HFold is an algorithm for predicting the pseudoknotted secondary structures of RNA using strict Hierarchical Folding.
HFold needs a 64bit Linux or macOS operating system to run
- CMake 3.15 or greater
- ViennaRNA 2.7.0
You will need to install ViennaRNA package in order to compile HFold
-
Download the ViennaRNA package from ViennaRNA-2.7.0.tar.gz
-
Install the ViennaRNA package:
tar -zxvf ViennaRNA-2.7.0.tar.gz
cd ViennaRNA-2.7.0
./configure --without-perl
sudo make -j$(nproc) # Linux
sudo make -j$(sysctl -n hw.ncpu) # macOS
sudo make install
Tip: To speed up compilation, you can parallelize compilation by replacing the sudo make command.:
- Linux: Use
sudo make -j$(nproc) - macOS: Use
sudo make -j$(sysctl -n hw.ncpu)
For more details about ViennaRNA, see https://github.com/ViennaRNA/ViennaRNA
CMake version 3.9 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
sudo apt update
sudo apt install cmake
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
- Download the repository and extract the files onto your system.
- 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: HFold[options] [input sequence]
-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
--noConv Do not convert DNA into RNA. This will use the Matthews 2004 parameters for DNA
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
(............................)
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
(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.
(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
For questions, you can email mateo2@ualberta.ca
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