Description Usage Arguments Details Value Author(s) References Examples
Calculates a corrected mutual information score (MIP), by substraction of the average product from the probability of joint occurrence of events.
1  mip(align, gap_ratio = 0.2)

align 
An object of class 'align' created by the 
gap_ratio 
Numeric value between 0 and 1 indicating the maximal gap ratio at a given position in the MSA for this position to be taken into account. Default is 0.2, positions with more than 20 percent of gaps will not be taken into account in the analysis. When gap_ratio is 1 or close to 1, only positions with at least 1 aa are taken into account (positions with only gaps are excluded). 
The MIp score at position [i,j] has been computed with the following formula :
{MIp(i,j)} = MI(i,j)  \frac{MI(i,\bar{j})MI(\bar{i},j)}{<MI>}
with :
{MI(i,j) = ∑_{x,y}^{ } p_{x,y}(i,j) ln\frac{p_{x,y}(i,j)}{p_{x}(i)p_{y}(j)}}
MI(i,\bar{j}) = \frac{1}{n1} ∑_{j \neq i}^{ } MI(i,j)
MI(\bar{i},j) = \frac{1}{n1} ∑_{i \neq j}^{ } MI(i,j)
<MI> = \frac{2}{n(n1)} ∑_{i,j}^{ }MI(i,j)
and where p_{x,y}(i,j) is the frequency of the amino acid pair (x,y) at positions i and j.
N.B. this formula has been widely applied in the field of sequence correlation/covariation but favors pairs with high entropy.
A list of two elements which are numerical matrices containing the MIP scores and Zscores for each pair of elements.
Madeline DENIAUD and Marie CHABBERT
Dunn SD, Wahl LM, Gloor GB. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinfor;atics 2008;24:333340. Martin LC, Gloor GB, Dunn SD, Wahl LM. Using infor;ation theory to search for coevolving residues in proteins. Bioinformatics 2005;21:41164124.
1 2 3 4 5  #Importing MSA file
align < import.fasta(system.file("msa/toy2_align.fa", package = "Bios2cor"))
#Creating correlation object with MIP method for positions with gap ratio < 0.2 (Default)
mip < mip(align)

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