Publications by authors named "G A Huttley"
The algorithms for phylogenetic reconstruction are central to computational molecular evolution. The relentless pace of data acquisition has exposed their poor scalability and the conclusion that the conventional application of these methods is impractical and not justifiable from an energy usage perspective. Furthermore, the drive to improve the statistical performance of phylogenetic methods produces increasingly parameter-rich models of sequence evolution, which worsens the computational performance.
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- Recent research highlights DNA methylation's role in gene regulation in cnidarians, particularly in corals, possibly aiding adaptation to stressors like rising seawater temperatures.
- The study reveals that DNA methylation specifically targets transposons in cnidarians, with younger, more active transposons showing higher levels of methylation.
- This suggests that the primary function of methylation in these animals may be to protect against genomic damage from transposon activity, reinforcing the idea that DNA methylation is essential for genome defense in diverse invertebrate species.
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- The study investigates how recombination and the sequence context affect human genome polymorphism using public genetic data.
- It finds that genomic diversity in recombination hotspots is mainly due to recombination's impact on mutation, rather than selective sweeps.
- Additionally, the analysis reveals that the influence of mutation context increases with larger context sizes, but is ultimately overshadowed by interactions between the central base and neighboring bases, particularly in the presence of transition mutations.
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- There is growing interest in creating diagnostic tools to differentiate between various mutagenic mechanisms, especially in cancer research and population studies.
- Researchers evaluated the origin of point mutations by comparing spontaneous mutations in mice to those caused by the chemical mutagen ENU, finding notable similarities that complicate classification.
- Using a new modeling approach and machine learning, the study achieved high accuracy in distinguishing mutation types based on neighboring base sequences, suggesting this method could be applied more broadly to mutation classification across different contexts.
Microbiome-based disease classification depends on well-validated disease-specific models or organismal markers. These are lacking for many diseases. Here, we present an alternative, search-based strategy for disease detection and classification, which detects diseased samples via their outlier novelty versus a database of samples from healthy subjects and then compares these to databases of samples from patients.
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