Gian Gaetano Tartaglia

HERCULES predicts RNA-binding regions and mutation effects using a deep-learning framework combining language models and physicochemical descriptors.

PHANES provides a comprehensive, interpretable database of protein features for the human proteome, integrating sequence, structure, and expression data to support biological analysis and machine learning.

The paper describes a synthetic circRNA-derived IRES-like element, SynFarsa, that enhances translation of an endogenous protein, HSP90, in trans by targeting its 3'UTR.

The paper identifies RSX interactors and shows they form phase-separation-prone networks, suggesting convergent evolution of XCI mechanisms through biophysical principles rather than sequence or structure.

This study investigates CHD8's role in female neuronal differentiation using multi-omics analysis, revealing its impact on chromatin structure and gene regulation in mouse models.

scRAPID-web predicts protein-RNA interactions from scRNA-seq data across eight organisms, offering GRN inference and visualization tools.

This study shows that LBR nucleoplasmic domains regulate X-chromosome solubility and nuclear organization during neural differentiation in mouse cells.

The paper shows that amygdala TDP-43 pathology correlates with behavioural dysfunction and ferritin accumulation in sporadic ALS.

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

The paper develops a computational framework to analyze IRES function by combining mutagenesis and RNA-protein interaction profiling, revealing structural and protein dependency differences between viral and cellular IRESs.

This study shows RNA binding alters the structure, function, and stability of alpha-synuclein fibrils, suggesting RNA as an active co-factor in amyloid formation.

The paper introduces a spatial long-read sequencing method to study developmental splicing and polyadenylation changes in cortical layers and cell types, finding layer-specific and age-related regulatory patterns.

The paper identifies PCBP2 as an inhibitor of SYNCRIP-mediated miRNA loading into EVs through direct interaction with an intracellular retention motif.

This study uses RNA aptamers to detect pathological FUS in ALS, showing improved specificity and sensitivity compared to antibodies.

This study investigates how RNA affects alpha-synuclein phase separation, showing RNA induces morphological changes and promotes liquid-to-solid transition.

This paper introduces catGRANULE 2.0 ROBOT, an algorithm predicting protein LLPS at single amino acid resolution, showing high performance and experimental validation.

This paper identifies low-complexity repeats as key drivers of RNA-RNA interactions and introduces RIME, a deep learning model that outperforms traditional methods in predicting these interactions.

This paper designs aptamers targeting RAD51-BRCA2 interaction to inhibit RAD51 nuclear recruitment, enhancing DNA damage and cytotoxicity in pancreatic cancer cells.

The paper investigates how RNA modifications affect protein-RNA interactions and introduces an algorithm to predict these effects.

The paper uses in silico analysis to explore interactions between flaviviral subgenomic RNAs and human proteins, identifying novel host factors involved in viral evasion.

This study compares machine learning methods for classifying multiple sclerosis and Alzheimer's disease using genomic data from UK Biobank, finding logistic regression is most robust while deep learning shows high variability.

This study uses patient-derived iPSCs with a GNAO1 variant to show altered neuronal differentiation and function in cortical neurons.

This study develops scRAPID, a pipeline that predicts protein-RNA interactions from single-cell transcriptomic data, showing comparable or better performance than transcription factor-target interaction prediction.

This study uses an RNA aptamer to detect TDP-43 aggregation and shows that nuclear TDP-43 pathology and STMN-2 cryptic splicing occur before clinical symptoms in ALS.

DEWSeq improves eCLIP data analysis by increasing the detection of RNA-binding protein binding sites and their functional elements.

This study identifies MOK as a kinase regulating microglial inflammatory responses and type-I IFN through Brd4, showing its involvement in ALS pathogenesis.

This study identifies SINEUP RNAs with Internal Ribosomal Entry Site (IRES) sequences as effector domains, enhancing translation of target mRNAs through both linear and circular transcripts.

The paper identifies and predicts RNA G-quadruplex-binding proteins, showing their roles in transcription and phase separation, and provides a web tool for prediction.

ORC1 binds to cis-transcribed RNAs from genes with replication origins at TSSs, which is required for optimal origin activation.

PRALINE is a database that catalogs proteins and RNAs in biological condensates, their interactions, and the impact of single-nucleotide variants on condensate properties.