Aradhana

An autonomous system for multi-objective continuous evolution at scale

TurboPRANCE enables large-scale, multi-objective continuous evolution by integrating robotic systems with phage-assisted evolution, generating high-resolution evolutionary data across diverse selection regimes.

Towards optimizing diversifying base editors for high-throughput studies of single-nucleotide variants

The paper evaluates how base editor design features affect editing efficiency and mutation patterns in mammalian cells, identifying DivA-BE as the most effective variant.

Development of compact transcriptional effectors using high-throughput measurements in diverse contexts

The paper develops HT-recruit to measure transcriptional effector function across contexts and identifies context-dependent effectors for CRISPR-based transcription control.

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning

The paper predicts and designs transcriptional repressor domains using mutational scans and a deep learning model called TENet, identifying functional variants and enabling synthetic design.

Large-scale mapping and systematic mutagenesis of human transcriptional effector domains

The paper systematically maps and characterizes transcriptional effector domains in human chromatin regulators and transcription factors, identifying new activation and repression domains and their functional features.

High-throughput discovery and characterization of human transcriptional effectors

The paper develops HT-recruit, a high-throughput assay to identify transcriptional effectors in human proteins, finding repressor function correlates with evolutionary age and discovering new activator domains.

Diverse transcriptomic signatures across human tissues identify functional rare genetic variation

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

Identification and mitigation of pervasive off-target activity in CRISPR-Cas9 screens for essential non-coding elements

The paper identifies off-target effects in CRISPR-Cas9 screens for non-coding elements and shows that strict filtering with specificity scores improves the identification of essential regulatory regions.