Sumaiya Iqbal

The yEvo Mutation Browser helps students and researchers visualize and interpret genome sequencing data from experimental evolution studies of yeast.

This paper introduces a framework for comparing embedding spaces in molecular biology, demonstrating its utility in pre-filtering machine learning tasks and identifying differential patterns across embeddings.

This study identifies a therapeutic target, the entrapment complex, that clears toxic protein accumulation in distinct proteinopathies by disrupting a cargo receptor complex.

PFES quantifies the protein context of missense variants, enabling mechanistically transparent interpretation and reclassification of variants of uncertain significance.

MAVISp provides a modular structural framework for analyzing protein variant effects, offering over eight million variants across 1000 proteins with curated data and a web server for accessibility.

The paper maps MAVE data to human reference sequences to improve variant functional assay utility for genomics applications.

The paper introduces the Genomics 2 Proteins Portal, which links genetic variants to protein sequences and structures to aid in understanding disease mechanisms and therapeutic design.

This study investigates how different GRIN2A mutations linked to schizophrenia, epilepsy, and developmental disorders affect NMDA receptor function, revealing distinct functional consequences.

The paper characterizes proteomic and transcriptomic features of missense variants in ALS genes and identifies enriched structural and functional properties associated with pathogenicity.

The paper analyzes GABRB3 variants in a cohort of 71 individuals to identify genotype-phenotype correlations, finding associations between variant locations and specific clinical features.

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

The paper predicts functional effects of missense variants in voltage-gated sodium and calcium channels using machine learning, achieving high accuracy in distinguishing gain and loss of function variants.

The paper analyzes missense variants in 1,330 disease genes using 3D protein structures to identify mutational hotspots and their functional impact.

The paper identifies regions in protein sequences enriched for pathogenic variants using gene family alignments and demonstrates their enrichment in neurodevelopmental disorder de novo variants and ClinVar pathogenic classifications.