Dataset Information

Revisiting the out of Africa event with a deep-learning approach.

ABSTRACT: Anatomically modern humans evolved around 300 thousand years ago in Africa. They started to appear in the fossil record outside of Africa as early as 100 thousand years ago, although other hominins existed throughout Eurasia much earlier. Recently, several studies argued in favor of a single out of Africa event for modern humans on the basis of whole-genome sequence analyses. However, the single out of Africa model is in contrast with some of the findings from fossil records, which support two out of Africa events, and uniparental data, which propose a back to Africa movement. Here, we used a deep-learning approach coupled with approximate Bayesian computation and sequential Monte Carlo to revisit these hypotheses from the whole-genome sequence perspective. Our results support the back to Africa model over other alternatives. We estimated that there are two sequential separations between Africa and out of African populations happening around 60-90 thousand years ago and separated by 13-15 thousand years. One of the populations resulting from the more recent split has replaced the older West African population to a large extent, while the other one has founded the out of Africa populations.

SUBMITTER: Montinaro F

PROVIDER: S-EPMC8595897 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Revisiting the out of Africa event with a deep-learning approach.

Montinaro Francesco F Pankratov Vasili V Yelmen Burak B Pagani Luca L Mondal Mayukh M

American journal of human genetics 20211008 11

Anatomically modern humans evolved around 300 thousand years ago in Africa. They started to appear in the fossil record outside of Africa as early as 100 thousand years ago, although other hominins existed throughout Eurasia much earlier. Recently, several studies argued in favor of a single out of Africa event for modern humans on the basis of whole-genome sequence analyses. However, the single out of Africa model is in contrast with some of the findings from fossil records, which support two o ...[more]

PMID: 34626535

Similar Datasets

Project description:Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Dataset Information

Revisiting the out of Africa event with a deep-learning approach.

Publications

Revisiting the out of Africa event with a deep-learning approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets