22,018 research outputs found
RNA secondary structure prediction from multi-aligned sequences
It has been well accepted that the RNA secondary structures of most
functional non-coding RNAs (ncRNAs) are closely related to their functions and
are conserved during evolution. Hence, prediction of conserved secondary
structures from evolutionarily related sequences is one important task in RNA
bioinformatics; the methods are useful not only to further functional analyses
of ncRNAs but also to improve the accuracy of secondary structure predictions
and to find novel functional RNAs from the genome. In this review, I focus on
common secondary structure prediction from a given aligned RNA sequence, in
which one secondary structure whose length is equal to that of the input
alignment is predicted. I systematically review and classify existing tools and
algorithms for the problem, by utilizing the information employed in the tools
and by adopting a unified viewpoint based on maximum expected gain (MEG)
estimators. I believe that this classification will allow a deeper
understanding of each tool and provide users with useful information for
selecting tools for common secondary structure predictions.Comment: A preprint of an invited review manuscript that will be published in
a chapter of the book `Methods in Molecular Biology'. Note that this version
of the manuscript may differ from the published versio
A new procedure to analyze RNA non-branching structures
RNA structure prediction and structural motifs analysis are challenging tasks in the investigation of RNA function. We propose a novel procedure to detect structural motifs shared between two RNAs (a reference and a target). In particular, we developed two core modules: (i) nbRSSP_extractor, to assign a unique structure to the reference RNA encoded by a set of non-branching structures; (ii) SSD_finder, to detect structural motifs that the target RNA shares with the reference, by means of a new score function that rewards the relative distance of the target non-branching structures compared to the reference ones. We integrated these algorithms with already existing software to reach a coherent pipeline able to perform the following two main tasks: prediction of RNA structures (integration of RNALfold and nbRSSP_extractor) and search for chains of matches (integration of Structator and SSD_finder)
Simultaneous alignment and folding of protein sequences
Accurate comparative analysis tools for low-homology proteins remains a difficult challenge in computational biology, especially sequence alignment and consensus folding problems. We presentpartiFold-Align, the first algorithm for simultaneous alignment and consensus folding of unaligned protein sequences; the algorithm’s complexity is polynomial in time and space. Algorithmically,partiFold-Align exploits sparsity in the set of super-secondary structure pairings and alignment candidates to achieve an effectively cubic running time for simultaneous pairwise alignment and folding. We demonstrate the efficacy of these techniques on transmembrane β-barrel proteins, an important yet difficult class of proteins with few known three-dimensional structures. Testing against structurally derived sequence alignments,partiFold-Align significantly outperforms state-of-the-art pairwise sequence alignment tools in the most difficult low sequence homology case and improves secondary structure prediction where current approaches fail. Importantly, partiFold-Align requires no prior training. These general techniques are widely applicable to many more protein families. partiFold-Align is available at http://partiFold.csail.mit.edu
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