Frequency of symbol occurrences in simple non-primitive stochastic models

We study the random variable Y-n representing the number of occurrences of a given symbol in a word of length n generated at random. The stochastic model we assume is a simple non-ergodic model defined by the product of two primitive rational formal series, which form two distinct ergodic components. We obtain asymptotic evaluations for the mean and the variance of Y-n and its limit distribution. It turns out that there are two main cases: if one component is dominant and non-degenerate we get a Gaussian limit distribution; if the two components are equipotent and have different leading terms of the mean, we get a uniform limit distribution. Other particular limit distributions are obtained in the case of a degenerate dominant component and in the equipotent case when the leading terms of the expectation values are equal

Conservation versus parallel gains in intron evolution

Orthologous genes from distant eukaryotic species, e.g. animals and plants, share up to 25–30% intron positions. However, the relative contributions of evolutionary conservation and parallel gain of new introns into this pattern remain unknown. Here, the extent of independent insertion of introns in the same sites (parallel gain) in orthologous genes from phylogenetically distant eukaryotes is assessed within the framework of the protosplice site model. It is shown that protosplice sites are no more conserved during evolution of eukaryotic gene sequences than random sites. Simulation of intron insertion into protosplice sites with the observed protosplice site frequencies and intron densities shows that parallel gain can account but for a small fraction (5–10%) of shared intron positions in distantly related species. Thus, the presence of numerous introns in the same positions in orthologous genes from distant eukaryotes, such as animals, fungi and plants, appears to reflect mostly bona fide evolutionary conservation

Flexible comparative genomics of prokaryotic transcriptional regulatory networks

Comparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed. Here we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota. Our results demonstrate the applicability of the CGB pipeline in multiple settings. CGB's ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks

Comparing Machine Learning Algorithms with or without Feature Extraction for DNA Classification

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 algorithms, namely Convolutional Neural Networks, Deep Neural Networks, and N-gram Probabilistic Models, are used for the task of DNA classification. Furthermore, we introduce a novel feature extraction method based on the Levenshtein distance and randomly generated DNA sub-sequences to compute information-rich features from the DNA sequences. We also use an existing feature extraction method based on 3-grams to represent amino acids and combine both feature extraction methods with a multitude of machine learning algorithms. Four different data sets, each concerning viral diseases such as Covid-19, AIDS, Influenza, and Hepatitis C, are used for evaluating the different approaches. The results of the experiments show that all methods obtain high accuracies on the different DNA datasets. Furthermore, the domain-specific 3-gram feature extraction method leads in general to the best results in the experiments, while the newly proposed technique outperforms all other methods on the smallest Covid-19 datasetComment: 17 page

Frequency of symbol occurrences in bicomponent stochastic models

We give asymptotic estimates of the frequency of occurrences of a symbol in a random word Generated by any bicomponent stochastic model. More precisely, we consider the random variable Y-n representing the number of occurrences of a given symbol in a word of length n generated at random; the stochastic model is defined by a rational formal series r having a linear representation with two primitive components. This model includes the case when r is the product or the sum of two primitive rational formal series. We obtain asymptotic evaluations for the mean value and the variance of Yn and its limit distribution

Algorithms and tools for splicing junction donor recognition in genomic DNA sequences

The consensus sequences at splicing junctions in genomic DNA are required for pre-mRNA breaking and rejoining which must be carried out precisely. Programs currently available for identification or prediction of transcribed sequences from within genomic DNA are far from being powerful enough to elucidate genomic structure completely[4]. In this research, we develop a degenerate pattern match algorithm for 5\u27 splicing site (Donor Site) recognition.. Using the Motif models we developed, we can mine out the degenerate pattern information from the consensus splicing junction sequences. Our experimental results show that, this algorithm can correctly recognize 93% of the total donor sites at the right positions in the test DNA group. And more than 91% of the donor sites the algorithm predicted are correct. These precision rates are higher than the best existing donor classification algorithm[25]. This research made a very important progress toward our full gene structure detection algorithm development

Species-specific protein sequence and fold optimizations

BACKGROUND: An organism's ability to adapt to its particular environmental niche is of fundamental importance to its survival and proliferation. In the largest study of its kind, we sought to identify and exploit the amino-acid signatures that make species-specific protein adaptation possible across 100 complete genomes. RESULTS: Environmental niche was determined to be a significant factor in variability from correspondence analysis using the amino acid composition of over 360,000 predicted open reading frames (ORFs) from 17 archae, 76 bacteria and 7 eukaryote complete genomes. Additionally, we found clusters of phylogenetically unrelated archae and bacteria that share similar environments by amino acid composition clustering. Composition analyses of conservative, domain-based homology modeling suggested an enrichment of small hydrophobic residues Ala, Gly, Val and charged residues Asp, Glu, His and Arg across all genomes. However, larger aromatic residues Phe, Trp and Tyr are reduced in folds, and these results were not affected by low complexity biases. We derived two simple log-odds scoring functions from ORFs (C(G)) and folds (C(F)) for each of the complete genomes. C(F )achieved an average cross-validation success rate of 85 ± 8% whereas the C(G )detected 73 ± 9% species-specific sequences when competing against all other non-redundant C(G). Continuously updated results are available at . CONCLUSION: Our analysis of amino acid compositions from the complete genomes provides stronger evidence for species-specific and environmental residue preferences in genomic sequences as well as in folds. Scoring functions derived from this work will be useful in future protein engineering experiments and possibly in identifying horizontal transfer events

A reexamination of information theory-based methods for DNA-binding site identification

<p>Abstract</p> <p>Background</p> <p>Searching for transcription factor binding sites in genome sequences is still an open problem in bioinformatics. Despite substantial progress, search methods based on information theory remain a standard in the field, even though the full validity of their underlying assumptions has only been tested in artificial settings. Here we use newly available data on transcription factors from different bacterial genomes to make a more thorough assessment of information theory-based search methods.</p> <p>Results</p> <p>Our results reveal that conventional benchmarking against artificial sequence data leads frequently to overestimation of search efficiency. In addition, we find that sequence information by itself is often inadequate and therefore must be complemented by other cues, such as curvature, in real genomes. Furthermore, results on skewed genomes show that methods integrating skew information, such as <it>Relative Entropy</it>, are not effective because their assumptions may not hold in real genomes. The evidence suggests that binding sites tend to evolve towards genomic skew, rather than against it, and to maintain their information content through increased conservation. Based on these results, we identify several misconceptions on information theory as applied to binding sites, such as negative entropy, and we propose a revised paradigm to explain the observed results.</p> <p>Conclusion</p> <p>We conclude that, among information theory-based methods, the most unassuming search methods perform, on average, better than any other alternatives, since heuristic corrections to these methods are prone to fail when working on real data. A reexamination of information content in binding sites reveals that information content is a compound measure of search and binding affinity requirements, a fact that has important repercussions for our understanding of binding site evolution.</p

Knowledge discovery and modeling in genomic databases

This dissertation research is targeted toward developing effective and accurate methods for identifying gene structures in the genomes of high eukaryotes, such as vertebrate organisms. Several effective hidden Markov models (HMMs) are developed to represent the consensus and degeneracy features of the functional sites including protein-translation start sites, mRNA splicing junction donor and acceptor sites in vertebrate genes. The HMM system based on the developed models is fully trained using an expectation maximization (EM) algorithm and the system performance is evaluated using a 10-way cross-validation method. Experimental results show that the proposed HMM system achieves high sensitivity and specificity in detecting the functional sites. This HMM system is then incorporated into a new gene detection system, called GeneScout. The main hypothesis is that, given a vertebrate genomic DNA sequence S, it is always possible to construct a directed acyclic graph G such that the path for the actual coding region of S is in the set of all paths on G. Thus, the gene detection problem is reduced to the analysis of paths in the graph G. A dynamic programming algorithm is employed by GeneScout to find the optimal path in G. Experimental results on the standard test dataset collected by Burset and Guigo indicate that GeneScout is comparable to existing gene discovery tools and complements the widely used GenScan system