Pytrf: a python package for finding tandem repeats from genomic sequences
Researchers from China released their new computational tool named pytrf. This Python-based tool is designed for identifying exact and approximate tandem repeats in genomic sequences. It integrates easily with Python workflows, supports command-line use, and outperforms other tools in speed while maintaining comparable memory usage. The tools is also available via PyPI and GitHub.
Publication Details:
Du, L., Sun, D., Chen, J. et al. Pytrf: a python package for finding tandem repeats from genomic sequences. BMC Bioinformatics 26, 151 (2025). https://doi.org/10.1186/s12859-025-06168-3
Performance of deep-learning-based approaches to improve polygenic scores
A team of scientists evaluated whether neural networks improve polygenic score prediction by capturing nonlinear effects. Despite testing across simulations and 28 UK Biobank traits, neural networks showed limited benefit over linear models, partly due to confounding from linkage disequilibrium rather than true gene-gene or gene-environment interactions.
Publication Details:
Kelemen, M., Xu, Y., Jiang, T. et al. Performance of deep-learning-based approaches to improve polygenic scores. Nat Commun 16, 5122 (2025). https://doi.org/10.1038/s41467-025-60056-1
Cell-free genome-wide transcriptomics through machine learning optimization
French researchers developed a machine learning–guided cell-free transcriptomics platform that screened over one million buffer conditions to boost mRNA yield 20-fold. This advance enables RNA-seq in vitro and offers a powerful method to dissect genome regulation without the complexity of living cells.
Publication Details:
Wagner, L., Hoang, A., Rue, O., et al. Cell-free genome-wide transcriptomics through machine learning optimization. bioRxiv 2025.06.04.657821; doi: https://doi.org/10.1101/2025.06.04.657821
Replaying germinal center evolution on a quantified affinity landscape
A team of researchers replayed over 100 germinal center reactions to study antibody evolution, revealing that affinity maturation results from consistent, cumulative selection rather than rare clonal bursts. Apparent permissiveness to low-affinity cells and early plateaus in affinity are explained by survivorship bias rather than true evolutionary stalling.
Publication Details:
DeWitt III, WSS., Vora, AA., Araki, T., et al. Replaying germinal center evolution on a quantified affinity landscape. bioRxiv 2025.06.02.656870; doi: https://doi.org/10.1101/2025.06.02.656870
Researchers from China released their new computational tool named pytrf. This Python-based tool is designed for identifying exact and approximate tandem repeats in genomic sequences. It integrates easily with Python workflows, supports command-line use, and outperforms other tools in speed while maintaining comparable memory usage. The tools is also available via PyPI and GitHub.
Publication Details:
Du, L., Sun, D., Chen, J. et al. Pytrf: a python package for finding tandem repeats from genomic sequences. BMC Bioinformatics 26, 151 (2025). https://doi.org/10.1186/s12859-025-06168-3
Performance of deep-learning-based approaches to improve polygenic scores
A team of scientists evaluated whether neural networks improve polygenic score prediction by capturing nonlinear effects. Despite testing across simulations and 28 UK Biobank traits, neural networks showed limited benefit over linear models, partly due to confounding from linkage disequilibrium rather than true gene-gene or gene-environment interactions.
Publication Details:
Kelemen, M., Xu, Y., Jiang, T. et al. Performance of deep-learning-based approaches to improve polygenic scores. Nat Commun 16, 5122 (2025). https://doi.org/10.1038/s41467-025-60056-1
Cell-free genome-wide transcriptomics through machine learning optimization
French researchers developed a machine learning–guided cell-free transcriptomics platform that screened over one million buffer conditions to boost mRNA yield 20-fold. This advance enables RNA-seq in vitro and offers a powerful method to dissect genome regulation without the complexity of living cells.
Publication Details:
Wagner, L., Hoang, A., Rue, O., et al. Cell-free genome-wide transcriptomics through machine learning optimization. bioRxiv 2025.06.04.657821; doi: https://doi.org/10.1101/2025.06.04.657821
Replaying germinal center evolution on a quantified affinity landscape
A team of researchers replayed over 100 germinal center reactions to study antibody evolution, revealing that affinity maturation results from consistent, cumulative selection rather than rare clonal bursts. Apparent permissiveness to low-affinity cells and early plateaus in affinity are explained by survivorship bias rather than true evolutionary stalling.
Publication Details:
DeWitt III, WSS., Vora, AA., Araki, T., et al. Replaying germinal center evolution on a quantified affinity landscape. bioRxiv 2025.06.02.656870; doi: https://doi.org/10.1101/2025.06.02.656870