Dataset Information

Genetic Algorithm model and data files to assess JONSWAP spectra coefficients: MATLAB code.

ABSTRACT: This data article presents the structure of a Genetic Algorithm model written in a MATLAB code for finding the 1D JONSWAP spectra parameters when measured raw spectra is not available. The JONSWAP spectra is widely used in Coastal, Offshore, and Ocean Engineering for determining wave parameters for structure designing and numerical modeling. However, finding proper spectra parameters may be difficult because of the limitations of parameterized equations to do so and the high non-linear relation between the alpha and gamma coefficients. This GA model can find the alpha and gamma parameters for specific locations considering sea-state's evolution and water-depth transitions. The application of the GA model of this data article is shown in Rueda-Bayona et al. [1].

SUBMITTER: Rueda-Bayona JG

PROVIDER: S-EPMC7452513 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic Algorithm model and data files to assess JONSWAP spectra coefficients: MATLAB code.

Rueda-Bayona Juan Gabriel JG Guzmán Andrés A

Data in brief 20200819

This data article presents the structure of a Genetic Algorithm model written in a MATLAB code for finding the 1D JONSWAP spectra parameters when measured raw spectra is not available. The JONSWAP spectra is widely used in Coastal, Offshore, and Ocean Engineering for determining wave parameters for structure designing and numerical modeling. However, finding proper spectra parameters may be difficult because of the limitations of parameterized equations to do so and the high non-linear relation ...[more]

PMID: 32904285

Similar Datasets

Project description:BACKGROUND:The standard genetic code (SGC) is a unique set of rules which assign amino acids to codons. Similar amino acids tend to have similar codons indicating that the code evolved to minimize the costs of amino acid replacements in proteins, caused by mutations or translational errors. However, if such optimization in fact occurred, many different properties of amino acids must have been taken into account during the code evolution. Therefore, this problem can be reformulated as a multi-objective optimization task, in which the selection constraints are represented by measures based on various amino acid properties. RESULTS:To study the optimality of the SGC we applied a multi-objective evolutionary algorithm and we used the representatives of eight clusters, which grouped over 500 indices describing various physicochemical properties of amino acids. Thanks to that we avoided an arbitrary choice of amino acid features as optimization criteria. As a consequence, we were able to conduct a more general study on the properties of the SGC than the ones presented so far in other papers on this topic. We considered two models of the genetic code, one preserving the characteristic codon blocks structure of the SGC and the other without this restriction. The results revealed that the SGC could be significantly improved in terms of error minimization, hereby it is not fully optimized. Its structure differs significantly from the structure of the codes optimized to minimize the costs of amino acid replacements. On the other hand, using newly defined quality measures that placed the SGC in the global space of theoretical genetic codes, we showed that the SGC is definitely closer to the codes that minimize the costs of amino acids replacements than those maximizing them. CONCLUSIONS:The standard genetic code represents most likely only partially optimized systems, which emerged under the influence of many different factors. Our findings can be useful to researchers involved in modifying the genetic code of the living organisms and designing artificial ones.

Project description:Background Of the different bioinformatic methods used to recover transposable elements (TEs) in genome sequences, one of the most commonly used procedures is the homology-based method proposed by the RepeatMasker program. RepeatMasker generates several output files, including the .out file, which provides annotations for all detected repeats in a query sequence. However, a remaining challenge consists of identifying the different copies of TEs that correspond to the identified hits. This step is essential for any evolutionary/comparative analysis of the different copies within a family. Different possibilities can lead to multiple hits corresponding to a unique copy of an element, such as the presence of large deletions/insertions or undetermined bases, and distinct consensus corresponding to a single full-length sequence (like for long terminal repeat (LTR)-retrotransposons). These possibilities must be taken into account to determine the exact number of TE copies. Results We have developed a perl tool that parses the RepeatMasker .out file to better determine the number and positions of TE copies in the query sequence, in addition to computing quantitative information for the different families. To determine the accuracy of the program, we tested it on several RepeatMasker .out files corresponding to two organisms (Drosophila melanogaster and Homo sapiens) for which the TE content has already been largely described and which present great differences in genome size, TE content, and TE families. Conclusions Our tool provides access to detailed information concerning the TE content in a genome at the family level from the .out file of RepeatMasker. This information includes the exact position and orientation of each copy, its proportion in the query sequence, and its quality compared to the reference element. In addition, our tool allows a user to directly retrieve the sequence of each copy and obtain the same detailed information at the family level when a local library with incomplete TE class/subclass information was used with RepeatMasker. We hope that this tool will be helpful for people working on the distribution and evolution of TEs within genomes.

Dataset Information

Genetic Algorithm model and data files to assess JONSWAP spectra coefficients: MATLAB code.

Publications

Genetic Algorithm model and data files to assess JONSWAP spectra coefficients: MATLAB code.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets