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MegaSSR: a web server for large scale microsatellite identification, classification, and marker development.
Mokhtar, Morad M; Alsamman, Alsamman M; El Allali, Achraf.
Afiliação
  • Mokhtar MM; Bioinformatics Laboratory, College of Computing, Mohammed VI Polytechnic University, Benguerir, Morocco.
  • Alsamman AM; Agricultural Genetic Engineering Research Institute, Agricultural Research Center, Giza, Egypt.
  • El Allali A; Bioinformatics Laboratory, College of Computing, Mohammed VI Polytechnic University, Benguerir, Morocco.
Front Plant Sci ; 14: 1219055, 2023.
Article em En | MEDLINE | ID: mdl-38162302
ABSTRACT
Next-generation sequencing technologies have opened new avenues for using genomic data to study and develop molecular markers and improve genetic resources. Simple Sequence Repeats (SSRs) as genetic markers are increasingly used in molecular diversity and molecular breeding programs that require bioinformatics pipelines to analyze the large amounts of data. Therefore, there is an ongoing need for online tools that provide computational resources with minimal effort and maximum efficiency, including automated development of SSR markers. These tools should be flexible, customizable, and able to handle the ever-increasing amount of genomic data. Here we introduce MegaSSR (https//bioinformatics.um6p.ma/MegaSSR), a web server and a standalone pipeline that enables the design of SSR markers in any target genome. MegaSSR allows users to design targeted PCR-based primers for their selected SSR repeats and includes multiple tools that initiate computational pipelines for SSR mining, classification, comparisons, PCR primer design, in silico PCR validation, and statistical visualization. MegaSSR results can be accessed, searched, downloaded, and visualized with user-friendly web-based tools. These tools provide graphs and tables showing various aspects of SSR markers and corresponding PCR primers. MegaSSR will accelerate ongoing research in plant species and assist breeding programs in their efforts to improve current genomic resources.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Front Plant Sci Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Marrocos

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: Front Plant Sci Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Marrocos