SMOOTH

SMOOTH corrects genotyping and scoring errors in high-density genetic linkage maps to improve map accuracy for applications such as fine-scale targeted gene cloning and anchoring physical maps.

Key Features:

• Data cleaning: Cleans genetic linkage data during the mapping process to remove erroneous genotype calls.
• Statistical discrepancy calculation: Computes discrepancies between observed and predicted values using neighboring loci within a specified marker order.
• Error identification: Identifies highly improbable data points (scoring errors) based on the calculated discrepancies.
• Iterative cleaning: Removes identified improbable data points through an iterative process integrated with a mapping algorithm.
• Map recalculation: Recalculates the linkage map after each cleaning iteration to refine marker order and distances.
• Error detection sensitivity: Detects high volumes of scoring errors in high-density datasets.
• Ordering ambiguity resolution: Resolves ordering ambiguities in marker clusters that other software cannot address.
• Empirical demonstration: Demonstrated accurate placement of nearly 1,000 markers in a single linkage group in potato experiments using simulated and experimental mapping data.

Scientific Applications:

• Fine-scale targeted gene cloning: Improves accuracy of linkage maps used for fine-scale targeted gene cloning.
• Anchoring physical maps: Enhances the anchoring of physical maps to genetic linkage maps by reducing scoring errors.
• High-density map construction: Enables construction of highly precise high-density genetic linkage maps by cleaning genotype data and refining marker order.
• Marker cluster ordering: Resolves ordering ambiguities within marker clusters to improve downstream genetic analyses.
• Potato mapping studies: Validated on simulated and experimental potato mapping data, facilitating placement of nearly 1,000 markers in a linkage group.

Methodology:

Computes discrepancies between observed and predicted values based on neighboring loci within a given marker order, identifies highly improbable data points, removes them through an iterative cleaning process integrated with a mapping algorithm, and recalculates the map after each iteration.