Calibrating Tetranucleotide-Frequency Distances for Metagenomic Binning with Right-Skewed Distribution Models
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ID: 322552
2026
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Abstract
Abstract Metagenomic binning is a pivotal step in reconstructing metagenome-assembled genomes (MAGs) from complex microbial communities, and it critically depends on reliable measures of similarity between contigs. In many workflows, tetranucleotide-frequency (TNF) distances are translated into probabilistic evidence of a shared genome of origin. Despite their central role, these distances are often modeled with convenient but poorly matched assumptions, even though they are intrinsically non-negative and frequently exhibit pronounced right-skewness—features that can distort tail behavior and weaken downstream thresholding decisions. In this work, we introduce a likelihood-based framework for characterizing intra- and inter-genomic TNF distance distributions with flexible right-skewed parametric models and for converting fitted distributions into calibrated distance-to-probability scores within a MaxBin-style scheme. Our approach provides a principled statistical basis for distributional assessment, probability calibration, and transparent operating-point selection, with the goal of improving robustness and interpretability in TNF-driven binning.
| Reference Key |
openalex_W7171174815
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| Authors | Omar Hajjaji, Al-Sayed Al-Soudy, Rachid Daoud, Rachid Benhida, Morad M. Mokhtar |
| Journal | Bioinformatics advances |
| Year | 2026 |
| DOI |
10.1093/bioadv/vbag207
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| URL | |
| Keywords | Keywords not found |
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