NCMHap

NCMHap reconstructs single-individual haplotypes from multiple input sequencing fragments of a specific chromosome using Neutrosophic c-means clustering to mitigate noise and outliers.

Key Features:

• Neutrosophic C-Means Clustering: Utilizes the Neutrosophic c-means (NCM) algorithm to cluster sequencing fragments and distinguish signal from noise and outliers.
• Noise Reduction: Reduces the impact of noisy input fragments on clustering to improve haplotype reconstruction accuracy.
• Scalability and Accuracy: Demonstrates improved accuracy and scalability relative to existing methods, particularly as noise levels increase.
• Addresses NP-hard Problem: Targets the NP-hard single individual haplotype reconstruction problem from multiple fragments of a chromosome.
• Parameter Tuning: Employs tuning of NCM framework parameters to enhance clustering results.
• Validation on Simulated and Real Data: Validated using both simulated datasets and real sequencing datasets, including cases with significant gaps and noise.

Scientific Applications:

• Pharmaceutical Research: Supports precise haplotype reconstruction for studies relevant to drug development and pharmacogenomics.
• Clinical Diagnostics: Aids clinical decision-making by reconstructing individual haplotypes from patient sequencing fragments.
• Genetic Disorder Studies: Facilitates investigation of genetic diseases by resolving haplotypes in noisy genomic datasets.

Methodology:

NCMHap applies the Neutrosophic c-means (NCM) algorithm to cluster input fragments, detect and reduce noise and outliers, tunes suitable NCM parameters, and validates results on simulated and real datasets, including datasets with significant gaps and noise.