Jennifer Listgarten

This study shows that large-scale recombination can generate millions of catalytically competent PETases compatible with high-throughput screening.

This study investigates the trade-off between activity and specificity in adenine base editors using machine learning and high-throughput screening, finding that activity is the primary determinant of editing outcomes.

This paper introduces a 'write-and-read' framework using synthetic DNA modifications to enable nanopore sequencing to detect chemically altered nucleotides and expand the sequence alphabet.

The paper uses allelic inclusion data to train models that identify antibody sequence constraints, finding stronger correlations with antibody properties than existing methods.

GENTANGLE is a computational tool for designing overlapping gene pairs in microbial genomes, enabling safer genetic engineering through gene entanglements.

This paper introduces a new method for differential sequencing analysis that improves accuracy by better utilizing sequencing data through model-based enrichment.

The paper studies the sparsity of fitness functions in biological sequences and develops a framework to estimate the number of samples needed for accurate recovery.

This paper introduces a method to improve deep neural network predictions of fitness functions by leveraging sparse epistatic interactions in genetic sequences.

The paper demonstrates that a hybrid HMM-DNN approach outperforms both pure HMM and DNN methods for 5mC calling with incomplete training data.

The paper evaluates methods for predicting protein fitness using both evolutionary and assay-labelled data, finding a simple baseline approach competitive with more complex methods.

This paper introduces a machine learning model, Elevation, for predicting CRISPR-Cas9 off-target effects, which outperforms existing methods and provides a new evaluation metric.