Funding and Sustainability

Funding, people, and long-term capacity
Sequencing all eukaryotic life requires more than technical expertise—it requires durable funding models. In this section, contributors explore what it takes to sustain genome sequencing beyond pilot grants and highlight the often-overlooked costs that truly define large-scale biodiversity genomics.

What funding models make genome sequencing sustainable beyond pilot grants?

Harris Lewin: Sustaining genome sequencing beyond pilot grants requires diversified and coordinated funding models.

While large-scale sequencing inevitably demands large-scale investment, the key challenge is aligning funding with clear scientific and societal value. Traditional government agencies remain the principal sources for projects tied to defined goals—such as evolutionary biology, conservation, human health, or bioeconomy applications. For these, principal investigators must continue to justify the need for high-quality reference genomes, though falling sequencing costs have made this easier.

Philanthropic foundations and private donors are increasingly vital, particularly as they seek to support transformative research in areas like biodiversity conservation, sustainability, and the integration of genomics with AI and data science. These sources often provide flexibility for exploratory or foundational initiatives that precede or complement government programs.

**Dr. Harris Lewin**, EBP Chair, and his wife, **Dr. Rosane Oliveira**, hiking the Costa Brava, Catalonia.

For global efforts such as the EBP, sustainability depends on creating a centralized, long-term funding framework — ideally through public–private partnerships that pool resources across nations, agencies, and foundations. This model would enable stable infrastructure, data standards, and equitable access to sequencing capacity worldwide.

Establishing such a framework is challenging, but emerging initiatives in open data, digital biology, and global biodiversity policy create real opportunities. With coordinated leadership and international cooperation, sustainable, large-scale genome sequencing is within reach.

**Harris Lewin** in a remote region of Brazil, southeast of Rio de Janeiro.

What’s the most misunderstood cost in genome sequencing?

Harris Lewin: The most misunderstood cost in genome sequencing is not the sequencing itself, but the upstream work required to obtain high-quality, well-identified biological samples. This includes field collection, taxonomic identification by experts, vouchering, and long-term biobanking. Most easily accessible species have already been sampled, yet they represent only about ~1% of the species thought to exist.

Many remaining species are exceptionally difficult to obtain. Some have not been seen in decades; others were described only once by a single taxonomist—who may no longer be available to verify identifications. Countless eukaryotic species live in remote or extreme environments, such as deep-sea ecosystems, tropical canopies, or isolated mountain regions. Organizing expeditions to reach these habitats can cost far more than the sequencing itself.

These logistical and taxonomic challenges—and their substantial costs—are often overlooked when people think about “the cost of sequencing.” The Earth BioGenome Project has explicitly accounted for these realities in its budgeting model, recognizing that collecting and correctly identifying life’s diversity is the true bottleneck and the most underestimated expense in large-scale genome sequencing effort.

What training models actually work to scale expertise?

**Verena Ras** collecting jellyfish in the Keurbooms River along the South African coast.

Verena Ras: Training in biodiversity genomics scales best when it builds lasting capacity. Not just individuals with new skills, but institutions that can keep teaching those skills long after a workshop ends. The most effective models don’t rely on short courses alone; they combine structured learning pathways with hands-on training inside real research or sequencing projects. When people learn while helping produce actual outputs like reference genomes, clean metadata, or analysis pipelines, the skills “stick”, and so do the systems and habits needed to sustain them.

This kind of training doesn’t just build technical expertise; it creates standard processes, good data practices, working collaborations, and shared infrastructure. And when universities, agencies, or sequencing centres formally adopt these programmes, the expertise continues to grow locally rather than fading when external trainers leave. In other words, training truly scales when it enables people and organisations to become teachers, problem-solvers, and builders of the next phase of capacity. The goal isn’t simply more trainees; it’s more places that can train others.

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