# An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology

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## Abstract

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## 1. Introduction

## 2. Methods

#### 2.1. Mean Squared Error

_{E}(i,j) and d

_{T}(i,j) are respectively the estimated and true pairwise distances between sequences i and j. Because pairwise distances directly influence the accuracy of transmission-clustering tools, such as HIV-TRACE, Mean Squared Error serves as a valuable indicator for the viability of any given MSA tool. We computed Mean Squared Error on pairwise distances computed directly from estimated MSAs under the Tamura–Nei 93 (TN93) model of sequence evolution [20] using the tn93 component of HIV-TRACE [2], as well as from the pairwise distances along the inferred phylogenies.

#### 2.2. Mantel Correlation

#### 2.3. Robinson–Foulds (RF) Distance

#### 2.4. Sum of Pairs (SP) Score

#### 2.5. Total Columns (TC) Score

#### 2.6. Compression Factor

## 3. Results

#### 3.1. Multiple Sequence Alignment

#### 3.2. Phylogenetic Inference

#### 3.3. Combinations of MSA and Phylogenetic Inference

#### 3.4. Combinations of MSA and Optimized FastTree Topologies

## 4. Discussion

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Kernel density estimates of the branch length distributions for the Ebola, HIV, and HCV true phylogenies.

**Figure 2.**Metrics of sequence alignment accuracy for MAFFT, MUSCLE, and Clustal Omega on 10 simulated replicate datasets of HIV, HCV, and Ebola. Violin plots are shown for Mean Squared Error, Spearman/Pearson Mantel Correlation, SP score, TC score, and Compression Factor.

**Figure 3.**Metrics of phylogenetic inference accuracy for FastTree, IQ-TREE (GTR), IQ-TREE (MFP), RAxML-NG, and PhyML on 10 simulated replicate datasets of HIV, HCV, and Ebola. Phylogenies which result from optimizing branch lengths along FastTree topology are also included. Violin plots are shown for URF, WRF, Pearson Mantel Correlation, and Mean Squared Error. Violin plots showing Spearman Mantel Correlation can be found in Supplementary Figure S1.

**Figure 4.**Heat maps comparing the accuracy of phylogenies inferred with FastTree, IQ-TREE (GTR), IQ-TREE (MFP), RAxML-NG, and PhyML from the MAFFT, Clustal Omega, MUSCLE, and true MSAs. Each value of Unweighted Robinson–Foulds (URF), Weighted Robinson–Foulds (WRF), Pearson Mantel Correlation, and Mean Squared Error shown is the average of 10 simulation replicates. Heatmaps showing Spearman Mantel Correlation can be found in Supplementary Figure S2.

**Figure 5.**Heat maps comparing the accuracy of FastTree topologies inferred from the MAFFT, Clustal Omega, MUSCLE, and true multiple sequence alignments with branch lengths optimized by IQ-TREE (GTR), IQ-TREE (MFP), RAxML-NG, and PhyML. Each value of Unweighted Robinson–Foulds (URF), Weighted Robinson–Foulds (WRF), Pearson Mantel Correlation, and Mean Squared Error shown is the average of 10 simulation replicates. Heatmaps showing Spearman Mantel Correlation can be found in Supplementary Figure S3.

**Table 1.**Total runtime for phylogenetic inference (top row) and runtime of branch length optimization on a fixed topology (bottom row) for FastTree 2, RAxML, IQ-TREE (GTR), and IQ-TREE MFP on a curated MSA of 2322 HIV-1 whole genome sequences from LANL. PhyML was unable to execute due to high memory consumption. All runs were executed sequentially on a 4-core 3.5 GHz Intel i5-6600k with 16 GB of memory, and each tool automatically selected an optimal number of threads to use internally.

(Seconds) | FastTree 2 | RaxML | PhyML | IQ-TREE | IQ-TREE MFP |
---|---|---|---|---|---|

Total | 645 | >604,800 | memory | 84,931 | 266,399 (142,286 MFP) |

BL optimization | N/A | 757 | memory | 1532 | 4885 |

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Young, C.; Meng, S.; Moshiri, N.
An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology. *Viruses* **2022**, *14*, 774.
https://doi.org/10.3390/v14040774

**AMA Style**

Young C, Meng S, Moshiri N.
An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology. *Viruses*. 2022; 14(4):774.
https://doi.org/10.3390/v14040774

**Chicago/Turabian Style**

Young, Colin, Sarah Meng, and Niema Moshiri.
2022. "An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology" *Viruses* 14, no. 4: 774.
https://doi.org/10.3390/v14040774