcl095.

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Timinskas, Albertas, Viktoras Butkus, and Arvydas Janulaitis. "Sequence motifs characteristic for DNA [cytosine-N4] and DNA [adenine-N6] methyltransferases. Classification of all DNA methyltransferases." Gene 157, no. 1-2 (1995): 3–11. http://dx.doi.org/10.1016/0378-1119(94)00783-o.

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T S, Farisa, and Elizabeth Isaac. "Sequence Based DNA-Binding Protein Prediction." International Journal of Recent Technology and Engineering 9, no. 6 (2021): 44–48. http://dx.doi.org/10.35940/ijrte.b3665.039621.

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Protein and DNA have vital role in our biological processes. For accurately predicting DNA binding protein, develop a new sequence based prediction method from the protein sequence. Sequence based method only considers the protein sequence information as input. For accurately predicting DBP, first develop a reliable benchmark data set from the protein data bank. Second, using Amino Acid Composition (AAC), Position Specific Scoring Matrix (PSSM), Predicted Solvent Accessibility (PSA), and Predicted Probabilities of DNA-Binding Sites (PDBS) to produce four specific protein sequence baselines. Us

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KAMATH, UDAY, AMARDA SHEHU, and KENNETH A. DE JONG. "A TWO-STAGE EVOLUTIONARY APPROACH FOR EFFECTIVE CLASSIFICATION OF HYPERSENSITIVE DNA SEQUENCES." Journal of Bioinformatics and Computational Biology 09, no. 03 (2011): 399–413. http://dx.doi.org/10.1142/s0219720011005586.

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Hypersensitive (HS) sites in genomic sequences are reliable markers of DNA regulatory regions that control gene expression. Annotation of regulatory regions is important in understanding phenotypical differences among cells and diseases linked to pathologies in protein expression. Several computational techniques are devoted to mapping out regulatory regions in DNA by initially identifying HS sequences. Statistical learning techniques like Support Vector Machines (SVM), for instance, are employed to classify DNA sequences as HS or non-HS. This paper proposes a method to automate the basic step

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Ryasik, Artem, Mikhail Orlov, Evgenia Zykova, Timofei Ermak, and Anatoly Sorokin. "Bacterial promoter prediction: Selection of dynamic and static physical properties of DNA for reliable sequence classification." Journal of Bioinformatics and Computational Biology 16, no. 01 (2018): 1840003. http://dx.doi.org/10.1142/s0219720018400036.

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Predicting promoter activity of DNA fragment is an important task for computational biology. Approaches using physical properties of DNA to predict bacterial promoters have recently gained a lot of attention. To select an adequate set of physical properties for training a classifier, various characteristics of DNA molecule should be taken into consideration. Here, we present a systematic approach that allows us to select less correlated properties for classification by means of both correlation and cophenetic coefficients as well as concordance matrices. To prove this concept, we have develope

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Chesnokov, Yu V. "GENETIC MARKERS: COMPARATIVE CLASSIFICATION OF MOLECULAR MARKERS." Vegetable crops of Russia, no. 3 (July 25, 2018): 11–15. http://dx.doi.org/10.18619/2072-9146-2018-3-11-15.

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With the creation of the molecular markers allowing to carry out analysis of genotypes on the level initial genetic information – DNA, onset one of the most multifarious and one of the most large in number class of markers at the present day. It is concerned with that each separate nucleic acid sequence is unique on its structure. Set of molecular and genetic methods, named as DNA-fingerprinting, most wide used in modern investigations for solving different problems in different biological areas. In this connection, necessity in comparative classification of modern molecular and genetic marker

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Wilson, John James. "Taxonomy and DNA sequence databases: A perfect match?" Terrestrial Arthropod Reviews 4, no. 3 (2011): 221–36. http://dx.doi.org/10.1163/187498311x591111.

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AbstractDespite the declining number of traditional taxonomists, our knowledge of Earth's biodiversity continues to grow in the form of DNA sequence data. Freely available through online databases, analyses of sequence datasets are increasingly used as an alternative for the traditional taxonomic process. Species identifications have become “DNA barcoding,” new species discoveries are characterised by genetic divergences, and traditional classification has been supplanted by molecular phylogenetics. These developments are illustrated through a case study investigating the identities of Taygeti

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Carels, Nicolas, Ramon Vidal, and Diego Frías. "Universal Features for the Classification of Coding and Non-coding DNA Sequences." Bioinformatics and Biology Insights 3 (January 2009): BBI.S2236. http://dx.doi.org/10.4137/bbi.s2236.

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In this report, we revisited simple features that allow the classification of coding sequences (CDS) from non-coding DNA. The spectrum of codon usage of our sequence sample is large and suggests that these features are universal. The features that we investigated combine (i) the stop codon distribution, (ii) the product of purine probabilities in the three positions of nucleotide triplets, (iii) the product of Cytosine, Guanine, Adenine probabilities in 1st, 2nd, 3rd position of triplets, respectively, (iv) the product of G and C probabilities in 1st and 2nd position of triplets. These feature

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Meysman, Pieter, Kathleen Marchal, and Kristof Engelen. "DNA Structural Properties in the Classification of Genomic Transcription Regulation Elements." Bioinformatics and Biology Insights 6 (January 2012): BBI.S9426. http://dx.doi.org/10.4137/bbi.s9426.

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It has been long known that DNA molecules encode information at various levels. The most basic level comprises the base sequence itself and is primarily important for the encoding of proteins and direct base recognition by DNA-binding proteins. A more elusive level consists of the local structural properties of the DNA molecule wherein the DNA sequence only plays an indirect supportive role. These properties are nevertheless an important factor in a large number of biomolecular processes and can be considered as informative signals for the presence of a variety of genomic features. Several rec

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Abe, Takashi, Yuta Hamano, and Toshimichi Ikemura. "Visualization of Genome Signatures of Eukaryote Genomes by Batch-Learning Self-Organizing Map with a Special Emphasis onDrosophilaGenomes." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/985706.

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A strategy of evolutionary studies that can compare vast numbers of genome sequences is becoming increasingly important with the remarkable progress of high-throughput DNA sequencing methods. We previously established a sequence alignment-free clustering method “BLSOM” for di-, tri-, and tetranucleotide compositions in genome sequences, which can characterize sequence characteristics (genome signatures) of a wide range of species. In the present study, we generated BLSOMs for tetra- and pentanucleotide compositions in approximately one million sequence fragments derived from 101 eukaryotes, fo

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Tapinos, Avraam, Bede Constantinides, My V. T. Phan, Samaneh Kouchaki, Matthew Cotten, and David L. Robertson. "The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences." Viruses 11, no. 5 (2019): 394. http://dx.doi.org/10.3390/v11050394.

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Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for

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Rahmani, Mohamed Elhadi, Abdelmalek Amine, and Reda Mohamed Hamou. "Bagging Approach for Medical Plants Recognition Based on Their DNA Sequences." International Journal of Social Ecology and Sustainable Development 9, no. 4 (2018): 45–60. http://dx.doi.org/10.4018/ijsesd.2018100103.

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Many drugs in modern medicines originate from plants and the first step in drug production, is the recognition of plants needed for this purpose. This article presents a bagging approach for medical plants recognition based on their DNA sequences. In this work, the authors have developed a system that recognize DNA sequences of 14 medical plants, first they divided the 14-class data set into bi class sub-data sets, then instead of using an algorithm to classify the 14-class data set, they used the same algorithm to classify the sub-data sets. By doing so, they have simplified the problem of cl

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Torroni, Antonio, Kirsi Huoponen, Paolo Francalacci, et al. "Classification of European mtDNAs From an Analysis of Three European Populations." Genetics 144, no. 4 (1996): 1835–50. http://dx.doi.org/10.1093/genetics/144.4.1835.

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Mitochondrial DNA (mtDNA) sequence variation was examined in Finns, Swedes and Tuscans by PCR amplification and restriction analysis. About 99% of the mtDNAs were subsumed within 10 mtDNA haplogroups (H, I, J, K, M, T, U, V, W, and X) suggesting that the identified haplogroups could encompass virtually all European mtDNAs. Because both hypervariable segments of the mtDNA control region were previously sequenced in the Tuscan samples, the mtDNA haplogroups and control region sequences could be compared. Using a combination of haplogroup-specific restriction site changes and control region nucle

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Sanjaya, Prima, and Dae-Ki Kang. "Could Decimal-binary Vector be a Representative of DNA Sequence for Classification?" International journal of advanced smart convergence 5, no. 3 (2016): 8–15. http://dx.doi.org/10.7236/ijasc.2016.5.3.8.

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Wang, Yuguo, Peter W. Fritsch, Suhua Shi, Frank Almeda, Boni C. Cruz, and Lawrence M. Kelly. "Phylogeny and infrageneric classification of Symplocos (Symplocaceae) inferred from DNA sequence data." American Journal of Botany 91, no. 11 (2004): 1901–14. http://dx.doi.org/10.3732/ajb.91.11.1901.

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Prince, Linda M., and W. John Kress. "Phylogenetic relationships and classification in Marantaceae: insights from plastid DNA sequence data." TAXON 55, no. 2 (2006): 281–96. http://dx.doi.org/10.2307/25065578.

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Shepard, Samuel S., Andrew McSweeny, Gursel Serpen, and Alexei Fedorov. "Exploiting mid-range DNA patterns for sequence classification: binary abstraction Markov models." Nucleic Acids Research 40, no. 11 (2012): 4765–73. http://dx.doi.org/10.1093/nar/gks154.

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SEEMuLLER, E., B. SCHNEIDER, R. MAURER, et al. "Phylogenetic Classification of Phytopathogenic Mollicutes by Sequence Analysis of 16S Ribosomal DNA." International Journal of Systematic Bacteriology 44, no. 3 (1994): 440–46. http://dx.doi.org/10.1099/00207713-44-3-440.

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Eisenhofer, Raphael, and Laura Susan Weyrich. "Assessing alignment-based taxonomic classification of ancient microbial DNA." PeerJ 7 (March 13, 2019): e6594. http://dx.doi.org/10.7717/peerj.6594.

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The field of palaeomicrobiology—the study of ancient microorganisms—is rapidly growing due to recent methodological and technological advancements. It is now possible to obtain vast quantities of DNA data from ancient specimens in a high-throughput manner and use this information to investigate the dynamics and evolution of past microbial communities. However, we still know very little about how the characteristics of ancient DNA influence our ability to accurately assign microbial taxonomies (i.e. identify species) within ancient metagenomic samples. Here, we use both simulated and published

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RASHEED, ZEEHASHAM, and HUZEFA RANGWALA. "METAGENOMIC TAXONOMIC CLASSIFICATION USING EXTREME LEARNING MACHINES." Journal of Bioinformatics and Computational Biology 10, no. 05 (2012): 1250015. http://dx.doi.org/10.1142/s0219720012500151.

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Next-generation sequencing technologies have allowed researchers to determine the collective genomes of microbial communities co-existing within diverse ecological environments. Varying species abundance, length and complexities within different communities, coupled with discovery of new species makes the problem of taxonomic assignment to short DNA sequence reads extremely challenging. We have developed a new sequence composition-based taxonomic classifier using extreme learning machines referred to as TAC-ELM for metagenomic analysis. TAC-ELM uses the framework of extreme learning machines t

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Huyen, Do Thi, Nguyen Minh Giang, Nguyen Thu Nguyet, and Truong Nam Hai. "Probe design for mining and selection of genes coding endo 1- 4 xylanase from dna metagenome data." TAP CHI SINH HOC 40, no. 1 (2018): 39–50. http://dx.doi.org/10.15625/0866-7160/v40n1.9200.

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According to the CAZY classification, endo 1- 4 xylanase belongs to GH 5, 8, 10, 11, 30, 51, 98. However only 03 sequences of GH8, 27 sequences of GH10, 18 sequence of GH11, only one sequence of each GH30 and GH51 from CAZy and NCBI database were thouroughly experimentally studied for biological activity and characteristics of the enzyme. Through the collected sequences, two probes for endo 1- 4 xylanase of GH10 and GH11 were designed, based on the sequence homology. The GH10 probe was 338 amino acids lenghth contained all the conserved amino acid residues (16 conserved residues in all sequenc

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Gusev, Vladimir D., and Liubov A. Miroshnichenko. "The complexity of DNA sequences. Different approaches and definitions." Mathematical Biology and Bioinformatics 15, no. 2 (2020): 313–37. http://dx.doi.org/10.17537/2020.15.313.

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An important quantitative characteristic of symbolic sequence (texts, strings) is complexity, which reflects at the intuitive level the degree of their "non-randomness". A.N. Kolmogorov formulated the most general definition of complexity. He proposed measuring the complexity of an object (symbolic sequence) by the length of the shortest descriptions by which this object can be uniquely reconstructed. Since there is no program guaranteed to search for the shortest description, in practice, various algorithmic approximations considered in this paper are used for this purpose. Along with definit

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Leite, Luis Anderson Ribeiro. "Mitochondrial pseudogenes in insect DNA barcoding: differing points of view on the same issue." Biota Neotropica 12, no. 3 (2012): 301–8. http://dx.doi.org/10.1590/s1676-06032012000300029.

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Molecular tools have been used in taxonomy for the purpose of identification and classification of living organisms. Among these, a short sequence of the mitochondrial DNA, popularly known as DNA barcoding, has become very popular. However, the usefulness and dependability of DNA barcodes have been recently questioned because mitochondrial pseudogenes, non-functional copies of the mitochondrial DNA incorporated into the nuclear genome, have been found in various taxa. When these paralogous sequences are amplified together with the mitochondrial DNA, they may go unnoticed and end up being analy

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Burks, David J., and Rajeev K. Azad. "Higher-order Markov models for metagenomic sequence classification." Bioinformatics 36, no. 14 (2020): 4130–36. http://dx.doi.org/10.1093/bioinformatics/btaa562.

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Abstract Motivation Alignment-free, stochastic models derived from k-mer distributions representing reference genome sequences have a rich history in the classification of DNA sequences. In particular, the variants of Markov models have previously been used extensively. Higher-order Markov models have been used with caution, perhaps sparingly, primarily because of the lack of enough training data and computational power. Advances in sequencing technology and computation have enabled exploitation of the predictive power of higher-order models. We, therefore, revisited higher-order Markov models

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