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NOBEL PRIZE 2026

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NIHNATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCESR01

Hypothesis Testing using Phylogenies for the 21st century

Active
Institution
TEMPLE UNIV OF THE COMMONWEALTH, PA
Principal investigator
Sergei L Kosakovsky Pond
Funding
$353K
Duration
2024–2028
Open portfolio

Moonbase score

Still being scored

Abstract

SUMMARY Between 2000, when the first version of Hypothesis Testing Using Phylogenies (HyPhy) was released, and 2022, the number of bases in GenBank increased ~250 fold, the number of sequenced genomes from a handful to >3,5003, and the number of PubMed papers studying molecular evolution ~12 fold. Data generation has ceased to be the bottleneck for biological and biomedical discoveries, and the new bottleneck is methods, tools, and software for data analysis and interpretation. Comparative evolutionary analyses remain an essential and powerful method for extracting meaning and insight from ever-expanding genomic sequence data. But, the lack of emphasis and incentive structure for developing, maintaining, benchmarking, and improving software and analysis tools, despite their essential and critical role in modern biology, biotechnology, and medicine remains a major concern. Analytical, infrastructure, and incentive challenges exposed by the genomic data deluge during the COVID-19 pandemic were aptly summarized. Over the last quarter century HyPhy has established itself as a useful, popular, and durable platform for studying diverse evolutionary processes, such as natural selection and recombination, across different taxonomic scales. Datamonkey, a web application providing free access to “one-click” popular HyPhy analyses, has seen increasing use by researchers worldwide over the last two decades. Through continued methodological innovation, improvements in performance and scalability, accessible web services, focus on data visualization, and user support, HyPhy developers were able to sustain and increase the reach and impact of the program. This proposal seeks to improve the software, enhance biological realism, relevance, accessibility, and interpretability of the methods, design novel approaches to address outstanding problems in evolutionary data analysis, and further lower access barriers to evolutionary comparative analyses through integration with the Galaxy ecosystem.

Research team

  • Spencer V. MusePI
  • Anton NekrutenkoPI
  • Sergei L Kosakovsky PondPI

LOADING MOONBASE SCORE

R01NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCESBiodata Management and Analysis Study Section[BDMA]centuryincentivehypothesis testingwebevolutionarybottleneckfoldpopular

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