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https://github.com/s-will/locarna

Alignment of RNAs
https://github.com/s-will/locarna

alignment rna-structural-analysis rna-structure-prediction

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Alignment of RNAs

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README 

        
[TOC]



LocARNA: Alignment of RNAs

==========================



The LocARNA package provides several tools for the structural analysis of

RNA. LocARNA's main functionality is to align a set of a priori unaligned

RNAs sequences and at the same time predict their common structure. In

this way, LocARNA performs simultaneous alignment and folding in the spirit

of the classical Sankoff algorithm, but implements strategies to perform

this computationally challenging task efficiently and comparably fast.



Due to the central ability to simultaneously assess sequence similarity and

the similarity of predicted structure, LocARNA is recommends itself for the

analysis of RNAs in the twilight zone (around or below 60% sequence

identity), where alignments based on only sequence similarity are

unreliable. Thus, it could be sometimes easier and typically faster to align highly

similar RNAs using pure sequence alignment tools; similarily, RNAs with existing trusted

alignments can be more efficiently analyzed based on specialized tools like

RNAalifold, R-scape, or Infernal.



### Example of standard usage



Most of the package's functionality is accessible via the command-line tool [`mlocarna`](https://s-will.github.io/LocARNA/md_src_Utils_mlocarna.html)

through its various options. In the simplest case, we provide the input

sequences in a fasta file.



```

$ mlocarna archaea.fa

```



yields text output and writes results (and intermediary results) to disk; here to folder ```archaea.out```.



As main result, it produces the alignment of the seven short RNA sequences in [`archaea.fa`](https://raw.githubusercontent.com/s-will/LocARNA/master/Data/Examples/archaea.fa) together with a consensus structure:



![](https://raw.githubusercontent.com/s-will/LocARNA/master/Data/Examples/archaea-aln.png)



The graphical RNAalifold-generated output shows the aligned RNAs (with

gaps), the consensus structure as dot-bracket string on top, and the column

similarities by bars at the bottom. In the way of alifold, columns are

color-coded to visualize compensatory and incompatible mutations at

predicted base pairs.



### More on features and alignment variants



LocARNA distinguishes itself from many other Sankoff-style multiple

alignment programs by its high performance (strongly improved in the 2.x

line) and low memory complexity, high accuracy, and a broad set of features.

As unique features, it offers structure-local alignment,

flexible structure constraints and anchor constraints, specialized

realignment modes for refining existing alignments, and provides efficient

computation of reliabilities in sequence-structure alignment.

The package offers a robust core of

features and is used as experimental platform for new RNA alignment related methods.



Multiple alignment can be performed in one of several different ways:



* progressive alignment using sequence-structure alignment of profiles



* progressive alignment after consistency transformation using

T-Coffee



* progressive alignment using probabilistic consistency transformation

  and sequence-structure profile alignments, optionally followed by

  iterative refinement.



Besides of global alignment, LocARNA supports two kinds of

locality. Local alignment as it is known from sequence alignment,

identifies and aligns the best matching subsequences. This form of

locality is called sequence local to distinguish it from structural

locality. When performing structure local alignment, LocARNA

identifies and aligns the best matching substructures in the RNAs. The

sequences of those substructures can be discontinuous on the sequence

level, but remain connected via structural bonds.



Alignment Reliabilities (LocARNA-P). In this special, probabilistic

mode of operation LocARNA supports the efficient computation of match

probabilities, probabilistic consistency transformation for more

accurate multiple alignment, and generates reliability profiles of

multiple alignments.



------------

Installation

------------



The software can be installed on recent Linux or MacOSX systems; Windows

is untested but should be supported via WSL.



### Installation from Conda package (recommended)



On Mac/Linux, LocARNA is installed most easily via Conda from a pre-compiled

package. For this purpose, install Conda and run from the command line:



```

conda install -c conda-forge -c bioconda locarna

```



### Alternative installation from source



Installing from source requires a C++ compiler (GNU C++,

Clang, ...) and Autotools. Moreover, it depends on the Vienna RNA package.



#### Installation from source distribution



Obtain the tar.gz source distribution, e.g. from Github



[https://github.com/s-will/LocARNA/releases](https://github.com/s-will/LocARNA/releases)



Then, build and install like



```

tar xzf locarna-xxx.tar.gz

cd locarna-xxx

./configure --prefix=/usr/local

make

make install

```



Is Vienna RNA installed in a non-standard location, this has to be

specified by configure option ```--with-vrna=path-to-vrna``.



Installing from source furthermore allows testing via



```

make check

```



and building documentation locally by



```

make doxygen-doc

```



Building documentation requires additional tools: doxygen, pod2markdown and pandoc.



#### Installation from the git repository



Installing from repository is possible after cloning and setting up the

autotools suite. This is most easily achieved by running



```

autoreconf -i

```



in the cloned repository. Then, the installation essentially works like

installing from source distribution. Note that, we will however require

additional tools to build the documentation: help2man, pod2man.



-----

Usage

-----



For instructions on the use of the tools, please see the documentation / man pages of

the single tools



* [mlocarna](https://s-will.github.io/LocARNA/md_src_Utils_mlocarna.html) --- for multiple alignment of

  RNAs.  This program supports most of the functionality of the package via

  a high level interface.



* [locarna](https://s-will.github.io/LocARNA/md__build_Doc_man_locarna.html) --- for pairwise alignment



* [locarna_p](https://s-will.github.io/LocARNA/md__build_Doc_man_locarna_p.html) --- for pairwise computation of alignment partition function

  and (sequence and structure) match probabilities



* [sparse](https://s-will.github.io/LocARNA/md__build_Doc_man_sparse.html) --- for structurally stronger sparsified pairwise alignment



For additional functionality and special purposes, see



* [exparna_p](https://s-will.github.io/LocARNA/md__build_Doc_man_exparna_p.html) --- for generating exact matches from the ensembles of two RNAs



* [locarnate](https://s-will.github.io/LocARNA/md_src_Utils_locarnate.html) --- for multiple alignment of

  RNAs via T-Coffee. This script offers multiple alignment of RNAs that is

  performed by sequence-structurally aligning all pairs of RNAs and then

  using T-Coffee to construct a common multiple alignment out of all

  pairwise ones.



----------

Web server

----------



The core functionality of the package is accessible through a web

interface at



   http://rna.informatik.uni-freiburg.de



-------

Contact

-------



Main author and contact: Sebastian Will sebastian.will (at) polytechnique.edu



------------

References

------------



* Sebastian Will, Kristin Reiche, Ivo L. Hofacker, Peter F. Stadler, and Rolf Backofen. Inferring non-coding RNA families and classes by means of genome-scale structure-based clustering. PLOS Computational Biology, 3 no. 4 pp. e65, 2007. doi:10.1371/journal.pcbi.0030065

* Sebastian Will, Tejal Joshi, Ivo L. Hofacker, Peter F. Stadler, and Rolf Backofen. LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs. RNA, 18 no. 5 pp. 900-914, 2012. doi:10.1261/rna.029041.111

* Sebastian Will, Michael Yu, and Bonnie Berger. Structure-based Whole Genome Realignment Reveals Many Novel Non-coding RNAs. Genome Research, no. 23 pp. 1018-1027, 2013. doi:10.1101/gr.137091.111

* Sebastian Will, Christina Otto, Milad Miladi, Mathias Mohl, and Rolf Backofen. SPARSE: quadratic time simultaneous alignment and folding of RNAs without sequence-based heuristics. Bioinformatics, 31(15):2489–2496, 2015. doi:10.1093/bioinformatics/btv185