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+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.