Mutation-Simulator: fine-grained simulation of random mutations in any genome

Kühl, Maren; Stich, Benjamin; Ries, David

doi:10.1093/bioinformatics/btaa716

Cited by 28 publications

(31 citation statements)

References 7 publications

(7 reference statements)

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“…The Protein Variation Effect Analyzer (PROVEAN ), a web server software tool, is used to predict any non-synonymous amino acid substitution or indel impacts on the biological function of a protein (Choi and Chan 2015). We utilize a recently developed mutation simulation tool (Kühl et al 2020) which generates fine-grained simulated random mutations in any genome.…”

Section: Software Tools and Programmingmentioning

confidence: 99%

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A study on non-synonymous mutational patterns in structural proteins of SARS-CoV-2

Das

Roy

2021

Genome

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SARS-CoV-2 is mutating and creating divergent variants across the world. An in-depth investigation of the amino acid substitutions in the genomic signature of SARS-CoV-2 proteins is highly essential for understanding its host adaptation and infection biology. A total of 9587 SARS-CoV-2 structural protein sequences collected from 49 different countries are used to characterize protein-wise variants, substitution patterns (type and location), and major substitution changes. The majority of the substitutions are distinct, mostly in a particular location, and leads to a change in amino acid's biochemical properties. In terms of mutational changes, Envelope (E) and Membrane (M) proteins are relatively stable than Nucleocapsid (N) and Spike (S) proteins. Several co-occurrence substitutions are observed, particularly in S and N proteins. Substitution specific to active sub-domains reveals that Heptapeptide Repeat, Fusion peptides, Transmembrane in S protein, and N-terminal and C-terminal domains in N protein are remarkably mutated. We also observe a few deleterious mutations in the above domains. The overall study on non-synonymous mutation in structural proteins of SARS-CoV-2 at early in the pandemic indicates a diversity amongst virus sequences.

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Section: Software Tools and Programmingmentioning

confidence: 99%

“…It may be interesting to verify whether mutations in SARS-CoV-2 follow similar distributions or not. We use a very recently developed mutation simulation tool (Kühl et al 2020) which generates fine-grained simulated random mutations in any genome.…”

Section: Comparison Of Real Vs Simulated Mutationsmentioning

confidence: 99%

A study on non-synonymous mutational patterns in structural proteins of SARS-CoV-2

Das

Roy

2021

Genome

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“…PROVEAN (Protein Variation Effect Analyzer), a web server software tool, is used to predict any non-synonymous amino acid substitution or indel impacts on the biological function of a protein [59]. We utilize a recently developed mutation simulation tool [60] which generates fine-grained simulated random mutations in any genome. We use Python scripting 3.7 for quantitative analysis.…”

Section: Software Tools and Programmingmentioning

confidence: 99%

“…It may be interesting to verify whether mutations in SARS-CoV-2 follow similar distributions or not. We use very recently developed mutation simulation tool [60] which generates fine-grained simulated random mutations in any genome. We consider reference sequence (Wuhan-Hu-1 ) and mutation rate (per base) (Table 4) and use ARGS mode as defined in [60] to generate simulated sequences.…”

Section: Comparison Of Real Vs Simulated Mutationsmentioning

confidence: 99%

“…We use very recently developed mutation simulation tool [60] which generates fine-grained simulated random mutations in any genome. We consider reference sequence (Wuhan-Hu-1 ) and mutation rate (per base) (Table 4) and use ARGS mode as defined in [60] to generate simulated sequences. The mutation rate is the number of mutations per genetic unit at (gene, base) per unit time (year, million years, or generation) [63].…”

Section: Comparison Of Real Vs Simulated Mutationsmentioning

confidence: 99%

See 1 more Smart Citation

A Study on Non-Synonymous Mutational Patterns in Structural Proteins of SARS-COV-2

Das

Roy

2021

Preprint

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SARS-CoV-2 is mutating and creating divergent variants across the world. An in-depth investigation of the amino acid substitution in the genomic signature of SARS-CoV-2 proteins is highly essential for understanding its host adaptation and infection biology. A total of 9587 SARS-CoV-2 structural protein sequences collected from 49 different countries are used to characterize protein-wise variants, substitution pattern (type and location), and major substitution changes. The majority of the substitutions are distinct, occurred mostly in a particular location, and leads to a change in amino acid's biochemical properties. In terms of mutational changes, Envelope (E) and Membrane (M) proteins are relatively stable than Nucleocapsid (N) and Spike (S) proteins. Several co-occurrence substitutions are observed, particularly in S and N proteins. Substitution specific to active sub-domains reveals that Heptapeptide Repeat, Fusion peptides, Transmembrane in S protein, and N-terminal and C-terminal domains in N protein are remarkably mutated, and also found few deleterious mutations in these domains.

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Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

Weisweiler

Arlt

et al. 2022

Theor Appl Genet

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Key message Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. Abstract In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.

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Mutation-Simulator: fine-grained simulation of random mutations in any genome

Cited by 28 publications

References 7 publications

A study on non-synonymous mutational patterns in structural proteins of SARS-CoV-2

A study on non-synonymous mutational patterns in structural proteins of SARS-CoV-2

A Study on Non-Synonymous Mutational Patterns in Structural Proteins of SARS-COV-2

Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation

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