About

Liana Lareau is an Associate Professor of Bioengineering and Molecular and Cell Biology (MCB) at the University of California, Berkeley. Her research focuses on understanding how post-transcriptional processes lead to robust and flexible control of gene expression. Her lab employs computational methods and high-throughput experiments to investigate how layers of regulation are encoded in gene sequences and how disruptions to this regulation can cause disease. She uses machine learning and other computational techniques, coupled with experimental approaches, to elucidate the mechanisms of post-transcriptional regulation. Her work aims to deepen the understanding of gene expression regulation at the post-transcriptional level, contributing to insights into disease mechanisms and potential therapeutic targets. She is involved in training students, including DE students Maria McSharry and Karinna Vivanco, as well as PhD students Helen Sakharova, Prakruthi Burra, Carmelle Catamura, Sahil Shah, and Rebecca Eliscu.

Research topics

• Molecular biology
• Chromatography
• Physics
• Chemistry
• Virology
• Biology
• Biochemistry

Selected publications

• Translation elongation as a rate-limiting step of protein production

  Cell Systems · 2026-02-01

  articleSenior author
  PublisherDOI

• RNA polymerase III transcription-associated polyadenylation promotes the accumulation of noncoding retrotransposons during infection

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-16 · 1 citations

  preprintOpen access

  The accumulation of RNA Polymerase III (Pol III) transcribed short interspersed nuclear element (SINE) retrotransposon RNA is a hallmark of various cellular stressors, including DNA virus infection. However, the molecular mechanisms driving the induction of these normally repressed loci are largely undefined. Here, we reveal that in addition to Pol III transcriptional induction, gammaherpesvirus infection stimulates mRNA-like 3' end processing of SINE RNAs that leads to their stabilization. We developed a convolutional neural network (CNN)-based model that identified a polyadenylation-associated motif as the key hallmark of infection-induced SINEs. Indeed, mRNA polyadenylation machinery is recruited in a Pol III-dependent manner to virus-induced loci, including B2 SINE and tRNA genes. Infection causes enhanced polyadenylation of SINE ncRNA, which is required for its stable accumulation. This virus-host interaction therefore highlights an inducible, coupled relationship between Pol III transcription and mRNA-like polyadenylation. It also reveals that co-option of the polyadenylation machinery by Pol III is a mechanism to increase the abundance of noncoding RNA during pathogenic stress. SIGNIFICANCE: Short interspersed nuclear elements (SINEs) are hyperabundant and transcribed by RNA polymerase III (Pol III) to produce noncoding retrotransposons. Although generally not detectable in healthy somatic cells, SINE RNA expression is upregulated during stress, including viral infection and inflammatory diseases. We used gammaherpesvirus infection to uncover pathways leading to increased SINE RNA expression. Using a newly developed deep learning model and genomics analyses, we reveal that infection-induced accumulation of SINEs is driven by increased Pol III transcription and Pol III-dependent recruitment of polyadenylation machinery. This stimulates polyadenylation of SINEs, which is a known stabilizer of these noncoding transcripts. Our findings suggest that inducible alterations to Pol III transcript 3' end processing modulate the abundance of noncoding retrotransposons during pathogenic stress.

  PublisherOA PDFDOI

• Protein language models reveal evolutionary constraints on synonymous codon choice

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-05

  preprintOpen accessSenior authorCorresponding

  Evolution has shaped the genetic code, with subtle pressures leading to preferences for some synonymous codons over others. Codons are translated at different speeds by the ribosome, imposing constraints on codon choice related to the process of translation. The structure and function of a protein may impose pressure to translate the associated mRNA at a particular speed in order to enable proper protein production, but the molecular basis and scope of these evolutionary constraints have remained elusive. Here, we show that information about codon constraints can be extracted from protein sequence alone. We leverage a protein language model to predict codon choice from amino acid sequence, combining implicit information about position and protein structure to learn subtle but generalizable constraints on codon choice in yeast. In parallel, we conduct a genome-wide screen of thousands of synonymous codon substitutions in endogenous loci in yeast, reliably identifying a small set of several hundred synonymous variants that increase or decrease fitness while showing that most positions have no measurable effect on growth. Our results suggest that cotranslational localization and translational accuracy, more than cotranslational protein folding, are major drivers of selective pressure on codon choice in eukaryotes. By considering both the small but wide-reaching effects of codon choice that can be learned from evolution and the strong but highly specific effects determined via experiment, we expose unappreciated biological constraints on codon choice.

  PublisherOA PDFDOI

• RNA polymerase III transcription-associated polyadenylation promotes the accumulation of noncoding retrotransposons during infection.

  PubMed · 2025-08-12 · 1 citations

  articleOpen access

  The accumulation of RNA Polymerase III (Pol III) transcribed short interspersed nuclear element (SINE) retrotransposon RNA is a hallmark of various cellular stressors, including DNA virus infection. However, the molecular mechanisms driving the induction of these normally repressed loci are largely undefined. Here, we reveal that in addition to Pol III transcriptional induction, gammaherpesvirus infection stimulates mRNA-like 3' end processing of SINE RNAs that leads to their stabilization. We developed a convolutional neural network (CNN)-based model that identified a polyadenylation-associated motif as the key hallmark of infection-induced SINEs. Indeed, mRNA polyadenylation machinery is recruited in a Pol III-dependent manner to virus-induced loci, including B2 SINE and tRNA genes. Infection causes enhanced polyadenylation of SINE ncRNA, which is required for its stable accumulation. This virus-host interaction therefore highlights an inducible, coupled relationship between Pol III transcription and mRNA-like polyadenylation. It also reveals that co-option of the polyadenylation machinery by Pol III is a mechanism to increase the abundance of noncoding RNA during pathogenic stress.

  PublisherOA PDFDOI

• A generative language model decodes contextual constraints on codon choice for mRNA design

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-13 · 5 citations

  preprintOpen accessSenior authorCorresponding

  The genetic code allows multiple synonymous codons to encode the same amino acid, creating a vast sequence space for protein-coding regions. Codon choice can impact mRNA function and protein output, a consideration newly relevant with advances in mRNA technology. Genomes preferentially use some codons, but simple optimization methods that select preferred codons miss complex contextual patterns. We present Trias, an encoder-decoder language model trained on millions of eukaryotic coding sequences. Trias learns codon usage rules directly from sequence data, integrating local and global dependencies to generate species-specific codon sequences that align with biological constraints. Without explicit training on protein expression, Trias generates sequences and scores that correlate strongly with experimental measurements of mRNA stability, ribosome load, and protein output. The model outperforms commercial codon optimization tools in generating sequences resembling high-expression codon sequence variants. By modeling codon usage in context, Trias offers a data-driven framework for synthetic mRNA design and for understanding the molecular and evolutionary principles behind codon choice.

  PublisherOA PDFDOI

• Translation elongation as a rate limiting step of protein production

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-11-28 · 18 citations

  preprintOpen accessSenior authorCorresponding

  The impact of synonymous codon choice on protein output has important implications for understanding endogenous gene expression and design of synthetic mRNAs. Synonymous codons are decoded at different speeds, but simple models predict that this should not drive protein output. Instead, translation initiation should be the rate limiting step for production of protein per mRNA, with little impact of codon choice. Previously, we used a neural network model to design a series of synonymous fluorescent reporters and showed that their protein output in yeast spanned a seven-fold range corresponding to their predicted translation elongation speed. Here, we show that this effect is not due primarily to the established impact of slow elongation on mRNA stability, but rather, that slow elongation further decreases the number of proteins made per mRNA. We combine simulations and careful experiments on fluorescent reporters to show that translation is limited on non-optimally encoded transcripts. Using a genome-wide CRISPRi screen, we find that impairing translation initiation attenuates the impact of slow elongation, showing a dynamic balance between rate limiting steps of protein production. Our results show that codon choice can directly limit protein production across the full range of endogenous variability in codon usage.

  PublisherOA PDFDOI

• Streamlined and sensitive mono- and di-ribosome profiling in yeast and human cells

  Nature Methods · 2023-10-02 · 59 citations

  articleOpen access
  PublisherOA PDFDOI

• choros: correction of sequence-based biases for accurate quantification of ribosome profiling data

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-02-22 · 5 citations

  preprintOpen accessSenior authorCorresponding

  Ribosome profiling quantifies translation genome-wide by sequencing ribosome-protected fragments, or footprints. Its single-codon resolution allows identification of translation regulation, such as ribosome stalls or pauses, on individual genes. However, enzyme preferences during library preparation lead to pervasive sequence artifacts that obscure translation dynamics. Widespread over- and under-representation of ribosome footprints can dominate local footprint densities and skew estimates of elongation rates by up to five fold. To address these biases and uncover true patterns of translation, we present choros, a computational method that models ribosome footprint distributions to provide bias-corrected footprint counts. choros uses negative binomial regression to accurately estimate two sets of parameters: (i) biological contributions from codon-specific translation elongation rates; and (ii) technical contributions from nuclease digestion and ligation efficiencies. We use these parameter estimates to generate bias correction factors that eliminate sequence artifacts. Applying choros to multiple ribosome profiling datasets, we are able to accurately quantify and attenuate ligation biases to provide more faithful measurements of ribosome distribution. We show that a pattern interpreted as pervasive ribosome pausing near the beginning of coding regions is likely to arise from technical biases. Incorporating choros into standard analysis pipelines will improve biological discovery from measurements of translation.

  PublisherOA PDFDOI

• Streamlined and sensitive mono- and diribosome profiling in yeast and human cells

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-02-03 · 6 citations

  preprintOpen access

  Abstract Ribosome profiling has unveiled diverse regulations and perturbations of translation through a transcriptome-wide survey of ribosome occupancy, read out by sequencing of ribosome-protected mRNA fragments. Generation of ribosome footprints and their conversion into sequencing libraries is technically demanding and sensitive to biases that distort the representation of physiological ribosome occupancy. We address these challenges by producing ribosome footprints with P1 nuclease rather than RNase I and replacing RNA ligation with Ordered Two-Template Relay, a single-tube protocol for sequencing library preparation that incorporates adapters by reverse transcription. Our streamlined approach reduced sequence bias and enhanced enrichment of ribosome footprints relative to ribosomal RNA. Furthermore, P1 nuclease preserved a myriad of distinct juxtaposed ribosome complexes informative about yeast and human ribosome fates during translation initiation, stalling, and termination. Our optimized methods for mRNA footprint generation and capture provides a richer translatome profile using lower input and fewer technical challenges.

  PublisherOA PDFDOI

• riboviz 2: a flexible and robust ribosome profiling data analysis and visualization workflow

  Bioinformatics · 2022-02-09 · 9 citations

  articleOpen accessCorresponding

  MOTIVATION: Ribosome profiling, or Ribo-seq, is the state-of-the-art method for quantifying protein synthesis in living cells. Computational analysis of Ribo-seq data remains challenging due to the complexity of the procedure, as well as variations introduced for specific organisms or specialized analyses. RESULTS: We present riboviz 2, an updated riboviz package, for the comprehensive transcript-centric analysis and visualization of Ribo-seq data. riboviz 2 includes an analysis workflow built on the Nextflow workflow management system for end-to-end processing of Ribo-seq data. riboviz 2 has been extensively tested on diverse species and library preparation strategies, including multiplexed samples. riboviz 2 is flexible and uses open, documented file formats, allowing users to integrate new analyses with the pipeline. AVAILABILITY AND IMPLEMENTATION: riboviz 2 is freely available at github.com/riboviz/riboviz.

  PublisherOA PDFDOI

Recent grants

• A computational framework for statistical analysis of ribosome profiling data

  NIH · $366k · 2015–2018

• Determinants and consequences of translation elongation rate

  NIH · $1.6M · 2019–2025

Frequent coauthors

• Jennifer A. Doudna

  University of California, Berkeley

  25 shared

• Richard E. Green

  University of California, Santa Cruz

  23 shared

• Mélanie Ott

  Gladstone Institutes

  21 shared

• Gavin J. Knott

  Monash University

  19 shared

• Nir Yosef

  University of California, Berkeley

  15 shared

• Noam Prywes

  14 shared

• Eli Dugan

  University of California, Berkeley

  14 shared

• G. Renuka Kumar

  Gladstone Institutes

  13 shared

Labs

• Liana LareauPI