Dynamics of nucleotide composition of the myocardial RNA in compensatory hyperfunction and hypertrophy of the heart

1967 ◽  
Vol 64 (2) ◽  
pp. 838-840
Author(s):  
F. Z. Meerson ◽  
L. A. Kopteva ◽  
V. V. Melekhov ◽  
G. I. Markovskaya
Keyword(s):  
Nucleotide Composition ◽  
Compensatory Hyperfunction ◽  
Hypertrophy Of The Heart

Nucleotide Content of Ribonucleic Acid in Compensatory Hyperfunction and Hypertrophy of the Heart

Nature ◽  
1966 ◽  
Vol 212 (5065) ◽  
pp. 927-928 ◽  
Author(s):  
F. Z. MEERSON ◽  
L. A. KOPTEVA ◽  
V. V. MELECHOV ◽  
G. J. MARKOVSKAYA
Keyword(s):  
Ribonucleic Acid ◽  
Nucleotide Content ◽  
Compensatory Hyperfunction ◽  
Hypertrophy Of The Heart

Structure and mass of mitochondria in the process of compensatory hyperfunction and hypertrophy of the heart

1964 ◽  
Vol 36 (3) ◽  
pp. 568-578 ◽  
Author(s):  
F.Z. Meerson ◽  
T.A. Zaletayeva ◽  
S.S. Lagutchev ◽  
M.G. Pshennikova
Keyword(s):  
Compensatory Hyperfunction ◽  
Hypertrophy Of The Heart

scholarly journals The Evolution of Isochores: Evidence From SNP Frequency Distributions

Genetics ◽  
2002 ◽  
Vol 162 (4) ◽  
pp. 1805-1810 ◽  
Author(s):  
Martin J Lercher ◽  
Nick G C Smith ◽  
Adam Eyre-Walker ◽  
Laurence D Hurst
Keyword(s):  
Population Genetics ◽  
Large Scale ◽  
Gc Content ◽  
Nucleotide Composition ◽  
Compositional Variation ◽  
Mutation Bias ◽  
Single Nucleotide ◽  
Frequency Distributions ◽  
Noncoding Regions ◽  
Standard Population

AbstractThe large-scale systematic variation in nucleotide composition along mammalian and avian genomes has been a focus of the debate between neutralist and selectionist views of molecular evolution. Here we test whether the compositional variation is due to mutation bias using two new tests, which do not assume compositional equilibrium. In the first test we assume a standard population genetics model, but in the second we make no assumptions about the underlying population genetics. We apply the tests to single-nucleotide polymorphism data from noncoding regions of the human genome. Both models of neutral mutation bias fit the frequency distributions of SNPs segregating in low- and medium-GC-content regions of the genome adequately, although both suggest compositional nonequilibrium. However, neither model fits the frequency distribution of SNPs from the high-GC-content regions. In contrast, a simple population genetics model that incorporates selection or biased gene conversion cannot be rejected. The results suggest that mutation biases are not solely responsible for the compositional biases found in noncoding regions.

Codon Usage Bias Covaries With Expression Breadth and the Rate of Synonymous Evolution in Humans, but This Is Not Evidence for Selection

Genetics ◽  
2001 ◽  
Vol 159 (3) ◽  
pp. 1191-1199
Author(s):  
Araxi O Urrutia ◽  
Laurence D Hurst
Keyword(s):  
Codon Usage ◽  
Codon Bias ◽  
Synonymous Codon ◽  
Nucleotide Composition ◽  
Synonymous Codon Usage ◽  
Synonymous Substitutions ◽  
Numerous Species ◽  
Nucleotide Content ◽  
Expression Breadth ◽  
Human Genes

Abstract In numerous species, from bacteria to Drosophila, evidence suggests that selection acts even on synonymous codon usage: codon bias is greater in more abundantly expressed genes, the rate of synonymous evolution is lower in genes with greater codon bias, and there is consistency between genes in the same species in which codons are preferred. In contrast, in mammals, while nonequal use of alternative codons is observed, the bias is attributed to the background variance in nucleotide concentrations, reflected in the similar nucleotide composition of flanking noncoding and exonic third sites. However, a systematic examination of the covariants of codon usage controlling for background nucleotide content has yet to be performed. Here we present a new method to measure codon bias that corrects for background nucleotide content and apply this to 2396 human genes. Nearly all (99%) exhibit a higher amount of codon bias than expected by chance. The patterns associated with selectively driven codon bias are weakly recovered: Broadly expressed genes have a higher level of bias than do tissue-specific genes, the bias is higher for genes with lower rates of synonymous substitutions, and certain codons are repeatedly preferred. However, while these patterns are suggestive, the first two patterns appear to be methodological artifacts. The last pattern reflects in part biases in usage of nucleotide pairs. We conclude that we find no evidence for selection on codon usage in humans.

scholarly journals Genomic Tackling of Human Satellite DNA: Breaking Barriers through Time

2021 ◽  
Vol 22 (9) ◽  
pp. 4707
Author(s):  
Mariana Lopes ◽  
Sandra Louzada ◽  
Margarida Gama-Carvalho ◽  
Raquel Chaves
Keyword(s):  
Human Genome ◽  
Satellite Dna ◽  
Repetitive Sequences ◽  
Nucleotide Composition ◽  
Genomic Component ◽  
Genomic Studies ◽  
Human Genomic ◽  
Definition Of ◽  
High Degree

(Peri)centromeric repetitive sequences and, more specifically, satellite DNA (satDNA) sequences, constitute a major human genomic component. SatDNA sequences can vary on a large number of features, including nucleotide composition, complexity, and abundance. Several satDNA families have been identified and characterized in the human genome through time, albeit at different speeds. Human satDNA families present a high degree of sub-variability, leading to the definition of various subfamilies with different organization and clustered localization. Evolution of satDNA analysis has enabled the progressive characterization of satDNA features. Despite recent advances in the sequencing of centromeric arrays, comprehensive genomic studies to assess their variability are still required to provide accurate and proportional representation of satDNA (peri)centromeric/acrocentric short arm sequences. Approaches combining multiple techniques have been successfully applied and seem to be the path to follow for generating integrated knowledge in the promising field of human satDNA biology.

scholarly journals DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 354
Author(s):  
Lu Zhang ◽  
Xinyi Qin ◽  
Min Liu ◽  
Ziwei Xu ◽  
Guangzhong Liu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Area Under The Curve ◽  
Nucleotide Composition ◽  
Computational Method ◽  
Feature Subset ◽  
Accurate Identification ◽  
Genome Wide ◽  
Dinucleotide Composition ◽  
Optimal Feature Subset

As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58%–83.38% and an area under the curve (AUC) of 81.39%–91.04%. Furthermore, the independent datasets achieved an ACC of 72.95%–83.04% and an AUC of 80.79%–91.09%, which shows an excellent generalization ability of our proposed method.

Patterns of Nucleotide Substitution in Mitochondrial Protein Coding Genes of Vertebrates

Genetics ◽  
1996 ◽  
Vol 143 (1) ◽  
pp. 537-548 ◽  
Author(s):  
Sudhir Kumar
Keyword(s):  
Nucleotide Substitution ◽  
Mitochondrial Protein ◽  
Likelihood Estimation ◽  
Nucleotide Composition ◽  
Substitution Rates ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Conserved Genes ◽  
Maximum Likelihood Methods ◽  
Position Data

Abstract Maximum likelihood methods were used to study the differences in substitution rates among the four nucleotides and among different nucleotide sites in mitochondrial protein-coding genes of vertebrates. In the lst+2nd codon position data, the frequency of nucleotide G is negatively correlated with evolutionary rates of genes, substitution rates vary substantially among sites, and the transition / transversion rate bias (R) is two to five times larger than that expected at random. Generally, largest transition biases and greatest differences in substitution rates among sites are found in the highly conserved genes. The 3rd positions in placental mammal genes exhibit strong nucleotide composition biases and the transitional rates exceed transversional rates by one to two orders of magnitude. Tamura-Nei and Hasegawa-Kishino-Yano models with gamma distributed variable rates among sites (gamma parameter, α) adequately describe the nucleotide substitution process in 1st+2nd position data. In these data, ignoring differences in substitution rates among sites leads to largest biases while estimating substitution rates. Kimura's two-parameter model with variable-rates among sites performs satisfactorily in likelihood estimation of R, α, and overall amount of evolution for lst+2nd position data. It can also be used to estimate pairwise distances with appropriate values of α for a majority of genes.

scholarly journals NUCLEOTIDE COMPOSITION OF PENTOSE NUCLEIC ACIDS FROM YEAST AND MAMMALIAN TISSUES

1950 ◽  
Vol 186 (1) ◽  
pp. 51-67 ◽  
Author(s):  
Erwin. Chargaff ◽  
Boris. Magasanik ◽  
Ernst. Vischer ◽  
Charlotte. Green ◽  
Ruth. Doniger ◽  
...  
Keyword(s):  
Nucleic Acids ◽  
Nucleotide Composition ◽  
Mammalian Tissues
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