MCQs on Bioinformatics

Introduction to Bioinformatics

Bioinformatics is an interdisciplinary field, develops the software tools and methods for understanding biological data in detail when the data sets are complex and broad. As an interdisciplinary science field, bioinformatics combines biology with computer science, mathematics, information engineering, and also statistics to analyze, interpret the biological data. Bioinformatics has been used in silico analyses of biological queries using statistical and mathematical techniques.

In addition, bioinformatics also includes the biological studies that use computer programming as their methodology part, and as specific analysis, "pipelines" repeatedly used especially in the field of genomics. Bioinformatics' common uses are the identification of candidates, genes, and Single Nucleotide Polymorphisms (SNPs). Such kind of identification is often made for a better understanding of the genetic basis of unique adaptations, disease, the differences between populations, or the desirable properties (especially in agricultural species). Less formally, bioinformatics also tries to understand the organizational principles within protein sequences and nucleic acid, known as proteomics.

The bioinformatics questions based on the bioinformatics MCQ book will help students crack the NEET in a better way.

Let us Look at Some of the MCQs on Bioinformatics.

1. Which of the Following Statements is Incorrect about the RNA Secondary Structure’s Prediction?

1. Every base is compared first to every other base by a type of analysis quite similar to that of dot matrix analysis

2. A matches row in the RNA matrix indicates a complementary nucleotides succession that can potentially produce a double-stranded region

3. The sequence is listed across the side of the page, top and down, G/C, G/U, and A/U base pairs are scored

4. A row of matches in the RNA matrix represents a failure of complementary nucleotides that can potentially produce a double-stranded region

Answer: (d)

Explanation

The energy of each structure, which is predicted, is estimated by the nearest-neighbor rule by summing the negative base-stacking energies for each base pair in the double-stranded regions. By adding the estimated positive energies of destabilizing regions like loops at the hairpins end, internal bulges, bulges within hairpins, and other unpaired regions.

2. Identify the following statement, either True or False?

Evaluation of all the different possible configurations is done through a single scoring matrix.

1. False

2. True

Answer: (a)

Explanation

To find the most energetically favorable, and to evaluate all the different possible configurations, different types of scoring matrices are used. The complementary regions are then evaluated by a dynamic programming algorithm to predict the most energetically stable molecule. The method is similar compared to the dynamic programming method that is used for sequence alignment.

3. Which Regions in Sequences, Similar to those in RNA that will Form the Secondary Structures, are the Examples of Such Kinds of Context-Free Sequences.

1. Non-Complementary

2. Complementary

3. Non-interlocking

4. Non-compatible

Answer: (c)

Explanation

Stochastic Context-Free Grammars (SCFG) introduce into the definition of such regions’ uncertainty. It also allows them to use alternative symbols, as found in the evolution of RNA molecules.

4. Identify the Following Statement, either True or False?

The SCFGs application to RNA secondary structure analysis is so similar in form to the probabilistic covariance models. Is this statement True or False?

a) False

b) True

Answer: (b)

Explanation

The alphabet symbols for RNA are A, C, G, and U. The Context-Free Grammar establishes a set of rules that are known as productions for generating the sequence from the alphabet, and in this case, an RNA molecule with sections that can base-pair and others that cannot base-pair.

5. Identify the Following Statement, either True or False?

The SCFGs that are used in the production analysis of RNA secondary structure are very similar compared to that of the covariance model, with the grammatical productions resembling the nodes in the ordered binary tree.

1. False

2. True

Answer: (b)

Explanation

According to the hidden Markov models, the probability distribution of each production must be derived by training with the known sequences. The algorithms used in SCFG training and for the sequence aligning with the SCFG are a bit different from those which are used with Markov models that are hidden, and the memory requirements, time are greater.

6. The Human Genome Approximately Contains ______ Pairs.

1. 3 billion base pairs

2. 5 billion base pairs

3. 6 billion base pairs

4. 4 billion base pairs

Answer: (a)

7. The SWISSPROT Protein Sequence Database has begun in the Year ______?

1. 1988

2. 1987

3. 1986

4. 1988

Answer: (b)

8. Which of These Following are not Bioinformatics Applications?

1. Data storage and management

2. Understand the relationships between organisms

3. Drug designing

4. None of the above

Answer: (d)

9. The Laboratory Work is Done using the Computers and Associated with Web-Based Analysis Online is Generally referred to as __________?

1. Insilico

2. Wet lab

3. Dry lab

4. All of the above

Answer: (b)

10. The Laboratory Work is Done using the Computers and Computer-Generated Models Offline Generally is referred to as _______?

1. Dry lab

2. Wet lab

3. Insilico

4. All of the above

Answer: (a)

11. The Latin Term “Invitro” refers to_______ ?

1. Outside the lab

2. Outside the glass

3. Within the lab

4. Within the glass

Answer: (d)

12. The computational methodology that tries to identify the best matching between two molecules, a ligand and receptor are known as _______?

1. Molecular matching

2. Molecule affinity checking

3. Molecular docking

4. Molecular fitting

Answer: (c)