Biomedical Engineering Reference
In-Depth Information
Some non-coding RNAs can be found by searching for likely transcripts
that do not contain an open reading frame. A survey of the
E. coli
genome for
DNA regions that contain a
)
70 promotor within a short distance of a
Rho
-
independent terminator, for instance, resulted in 144 novel possible ncRNAs
(164). This approach is limited, however, to functional RNAs that are tran-
scribed in the "usual" manner.
Comparative approaches such as the
QRNA
program (113) can detect novel
structural RNA genes in a pair of aligned homologous sequences by deciding
whether the substitution pattern fits better with (a) synonymous substitutions,
which are expected in protein-coding regions, (b) the compensatory mutations
consistent with some base-paired secondary structure, or (c) uncorrelated muta-
tions.
Another approach tries to determine functional RNAs by means of structure
prediction. The basic assumption is that functional and hence conserved struc-
tures will be thermodynamically more stable (64,78). While such procedures are
capable of detecting some particularly stable features, a recent study (110) con-
cludes that "although a distinct, stable secondary structure is undoubtedly impor-
tant in most non-coding RNAs, the stability of most noncoding RNA secondary
structures is not sufficiently different from the predicted stability of a random
sequence to be useful as a general genefinding approach." Nevertheless, in some
special cases such as hyperthermophilic organisms,
GC
-content (and hence
thermodynamic stability) proved sufficient (74).
Since most classes of functional RNAs are relatively well conserved while
their sequences show little similarities, both comparative procedures and search
in single sequences have to rely on structural information. While the prediction
of RNA tertiary structures faces much the same problems as protein structure
prediction, efficient algorithms exist for handling RNA secondary structure. As
we have seen, these methods provide powerful tools for computational studies of
RNA structure.
6.
ACKNOWLEDGMENTS
This work is supported by the Austrian
Fonds zur Föderung der Wissen-
schaftlichen Forschung
, Project Nos. P-13545-MAT and P-15893, and the
DFG
Bioinformatics Initiative.
7.
REFERENCES
1.
Akutsu, T. 2001. Dynamic programming algorithms for RNA secondary structure prediction
with pseudoknots.
Discr Appl Math
104
:45-62.
2.
Avner P, Heard E. 2001. X-chromosome inactivation: counting, choice, and initiation.
Nature
Rev Genet
2
:59-67.
