Applying Shannon's information theory to bacterial and phage genomes and metagenomes

Informational rescaling of PCA maps with application to genetic distance

Principal Component Analysis (PCA) is a powerful multivariate tool allowing the projection of data in low-dimensional representations. Nevertheless, datapoint distances on these low-dimensional projections are challenging to interpret. Here, we propose a computationally simple heuristic to transform a map based on standard PCA (when the variables are asymptotically Gaussian) into an entropy-based map where distances are based on mutual information (MI). Moreover, we show that in certain instances our proposed scaled PCA can improve cluster identification. Rescaling principal component-based distances using MI results in a representation of relative statistical associations when, as in genetics, it is applied on bit measurements between individuals' genomic mutual information. This entropy-rescaled PCA, while preserving order relationships (along a dimension), quantifies relative distances into information units, such as "bits". We illustrate the effect of this rescaling using genomics data derived from world populations and describe how the interpretation of results is impacted.

Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy

In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index q; (2) performance testing of the best entropic indices on new datasets; (3) a comparison made with Shannon entropy and (4) generalized entropies; (5) an investigation of the Tsallis entropy in the context of dimensionality reduction. As a result, our proposal proved to be effective, being superior to Shannon entropy and robust in terms of generalization, and also potentially representative for collecting information in fewer dimensions compared with methods such as Singular Value Decomposition and Uniform Manifold Approximation and Projection.

MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors

One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350-0.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.

A Genetic Toolkit for Investigating Clavibacter Species: Markerless Deletion, Permissive Site Identification, and an Integrative Plasmid

The development of knockout mutants and expression variants are critical for understanding genotype-phenotype relationships. However, advances in these techniques in gram-positive actinobacteria have stagnated over the last decade. Actinobacteria in the Clavibacter genus are composed of diverse crop pathogens that cause a variety of wilt and cankering diseases. Here, we present a suite of tools for genetic manipulation in the tomato pathogen Clavibacter michiganensis including a markerless deletion system, an integrative plasmid, and an R package for identification of permissive sites for plasmid integration. The vector pSelAct-KO is a recombination-based, markerless knockout system that uses dual selection to engineer seamless deletions of a region of interest, providing opportunities for repeated higher-order genetic knockouts. The efficacy of pSelAct-KO was demonstrated in C. michiganensis and was confirmed using whole-genome sequencing. We developed permissR, an R package to identify permissive sites for chromosomal integration, which can be used in conjunction with pSelAct-Express, a nonreplicating integrative plasmid that enables recombination into a permissive genomic location. Expression of enhanced green fluorescent protein by pSelAct-Express was verified in two candidate permissive regions predicted by permissR in C. michiganensis. These molecular tools are essential advances for investigating gram-positive actinobacteria, particularly for important pathogens in the Clavibacter genus.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Information Entropy in Chemistry: An Overview

Basic applications of the information entropy concept to chemical objects are reviewed. These applications deal with quantifying chemical and electronic structures of molecules, signal processing, structural studies on crystals, and molecular ensembles. Recent advances in the mentioned areas make information entropy a central concept in interdisciplinary studies on digitalizing chemical reactions, chemico-information synthesis, crystal engineering, as well as digitally rethinking basic notions of structural chemistry in terms of informatics.

Information, Thermodynamics and Life: A Narrative Review

Information is probably one of the most difficult physical quantities to comprehend. This applies not only to the very definition of information, but also to the physical entity of information, meaning how can it be quantified and measured. In recent years, information theory and its function in systems has been an intense field of study, due to the large increase of available information technology, where the notion of bit dominated the information discipline. Information theory also expanded from the “simple” “bit” to the quantal “qubit”, which added more variables for consideration. One of the main applications of information theory could be considered the field of “autonomy”, which is the main characteristic of living organisms in nature since they all have self-sustainability, motion and self-protection. These traits, along with the ability to be aware of existence, make it difficult and complex to simulate in artificial constructs. There are many approaches to the concept of simulating autonomous behavior, yet there is no conclusive approach to a definite solution to this problem. Recent experimental results have shown that the interaction between machines and neural cells is possible and it consists of a significant tool for the study of complex systems. The present work tries to review the question on the interactions between information and life. It attempts to build a connection between information and thermodynamics in terms of energy consumption and work production, as well as present some possible applications of these physical quantities.

Shannon entropy as a metric for conditional gene expression in Neurospora crassa

Neurospora crassa has been an important model organism for molecular biology and genetics for over 60 years. Neurospora crassa has a complex life cycle, with over 28 distinct cell types and is capable of transcriptional responses to many environmental conditions including nutrient availability, temperature, and light. To quantify variation in N. crassa gene expression, we analyzed public expression data from 97 conditions and calculated the Shannon Entropy value for Neurospora’s approximately 11,000 genes. Entropy values can be used to estimate the variability in expression for a single gene over a range of conditions and be used to classify individual genes as constitutive or condition-specific. Shannon entropy has previously been used measure the degree of tissue specificity of multicellular plant or animal genes. We use this metric here to measure variable gene expression in a microbe and provide this information as a resource for the N. crassa research community. Finally, we demonstrate the utility of this approach by using entropy values to identify genes with constitutive expression across a wide range of conditions and to identify genes that are activated exclusively during sexual development.

Rapid discovery of novel prophages using biological feature engineering and machine learning

Prophages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

Similarity Studies of Corona Viruses through Chaos Game Representation

The novel coronavirus (SARS-COV-2) is generally referred to as Covid-19 virus has spread to 213 countries with nearly 7 million confirmed cases and nearly 400,000 deaths. Such major outbreaks demand classification and origin of the virus genomic sequence, for planning, containment, and treatment. Motivated by the above need, we report two alignment-free methods combing with CGR to perform clustering analysis and create a phylogenetic tree based on it. To each DNA sequence we associate a matrix then define distance between two DNA sequences to be the distance between their associated matrix. These methods are being used for phylogenetic analysis of coronavirus sequences. Our approach provides a powerful tool for analyzing and annotating genomes and their phylogenetic relationships. We also compare our tool to ClustalX algorithm which is one of the most popular alignment methods. Our alignment-free methods are shown to be capable of finding closest genetic relatives of coronaviruses.

Phage Genome Annotation Using the RAST Pipeline

Phages are complex biomolecular machineries that have to survive in a bacterial world. Phage genomes show many adaptations to their lifestyle such as shorter genes, reduced capacity for redundant DNA sequences, and the inclusion of tRNAs in their genomes. In addition, phages are not free-living, they require a host for replication and survival. These unique adaptations provide challenges for the bioinformatics analysis of phage genomes. In particular, ORF calling, genome annotation, noncoding RNA (ncRNA) identification, and the identification of transposons and insertions are all complicated in phage genome analysis. We provide a road map through the phage genome annotation pipeline, and discuss the challenges and solutions for phage genome annotation as we have implemented in the rapid annotation using subsystems (RAST) pipeline.

Kullback Leibler divergence in complete bacterial and phage genomes

The amino acid content of the proteins encoded by a genome may predict the coding potential of that genome and may reflect lifestyle restrictions of the organism. Here, we calculated the Kullback–Leibler divergence from the mean amino acid content as a metric to compare the amino acid composition for a large set of bacterial and phage genome sequences. Using these data, we demonstrate that (i) there is a significant difference between amino acid utilization in different phylogenetic groups of bacteria and phages; (ii) many of the bacteria with the most skewed amino acid utilization profiles, or the bacteria that host phages with the most skewed profiles, are endosymbionts or parasites; (iii) the skews in the distribution are not restricted to certain metabolic processes but are common across all bacterial genomic subsystems; (iv) amino acid utilization profiles strongly correlate with GC content in bacterial genomes but very weakly correlate with the G+C percent in phage genomes. These findings might be exploited to distinguish coding from non-coding sequences in large data sets, such as metagenomic sequence libraries, to help in prioritizing subsequent analyses.

Recovering complete and draft population genomes from metagenome datasets

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution.

Relationship between digital information and thermodynamic stability in bacterial genomes

Ever since the introduction of the Watson-Crick model, numerous efforts have been made to fully characterize the digital information content of the DNA. However, it became increasingly evident that variations of DNA configuration also provide an “analog” type of information related to the physicochemical properties of the DNA, such as thermodynamic stability and supercoiling. Hence, the parallel investigation of the digital information contained in the base sequence with associated analog parameters is very important for understanding the coding capacity of the DNA. In this paper, we represented analog information by its thermodynamic stability and compare it with digital information using Shannon and Gibbs entropy measures on the complete genome sequences of several bacteria, including Escherichia coli (E. coli), Bacillus subtilis (B. subtilis), Streptomyces coelicolor (S. coelicolor), and Salmonella typhimurium (S. typhimurium). Furthermore, the link to the broader classes of functional gene groups (anabolic and catabolic) is examined. Obtained results demonstrate the couplings between thermodynamic stability and digital sequence organization in the bacterial genomes. In addition, our data suggest a determinative role of the genome-wide distribution of DNA thermodynamic stability in the spatial organization of functional gene groups.

Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification

MOTIVATION

Alignment-based sequence similarity searches, while accurate for some type of sequences, can produce incorrect results when used on more divergent but functionally related sequences that have undergone the sequence rearrangements observed in many bacterial and viral genomes. Here, we propose a classification model that exploits the complementary nature of alignment-based and alignment-free similarity measures with the aim to improve the accuracy with which DNA and protein sequences are characterized.

RESULTS

Our model classifies sequences using a combined sequence similarity score calculated by adaptively weighting the contribution of different sequence similarity measures. Weights are determined independently for each sequence in the test set and reflect the discriminatory ability of individual similarity measures in the training set. Because the similarity between some sequences is determined more accurately with one type of measure rather than another, our classifier allows different sets of weights to be associated with different sequences. Using five different similarity measures, we show that our model significantly improves the classification accuracy over the current composition- and alignment-based models, when predicting the taxonomic lineage for both short viral sequence fragments and complete viral sequences. We also show that our model can be used effectively for the classification of reads from a real metagenome dataset as well as protein sequences.

AVAILABILITY AND IMPLEMENTATION

All the datasets and the code used in this study are freely available at https://collaborators.oicr.on.ca/vferretti/borozan_csss/csss.html.

CONTACT

ivan.borozan@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Hepatitis C Virus (HCV) NS3 sequence diversity and antiviral resistance-associated variant frequency in HCV/HIV coinfection

HIV coinfection accelerates disease progression in chronic hepatitis C and reduces sustained antiviral responses (SVR) to interferon-based therapy. New direct-acting antivirals (DAAs) promise higher SVR rates, but the selection of preexisting resistance-associated variants (RAVs) may lead to virologic breakthrough or relapse. Thus, pretreatment frequencies of RAVs are likely determinants of treatment outcome but typically are below levels at which the viral sequence can be accurately resolved. Moreover, it is not known how HIV coinfection influences RAV frequency. We adopted an accurate high-throughput sequencing strategy to compare nucleotide diversity in HCV NS3 protease-coding sequences in 20 monoinfected and 20 coinfected subjects with well-controlled HIV infection. Differences in mean pairwise nucleotide diversity (π), Tajima's D statistic, and Shannon entropy index suggested that the genetic diversity of HCV is reduced in coinfection. Among coinfected subjects, diversity correlated positively with increases in CD4(+) T cells on antiretroviral therapy, suggesting T cell responses are important determinants of diversity. At a median sequencing depth of 0.084%, preexisting RAVs were readily identified. Q80K, which negatively impacts clinical responses to simeprevir, was encoded by more than 99% of viral RNAs in 17 of the 40 subjects. RAVs other than Q80K were identified in 39 of 40 subjects, mostly at frequencies near 0.1%. RAV frequency did not differ significantly between monoinfected and coinfected subjects. We conclude that HCV genetic diversity is reduced in patients with well-controlled HIV infection, likely reflecting impaired T cell immunity. However, RAV frequency is not increased and should not adversely influence the outcome of DAA therapy.

A virulent strain of deformed wing virus (DWV) of honeybees (Apis mellifera) prevails after Varroa destructor-mediated, or in vitro, transmission

The globally distributed ectoparasite Varroa destructor is a vector for viral pathogens of the Western honeybee (Apis mellifera), in particular the Iflavirus Deformed Wing Virus (DWV). In the absence of Varroa low levels DWV occur, generally causing asymptomatic infections. Conversely, Varroa-infested colonies show markedly elevated virus levels, increased overwintering colony losses, with impairment of pupal development and symptomatic workers. To determine whether changes in the virus population were due Varroa amplifying and introducing virulent virus strains and/or suppressing the host immune responses, we exposed Varroa-naïve larvae to oral and Varroa-transmitted DWV. We monitored virus levels and diversity in developing pupae and associated Varroa, the resulting RNAi response and transcriptome changes in the host. Exposed pupae were stratified by Varroa association (presence/absence) and virus levels (low/high) into three groups. Varroa-free pupae all exhibited low levels of a highly diverse DWV population, with those exposed per os (group NV) exhibiting changes in the population composition. Varroa-associated pupae exhibited either low levels of a diverse DWV population (group VL) or high levels of a near-clonal virulent variant of DWV (group VH). These groups and unexposed controls (C) could be also discriminated by principal component analysis of the transcriptome changes observed, which included several genes involved in development and the immune response. All Varroa tested contained a diverse replicating DWV population implying the virulent variant present in group VH, and predominating in RNA-seq analysis of temporally and geographically separate Varroa-infested colonies, was selected upon transmission from Varroa, a conclusion supported by direct injection of pupae in vitro with mixed virus populations. Identification of a virulent variant of DWV, the role of Varroa in its transmission and the resulting host transcriptome changes furthers our understanding of this important viral pathogen of honeybees.

Honeybees are the most important managed pollinating insect, contributing billions of dollars to annual global agricultural production. Over the last century a parasitic mite, Varroa, has spread worldwide, with significant impacts on honeybee colony health as a consequence of its transmission of a cocktail of viruses while feeding on honeybee ‘blood’. The most important virus for colony health is deformed wing virus (DWV), high levels of which cause developmental deformities and premature ageing resulting in high overwintering colony losses. In experiments on individual Varroa-exposed pupae we demonstrate that a single type of virulent DWV is amplified 1,000–10,000 times in the recipient pupae, despite the mite containing a high diversity of replicating DWV strains. We could recapitulate this by direct injection of pupae with mixed virus populations, showing the virulent strain is advantaged by the route of transmission. In parallel, we detected changes in the immune response and developmental gene expression of the honeybee and propose that these contribute to the characteristic pathogenesis of DWV. Identification of a virulent strain of DWV has implications for therapeutic or prophylactic interventions to improve honeybee colony health, as well as contributing to our understanding of the biology of this important honeybee viral pathogen.

Information theory applications for biological sequence analysis

Information theory (IT) addresses the analysis of communication systems and has been widely applied in molecular biology. In particular, alignment-free sequence analysis and comparison greatly benefited from concepts derived from IT, such as entropy and mutual information. This review covers several aspects of IT applications, ranging from genome global analysis and comparison, including block-entropy estimation and resolution-free metrics based on iterative maps, to local analysis, comprising the classification of motifs, prediction of transcription factor binding sites and sequence characterization based on linguistic complexity and entropic profiles. IT has also been applied to high-level correlations that combine DNA, RNA or protein features with sequence-independent properties, such as gene mapping and phenotype analysis, and has also provided models based on communication systems theory to describe information transmission channels at the cell level and also during evolutionary processes. While not exhaustive, this review attempts to categorize existing methods and to indicate their relation with broader transversal topics such as genomic signatures, data compression and complexity, time series analysis and phylogenetic classification, providing a resource for future developments in this promising area.

Information measure for long-range correlated sequences: the case of the 24 human chromosomes

A new approach to estimate the Shannon entropy of a long-range correlated sequence is proposed. The entropy is written as the sum of two terms corresponding respectively to power-law (ordered) and exponentially (disordered) distributed blocks (clusters). The approach is illustrated on the 24 human chromosome sequences by taking the nucleotide composition as the relevant information to be encoded/decoded. Interestingly, the nucleotide composition of the ordered clusters is found, on the average, comparable to the one of the whole analyzed sequence, while that of the disordered clusters fluctuates. From the information theory standpoint, this means that the power-law correlated clusters carry the same information of the whole analysed sequence. Furthermore, the fluctuations of the nucleotide composition of the disordered clusters are linked to relevant biological properties, such as segmental duplications and gene density.