Explanation: This sequence format, which is sometimes also called the PIR format. It has been used by the National Biomedical Research Foundation/Protein Information Resource (NBRF) and also by other sequence analysis programs.

Explanation: It is a resource prepared by the staff of the National Center for Biotechnology Information and National Library of Medicine, Bethesda, Maryland. After search for either a protein or a DNA sequence is chosen at the above address, another Web page is provided with a form to fill out for the search.

Explanation: Once the gene is cloned, the determination of DNA sequence is possible. Further, the study of target protein is carried out.

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.

Explanation: Multiple Expectation Maximization for Motif Elicitation is a web-based program that uses the EM algorithm to find motifs either for DNA or protein sequences. It uses modified EM algorithm to avoid the local minimum problem.

Explanation: For the purpose of dot plot interpretation there are various softwares currently present. Among these SIM is used for these kinds of alignments through dot-plot method that is wrongly abbreviated.

Explanation: BLASTP is used under BLAST 2 sequence alignment. Also, The BLAST algorithm normally used for database similarity searches can also be used to align two sequences. SIM is known as Local similarity program for finding alternative alignments.

Explanation: In this process, first, it is assumed that each amino acid position is equally mutable, whereas, in fact, sites vary considerably in their degree of mutability. Mutagenesis hot spots are well known in molecular genetics, and variations in the mutability of different amino acid sites in proteins are well known.

Explanation: One disadvantage of the Bayesian approach is that a specific mutational model is required, whereas other methods, such as the maximum likelihood approach, can be used to estimate the best mutational model as well as the distance. Computationally, however, the Bayesian method is much more practical.

Explanation: Homologous relationships are more certain when the sequences have common evolutionary origin. A related but different term is sequence similarity, which is the percentage of aligned residues that are similar in physiochemical properties such as size, charge, and hydrophobicity.

Explanation: The residues that perform key functional and structural roles tend to be preserved by natural selection. For example, active site residues of an enzyme family tend to be conserved because they are responsible for catalytic functions. Therefore, by comparing sequences through alignment, patterns of conservation and variation can be identified.

Explanation: In the self comparison, there is a main diagonal for perfect matching of each residue. If repeats are present, short parallel lines are observed above and below the main diagonal.

Explanation: Maximum scores are obtained through repeated shuffling. Then the pool of alignment scores from the shuffled sequences is used to generate parameters for the extreme distribution. The original alignment score is then compared against the distribution of random alignments to determine whether the score is beyond random chance.

Explanation: The major feature of the program is that it allows partial shuffling. For example, shuffling can be restricted to residues within a local window of 25–40, whereas the residues outside the window remain unchanged.

Explanation: In calculating the SP scores, each column is scored by summing the scores for all possible pair wise matches, mismatches and gap costs. The score of the entire alignment is the sum of all of the column scores. The score of the entire alignment is the sum of all of the column scores. In that case, option d becomes an irrelevant choice here.

Explanation: This method is based on heuristic methods. The procedure starts by producing a low-quality alignment and gradually improves it by iterative realignment through well-defined procedures until no more improvements in the alignment scores can be achieved.

Explanation: To assess whether it is likely that two sequences evolved from the same sequence comparison is required. Also, to find out which sequences from the database are similar to the sequence at hand, sequence comparison is carried out.

Explanation: The methods used, might be fast k-tuple or pattern-finding approach similar to FASTA that is useful for many sequences and the full dynamic programming method as well. But the option d becomes incorrect as full dynamic programming method is slower as compared to rest of the methods in options.

Explanation: In the mutation process, the sequence is not changed (else it would no longer be an alignment), but gaps are inserted and rearranged in an attempt to create a better-scoring MSA. In the gap insertion process, the sequences in a given MSA are divided into two groups based on an estimated phylogenetic tree, and gaps of random length are inserted into random positions in the alignment.