ArticleBioinformatics (Oxford, England)2022
The effect of genome graph expressiveness on the discrepancy between genome graph distance and string set distance.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Revisiting the complexity of and algorithms for the graph traversal edit distance and its variants.Algorithms for molecular biology : AMB · 2024Article
- A hepatitis B virus (HBV) sequence variation graph improves alignment and sample-specific consensus sequence construction.PloS one · 2024Article
- A hepatitis B virus (HBV) sequence variation graph improves sequence alignment and sample-specific consensus sequence construction for genetic analysis of HBV.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
motivationIntra-sample heterogeneity describes the phenomenon where a genomic sample contains a diverse set of genomic sequences. In practice, the true string sets in a sample are often unknown due to limitations in sequencing technology. In order to compare heterogeneous samples, genome graphs can be used to represent such sets of strings. However, a genome graph is generally able to represent a string set universe that contains multiple sets of strings in addition to the true string set. This difference between genome graphs and string sets is not well characterized. As a result, a distance metric between genome graphs may not match the distance between true string sets.
resultsWe extend a genome graph distance metric, Graph Traversal Edit Distance (GTED) proposed by Ebrahimpour Boroojeny et al., to FGTED to model the distance between heterogeneous string sets and show that GTED and FGTED always underestimate the Earth Mover's Edit Distance (EMED) between string sets. We introduce the notion of string set universe diameter of a genome graph. Using the diameter, we are able to upper-bound the deviation of FGTED from EMED and to improve FGTED so that it reduces the average error in empirically estimating the similarity between true string sets. On simulated T-cell receptor sequences and actual Hepatitis B virus genomes, we show that the diameter-corrected FGTED reduces the average deviation of the estimated distance from the true string set distances by more than 250%. AVAILABILITY AND IMPLEMENTATION: Data and source code for reproducing the experiments are available at: https://github.com/Kingsford-Group/gtedemedtest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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