Eduardo Eyras

This study identifies three distinct spliceosomic profiles in pancreatic neuroendocrine tumors linked to clinical and molecular characteristics and potential therapeutic targets.

RICO quantifies rDNA copy number and methylation using nanopore sequencing, showing accurate results in simulations and human/mouse samples, and revealing correlations between rDNA CN and methylation.

ModkitOpt optimizes RNA modification site calling by selecting the best parameters and stoichiometry cutoff for nanopore sequencing data, improving precision and recall across modification types.

SWARM improves detection of RNA modifications by reducing false positives and reveals new splicing-related pseudouridylation patterns.

The paper develops Nano-Mod-Amp, a Nanopore sequencing tool, to analyze PUS7-dependent pseudouridylation, revealing structural and cell-type specific regulatory mechanisms.

This study directly sequences pre-mRNA to resolve transcription and processing at single-molecule resolution, revealing delayed splicing and m6A methylation preceding splicing.

This study shows PMR-116, a second-generation RiBi inhibitor, effectively targets high-MYC cancers without causing DNA damage, suggesting RiBi as a viable cancer therapy.

The paper introduces DTI, a new RNA integrity metric based on nanopore sequencing, and the INDEGRA package to correct biases and enable accurate transcriptome comparisons.

The paper presents a long-read RNA sequencing atlas across mammals, revealing conserved transcript complexity and m6A modifications, and their evolutionary roles.

R2Dtool integrates and visualizes isoform-resolved RNA features using transcript and genome annotations to enable comprehensive analysis of RNA modifications and structures.

The paper develops a machine learning model using transcriptomic data to predict chemotherapy response in muscle-invasive bladder cancer with high accuracy.

RISER enables real-time RNA class enrichment or depletion during direct RNA sequencing, improving detection of low-abundance RNAs by reducing dominant RNA reads.

This study uses nanopore adaptive sampling to profile age and body fluid DNA methylation markers, identifying new age-correlated sites and validating known markers.

This paper introduces a language-informed basecalling method for Nanopore direct RNA sequencing, showing that incorporating mRNA sequence biases improves basecalling accuracy.

RATTLE reconstructs and quantifies transcripts from Nanopore sequencing without a reference genome, showing accuracy and scalability across simulated and experimental data.

CHEUI predicts m6A and m5C modifications at single-molecule resolution, revealing co-occurrence of these modifications in mRNAs across cell lines and tissues.

The paper introduces a method to quantify protein synthesis rates by analyzing ribosome co-localization dynamics on mRNA.

ISOTOPE identifies tumor-specific splicing-derived epitopes using RNA sequencing and mass spectrometry, showing splicing neoepitopes have higher MHC-I binding affinity in immune responders.

The paper benchmarks CpG methylation detection tools from Nanopore sequencing, finding tradeoffs in accuracy and introducing a consensus method, METEORE, to improve results.

PACIFIC detects SARS-CoV-2 and co-infecting RNA viruses in RNA-seq data with high precision and recall across multiple datasets.

The paper introduces ORQAS, a pipeline for quantifying translation of transcript isoforms using ribosome profiling, and finds that differential splicing impacts translation across species.

ReorientExpress is a reference-free tool that uses deep learning to orient nanopore cDNA reads, improving transcriptome analysis without requiring a genome or transcriptome reference.

SARNAclust identifies RNA motifs from immunoprecipitation data by combining structure/sequence analysis, revealing known and novel motifs including a new ILF3 motif similar to hnRNPC.

The paper introduces SeRPeNT, a method to discover and characterize small non-coding RNAs using RNA processing profiles, identifying new sncRNAs and differential processing patterns.

The paper analyzes transcript isoform abundance across 12 tumors to build prognostic signatures, finding they predict tumor stage and survival better than gene expression signatures.

The paper analyzes genome and transcriptome alterations in multiple tumors to identify novel cancer-relevant splicing networks involving RNA-binding proteins and alternative splicing changes.

SUPPA computes alternative splicing profiles quickly and accurately using fast transcript quantification, outperforming standard methods in speed while maintaining comparable accuracy.

The paper introduces a semi-supervised method to identify intragenic enhancers differentially active in human cell lines and predicts thousands of such enhancers with functional properties.