David Ross

The paper introduces GROQ-seq, a high-throughput platform for linking protein function to sequencing data, and presents pilot datasets for transcription factors, polymerases, and proteases.

This study demonstrates that GROQ-seq provides reproducible, scalable measurements of protein function across different facilities.

The paper benchmarks sequence- and structure-based protein models using a unified framework, showing structure-based models outperform others in generating functional variants.

The paper uses GROQ-seq to profile function across a diverse protease library, showing robust activity across sequence divergence.

evedesign provides a unified framework for conditional biosequence design, enabling multiobjective workflows and experimental integration across various applications.

The paper uses Deep Mutational Scanning and Growth-based Quantitative Sequencing to analyze CymR variants and identify amino acids essential for repressor function and mutations that improve ligand sensitivity.

The paper compares biophysical and phenomenological models to predict epistasis in allosteric proteins, finding biophysical models better capture mutational effects with less epistasis.

Ligify predicts transcription factors responsive to user-defined chemicals and generates plasmids for biosensor development, achieving 31/100 correct predictions in benchmarking.

The paper presents two methods for precision engineering of genetic sensors using large-scale data and machine learning to achieve quantitative dose-response specifications.

LANTERN is a hierarchical Bayesian model that improves interpretability and predictive power in genotype-phenotype landscape analysis, outperforming alternatives across benchmark datasets.

This paper introduces a flow-FISH assay for high-throughput, in situ measurement of RNA degradation rates without RNA purification.

FlowGateNIST automates bacterial flow cytometry data analysis by gating cells, distinguishing singlets from multiplets, and calibrating fluorescence signals.

Dose-response curves for 62 472 LacI coding sequences train a recurrent network for EC50 and G∞, enable EC50 targeting within 1.25-fold, and reveal band-stop sensors.

The paper shows that adapting bacterial strains to growth conditions improves the stability of engineered functions across generations without genomic changes.