SMAP: A pipeline for sample matching in proteogenomics

Abstract

Integration of genomics and proteomics (proteogenomics) offers unprecedented promise for in-depth understanding of human diseases. However, sample mix-up is a pervasive, recurring problem, due to complex sample processing in proteogenomics. Here we present a pipeline for Sample Matching in Proteogenomics (SMAP) for verifying sample identity to ensure data integrity. SMAP infers sample-dependent protein-coding variants from quantitative mass spectrometry (MS), and aligns the MS-based proteomic samples with genomic samples by two discriminant scores. Theoretical analysis with simulation data indicates that SMAP is capable of uniquely match proteomic and genomic samples, when [≥]20% genotypes of individual samples are available. When SMAP was applied to a large-scale proteomics dataset from 288 biological samples generated by the PsychENCODE BrainGVEX project, we identified and corrected 18.8% (54/288) mismatched samples. The correction was further confirmed by ribosome profiling and assay for transposase-accessible chromatin sequencing data from the same set of samples. Thus our results demonstrate that SMAP is an effective tool for sample verification in a large-scale MS-based proteogenomics study. The source code, manual, and sample data of the SMAP are publicly available at https://github.com/UND-Wanglab/SMAP, and a web-based SMAP can be accessed at https://smap.shinyapps.io/smap/.