Francisca Vázquez

Synergistic targeting of EP300/CBP and EYA co-activators collapses the rhabdomyosarcoma core regulatory circuit

The paper identifies a core regulatory circuit in rhabdomyosarcoma and shows that targeting two co-activators synergistically inhibits tumor growth.

Dissecting context-dependent cancer vulnerabilities using Perturb-seq

The paper uses Perturb-seq to analyze gene knockouts in cancer cell lines, identifying context-specific gene dependencies and transcriptional responses.

Tabular foundation model predicts alternative lengthening of telomeres (ALT) and identifies SMARCAL1 as a target in ALT-driven cancers

The paper develops ALTitude, a machine learning model to predict ALT status from whole genome sequencing data, identifies SMARCAL1 as a therapeutic target in ALT-driven cancers.

Generation of a biliary tract cancer cell line atlas reveals molecular subtypes and therapeutic targets

This study creates a biliary tract cancer cell line atlas using multi-omics data to identify molecular subtypes and therapeutic targets.

Controlling False Discovery in CRISPR Screens

This paper introduces Chronos, a module for estimating false discovery rates in CRISPR screens, showing it outperforms existing methods like MAGeCK in controlling false positives.

A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data

This study benchmarks seven computational methods for correcting CN and proximity biases in CRISPR-Cas9 screens, finding AC-Chronos and CRISPRcleanR perform best in different scenarios.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

CTAT-LR-fusion detects fusion transcripts from long and short read RNA-seq data, improving accuracy in bulk and single cell transcriptomes.

Chronos: a CRISPR cell population dynamics model

Chronos infers gene knockout fitness effects by explicitly modelling cell proliferation dynamics after knockout, using longitudinal readouts and correcting several known screen biases.

Predicting cell health phenotypes using image-based morphology profiling

The paper develops a method to predict cell health phenotypes from Cell Painting images using machine learning, validated across multiple perturbations and assays.

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

The paper improves gene dependency estimation from RNAi screens by using model-based normalization and data integration, enhancing accuracy and enabling cross-dataset analysis.

Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

The paper develops CERES, a computational method to correct copy-number effects in CRISPR-Cas9 essentiality screens, improving specificity in cancer cell line studies.