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Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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Updated: Aug 20, 2025

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

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

Adrien Vidal¹, Franck Gauthier¹, Willy Rodrigez¹

¹Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France.

BMC Bioinformatics

|November 19, 2022

Summary

This summary is machine-generated.

SeSAM software automates genetic map construction, producing robust framework and high-density maps from large SNP datasets. It handles missing data and errors, supporting diverse populations and offering advanced features for geneticists.

Keywords:

Automated software Genetic mapping Linkage Marker order robustness Seriation

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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.