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   https://doi.org/10.1145/3535508.3545559  

   Gao, Meijun; Wang, Wei; Liu, Kevin J. (August 2022, Proceedings of the 13th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB ’22))
2. Phylogenomics: Constrained gene tree inference

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   Mirarab, Siavash (January 2017, Nature Ecology & Evolution)
3. Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells

   https://doi.org/10.35459/tbp.2019.000101  

   Chen, Kevin Y.; Zuckerman, Daniel M.; Nelson, Philip C. (January 2020, The Biophysicist)

   Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential equation approach by generating typical system histories, instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch—that is, without recourse to black-box packages. In some cases, their results can also be compared directly with single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies involving gene expression and error correction, respectively. For gene expression, stochastic simulation results are compared with experimental data, an important research exercise for biophysics students. For error correction, several proofreading models are compared to find the minimal components necessary for sufficient accuracy in translation. Animations of the stochastic error correction models provide insight into the proofreading mechanisms. Code samples and resulting animations showing results are given in the online Supplemental Material . 

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4. “Correcting” Gene Trees to be More Like Species Trees Frequently Increases Topological Error

   https://doi.org/10.1093/gbe/evad094  

   Yan, Zhi; Ogilvie, Huw A; Nakhleh, Luay (June 2023, Genome Biology and Evolution)

   Holland, Barbara (Ed.)

   Abstract The evolutionary histories of individual loci in a genome can be estimated independently, but this approach is error-prone due to the limited amount of sequence data available for each gene, which has led to the development of a diverse array of gene tree error correction methods which reduce the distance to the species tree. We investigate the performance of two representatives of these methods: TRACTION and TreeFix. We found that gene tree error correction frequently increases the level of error in gene tree topologies by “correcting” them to be closer to the species tree, even when the true gene and species trees are discordant. We confirm that full Bayesian inference of the gene trees under the multispecies coalescent model is more accurate than independent inference. Future gene tree correction approaches and methods should incorporate an adequately realistic model of evolution instead of relying on oversimplified heuristics. 

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5. Ocean to Tree: Leveraging Single-Molecule RNA-Seq to Repair Genome Gene Models and Improve Phylogenomic Analysis of Gene and Species Evolution

   https://doi.org/10.1007/978-1-0716-3642-8_19  

   Hsiao, Jan; Deng, Lola Chenxi; Moroz, Leonid L; Chalasani, Sreekanth H; Edsinger, Eric (January 2024, Springer US)