This set of Bioinformatics Multiple Choice Questions & Answers (MCQs) focuses on “Distance Based Methods”.
1. Which of the following is untrue about distance based methods?
a) The computed evolutionary distances can be used to construct a matrix of distances between all individual pairs of taxa
b) Clustering is the only method among the algorithms for the distance-based tree-building method
c) The clustering-type algorithms compute a tree based on a distance matrix starting from the most similar sequence pairs
d) Based on the pairwise distance scores in the matrix, a phylogenetic tree can be constructed for all the taxa involved
Explanation: The algorithms for the distance-based tree-building method can be subdivided into either clustering based or optimality based. These algorithms include an unweighted pair group method using arithmetic average (UPGMA) and neighbor joining. The optimality-based algorithms compare many alternative tree topologies and select one that has the best fit between estimated distances in the tree and the actual evolutionary distances.
2. Which of the following is untrue about the Unweighted Pair Group Method Using Arithmetic Average?
a) The simplest clustering method is UPGMA, which builds a tree by a sequential clustering method
b) Given a distance matrix, it starts by grouping two taxa with the largest pairwise distance in the distance matrix
c) The distances between this new composite taxon and all remaining taxa are calculated to create a reduced matrix
d) The grouping process is repeated and another newly reduced matrix is created
Explanation: It starts by grouping two taxa with the smallest pairwise distance in the distance matrix. A node is placed at the midpoint or half distance between them. It then creates a reduced matrix by treating the new cluster as a single taxon.
3. The basic assumption of the UPGMA method is that all taxa evolve at a constant rate and that they are equally distant from the root, implying that a molecular clock is in effect.
Explanation: However, real data rarely meet this assumption. Thus, UPGMA often produces erroneous tree topologies. However, owing to its fast speed of calculation, it has found extensive usage in clustering analysis of DNA microarray data.
4. In the Neighbor Joining step, The UPGMA method uses unweighted distances and assumes that all taxa have constant evolutionary rates.
Explanation: Since this molecular clock assumption is often not met in biological sequences, to build a more accurate phylogenetic trees, the neighbor joining (NJ) method can be used, which is somewhat similar to UPGMA in that it builds a tree by using stepwise reduced distance matrices. However, the NJ method does not assume the taxa to be equidistant from the root.
5. Corrects for unequal evolutionary rates between sequences by using a conversion step. This conversion requires the calculations of “r-values” and “transformed r-values” using the following formula ______
a) dAB’= dAB − 1/4 × (rA + rB)
b) dAB’= dAB − 1/2 × (rA + rB)
c) dAB’= dAB − 1/3 × (rA + rB)
d) dAB’= (dAB/3) − 1/2 × (rA + rB)
Explanation: AB is the converted distance between A and B and dAB is the actual evolutionary distance between A and B. The value of rsub>A (or rB) is the sum of distances of A (or B) to all other taxa.
6. A generalized expression of the r-value is ri calculated based on the following formula _______
a) ri = ∑dij + dj2
b) ri = ∑dij
c) ri = ∑dij + di
d) ri = ∑dij + dj
Explanation: i and j are two different taxa. The r-values are needed to create a modified distance matrix. The transformed r-values (r ‘) are used to determine the distances of an individual taxon to the nearest node: r i2= ri/ (n−2)
7. The tree construction process is somewhat similar to that used UPGMA.
Explanation: Rather than building trees from the closest pair of branches and progressing to the entire tree, the NJ tree method begins with a completely unresolved star tree by joining all taxa onto a single node and progressively decomposes the tree by selecting pairs of taxa based on the above modified pairwise distances. This allows the taxa with the shortest corrected distances to be joined first as a node.
8. Which of the following is untrue about the Optimality-Based Methods?
a) The clustering-based methods produce multiple trees as output
b) Optimality-based methods select a tree that best fits the actual evolutionary distance matrix
c) There is no criterion in judging how this tree is compared to other alternative trees
d) Optimality-based methods have a well-defined algorithm to compare all possible tree topologies
Explanation: The clustering-based methods produce a single tree as output. Based on the differences in optimality criteria, there are two types of algorithms, Fitch–Margoliash and minimum evolution, that are described next. The exhaustive search for an optimal tree necessitates a slow computation, which is a clear drawback especially when the dataset is large.
9. Which of the following is untrue about the Fitch–Margoliash?
a) Method selects a best tree among all possible trees based on minimal deviation between the distances calculated in the overall branches in the tree and the distances in the original dataset
b) It starts by randomly clustering two taxa in a node
c) It starts by creating three equations to describe the distances
d) The method searches for some specific tree topologies
Explanation: It solves the three algebraic equations for unknown branch lengths. The clustering of the two taxa helps to create a newly reduced matrix. This process is iterated until a tree is completely resolved. The method searches for all tree topologies and selects the one that has the lowest squared deviation of actual distances and calculated tree branch lengths.
10. Minimum evolution (ME) constructs a tree with a similar procedure, but uses a different optimality criterion that finds a tree among all possible trees with a minimum overall branch length. The optimality criterion relies on the formula S = ∑bi where bi is the (i)th branch length.
Explanation: Searching for the minimum total branch length is an indirect approach to achieving the best fit of the branch lengths with the original dataset. Analysis has shown that minimum evolution in fact slightly outperforms the least square-based FM method.
Sanfoundry Global Education & Learning Series – Bioinformatics.
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