yafshar/intarna

Efficient target prediction incorporating accessibility of interaction sites

IntaRNA version 2.*

Efficient RNA-RNA interaction prediction incorporating accessibility and seeding of interaction sites

During the last few years, several new small regulatory RNAs (sRNAs) have been discovered in bacteria. Most of them act as post-transcriptional regulators by base pairing to a target mRNA, causing translational repression or activation, or mRNA degradation. Numerous sRNAs have already been identified, but the number of experimentally verified targets is considerably lower. Consequently, computational target prediction is in great demand. Many existing target prediction programs neglect the accessibility of target sites and the existence of a seed, while other approaches are either specialized to certain types of RNAs or too slow for genome-wide searches.

IntaRNA, developed by Prof. Backofen's bioinformatics group at Freiburg University, is a general and fast approach to the prediction of RNA-RNA interactions incorporating both the accessibility of interacting sites as well as the existence of a user-definable seed interaction. We successfully applied IntaRNA to the prediction of bacterial sRNA targets and determined the exact locations of the interactions with a higher accuracy than competing programs.

For testing or ad hoc use of IntaRNA, you can use its webinterface at the

==> Freiburg RNA tools IntaRNA webserver <==

Contribution

Feel free to contribute to this project by writing Issues with feature requests, bug reports, or just contact messages.

Citation

If you use IntaRNA, please cite our articles

• IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions Martin Mann, Patrick R. Wright, and Rolf Backofen, Nucleic Acids Research, 45 (W1), W435–W439, 2017, DOI(10.1093/nar/gkx279).
• CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains Patrick R. Wright, Jens Georg, Martin Mann, Dragos A. Sorescu, Andreas S. Richter, Steffen Lott, Robert Kleinkauf, Wolfgang R. Hess, and Rolf Backofen, Nucleic Acids Research, 42 (W1), W119-W123, 2014, DOI(10.1093/nar/gku359).
• IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions Anke Busch, Andreas S. Richter, and Rolf Backofen, Bioinformatics, 24 no. 24 pp. 2849-56, 2008, DOI(10.1093/bioinformatics/btn544).

Documentation

Overview

The following topics are covered by this documentation:

• Installation
  • IntaRNA via conda
  • IntaRNA docker container
  • Dependencies
  • Cloning from github
  • Source code distribution
  • Microsoft Windows installation
  • OS X installation with homebrew
• Usage and Parameters
  • Just run ...
  • Prediction modes, their features and emulated tools
  • Interaction restrictions
  • Seed constraints
  • Explicit seed input
  • Output modes
  • Suboptimal RNA-RNA interaction prediction and output restrictions
  • Energy parameters and temperature
  • Additional output files
    • Minimal energy profiles
    • Minimal energy for all intermolecular index pairs
    • Accessibility and unpaired probabilities
      • Local versus global unpaired probabilities
      • Read/write accessibility from/to file or stream
  • Multi-threading and parallelized computation
• Library for integration in external tools

Installation

IntaRNA via conda (bioconda channel)

The most easy way to locally install IntaRNA is via conda using the bioconda channel (linux only). This way, you will install a pre-built IntaRNA binary along with all dependencies. Follow to get detailed information or run

conda install -c bioconda intarna

if you are using bioconda already.

IntaRNA docker container (via QUAY)

An IntaRNA docker container (?) is provided from the bioconda package via Quay.io. This provides you with an encapsulated IntaRNA installation.

Dependencies

If you are going to compile IntaRNA from source, ensure you meet the following dependencies:

• compiler supporting C++11 standard and OpenMP
• boost C++ library version >= 1.50.0 (ensure the following libraries are installed; or install all e.g. in Ubuntu via package libboost-all-dev)
  • libboost_regex
  • libboost_program_options
  • libboost_filesystem
  • libboost_system
• Vienna RNA package version >= 2.4.1
• if cloning from github: GNU autotools (automake, autoconf, ..)

Also used by IntaRNA, but already part of the source code distribution (and thus not needed to be installed separately):

• Catch test framework
• Easylogging++ logging framework

Cloning Source code from github (or downloading ZIP-file)

The data provided within the github repository (or within the Source code archives provided at the
IntaRNA release page) is no complete distribution and lacks all system specifically generated files. Thus, in order to get started with a fresh clone of the IntaRNA source code repository you have to run the GNU autotools to generate all needed files for a proper configure and make. To this end, we provide the helper script autotools-init.sh that can be run as shown in the following.

# call aclocal, automake, autoconf
bash ./autotools-init.sh

Afterwards, you can continue as if you would have downloaded an IntaRNA package distribution.

IntaRNA package distribution (e.g. intaRNA-2.0.0.tar.gz)

When downloading an IntaRNA package distribution (e.g. intaRNA-2.0.0.tar.gz) from the IntaRNA release page, you should first ensure, that you have all dependencies installed. If so, you can simply run the following (assuming bash shell).

# generate system specific files (use -h for options)
./configure
# compile IntaRNA from source
make
# run tests to ensure all went fine
make tests
# install (use 'configure --prefix=XXX' to change default install directory)
make install
# (alternatively) install to directory XYZ
make install prefix=XYZ

If you installed one of the dependencies in a non-standard directory, you have to use the according configure options:

• --with-vrna : the prefix where the Vienna RNA package is installed
• --with-boost : the prefix where the boost library is installed

Microsoft Windows installation

... from source

IntaRNA can be compiled, installed, and used on a Microsoft Windows system when e.g. using Cygwin as 'linux emulator'. Just install Cygwin with the following packages:

• Devel:
  • make
  • gcc-g++
  • autoconf
  • automake
  • pkg-config
• Libs:
  • libboost-devel
• Perl:
  • perl

and follow either install from github or install from package.

... using pre-compiled binaries

For some releases, we also provide precompiled binary packages for Microsoft Windows at the IntaRNA release page that enable 'out-of-the-box' usage. If you want to use them:

• download the according ZIP archive and extract
• open a Windows command prompt
• run IntaRNA

Note, these binaries come without any waranties, support or what-so-ever! They are just an offer due to according user requests.

If you do not want to work within the IntaRNA directory or don't want to provide the full installation path with every IntaRNA call, you should add the installation directory to your Path System variable (using a semicolon ; separator).

OS X installation with homebrew (thanks to Lars Barquist)

If you do not want to or can use the pre-compiled binaries for OS X available from bioconda, you can compile IntaRNA locally.

The following wraps up how to build IntaRNA-2.0.2 under OS X (Sierra 10.12.4) using homebrew.

First, install homebrew! :)

brew install gcc --without-multilib

--without-multilib is necessary for OpenMP multithreading support -- note OS X default gcc/clang doesn't support OpenMP, so we need to install standard gcc/g++

brew install boost --cc=gcc-6

--cc=gcc-6 is necessary to build boost with standard gcc, rather than the default bottle which appears to have been built with the system clang. Brew installs gcc/g++ as /usr/local/bin/gcc-VERSION by default to avoid clashing with the system's gcc/clang. 6 is the current version as of writing, but may change.

brew install viennarna
brew install doxygen

Download and extract the IntaRNA source code package (e.g. intaRNA-2.0.2.tar.gz) from the release page.

./configure CC=gcc-6 CXX=g++-6

This sets up makefiles to use standard gcc/g++ from brew, which will need an update to the appropriate compiler version if not still 6. You might also want to set --prefix=INSTALLPATH if you dont want to install IntaRNA globally.

Make
make tests
make install

Usage and parameters

IntaRNA comes with a vast variety of ways to tune or enhance YOUR RNA-RNA prediction. To this end, different prediction modes are implemented that allow to balance predication quality and runtime requirement. Furthermore, it is possible to define interaction restrictions, seed constraints, explicit seed information, output modes, suboptimal enumeration, energy parameters, temperature, and the accessibility handling. If you are doing high-throughput computations, you might also want to consider multi-threading support.

For ad hoc usage you can use the Freiburg RNA tools IntaRNA webserver (with limited parameterization).

Just run ...

If you just want to start and are fine with the default parameters set, you only have to provide two RNA sequences, a (long) target RNA (using -t or --target) and a (short) query RNA (via -q or --query), in IUPAC RNA encoding. You can either directly input the sequences

# running IntaRNA with direct sequence input
# call : IntaRNA -t CCCCCCCCGGGGGGGGGGGGGG -q CCCCCCC

target
             9     15
             |     |
  5'-CCCCCCCC       GGGGGGG-3'
             GGGGGGG
             |||||||
             CCCCCCC
          3'-       -5'
             |     |
             7     1
query

interaction energy = -10.7116 kcal/mol

or provide (multiple) sequence(s) in FASTA-format. It is possible to provide either file input or to read the FASTA input from the STDIN stream.

# running IntaRNA with FASTA files
IntaRNA -t myTargets.fasta -q myQueries.fasta
# reading query FASTA input from stream via pipe
cat myQueries.fasta | IntaRNA -q STDIN -t myTargets.fasta

Nucleotide encodings different from ACGUT are rewritten as N and the respective positions are not considered to form base pairs (and this ignored). Thymine T encodings are replaced by uracil U, since a ACGU-only energy model is used.

For a list of general program argument run -h or --help. For a complete list covering also more sophisticated options, run --fullhelp.

Prediction modes, their features and emulated tools

For the prediction of minimum free energy interactions, the following modes and according features are supported and can be set via the --mode parameter. The tiem and space complexities are given for the prediction of two sequences of equal length n.

Features	Heuristic --mode=H	Exact-SE --mode=S	Exact --mode=E
Time complexity (prediction only)	O(n^2)	O(n^4)	O(n^4)
Space complexity	O(n^2)	O(n^2)	O(n^4)
Seed constraint	x	x	x
Explicit seeds	x	x	x
No seed constraint	x	x	x
Minimum free energy interaction	not guaranteed	x	x
Overlapping suboptimal interactions	x	x	x
Non-overlapping suboptimal interactions	x	-	x

Note, due to the low run-time requirement of the heuristic prediction mode (--mode=H), heuristic IntaRNA interaction predictions are widely used to screen for interaction in a genome-wide scale. If you are more interested in specific details of an interaction site or of two relatively short RNA molecules, you should investigate the exact prediction mode (--mode=S, or --mode=E if non-overlapping suboptimal prediction is required).

Given these features, we can emulate and extend a couple of RNA-RNA interaction tools using IntaRNA.

TargetScan and RNAhybrid are approaches that predict the interaction hybrid with minimal interaction energy without consideratio whether or not the interacting subsequences are probably involved involved in intramolecular base pairings. Furthermore, no seed constraint is taken into account. This prediction result can be emulated (depending on the used prediction mode) by running IntaRNA when disabling both the seed constraint as well as the accessibility integration using

# prediction results similar to TargetScan/RNAhybrid
IntaRNA [..] --noSeed --qAcc=N --tAcc=N

We add seed-constraint support to TargetScan/RNAhybrid-like computations by removing the --noSeed flag from the above call.

RNAup was one of the first RNA-RNA interaction prediction approaches that took the accessibility of the interacting subsequences into account while not considering the seed feature. IntaRNA's exact prediction mode is eventually an alternative implementation when disabling seed constraint incorporation. Furthermore, the unpaired probabilities used by RNAup to score the accessibility of subregions are covering the respective overall structural ensemble for each interacting RNA, such that we have to disable accessibility computation based on local folding (RNAplfold) using

# prediction results similar to RNAup
IntaRNA --mode=S --noSeed --qAccW=0 --qAccL=0 --tAccW=0 --tAccL=0

We add seed-constraint support to RNAup-like computations by removing the --noSeed flag from the above call.

Interaction restrictions

The predicted RNA-RNA interactions can be enhanced if additional knowledge is available. To this end, IntaRNA provides different options to restrict predicted interactions.

A general most general restriction is the maximal energy (inversely related to stability) an RNA-RNA interaction is allowed to have. Per default, a reported interaction should have a negative energy (<0) to be energetically favorable. This report barrier can be altered using --outMaxE. For suboptimal interaction restriction, please refer to suboptimal interaction prediction section.

Furthermore, the region where interactions are supposed to occur can be restricted for target and query independently. To this end, a list of according index pairs can be provided using --qRegion and --tRegion, respectively. The indexing starts with 1 and should be in the format from1-end1,from2-end2,.. using integers.

Finally, it is possible to restrict the overall length an interaction is allowed to have. This can be done independently for the query and target sequence using --qIntLenMax and --tIntLenMax, respectively. Both default to the full sequence length (by setting both to 0).

Seed constraints

For different types of RNA-RNA interactions it was found that experimentally verified interactions were showing a short and compact subinteraction of high stability (= low energy). It was hypothesized that these regions are among the first formed parts of the full RNA-RNA interaction, and thus considered as the seed of the overall interaction.

Based on this observation, RNA-RNA interaction predictors were enhanced by incorporating such seed constraints into their prediction pipeline, i.e. a reported interaction has to feature at least one seed. Typically, a seed is defined as a short subinteraction of 5-8 consecutive base pairs that are not enclosing any unpaired nucleotides (or if so only very few).

IntaRNA supports the definition of such seed constraints and adds further options to even more constrain the seed selection. The list of options is given by

• --seedBP : the number of base pairs the seed has to show
• --seedMaxUP : the maximal overall number of unpaired bases within the seed
• --seedQMaxUP : the maximal number of unpaired bases within the query's seed region
• --seedTMaxUP : the maximal number of unpaired bases within the target's seed region
• --seedMaxE : the maximal overall energy of the seed (to exclude weak seed interactions)
• --seedMinPu : the minimal unpaired probability of each seed region in query and target
• --seedQRange : a list of index intervals where a seed in the query is allowed
• --seedTRange : a list of index intervals where a seed in the target is allowed

Alternatively, you can set

• --seedTQ : to specify explicit seed interactions

Seed constraint usage can be globally disabled using the --noSeed flag.

Explicit seed input

Some experiments provide hints or explicit knowledge about the seed or even provide details about some intermolecular base pairs formed between two RNAs. This information can be incorporated into IntaRNA predictions by providing explicit seed information. To this end, the --seedTQ parameter can be used. It takes a comma-separated list of seed string encodings in the format startTbpsT&startQbpsQ, which is in the same format as the IntaRNA hybridDB output (see below), i.e. e.g. --seedTQ='4|||.|&7||.||' (ensure you quote the seed encoding to avoid a shell interpretation of the pipe symbol '|') to encode a seed interaction like the following

target
             4    8
             |    |
      5'-AAAC    C UGGUUUGG-3'
             AC C C
             || | |
             UG G G
      3'-GGUU  U   CCCACAAA-5'
             |    |
            11    7
query

If several or alternative seeds are known, you can provide all as a comma-separated list and IntaRNA will consider all interactions that cover at least one of them.

Output modes

The RNA-RNA interactions predicted by IntaRNA can be provided in different formats. The style is set via the argument --outMode and the different modes will be discussed below.

Furthermore, it is possible to define where to output, i.e. using --out you can either name a file or one of the stream names STDOUT|STDERR. Note, any string not matching one of the two stream names is considered a file name. The file will be overwritten by IntaRNA!

### Standard RNA-RNA interaction output with ASCII chart

The standard output mode --outMode=D provides a detailed ASCII chart of the interaction together with its overall interaction energy. For an example see the Just run ... section.

### Detailed RNA-RNA interaction output with ASCII chart

Using --outMode=D, a detailed ASCII chart of the interaction together with various interaction details will be provided. An example is given below.

# call: IntaRNA -t AAACACCCCCGGUGGUUUGG -q AAACACCCCCGGUGGUUUGG --outMode=D --seedBP=4

target
             5      12
             |      |
      5'-AAAC   C    UGGUUUGG-3'
             ACC CCGG
             ||| ||||
             UGG GGCC
      3'-GGUU   U    CCCACAAA-5'
             |      |
            16      9
query

interaction seq1   = 5 -- 12
interaction seq2   = 9 -- 16

interaction energy = -2.78924 kcal/mol
  = E(init)        = 4.1
  + E(loops)       = -13.9
  + E(dangleLeft)  = -0.458042
  + E(dangleRight) = -0.967473
  + E(endLeft)     = 0.5
  + E(endRight)    = 0
  + ED(seq1)       = 3.91068
  + ED(seq2)       = 4.0256
  + Pu(seq1)       = 0.00175516
  + Pu(seq2)       = 0.00145659

seed seq1   = 9 -- 12
seed seq2   = 9 -- 12
seed energy = -1.4098 kcal/mol
seed ED1    = 2.66437 kcal/mol
seed ED2    = 2.66437 kcal/mol
seed Pu1    = 0.0132596
seed Pu2    = 0.0132596

Position annotations start indexing with 1 at the 5'-end of each RNA. ED values are the energy penalties for reduced accessibility and Pu denote unpaired probabilities of the respective interacting subsequences.

### Customizable CSV RNA-RNA interaction output

IntaRNA provides via --outMode=C a flexible interface to generate RNA-RNA interaction output in CSV format (using ; as separator).

# call: IntaRNA -t AAACACCCCCGGUGGUUUGG -q AAACACCCCCGGUGGUUUGG --outMode=C --noSeed --outOverlap=B -n 3
id1;start1;end1;id2;start2;end2;subseqDP;hybridDP;E
target;4;14;query;4;14;CACCCCCGGUG&CACCCCCGGUG;((((...((((&))))...))));-4.14154
target;5;16;query;5;16;ACCCCCGGUGGU&ACCCCCGGUGGU;(((((.((.(((&))))).)).)));-4.04334
target;1;14;query;4;18;AAACACCCCCGGUG&CACCCCCGGUGGUUU;(((((((...((((&))))...)))).)));-2.94305

For each prediction, a row in the CSV is generated.

Using the argument --outCsvCols, the user can specify what columns are printed to the output using a comma-separated list of colIds. Available colIds are

• id1 : id of first sequence
• id2 : id of second sequence
• seq1 : full first sequence
• seq2 : full second sequence
• subseq1 : interacting subsequence of first sequence
• subseq2 : interacting subsequence of second sequence
• subseqDP : hybrid subsequences compatible with hybridDP
• subseqDB : hybrid subsequences compatible with hybridDB
• start1 : start index of hybrid in seq1
• end1 : end index of hybrid in seq1
• start2 : start index of hybrid in seq2
• end2 : end index of hybrid in seq2
• hybridDP : hybrid in VRNA dot-bracket notation
• hybridDB : hybrid in dot-bar notation
• E : overall hybridization energy
• ED1 : ED value of seq1
• ED2 : ED value of seq2
• Pu1 : probability to be accessible for seq1
• Pu2 : probability to be accessible for seq2
• E_init : initiation energy
• E_loops : sum of loop energies (excluding E_init)
• E_dangleL : dangling end contribution of base pair (start1,end2)
• E_dangleR : dangling end contribution of base pair (end1,start2)
• E_endL : penalty of closing base pair (start1,end2)
• E_endR : penalty of closing base pair (end1,start2)
• E_hybrid : energy of hybridization only = E - ED1 - ED2
• E_norm : length normalized energy = E / ln(length(seq1)*length(seq2))
• E_hybridNorm : length normalized energy of hybridization only = E_hybrid / ln(length(seq1)*length(seq2))
• seedStart1 : start index of the seed in seq1
• seedEnd1 : end index of the seed in seq1
• seedStart2 : start index of the seed in seq2
• seedEnd2 : end index of the seed in seq2
• seedE : overall hybridization energy of the seed only (excluding rest)
• seedED1 : ED value of seq1 of the seed only (excluding rest)
• seedED2 : ED value of seq2 of the seed only (excluding rest)
• seedPu1 : probability of seed region to be accessible for seq1
• seedPu2 : probability of seed region to be accessible for seq2

Using --outCsvCols '', all available columns are added to the output.

Energies are provided in unit kcal/mol, probabilities in the interval [0,1]. Position annotations start indexing with 1.

The hybridDP format is a dot-bracket notation as e.g. generated by RNAup. Here, for each target sequence position within the interaction, a '.' represents a position not involved in the interaction while a '(' marks an interacting position. For the query sequence this is done analogously but using a ')' for interacting positions. Both resulting strings are concatenated by a separator '&' to yield a single string encoding of the interaction's base pairing details.

The hybridDB format is similar to the hybridDP but also provides site information. Here, a bar '|' is used in both base pairing encodings (which makes it a 'dot-bar encoding'). Furthermore, each interaction string is prefixed with the start position of the respective interaction site.

In the following, an altered CSV output for the example from above is generated.

# call: IntaRNA --outCsvCols=Pu1,Pu2,subseqDB,hybridDB -t AAACACCCCCGGUGGUUUGG -q AAACACCCCCGGUGGUUUGG --outMode=C --noSeed --outOverlap=B -n 3
Pu1;Pu2;subseqDB;hybridDB
0.00133893;0.00133893;4CACCCCCGGUG&4CACCCCCGGUG;4||||...||||&4||||...||||
0.00134094;0.00134094;5ACCCCCGGUGGU&5ACCCCCGGUGGU;5|||||.||.|||&5|||||.||.|||
0.00133686;0.0013368;1AAACACCCCCGGUG&4CACCCCCGGUGGUUU;1|||||||...||||&4||||...||||.|||

### Backward compatible IntaRNA v1.* output

If your scripts/whatever is tuned to the old IntaRNA v1.* output, you can use

• --outMode=1 : IntaRNA v1.* normal output
• --outMode=O : IntaRNA v1.* detailed output (former -o option)

Suboptimal RNA-RNA interaction prediction and output restrictions

Besides the identification of the optimal (e.g. minimum-free-energy) RNA-RNA interaction, IntaRNA enables the enumeration of suboptimal interactions. To this end, the argument -n N or --outNumber=N can be used to generate up to N interactions for each query-target pair (including the optimal one). Note, the suboptimal enumeration is increasingly sorted by energy.

Furthermore, it is possible to restrict (sub)optimal enumeration using

• --outMaxE : maximal energy for any interaction reported
• --outDeltaE : maximal energy difference of suboptimal interactions' energy to the minimum free energy interaction
• --outOverlap : defines if an where overlapping of reported interaction sites is allowed (Note, IntaRNA v1.* used implicitly the 'T' mode):
  • 'N' : no overlap neither in target nor query allowed for reported interactions
  • 'B' : overlap allowed for interacting subsequences for both target and query
  • 'T' : overlap allowed for interacting subsequences in target only
  • 'Q' : overlap allowed for interacting subsequences in query only

Energy parameters and temperatures

The selection of the correct temperature and energy parameters is cruicial for a correct RNA-RNA interaction prediction. To this end, various settings are supported by IntaRNA.

The temperature can be set via --temperature=Cto set a temperature C in degree Celsius. Note, this is important especially for predictions within plants etc., since the default temperature is 37°C.

The energy model used can be specified using the --energy parameters using

• 'B' for base pair maximization similar to the Nussinov intramolecular structure prediction. Here, each base pair contributes an energy term of -1 independently of its structural or sequence context. This mode is mainly useful for study or teaching purpose.
• 'V' enables Nearest Neighbor Model energy computation similar to the Zuker intramolecular structure prediction using the Vienna RNA package routines. Within this model, the energy contribution of a base pair depends on its directly enclosed (neighbored) basepair and the subsequence(s) involved. Different energy parameter sets have been experimentally derived in the last decades. Since IntaRNA makes use of the energy evaluation routines of the Vienna RNA package, all parameter sets from the Vienna RNA package are available for RNA-RNA interaction prediction. Per default, the default parameter set of the linked Vienna RNA package version is used. You can change the parameter set using the --energyVRNA parameter as explained below.

If Vienna RNA package is used for energy computation (--energy=V), per default the default parameter set of the linked Vienna RNA package is used (e.g. the set Turner04 for VRNA 2.3.0). If you want to use a different parameter set, you can provide an according parameter file via --energVRNA=MyParamFile. The following example exemplifies the use of the old Turner99 parameter set as used by IntaRNA v1.*.

# IntaRNA v1.* like energy parameter setup
IntaRNA --energyVRNA=/usr/local/share/Vienna/rna_turner1999.par --seedMaxE=999

To increase prediction quality and to reduce the computational complexity, the number of unpaired bases between intermolecular base pairs is restricted (similar to internal loop length restrictions in the Zuker algorithm). The upper bound can be set independent for the query and target sequence via --qIntLoopMax and --tIntLoopMax, respectively, and default to 16.

Additional output files

IntaRNA v2 enables the generation of various additional information in dedicated files/streams. The generation of such output is guided by an according (repeated) definition of the --out argument in combination with one of the following argument prefixes (case insensitive) that have to be colon-separated to the targeted file/stream name:

• qMinE:|tMinE: the query/target's minimal interaction energy profile (CSV format), respectively
• pMinE: the minimal interaction energy for all pairs of query-target index pairs (CSV format)
• qAcc:|tAcc: the query/target's ED accessibility values (RNAplfold-like format), respectively
• qPu:|tPu: the query/target's unpaired probabilities (RNAplfold format), respectively

Note, for multiple sequences in FASTA input, the provided file names are suffixed with with -s and the according sequence's number (where indexing starts with 1) within the FASTA input to generate an individual file for each sequence. For pMinE output, if multiple query-target combinations are to be reported, the file name is suffixed with -q#t# (where # denotes the according sequence number within the input.

### Minimal energy profiles

To get a more global view of possible interaction sites for a pair of interacting RNAs, one can generate the minimal energy profile for each sequence (independently).

For instance, to generate the target's profile, add the following to your IntaRNA call: --out=tMinE:MYPROFILEFILE.csv. For the query's profile, use --out=qMinE:.. respectively. This will produce an according CSV-file (; separated) with the according minimal energy profile data that can be visualized with any program of your liking.

In the following, such an output was visualized using R:

d <- read.table("MYPROFLEFILE.csv", header=T, sep=";");
plot( d[,1], d[,3], xlab="sequence index", ylab="minimal energy", type="l", col="blue", lwd=2)
abline(h=0, col="red", lty=2, lwd=2)

This plot reveals two less but still stable (E below 0) interaction sites beside the mfe interaction close to the 5'-end of the molecule.

### Minimal energy for all intermolecular index pairs

To investigate how stable RNA-RNA interactions are distributed for a given pair of RNAs, you can also generate the minimal energy for all intermolecular index pairs using --out=pMinE:MYPAIRMINE.csv. This generates a CSV file (;separated) holding for each index pair the minimal energy of any interaction covering this index combination or NA if no covers it at all.

This information can be visualized with your preferred program. In the following, the provided R call is used to generate a heatmap visualization of the RNA-RNA interaction possibilities.

# read data, skip first column, and replace NA and E>0 values with 0
d <- read.table("MYPAIRMINE.csv",header=T,sep=";");
d <- d[,2:ncol(d)];
d[is.na(d)] = 0;
d[d>0] = 0;
# plot
image( 1:nrow(d), 1:ncol(d), as.matrix(d), col = heat.colors(100), xlab="index in sequence 1", ylab="index in sequence 2");
box();

The following plot (for the minimal energy profile example from above) reveals, that the alternative stable (E<0) interactions all involve the mfe-site in the second sequence and are thus less likely to occure.

### Accessibility and unpaired probabilities

Accessibility describes the availability of an RNA subsequence for intermolecular base pairing. It can be expressed in terms of the probability of the subsequence to be unpaired (its unpaired probability Pu).

A limited accessibility, i.e. a low unpaired probability, can be incorporated into the RNA-RNA interaction prediction by adding according energy penalties. These so called ED values are transformed unpaired probabilities, i.e. the penalty for a subsequence partaking in an interaction is given by ED=-RT log(Pu), where Pu denotes the unpaired probability of the subsequence. Within the IntaRNA energy model, ED values for both interacting subsequences are considered.

Accessibility incorporation can be disabled for query or target sequences using --qAcc=N or --tAcc=N, respectively.

A setup of --qAcc=C or --tAcc=C (default) enables accessibility computation using the Vienna RNA package routines for query or target sequences, respectively.

#### Local versus global unpaired probabilities

Exact computation of unpaired probabilities (Pu terms) is considers all possible structures the sequence can adopt (the whole structure ensemble). This is referred to as global unpaired probabilities as computed e.g. by RNAup.

Since global probability computation is (a) computationally demanding and (b) not reasonable for long sequences, local RNA folding was suggested, which also enables according local unpaired probability computation, as e.g. done by RNAplfold. Here, a folding window of a defined length 'screens' along the RNA and computes unpaired probabilities within the window (while only intramolecular base pairs within the window are considered).

IntaRNA enables both global as well as local unpaired probability computation. To this end, the sliding window length has to be specified in order to enable/disable local folding.

Use case examples global/local unpaired probability computation

The use of global or local accessibilities can be defined independently for query and target sequences using --qAccW|L and --tAccW|L, respectively. Here, --?AccW defines the sliding window length (0 sets it to the whole sequence length) and --?AccL defines the maximal length of considered intramolecular base pairs, i.e. the maximal number of positions enclosed by a base pair (0 sets it to the whole sequence length). Both can be defined independently while respecting AccL <= AccW.

# using global accessibilities for query and target
IntaRNA [..] --qAccW=0 --qAccL=0 --tAccW=0 --qAccL=0
# using local accessibilities for target and global for query
IntaRNA [..] --qAccW=0 --qAccL=0 --tAccW=150 --qAccL=100

#### Read/write accessibility from/to file or stream

It is possible to read precomputed accessibility values from file or stream to avoid their runtime demanding computation. To this end, we support the following formats

Input format	produced by
RNAplfold unpaired probabilities	RNAplfold -u or IntaRNA --out=*Pu:
RNAplfold-styled ED values	IntaRNA --out=*Acc:

The RNAplfold format is a table encoding of a banded upper triangular matrix with band width l. First row contains a header comment on the data starting with #. Second line encodes the column headers, i.e. the window width per column. Every successive line starts with the index (starting from 1) of the window end followed by a tabulator separated list for each windows value in increasing window length order. That is, column 2 holds values for window length 1, column 3 for length 2, ... . The following provides a short output/input example for a sequence of length 5 with a maximal window length of 3.

#unpaired probabilities
 #i$	l=1	2	3	
1	0.9949492	NA	NA	
2	0.9949079	0.9941056	NA	
3	0.9554214	0.9518663	0.9511048		
4	0.9165814	0.9122866	0.9090283		
5	0.998999	0.915609	0.9117766		
6	0.8549929	0.8541667	0.8448852		

Use case examples for read/write accessibilities and unpaired probabilities

If you have precomputed data, e.g. the file plfold_lunp with unpaired probabilities computed by RNAplfold, you can run

# fill accessibilities from RNAplfold unpaired probabilities
IntaRNA [..] --qAcc=P --qAccFile=plfold_lunp
# fill accessibilities from RNAplfold unpaired probabilities via pipe
cat plfold_lunp | IntaRNA [..] --qAcc=P --qAccFile=STDIN

Another option is to store the accessibility data computed by IntaRNA for successive calls using

# storing and reusing (target) accessibility (Pu) data for successive IntaRNA calls
IntaRNA [..] --out=tPu:intarna.target.pu
IntaRNA [..] --tAcc=P --tAccFile=intarna.target.pu
# piping (target) accessibilities (ED values) between IntaRNA calls
IntaRNA [..] --out=tAcc:STDOUT | IntaRNA [..] --tAcc=E --tAccFile=STDIN

Note, for multiple sequences in FASTA input, one can also load the accessibilities (for all sequencces) from file. To this end, the file names have to be prefixed with with s and the according sequence's number (where indexing starts with 1) within the FASTA input using a common suffix after the index. This suffix is to be provided to the according --?AccFile argument. The files generated by --out=?Acc:... are already conform to this requirement, such that you can use the use case examples from above also for multi-sequence FASTA input. Note, this is not supported for a piped setup (e.g. via --out=tAcc:STDOUT as shown above), since this does not produce the according output files!

Multi-threading and parallelized computation

IntaRNA supports the parallelization of the target-query-combination processing. The maximal number of threads to be used can be specified using the --threads parameter. If --threads=k > 0, than k predictions are processed in parallel.

When using parallelization, you should have the following things in mind:

• Most of the IntaRNA runtime (in heuristic prediction mode) is consumed by accessibility computation (if not loaded from file). Currently, due to some thread-safety issues with the routines from the Vienna RNA package, the IntaRNA accessibility computation is done serially. This significantly reduces the multi-threading effect when running IntaRNA in the fast heuristic mode (--mode=H). If you run a non-heuristic prediction mode, multi-threading will show a more dramatic decrease in runtime performance, since here the interaction prediction is the computationally more demanding step.
• The memory consumption will be much higher, since each thread runs an independent prediction (with according memory consumption). Thus, ensure you have enough RAM available when using many threads of memory-demanding prediction modes.

The support for multi-threading can be completely disabled before compilation using configure --disable-multithreading.

Library for integration in external tools

The IntaRNA package also comes with a C++ library libIntaRNA.a containing the core classes and functionalities used within the IntaRNA tool. The whole library comes with an IntaRNA namespace and exhaustive class and member API documentation that is processed using doxygen to generate html/pdf versions.

When IntaRNA is build while pkg-config is present, according pkg-config information is generated and installed too.

Mandatory Easylogging++ initalization !

Since IntaRNA makes heavy use of the Easylogging++ library, you have to add (and adapt) the following code to your central code that includes the main() function:

// get central IntaRNA-lib definitions and includes
#include <IntaRNA/RnaSequence.h>
// initialize logging for binary
INITIALIZE_EASYLOGGINGPP

[...]

int main(int argc, char **argv){

[...]
		// set overall logging style
		el::Loggers::reconfigureAllLoggers(el::ConfigurationType::Format, std::string("# %level : %msg"));
		// no log file output
		el::Loggers::reconfigureAllLoggers(el::ConfigurationType::ToFile, std::string("false"));
		// set additional logging flags
		el::Loggers::addFlag(el::LoggingFlag::ColoredTerminalOutput);
		el::Loggers::addFlag(el::LoggingFlag::DisableApplicationAbortOnFatalLog);
		el::Loggers::addFlag(el::LoggingFlag::LogDetailedCrashReason);
		el::Loggers::addFlag(el::LoggingFlag::AllowVerboseIfModuleNotSpecified);

		// setup logging with given parameters
		START_EASYLOGGINGPP(argc, argv);
[...]
}