Calculating Shannon Entropy of DNA Sequences

In summary: The calculation then is as follows:$$I=-0.4log_2(0.4)-0.4log_2(0.4)-0.6log_2(0.6)-0.6log_2(0.6)=1.94$$In summary, the person is struggling with two tasks and is not sure about the calculation for task 2. They use a GC content to help determine what AT and GC percentages are needed.
  • #1
GravityX
19
1
Homework Statement
Calculate the information content of a DNA base pair
Relevant Equations
##I(A)=-\sum\limits_{x=A}^{}P_xlog_2(P_x)##
Unfortunately, I have problems with the following task

Bildschirmfoto 2023-01-10 um 16.07.44.png

For task 1, I proceeded as follows. Since the four bases have the same probability, this is ##P=\frac{1}{4}## I then simply used this probability in the formula for the Shannon entropy:

$$I=-\frac{1}{4}log_2(\frac{1}{4})-\frac{1}{4}log_2(\frac{1}{4})-\frac{1}{4}log_2(\frac{1}{4})-\frac{1}{4}log_2(\frac{1}{4})=2$$

Unfortunately, I am not quite sure about task 2, but a GC content indicates how high the proportion of GC is in the DNA, so it means that AT must be present at 60 % and GC at 40 %. Is the calculation then as follows:

$$I=-0.4log_2(0.4)-0.4log_2(0.4)-0.6log_2(0.6)-0.6log_2(0.6)=1.94$$
 
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  • #2
GravityX said:
Unfortunately, I am not quite sure about task 2, but a GC content indicates how high the proportion of GC is in the DNA, so it means that AT must be present at 60 % and GC at 40 %. Is the calculation then as follows:$$I=-0.4log_2(0.4)-0.4log_2(0.4)-0.6log_2(0.6)-0.6log_2(0.6)=1.94$$
Not familiar with the topic but if the formula in 'Relevant Equations' is correct then, for task 2, the four probabilties should presumably be:
P(G) = 0.2 (i.e. not 0.4)
P(C) = 0.2 (i.e. not 0.4)
P(A) = 0.3 (i.e. not 0.6)
P(T) = 0.3 (i.e. not 0.6)
(They have to add-up to 1.)
 
  • #3
Not sure I understand the first question. It lists A, C, G and T as the four "base pairs". No, they are bases, the pairs being GC, AT.
But treating it as a matter of base pairs would yield ##-\frac 12\ln(\frac 12)-\frac 12\ln(\frac 12)=1##.
The question ought to read "for a single DNA base" (i.e., per base in a single strand).

This may be what fooled you into using 0.4 and 0.6 instead of 0.2 and 0.3 in the second question. But note that we can only get 0.2 and 0.3 by assuming that the orientations of the base pairs (which base is in which strand) are independent. One could imagine some sort of autocorrelation instead.

As to whether it would be surprising, that might depend whether we consider also the relative stabilities of the base pairs and the scheme that maps codons to amino acids.
 
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  • #4

haruspex said:
Not sure I understand the first question. It lists A, C, G and T as the four "base pairs". No, they are bases, the pairs being GC, AT.
By convention, when writing base-pair sequences, to be concise we use a single letter for each base pair. The letter is the base present on the ‘forward’ strand. See link below.

With this convention:
‘A’ represents adenine-thymine on the double strand.
‘C’ represents cytosine-guanine on the double strand.
‘G’ represents guanine- cytosine on the double strand.
‘T’ represents thymine- adenine on the double strand.

For example (using lower case for the bases) the sequence TATAGC represents the double strand: tatagc atatcg
From https://www.futurelearn.com/info/courses/bacterial-genomes-bioinformatics/0/steps/47002:
“Despite being a double helix of complementary DNA sequences, DNA is almost always represented as a single sequence.”
 
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  • #5
Steve4Physics said:
By convention, when writing base-pair sequences, to be concise we use a single letter for each base pair. The letter is the base present on the ‘forward’ strand.
Ok, thanks.
 
  • #6
Thank you Steve4Physics and haruspex for your help 👍, I had completely forgotten that the 0.4 was for the pair and not one base alone.
 

FAQ: Calculating Shannon Entropy of DNA Sequences

What is Shannon Entropy and why is it important for DNA sequences?

Shannon Entropy is a measure of the uncertainty or randomness in a set of data. For DNA sequences, it quantifies the variability in the sequence composition, providing insights into the complexity and information content of the genetic material. High entropy indicates a more diverse sequence, while low entropy suggests a more repetitive or uniform sequence.

How do you calculate Shannon Entropy for a DNA sequence?

To calculate Shannon Entropy for a DNA sequence, follow these steps:1. Count the frequency of each nucleotide (A, T, C, G) in the sequence.2. Calculate the probability of each nucleotide by dividing its frequency by the total number of nucleotides.3. Use the formula: Entropy = -Σ (p(x) * log2(p(x))), where p(x) is the probability of nucleotide x.4. Sum the values for all nucleotides to get the total entropy.

What tools or software can be used to calculate Shannon Entropy of DNA sequences?

Several bioinformatics tools and software can be used to calculate Shannon Entropy of DNA sequences, including:- Biopython: A Python library for computational biology.- R packages like seqinr or entropy.- Online calculators and web servers specifically designed for entropy calculations.- Custom scripts written in programming languages like Python, R, or MATLAB.

How does sequence length affect Shannon Entropy calculation?

Sequence length can influence the calculated Shannon Entropy. Longer sequences tend to provide a more accurate representation of nucleotide variability, leading to more reliable entropy values. Shorter sequences may not capture the full complexity of the DNA and can result in biased or less precise entropy measurements.

Can Shannon Entropy be used to compare different DNA sequences?

Yes, Shannon Entropy can be used to compare different DNA sequences. By calculating and comparing the entropy values of various sequences, researchers can assess their relative complexity and information content. This can be useful in identifying regions of high variability or conservation, understanding evolutionary relationships, and studying genetic diversity.

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