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Bioinformatics Practice Test Questions: Pairwise Sequence Alignment (PSA)
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Pairwise Sequence Alignment (PSA) is used to identify regions of similarity that may indicate functional, structural and/or evolutionary relationships between two biological sequences (protein or nucleic acid).

Topics include: Sequence Homology Versus Sequence Similarity and Identity, Methods, & Statistical Significance of Sequence Alignment.

Related Test: Bioinformatics Practice Test Questions: Sequence Alignment

Bioinformatics Practice Test Questions: Pairwise Sequence Alignment (PSA)
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25 Questions

1. The overall goal of pair wise sequence alignment is to find the best pairing of two sequences, such that there is maximum correspondence among residues.
2. It is not known whether the Gumble distribution applies equally well to gapped alignments.
3. Self complementarity of DNA sequences cannot be identified using a dot plot.
4. Which of the following is incorrect regarding pair wise sequence alignment?
5. By calculating alignment scores of a large number of ______ sequence pairs, a distribution model of the ______ sequence scores can be derived.
6. The major disadvantage of the PRSS program is that it doesn’t allow partial shuffling.
7. If the two sequences share significant similarity, it is extremely ______ that the extensive similarity between the two sequences has been acquired randomly, meaning that the two sequences must have derived from a common evolutionary origin.
8. Sequence similarity and sequence identity are synonymous for nucleotide sequences and protein sequences as well.
9. In the statistical test, randomization process in which one of the two given sequences is randomly shuffled.
10. A problem exists when comparing _____ sequences using the dot matrix method, namely, the _______
11. In local alignment, the two sequences to be aligned cannot be of unequal lengths.
12. Shorter sequences require higher cutoffs for inferring homologous relationships than longer sequences.
13. In a dot matrix, two sequences to be compared are written in the _____________ of the matrix.
14. A sequence can be aligned with itself to identify internal repeat elements.
15. Sequence similarity can be quantified using ________ homology is a ______ statement.
16. Many studies have demonstrated that the distribution of similarity scores assumes a peculiar shape that resembles a highly skewed normal distribution with a long tail on one side. The distribution matches the _______
17. The degree of sequence variation in the alignment reveals evolutionary relatedness of different sequences, whereas the conservation between sequences reflects the changes that have occurred during evolution in the form of substitutions, insertions, and deletions.
18. If the score is located in the extreme margin of the distribution, that means that the alignment between the two sequences is ______ due to random chance and is thus considered ______
19. Which of the following is a part of the statistical test of sequences?
20. The truly statistically significant sequence alignment will be able to provide evidence of homology between the sequences involved.
21. The presence of evolutionary traces is because some of the residues that perform key functional and structural roles tend to be preserved by natural selection; other residues that may be less crucial for structure and function tend to mutate more frequently.
22. Alignment algorithms, both global and local, are fundamentally similar and only differ in the optimization strategy used in aligning similar residues.
23. If the selected window size is too long, sensitivity of the alignment is lost.
24. Sometimes, it is also possible that two sequences have derived from a common ancestor, but may have diverged to such an extent that the common ancestral relationships are not recognizable at the sequence level.
25. What is used to generate parameters for the extreme distribution?

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