GROQ-seq Datasets Across Transcription Factors (LacI, RamR, VanR), T7 RNA Polymerase and TEV Protease

Published

April 19, 2026

Author(s)

Aviv Spinner, Shwetha Sreenivasan, James McLellan, Svetlana Ikonomova, Dana Cortade, Simon d'Oelsnitz, Kristen Sheldon, Olga Vasilyeva, Nina Alperovich, Anjali Chadha, Lily Nematollahi, Andi Dhroso, Zach Sisson, Corey Hudson, Erika DeBenedictis, Peter Kelly, Amanda Reider Apel, David Ross, Catherine Baranowski

Abstract

Predicting any protein's function from its sequence alone would be a significant breakthrough in molecular biology. Although machine learning approaches have sought to tackle this, their limited generalizability reflects the absence of sufficiently large, open, diverse, and unified datasets. To address this data gap, we developed a high-throughput experimental platform called GROQ-seq (Growth-based Quantitative Sequencing). In GROQ-seq, a protein's function can be linked to a sequencing-based readout that enables scalable characterization of large variant libraries in Escherichia coli. Here, we present pilot datasets demonstrating its performance across three distinct protein function classes: transcription factors, polymerases, and proteases. The objective of this report is to present the datasets and to provide users with a clear and transparent characterization of their properties, including both the strengths and limitations.

Citation

BioRxiv

Pub Weblink

https://www.biorxiv.org/

Pub Type

Websites

Keywords

AI-Ready Biological Data

Citation

Spinner, A. , Sreenivasan, S. , McLellan, J. , Ikonomova, S. , Cortade, D. , d'Oelsnitz, S. , Sheldon, K. , Vasilyeva, O. , Alperovich, N. , Chadha, A. , Nematollahi, L. , Dhroso, A. , Sisson, Z. , Hudson, C. , DeBenedictis, E. , Kelly, P. , Reider Apel, A. , Ross, D. and Baranowski, C. (2026), GROQ-seq Datasets Across Transcription Factors (LacI, RamR, VanR), T7 RNA Polymerase and TEV Protease, BioRxiv, [online], https://www.biorxiv.org/ (Accessed April 20, 2026)