Omar Wagih

Mutational signatures in cancer genomes alter protein sequence motifs in cellular signaling networks

The paper identifies mutational signatures in cancer genomes that alter protein sequence motifs in signaling networks, linking mutation etiology to functional consequences.

Improved cloning-free one-step CRISPR-Cas12a-assisted tagging of mammalian genes using PCR generated reagents (PCR tagging)

The paper improves CRISPR-Cas12a-assisted gene tagging in mammalian cells by enhancing HDR efficiency and reducing off-target integrations through modified cassettes and repair pathway modulation.

Sequence based prediction of cell type specific microRNA binding and mRNA degradation for therapeutic discovery

This paper introduces REPRESS, a deep learning model that predicts cell-type-specific microRNA binding and mRNA degradation from RNA sequences, outperforming existing methods in multiple tasks.

An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics

The paper introduces BigRNA, an RNA foundation model that predicts RNA biology from DNA sequence and identifies therapeutic effects of SBOs on gene expression and splicing.

A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect prediction

The paper curates a high-confidence set of pathogenic and likely pathogenic UTR variants and evaluates deep learning models for predicting their functional impact.

Capturing variation impact on molecular interactions: the IMEx Consortium mutations data set

The paper presents a dataset of 28,000 annotations describing how amino acid mutations affect protein interactions, available through the IntAct website.

Comprehensive variant effect predictions of single nucleotide variants in model organisms

The paper benchmarks variant effect predictors and analyzes SNVs in H. sapiens, S. cerevisiae, and E. coli to predict molecular effects and generate protein complex burden scores.

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

The paper benchmarks variant impact predictors using allele-specific binding ChIP-seq data, finding deep learning methods perform best but have room for improvement.

Phenotype prediction in an Escherichia coli strain panel

The paper predicts growth defects in Escherichia coli strains using genetic variant impact models and gene function data, achieving reliable predictions for up to 38% of tested conditions.

Systematic analysis of transcriptional and post-transcriptional regulation of metabolism in yeast

This study integrates metabolomics, phosphoproteomics, and transcriptomics data in yeast to identify regulatory mechanisms linking signaling kinases to metabolic changes.