OligoRL

Contents

Description
Instructions to Use OligoRL
CutFreeRL
OligoCompressor
NSR-RL
Instructions to Replicate Published Data

Description

A Reinforcemnt Learning Framework for Pooled Oligonucleotide Design. Solve oligo design problems with complex constraints.

OligoRL has three associated tools:

• CutFreeRL: finds degenerate barcodes that lack specified restriction enzyme sites
• OligoCompressor: compresses a set of non-degenerate oligos into a smaller set of degenerate ones
• NSR-RL: creates semi-random hexamers that avoid rRNA when preparing RNA-seq libraries

Instructions to Use OligoRL

Each software application is a standalone Julia file containing all of the necessary functions to run the application. Requires installation of Julia. Once Julia is installed. Install the dependent Julia Packages. Navigate to package mode by pressing ]

add Random
add Statistics
add CSV
add BioSequences
add DataFrames
add XLSX
add ArgParse
add RCall

CutFreeRL

The CutFreeRL application is located in the CutFreeRL folder as CutFreeRL.jl. CutFree is available as a web-tool at the Jensen Lab website. We have also created a command line interface for the CutFreeRL implementation.

$ julia CutFreeRL.jl -sequence NNNNNNNNNNN -restrictionsites GGTCTC,GGCCGG -nsims 1000

Required Arguments

• --sequence,-s: Starting DNA sequence that should be blocked from containing restriction sites. To generate a set of barcodes with the highest diversity, start with a string of N's the length of your oligo.

• --restrictionsites,-r: Sequences to block from the oligo pools. Separate multiple sequences by commas. Do not include spaces.

Optional Arugments

• --nsims,-n =1000: The number of rollout simulations per action. Decreasing nsims results in faster runtimes but potentially decreases solution optimality.

OligoCompressor

The OligoCompressor application is located in the OligoCompressor folder as OligoCompressor.jl To use OligoCompressor on your own data, we have created a command line interface.

$ julia OligoCompressor.jl --targetpool mysequences.txt 

Required Arguments

• --targetpool,-t: A text file containing a set of non-degenerate oligos to be compressed. An example file called test_seqs.text is located in the OligoCompressor folder

Optional Arguments

• --output,-o: Output .txt file name. The compressed sequences will print if no input is given

• --bases,-b =dna"AGCTMRWSYKVHDBN": Nucleotide codes available to be used. Default is all 15.

• --nsims,-n =1000: Number of rollout simulations per action.

NSR-RL

The NSR-RL tool reqiures the use of R in order to download the genomic data for your organism of interest. We have created the R package nsrgenomes to automate this process. Please refer to nsrgenomes to install the package before you use the NSR-RL tool.

The NSR-RL application is located in the NSR_RL folder as NSR_RL.jl. To use NSR-RL to design your own NSR primers for any organism of interest, we have created a command line interface.

$ julia  NSR_RL.jl --species E_coli --accession NC_000913.3 --poolsize 100

Arguments

• --species,-s: The species name of interest. Ex. "E_coli"
• --poolsize,-p: The number of NSR primers in the final pool

Either

• --accession,-a: The genome accession number listed on the NCBI database

Or

• --genefile: The file name for the file containing the mRNA sequences of the organsim of interest made by the nsrgenomes R package
• --rRNAtRNAfile: the file name for the file containing the rRNA and tRNA sequences made by the nsrgenomes R package

Directly supplying the genefile and rRNAtRNAfile bypasses the genome download and formatting process.

Optional Arguments

• --outfile,-o: The file name for the NSR-RL output. Provides a more detailed set of statistics.
• --length_primers,-l =6: The desired length of each primer. Default is hexamer.
• --bases,-b =AGCTMRWSYKVHDBN: The available base codes for the primers
• --nsims,-n =1000: The number of rollout simulations per action

The following arguments allow uses to change the weights in the reward function:

$$Reward = \beta_{GenesHit}GenesHit + \beta_{TotalHits}TotalHits + \beta_{Inter}Interuniformity + \beta_{Intra}Intrauniformity$$ This allows users to empiracally tune NSR pools.

• --genes_hit_weight =1: Weight given to hitting each gene at least once in the reward function
• --total_hits_weight =1: Weight given to maximizing the total number of hits
• --interuniformity_weight =1: Weight given to placing hits equally across all genes
• --intrauniformity_weight =1: Weight given to placing hits equally within each gene

Instructions to Replicate Published Data

We provide pre-written scripts to generate the data shown in each of the figures. Requires installations of R , Julia, and Gurobi Optimizer (For replicating the original CutFree data only). While not required, it is helpful to have a text editing IDE installed such as VScode or atom.

1. Install required R and Julia Packages

Install R Packages

install.packages(ggplot2)
install.packages(gurobi)
install.packages(dplyr)
install.packages(stringr)

Istall Julia Packages. Navigate to package mode by pressing ]

add Random
add Statistics
add CSV
add Biosequences
add DataFrames
add XLSX

2. Replicate Figure 1 Data

1. Navigate to Figures -> Figure1. Open and run the rebase.R file. This file designs the experiments and runs the orignal Cutfree algorithm. Information about the experiments and the CutFree benchmarking data are saved to .csv files in the open directory.
2. Open and run the Figure1.jl file. This file extracts the experimental settings used for the original CutFree algorithm and runs CutFreeRL using these settings. Data for plotting and analysis are saved to .csv files in the open directory
3. Open and run the plot_Figure1.R file. This file plots the data as it appears in Figure 1 and performs the statistical analyses reported in the paper.

3. Replicate Figure 2 Data

1. Navigate to Figures -> Figure2. Open and run the Figure2.jl file. This file contains funcitons that design and run the experiments for OligoCompressor and then saves the data from those experiments to .csv files. Along with the data that appears in the figure, this file also runs the comparision experiment for a published NSR pool described in text. The compressed pool is saved as a .csv file in the open directory.
2. Open and run the plot_Figure2.R file. This file plots the data gathered from the julia script as it appears in Figure 3.

4. Replicate Figure 3 Data

1. Navigate to Figures -> Figure3. Open and run the Figure3.jl file. This file contains the NSR-RL base funcitons along with funcitons that design and run the experiments described in the text. Data from these experiments are saved to .csv files for visualization and analysis. The Genome Data folder contains the transcriptomes and rRNA/tRNA sequences of 25 prokaryotic species used to benchmark NSR-RL.

2. Open and run the plot_Figure3.R file. This file plots the data gathered from the julia script as it appears in Figure 3.