Weighted relative entropy for alignment-free sequence comparison based on Markov model

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

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.

Alignment-Free Sequence Comparison With Multiple k Values

Alignment-free sequence comparison approaches have become increasingly popular in computational biology, because alignment-based approaches are inefficient to process large-scale datasets. Still, there is no way to determine the optimal value of the critical parameter k for alignment-free approaches in general. In this article, we tried to solve the problem by involving multiple k values simultaneously. The method counts the occurrence of each k-mer with different k values in a sequence. Two weighting schemes, based on maximizing deviation method and genetic algorithm, are then used on these counts. We applied the method to enhance the three common alignment-free approaches D₂, D₂^S, and D₂^*, and evaluated its performance on similarity search and functionally related regulatory sequences recognition. The enhanced approaches achieve better performance than the original approaches in all cases, and much better performance than some other common measures, such as Pcc, Eu, Ma, Ch, Kld, and Cos.

Reads Binning Improves Alignment-Free Metagenome Comparison

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,d 2 S andd 2 * , 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 ind 2 S andd 2 * , 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,d 2 S andd 2 * , 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.

One size does not fit all: on how Markov model order dictates performance of genomic sequence analyses

The structural simplicity and ability to capture serial correlations make Markov models a popular modeling choice in several genomic analyses, such as identification of motifs, genes and regulatory elements. A critical, yet relatively unexplored, issue is the determination of the order of the Markov model. Most biological applications use a predetermined order for all data sets indiscriminately. Here, we show the vast variation in the performance of such applications with the order. To identify the 'optimal' order, we investigated two model selection criteria: Akaike information criterion and Bayesian information criterion (BIC). The BIC optimal order delivers the best performance for mammalian phylogeny reconstruction and motif discovery. Importantly, this order is different from orders typically used by many tools, suggesting that a simple additional step determining this order can significantly improve results. Further, we describe a novel classification approach based on BIC optimal Markov models to predict functionality of tissue-specific promoters. Our classifier discriminates between promoters active across 12 different tissues with remarkable accuracy, yielding 3 times the precision expected by chance. Application to the metagenomics problem of identifying the taxum from a short DNA fragment yields accuracies at least as high as the more complex mainstream methodologies, while retaining conceptual and computational simplicity.

Phylogenetic analysis of protein sequences based on distribution of length about common sub-string

Up to now, various approaches for phylogenetic analysis have been developed. Almost all of them put stress on analyzing nucleic acid sequences or protein primary sequences. In this paper, we propose a new sequence distance for efficient reconstruction of phylogenetic trees based on the distribution of length about common sub-sequences between two sequences. We describe some applications of this method, which not only show the validity of the method, but also suggest a number of novel phylogenetic insights.

Genome analysis with the conditional multinomial distribution profile

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.