Bold claim: We’re unlocking the microscopic world in ways that sharpen our health, our environment, and our future. But here’s where it gets controversial: the tools we rely on shape what we can discover—and who gets to use them. New research from Arizona State University delivers two powerful advances that make microbiome science easier, more accurate, and scalable for the long haul. One study refines how scientists build microbial family trees. The other offers an open‑source software foundation that researchers worldwide rely on to analyze biological data. Together, these developments strengthen the scientific underpinnings of microbiome research, disease surveillance, environmental monitoring, and cutting‑edge fields like precision medicine.
Lead author Qiyun Zhu, from ASU’s Biodesign Center for Fundamental and Applied Microbiomics and an ASU School of Life Sciences faculty member, emphasizes a core idea: open tools accelerate discovery. “Our team builds open‑source software tools because we believe that when everyone can access and extend scientific tools, the entire community benefits and discovery accelerates,” he says. Zhu is supported by ASU colleagues and international collaborators.
The first study, published in Nature Communications, focuses on improving marker genes—the DNA signposts that trace microbial lineages. The second study, presented in Nature Methods, introduces scikit-bio, an open‑source software library that serves as a global foundation for analyzing biological data. Collectively, these works push microbiome research, disease tracking, environmental monitoring, and emerging disciplines like precision medicine toward more robust and scalable practices.
Family matters in microbiology
Constructing precise evolutionary trees is crucial for understanding how microbes evolve, interact, and influence ecosystems. Clearer trees enhance disease tracking by revealing how harmful microbes mutate over time. They also improve environmental research by showing how microbial communities respond to pollution or climate change. In health research, sharper microbial identification strengthens studies of the gut microbiome and its impact on well‑being.
At the heart of this effort is the choice of marker genes—the DNA signposts used to map evolutionary history.
For years, scientists leaned on a small set of traditional marker genes. But the metagenomics era changes the game: researchers now sequence millions of genomes, often directly from environmental samples. Metagenomics lets researchers capture all DNA in an environment and sequence it at once, exposing entire, previously hidden microbial communities.
Those genomes are incredibly valuable yet frequently incomplete or uneven in quality. Relying on a fixed set of marker genes can yield unreliable evolutionary interpretations.
To tackle this, Zhu and colleagues developed TMarSel (Tree-based Marker Selection). Instead of handpicking a few marker genes, TMarSel automatically searches thousands of gene families and selects the combination that yields the most reliable evolutionary tree. It evaluates each gene for prevalence, informativeness, and its contribution to a stable, meaningful portrait of microbial relationships.
The outcome is a flexible, data‑driven approach to building microbial trees that scales to large, diverse groups of organisms—even when many genomes are only partially complete.
Scikit-bio: A microbial ancestry toolkit
Zhu also leads the development of scikit-bio, a broad, open‑source software library that gives scientists the tools to analyze enormous biological datasets. It’s especially valuable for microbiome studies—environments like the human gut or soil harboring complex microbial communities.
Biological data are distinct: they’re massive, sparse, and highly interconnected. Standard data‑analysis tools aren’t built to handle that level of fragmentation. Scikit-bio fills the gap with more than 500 functions for tasks such as:
- Comparing microbial communities
- Measuring diversity
- Transforming compositional data
- Analyzing DNA, RNA, and protein sequences
- Building and editing phylogenetic trees
- Preparing data for machine learning
The project is community‑driven, supported by more than 80 contributors, and maintained with rigorous testing and documentation. It has been cited in tens of thousands of scientific papers across medicine, ecology, climate science, and cancer biology, cementing its role as a cornerstone for microbiome research and other data‑rich areas of modern biology.
A new era for microbial science
As biological datasets keep expanding, tools like scikit-bio and TMarSel make large‑scale research more reliable and reproducible. The studies also highlight ASU’s growing leadership at the crossroads of biology and computation. Zhu’s work demonstrates that merging evolutionary insight with sophisticated software engineering can produce tools used by researchers worldwide.
As DNA sequencing becomes faster and cheaper, scientists will uncover even more of the microbial universe. Tools like TMarSel and scikit-bio ensure that this surge of data translates into real, actionable scientific understanding.
