Liana F. Lareau

This study identifies Gim3 as a critical regulator of meiotic chromosome segregation in budding yeast, revealing its role in spindle sensitivity to tubulin levels and meiotic cellular remodeling.

This paper develops yeast-selected SpCas9 variants with re-specified PAM recognition to enable allele-specific editing, demonstrating improved specificity and catalytic efficiency compared to PAM-broadened variants.

The paper identifies a gene expression program in active neurons that supports sustained network activity, distinct from immediate early gene responses and enriched for excitability regulators.

The paper shows that slow translation elongation reduces protein output per mRNA, independent of mRNA stability, and demonstrates a dynamic balance between initiation and elongation rates.

The paper uses protein language models to uncover evolutionary constraints on synonymous codon choice in yeast by combining sequence analysis with experimental validation.

The paper identifies polyadenylation as a mechanism driving SINE RNA accumulation during gammaherpesvirus infection, linking Pol III transcription to mRNA-like processing.

This paper introduces Trias, a generative language model that designs mRNAs with high protein output by learning codon usage rules from natural sequences.

The paper introduces a streamlined ribosome profiling method using P1 nuclease and Ordered Two-Template Relay, improving sensitivity and reducing bias in yeast and human cells.

Choros corrects sequence-based biases in ribosome profiling data to provide more accurate quantification of translation dynamics.

The paper introduces Psix, a method to identify cell-state associated alternative splicing events using scRNA-seq data, revealing co-regulated splicing modules and their regulatory networks.

riboviz 2 provides a flexible and robust workflow for analyzing and visualizing Ribo-seq data with improved functionality and usability.

The paper shows that apparent binary splicing in single cells is caused by technical biases, not biological reality, and proposes methods to correct for these biases.

The paper introduces a neural network model to predict translation elongation rates and validates it by designing synonymous variants that match predicted protein output in yeast.