Weighted Relative Entropy for Alignment-free Sequence Comparison Based on Markov Model

Chang, Guisong; Wang, Tianming

doi:10.1080/07391102.2011.10508594

Cited by 10 publications

(4 citation statements)

References 52 publications

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“…These models were then used for grouping NGS reads into different bins, followed by extracting the k -tuples and calculating their expectation in each bin. Markov models have been used extensively for genome modeling (Narlikar et al, 2013), motif discovery (D’haeseleer, 2006), computational gene search (Lomsadze et al, 2005), classification of metagenomic sequences (Brady and Salzberg, 2009) and alignment-free sequence comparison (Chang and Wang, 2011). Next, we extended the definition of our previous alignment-free measures, d2S and d2*, to make them more compatible with a scheme of analysis that uses the proposed reads binning datasets.…”

Section: Introductionmentioning

confidence: 99%

Reads Binning Improves Alignment-Free Metagenome Comparison

2019

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Comparing metagenomic samples is a critical step in understanding the relationships among microbial communities. Recently, next-generation sequencing (NGS) technologies have produced a massive amount of short reads data for microbial communities from different environments. The assembly of these short reads can, however, be time-consuming and challenging. In addition, alignment-based methods for metagenome comparison are limited by incomplete genome and/or pathway databases. In contrast, alignment-free methods for metagenome comparison do not depend on the completeness of genome or pathway databases. Still, the existing alignment-free methods, d2S and d2*, which model k-tuple patterns using only one Markov chain for each sample, neglect the heterogeneity within metagenomic data wherein potentially thousands of types of microorganisms are sequenced. To address this imperfection in d2S and d2*, we organized NGS sequences into different reads bins and constructed several corresponding Markov models. Next, we modified the definition of our previous alignment-free methods, d2S and d2*, to make them more compatible with a scheme of analysis which uses the proposed reads bins. We then used two simulated and three real metagenomic datasets to test the effect of the k-tuple size and Markov orders of background sequences on the performance of these de novo alignment-free methods. For dependable comparison of metagenomic samples, our newly developed alignment-free methods with reads binning outperformed alignment-free methods without reads binning in detecting the relationship among microbial communities, including whether they form groups or change according to some environmental gradients.

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Section: Introductionmentioning

confidence: 99%

Reads Binning Improves Alignment-Free Metagenome Comparison

2019

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“…A multitude of alignment-free sequence comparison algorithms have been developed in recent years, such as conditional Lempel-Ziv [4] and Kolmogorov complexity [5], measure representation [6], Markov model comparisons and frequent substring lengths [7,8], which divides the genome into regions that represent a system that is evolving over time with hidden states. Base-base correlation [9], spectral distortion [10], primitive discrimination substrings [11], Burrows-Wheeler similarity [12], normalized central moments, nearest-neighbor interactions [13], subword composition [14], prefix codes [15], information correlation [16], the context-object model [17], and spaced word frequencies [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach

Hossain,

Kim,

Uddin

et al. 2023

Bioengineering

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The application of deep learning for taxonomic categorization of DNA sequences is investigated in this study. Two deep learning architectures, namely the Stacked Convolutional Autoencoder (SCAE) with Multilabel Extreme Learning Machine (MLELM) and the Variational Convolutional Autoencoder (VCAE) with MLELM, have been proposed. These designs provide precise feature maps for individual and inter-label interactions within DNA sequences, capturing their spatial and temporal properties. The collected features are subsequently fed into MLELM networks, which yield soft classification scores and hard labels. The proposed algorithms underwent thorough training and testing on unsupervised data, whereby one or more labels were concurrently taken into account. The introduction of the clade label resulted in improved accuracy for both models compared to the class or genus labels, probably owing to the occurrence of large clusters of similar nucleotides inside a DNA strand. In all circumstances, the VCAE-MLELM model consistently outperformed the SCAE-MLELM model. The best accuracy attained by the VCAE-MLELM model when the clade and family labels were combined was 94%. However, accuracy ratings for single-label categorization using either approach were less than 65%. The approach’s effectiveness is based on MLELM networks, which record connected patterns across classes for accurate label categorization. This study advances deep learning in biological taxonomy by emphasizing the significance of combining numerous labels for increased classification accuracy.

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“…Many neutral mutations may remain and play a role of random background. One should subtract the random background from the simple counting result in order to highlight the contribution of selective evolution (Chang and Wang, 2011;Ding et al, 2010;Gao et al, 2006). In this work, we propose a new conditional multinomial distribution representation which reveals the relative difference of biological sequence from sequence generated by an independent random process to remove the random background.…”

Section: Complete Multinomial Composition Vectormentioning

confidence: 99%

Genome analysis with the conditional multinomial distribution profile

Chang

Wang

2011

Journal of Theoretical Biology

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The focus of the research is on the analysis of genome sequences. Based on the inter-nucleotide distance sequence, we propose the conditional multinomial distribution profile for the complete genomic sequence. These profiles can be used to define a very simple, computationally efficient, alignment-free, distance measure that reflects the evolutionary relationships between genomic sequences. We use this distance measure to classify chromosomes according to species of origin, to build the phylogenetic tree of 24 complete genome sequences of coronaviruses. Our results demonstrate the new method is powerful and efficient.

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Weighted Relative Entropy for Alignment-free Sequence Comparison Based on Markov Model

Cited by 10 publications

References 52 publications

Reads Binning Improves Alignment-Free Metagenome Comparison

Reads Binning Improves Alignment-Free Metagenome Comparison

Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach

Genome analysis with the conditional multinomial distribution profile

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