Release Tar:
Conda:
For a given RNA, pourRNA identifies local minima and computes respective gradient basins and transition rates of the energy landscape. This is done by efficient local flooding techniques in combination with heuristics to guide which gradient basins are explored and considered.
If you are used to conda, the easiest way to install pourRNA is:
conda install -c bioconda pourrna
| Arch | Debian | Ubuntu | openSUSE |
|---|---|---|---|
Arch_Extra |
xUbuntu_19.04xUbuntu_18.10xUbuntu_18.04xUbuntu_17.10 |
openSUSE_TumbleweedopenSUSE_Leap_42.3openSUSE_Leap_42.2openSUSE_Leap_15.1openSUSE_Leap_15.0 |
An alternative download location is here: https://www.tbi.univie.ac.at/~entzian/pourRNA_build/
From release Source
You can download the source tar balls for the individual releases from the release page.
To configure, compile and install execute the following commands on your command line:
./configure [--help for additional configuration options]
make
make install
Dependencies:
If you installed the ViennaRNA library in a non-standard directory, you have to give the path to the main directory of the ViennaRNA library:
./configure --with-RNA=/path/to/ViennaRNA
To configure, compile and install execute the following commands on your command line:
autoreconf -i
./configure [--help for additional configuration options]
make
make install
Dependencies:
If you installed the ViennaRNA library in a non-standard directory, you have to give the path to the main directory of the ViennaRNA library:
./configure --with-RNA=/path/to/ViennaRNA
The minimal input is an RNA sequence. Either per standard input (from a *.fasta file)
cat rna.fasta | pourRNA
or per command line argument
pourRNA --sequence="CUAGUUAGGAACGGAAUUAAUUAGGAAAAAGCUGAUUAG"
The content of the file rna.fasta should look similar to this:
> fasta header
CUAGUUAGGAACGGAAUUAAUUAGGAAAAAGCUGAUUAG
The output consists of the representative structures (local minima) of the explored gradient basins and the transition rates between them. You can adjust the output and speed up the computation by using additional command line parameters. All parameters are shown by
pourRNA --help
- gracebat alternative R (with additional packages)
- treekin alternative R (with additional packages)
- ps2pdf
- meld alternative any other diff-tool
If you are only interested in the thermodynamic equilibrium of the Markov process, you can simply extract the line with the equilibrium densities for the local minima:
cat rna.fasta | pourRNA | grep "Equilibrium Densities:" -A1
If you are interested in the dynamic folding behaviour, you need additional tools in order to post-process the output of pourRNA. Usually the tool treekin is used, which needs two input files that should have the output format of the tool 'barriers'. pourRNA can also be used to produce a barriers-like output.
cat rna.fasta | pourRNA --barriers-like-output=rna_barriers
As a second step you need one or several start structures for the initial population of the Markov process. In this example we extract the open chain structure from the barriers like output:
cat rna_barriers_states.out | grep -P "\s*\d+\s[\.]+\s+\-?\d*\.?\d*"
In this example the index of our start structure is 34. With this we can start treekin:
cat ./rna_barriers_rates.out | treekin -m I --bar=./rna_barriers_states.out --p0 34=1.0 > treekin.out
The treekin output is a matrix with the time steps in the first column and the population densities for all local minima in the other columns. This file can be visualized for example with the tool 'gracebat'.
gracebat -log x -nxy treekin.out -hdevice PostScript -hardcopy -printfile kinetics.ps
ps2pdf kinetics.ps
The final kinetics.pdf shows the folding process from the initial population until the thermodynamic equilibrium is reached.
If you don't have access to treekin and want to use the R script within the scripts directory of this project, you can compute the kinetics pdf file as follows:
cat rna.fasta | pourRNA --barriers-like-output=rna_barriers --binary-rates-file=rna_rate_matrix
Rscript ./scripts/read_matrix_plot_kinetics.R --binary_matrix ./rna_rate_matrix --states_file ./rna_barriers_states.out --initial_state=34
The output of this call is the file rna_rate_matrix.pdf within the current directory.
You can easily test if the printed rate matrix is similar to the output of the tool barriers. However, you should know that the default parameters will not work.
At first you have to make shure that you compute the whole state space for both tools by setting the same maximal energy threshold (RNAsubopt -e 1000 and pourRNA --max-energy 1000).
Note that the RNAsubopt threshold is relative to the MFE and the pourRNA threshold is absolute.
In barriers you have to set the minimum height to introduce a new basin to 0 (i.e. --minh 0). In barriers versions <= 1.6.0 you have to make shure that shift moves are disabled (-M RNA-noShift).
Compute the barriers rates file (rates.out):
echo "GGGGGGACCCCCC" | RNAsubopt -e 1000 -s | barriers --rates --minh 0 -M RNA-noShift > 13nt_barriers.out
Compute the pourRNA rates file (13nt_pourRNA_rates.out):
echo "GGGGGGACCCCCC" | pourRNA --max-energy 1000 --barriers-like-output=13nt_pourRNA
Compare both files:
meld rates.out 13nt_pourRNA_rates.out
All rates should be exactly the same in both files, except for the diagonal.
The barriers diagonal is different from pourRNA. However, often it does not matter because some post-processing tools (e.g. treekin) recompute the diagonal.
The correct diagonal has the rates q(i,i) = -sum_{j not equal i} q(i,j) for the i-th row and the j-th column.
Use this parameter in order to speed up the rate computation by distributing the flooding of several basins among several threads.
cat rna.fasta | pourRNA --max-threads=8
A global energy threshold can be set with --max-energy= a value in kcal/mol. This energy is an absolute threshold.
If you want to include, for example, the open chain structure (without any base pairs), then you should set this threshold above zero. The default value is 5 kcal/mol.
This filter can also be applied on a local level, which is called --delta-e= a value in kcal/mol. This energy is the maximum energy threshold relative to the minimal energy (local minimum) of each basin that is currently flooded. So it makes a difference if you start the exploration with the open chain structure or with the MFE structure.
Using these filters you can compute approximate RNA folding kinetics, which is much faster.
However, these filters can lead to very different results if they are too restrictive.
The --max-neigh-e filter reduces outgoing transitions to the neighbored minima, for which the energy is lower than
the energy of the current minimum plus the filter value (E(neighbored minimum) < E(current minimum) + filterValue).
This helps to avoid the exploration of minima that are too high in the energy landscape and thus often have a low contribution to the final folding kinetics.
The --filter-best-k filter follows takes an integer k as input. From a given initial structure it explores only the k neighbored basins, with the highest transition rates.
Both filters can be applied at the same time, however, the order of these filters is important (in order to get reproducible results) because the highest rates are not not necessarily leading to energetically lower neighbored gradient basins. Thus, we first have to prune all "up-hill" transitions before reducing the remaining to the k best
The kBest filter can be dynamically adjusted with --dynamic-best-k, which increases the number of transitions iteratively until the MFE structure is discovered.
- compute the partition functions for all local minima and their probability in the thermodynamic ensemble
- compute the rate matrix and the folding kinetics from an ensemble of structures to the thermodynamic equilibrium
- compute refolding paths on macro-state level
This is the example output:
echo "ACGUUGCAACGU" | pourRNA
Sequence: ACGUUGCAACGU
MFE structure: ((((....))))
The start state is: ............
The start state ends in basin: ............
The final state is:
The final minimum is:
Number of minima: 3
--------------THE FINAL RATE MATRIX-----------------------
from : to
0 [((((....))))] : 1 = 5.601344e-03
1 [............] : 0 = 3.844359e-02, 2 = 1.743583e-03
2 [....((...)).] : 1 = 1.000000e+00
Equilibrium Densities:
(8.726331e-01, 1.271452e-01, 2.216883e-04)
The overall partition function is: 8.055290e+00
number of rates: 4
finished computation at Wed Apr 3 11:12:33 2019
elapsed time: 7.505980e-04s
At first it prints the most important initial conditions. The sequence, the most stable structure, the initial state for the exploration and the final state that stops the exploration (if it was set). Then it prints the rate matrix in a sparse text format (state index, representative local minimum structure and then all outgoing transition rates). Finally it prints the equilibrium densities for all local minima (in the same order as the rate matrix indices).
The tool can also output transition rates in different matrix formats.
-
--binary-rates-file=writes a binary file with all matrix entries (number of states, rates from 0 to n, rates from 1 to n, etc. as double values) -
--binary-rates-file-sparse=the sparse format is: First value is the number of states (uint_32), then <uint_32 from>, <uint_32 number of how many value pairs to>, <value pair <uint_32 to, double rate from, to>> etc. -
--barriers-like-output=produces the same output format as the tool barriers. The result is a file with the rate matrix in text format and a file with the structures in dot-parentheses format. Both files have the same prefix.
If you are interested in refolding paths, you can output a file with all saddle heights (--saddle-file=) and apply a Dijkstra algorithm in order to find a path with the minimal saddle height between two structures in the landscape. A python script that reads the saddle file and computes the path can be found in the scripts directory of this project.
For additional output options or parameters you can look up the description in the manual or in the help text of the tool. Simply type:
man pourRNA
or
pourRNA --help
If you are using this software, we recommend to read and cite the corresponding publication:
G. Entzian and M. Raden (2019) "pourRNA - a time- and memory-efficient approach for the guided exploration of RNA energy landscapes", Bioinformatics
In case you run into problems, please contact us!
© Gregor Entzian, 2019