Juan Antonio Vizcaíno

The paper presents a high-quality acetylation dataset and shows that transfer learning can achieve good performance with minimal data for identifying PTMs.

The paper creates a high-confidence human methylation atlas and develops a deep learning model for methylated peptide detection.

The paper identifies 25% of 7,264 non-canonical open reading frames (ncORFs) as producing translated gene products, supported by over 3,000 peptides from 3.8 billion mass spectra.

This study characterizes the reuse of MetaboLights metabolomics datasets in the literature, finding most reuse is for method development and highlighting issues with inconsistent citation.

The paper analyzes proteomics dataset reuse using PRIDE download statistics and a semi-supervised LLM-based framework to classify download sources.

The paper maps the functional importance of site-specific ubiquitination across the human proteome by analyzing conservation, perturbation, and machine learning scores.

The paper expands and refines a reference catalog of human non-canonical open reading frames (ncORFs) by incorporating more datasets and introducing a translation signature score framework to prioritize high-quality ncORFs.

The paper assesses MS-based identifications of unannotated human microproteins, finding large variation in reported proteins and PSM quality across studies.

The paper reanalyzes public DIA proteomics datasets to provide a baseline view of human protein expression across tissues and compares it with previous DDA results.

The paper reanalyzes public pig proteomics data to map baseline protein expression across 14 organs and compares it with human orthologs.

The paper presents open-source software libraries for processing mzQC data in Python, R, and Java, enabling QC analysis and integration in mass spectrometry workflows.

WOMBAT-P benchmarks label-free proteomics workflows by comparing four analysis pipelines using standardized metrics and datasets.

This paper performs a meta-analysis of 11 phosphoproteomics datasets to identify phosphorylated sites in Plasmodium falciparum and related species, revealing sequence motifs, conservation patterns, and structural context of phosphosites.

This study analyzes rice phosphoproteomics data to identify phosphosite variations across the rice pan-genome and their functional implications.

The paper reanalyzes 24 public proteomics datasets to assess baseline protein abundance in 31 human organs and integrates the results into the Expression Atlas for combined proteomics and transcriptomics analysis.

The paper reanalyzes proteomics datasets from mouse and rat tissues to compare baseline protein expression across species and organs, finding high correlation among orthologs in brain, kidney, heart, and liver.

The paper introduces a pipeline to re-analyze public DIA proteomics datasets, integrating results into Expression Atlas to enhance data availability and utility.

The paper introduces a method to independently estimate the false localisation rate in phosphoproteomics using decoy amino acids, showing that alanine and leucine provide reliable results.

The paper introduces a standardized metadata format for proteomics datasets to improve reusability and integration across public repositories.

The paper introduces the Universal Spectrum Identifier (USI) to standardize referencing mass spectra in proteomics repositories, enabling transparent traceability of spectral evidence in publications.

This study compiles a proteomics resource for cancer cell lines and tumour samples, finding that protein abundance in cell lines often reflects tumours but fails to capture subtype-specific variations.

The paper introduces the BioContainers Registry and API to improve the findability, accessibility, interoperability, and reusability of bioinformatics tools and containers.

The paper assesses the value and sustainability of ELIXIR Core Data Resources, showing their critical role in preserving biological data and highlighting funding challenges.

The paper creates a comprehensive human phosphoproteome by integrating 6,801 phospho-enriched mass spectrometry experiments and prioritizes functional phosphosites using machine learning.

This paper explores how to quantify the impact of public omics data by examining usage patterns and the role of FAIR principles in data discovery and reuse.

The paper introduces PEFF, a new format for proteomics data that enhances FASTA with metadata for sequence variants and PTMs, enabling better analysis in proteogenomics and top-down proteomics.

The paper introduces two new data formats, proBAM and proBed, to integrate proteomics with genomics and transcriptomics data in proteogenomics studies.

The paper develops an efficient feature selection workflow and R package for high-dimensional omics data, evaluated on proteomics and gene expression datasets.

The paper introduces the Omics Discovery Index, a platform to search and link public omics datasets across different fields, addressing challenges in data organization and accessibility.