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Introduction

SeqFold.jl is a high-performance Julia reimplementation of seqfold Python library for predicting nucleic acid secondary structures and calculating melting temperatures, which is, in turn, an implementation of the Zuker, 1981 dynamic programming algorithm, the basis for UNAFold/mfold, with energy functions from SantaLucia, 2004 (DNA) and Turner, 2009 (RNA).

Motivation

• Secondary structure prediction is essential for:

  • Designing PCR primers with minimal secondary structure;
  • Creating oligos for genome editing techniques like MAGE;
  • Tuning ribosome binding site (RBS) expression rates;
  • Analyzing potential off-target binding in CRISPR applications.

• While the Python seqfold library provides a minimalist open-source alternative to proprietary UNAFold/mfold, it has several limitations:

  • Performance bottlenecks in the pure Python implementation;
  • Undocumented bugs in melting temperature calculations;
  • Limited control over buffer conditions for $T_m$ calculations.

• SeqFold.jl addresses these issues, here are the key points:

  • For sequence folding the compatibility with the original seqfold library results is preserved (identical folding algorithm is used);
  • For accurate $T_m$ calculation buffer condition control is provided to user (physically impossible buffer conditions cause errors with meaningful error messages);
  • Results of $T_m$ calculation are validated against Biopython's reference implementation;
  • Significantly improved performance through Julia's JIT compilation. For oligs of 20 nt (a typical length for primer sequences) more than 4× speedup for tm and more than 20× speedup for fold is achieved:

Installation

First option (inside Julia REPL):

julia> ]
pkg> add SeqFold

Second option (from Julia scripts):

using Pkg
Pkg.add("SeqFold")

either way you should now be able to call the package:

using SeqFold