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Bioinformatics Multiple Choice Questions on “Local Sequence Alignment”.
1. When did Smith–Waterman first describe the algorithm for local alignment? Answer: c 2. Which of the following does not describe local alignment? Answer: c 3. Which of the following does not describe local alignment algorithm? Answer: A 4. Local alignments are more used when _____________ Answer: A 5. Which of the following does not describe BLOSUM matrices? Answer: D 6. Which of the following is untrue regarding the gap penalty used in dynamic programming? Answer: B 7. Among the following which one is not the approach to the local alignment? Answer: D 8. Which of the following does not describe k-tuple methods? Answer: D 9. Which of the following does not describe BLAST? Answer: D 10. Which of the following is untrue regarding BLAST and FASTA? Answer: A
A. 1950
B. 1970
C. 1981
D. 1925
Explanation: The algorithm was first proposed by Temple F. Smith and Michael S. Waterman in 1981. The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings of nucleic acid sequences or protein sequences.
A. A local alignment aligns a substring of the query sequence to a substring of the target sequence
B. A local alignment is defined by maximizing the alignment score, so that deleting a column from either end would reduce the score, and adding further columns at either end would also reduce the score
C. Local alignments have terminal gaps
D. The substrings to be examined may be all of one or both sequences; if all of both are included then the local alignment is also global
Explanation: Local alignments never have terminal gaps, because a higher score could be obtained by deleting the gaps (which always have negative scores, i.e. penalties). In case of global alignment there are terminal gaps while analyzing.
A. Score can be negative
B. Negative score is set to 0
C. First row and first column are set to 0 in initialization step
D. In traceback step, beginning is with the highest score, it ends when 0 is encountered
Explanation: Score can be negative. When any element has a score lower than zero, it means that the sequences up to this position have no similarities; this element will then be set to zero to eliminate influence from previous alignment. In this way, calculation can continue to find alignment in any position afterward.
A. There are totally similar and equal length sequences
B. Dissimilar sequences are suspected to contain regions of similarity
C. Similar sequence motif with larger sequence context
D. Partially similar, different length and conserved region containing sequences
Explanation: The given description is suitable for global alignment. It attempts to align maximum of the entire sequence unlike local alignment where the partially similar sequences are analyzed.
A. It stands for BLOcks SUbstitution Matrix
B. It was developed by Henikoff and Henikoff
C. The year it was developed was 1992
D. These matrices are logarithmic identity values
Explanation: These matrices are actual percentage identity values. Or simply, they depend on similarity. Blosum 62 means there is 62 % similarity.
A. Gap penalty is subtracted for each gap that has been introduced
B. Gap penalty is added for each gap that has been introduced
C. The gap score defines a penalty given to alignment when we have insertion or deletion
D. Gap open and gap extension has been introduced when there are continuous gaps (five or more)
Explanation: Dynamic programming algorithms use gap penalties to maximize the biological meaning. The open penalty is always applied at the start of the gap, and then the other gaps following it is given with a gap extension penalty which will be less compared to the open penalty. Typical values are –12 for gap opening, and –4 for gap extension.
A. Smith-Waterman algorithm
B. K-tuple method
C. Words method
D. Needleman-Wunsch algorithm
Explanation: Local alignment can be distinguished on two broad approaches, Smith-Waterman algorithm and word methods, also known as k-tuple methods and they are implemented in the well-known families of programs FASTA and BLAST.
A. k-tuple methods are best known for their implementation in the database search tools FASTA and the BLAST family
B. They are also known as words methods
C. They are basically heuristic methods to find local alignment
D. They are useful in small scale databases
Explanation: k-tuple or word methods are especially useful in large-scale database searches where a large proportion of stored sequences will have essentially no significant match with the query sequence. They are heuristic methods that are not guaranteed to find an optimal alignment solution but are significantly more efficient than Smith-Waterman algorithm.
A. It stands for Basic Local Alignment Search Tool
B. It uses word matching like FASTA
C. It is one of the tools of the NCBI
D. Even if no words are similar, there is an alignment to be considered
Explanation: If no words are similar, there is no alignment i. e. it will not find matches for very short sequences. But it is considerably accurate as compared to other tools and hence is quite popular.
A. FASTA is faster than BLAST
B. FASTA is the most accurate
C. BLAST has limited choices of databases
D. FASTA is more sensitive for DNA-DNA comparisons
Explanation: BLAST is faster than FASTA and most other tools. The speed and relatively good accuracy of BLAST is the key why the tool is the most popular bioinformatics search tool.