slackbuilds/academic/locarna/README

LocARNA: Alignment of RNAs
==========================

LocARNA is a collection of alignment tools for the structural analysis
of RNA. Given a set of RNA sequences, LocARNA simultaneously aligns
and predicts common structures for your RNAs. In this way, LocARNA
performs Sankoff-like alignment and is in particular suited for
analyzing sets of related RNAs without known common structure.

LocARNA distinguishes itself from many other Sankoff-style multiple
alignment programs by its performance and low memory complexity, high
accuracy, and richness of features. As unique features, it offers
structure-local alignment, flexible structure and anchor constraints,
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 the incorporation of
new features in RNA sequence-structure alignment.


At its core, the package offers global and local multiple alignment of
RNAs.

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.