Genotype likelihoods incorporated in non-linear dimensionality reduction techniques infer fine-scale population genetic structure

This repository contains genotype likelihood estimations derived from open access whole-genome re-sequencing datasets of the scimitar-horned oryx (SO) and Galápagos giant tortoises (GT). The datasets were downsampled to exhibit varying coverage levels, including 6x/8x, 2x, and 0.5x. Genotype likelih...

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description This repository contains genotype likelihood estimations derived from open access whole-genome re-sequencing datasets of the scimitar-horned oryx (SO) and Galápagos giant tortoises (GT). The datasets were downsampled to exhibit varying coverage levels, including 6x/8x, 2x, and 0.5x. Genotype likelihoods were estimated, followed by the calculation of principal components, and subsequent application of UMAP and t-SNE with varying parameter settings, as detailed in Çilingir et al. (2024). All intermediate and input files generated from these datasets are available here. Genotype likelihood estimations are provided in the formats '.beagle.gz' and '.mafs.gz'. Additionally, the repository contains the input covariance matrix ('.cov') for each dataset and the population information file for each group, which were employed in the non-linear dimensionality reduction steps described in Çilingir et al. (2024). Raw data resources All raw sequencing data we used in this study were downloaded from public databases, and no new data were generated. The scimitar-horned oryx data were acquired from NCBI BioProject PRJEB37295  (Humble et al. 2023) The Galápagos giant tortoises data were acquired from NCBI BioProject PRJNA761229 (Jensen et al. 2021, 2022) Code/Software All bioinformatic codes used for generating the results and guidelines presented in Çilingir et al. (2024) are available at https://github.com/fgcilingir/lcUMAPtSNE. Literature Cited Çilingir, F.G., Uzel, K., Grossen, C. (2024) Genotype likelihoods incorporated in non-linear dimensionality reduction techniques infer fine-scale population genetic structure. bioRxiv, https://doi.org/10.1101/2024.04.01.587545 Humble, E., Stoffel, M. A., Dicks, K., Ball, A. D., Gooley, R. M., Chuven, J., Pusey, R., Remeithi, M. A., Koepfli, K.-P., Pukazhenthi, B., Senn, H., & Ogden, R. (2023). Conservation management strategy impacts inbreeding and mutation load in scimitar-horned oryx. Proceedings of the National Academy of Sciences of the United States of America, 120(18), e2210756120. Jensen, E. L., Gaughran, S. J., Fusco, N. A., Poulakakis, N., Tapia, W., Sevilla, C., Málaga, J., Mariani, C., Gibbs, J. P., & Caccone, A. (2022). The Galapagos giant tortoise Chelonoidis phantasticus is not extinct. Communications Biology, 5(1), 546. Jensen, E. L., Gaughran, S. J., Garrick, R. C., Russello, M. A., & Caccone, A. (2021). Demographic history and patterns of molecular evolution from whole genome sequencing in the radiation of Galapago
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Gözde ; Uzel, Kerem ; Grossen, Christine</creator><creatorcontrib>Çilingir, F. Gözde ; Uzel, Kerem ; Grossen, Christine</creatorcontrib><description>This repository contains genotype likelihood estimations derived from open access whole-genome re-sequencing datasets of the scimitar-horned oryx (SO) and Galápagos giant tortoises (GT). The datasets were downsampled to exhibit varying coverage levels, including 6x/8x, 2x, and 0.5x. Genotype likelihoods were estimated, followed by the calculation of principal components, and subsequent application of UMAP and t-SNE with varying parameter settings, as detailed in Çilingir et al. (2024). All intermediate and input files generated from these datasets are available here. Genotype likelihood estimations are provided in the formats '.beagle.gz' and '.mafs.gz'. Additionally, the repository contains the input covariance matrix ('.cov') for each dataset and the population information file for each group, which were employed in the non-linear dimensionality reduction steps described in Çilingir et al. (2024). Raw data resources All raw sequencing data we used in this study were downloaded from public databases, and no new data were generated. The scimitar-horned oryx data were acquired from NCBI BioProject PRJEB37295  (Humble et al. 2023) The Galápagos giant tortoises data were acquired from NCBI BioProject PRJNA761229 (Jensen et al. 2021, 2022) Code/Software All bioinformatic codes used for generating the results and guidelines presented in Çilingir et al. (2024) are available at https://github.com/fgcilingir/lcUMAPtSNE. Literature Cited Çilingir, F.G., Uzel, K., Grossen, C. (2024) Genotype likelihoods incorporated in non-linear dimensionality reduction techniques infer fine-scale population genetic structure. bioRxiv, https://doi.org/10.1101/2024.04.01.587545 Humble, E., Stoffel, M. A., Dicks, K., Ball, A. D., Gooley, R. M., Chuven, J., Pusey, R., Remeithi, M. A., Koepfli, K.-P., Pukazhenthi, B., Senn, H., &amp; Ogden, R. (2023). Conservation management strategy impacts inbreeding and mutation load in scimitar-horned oryx. Proceedings of the National Academy of Sciences of the United States of America, 120(18), e2210756120. Jensen, E. L., Gaughran, S. J., Fusco, N. A., Poulakakis, N., Tapia, W., Sevilla, C., Málaga, J., Mariani, C., Gibbs, J. P., &amp; Caccone, A. (2022). The Galapagos giant tortoise Chelonoidis phantasticus is not extinct. Communications Biology, 5(1), 546. Jensen, E. L., Gaughran, S. J., Garrick, R. C., Russello, M. A., &amp; Caccone, A. (2021). 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Gözde</creatorcontrib><creatorcontrib>Uzel, Kerem</creatorcontrib><creatorcontrib>Grossen, Christine</creatorcontrib><title>Genotype likelihoods incorporated in non-linear dimensionality reduction techniques infer fine-scale population genetic structure</title><description>This repository contains genotype likelihood estimations derived from open access whole-genome re-sequencing datasets of the scimitar-horned oryx (SO) and Galápagos giant tortoises (GT). The datasets were downsampled to exhibit varying coverage levels, including 6x/8x, 2x, and 0.5x. Genotype likelihoods were estimated, followed by the calculation of principal components, and subsequent application of UMAP and t-SNE with varying parameter settings, as detailed in Çilingir et al. (2024). All intermediate and input files generated from these datasets are available here. Genotype likelihood estimations are provided in the formats '.beagle.gz' and '.mafs.gz'. Additionally, the repository contains the input covariance matrix ('.cov') for each dataset and the population information file for each group, which were employed in the non-linear dimensionality reduction steps described in Çilingir et al. (2024). Raw data resources All raw sequencing data we used in this study were downloaded from public databases, and no new data were generated. The scimitar-horned oryx data were acquired from NCBI BioProject PRJEB37295  (Humble et al. 2023) The Galápagos giant tortoises data were acquired from NCBI BioProject PRJNA761229 (Jensen et al. 2021, 2022) Code/Software All bioinformatic codes used for generating the results and guidelines presented in Çilingir et al. (2024) are available at https://github.com/fgcilingir/lcUMAPtSNE. Literature Cited Çilingir, F.G., Uzel, K., Grossen, C. 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Genotype likelihoods were estimated, followed by the calculation of principal components, and subsequent application of UMAP and t-SNE with varying parameter settings, as detailed in Çilingir et al. (2024). All intermediate and input files generated from these datasets are available here. Genotype likelihood estimations are provided in the formats '.beagle.gz' and '.mafs.gz'. Additionally, the repository contains the input covariance matrix ('.cov') for each dataset and the population information file for each group, which were employed in the non-linear dimensionality reduction steps described in Çilingir et al. (2024). Raw data resources All raw sequencing data we used in this study were downloaded from public databases, and no new data were generated. The scimitar-horned oryx data were acquired from NCBI BioProject PRJEB37295  (Humble et al. 2023) The Galápagos giant tortoises data were acquired from NCBI BioProject PRJNA761229 (Jensen et al. 2021, 2022) Code/Software All bioinformatic codes used for generating the results and guidelines presented in Çilingir et al. (2024) are available at https://github.com/fgcilingir/lcUMAPtSNE. Literature Cited Çilingir, F.G., Uzel, K., Grossen, C. (2024) Genotype likelihoods incorporated in non-linear dimensionality reduction techniques infer fine-scale population genetic structure. bioRxiv, https://doi.org/10.1101/2024.04.01.587545 Humble, E., Stoffel, M. A., Dicks, K., Ball, A. D., Gooley, R. M., Chuven, J., Pusey, R., Remeithi, M. A., Koepfli, K.-P., Pukazhenthi, B., Senn, H., &amp; Ogden, R. (2023). Conservation management strategy impacts inbreeding and mutation load in scimitar-horned oryx. 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title Genotype likelihoods incorporated in non-linear dimensionality reduction techniques infer fine-scale population genetic structure
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