Shannon information entropy in the canonical genetic code

Molecular epidemiology analysis of symptomatic and asymptomatic norovirus infections in Chinese infants

BACKGROUND

Norovirus is a leading cause of acute gastroenteritis among children. Previous studies based on symptomatic infections indicated that mutations, rather than recombination drove the evolution of the norovirus ORF2. These characteristics were found in hospital-based symptomatic infections, whereas, asymptomatic infections are frequent and contribute significantly to transmission.

METHODS

We conducted the first norovirus molecular epidemiology analysis covering both symptomatic and asymptomatic infections derived from a birth cohort study in the northern China.

RESULTS

During the study, 14 symptomatic and 20 asymptomatic norovirus infections were detected in 32 infants. Out of the 14 strains that caused symptomatic infections, 12 strains were identified as GII.3[P12], and others were GII.4[P31]. Conversely, 17 asymptomatic infections were caused by GII.4[P31], two by GII.2[P16], and one by GII.4[P16]. Regardless of symptomatic and asymptomatic infections, the mutations were detected frequently in the ORF2 region, and almost all recombination were identified in the RdRp-ORF2 region. The majority of the mutations were located around the predefined epitope regions of P2 subdomain indicating a potential for immune evasion.

CONCLUSION

The role of symptomatic as well as asymptomatic infections in the evolution of norovirus needs to be evaluated continuously.

Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences

BACKGROUND AND OBJECTIVE

The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters.

METHOD

To identify the spatial distribution of amino acids over the primary protein sequences of SARS-CoV-2, the Hurst exponent and Shannon entropy were applied as parameters to fetch the autocorrelation and amount of information over the spatial representations. The frequency distribution of each amino acid over the protein sequences was also evaluated. In the case of a one-dimensional sequence, the Hurst exponent (HE) was utilized due to its linear relationship with the fractal dimension (D), i.e. D+HE=2, to characterize fractality. Moreover, binary Shannon entropy was considered to measure the uncertainty in a binary sequence then further applied to calculate amino acid conservation in the primary protein sequences.

RESULTS AND CONCLUSION

Fourteen (14) SARS-CoV protein sequences were evaluated and compared with 105 SARS-CoV-2 proteins. The simulation results demonstrate the differences in the collected information about the amino acid spatial distribution in the SARS-CoV-2 and SARS-CoV proteins, enabling researchers to distinguish between the two types of CoV. The spatial arrangement of amino acids also reveals similarities and dissimilarities among the important structural proteins, E, M, N and S, which is pivotal to establish an evolutionary tree with other CoV strains.

A model of k-mer surprisal to quantify local sequence information content surrounding splice regions

Molecular sequences carry information. Analysis of sequence conservation between homologous loci is a proven approach with which to explore the information content of molecular sequences. This is often done using multiple sequence alignments to support comparisons between homologous loci. These methods therefore rely on sufficient underlying sequence similarity with which to construct a representative alignment. Here we describe a method using a formal metric of information, surprisal, to analyse biological sub-sequences without alignment constraints. We applied our model to the genomes of five different species to reveal similar patterns across a panel of eukaryotes. As the surprisal of a sub-sequence is inversely proportional to its occurrence within the genome, the optimal size of the sub-sequences was selected for each species under consideration. With the model optimized, we found a strong correlation between surprisal and CG dinucleotide usage. The utility of our model was tested by examining the sequences of genes known to undergo splicing. We demonstrate that our model can identify biological features of interest such as known donor and acceptor sites. Analysis across all annotated coding exon junctions in Homo sapiens reveals the information content of coding exons to be greater than the surrounding intron regions, a consequence of increased suppression of the CG dinucleotide in intronic space. Sequences within coding regions proximal to exon junctions exhibited novel patterns within DNA and coding mRNA that are not a function of the encoded amino acid sequence. Our findings are consistent with the presence of secondary information encoding features such as DNA and RNA binding sites, multiplexed through the coding sequence and independent of the information required to define the corresponding amino-acid sequence. We conclude that surprisal provides a complementary methodology with which to locate regions of interest in the genome, particularly in situations that lack an appropriate multiple sequence alignment.

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

Global importance of RNA secondary structures in protein-coding sequences

Motivation

The protein-coding sequences of messenger RNAs are the linear template for translation of the gene sequence into protein. Nevertheless, the RNA can also form secondary structures by intramolecular base-pairing.

Results

We show that the nucleotide distribution within codons is biased in all taxa of life on a global scale. Thereby, RNA secondary structures that require base-pairing between the position 1 of a codon with the position 1 of an opposing codon (here named RNA secondary structure class c₁) are under-represented. We conclude that this bias may result from the co-evolution of codon sequence and mRNA secondary structure, suggesting that RNA secondary structures are generally important in protein-coding regions of mRNAs. The above result also implies that codon position 2 has a smaller influence on the amino acid choice than codon position 1.

Supplementary information

Supplementary data are available at Bioinformatics online.

Systems protobiology: origin of life in lipid catalytic networks

Life is that which replicates and evolves, but there is no consensus on how life emerged. We advocate a systems protobiology view, whereby the first replicators were assemblies of spontaneously accreting, heterogeneous and mostly non-canonical amphiphiles. This view is substantiated by rigorous chemical kinetics simulations of the graded autocatalysis replication domain (GARD) model, based on the notion that the replication or reproduction of compositional information predated that of sequence information. GARD reveals the emergence of privileged non-equilibrium assemblies (composomes), which portray catalysis-based homeostatic (concentration-preserving) growth. Such a process, along with occasional assembly fission, embodies cell-like reproduction. GARD pre-RNA evolution is evidenced in the selection of different composomes within a sparse fitness landscape, in response to environmental chemical changes. These observations refute claims that GARD assemblies (or other mutually catalytic networks in the metabolism first scenario) cannot evolve. Composomes represent both a genotype and a selectable phenotype, anteceding present-day biology in which the two are mostly separated. Detailed GARD analyses show attractor-like transitions from random assemblies to self-organized composomes, with negative entropy change, thus establishing composomes as dissipative systems—hallmarks of life. We show a preliminary new version of our model, metabolic GARD (M-GARD), in which lipid covalent modifications are orchestrated by non-enzymatic lipid catalysts, themselves compositionally reproduced. M-GARD fills the gap of the lack of true metabolism in basic GARD, and is rewardingly supported by a published experimental instance of a lipid-based mutually catalytic network. Anticipating near-future far-reaching progress of molecular dynamics, M-GARD is slated to quantitatively depict elaborate protocells, with orchestrated reproduction of both lipid bilayer and lumenal content. Finally, a GARD analysis in a whole-planet context offers the potential for estimating the probability of life's emergence. The invigorated GARD scrutiny presented in this review enhances the validity of autocatalytic sets as a bona fide early evolution scenario and provides essential infrastructure for a paradigm shift towards a systems protobiology view of life's origin.

Properties of network structures, structure coefficients, and benefit-to-cost ratios

In structured populations the spatial arrangement of cooperators and defectors on the interaction graph together with the structure of the graph itself determines the game dynamics and particularly whether or not fixation of cooperation (or defection) is favored. For networks described by regular graphs and for a single cooperator (and a single defector) the question of fixation can be addressed by a single parameter, the structure coefficient. This quantity is invariant with respect to the location of the cooperator on the graph and also does not vary over different networks. We may therefore consider it to be generic for regular graphs and call it the generic structure coefficient. For two and more cooperators (or several defectors) fixation properties can also be assigned by structure coefficients. These structure coefficients, however, depend on the arrangement of cooperators and defectors which we may interpret as a configuration of the game. Moreover, the coefficients are specific for a given interaction network modeled as a regular graph, which is why we may call them specific structure coefficients. In this paper, we study how specific structure coefficients vary over interaction graphs and analyze how spectral properties of interaction networks relate to specific structure coefficients. We also discuss implications for the benefit-to-cost ratios of donation games.

QuantumIS: A Qualia Consciousness Awareness and Information Theory Quale Approach to Reducing Strategic Decision-Making Entropy

This paper investigates the underlying driving force in strategic decision-making. From a conceptual standpoint, few studies empirically studied the decision-maker's intrinsic state composed of entropy and uncertainty. This study examines a mutual information theory approach integrated into a state of qualia complexity that minimizes exclusion and maximizes the interactions of the information system and its dynamic environment via logical metonymy, illusion, and epigenetics. The article questions whether decision-makers at all levels of the organization are responding from the consciousness of an objective quale from a more subjective qualia awareness in the narrow-sense perspective of individual instances of their conscious experience. To quantify this research question, we explore several hypotheses revolving around strategic information system decisions. In this research, we posit that the eigenvalues of factor analysis along with the reduction in the uncertainty coefficients of the qualia entropy will be balanced by the quale enthalpy of our information theory structural equation model of trust, flexibility, expertise, top management support, and competitive advantage performance. We operationalize the integration of the aforementioned top management support, information systems competencies, and competitive advantage performance concepts into the qualia consciousness awareness and information theory quale framework.

Association of the characteristics of B- and T-cell repertoires with papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Complementarity-determining region 3 (CDR3) of B-cell receptors (BCRs) and T-cell receptors are the major site of antigen recognition, which determines a unique clone type, and are considered to be the representative of the disease. In the present study, high-throughput sequencing was used to analyze the association of characteristics of the BCR immunoglobulin heavy chain (IGH) and the T-cell receptor β chain (TRB) CDR3 genes in PTC and corresponding pericarcinous tissues from patients. A difference of CDR3 length distributions of total IGH CDR3 sequences between the two groups was revealed. IGHV3-11/IGHJ6, TRBV2/TRBJ1-2 and TRBV2/TRBJ1-1 may be biomarkers for the development of PTC. Furthermore, it was revealed that the extent of the common clonotype expressions at the amino acid level was slightly higher compared with the nucleotide level. The Shannon entropy demonstrated a diversity reduction in PTC compared with the pericarcinous group, and the highly expended clone (HEC) expression of PTC was higher compared with that of the corresponding pericarcinous group. Additionally, the highest clone frequency percentage of IGH and TRB was at 0.1–1.0% degree of expansion, as HEC expression was higher in PTC compared with the matched group. There was no shared clone of HECs in the two groups either at the amino acid level or at the nucleotide expression level. The differential expression of CDR3 sequences of PTC have been identified in the present study. Further research is required for assessing the immune repertoire size, diversity, cloning tracking and finding public clones of T-cell and B-cell populations in the development of PTC.

Information Entropy in Predicting Location of Observation Points for Long Tunnel

Based on the Markov model and the basic theory of information entropy, this paper puts forward a new method for optimizing the location of observation points in order to obtain more information from limited geological investigation. According to the existing data from observation points data, classification of tunnel geological lithology was performed, and various lithology distribution were determined along the tunnel using the Markov model and theory. On the basis of the information entropy theory, the distribution of information entropy was obtained along the axis of the tunnel. Therefore, different information entropy could be acquired by calculating different classification of rocks. Furthermore, uncertainty increases when information entropy increased. The maximum entropy indicates maximum uncertainty and thus, this value determines the position of the new drilling hole. A new geology situation will be decided by the maximum entropy for the lowest accuracy. Optimal distribution will be obtained after recalculating, using the new location of the geology situation. Taking the engineering for the Bashiyi Daban water diversion tunneling in Xinjiang as a case, the maximum information entropy of the geological conditions was analyzed by the method proposed in the present study, with 25 newly added geology observation points along the axis of the 30-km tunnel. The results proved the validity of the present method. The method and results in this paper may be used not only to predict the geological conditions of underground engineering based on the investigated geological information, but also to optimize the distribution of the geology observation points.

A binary representation of the genetic code

This article introduces a novel binary representation of the canonical genetic code based on both the structural similarities of the nucleotides, as well as the physicochemical properties of the encoded amino acids. Each of the four mRNA bases is assigned a unique 2-bit identifier, so that the 64 triplet codons are each indexed by a 6-bit label. The ordering of the bits reflects the hierarchical organization manifested by the DNA replication/repair and tRNA translation systems. In this system, transition and transversion mutations are naturally expressed as binary operations, and the severities of the different point mutations can be analyzed. Using a principal component analysis, it is shown that the physicochemical properties of amino acids related to protein folding also correlate with certain bit positions of their respective labels. Thus, the likelihood for a point mutation to be conservative, and less likely to cause a change in protein functionality, can be estimated.