Estimating the entropy of DNA sequences

Efficient CRISPR-Cas13d-Based Antiviral Strategy to Combat SARS-CoV-2

The current SARS-CoV-2 pandemic forms a major global health burden. Although protective vaccines are available, concerns remain as new virus variants continue to appear. CRISPR-based gene-editing approaches offer an attractive therapeutic strategy as the CRISPR-RNA (crRNA) can be adjusted rapidly to accommodate a new viral genome sequence. This study aimed at using the RNA-targeting CRISPR-Cas13d system to attack highly conserved sequences in the viral RNA genome, thereby preparing for future zoonotic outbreaks of other coronaviruses. We designed 29 crRNAs targeting highly conserved sequences along the complete SARS-CoV-2 genome. Several crRNAs demonstrated efficient silencing of a reporter with the matching viral target sequence and efficient inhibition of a SARS-CoV-2 replicon. The crRNAs that suppress SARS-CoV-2 were also able to suppress SARS-CoV, thus demonstrating the breadth of this antiviral strategy. Strikingly, we observed that only crRNAs directed against the plus-genomic RNA demonstrated antiviral activity in the replicon assay, in contrast to those that bind the minus-genomic RNA, the replication intermediate. These results point to a major difference in the vulnerability and biology of the +RNA versus −RNA strands of the SARS-CoV-2 genome and provide important insights for the design of RNA-targeting antivirals.

Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles

The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity-entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.

In Silico Prediction and Selection of Target Sequences in the SARS-CoV-2 RNA Genome for an Antiviral Attack

The SARS-CoV-2 pandemic has urged the development of protective vaccines and the search for specific antiviral drugs. The modern molecular biology tools provides alternative methods, such as CRISPR-Cas and RNA interference, that can be adapted as antiviral approaches, and contribute to this search. The unique CRISPR-Cas13d system, with the small crRNA guide molecule, mediates a sequence-specific attack on RNA, and can be developed as an anti-coronavirus strategy. We analyzed the SARS-CoV-2 genome to localize the hypothetically best crRNA-annealing sites of 23 nucleotides based on our extensive expertise with sequence-specific antiviral strategies. We considered target sites of which the sequence is well-conserved among SARS-CoV-2 isolates. As we should prepare for a potential future outbreak of related viruses, we screened for targets that are conserved between SARS-CoV-2 and SARS-CoV. To further broaden the search, we screened for targets that are conserved between SARS-CoV-2 and the more distantly related MERS-CoV, as well as the four other human coronaviruses (OC43, 229E, NL63, HKU1). Finally, we performed a search for pan-corona target sequences that are conserved among all these coronaviruses, including the new Omicron variant, that are able to replicate in humans. This survey may contribute to the design of effective, safe, and escape-proof antiviral strategies to prepare for future pandemics.

DnoisE: distance denoising by entropy. An open-source parallelizable alternative for denoising sequence datasets

DNA metabarcoding is broadly used in biodiversity studies encompassing a wide range of organisms. Erroneous amplicons, generated during amplification and sequencing procedures, constitute one of the major sources of concern for the interpretation of metabarcoding results. Several denoising programs have been implemented to detect and eliminate these errors. However, almost all denoising software currently available has been designed to process non-coding ribosomal sequences, most notably prokaryotic 16S rDNA. The growing number of metabarcoding studies using coding markers such as COI or RuBisCO demands a re-assessment and calibration of denoising algorithms. Here we present DnoisE, the first denoising program designed to detect erroneous reads and merge them with the correct ones using information from the natural variability (entropy) associated to each codon position in coding barcodes. We have developed an open-source software using a modified version of the UNOISE algorithm. DnoisE implements different merging procedures as options, and can incorporate codon entropy information either retrieved from the data or supplied by the user. In addition, the algorithm of DnoisE is parallelizable, greatly reducing runtimes on computer clusters. Our program also allows different input file formats, so it can be readily incorporated into existing metabarcoding pipelines.

Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy

Information theoretic approaches are ubiquitous and effective in a wide variety of bioinformatics applications. In comparative genomics, alignment-free methods, based on short DNA words, or k-mers, are particularly powerful. We evaluated the utility of varying k-mer lengths for genome comparisons by analyzing their sequence space coverage of 5805 genomes in the KEGG GENOME database. In subsequent analyses on four k-mer lengths spanning the relevant range (11, 21, 31, 41), hierarchical clustering of 1634 genus-level representative genomes using pairwise 21- and 31-mer Jaccard similarities best recapitulated a phylogenetic/taxonomic tree of life with clear boundaries for superkingdom domains and high subtree similarity for named taxons at lower levels (family through phylum). By analyzing ~14.2M prokaryotic genome comparisons by their lowest-common-ancestor taxon levels, we detected many potential misclassification errors in a curated database, further demonstrating the need for wide-scale adoption of quantitative taxonomic classifications based on whole-genome similarity.

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.

Single-Strand Conformation Polymorphism Fingerprint Method for Dictyostelids

Dictyostelid social amoebae are a highly diverse group of eukaryotic soil microbes that are valuable resources for biological research. Genetic diversity study of these organisms solely relies on molecular phylogenetics of the SSU rDNA gene, which is not ideal for large-scale genetic diversity study. Here, we designed a set of PCR–single-strand conformation polymorphism (SSCP) primers and optimized the SSCP fingerprint method for the screening of dictyostelids. The optimized SSCP condition required gel purification of the SSCP amplicons followed by electrophoresis using a 9% polyacrylamide gel under 4°C. We also tested the optimized SSCP procedure with 73 Thai isolates of dictyostelid that had the SSU rDNA gene sequences published. The SSCP fingerprint patterns were related to the genus-level taxonomy of dictyostelids, but the fingerprint dendrogram did not reflect the deep phylogeny. This method is rapid, cost-effective, and suitable for large-scale sample screening as compared with the phylogenetic analysis of the SSU rDNA gene sequences.

Like Father Like Son: Cultural and Genetic Contributions to Song Inheritance in an Estrildid Finch

Social learning of vocalizations is integral to song inheritance in oscine passerines. However, other factors, such as genetic inheritance and the developmental environment, can also influence song phenotype. The relative contributions of these factors can have a strong influence on song evolution and may affect important evolutionary processes such as speciation. However, relative contributions are well-described only for a few species and are likely to vary with taxonomy. Using archived song data, we examined patterns of song inheritance in a domestic population of Java sparrows (Lonchura oryzivora), some of which had been cross-fostered. Six-hundred and seventy-six songs from 73 birds were segmented and classified into notes and note subtypes (N = 22,972), for which a range of acoustic features were measured. Overall, we found strong evidence for cultural inheritance of song structure and of the acoustic characteristics of notes; sons’ song syntax and note composition were similar to that of their social fathers and were not influenced by genetic relatedness. For vocal consistency of note subtypes, a measure of vocal performance, there was no apparent evidence of social or genetic inheritance, but both age and developmental environment influenced consistency. These findings suggest that high learning fidelity of song material, i.e., song structure and note characteristics, could allow novel variants to be preserved and accumulate over generations, with implications for evolution and conservation. However, differences in vocal performance do not show strong links to cultural inheritance, instead potentially serving as condition dependent signals.

To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography

BACKGROUND

The recent blooming of metabarcoding applications to biodiversity studies comes with some relevant methodological debates. One such issue concerns the treatment of reads by denoising or by clustering methods, which have been wrongly presented as alternatives. It has also been suggested that denoised sequence variants should replace clusters as the basic unit of metabarcoding analyses, missing the fact that sequence clusters are a proxy for species-level entities, the basic unit in biodiversity studies. We argue here that methods developed and tested for ribosomal markers have been uncritically applied to highly variable markers such as cytochrome oxidase I (COI) without conceptual or operational (e.g., parameter setting) adjustment. COI has a naturally high intraspecies variability that should be assessed and reported, as it is a source of highly valuable information. We contend that denoising and clustering are not alternatives. Rather, they are complementary and both should be used together in COI metabarcoding pipelines.

RESULTS

Using a COI dataset from benthic marine communities, we compared two denoising procedures (based on the UNOISE3 and the DADA2 algorithms), set suitable parameters for denoising and clustering, and applied these steps in different orders. Our results indicated that the UNOISE3 algorithm preserved a higher intra-cluster variability. We introduce the program DnoisE to implement the UNOISE3 algorithm taking into account the natural variability (measured as entropy) of each codon position in protein-coding genes.  This correction increased the number of sequences retained by 88%. The order of the steps (denoising and clustering) had little influence on the final outcome.

CONCLUSIONS

We highlight the need for combining denoising and clustering, with adequate choice of stringency parameters, in COI metabarcoding. We present a program that uses the coding properties of this marker to improve the denoising step. We recommend researchers to report their results in terms of both denoised sequences (a proxy for haplotypes) and clusters formed (a proxy for species), and to avoid collapsing the sequences of the latter into a single representative. This will allow studies at the cluster (ideally equating species-level diversity) and at the intra-cluster level, and will ease additivity and comparability between studies.

Nucleotide spacing distribution analysis for human genome

The distribution of nucleotides spacing in human genome was investigated. An analysis of the frequency of occurrence in the human genome of different sequence lengths flanked by one type of nucleotide was carried out showing that the distribution has no self-similar (fractal) structure. The results nevertheless revealed several characteristic features: (i) the distribution for short-range spacing is quite similar to the purely stochastic sequences; (ii) the distribution for long-range spacing essentially deviates from the random sequence distribution, showing strong long-range correlations; (iii) the differences between (A, T) and (C, G) nucleotides are quite significant; (iv) the spacing distribution displays tiny oscillations.

Word Entropy-Based Approach to Detect Highly Variable Genetic Markers for Bacterial Genotyping

Genotyping methods are used to distinguish bacterial strains from one species. Thus, distinguishing bacterial strains on a global scale, between countries or local districts in one country is possible. However, the highly selected bacterial populations (e.g., local populations in hospital) are typically closely related and low diversified. Therefore, currently used typing methods are not able to distinguish individual strains from each other. Here, we present a novel pipeline to detect highly variable genetic segments for genotyping a closely related bacterial population. The method is based on a degree of disorder in analyzed sequences that can be represented by sequence entropy. With the identified variable sequences, it is possible to find out transmission routes and sources of highly virulent and multiresistant strains. The proposed method can be used for any bacterial population, and due to its whole genome range, also non-coding regions are examined.

Entropy-Based Approach in Selection Exact String-Matching Algorithms

The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns' entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology.

Identification of Regulatory SNPs Associated with Vicine and Convicine Content of Vicia faba Based on Genotyping by Sequencing Data Using Deep Learning

Faba bean (Vicia faba) is a grain legume, which is globally grown for both human consumption as well as feed for livestock. Despite its agro-ecological importance the usage of Vicia faba is severely hampered by its anti-nutritive seed-compounds vicine and convicine (V+C). The genes responsible for a low V+C content have not yet been identified. In this study, we aim to computationally identify regulatory SNPs (rSNPs), i.e., SNPs in promoter regions of genes that are deemed to govern the V+C content of Vicia faba. For this purpose we first trained a deep learning model with the gene annotations of seven related species of the Leguminosae family. Applying our model, we predicted putative promoters in a partial genome of Vicia faba that we assembled from genotyping-by-sequencing (GBS) data. Exploiting the synteny between Medicago truncatula and Vicia faba, we identified two rSNPs which are statistically significantly associated with V+C content. In particular, the allele substitutions regarding these rSNPs result in dramatic changes of the binding sites of the transcription factors (TFs) MYB4, MYB61, and SQUA. The knowledge about TFs and their rSNPs may enhance our understanding of the regulatory programs controlling V+C content of Vicia faba and could provide new hypotheses for future breeding programs.

AnnoGen: annotating genome-wide pragmatic features

MOTIVATION

Genome annotation is an important step for all in-depth bioinformatics analysis. It is imperative to augment quantity and diversity of genome-wide annotation data for the latest reference genome to promote its adoption by ongoing and future impactful studies.

RESULTS

We developed a python toolkit AnnoGen, which at the first time, allows the annotation of three pragmatic genomic features for the GRCh38 genome in enormous base-wise quantities. The three features are chemical binding Energy, sequence information Entropy and Homology Score. The Homology Score is an exceptional feature that captures the genome-wide homology through single-base-offset tiling windows of 100 continual nucleotide bases. AnnoGen is capable of annotating the proprietary pragmatic features for variable user-interested genomic regions and optionally comparing two parallel sets of genomic regions. AnnoGen is characterized with simple utility modes and succinct HTML report of informative statistical tables and plots.

AVAILABILITY AND IMPLEMENTATION

https://github.com/shengqh/annogen.

From metabarcoding to metaphylogeography: separating the wheat from the chaff

Metabarcoding is by now a well-established method for biodiversity assessment in terrestrial, freshwater, and marine environments. Metabarcoding data sets are usually used for α- and β-diversity estimates, that is, interspecies (or inter-MOTU [molecular operational taxonomic unit]) patterns. However, the use of hypervariable metabarcoding markers may provide an enormous amount of intraspecies (intra-MOTU) information-mostly untapped so far. The use of cytochrome oxidase (COI) amplicons is gaining momentum in metabarcoding studies targeting eukaryote richness. COI has been for a long time the marker of choice in population genetics and phylogeographic studies. Therefore, COI metabarcoding data sets may be used to study intraspecies patterns and phylogeographic features for hundreds of species simultaneously, opening a new field that we suggest to name metaphylogeography. The main challenge for the implementation of this approach is the separation of erroneous sequences from true intra-MOTU variation. Here, we develop a cleaning protocol based on changes in entropy of the different codon positions of the COI sequence, together with co-occurrence patterns of sequences. Using a data set of community DNA from several benthic littoral communities in the Mediterranean and Atlantic seas, we first tested by simulation on a subset of sequences a two-step cleaning approach consisting of a denoising step followed by a minimal abundance filtering. The procedure was then applied to the whole data set. We obtained a total of 563 MOTUs that were usable for phylogeographic inference. We used semiquantitative rank data instead of read abundances to perform AMOVAs and haplotype networks. Genetic variability was mainly concentrated within samples, but with an important between seas component as well. There were intergroup differences in the amount of variability between and within communities in each sea. For two species, the results could be compared with traditional Sanger sequence data available for the same zones, giving similar patterns. Our study shows that metabarcoding data can be used to infer intra- and interpopulation genetic variability of many species at a time, providing a new method with great potential for basic biogeography, connectivity and dispersal studies, and for the more applied fields of conservation genetics, invasion genetics, and design of protected areas.

Methods for automatic reference trees and multilevel phylogenetic placement

MOTIVATION

In most metagenomic sequencing studies, the initial analysis step consists in assessing the evolutionary provenance of the sequences. Phylogenetic (or Evolutionary) Placement methods can be employed to determine the evolutionary position of sequences with respect to a given reference phylogeny. These placement methods do however face certain limitations: The manual selection of reference sequences is labor-intensive; the computational effort to infer reference phylogenies is substantially larger than for methods that rely on sequence similarity; the number of taxa in the reference phylogeny should be small enough to allow for visually inspecting the results.

RESULTS

We present algorithms to overcome the above limitations. First, we introduce a method to automatically construct representative sequences from databases to infer reference phylogenies. Second, we present an approach for conducting large-scale phylogenetic placements on nested phylogenies. Third, we describe a preprocessing pipeline that allows for handling huge sequence datasets. Our experiments on empirical data show that our methods substantially accelerate the workflow and yield highly accurate placement results.

AVAILABILITY AND IMPLEMENTATION

Freely available under GPLv3 at http://github.com/lczech/gappa.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

riboSeed: leveraging prokaryotic genomic architecture to assemble across ribosomal regions

The vast majority of bacterial genome sequencing has been performed using Illumina short reads. Because of the inherent difficulty of resolving repeated regions with short reads alone, only ∼10% of sequencing projects have resulted in a closed genome. The most common repeated regions are those coding for ribosomal operons (rDNAs), which occur in a bacterial genome between 1 and 15 times, and are typically used as sequence markers to classify and identify bacteria. Here, we exploit the genomic context in which rDNAs occur across taxa to improve assembly of these regions relative to de novo sequencing by using the conserved nature of rDNAs across taxa and the uniqueness of their flanking regions within a genome. We describe a method to construct targeted pseudocontigs generated by iteratively assembling reads that map to a reference genome’s rDNAs. These pseudocontigs are then used to more accurately assemble the newly sequenced chromosome. We show that this method, implemented as riboSeed, correctly bridges across adjacent contigs in bacterial genome assembly and, when used in conjunction with other genome polishing tools, can assist in closure of a genome.

Integrated entropy-based approach for analyzing exons and introns in DNA sequences

BACKGROUND

Numerous essential algorithms and methods, including entropy-based quantitative methods, have been developed to analyze complex DNA sequences since the last decade. Exons and introns are the most notable components of DNA and their identification and prediction are always the focus of state-of-the-art research.

RESULTS

In this study, we designed an integrated entropy-based analysis approach, which involves modified topological entropy calculation, genomic signal processing (GSP) method and singular value decomposition (SVD), to investigate exons and introns in DNA sequences. We optimized and implemented the topological entropy and the generalized topological entropy to calculate the complexity of DNA sequences, highlighting the characteristics of repetition sequences. By comparing digitalizing entropy values of exons and introns, we observed that they are significantly different. After we converted DNA data to numerical topological entropy value, we applied SVD method to effectively investigate exon and intron regions on a single gene sequence. Additionally, several genes across five species are used for exon predictions.

CONCLUSIONS

Our approach not only helps to explore the complexity of DNA sequence and its functional elements, but also provides an entropy-based GSP method to analyze exon and intron regions. Our work is feasible across different species and extendable to analyze other components in both coding and noncoding region of DNA sequences.

Landauer's Principle as a Special Case of Galois Connection

It is demonstrated how to construct a Galois connection between two related systems with entropy. The construction, called the Landauer’s connection, describes coupling between two systems with entropy. It is straightforward and transfers changes in one system to the other one, preserving ordering structure induced by entropy. The Landauer’s connection simplifies the description of the classical Landauer’s principle for computational systems. Categorification and generalization of the Landauer’s principle opens the area of modeling of various systems in presence of entropy in abstract terms.

Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity

Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations. Here, we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations (no recombination) in various examples, particularly binary matrices, in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistically uniform but algorithmically uniform. We show that algorithmic distributions can evolve modularity and genetic memory by preservation of structures when they first occur sometimes leading to an accelerated production of diversity but also to population extinctions, possibly explaining naturally occurring phenomena such as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the End Triassic) whose causes are currently a cause for debate. The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results. These results validate some suggestions in the direction that computation may be an equally important driver of evolution. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to accelerate convergence of artificial evolutionary algorithms.

Information-theoretic signatures of biodiversity in the barcoding gene

Analyzing the information content of DNA, though holding the promise to help quantify how the processes of evolution have led to information gain throughout the ages, has remained an elusive goal. Paradoxically, one of the main reasons for this has been precisely the great diversity of life on the planet: if on the one hand this diversity is a rich source of data for information-content analysis, on the other hand there is so much variation as to make the task unmanageable. During the past decade or so, however, succinct fragments of the COI mitochondrial gene, which is present in all animal phyla and in a few others, have been shown to be useful for species identification through DNA barcoding. A few million such fragments are now publicly available through the BOLD systems initiative, thus providing an unprecedented opportunity for relatively comprehensive information-theoretic analyses of DNA to be attempted. Here we show how a generalized form of total correlation can yield distinctive information-theoretic descriptors of the phyla represented in those fragments. In order to illustrate the potential of this analysis to provide new insight into the evolution of species, we performed principal component analysis on standardized versions of the said descriptors for 23 phyla. Surprisingly, we found that, though based solely on the species represented in the data, the first principal component correlates strongly with the natural logarithm of the number of all known living species for those phyla. The new descriptors thus constitute clear information-theoretic signatures of the processes whereby evolution has given rise to current biodiversity, which suggests their potential usefulness in further related studies.

unitas: the universal tool for annotation of small RNAs

BACKGROUND

Next generation sequencing is a key technique in small RNA biology research that has led to the discovery of functionally different classes of small non-coding RNAs in the past years. However, reliable annotation of the extensive amounts of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise. Moreover, existing tools have a number of shortcomings including a lack of sensitivity under certain conditions, limited number of supported species or detectable sub-classes of small RNAs.

RESULTS

Here we introduce unitas, an out-of-the-box ready software for complete annotation of small RNA sequence datasets, supporting the wide range of species for which non-coding RNA reference sequences are available in the Ensembl databases (currently more than 800). unitas combines high quality annotation and numerous analysis features in a user-friendly manner. A complete annotation can be started with one simple shell command, making unitas particularly useful for researchers not having access to a bioinformatics facility. Noteworthy, the algorithms implemented in unitas are on par or even outperform comparable existing tools for small RNA annotation that map to publicly available ncRNA databases.

CONCLUSIONS

unitas brings together annotation and analysis features that hitherto required the installation of numerous different bioinformatics tools which can pose a challenge for the non-expert user. With this, unitas overcomes the problem of read normalization. Moreover, the high quality of sequence annotation and analysis, paired with the ease of use, make unitas a valuable tool for researchers in all fields connected to small RNA biology.

A new model for ancient DNA decay based on paleogenomic meta-analysis

The persistence of DNA over archaeological and paleontological timescales in diverse environments has led to a revolutionary body of paleogenomic research, yet the dynamics of DNA degradation are still poorly understood. We analyzed 185 paleogenomic datasets and compared DNA survival with environmental variables and sample ages. We find cytosine deamination follows a conventional thermal age model, but we find no correlation between DNA fragmentation and sample age over the timespans analyzed, even when controlling for environmental variables. We propose a model for ancient DNA decay wherein fragmentation rapidly reaches a threshold, then subsequently slows. The observed loss of DNA over time may be due to a bulk diffusion process in many cases, highlighting the importance of tissues and environments creating effectively closed systems for DNA preservation. This model of DNA degradation is largely based on mammal bone samples due to published genomic dataset availability. Continued refinement to the model to reflect diverse biological systems and tissue types will further improve our understanding of ancient DNA breakdown dynamics.

Genotypic Complexity of Fisher's Geometric Model

Fisher's geometric model was originally introduced to argue that complex adaptations must occur in small steps because of pleiotropic constraints. When supplemented with the assumption of additivity of mutational effects on phenotypic traits, it provides a simple mechanism for the emergence of genotypic epistasis from the nonlinear mapping of phenotypes to fitness. Of particular interest is the occurrence of reciprocal sign epistasis, which is a necessary condition for multipeaked genotypic fitness landscapes. Here we compute the probability that a pair of randomly chosen mutations interacts sign epistatically, which is found to decrease with increasing phenotypic dimension n, and varies nonmonotonically with the distance from the phenotypic optimum. We then derive expressions for the mean number of fitness maxima in genotypic landscapes comprised of all combinations of L random mutations. This number increases exponentially with L, and the corresponding growth rate is used as a measure of the complexity of the landscape. The dependence of the complexity on the model parameters is found to be surprisingly rich, and three distinct phases characterized by different landscape structures are identified. Our analysis shows that the phenotypic dimension, which is often referred to as phenotypic complexity, does not generally correlate with the complexity of fitness landscapes and that even organisms with a single phenotypic trait can have complex landscapes. Our results further inform the interpretation of experiments where the parameters of Fisher's model have been inferred from data, and help to elucidate which features of empirical fitness landscapes can be described by this model.

Genome-Wide Prediction of DNA Methylation Using DNA Composition and Sequence Complexity in Human

DNA methylation plays a significant role in transcriptional regulation by repressing activity. Change of the DNA methylation level is an important factor affecting the expression of target genes and downstream phenotypes. Because current experimental technologies can only assay a small proportion of CpG sites in the human genome, it is urgent to develop reliable computational models for predicting genome-wide DNA methylation. Here, we proposed a novel algorithm that accurately extracted sequence complexity features (seven features) and developed a support-vector-machine-based prediction model with integration of the reported DNA composition features (trinucleotide frequency and GC content, 65 features) by utilizing the methylation profiles of embryonic stem cells in human. The prediction results from 22 human chromosomes with size-varied windows showed that the 600-bp window achieved the best average accuracy of 94.7%. Moreover, comparisons with two existing methods further showed the superiority of our model, and cross-species predictions on mouse data also demonstrated that our model has certain generalization ability. Finally, a statistical test of the experimental data and the predicted data on functional regions annotated by ChromHMM found that six out of 10 regions were consistent, which implies reliable prediction of unassayed CpG sites. Accordingly, we believe that our novel model will be useful and reliable in predicting DNA methylation.

Characterization of DNA methylation as a function of biological complexity via dinucleotide inter-distances

We perform a statistical study of the distances between successive occurrences of a given dinucleotide in the DNA sequence for a number of organisms of different complexity. Our analysis highlights peculiar features of the CG dinucleotide distribution in mammalian DNA, pointing towards a connection with the role of such dinucleotide in DNA methylation. While the CG distributions of mammals exhibit exponential tails with comparable parameters, the picture for the other organisms studied (e.g. fish, insects, bacteria and viruses) is more heterogeneous, possibly because in these organisms DNA methylation has different functional roles. Our analysis suggests that the distribution of the distances between CG dinucleotides provides useful insights into characterizing and classifying organisms in terms of methylation functionalities.

Characterizing Protease Specificity: How Many Substrates Do We Need?

Calculation of cleavage entropies allows to quantify, map and compare protease substrate specificity by an information entropy based approach. The metric intrinsically depends on the number of experimentally determined substrates (data points). Thus a statistical analysis of its numerical stability is crucial to estimate the systematic error made by estimating specificity based on a limited number of substrates. In this contribution, we show the mathematical basis for estimating the uncertainty in cleavage entropies. Sets of cleavage entropies are calculated using experimental cleavage data and modeled extreme cases. By analyzing the underlying mathematics and applying statistical tools, a linear dependence of the metric in respect to 1/n was found. This allows us to extrapolate the values to an infinite number of samples and to estimate the errors. Analyzing the errors, a minimum number of 30 substrates was found to be necessary to characterize substrate specificity, in terms of amino acid variability, for a protease (S4-S4’) with an uncertainty of 5 percent. Therefore, we encourage experimental researchers in the protease field to record specificity profiles of novel proteases aiming to identify at least 30 peptide substrates of maximum sequence diversity. We expect a full characterization of protease specificity helpful to rationalize biological functions of proteases and to assist rational drug design.

IVT-seq reveals extreme bias in RNA sequencing

BACKGROUND

RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value.

RESULTS

We present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation.

CONCLUSIONS

These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results.

Clustering of giant virus-DNA based on variations in local entropy

We present a method for clustering genomic sequences based on variations in local entropy. We have analyzed the distributions of the block entropies of viruses and plant genomes. A distinct pattern for viruses and plant genomes is observed. These distributions, which describe the local entropic variability of the genomes, are used for clustering the genomes based on the Jensen-Shannon (JS) distances. The analysis of the JS distances between all genomes that infect the chlorella algae shows the host specificity of the viruses. We illustrate the efficacy of this entropy-based clustering technique by the segregation of plant and virus genomes into separate bins.

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 compression exploits patterns of genome composition to discriminate populations and highlight regions of evolutionary interest

Background

Genomic information allows population relatedness to be inferred and selected genes to be identified. Single nucleotide polymorphism microarray (SNP-chip) data, a proxy for genome composition, contains patterns in allele order and proportion. These patterns can be quantified by compression efficiency (CE). In principle, the composition of an entire genome can be represented by a CE number quantifying allele representation and order.

Results

We applied a compression algorithm (DEFLATE) to genome-wide high-density SNP data from 4,155 human, 1,800 cattle, 1,222 sheep, 81 dogs and 49 mice samples. All human ethnic groups can be clustered by CE and the clusters recover phylogeography based on traditional fixation index (F_ST) analyses. CE analysis of other mammals results in segregation by breed or species, and is sensitive to admixture and past effective population size. This clustering is a consequence of individual patterns such as runs of homozygosity. Intriguingly, a related approach can also be used to identify genomic loci that show population-specific CE segregation. A high resolution CE ‘sliding window’ scan across the human genome, organised at the population level, revealed genes known to be under evolutionary pressure. These include SLC24A5 (European and Gujarati Indian skin pigmentation), HERC2 (European eye color), LCT (European and Maasai milk digestion) and EDAR (Asian hair thickness). We also identified a set of previously unidentified loci with high population-specific CE scores including the chromatin remodeler SCMH1 in Africans and EDA2R in Asians. Closer inspection reveals that these prioritised genomic regions do not correspond to simple runs of homozygosity but rather compositionally complex regions that are shared by many individuals of a given population. Unlike F_ST, CE analyses do not require ab initio population comparisons and are amenable to the hemizygous X chromosome.

Conclusions

We conclude with a discussion of the implications of CE for a complex systems science view of genome evolution. CE allows one to clearly visualise the evolution of individual genomes and populations through a formal, mathematically-rigorous information space. Overall, CE makes a set of biological predictions, some of which are unique and await functional validation.

Study of the HIV-2 Env cytoplasmic tail variability and its impact on Tat, Rev and Nef

Background

The HIV-2 env’s 3’ end encodes the cytoplasmic tail (CT) of the Env protein. This genomic region also encodes the rev, Tat and Nef protein in overlapping reading frames. We studied the variability in the CT coding region in 46 clinical specimens and in 2 reference strains by sequencing and by culturing. The aims were to analyse the variability of Env CT and the evolution of proteins expressed from overlapping coding sequences.

Results

A 70% reduction of the length of the CT region affected the HIV-2 ROD and EHO strains invitro due to a premature stop codon in the env gene. In clinical samples this wasn’t observed, but the CT length varied due to insertions and deletions. We noted 3 conserved and 3 variable regions in the CT. The conserved regions were those containing residues involved in Env endocytosis, the potential HIV-2 CT region implicated in the NF-kB activation and the potential end of the lentiviral lytic peptide one. The variable regions were the potential HIV-2 Kennedy region, the potential lentiviral lytic peptide two and the beginning of the potential lentiviral lytic peptide one. A very hydrophobic region was coded downstream of the premature stop codon observed invitro, suggesting a membrane spanning region. Interestingly, the nucleotides that are responsible for the variability of the CT don’t impact rev and Nef. However, in the Kennedy-like coding region variability resulted only from nucleotide changes that impacted Env and Tat together.

Conclusion

The HIV-2 Env, Tat and Rev C-terminal part are subject to major length variations in both clinical samples and cultured strains. The HIV-2 Env CT contains variable and conserved regions. These regions don’t affect the rev and Nef amino acids composition which evolves independently. In contrast, Tat co-evolves with the Env CT.

On the fractal geometry of DNA by the binary image analysis

The multifractal analysis of binary images of DNA is studied in order to define a methodological approach to the classification of DNA sequences. This method is based on the computation of some multifractality parameters on a suitable binary image of DNA, which takes into account the nucleotide distribution. The binary image of DNA is obtained by a dot-plot (recurrence plot) of the indicator matrix. The fractal geometry of these images is characterized by fractal dimension (FD), lacunarity, and succolarity. These parameters are compared with some other coefficients such as complexity and Shannon information entropy. It will be shown that the complexity parameters are more or less equivalent to FD, while the parameters of multifractality have different values in the sense that sequences with higher FD might have lower lacunarity and/or succolarity. In particular, the genome of Drosophila melanogaster has been considered by focusing on the chromosome 3r, which shows the highest fractality with a corresponding higher level of complexity. We will single out some results on the nucleotide distribution in 3r with respect to complexity and fractality. In particular, we will show that sequences with higher FD also have a higher frequency distribution of guanine, while low FD is characterized by the higher presence of adenine.

A novel hierarchical clustering algorithm for gene sequences

BACKGROUND

Clustering DNA sequences into functional groups is an important problem in bioinformatics. We propose a new alignment-free algorithm, mBKM, based on a new distance measure, DMk, for clustering gene sequences. This method transforms DNA sequences into the feature vectors which contain the occurrence, location and order relation of k-tuples in DNA sequence. Afterwards, a hierarchical procedure is applied to clustering DNA sequences based on the feature vectors.

RESULTS

The proposed distance measure and clustering method are evaluated by clustering functionally related genes and by phylogenetic analysis. This method is also compared with BlastClust, CD-HIT-EST and some others. The experimental results show our method is effective in classifying DNA sequences with similar biological characteristics and in discovering the underlying relationship among the sequences.

CONCLUSIONS

We introduced a novel clustering algorithm which is based on a new sequence similarity measure. It is effective in classifying DNA sequences with similar biological characteristics and in discovering the relationship among the sequences.

Energetic loads and informational entropy during insect metamorphosis: measuring structural variability and self-organization

In this work an information theory approach is presented for measuring structural variability during insect metamorphosis. Following a self-organizational perspective, the underlying assumption is that an insect pupa is a cybernetic bio-system, which displays a homeostatic control during its metamorphosis. The description of structural variability was based on biochemical data (lipids, glycogen, carbohydrates and proteins) analysed at different time intervals during the metamorphosis of Anarsia lineatella Zeller (Lepidoptera: Gelechiidae). Probabilities of biochemical variables were further treated by considering a finite countable set of progressive metamorphosis states having Markov properties at isothermal conditions (25 °C, 16:8h L:D, 65 ± 5%RH). The probabilities of the biochemical variables, as well as the related informational entropies, are affected when the system moves one step forward for each successive state. In most cases, but protein, there is some observable evidence that histolysis could be related to a decrease in informational entropy H ('disorganization of the system'), followed by a 'stable balance period' during the middle stages of metamorphosis. An initial increase in H is measured at the last stages of metamorphosis, which theoretically correspond to histogenesis ('reorganization of the system'). In this context, the temporal evolution of pupal structural variability was probabilistically quantified according to the classical information theory. The principles of the proposed holistic system are independent of its detailed dynamics and the proposed model can potentially describe part of the observable experimental data during metamorphosis of a holometabolous insect.

Topological entropy of DNA sequences

MOTIVATION

Topological entropy has been one of the most difficult to implement of all the entropy-theoretic notions. This is primarily due to finite sample effects and high-dimensionality problems. In particular, topological entropy has been implemented in previous literature to conclude that entropy of exons is higher than of introns, thus implying that exons are more 'random' than introns.

RESULTS

We define a new approximation to topological entropy free from the aforementioned difficulties. We compute its expected value and apply this definition to the intron and exon regions of the human genome to observe that as expected, the entropy of introns are significantly higher than that of exons. We also find that introns are less random than expected: their entropy is lower than the computed expected value. We also observe the perplexing phenomena that introns on chromosome Y have atypically low and bimodal entropy, possibly corresponding to random sequences (high entropy) and sequences that posses hidden structure or function (low entropy).

AVAILABILITY

A Mathematica implementation is available at http://www.math.psu.edu/koslicki/entropy.nb

CONTACT

koslicki@math.psu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Scaling properties and fractality in the distribution of coding segments in eukaryotic genomes revealed through a block entropy approach

Statistical methods, including block entropy based approaches, have already been used in the study of long-range features of genomic sequences seen as symbol series, either considering the full alphabet of the four nucleotides or the binary purine or pyrimidine character set. Here we explore the alternation of short protein-coding segments with long noncoding spacers in entire chromosomes, focusing on the scaling properties of block entropy. In previous studies, it has been shown that the sizes of noncoding spacers follow power-law-like distributions in most chromosomes of eukaryotic organisms from distant taxa. We have developed a simple evolutionary model based on well-known molecular events (segmental duplications followed by elimination of most of the duplicated genes) which reproduces the observed linearity in log-log plots. The scaling properties of block entropy H(n) have been studied in several works. Their findings suggest that linearity in semilogarithmic scale characterizes symbol sequences which exhibit fractal properties and long-range order, while this linearity has been shown in the case of the logistic map at the Feigenbaum accumulation point. The present work starts with the observation that the block entropy of the Cantor-like binary symbol series scales in a similar way. Then, we perform the same analysis for the full set of human chromosomes and for several chromosomes of other eukaryotes. A similar but less extended linearity in semilogarithmic scale, indicating fractality, is observed, while randomly formed surrogate sequences clearly lack this type of scaling. Genomic sequences always present entropy values much lower than their random surrogates. Symbol sequences produced by the aforementioned evolutionary model follow the scaling found in genomic sequences, thus corroborating the conjecture that "segmental duplication-gene elimination" dynamics may have contributed to the observed long rangeness in the coding or noncoding alternation in genomes.

A Markov model of the Indus script

Although no historical information exists about the Indus civilization (flourished ca. 2600-1900 B.C.), archaeologists have uncovered about 3,800 short samples of a script that was used throughout the civilization. The script remains undeciphered, despite a large number of attempts and claimed decipherments over the past 80 years. Here, we propose the use of probabilistic models to analyze the structure of the Indus script. The goal is to reveal, through probabilistic analysis, syntactic patterns that could point the way to eventual decipherment. We illustrate the approach using a simple Markov chain model to capture sequential dependencies between signs in the Indus script. The trained model allows new sample texts to be generated, revealing recurring patterns of signs that could potentially form functional subunits of a possible underlying language. The model also provides a quantitative way of testing whether a particular string belongs to the putative language as captured by the Markov model. Application of this test to Indus seals found in Mesopotamia and other sites in West Asia reveals that the script may have been used to express different content in these regions. Finally, we show how missing, ambiguous, or unreadable signs on damaged objects can be filled in with most likely predictions from the model. Taken together, our results indicate that the Indus script exhibits rich synactic structure and the ability to represent diverse content. both of which are suggestive of a linguistic writing system rather than a nonlinguistic symbol system.

Shallow Reductionism and the Problem of Complexity in Psychology

In his recent book The Mind Doesn't Work That Way, Fodor argues that computational modeling of global cognitive processes, such as abductive everyday reasoning, has not been successful. In this article the problem is analyzed in the framework of algorithmic information theory. It is argued that the failed approaches are characterized by shallow reductionism, which is rejected in favor of deep reductionism and nonreductionism.

Using the SERV Applet to Detect Tandem Repeats in DNA Sequences and to Predict Their Variability

INTRODUCTIONTandem repeats (satellite repeats) are short stretches of DNA that are repeated head-to-tail. Tandem repeats mutate at rates that are between 100- and 10,000-fold greater than normal (point) mutation rates in the rest of the genome. As a consequence of these frequent mutation events, "homologous" tandem repeats in closely related species, strains, or even individuals in the same population often contain a different number of repeat units. This heterogeneity is extensively used in today's molecular forensics and genotyping research. However, while all repeats are unstable, precise mutation rates vary greatly between different repeat loci. This implies that not all tandem repeats are suited as markers for forensics, genotyping, or putative hypervariable functional modules. The SERV ("Sequence-Based Estimation of Repeats Variability") applet enables finding repeats in DNA sequences and estimating their variability. Hence, it can be used to select repeats that are suitable markers for genotyping or interesting candidates for functional studies.

Biological models: Measuring variability with classical and quantum information

This essay proposes methods to analyse the variability of biological data. The idea is to express the state of a biological system as a linear combination of base states in a Hilbert space. Coefficients of the linear combination can be interpreted as probabilities and informational entropy is associated to each state allowing the definition of a classical variability measure. Besides, state transition matrices can also be calculated and their norms express the dynamics of the system organization and a quantum variability measure. As the examples show, the classical measure expresses a structural variability and the quantum measure expresses a functional variability.

Genomes are covered with ubiquitous 11 bp periodic patterns, the "class A flexible patterns"

Background

The genomes of prokaryotes and lower eukaryotes display a very strong 11 bp periodic bias in the distribution of their nucleotides. This bias is present throughout a given genome, both in coding and non-coding sequences. Until now this bias remained of unknown origin.

Results

Using a technique for analysis of auto-correlations based on linear projection, we identified the sequences responsible for the bias. Prokaryotic and lower eukaryotic genomes are covered with ubiquitous patterns that we termed "class A flexible patterns". Each pattern is composed of up to ten conserved nucleotides or dinucleotides distributed into a discontinuous motif. Each occurrence spans a region up to 50 bp in length. They belong to what we named the "flexible pattern" type, in that there is some limited fluctuation in the distances between the nucleotides composing each occurrence of a given pattern. When taken together, these patterns cover up to half of the genome in the majority of prokaryotes. They generate the previously recognized 11 bp periodic bias.

Conclusion

Judging from the structure of the patterns, we suggest that they may define a dense network of protein interaction sites in chromosomes.

“Word” Preference in the Genomic Text and Genome Evolution: Different Modes of n-tuplet Usage in Coding and Noncoding Sequences

Extensive work on n-tuplet occurrence in genomic sequences has revealed the correlation of their usage with sequence origin. Parallel to that, there exist different restrictions in the nucleotide composition of coding and noncoding sequences that may result in distinct modes of usage of n-tuplets. The relatively simple approaches described herein focus on such differences. They are based on simple summation measures of n-tuplet frequencies, computed after filtering the background nucleotide composition. Among the main targets of this work is to draw some conclusions on the qualitative differences in the composition of genomic sequences depending on their functionality. Moreover, an evolutionary model is formulated, including simple forms of ubiquitous events of genome dynamics: genomic fusions, genome shuffling due to transpositions, replication slippage, and point mutations. This model is shown to be able to reproduce all the statistical features of genomic sequences discussed herein.