Pitfalls in understanding PhIP-Seq data: technical variability, replicability, and best practices for interpretation

Trgovec-Greif, L.; Vogl, T.

doi:10.64898/2026.09.15.751724

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bioRxiv · 10.64898/2026.09.15.751724

Pitfalls in understanding PhIP-Seq data: technical variability, replicability, and best practices for interpretation

Trgovec-Greif, L.·Vogl, T.

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Abstract

Phage Display Immunoprecipitation sequencing (PhIP-Seq) is a high throughput method allowing to measure antibody binding against hundreds of thousands of potential antigens. Typically, blood samples of hundreds to thousands of individuals are measured in 96-well plates, necessitating distribution of samples on multiple plates and processing in batches. To correctly interpret PhIP-Seq results, it is crucial to understand its nature and technical limitations. Here, we analyzed 195 technical replicates of controls (anchor samples) from 49 plates measured in 13 immunoprecipitation runs to gauge technical variability and replicability. While there were few false-positive enriched peptides, we observed a substantial fraction of false negatives. The total number of enriched peptides can be affected by batch effects. Additionally, the type of biological material used (serum or plasma) influences enrichment profiles with an antigen library containing bacterial proteins due to the interaction of phages with plasma proteins. Finally, replicating measurements in different labs produces comparable enrichment profiles, but slight differences in a subset of peptides are noticeable. These results suggest best practices for PhIP-Seq experiments. Most importantly, due to batch effects, samples of cases and controls should be evenly distributed between 96-well plates to avoid confounding with biological effects. Also, for highly complex libraries, the total number of enriched peptides can be a technical artifact and potentially not a feature useful in comparisons.

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Trgovec-Greif, L., Vogl, T.. 2026-09-21. Pitfalls in understanding PhIP-Seq data: technical variability, replicability, and best practices for interpretation. https://doi.org/10.64898/2026.09.15.751724

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