SeSAM: software for automatic construction of order-robust linkage maps.

Area of Science:

• Genomics and Bioinformatics
• Plant and Animal Breeding
• Population Genetics

Background:

• Increasingly large genetic marker datasets from genotyping and sequencing present challenges for linkage map construction.
• High rates of missing or erroneous data complicate robust marker ordering.
• Cost constraints limit segregating population sizes, necessitating efficient marker selection for accurate genetic maps.

Purpose of the Study:

• To develop an automated software solution for robust and high-density genetic linkage map construction.
• To address challenges posed by large datasets, missing data, and genotyping errors in genetic mapping.
• To provide a versatile tool applicable to various biparental populations, including outcrossing species.

Main Methods:

• Utilized seriation and placement algorithms for automatic genetic map construction.
• Implemented methods to mitigate the impact of genotyping errors and missing data on mapping quality.
• Developed functionalities for data simulation, format conversion, error detection, visualization, and map merging.

Main Results:

• Successfully developed SeSAM software for automatic genetic map construction.
• Generated a high-robustness framework map by optimizing marker subset selection.
• Produced a high-density total map incorporating nearly all polymorphic markers, while managing data quality issues.

Conclusions:

• SeSAM provides a fully automated approach to construct genetic linkage maps with user-defined robustness.
• The software supports a wide range of biparental populations, including outcrossing species, with on-the-fly phase inference.
• SeSAM is freely available for Linux, Windows, and macOS, facilitating its adoption in genetic research.