PT  - JOURNAL ARTICLE
AU  - Benjamin M. David
AU  - Ryan M. Wyllie
AU  - Ramdane Harouaka
AU  - Paul A. Jensen
TI  - A Reinforcement Learning Framework for Pooled Oligonucleotide Design
AID  - 10.1101/2021.08.25.455853
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.08.25.455853
4099  - http://biorxiv.org/content/early/2021/08/27/2021.08.25.455853.short
4100  - http://biorxiv.org/content/early/2021/08/27/2021.08.25.455853.full
AB  - The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every possible combination of oligos, so heuristic approaches must be used to find near-optimal solutions. We present a general reinforcement learning framework, called OligoRL, to solve oligo design problems with complex constraints. OligoRL allows “black-box” design criteria and can be adapted to solve many oligo design problems. We highlight the flexibility of OligoRL by building tools to solve three distinct design problems: 1.) finding pools of random DNA barcodes that lack restriction enzyme recognition sequences (CutFreeRL); 2.) compressing large, non-degenerate oligo pools into smaller degenerate ones (OligoCompressor); and 3.) finding Not-So-Random hexamer primer pools that avoid rRNA and other unwanted transcripts during RNA-seq library preparation (NSR-RL). OligoRL demonstrates how reinforcement learning offers a general solution for complex oligo design problems. OligoRL and its associated software tools are available as a Julia package at http://jensenlab.net/tools.Competing Interest StatementThe authors have declared no competing interest.