PoolParty: streamlined design of DNA sequence libraries in Python

Liu, Zhihan, Cordero, Aidan, Kinney, Justin B (October 2026) PoolParty: streamlined design of DNA sequence libraries in Python. BMC Bioinformatics, 27 (1). ISSN 1471-2105

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Abstract

BACKGROUND: Computationally designed DNA sequence libraries are essential components of massively parallel reporter assays (MPRAs), deep mutational scanning (DMS) experiments, and other multiplex assays of variant effect (MAVEs). They are also increasingly used in silico to analyze genomic AI models. Designing these libraries, however, remains tedious and error-prone due to the scarcity of purpose-built software. RESULTS: Here we describe PoolParty, a Python package that streamlines the design of complex oligo pools using a simple but flexible API. In PoolParty, each library is represented by a computational graph that can be specified in just a few lines of code. Over 50 built-in operations cover nucleotide- and codon-level mutagenesis, motif insertion, barcode generation, and more. PoolParty automatically generates informative names for each sequence and provides "design cards" detailing how each sequence was generated. Visualization methods let users quickly audit library content and inspect the underlying graph. PoolParty thus transforms oligo pool design from a tedious task requiring custom functions and scripts into a structured, transparent, and reproducible process. CONCLUSIONS: PoolParty streamlines the design of DMS, MPRA, and other multiplex assay libraries, and the design cards it provides can help researchers systematically probe and interpret genomic AI models. PoolParty can also be extended to support new assays and analysis strategies as they emerge.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > genomics and proteomics bioinformatics > quantitative biology bioinformatics > computational biology
CSHL Authors:	Kinney, Justin B. Liu, Zhihan Cordero, Aidan
Communities:	CSHL labs > Kinney lab CSHL Post Doctoral Fellows
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	30 October 2026
Date Deposited:	02 Nov 2026 16:28
Last Modified:	02 Nov 2026 16:28
PMCID:	PMC13723123
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/42352

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