Klara Kuret

PARNET: A CLIP-SEQ-BASED FOUNDATION MODEL FOR RNA SEQUENCE REPRESENTATION LEARNING

Parnet is a CLIP-seq-based foundation model for RNA sequence representation learning that outperforms existing methods in binding profile prediction and generalizes across tasks.

Mutual homeostasis of charged proteins

The paper identifies a homeostatic mechanism called interstasis that co-regulates the dosage of proteins with low complexity domains through RNA-based negative feedback.

Flow: a web platform and open database to analyse, store, curate and share bioinformatics data at scale

Flow provides a web platform and database for managing and analyzing large-scale bioinformatics data, addressing challenges in data processing, quality, and accessibility.

Towards In-Silico CLIP-seq: Predicting Protein-RNA Interaction via Sequence-to-Signal Learning

RBPNet predicts CLIP crosslink count distributions from RNA sequences, outperforming existing methods and enabling variant impact scoring.

Positional motif analysis reveals the extent of specificity of protein-RNA interactions observed by CLIP

The paper introduces PEKA, a tool for analyzing CLIP data to assess motif specificity, and shows that technical biases affect motif detection.