SeSAM

SeSAM constructs genetic linkage maps from high-throughput genotyping and sequencing data to produce robust framework maps and high-density total maps while accounting for genotyping errors, missing data, and complex segregation in biparental and outcrossing populations.

Key Features:

• Seriation and placement approaches: Implements seriation and placement approaches for marker ordering.
• High-Robustness Framework Map: Selects a subset of markers that maintain order reliability beyond a specified statistical threshold to form a robust framework.
• High-Density Total Map: Builds a comprehensive map by incorporating nearly all polymorphic markers on top of the framework map.
• Error mitigation for genotyping and missing data: Mitigates the impact of genotyping errors and missing data during mapping.
• Support for biparental and outcrossing populations: Supports a wide range of biparental populations, including outcrossing species, with on-the-fly phase inference by maximum-likelihood during map elongation.
• Data simulation and format conversion: Provides functions to simulate datasets and convert data formats.
• Error detection and visualization: Detects putative genotyping errors and visualizes data and map quality, including graphical representations of genotypes.
• Map merging: Merges multiple maps into a consensus map.
• 2-point and multipoint Expectation-Maximization analyses: Exposes lower-level functions for 2-point and multipoint EM analyses to support interactive map construction and detailed analyses.
• Implementation: Implemented in R with core functionalities written in C++.

Scientific Applications:

• Genetic linkage map construction: Construction of linkage maps from marker-dense genotyping and sequencing datasets.
• Biparental and outcrossing species analysis: Linkage analysis and map construction for biparental populations and outcrossing species with phase inference.
• Robust marker ordering: Production of framework maps that prioritize reliable marker order for downstream analyses.
• High-density mapping studies: Generation of high-density total maps for marker-rich genomic studies.
• Genotyping quality control: Detection and visualization of putative genotyping errors for dataset QC.
• Method development and benchmarking: Use of simulated datasets to benchmark mapping methods and parameters.
• Comparative and consensus mapping: Merging multiple maps into consensus maps for comparative genomics and integration of mapping experiments.

Methodology:

Uses seriation and placement approaches for marker ordering; constructs a high-robustness framework by selecting markers above a statistical reliability threshold and extends it into a high-density total map by incorporating nearly all polymorphic markers; infers phases on-the-fly by maximum-likelihood during map elongation; provides 2-point and multipoint Expectation-Maximization analyses.