This set of Bioinformatics Multiple Choice Questions & Answers (MCQs) focuses on “Searching Genomes for RNA & RNA Structure Modeling Applications”.
1. ______ molecules can simply be identified based on their sequence similarity with already-known sequences.
a) Larger, less conserved
b) Larger, highly conserved
c) smaller, highly conserved
d) shorter, highly conserved
View Answer
Explanation: For smaller sequences with more sequence variation, this method does not work. A number of methods for finding small RNA genes have been described and are available on the Web. A major problem with these methods in searches of large genomes is that a small false positive rate becomes quite unacceptable because there are so many false positives to check out.
2. One of the first methods used to find tRNA genes was to search for sequences that are complementary and can fold into a knot like the three found in tRNAs.
a) True
b) False
View Answer
Explanation: One of the first methods used to find tRNA genes was to search for sequences that are self-complementary and can fold into a hairpin like the three found in tRNAs (Staden 1980). Through the regions of self-complimentarity it was first possible to find the tRNA.
3. Fichant and Burks (1991) described a program, tRNAscan, that searches a genomic sequence with a sliding window searching simultaneously for matches to a set of invariant bases and conserved self-complementary regions in tRNAs with an accuracy of 97.5%.
a) True
b) False
View Answer
Explanation: A method for finding the RNA polymerase III transcriptional control regions of tRNA genes using a scoring matrix derived from known control regions, was derived. That is also very accurate. Finally, Lowe and Eddy (1997) have devised a search algorithm tRNAscan-SE that uses a combination of three methods to find tRNA genes in genomic sequences—tRNAscan, the Pavesi algorithm, and the COVELS program based on sequence covariance analysis (Eddy and Durbin 1994). This method is reportedly 99–100% accurate with an extremely low rate of false positives.
4. The probabilistic model was used to identify small nucleolar (sno) RNAs in the yeast genome that methylate ribosomal RNA.
a) True
b) False
View Answer
Explanation: The model is not used to search genomic sequences directly. Instead, a list of candidate sequences is first found by searching for patterns that match the sequences in the model (Lowe and Eddy 1999).
5. The probability model mentioned above was a hybrid combination of HMMs and SCFGs trained on sno RNAs.
a) True
b) False
View Answer
Explanation: These RNAs vary sufficiently in sequence and structure that they are not found by straightforward similarity searches. The RNAs found were shown to be sno RNAs by insertional mutagenesis.
6. Which of the following is untrue regarding RNA structure?
a) RNA structure 4.6 is a Windows implementation of the Zuker algorithm
b) It includes additional options for other folding algorithms and incorporation of experimental data
c) The authors of RNA structure collaborate very closely with the Turner laboratory and keep the most up-to-date thermodynamic parameters
d) The OligoWalk program cannot be used for siRNA design
View Answer
Explanation: Two unique ways of incorporating experimental data in the RNA folding is done with Dynalign and chemical modification. The Dynalign program computes the lowest free-energy sequence alignment and secondary structure common to two RNA sequences.
7. Which of the following is untrue about Vienna RNA Websuite?
a) It introduced the Wuchty algorithm, developed applications of the McCaskill algorithm
b) It also offers a wide variety of algorithms and functions
c) The Wuchty algorithm generates a small but complete set of suboptimal structures
d) The Wuchty algorithm computes some possible tertiary structures within a narrow free-energy range
View Answer
Explanation: The Wuchty algorithm computes all possible secondary structures within a narrow free-energy range. The Wuchty algorithm generates a small but complete set of suboptimal structures that may include some very different secondary structures but also very many highly similar structures. However, structures containing more than one suboptimal region may occur in the Wuchty set of structures but would be absent if the Zuker method for sampling suboptimal structures were used.
8. Which of the following is untrue about the Sfold algorithm?
a) It uses a unique algorithm to aid in the design of siRNA
b) The algorithm combines thermodynamic stabilities, calculations of target accessibility, and empirical rules
c) The website offers specialized programs for the design of siRNA, antisense RNA, trans-cleaving RNA, and mRNA-microRNA interactions
d) The website doesn’t offer programs for the design of a general program for statistically sampling suboptimal RNA structures
View Answer
Explanation: The algorithm uses a partition function calculation and then groups suboptimal structures by similarity. The centroid structure is the most-representative structure that is closest in similarity to all the other structures.
9. If the centroid structure is different from the minimum free-energy structure, the centroid structure is often closer to the phylogenetic prediction and contains fewer base pairs, or fewer false-positive base pair predictions, than the minimum free-energy prediction.
a) True
b) False
View Answer
Explanation: The point is to show a structure that represents a group of structures rather than a single predicted structure. Many long RNA sequences, such as viral genomes or mRNA, may not have a single structure but instead have a dynamic structure that has some conserved features but also varies and changes, and these many conformations may all exist simultaneously in the cell.
10. The ILM program uses an iterative loop matching algorithm to maximize base pairs and allows pseudoknots to form by allowing base. pairs to be added or removed in successive rounds.
a) True
b) False
View Answer
Explanation: The Nussinov algorithm, or maximum loop matching algorithm, is the basic framework for generating a structure with the most possible base pairs. The base pairs are ranked using both thermodynamic parameters and covariation data for aligned sequences. ILM requires the RnaViz program to visualize the RNA secondary structure with pseudoknots.
Sanfoundry Global Education & Learning Series – Bioinformatics.
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