Shaimae I. Elhajjajy

This paper introduces a linguistics-based algorithm for discovering RNA-binding protein motifs and their sequence contexts, showing improved accuracy over existing methods.

This paper introduces an NLP-based method to predict RBP binding motifs, contexts, and interactions, identifying novel motifs and RBP-RBP interactions in HepG2 and K562 cells.

The paper introduces a natural language-based approach to model RBP binding by decomposing RNA sequences into linguistic regions and using iterative relabeling for prediction.

SPARK generates simulated nascent RNA sequencing data to benchmark computational tools for analyzing transcriptional dynamics.

The paper expands the ENCODE Registry of candidate cis-regulatory elements (cCREs) to include 2.35 million human and 927 thousand mouse cCREs, revealing novel regulatory functions and identifying KLF1 as a causal gene for red blood cell traits.

Long-read sequencing of 81 human and mouse samples yields 200,000 full-length transcripts plus a triplet framework that separates promoter choice, splicing and 3' end use across tissues.

The paper generates a catalog of experimentally validated transcription start sites (TSSs) across 115 human tissues and cell types, updating TSS annotations for 20% of GENCODE genes and identifying new GWAS candidate genes.