TrendBio

#7 · Genomics

Graphs resolve haplotypes from ambiguous, mixed, and low-coverage sequencing reads

2 preprints · 6 published · ↑ 1.6× vs 4-wk avg
Items per day, last 8 weekstoday: 8

The dominant thread across these papers is casting haplotype reconstruction from sequencing reads as a graph problem. pHapCompass models read ambiguity probabilistically for polyploid phase and adds graph-based uncertainty quantification. devider applies a positional de Bruijn graph to long reads to resolve diverse viral and AMR gene haplotypes in mixtures. A pangenome-graph method uses integer programming in an alignment-free framework and is benchmarked on downsampled human MHC data at 0.1x. Other papers apply related structured inference to adjacent problems: PBML enumerates shared SMEMs on a run-length PBWT index, EquiRep reconstructs tandem repeat units via equivalence classes, and ScisTree2 uses local search for lineage trees and genotype calling. What is new is explicit handling of ambiguity and uncertainty, or of mixed and low-coverage samples, within graph-based inference.

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