Dataset Information

Uncovering Signals from the Coronavirus Genome.

ABSTRACT: A signal analysis of the complete genome sequenced for coronavirus variants of concern-B.1.1.7 (Alpha), B.1.135 (Beta) and P1 (Gamma)-and coronavirus variants of interest-B.1.429-B.1.427 (Epsilon) and B.1.525 (Eta)-is presented using open GISAID data. We deal with a certain new type of finite alternating sum series having independently distributed terms associated with binary (0,1) indicators for the nucleotide bases. Our method provides additional information to conventional similarity comparisons via alignment methods and Fourier Power Spectrum approaches. It leads to uncover distinctive patterns regarding the intrinsic data organization of complete genomics sequences according to its progression along the nucleotide bases position. The present new method could be useful for the bioinformatics surveillance and dynamics of coronavirus genome variants.

SUBMITTER: Canessa E

PROVIDER: S-EPMC8303286 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Uncovering Signals from the Coronavirus Genome.

Canessa Enrique E

Genes 20210625 7

A signal analysis of the complete genome sequenced for coronavirus variants of concern-B.1.1.7 (Alpha), B.1.135 (Beta) and P1 (Gamma)-and coronavirus variants of interest-B.1.429-B.1.427 (Epsilon) and B.1.525 (Eta)-is presented using open GISAID data. We deal with a certain new type of finite alternating sum series having independently distributed terms associated with binary (0,1) indicators for the nucleotide bases. Our method provides additional information to conventional similarity comparis ...[more]

PMID: 34202172

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Project description:BackgroundLarge scale genome arrangement, such as whole gene insertion/deletion, plays an important role in bacterial genome evolution. Various methods have been employed to study the dynamic process of gene insertions and deletions, such as parsimony methods and maximum likelihood methods. Previous maximum likelihood studies have assumed that the rate of gene insertions/deletions is constant over different genes. This assumption is unrealistic. For instance, it has been shown that informational genes are less likely to be laterally transferred than non-informational genes. However, how much of the variation in gene transfer rates is due to the difference between informational genes and non-informational genes is unclear. In this study, a Gamma-distribution was incorporated in the likelihood estimation by considering rate variation for gene insertions/deletions between genes. This makes it possible to address whether a difference between informational genes and non-informational genes is the main contributor to rate variation of lateral gene transfers.ResultsThe results show that models incorporating rate variation fit the data better than do constant rate models in many phylogenetic groups. Even though informational genes are less likely to be laterally transferred than non-informational genes, the degree of rate variation for insertions/deletions did not change dramatically and remained high even when informational genes were excluded from the study. This suggests that the variation in rate of insertions/deletions is not due mainly to the simple difference between informational genes and non-informational genes. Among genes that are not classified as informational and among the informational genes themselves, there are still large differences in the rates that these genes are inserted and deleted.ConclusionWhile the difference in informational gene rates contributes to rate variation, it is only a small fraction of the variation present; instead, a substantial amount of rate variation for insertions/deletions remains among both informational genes and among non-informational genes.

Project description:Genome-wide association studies (GWAS) aim to detect single nucleotide polymorphisms (SNP) associated with trait variation. However, due to the large number of tests, standard analysis techniques impose highly stringent significance thresholds, leaving potentially associated SNPs undetected, and much of the trait genetic variation unexplained. Pathway- and network-based methodologies applied to GWAS aim to detect associations missed by standard single-marker approaches. The complex and non-random architecture of the genome makes it a challenge to derive an appropriate testing framework for such methodologies. We developed a rapid and simple permutation approach that uses GWAS SNP association results to establish the significance of pathway associations while accounting for the linkage disequilibrium structure of SNPs and the clustering of functionally related elements in the genome. All SNPs used in the GWAS are placed in a "circular genome" according to their location. Then the complete set of SNP association P values are permuted by rotation with respect to the genomic locations of the SNPs. Once these "simulated" P values are assigned, the joint gene P values are calculated using Fisher's combination test, and the association of pathways is tested using the hypergeometric test. The circular genomic permutation approach was applied to a human genome-wide association dataset. The data consists of 719 individuals from the ORCADES study genotyped for ~300,000 SNPs and measured for 51 traits ranging from physical to biochemical measurements. KEGG pathways (n = 225) were used as the sets of pathways to be tested. Our results demonstrate that the circular genomic permutations provide robust association P values. The non-permuted hypergeometric analysis generates ~1400 pathway-trait combination results with an association P value more significant than P ≤ 0.05, whereas applying circular genomic permutation reduces the number of significant results to a more credible 40% of that value. The circular permutation software ("genomicper") is available as an R package at http://cran.r-project.org/.

Dataset Information

Uncovering Signals from the Coronavirus Genome.

Publications

Uncovering Signals from the Coronavirus Genome.

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