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RNA Pseudoknotted Secondary Structure Prediction Using Strict Hierarchical Folding

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HFold

HFold is an algorithm for predicting the pseudoknotted secondary structures of RNA using strict Hierarchical Folding.

System Requirements

HFold needs a 64bit Linux or macOS operating system to run

Software Requirements:

  • CMake 3.15 or greater

CMake installation

CMake version 3.15 or greater must be installed in a way that HFold can find it.

To check if your system already has CMake, run this in terminal:

cmake --version

Linux:

sudo apt update
sudo apt install cmake

Mac:

Homebrew is required to download CMake on Mac. Run this command to install homebrew.

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"   

You may be prompted to add homebrew to PATH. If prompted, please follow the instructions presented on the terminal.

Once finished, install CMake

brew install cmake   

Install HFold

Steps for installation

  1. Download the repository and extract the files onto your system.
  2. From a command line in the root directory (where this README.md is) run
cmake -S . -B build
cmake --build build --parallel

If you want to run DEBUG mode, you can run

cmake -S . -B build -DCMAKE_BUILD_TYPE=Debug
cmake --build build --parallel

Usage

Usage: HFold[options] [input sequence]

Flags:

  -h, --help             Print help and exit
  -V, --version          Print version and exit
  -r, --input-structure  Give a restricted structure as an input structure
  -i, --input-file       Give a path to an input file containing the sequence (and input structure if known)
  -o, --output-file      Give a path to an output file which will the sequence, and its structure and energy
  -n, --opt              Specify the number of suboptimal structures to output (default is 1)
  -p  --pk-free          Specify whether you only want the pseudoknot-free structure to be calculated
  -k  --pk-only          Only add base pairs which cross the constraint structure. The constraint structure is returned if there are no energetically favorable crossing base pairs
  -d  --dangles          Specify the dangle model to be used (base is 2)
  -P, --paramFile        Read energy parameters from paramfile, instead of using the default parameter set.\n
  -S, --shape            Give a path to a shape file corresponding to the sequence given
      --noConv           Do not convert DNA into RNA. This will use the Matthews 2004 parameters for DNA

How to use:

    Remarks:
        make sure the <arguments> are enclosed in "", for example -r "..().." instead of -r ..()..
        input file for -i must be .txt
        if -i is provided with just a file name without a path, it is assuming the file is in the diretory where the executable is called
        if -o is provided with just a file name without a path, the output file will be generated in the diretory where the executable is called
        if -o is provided with just a file name without a path, and if -i is provided, then the output file will be generated in the directory where the input file is located
        if suboptimal structures are specified, repeated structures are skipped. That is, if different input structures come to the same conclusion, only those that are different are shown
        If no input structure is given, or suboptimal structures are greater than the number given, CParty generates hotspots to be used as input structures -- where hotspots are energetically favorable stems
        The default parameter file is DP09. This can be changed via -P and specifying the parameter file you would like

Sequence requirements:
    containing only characters GCAU

Structure requirements:
    -pseudoknot free
    -containing only characters .x()
    Remarks:
        Restricted structure symbols:
            () restricted base pair
            . no restriction
            x restricted to unpaired


Input file requirements:
        Line1: FASTA name (optional)
        Line2: Sequence
        Line3: Structure
    sample:
        >Sequence1 (optional)
        GCAACGAUGACAUACAUCGCUAGUCGACGC
        (............................)

Examples:

assume you are in the directory where the HFold executable is loacted
./build/HFold -i "/home/username/Desktop/myinputfile.txt"
./build/HFold -i "/home/username/Desktop/myinputfile.txt" -o "outputfile.txt"
./build/HFold -r "(............................)" GCAACGAUGACAUACAUCGCUAGUCGACGC
./build/HFold -r "(((((.........................)))))................" -d1 GGGGGAAAAAAAGGGGGGGGGGAAAAAAAACCCCCAAAAAACCCCCCCCCC
./build/HFold -p -r "(............................)" -o "/home/username/Desktop/some_folder/outputfile.txt" GCAACGAUGACAUACAUCGCUAGUCGACGC
./build/HFold -n 3 -r "(............................)" -o "/home/username/Desktop/some_folder/outputfile.txt" GCAACGAUGACAUACAUCGCUAGUCGACGC
./build/HFold -k -r "(............................)" GCAACGAUGACAUACAUCGCUAGUCGACGC
./build/HFold -P "params/rna_Turner04.par" -r "(............................)" GCAACGAUGACAUACAUCGCUAGUCGACGC

Changes

(Mateo 03/11/24) HFold has been given a full rework and has been changed from the simfold style of code to the ViennaRNA style.
Many of the original files have been condensed or removed due to this. 
Along with this, is the use of a partial library from ViennaRNA. This change comes with ~60-70x faster prediction time. Users can also
use pk-only prediction -- see Iterative HFold, and pseudoknot-free if desired. 

Bug fixes

(Mateo 03/11/24) VP case 1-3 did not allow for pseudoknots within multiloops, this has been fixed.
                 VM did not previously update with the pseudoknots predicted. This was fixed in the rework as the prediction was combined
                 WMBP case 1 did not allow for kissing hairpins where the middle band was in G. The bounds for l have been changed to bp(i,l) to
                 Bp(l,j) to fix this

Questions

For questions, you can email mateo2@ualberta.ca

Cite:

Jabbari, H., Condon, A., Pop, A., Pop, C., Zhao, Y. (2007). HFold: RNA Pseudoknotted Secondary Structure Prediction Using Hierarchical Folding. In: Giancarlo, R., Hannenhalli, S. (eds) Algorithms in Bioinformatics. WABI 2007. Lecture Notes in Computer Science, vol 4645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74126-8_30

Jabbari, H., Condon, A., Zhao Y. Novel and Efficient RNA Secondary Structure Prediction Using Hierarchical Folding.Journal of Computational Biology.Mar 2008.139-163. http://doi.org/10.1089/cmb.2007.0198

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