scholarly journals Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning.

2021 ◽  
Author(s):  
Md. Shahadat Hossain ◽  
A. Q. M. Sala Uddin Pathan ◽  
Md. Nur Islam ◽  
Mahafujul Islam Quadery Tonmoy ◽  
Mahmudul Islam Rakib ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Fundamental System ◽  
Genomic Data ◽  
Rate Analysis ◽  
Neural Learning ◽  
Genome Wide ◽  
The Future ◽  
Frequent Mutations ◽  
Genomic Data Analysis ◽  
Integrated Study

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identify 3334545 mutations (14.01 mutations per isolate), suggesting a high mutation rate. Strains from India showed the highest no. of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. Besides the most prominently occurring mutations (D416G, F106F, P314L, and UTR:C241T), we identify L93L, A222V, A199A, V30L, and A220V mutations which are in the top 10 most frequent mutations. Multi-nucleotide mutations GGG>AAC, CC>TT, TG>CA, and AT>TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C>T, A>G, and A>T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T>C, G>A, and G>T mutations, respectively. T>G\A, C>G\A, and A>T\C are not anticipated in the future. Since SARS-CoV-2 is evolving continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide.

scholarly journals Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning

2021 ◽  
pp. 100798
Author(s):  
Md Shahadat Hossain ◽  
A.Q.M. Sala Uddin Pathan ◽  
Md Nur Islam ◽  
Mahafujul Islam Quadery Tonmoy ◽  
Mahmudul Islam Rakib ◽  
...  
Keyword(s):  
Neural Learning ◽  
Genome Wide ◽  
Integrated Study

scholarly journals Mutation rate analysis via parent–progeny sequencing of the perennial peach. II. No evidence for recombination-associated mutation

2016 ◽  
Vol 283 (1841) ◽  
pp. 20161785 ◽  
Author(s):  
Long Wang ◽  
Yanchun Zhang ◽  
Chao Qin ◽  
Dacheng Tian ◽  
Sihai Yang ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Recombination Rate ◽  
Mutation Rates ◽  
Recombination Rates ◽  
Rate Analysis ◽  
A Genome ◽  
Break Points ◽  
New Mutations

Mutation rates and recombination rates vary between species and between regions within a genome. What are the determinants of these forms of variation? Prior evidence has suggested that the recombination might be mutagenic with an excess of new mutations in the vicinity of recombination break points. As it is conjectured that domesticated taxa have higher recombination rates than wild ones, we expect domesticated taxa to have raised mutation rates. Here, we use parent–offspring sequencing in domesticated and wild peach to ask (i) whether recombination is mutagenic, and (ii) whether domesticated peach has a higher recombination rate than wild peach. We find no evidence that domesticated peach has an increased recombination rate, nor an increased mutation rate near recombination events. If recombination is mutagenic in this taxa, the effect is too weak to be detected by our analysis. While an absence of recombination-associated mutation might explain an absence of a recombination–heterozygozity correlation in peach, we caution against such an interpretation.

scholarly journals The theory on and software simulating large-scale genomic data for genotype-by-environment interactions

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiujin Li ◽  
Hailiang Song ◽  
Zhe Zhang ◽  
Yunmao Huang ◽  
Qin Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Simulated Data ◽  
Genomic Data ◽  
Efficient Tool ◽  
Phenotypic Data ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Threshold Trait ◽  
Genome Wide ◽  
Increasing Demand

Abstract Background With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. Results We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. Conclusions This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

scholarly journals Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria

2017 ◽  
Author(s):  
Antoine Frénoy ◽  
Sebastian Bonhoeffer
Keyword(s):  
Population Dynamics ◽  
Mutation Rate ◽  
Death Rate ◽  
Mutation Rates ◽  
Resistance Mutations ◽  
Bacterial Populations ◽  
Plasmid Segregation ◽  
Genome Wide ◽  
Response To Stress ◽  
Induced Mutagenesis

AbstractThe stress-induced mutagenesis paradigm postulates that in response to stress, bacteria increase their genome-wide mutation rate, in turn increasing the chances that a descendant is able to withstand the stress. This has implications for antibiotic treatment: exposure to sub-inhibitory doses of antibiotics has been reported to increase bacterial mutation rates, and thus probably the rate at which resistance mutations appear and lead to treatment failure.Measuring mutation rates under stress, however, is problematic, because existing methods assume there is no death. Yet sub-inhibitory stress levels may induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size, thus giving more opportunities to acquire mutations. We show that ignoring death leads to a systematic overestimation of mutation rates under stress.We developed a system using plasmid segregation to measure death and growth rates simultaneously in bacterial populations. We use it to replicate classical experiments reporting antibiotic-induced mutagenesis. We found that a substantial death rate occurs at the tested sub-inhibitory concentrations, and taking this death into account lowers and sometimes removes the signal for stress-induced mutagenesis. Moreover even when antibiotics increase mutation rate, sub-inhibitory treatments do not increase genetic diversity and evolvability, again because of effects of the antibiotics on population dynamics.Beside showing that population dynamic is a crucial but neglected parameter affecting evolvability, we provide better experimental and computational tools to study evolvability under stress, leading to a re-assessment of the magnitude and significance of the stress-induced mutagenesis paradigm.

scholarly journals Uncovering Effective Explanations for Interactive Genomic Data Analysis

Patterns ◽  
2020 ◽  
Vol 1 (6) ◽  
pp. 100093
Author(s):  
Silu Huang ◽  
Charles Blatti ◽  
Saurabh Sinha ◽  
Aditya Parameswaran
Keyword(s):  
Data Analysis ◽  
Genomic Data ◽  
Genomic Data Analysis
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