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Rotation and translation motions were removed prior to covariance matrix calculation by least-squares superposition.
The alignments were then used for subsequent distance matrices (JTT matrix) calculation by PROTDIST to obtain 1000 matrices.
As discussed earlier, PLINK 1.9 employs an XOR/bit population count algorithm which speeds up the matrix calculation by a large constant factor; the computational complexity of the clustering algorithm has also been reduced, from O(n) to O(n log n).
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The modal information required for the experimental flexibility matrix calculation are obtained by means of Eigensystem Realization Algorithm (ERA) and common-based structure identification (CBSI).
The PLINK 1.0 –cluster –matrix flag combination launches an identity-by-state matrix calculation and writes the result to disk, and then performs complete linkage clustering on the data; when –ppc is added, a pairwise population concordance constraint is applied to the clustering process.
The structure is characterized by using transfer matrix calculation.
The unknown parameter of CIR (hat {h}_{l}(m)) at the mth symbol can be solved by the following matrix calculation of using the inverse matrix of [d 2 m,n 2−l)] S×S.
By following the matrix calculation procedure, it is straightforward to determine the full composition distribution of polymers with a given chain length numerically, with the help of computer softwares capable of conducting symbolic calculations.
Nauty is implemented by C++, while the other two are implemented by matlab, because of more matrix calculation.
This technique reduces the total number of sequences and therefore reduces the time taken by k-mer pairwise distance matrix calculation.
By using this algorithm, the inverse matrix of (24) can be calculated perfectly with the same accuracy as that for the direct inverse matrix calculation.
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