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Journal Article

LaRA 2: parallel and vectorized program for sequence–structure alignment of RNA sequences


Reinert,  Knut
Efficient Algorithms for Omics Data (Knut Reinert), Max Planck Fellow Group, Max Planck Institute for Molecular Genetics, Max Planck Society;

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Winkler, J., Urgese, G., Ficarra, E., & Reinert, K. (2022). LaRA 2: parallel and vectorized program for sequence–structure alignment of RNA sequences. BMC Bioinformatics, 23: 18. doi:10.1186/s12859-021-04532-7.

Cite as: https://hdl.handle.net/21.11116/0000-0009-E229-A

The function of non-coding RNA sequences is largely determined by their spatial conformation, namely the secondary structure of the molecule, formed by Watson–Crick interactions between nucleotides. Hence, modern RNA alignment algorithms routinely take structural information into account. In order to discover yet unknown RNA families and infer their possible functions, the structural alignment of RNAs is an essential task. This task demands a lot of computational resources, especially for aligning many long sequences, and it therefore requires efficient algorithms that utilize modern hardware when available. A subset of the secondary structures contains overlapping interactions (called pseudoknots), which add additional complexity to the problem and are often ignored in available software.

We present the SeqAn-based software LaRA 2 that is significantly faster than comparable software for accurate pairwise and multiple alignments of structured RNA sequences. In contrast to other programs our approach can handle arbitrary pseudoknots. As an improved re-implementation of the LaRA tool for structural alignments, LaRA 2 uses multi-threading and vectorization for parallel execution and a new heuristic for computing a lower boundary of the solution. Our algorithmic improvements yield a program that is up to 130 times faster than the previous version.

With LaRA 2 we provide a tool to analyse large sets of RNA secondary structures in relatively short time, based on structural alignment. The produced alignments can be used to derive structural motifs for the search in genomic databases.