Question

Which of the following is a wrong statement regarding Gene Prediction Using Markov Models and Hidden Markov Models?

(a) Markov models and HMMs can be very helpful in providing finer statistical description of a gene

(b) A Markov model describes the probability of the distribution of nucleotides in a DNA sequence

(c) In a Markov model the conditional probability of a particular sequence position depends on k alternate positions

(d) A zero-order Markov model assumes each base occurs independently with a given probability

The question was posed to me by my school principal while I was bunking the class.

The question is from Gene Prediction in Prokaryotes topic in portion Gene and Promoter Prediction of Bioinformatics

Answer 1

Correct option is (c) In a Markov model the conditional probability of a particular sequence position depends on k alternate positions

For explanation: In a Markov model the conditional probability of a particular sequence position depends on k previous positions. In this case, k is the order of a Markov model. In a zero-order Markov model, it is often the case for noncoding sequences. A first-order Markov model assumes that the occurrence of a base depends on the base preceding it. A second-order model looks at the preceding two bases to determine which base follows, which is more characteristic of codons in a coding sequence.