# Bioinformatics Questions and Answers – Minimum Free – Energy Method & Stochastic Context – Free Grammars

This set of Bioinformatics Multiple Choice Questions & Answers focuses on “Minimum Free – Energy Method & Stochastic Context – Free Grammars”.

1. Which of the following is incorrect about the prediction of RNA secondary structure?
a) Every base is first compared to every other base by a type of analysis very similar to the dot matrix analysis
b) A row of matches in the RNA matrix indicates a succession of complementary nucleotides that can potentially form a double-stranded region
c) A row of matches in the RNA matrix indicates a failure of complementary nucleotides that can potentially form a double-stranded region
d) The sequence is listed across the top and down the side of the page, and G/C, A/U, and G/U base pairs are scored

Explanation: The energy of each predicted structure is estimated by the nearest- neighbor rule by summing the negative base-stacking energies for each pair of bases in double-stranded regions. By adding the estimated positive energies of destabilizing regions such as loops at the end of hairpins, bulges within hairpins, internal bulges, and other unpaired regions.

2. Through a single scoring matrix, evaluation of all the different possible configurations is done.
a) True
b) False

Explanation: To evaluate all the different possible configurations and to find the most energetically favorable, several types of scoring matrices are used. The complementary regions are evaluated by a dynamic programming algorithm to predict the most energetically stable molecule. The method is similar to the dynamic programming method used for sequence alignment.

3. The object is to find a diagonal row of matches that goes from upper left to lower right.
a) True
b) False

Explanation: The object is to find a diagonal row of matches that goes from upper right to lower left. In general, each matrix value is obtained by considering the minimum energy values, obtained by all previous complementary pairs, decreased by the stacking energy of any additional complementary base pairs or increased by the destabilizing energy associated with non-complementary bases.

4. The increase depends on the type and length of loop that is introduced by the non-complementary base pair, whether internal loop, bulge loop, or hairpin loop.
a) True
b) False

Explanation: This comparison of all possible matches and energy values is continued until all nucleotides have been compared. There is a pattern followed in comparing bases within the RNA molecule.

5. The sequence is listed down the first column of base comparisons’ table and free energy calculations’ table in the 5’→3’ orientation.
a) True
b) False

Explanation: The first four bases of the sequence are also listed in the first row of the tables in the 5’→3’ direction. Several complementary base pairs between the first and last four bases that could lead to secondary structure are shown in the tables.

6. A general theory for modeling strings of symbols, such as bases in DNA sequences, has been developed by linguists. There is a hierarchy of these so-called transformational grammars that deal with situations of increasing complexity.
a) True
b) False

Explanation: The application of these grammars to sequence analysis has been extensively discussed elsewhere. The context-free grammar is suitable for finding groups of symbols in different parts of the input sequence that thus are not in the same context.

7. ______ regions in sequences, such as those in RNA that will form secondary structures, are an example of such context-free sequences.
a) non-interlocking
b) non-Complementary
c) complementary
d) non-compatible

Explanation: Stochastic context-free grammars (SCFG) introduce uncertainty into the definition of such regions. It allows them to use alternative symbols as found in the evolution of RNA molecules.

8. The use of SCFGs in RNA secondary structure production analysis is in fact very similar to that of the covariance model, with the grammatical productions resembling the nodes in the ordered binary tree.
a) True
b) False

Explanation: As with hidden Markov models, the probability distribution of each production must be derived by training with known sequences. The algorithms used for training the SCFG and for aligning a sequence with the SCFG are somewhat different from those used with hidden Markov models, and the time and memory requirements are greater.

9. In a SCFG, each production of a non-terminal symbol has an associated probability for giving rise to the resulting product, and there are a set of productions, each giving a different result.
a) True
b) False

Explanation: For example, the production S1 → C S2 G could also be represented by 15 other base-pair combinations, and each of these has a corresponding probability. Thus, each production can be considered to be represented by a probability distribution over the possible outcomes.

10. The application of SCFGs to RNA secondary structure analysis is very similar in form to the probabilistic covariance models.
a) True
b) False

Explanation: For RNA, the symbols of the alphabet are A, C, G, and U. The context-free grammar establishes a set of rules called productions for generating the sequence from the alphabet, in this case an RNA molecule with sections that can base-pair and others that cannot base-pair.

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