CPpab

CPpab, short for Comparative Protein Profiling and Benchmarking, is a framework for standardizing the evaluation of proteomics workflows. It provides an open specification for data formats, benchmarking protocols, and performance metrics used to compare methods for protein identification, quantification, and annotation across laboratories and studies.

Origins and scope: CPpab was proposed by an international consortium of proteomics researchers in 2019 to address reproducibility and comparability challenges in high-throughput protein analysis. The initial release, CPpab 1.0, defined a core data schema (CPpab-DS), a set of evaluation rules (CPpab-Metrics), and reference datasets (CPpab-Ref) to enable consistent benchmarking.

Architecture and interoperability: The framework is modular, with components for data representation, benchmarking workflows, and scoring engines. It is designed to interoperate with established formats such as mzML, mzIdentML, and FASTA, and supports extension via plugins for alternative scoring functions, normalization schemes, and multi-omics data integration.

Impact and adoption: CPpab has been adopted by several research consortia and academic labs to compare proteomics pipelines, quantify improvements in reproducibility, and guide method selection. Proponents emphasize transparency and reproducibility, while critics note the ongoing need for diverse, high-quality reference datasets and careful interpretation of benchmark results.

Limitations and governance: Ongoing governance aims to converge on consensus metrics and data models. Users must ensure input data quality and be aware of potential biases introduced by dataset selection or processing pipelines.

See also: proteomics, benchmarking, data standards, open science.