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Identifying Selections Operating on HIV-1 Reverse Transcriptase via Uniform Manifold Approximation and Projection
Genomics · Submitted 1 Oct 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Thus, this method for visualization of areas of HIV RT undergoing evolution can help infer information about selective pressures, although it is correlative.
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The abstract, sentence by sentence
We analyze 14,651 HIV1 reverse transcriptase (HIV RT) sequences from the Stanford HIV Drug Resistance Database labeled with treatment regimen in order to study the evolution this enzyme under drug selection in the clinic. Our goal is to identify distinct sectors of HIV RT's sequence space that are undergoing evolution as a way to identify individual selections and/or evolutionary solutions. We utilize Uniform Manifold Approximation and Projection (UMAP), a graph-based dimensionality reduction technique uniquely suited for the detection of non-linear dependencies and visualize the results using an unsupervised clustering algorithm based on density analysis. Our analysis produced 21 distinct clusters of sequences. Supporting the biological significance of these clusters, they tend to represent phylogenetically related sequences with strong correspondence to distinct treatment regimens. Thus, this method for visualization of areas of HIV RT undergoing evolution can help infer information about selective pressures, although it is correlative. The mutation signatures associated with each cluster may represent the higher-order epistatic context facilitating these evolutionary pathways, information that is generally not accessible by other types of mutational co-dependence analyses.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We analyze 14,651 HIV1 reverse transcriptase (HIV RT) sequences from the Stanford HIV Drug Resistance Database labeled with treatment regimen in order to study the evolution this enzyme under drug selection in the clinic. | method 0.69 | 0.95 | 0.03 | asserted 0.98 |
| s1 | Our goal is to identify distinct sectors of HIV RT's sequence space that are undergoing evolution as a way to identify individual selections and/or evolutionary solutions. | contribution 0.74 | 0.21 | 0.04 | asserted 0.89 |
| s2 | We utilize Uniform Manifold Approximation and Projection (UMAP), a graph-based dimensionality reduction technique uniquely suited for the detection of non-linear dependencies and visualize the results using an unsupervised clustering algorithm based on density analysis. | method 1.00 | 0.67 | 0.03 | asserted 1.00 |
| s3 | Our analysis produced 21 distinct clusters of sequences. | result 1.00 | 0.97 | 0.11 | asserted 1.00 |
| s4 | Supporting the biological significance of these clusters, they tend to represent phylogenetically related sequences with strong correspondence to distinct treatment regimens. | interpretation 0.56 | 0.92 | 0.21 | asserted 0.81 |
| s5 | Thus, this method for visualization of areas of HIV RT undergoing evolution can help infer information about selective pressures, although it is correlative. | interpretation 0.84 | 0.54 | 0.57 | hedged possibility 0.96 |
| s6 | The mutation signatures associated with each cluster may represent the higher-order epistatic context facilitating these evolutionary pathways, information that is generally not accessible by other types of mutational co-dependence analyses. | interpretation 1.00 | 0.54 | 0.21 | hedged possibility 1.00 |
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Original abstract
We analyze 14,651 HIV1 reverse transcriptase (HIV RT) sequences from the Stanford HIV Drug Resistance Database labeled with treatment regimen in order to study the evolution this enzyme under drug selection in the clinic. Our goal is to identify distinct sectors of HIV RT's sequence space that are undergoing evolution as a way to identify individual selections and/or evolutionary solutions. We utilize Uniform Manifold Approximation and Projection (UMAP), a graph-based dimensionality reduction technique uniquely suited for the detection of non-linear dependencies and visualize the results using an unsupervised clustering algorithm based on density analysis. Our analysis produced 21 distinct clusters of sequences. Supporting the biological significance of these clusters, they tend to represent phylogenetically related sequences with strong correspondence to distinct treatment regimens. Thus, this method for visualization of areas of HIV RT undergoing evolution can help infer information about selective pressures, although it is correlative. The mutation signatures associated with each cluster may represent the higher-order epistatic context facilitating these evolutionary pathways, information that is generally not accessible by other types of mutational co-dependence analyses.
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