The path to becoming an independent researcher was never easy. I am sincerely grateful to my wife Hayeon, my parents, and all my colleagues and friends for their unwavering support and encouragement along the way.🥹

Grateful for the innovation behind today’s primate genomics— @pacbio.bsky.social ‬(HiFi long reads), @nanoporetech.com (ultra-long reads, real-time), Illumina (high-accuracy short reads). Your platforms enable T2T assemblies and pangenome insights. Thank you! 🙌

Shout-out to the amazing teams behind @ebpgenome.bsky.social, @vertebrategenomes.bsky.social, @bat1kgenomes.bsky.social — and all those working to make high-quality genomes of new species openly available.
You’re fueling the future of comparative genomics!🔥

Huge thanks to the teams whose work inspired Gencube:
🔹 GenArk team (e.g., @genomebrowser.bsky.social )
🔹 Ensembl Beta ( @ensembl.bsky.social )
🔹 Zoonomia Consortium ( @elinork.bsky.social & @KltLab)
Your contributions to scalable infrastructure and comparative genomics were truly foundational. 🙏

🎥 Watch the YouTube walkthrough:
youtu.be/ftQP0_Dv9yI

If you're working with multi-omics data across species, we hope Gencube makes your workflow smoother and faster. Try it and let us know what you think!

📄 Read the full paper in Bioinformatics :
doi.org/10.1093/bioi...

🔧 GitHub (with step-by-step usage guide):
github.com/snu-cdrc/gen...

It also enables efficient discovery of research-ready NGS datasets and generates integrated metadata for immediate downstream use. No more hunting through multiple portals.

With a few simple commands, Gencube lets users download a wide range of genomics data types and automatically unify their formats — saving time and reducing preprocessing burden.

To address these challenges, we developed Gencube, a command-line tool that allows easy retrieval and integration of genome and omics data from major databases like UCSC GenArk, Ensembl Beta, Zoonomia TOGA, and more.

In addition, finding suitable NGS datasets and compiling clean, usable metadata for analysis often requires extensive manual effort. It’s inefficient and difficult to scale.

Data are spread across multiple databases, using different query systems and inconsistent chromosome naming schemes.
As a result, integrating datasets becomes a slow and error-prone process.

With sequencing costs dropping and computational methods advancing, genomic and multi-omics data across species is expanding rapidly.
The opportunities are enormous — but so are the challenges.