This set of Bioinformatics Multiple Choice Questions & Answers (MCQs) focuses on “Microarray-Based Approaches”.
1. Which of the following is incorrect about a microarray?
a) It is a slide attached with a high-density array of immobilized DNA oligomers representing the entire genome of the species under study
b) Array of immobilized DNA oligomers cannot be cDNAs
c) Each oligomer is spotted on the slide and serves as a probe for binding to a unique complementary cDNA
d) It is the most commonly used global gene expression profiling method
Explanation: The entire cDNA population, labeled with fluorescent dyes or radioisotopes, is allowed to hybridize with the oligo probes on the chip. The amount of fluorescent or radiolabels at each spot position reflects the amount of corresponding mRNA in the cell. Using this analysis, patterns of global gene expression in a cell can be examined.
2. which of the following is incorrect about Oligonucleotide Design in A microarray?
a) DNA microarrays are generated by fixing oligonucleotides onto a solid support
b) The oligonucleotide array slide represents thousands of preselected genes from an organism
c) The length of oligonucleotides is typically in the range of twenty-five to seventy bases long
d) The oligonucleotides don’t react with cDNA samples
Explanation: The oligonucleotides are called probes that hybridize to labeled cDNA samples. Shorter oligo probes tend to be more specific in hybridization because they are better at discriminating perfect complementary sequences fromsequences containing mismatches. However, longer oligos can be more sensitive in binding cDNAs.
3. Which of the following is incorrect about Data Collection?
a) The two-color microarray uses multiple dyes at times
b) The most common type of microarray protocol is the two-color microarray
c) The cDNAs are obtained by extracting total RNA or mRNA from tissues or cells and incorporating fluorescent dyes in the DNA strands during the cDNA biosynthesis
d) The expression of genes is measured via the signals from cDNAs hybridizing with the specific oligonucleotide probes on the microarray
Explanation: The most common type of microarray protocol is the two-color microarray, which involves labeling one set of cDNA from an experimental condition with one dye (Cy5, red fluorescence) and another set of cDNA from a reference condition (the controls) with another dye (Cy3, green fluorescence). When the two differently labeled cDNA samples are mixed in equal quantity and allowed to hybridize with the DNA probes on the chips, gene expression patterns of both samples can be measured simultaneously.
4. In the analysis of microarray data–If replicated datasets are available, rigorous statistical tests such as t-test and analysis of variance (ANOVA) can be performed to test the null hypothesis that a given data point is not significantly different from the mean of the data distribution.
Explanation: For such tests, it is common to use a P-value cutoff of .05, which means a confidence level of 95% to distinguish the data groups. This level also corresponds to a gene expression level with two standard deviations from the mean of distribution.
5. Which of the following is incorrect about Classification of microarray data?
a) For microarray data, clustering analysis identifies coexpressed and coregulated genes
b) For microarray data, clustering analysis identifies coexpressed but not coregulated genes
c) For microarray data, clustering analysis identifies and coregulated but not coexpressed genes
d) Genes within a category have more similarity in expression than genes from different categories.
Explanation: When genes are co-regulated, they normally reflect related functionality. Through gene clustering, functions of previously uncharacterized genes may be discovered. Clustering methods include hierarchical clustering and partitioning clustering (e.g., k-means, self-organizing maps [SOMs]).
6. A supervised analysis refers to classification of data into a set of predefined categories. For example, depending on the purpose of the experiment, the data can be classified into predefined ‘diseased’ or ‘normal’ categories.
Explanation: Similarly, an unsupervised analysis does not assume predefined categories, but identifies data categories according to actual similarity patterns. The unsupervised analysis is also called clustering, which is to group patterns into clusters of genes with correlated profiles.
7. Which of the following is incorrect about Hierarchical Clustering?
a) The tree-branching pattern illustrates a higher degree of relationship between related gene groups
b) It is not similar to the distance phylogenetic tree-building method
c) It produces a treelike structure that represents a hierarchy or relative relatedness of data groups
d) In the tree leaves, similar gene expression profiles are placed more closely together than dissimilar gene expression profiles
Explanation: A hierarchical clustering method is in principle similar to the distance phylogenetic tree-building method. When genes with similar expression profiles are grouped in such a way, functions for unknown genes can often be inferred. Hierarchical clustering uses the agglomerative approach that works in much the same way as the UPGMA method, in which the most similar data pairs are joined first to form a cluster.
8. Which of the following is incorrect about k-Means Clustering?
a) k-means clustering produces a dendrogram
b) It classifies data through a single step partition
c) It is a divisive approach
d) In this method, data are partitioned into k-clusters, which are prespecified at the outset
Explanation: In contrast to hierarchical clustering algorithms, k-means clustering does not produce a dendrogram, but instead classifies data through a single step partition. The value of k is normally randomly set but can be adjusted if results are found to be unsatisfactory.
9. Which of the following is incorrect about Self-Organizing Maps?
a) Clustering by SOMs is in principle similar to the k-means method
b) It doesn’t involve neural networks
c) The data points are initially assigned to the nodes at random
d) It starts by defining a number of nodes
Explanation: This pattern recognition algorithm employs neural networks. The distance between the input data points and the centroids are calculated. The data points are successively adjusted among the nodes, and their distances to the centroids are recalculated. After many iterations, a stabilized clustering pattern are reached with the minimum distances of the data points to the centroids. The differences between SOM and k-means are that, in SOM, the nodes are not treated as isolated entities, but as connected to other nodes.
10. TIGR TM4 is a suite of multiplatform programs for analyzing microarray data.
Explanation: This comprehensive package includes four interlinked programs, TIGR spot finder (for image analysis), MIDAS (for data normalization), MeV (for clustering analysis and visualization), and MADAM (for data management). The package provides different data normalization schemes and clustering options. Other Similar Clustering Programs are EPCLUST, SOTA.
Sanfoundry Global Education & Learning Series – Bioinformatics.
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