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README for the software

by Kengo Sato — last modified 2008-12-20 01:09

CentroidFold for predicting RNA secondary structures

CentroidFold is one of the most accurate tools for predicting RNA secondary structures. It is based on the gamma-centroid estimator (Hamada et.al., 2008) for high-dimensional discrete spaces. Generally, a gamma-centroid estimator is slightly more accurate than an MEA estimator (Do et.al., 2005) under the same probability distribution.

Furthermore, CentroidFold can predict common secondary structures for multiple alignments of RNA sequences by using an averaged gamma-centroid estimator (Hamada et al., 2008).

CentroidFold can employ various probability distributions. Currently,

  • McCaskill models implemented in Vienna RNA package,
  • CONTRAfold models, and
  • CG optimized models implemented in MultiRNAFold package

are supported.

According to our benchmark, CentroidFold with CONTRAfold models will predict the most accurate RNA secondary structures among currently available prediction tools at this time.

Requirements

Install

Build without linking CONTRAfold

./configure && make && make install

Build with linking CONTRAfold

Currently, a patch for CONTRAfold v2.0.0 is provided, which makes it possible to link the routine for calculating base-pairing probabilities in CONTRAfold with CentroidFold.

After applying this patch, you can build libcontrafold.a.

cd /somewhere/contrafold/src
patch -p2 < /somewhere/centroid_fold-x.x.x/contrafold.patch
make lib

Then, put libcontrafold.a into the CentroidFold build tree.

cp libcontrafold.a /somewhere/centroid_fold-x.x.x/src/

Finally, build CentroidFold.

cd /somewhere/centroid_fold-x.x.x/
./configure --with-contrafold && make && make install

If you configure CentroidFold with --with-contrafold, the CONTRAfold model are used as the default posterior probability distribution rather than the McCaskill model.

Usage

centroid_fold can take the FASTA format and the CLUSTAL format as an input sequence or a multiple alignment.

Secondary structure prediction for single sequences

centroid_fold is the main program of this package. It calculates a base-pairing probability matrix for a given sequence with the McCaskill algorithm using pf_fold routine in Vienna RNA package (If you configure CentroidFold --with-contrafold, the CONTRAfold model are used instead of the McCaskill model). Then, centroid_fold estimates a gamma-centroid estimator for the base-pairing probability matrix.

centroid_fold [options] seq

Options:
  -g [ --gamma ] arg    weight of base pairs (default: 1.0 for Centroid, 6.0 for MEA)
  --mea                 run as an MEA estimator
  --aux                 use auxiliary base-pairing probabilities

If a negative value is given for the option '--gamma', centroid_fold calculates secondary structures for several values of gamma at the same time ({2^k | -5 <= k <= 10} and 6).

Using the option '--aux', centroid_fold can take an auxiliary base-pairing probability matrix instead of the McCaskill model.

Example:

% centroid_fold -g -1 RF00008_B.fa
> RF00008_B
CAAAAGUCUGGGCUAAGCCCACUGAUGAGCCGCUGAAAUGCGGCGAAACUUUUG
.(((((...(((.....)))........(((((......)))))....))))). (g=0.03125,th=0.969697)
.(((((...(((.....)))........(((((......)))))....))))). (g=0.0625,th=0.941176)
((((((...(((.....)))........(((((......)))))....)))))) (g=0.125,th=0.888889)
((((((...((((...))))........(((((......)))))....)))))) (g=0.25,th=0.8)
(((((((..((((...))))........(((((......)))))...))))))) (g=0.5,th=0.666667)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=1,th=0.5)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=2,th=0.333333)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=4,th=0.2)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=6,th=0.142857)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=8,th=0.111111)
(((((((((((((...))))).......(((((......))))))..))))))) (g=16,th=0.0588235)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=32,th=0.030303)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=64,th=0.0153846)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=128,th=0.00775194)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=256,th=0.00389105)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=512,th=0.00194932)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=1024,th=0.00097561)

Common secondary structure predicion for multiple alignments

centroid_fold can automatically recognize the format of input sequences. For the FASTA format file, centroid_fold predicts secondary structures for every contained sequence. For the CLUSTAL format, centroid_fold predicts common secondary structures for the given multiple alignments.

Example:

% centroid_fold -g -1 RF00436.aln
>AB029447-1/1210-1265
--BCAHuUGYAVgUCGCUUUGGAYAaaAG--CGUCUGCUAAAUGM-VURwrukKAAAUDu-
............................................................. (g=0.03125,th=0.969697)
............................................................. (g=0.0625,th=0.941176)
............................................................. (g=0.125,th=0.888889)
............................................................. (g=0.25,th=0.8)
...............(((.........))..)............................. (g=0.5,th=0.666667)
..............(((((.......)))..))............................ (g=1,th=0.5)
............(.((((((.....))))..)).).......................... (g=2,th=0.333333)
........((..(.((((((.....))))..)).).))....................... (g=4,th=0.2)
...((((.((.((.((((((.....))))..)).))))..))))................. (g=6,th=0.142857)
...(((((((.((.((((((.....))))..)).)))).)))))................. (g=8,th=0.111111)
..((((((((.((.((((((.....))))..)).)))).))))))................ (g=16,th=0.0588235)
..((((((((.((.((((((.....))))..)).)))).))))))((.....))....... (g=32,th=0.030303)
..((((((((.((.((((((.....))))..)).)))).))))))((.....))....... (g=64,th=0.0153846)
..((((((((.((.((((((.....))))..)).)))).))))))((.....))....... (g=128,th=0.00775194)
..((((((((.((.((((((.....))))..)).)))).))))))(((...)))....... (g=256,th=0.00389105)
.(((((((((.((.((((((.....))))..)).)))).)))))).).((((....)))). (g=512,th=0.00194932)
.(((((((((.((.((((((.....))))..)).)))).)))))).)(((((....))))) (g=1024,th=0.00097561)

The first line of the result is the description of the first sequence in the given alignment. The second is the "most informative sequence" (Freyhult et al., 2005), which is similar to IUPAC ambiguity characters, produced by a library routine of the Vienna Package.

CentroidFold with CONTRAfold models

Even centroid_fold without libcontrafold.a can employ CONTRAfold models using a wrapper script, contrafold.rb, which executes contrafold to calculate a base-pairing probability matrix for a given sequence, and then executes centroid_fold with '--aux' to estimate the most accurate secondary structure from the base-pairing probability matrix.

Usage: contrafold.rb [options] seq
   -g, --gamma gamma                weight of base-pairs
       --mea                        MEA estimators
       --viterbi                    viterbi estimators
   -t, --threshold th               threshold of posteriors
       --centroid_fold path         exec path of centroid_fold
       --contrafold path            exec path of contrafold
       --params params              specify a parameter file for contrafold

Example:

% contrafold.rb -g -1 RF00008_B.fa
> RF00008_B
CAAAAGUCUGGGCUAAGCCCACUGAUGAGCCGCUGAAAUGCGGCGAAACUUUUG
...................................................... (g=0.03125,th=0.969697)
.............................(((........)))........... (g=0.0625,th=0.941176)
.........(((.....)))........((((........)))).......... (g=0.125,th=0.888889)
.........(((.....)))........(((((......))))).......... (g=0.25,th=0.8)
.(((((..((((.....)))).......(((((......)))))....))))). (g=0.5,th=0.666667)
((((((..(((((...))))).......(((((......)))))....)))))) (g=1,th=0.5)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=2,th=0.333333)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=4,th=0.2)
(((((((((((((...)))))..)....(((((......)))))...))))))) (g=6,th=0.142857)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=8,th=0.111111)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=16,th=0.0588235)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=32,th=0.030303)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=64,th=0.0153846)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=128,th=0.00775194)
(((((((((((((...)))))..).(..((((((...).)))))..)))))))) (g=256,th=0.00389105)
(((((((((((((...)))))..).(..((((((...).)))))..)))))))) (g=512,th=0.00194932)
(((((((((((((...)))))..).(..((((((...).)))))..)))))))) (g=1024,th=0.00097561)

CentroidFold with CG optimized models

simfold.rb is also a wrapper script which executes simfold_pf instead of contrafold.

Usage: simfold.rb [options] seq
   -g, --gamma gamma                weight of base-pairs
       --mea                        MEA estimators
       --viterbi                    viterbi estimators
   -t, --threshold th               threshold of posteriors
       --centroid_fold path         exec path of centroid_fold
       --simfold path               exec path of simfold
       --simfold_pf path            exec path of simfold_pf
       --params params              specify a parameter file for simfold

Example:

simfold.rb -g -1 --simfold_pf ~/MultiRNAFold-1.10/simfold_pf --params ~/MultiRNAFold-1.10/params/CG_best_parameters_ISMB2007.txt RF00008_B.fa
> RF00008_B
CAAAAGUCUGGGCUAAGCCCACUGAUGAGCCGCUGAAAUGCGGCGAAACUUUUG
.........(((.....))).........((((......))))........... (g=0.03125,th=0.969697)
.........(((.....)))........(((((......))))).......... (g=0.0625,th=0.941176)
.........(((.....)))........(((((......))))).......... (g=0.125,th=0.888889)
.........((((...))))........(((((......))))).......... (g=0.25,th=0.8)
....(...(((((...))))).......(((((......))))).....).... (g=0.5,th=0.666667)
((((((..(((((...))))).......(((((......)))))....)))))) (g=1,th=0.5)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=2,th=0.333333)
(((((((.(((((...))))).......(((((......)))))...))))))) (g=4,th=0.2)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=6,th=0.142857)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=8,th=0.111111)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=16,th=0.0588235)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=32,th=0.030303)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=64,th=0.0153846)
(((((((((((((...)))))..).(..(((((......)))))..)))))))) (g=128,th=0.00775194)
(((((((((((((...)))))..).(..((((((...).)))))..)))))))) (g=256,th=0.00389105)
(((((((((((((...)))))..).(..((((((...).)))))..)))))))) (g=512,th=0.00194932)
(((((((((((((...)))))..).(..((((((...).)))))..)))))))) (g=1024,th=0.00097561)

References

  • Centroid estimators
    • Carvalho, L. E. and Lawrence, C. E.: Centroid estimation in discrete high-dimensional spaces with applications in biology. Proc Natl Sci USA, 105:3209-3214, 2008.
    • Ding, Y., Chan, C. Y., and Lawrence, C. E.: RNA secondary structure prediction by centroids in a Boltzmann weighted ensemble, RNA, 11:1157-1166, 2005
    • Hamada, M., Kiryu, H., Sato, K., Mituyama, T. and Asai, K.: Prediction of RNA secondary structure using generalized centroid estimators, Bioinformatics 2008; doi: 10.1093/bioinformatics/btn601. [Abstract] [PDF]
  • CONTRAfold models and MEA estimators
    • Do, C. B., Woods, D. A. and Batzoglou, S.: CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics, 22:e90-e98, 2006.
  • McCaskill models
    • McCaskill, J. S.: The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers, 29, 1105-1119, 1990.
    • Hofacker, I. L.: Vienna RNA secondary structure server. Nucleic Acids Res, 31:3429-3431, 2003
  • CG optimized models
    • Andronescu, M., Condon, A., Hoos, H. H., Mathews, D. H. and Murphy, K. P.: Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics, 23:i19-i28, 2007
  • Others
    • Freyhult, E., Moulton, V., and Gardner, PP.: Predicting RNA structure using mutual information. Appl Bioinformatics. 4:53-59, 2004.
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