EPIC: An open community challenge for sequence-based prediction of transcription initiation in five non-model metazoans
Year: 2026
Type: preprint
Abstract: Predicting gene expression from DNA sequence is a central problem with critical biological and clinical implications. Recent sequence-to-function models are reported to achieve improved performance, yet it remains unclear how much of what they learn reflects genuine principles of eukaryotic transcription initiation and to what extent they are able to generalize beyond humans and other primary model species. A fair and blind benchmark has likewise been missing. Here we introduce EPIC, the Eukaryotic Promoter and transcription Initiation prediction Challenge. Teams receive strand-specific, single-nucleotide-resolution initiation profiles for 80-95% of the genome and are asked to predict the remainder from DNA sequence alone. To level the field, the challenge relies on understudied animals spanning three phyla: octopus, oyster, milkweed bug, Indian meal moth, and shark. EPIC is open to everyone and closes on December 31, 2026. All teams clearing the dinucleotide precision baseline are invited to join the consortium authorship of the post-challenge publication, and winners are invited for personal authorship.
Institutions: Russian Academy of Sciences, Vavilov Institute of General Genetics, Institute of Protein Research, Washington State University, National Research University Higher School of Economics, Microsoft Research New England (United States), Martin Luther University Halle-Wittenberg, University of Manchester, SIB Swiss Institute of Bioinformatics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214242152