Skip to main content
  • Poster presentation
  • Open access
  • Published:

Multiple alignment and structure prediction of non-coding RNA sequences


As the importance of non-coding RNAs becomes more evident, the need for computational methods for ncRNAs grows. Predicting the secondary structure is of great importance, and combining this with multiple alignment yields a useful tool for researchers. The exact solution to the problem of simultaneous multiple alignment and structure prediction for RNA sequences was described by Sankoff [1], but to date only pairwise implementations (e.g. Foldalign [2], Dynalign [3]) or heuristics for multiple sequences (e.g. FoldalignM [4], LocARNA [5], RNA Sampler [6]) exist.


We present a novel approach to the problem: Using Markov chain Monte Carlo in a simulated annealing framework, we sample multiple alignments and secondary structures. The sampling is based on a scoring system that combines a sequence measure with a structure measure: The sequence alignment is scored using the log-likelihood, and the structure is scored using basepair probabilities and a covariation term. The sampling procedure itself uses simple local moves to optimize the solution. These moves either act on the sequence alignment or the predicted structure. The input to the program can be unaligned sequences or an alignment obtained using e.g. Clustal. The structure can be constrained by indicating e.g. basepairs or unpaired positions in one of the sequences. The program MASTR (M ultiple A lignment of ST ructural R NAs) is implemented in C++.


MASTR is compared to LocARNA, FoldalignM, RNA Sampler and Clustal+RNAalifold on various RNA families. The datasets are unaligned and of varying average pairwise identities ranging from 30% to 100%. The sequence alignments are consistently better than or comparable to all other methods, the running time is significantly faster than FoldalignM and RNA Sampler, and MASTR can handle larger datasets than both these programs. RNA Sampler is best on datasets with identities between 30% and 45%, but MASTR is better than all other programs tested from 45% ID up to 80% ID, where the structure predictions deteriorate due to the poor covariation signal.


  1. Sankoff D: Simultaneous solution of the RNA folding, alignment, and protosequence problems. SIAM J Appl Math 1985, 45: 810–825. 10.1137/0145048

    Article  Google Scholar 

  2. Havgaard JH, Lyngsø RB, Stormo GD, Gorodkin J: Pairwise local structural alignment of RNA sequences with sequence similarity less than 40%. Bioinformatics 2005, 21(9):1815–1824. 10.1093/bioinformatics/bti279

    Article  CAS  PubMed  Google Scholar 

  3. Mathews DH, Turner DH: Dynalign: an algorithm for finding the secondary structure common to two RNA sequences. J Mol Biol 2002, 317(2):191–203. 10.1006/jmbi.2001.5351

    Article  CAS  PubMed  Google Scholar 

  4. Torarinsson E, Havgaard JH, Gorodkin J: Multiple structural alignment and clustering of RNA sequences. Bioinformatics 2007, 23(8):926–932. 10.1093/bioinformatics/btm049

    Article  CAS  PubMed  Google Scholar 

  5. Will S, Reiche K, Hofacker IL, Stadler PF, Backofen R: Inferring noncoding RNA families and classes by means of genome-scale structure-based clustering. PLoS Comput Biol 2007, 3(4):e65. 10.1371/journal.pcbi.0030065

    Article  PubMed Central  PubMed  Google Scholar 

  6. Xu X, Ji Y, Stormo GD: RNA Sampler: A new sampling based algorithm for common RNA secondary structure prediction and structural alignment. Bioinformatics 2007, in press.

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Stinus Lindgreen.

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Lindgreen, S., Gardner, P.P. & Krogh, A. Multiple alignment and structure prediction of non-coding RNA sequences. BMC Bioinformatics 8 (Suppl 8), P8 (2007).

Download citation

  • Published:

  • DOI: