Evidence map›Paper›PMID 42817935›Full record

ArticlebioRxiv : the preprint server for biology2026

EPIC: An open community challenge for sequence-based prediction of transcription initiation in five non-model metazoans.

Ilya E Vorontsov, Nikita Gryzunov, Bayley R McDonald, Vasily Kamenets, Timur Salimgareev, Ivan Kozin, EPIC Consortium, Ashley M Conard, Dmitry Penzar, Ivo Grosse and 4 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Ilya E VorontsovVavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
Nikita GryzunovInstitute of Protein Research, Russian Academy of Sciences, Pushchino, Russia.
Bayley R McDonaldSchool of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, WA, USA.
Vasily KamenetsVavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
Timur SalimgareevHigher School of Economics, Moscow, Russia.
Ivan KozinVavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
EPIC Consortium
Ashley M ConardMicrosoft Research, Cambridge, MA, USA.
Dmitry PenzarVavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
Ivo GrosseInstitute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany.
Vsevolod J MakeevCancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK.
Philipp BucherSwiss Institute of Bioinformatics, Lausanne, Switzerland.
Ivan V KulakovskiyVavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.
Sascha H DuttkeSchool of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Pullman, WA, USA.ORCID 0000-0003-2551-3059

Funding

Exploiting Natural Genetic and Organismic Variation to Identify the DNA Motifs Regulating TranscriptionR00GM135515 · NIGMS · WASHINGTON STATE UNIVERSITY · PI DUTTKE, SASCHA H. · 2022 to 2024
$747k
NIGMS NIH HHS R00 GM135515
6 · The paper itself

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.

Indexed as

community benchmarkcsRNA-seqpromotersequence-to-function modelstranscription initiation

Identifiers

PMID42817935
PMCPMC13621998

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.