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GREMLIN - learn MRF/potts model from input multiple sequence alignment! Implementation now available in C++ and Tensorflow/Python!

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whbpt/GREMLIN_CPP

 
 

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Installation

$ g++ -O3 -std=c++0x -o gremlin_cpp gremlin_cpp.cpp -fopenmp

invoke-fopenmp to allow usage of multiple CPU(s), the code is openmp friendly.

Usage

Note, openmp uses the system variableOMP_NUM_THREADS to decide how many threads/CPU(s) to use.

$ export OMP_NUM_THREADS=16$ ./gremlin_cpp -i alignment_file -o results# ---------------------------------------------------------------------------------------------#                                GREMLIN_CPP v1.0                                              # ---------------------------------------------------------------------------------------------#   -i            input alignment (either one sequence per line or in fasta format)#   -o            save output to# ---------------------------------------------------------------------------------------------#  Optional settings                                                                           # ---------------------------------------------------------------------------------------------#   -only_neff    only compute neff (effective num of seqs)      [Default=0]#   -only_v       only compute v (1body-term)                    [Default=0]#   -gap_cutoff   remove positions with > X fraction gaps        [Default=0.5]#   -alphabet     select: [protein|rna|binary]                   [Default=protein]#   -eff_cutoff   seq id cutoff for downweighting similar seqs   [Default=0.8]#   -lambda       L2 regularization weight                       [Default=0.01]#   -mrf_i        load MRF#   -mrf_o        save MRF#   -pair_i       load list of residue pairs (one pair per line, index 0)# ---------------------------------------------------------------------------------------------#  Minimizer settings                                                                          # ---------------------------------------------------------------------------------------------#   -min_type     select: [lbgfs|cg|none]                        [Default=lbfgs]#   -max_iter     number of iterations                           [Default=100]# ---------------------------------------------------------------------------------------------

parsing output

i j raw apc ii jji = index ij = index jraw = l2norm(W)apc = raw - mean(row) * mean(col) / mean(all)ii = char-position ijj = char-position j

The out MRF contains 21 values for each position (V) and 21 x 21 values for each pair of positions (W).

The order of the values is as follows: "ARNDCQEGHILKMFPSTWYV-" (where "-" is the gap).

For RNA the order is "AUCG-", with 5 values for V and 5x5 for W.

For Binary the order is "01-", with 3 values for V and 3x3 for W.

Alternative implementations

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GREMLIN - learn MRF/potts model from input multiple sequence alignment! Implementation now available in C++ and Tensorflow/Python!

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