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Design orthogonal DNA sequences

Project description

seqwalk

seqwalk is a package for designing orthogonal DNA sequence libraries. It can efficiently generate libraries of sequences that satisfy sequence symmetry minimization constraints (i.e. minimizing longest common substrings). seqwalk additionally includes off-the-shelf orthogonal sequence libraries, as well as some tools for analyzing orthogonal sequence libraries. A code-free, interactive version of seqwalk can be found here.

For more details, see the paper.

Installation

$ pip install seqwalk

Usage

Designing a set of barcodes with minimal sequence symmetry

If you want a certain number of barcodes with maximum orthogonality, you can use the max_orthogonality function from the design module. You must specify the length of desired sequences (L) and the number of desired sequences (N). Optionally, specify the prevention of reverse complementary sequences, GC content limits, allowable alphabet, and specific prevented patterns. By default, reverse complementary sequences are allowed, there are no GC content constraints, a 3 letter (A/C/T, no G) code is used and any 4N sequence is prevented.

For example, if you want 100 barcodes with length 25, with prevented reverse complements, and a 4 letter alphabet, and between 10 and 15 G/C bases, you can use the following code:

from seqwalk import design

library = design.max_orthogonality(100, 25, alphabet="ACGT", RCfree=True, GClims=(10, 15))

This will generate a library of at least the specified size, with the strongest possible sequence symmetry constraint.

Designing a set of orthogonal barcodes with maximum size

If you have an orthogonality constraint in mind, you can use the max_size function from the design module to generate a maximally sized library with the given sequence symmetry minimization k values. That is, the shortest k for which no substring of length k appears twice.

If you want sequences that satisfy SSM for k=12, and you want barcodes of length 25, without considering reverse complementarity, and using a 4 letter alphabet, with no GC constraints, you can use the following code:

from seqwalk import design

library = design.max_size(25, 12, alphabet="ACGT")

Importing "off-the-shelf" experimentally characterized libraries

The io module provides the ability to import libraries that have been previously experimentally characterized, using code of the following format.

from seqwalk import io

PERprimers = io.load_library("kishi2018")

We provide the following libraries, accessible with the identifier tag.

identifier # of seqs seq length original use case ref
kishi2018 50 9nt PER primers Kishi et al, 2018

If you have an orthogonal library you would like to add, please submit a PR!

Quality control using pairwise comparisons

Once you have a library in the form of a list of sequences, you can use the analysis module to perform additional quality control. For example, we provide a function to compute pairwise Hamming distances.

from seqwalk import analysis

h_crosstalk = analysis.hamming_matrix(seqs)

Since sequence symmetry minimization does not explicitly guarantee low off-target hybridization strength, a simple function for using NUPACK to identify "bad" sequences is included in the analysis.py file. However, it is commented out to avoid the NUPACK dependency in the package (problematic due to NUPACK licensing).

License

seqwalk is licensed under the terms of the MIT license.

Credits

seqwalk was created with cookiecutter and the py-pkgs-cookiecutter template.

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