# Import required packages import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.stats import pearsonr # Basepairs possible in RNA and score PAIRS = { "CG": 1, "GC": 1, "GU": 1, "UG": 1, "AU": 1, "UA": 1 } # Minimum free energy table #bp_scoring = pd.DataFrame([[2.4, 3.4, 3.5, 2.7, 2.2, 1.9], # [3.4, 3.7, 4.6, 3.4, 3.4, 2.7], # [3.5, 4.6, 4.7, 3.8, 3.7, 2.8], # [2.7, 3.4, 3.8, 0.0, 2.6, 1.8], # [2.2, 3.4, 3.7, 2.6, 2.6, 2.3], # [1.9, 2.7, 2.8, 1.8, 2.3, 1.0]], # index=["AU", "CG", "GC", "GU", "UA", "UG"], # columns=["AU", "CG", "GC", "GU", "UA", "UG"]) # Minimum free energy table provided from https://rth.dk/resources/bioinf2024/basepair_stack_energy_score.py energy_scores = { "CG": { "AA": 1.0, "AC": 1.0, "AG": 1.0, "CA": 1.0, "CC": 1.0, "CU": 1.0, "GA": 1.0, "GG": 1.0, "UC": 1.0, "UU": 1.0, "CG": 3.7, "GC": 4.6, "GU": 3.4, "UG": 2.7, "AU": 3.4, "UA": 3.4, }, "GC": { "AA": 1.0, "AC": 1.0, "AG": 1.0, "CA": 1.0, "CC": 1.0, "CU": 1.0, "GA": 1.0, "GG": 1.0, "UC": 1.0, "UU": 1.0, "CG": 4.6, "GC": 4.7, "GU": 3.8, "UG": 2.8, "AU": 3.5, "UA": 3.7, }, "GU": { "AA": 1.0, "AC": 1.0, "AG": 1.0, "CA": 1.0, "CC": 1.0, "CU": 1.0, "GA": 1.0, "GG": 1.0, "UC": 1.0, "UU": 1.0, "CG": 3.4, "GC": 3.8, "GU": 0.0, "UG": 1.8, "AU": 2.7, "UA": 2.6, }, "UG": { "AA": 1.0, "AC": 1.0, "AG": 1.0, "CA": 1.0, "CC": 1.0, "CU": 1.0, "GA": 1.0, "GG": 1.0, "UC": 1.0, "UU": 1.0, "CG": 2.7, "GC": 2.8, "GU": 1.8, "UG": 1.0, "AU": 1.9, "UA": 2.3, }, "AU": { "AA": 1.0, "AC": 1.0, "AG": 1.0, "CA": 1.0, "CC": 1.0, "CU": 1.0, "GA": 1.0, "GG": 1.0, "UC": 1.0, "UU": 1.0, "CG": 3.4, "GC": 3.5, "GU": 2.7, "UG": 1.9, "AU": 2.4, "UA": 2.2, }, "UA": { "AA": 1.0, "AC": 1.0, "AG": 1.0, "CA": 1.0, "CC": 1.0, "CU": 1.0, "GA": 1.0, "GG": 1.0, "UC": 1.0, "UU": 1.0, "CG": 3.4, "GC": 3.7, "GU": 2.6, "UG": 2.3, "AU": 2.2, "UA": 2.6, }, } bp_scoring = pd.DataFrame(energy_scores).T def seq_to_matrix(text): """ Make a zeroed matrix with dimensions of the string length as columns and rows Args: text (Str): String used to determine matrix dimensions Returns: Zeroed matrix of dimensions determined by text length """ # Determine amount of characters in text and create a zeroed matrix shaped with this information size = len(text) matrix = np.zeros((size, size)) # Return the created matrix return matrix def loop_matrix_diag(matrix, offset=3, min_loop=0): """ Determine the upper diagonal path through a given matrix Args: matrix (Matrix): Zeroed matrix of dimensions matching the amount of characters in a string offset (Int): Skip the given amount of columns, default: 3 min_loop (Int): Minimum basepairs in loop, default: 0 Returns: Diagonal paths of given matrix as a list of tuples """ # Remember that we're working with 0-index matrix offset -= 1 # Quirky fix for minimum loop size if min_loop > 0: min_loop -= 1 # Calculations for loop sizes matrix_length = len(matrix) limit = matrix_length - offset # Default values of where to start in column and row default_row = 0 default_column = offset # List containing positions of diagonals positions = [] # Loop for the amount of times determined to get all diagonals for i in range(min_loop, limit): # Reset row and column, but remember which iteration we're in row = default_row column = default_column + i # Loop for the amount of times determined to get all positions in current diagonal for j in range(matrix_length - column): # Append the current cell of diagonal positions.append((row, column)) # Go to next cell of diagonal row += 1 column += 1 # Return list of positions to work with return positions def basepairs_to_string(pairs, sequence): """ Create a dot-bracket notation for a given string using its determined pairs Args: pairs (List): List containing tuples of pairs in sequence sequence (Str): RNA sequence which is to be converted to dot-bracket string Returns: A dot-bracket notation for the sequence given the list of pairs """ # Create a list of dots with the same length of the sequence sequence = ["."] * len(sequence) # Loop through basepairs and replace dots in sequence with corresponding brackets for pairs for i, j in pairs: sequence[i] = "(" sequence[j] = ")" # Concatenate list to a string dot_bracket_sequence = "".join(sequence) # Return sequence in dot-bracket format return dot_bracket_sequence def nussinov(sequence, min_loop_size=0, use_scoring=False): """ Implementation of the Nussinov algorithm to generate RNA secondary structure Args: sequence (Str): RNA sequence min_loop_size (Int): Minimum loop size, default: 0 use_scoring (Bool): Use scoring matrix for stacked basepairs, default: False Returns: A matrix of Nussinov scores """ # Get a zeroed matrix of the sequence in question seq_matrix = seq_to_matrix(sequence) # Loop through the cells of diagonals for i, j in loop_matrix_diag(seq_matrix, min_loop=min_loop_size): # Get the value of the cell to the left of the current cell (i, j-1) left_cell = seq_matrix[i, j - 1] # Get the value of the cell below the current cell (i+1, j) down_cell = seq_matrix[i + 1, j] # Check if the current cell is a basepair if sequence[i] + sequence[j] in PAIRS.keys(): # Add score to value of cell diagonally down left if sequence[i + 1] + sequence[j - 1] in PAIRS.keys() and use_scoring: diagonal_down_left = seq_matrix[i + 1, j - 1] + bp_scoring.at[sequence[i] + sequence[j], sequence[i + 1] + sequence[j - 1]] else: diagonal_down_left = seq_matrix[i + 1, j - 1] + 1 else: # Get value of the cell diagonally down left diagonal_down_left = seq_matrix[i + 1, j - 1] # Max value from bifurcation bifurcation = 0 for k in range(i, j - 1): if (seq_matrix[i, k] + seq_matrix[k + 1, j]) > bifurcation: bifurcation = seq_matrix[i, k] + seq_matrix[k + 1, j] # Determine which value of the cells and bifurcation were biggest and write it to the current cell biggest_value = max(left_cell, down_cell, diagonal_down_left, bifurcation) seq_matrix[i, j] = biggest_value # Return the matrix of Nussinov values return seq_matrix def backtrack(matrix, sequence): """ Backtrack through a matrix using the same rules as in the Nussinov algorithm to predict a structure Args: matrix (Matrix): Matrix containing Nussinov scores sequence (Str): RNA sequence in interest Returns: A dot-bracket notation of RNA secondary structure """ # Starting values of in the matrix depending on the sequence in question stack = [(0, len(sequence) - 1)] # Basepairs found basepairs = [] # Continue to backtrack as long as the stack is not empty while len(stack) > 0: # Get first tuple values from stack i, j = stack.pop() # If i is equal to or bigger than j, move to next iteration if i >= j: continue # Check the value of the cell to the left of the current cell (i, j-1) if matrix[i, j - 1] == matrix[i, j]: stack.append((i, j - 1)) # Move one column to the left # Check the value of the cell below the current cell (i+1, j) elif matrix[i + 1, j] == matrix[i, j]: stack.append((i + 1, j)) # Move one row down # Check the value of the cell diagonally down left from the current cell (i+1, j-1) elif sequence[i] + sequence[j] in PAIRS.keys(): basepairs.append((i, j)) # Add found basepair to list stack.append((i + 1, j - 1)) # Move diagonally down left # If none of above is true, check if bifurcation occurred else: for k in range(i + 1, j - 1): stack.append((k + 1, j)) stack.append((i, k)) break # Return dot-bracket string of found basepairs (predicted secondary structure) dot_bracket_string = basepairs_to_string(basepairs, sequence) return dot_bracket_string def basepairs(sequence): """ Find basepair positions of sequence given its dot-bracket notation Args: sequence (Str): Dot-bracket notation of sequence in interest Returns: List of basepair positions in the given dot-bracket notation """ # List for basepair found and the stack found_basepairs = [] stack = [] # Loop through all characters in the sequence for i in range(len(sequence)): # If start of basepair found, add current index to stack if sequence[i] == "(": stack.append(i) # Else-if end of basepair found, remove first item from stack and add tuple of basepair position to found list elif sequence[i] == ")": first_base = stack.pop() found_basepairs.append((first_base, i)) # Return a list of found basepairs return found_basepairs def check_differences(list_1, list_2): """ Find basepair positions of sequence given its dot-bracket notation Args: list_1 (List): List containing basepairs list_2 (List): List containing basepairs to compare with the first list Returns: Amount of differences between the first and second list """ # Count of differences found between two list differences = 0 # Loop through all basepairs (tuples) in list first list for item in list_1: # Check if basepair doesn't exist in the second list of basepairs if item not in list_2: differences += 1 # Return amount of differences between list one and and two return differences def basepair_distance(sequence_1, sequence_2): """ Calculate the basepair distance between two sequences given their dot-bracket notation, must be equal sizes Args: sequence_1 (Str): Dot-bracket notation of one sequence of interest sequence_2 (Str): Dot-bracket notation of another sequence of interest Returns: Basepair distance (differences) between the two sequences """ # Check if the sequences are of equal length; otherwise raise error if len(sequence_1) != len(sequence_2): raise ValueError("Sequences must be of equal length!") # Get the basepairs from both sequences basepairs_sequence_1 = basepairs(sequence_1) basepairs_sequence_2 = basepairs(sequence_2) # Calculate differences between the two sequence's basepairs differences_seq_1_2 = check_differences(basepairs_sequence_1, basepairs_sequence_2) differences_seq_2_1 = check_differences(basepairs_sequence_2, basepairs_sequence_1) # Find the total differences differences_total = differences_seq_1_2 + differences_seq_2_1 # Return the total differences return differences_total if __name__ == "__main__": # Sequences to go through, downloaded from https://rth.dk/resources/bioinf2024/submit.php using KUID pgt781 rnafold = { "seq_pgt781_001": { "energy": -33.5, "seq": "CCUGGGCCUCAGGGAGUCGACACGUAUAGUACGCGACGUCCCAAGCGUGACGAUACGCCUGUGCAAUUGUGUGGUCCUGUGAAAGCAGAGCGUGCCAGCACAAACCUAUUCUGGUCAUCU", "struct": "...(((((.((((((((((...((((...)))))))).))))..(((((....)))))...........)).)))))((((...(((.....)))...)))).(((......))).....", }, "seq_pgt781_002": { "energy": -30.0, "seq": "ACUAUUCAAACACGGAAGAAAUCUGCAUUGAGCAUCCUCCAGCCACUCUGUAUAGUCGGAGCUGCACUUAGUUGUAAGCAACACAACUUACGGCCCGGCGCCAGUAGCACAUGCGGGAUA", "struct": "....(((((...((((.....))))..))))).(((((.((....(((((....(((((.((((.....((((((.......))))))..)))))))))..))).))....)).))))).", }, "seq_pgt781_003": { "energy": -23.0, "seq": "GAUAUCCACCAGUCCCUUAUGAUACCCCCGAUACAGACACUUCCCAACGAGAUGCGAUUGUCGGGCUUACUAGUUGCAUCGGACUUUUCGAGUUCACUAUACCCUUGUGAGUUGACGCGC", "struct": "..........(((((...(((..((.(((((((..(.(((((......))).)))...))))))).......))..))).)))))..((((.(((((.........))))))))).....", }, "seq_pgt781_004": { "energy": -29.9, "seq": "UCCCUUCCGCGGAUGAAGAUAGCGCUAGAAUACGAAUCAGCACGGUUGGACGUGACCGAGCUUGAGUGGAGACAAGUCAGCAGCCCAUGCCGAGCUUAGGUGCGGCGCCAUCACUAGUAU", "struct": "((((....).)))....(((.(((((..............((((......))))((((((((((.((((...............))))..))))))).)))..))))).)))........", }, "seq_pgt781_005": { "energy": -36.8, "seq": "GAAUGAACCGACCGAAAUAAAGGCGGCGGUGACGGUCGAAUAUCCAACAUUCCCACCGGUUAACGACUACAUUGGAUUAUCCAUAUGGAUCUGGCCGAUGUUAGGUGGCCGGGGAUCAGA", "struct": "(((((.((((.(((.........)))))))...((........))..)))))((.(((((((.(....(((((((...((((....))))....)))))))...))))))))))......", }, "seq_pgt781_006": { "energy": -22.5, "seq": "GACAGCGUUUAGGUUCGCACACAGAUUGUGUUAUUAUAGUGUAAUGGAUGCUUAAGUGACUUCACUAAUUCCCCGACACACCUGACUUAACGUUUUAGCACUCUUUCACAAGGAGAACAG", "struct": "...(((((((((((..(((((.....))))).......((((...(((......((((....))))...)))...))))))))))....)))))......((((((...)))))).....", }, "seq_pgt781_007": { "energy": -40.2, "seq": "UUGCUCUCCCUUCGAGCUCAGUAAAGAACGAAGCCGAUUGGGGGUGGACAACUAUGCGCGCAAUUGUUACCAGUACGGUGCCGCGGGAGAGUGGUCAGACUAUCCCCUGGUAUGGGGAGU", "struct": "..((((((((((((..((......))..))))).((((((.(.(((........))).).))))))..(((.....)))((((.((((.(((......))).)))).))))..)))))))", }, "seq_pgt781_008": { "energy": -22.5, "seq": "UAACGCACCGACCAGACUAACGCGGACAAGCUUUACGACGCAAGGCAGGAAUAUUCGUAUACUGUUCUCGUACCAAUUUCUCCUUUUGCUCCAAAUCUAGGAUCCCGGAUUCGGGUCGCG", "struct": "........(((((.((....((.((((.((.(((..((.((((((.((((((....((((.........))))..)))))))))..))))).))).))..).)))))...)).)))))..", }, "seq_pgt781_009": { "energy": -28.3, "seq": "CACCAGCUAAAUUCUGACUAUUAGCUUGGACCCGCAUACUAAUCAAGCGGGGCAAAUAAACUCGAUCCUCUGCAUCAGUCCUGACAGUGAUAUCGGAAACUUAUCAGGAAGUUGGCCGGG", "struct": "..((((((((..........))))).))).(((((...........)))))................(((.((.....((((((.(((..........)))..)))))).....)).)))", }, "seq_pgt781_010": { "energy": -29.2, "seq": "GGAUGUGCUGAUAAGAUCCGAUAACAUACGGUUGUGUCGUCAGUUCCCACACCUUAUCCUAGUGACUUAGGGCCAUGCACCUCCACGUCUGAGUUAUUUCAGAUGAGCGAUGUCCUGAAG", "struct": "(((((((((((((....(((........)))...))))((((.(................).))))..(((((...)).)))...((((((((....))))))))))).)))))).....", }, "seq_pgt781_011": { "energy": -23.6, "seq": "CGUGGGGUCGGACCAAAGUAUAAGUGUCUAAACAUAACCUCUAAAACAGGGACAAUCCGCUUUGGACCUUGCUGUUAAUUCAUUUACCUACCAACUUCGGGUGCAAACUUUUUUGUGCUG", "struct": ".(((((((....(((((((.....(((((....................)))))....))))))))))))))...........................((((((((....)))))))).", }, "seq_pgt781_012": { "energy": -27.5, "seq": "AGGUAACUACUUCCCCUAAGAGAUAAAUGAUUCAACAUGCAAUAAUCGUGGGGAGCCCCGCAGACGCCUAGGUCUAUGAUCUCCCCAGGCUCACUGAGCGCGGUAUCCUCUUGAGUAAGA", "struct": ".......(((((.....(((((....((((((...........))))))((((((...((.((((......)))).))..))))))..((((...))))........))))))))))...", }, "seq_pgt781_013": { "energy": -28.5, "seq": "CUGUACACUGUAGUUCAUUUGCAUGGCAAAAAAAUGGAUACAAUGAGCUAAUCUCGUGGUGGGCACGGACACUGAUUACCUCUUACCAUCCAUUGGCGUCGACGCUGCUGUGGCCCGCGU", "struct": ".....(((.(((((....(((.(((.((.....((((((.....(((.((((((((((.....))))).....))))).))).....)))))))).)))))).))))).)))........", }, "seq_pgt781_014": { "energy": -25.5, "seq": "UGUCGUUCGAGUUCGUGUUGUUGCCUAUUAUCGUUUGUGCGUGAUACAGCGCUAUCGGUUUAUUCUCGCGUUGGCGACUCACCCUGUGAGCAUCUAUCAAAGCAUGCAUUCCGUCCAAUA", "struct": "........((((.((((((.(((......((((...((((........))))...))))......((((....))))(((((...))))).......))))))))).)))).........", }, "seq_pgt781_015": { "energy": -29.0, "seq": "AUAAAAUGGUACAAGCGAGGUCCUGUAAAGAGCUAUAUGCACUUCAUUCAUCUGCUAGGACUUCCUCGAGUGGUCGCUCCUCUCGCAACAGUCCAGCGACGAGCCGGCAAUAAAUGCGCG", "struct": ".............((.((((((((((((((.((.....)).)))........))).))))))))))((..(.((((((...((......))...)))))).)..))(((.....)))...", }, "seq_pgt781_016": { "energy": -22.8, "seq": "CGGGCCAUGUUAGGAUACAGAUUUGUGAACCUAGUAAGCUGACUCUCAAAUCGAAACCUCCUUACCCUCAGAUAGAUACAGCUGACAAUGGGAGCCUUGACCGAAUAAAUUCCUCAGGCC", "struct": "..((((.((..((((.....((((((.((((((...(((((..(((...(((..................))))))..))))).....))))....)).).)))))....))))))))))", }, "seq_pgt781_017": { "energy": -26.2, "seq": "GAUGUACUGUUGUCAUACCUAGGGACACACCUUUAUACGUCGGAAGAUUGGAAAGGUAGGCUCUUGUUAAUUAGGACUUGACCAUCCUCUGACAUCUGCUUGACAAGCAAAAUUGUCAAA", "struct": ".(((.((....)))))....(((......)))......((((((.....(((..((((((.(((((.....)))))))).))).)))))))))......(((((((......))))))).", }, "seq_pgt781_018": { "energy": -21.1, "seq": "GUCAUGAUGAAUCGUCAAACCCCCAUGCCCUAUCAACAGACGUGAUACAUGCCGGAUAUCACGACAAACAAUGCGCACGUCUCGCAGCGGGCUACCAUUGAAUUUACCGUAGACCUUAAC", "struct": "((((((.(((((..((((........((((.......(((((((.....((.((.......)).)).........)))))))......)))).....))))))))).))).)))......", }, "seq_pgt781_019": { "energy": -40.0, "seq": "UAAGAAGAUCGUAUGGAAUGGUUUAGUUGAGGCAGCCUAGCUGCGAACGUCGGAGCGGGAUUCCAGGGAUCCAGAGCGAAGUCUAUCCCUAAGCAAACCGCUGCCUAACGUUCCAUUCUC", "struct": "..............(((((((....(((.(((((((...(((.(((...))).)))((....))((((((..(((......))))))))).........))))))))))...))))))).", }, "seq_pgt781_020": { "energy": -17.5, "seq": "GCGAUGACUACCGAUAGAGAGCGCCUACUAAAUCUUAGAGCUAACUCAGUAUCUAUUAUUUAACUGAUAGAUCUAGUAAGGACUUGCCGACGUAAGAAUGAGAUACGCAGAAUACAAAAA", "struct": "(((....(((....)))....))).......((((((...((.(((.((.((((((((......))))))))))))).))..(((((....)))))..))))))................", }, "seq_pgt781_021": { "energy": -27.4, "seq": "GAGAAAUUAUUUUUUGGGGGAUUAUAGGCUUCUAGUAGUAGGCCGCAUAGGAUGCCCUACAGCCGGGCCACUGUCGCCAGCAUAAGAAUUAUCGCACACCCUAAACGUGAUUAUAAUGGU", "struct": "....((((((..(((((((.......(((((((...)).)))))((((((...((((.......))))..)))).))..((((((...)))).))...))))))).))))))........", }, "seq_pgt781_022": { "energy": -19.4, "seq": "UAAACACAUGGUAAUUGAUCCUUUGGGUGGCCCGGUGGAACCACCACUGAUAAAUAGCAAUAGAAGCAUCCUCGUUCAUACAGUCUAGUCAAGUUAUAAAAACGAGUCAAACAGGCUCAC", "struct": "......((.((........))..)).((((((((((((.....)))))).......((.......))...((((((..((.((.((.....)))).))..)))))).......))).)))", }, "seq_pgt781_023": { "energy": -29.1, "seq": "CAUGACGUAAGAAAAAAUCCCAGUCCUCGUAGCACGCGGGAAUAUCAAUCCGUCGAGUUUAAGGCCUCCGGAUGGGCUUUAGAUUUACACGCACCCACUGCAGGAGAAGGACAUUGAGAA", "struct": ".................(((...((((.((((...((((((.......)))((.(((((((((((((......))))))))))))))).))).....))))))))...))).........", }, "seq_pgt781_024": { "energy": -30.0, "seq": "UCUAGAGAGUAUUCGUGACGUCGAGGCUGAACAAACUUAAGCAUAAAAUGACCGAGCGUAUCGGUUCAUAUGGAGGUCAUGGUCUAGUGGCGCCUGUGGCUAGGGUCAUAACCAACACGU", "struct": "............(((((((.((.(.(((...........))).....((((((((.....)))).)))).).)).))))))).(((((.((....)).)))))(((....))).......", }, "seq_pgt781_025": { "energy": -36.4, "seq": "CGUCUGAAGGAGGGCACGACUGCGGCCCGCCGGCGUCGACUUGGACGAUGAGAGACUCCUAGCUGAAUUCGAUAAAGGAAAAUUUGGCCCUCUGUCCGGGGACAGGGAUUGCCAUACGGC", "struct": ".((((...((.((((.((....)))))).))..((((......)))).....))))((((...............)))).....((((.(((((((....)))))))...))))......", }, "seq_pgt781_026": { "energy": -23.5, "seq": "GUACGGUCGUUACAGAACGAAUUAAUGUCCGUCCUUGAGUCUCAGAUACGUCGAGAUCGGUUACGCCUAUGAAACAGCGUAUGUAGGAACGAGCAAUAUGGGCACAGGAAGUCGCUGGCU", "struct": "..((((.(((((..........))))).)))).((((..(((.(.((((((....((.(((...))).))......)))))).).))).)))).......(((.(((.......))))))", }, "seq_pgt781_027": { "energy": -28.1, "seq": "CCAGGAACCUUGUAGCCCCGGCGUGUCUUCAAUCGCCUGCGAUGGUAAGUUUUUGUGCGGAACUUCAAACCCCGGUCGGGGCCUAGUACUCGAGUGCAGGCAUCAAUAUUGGCAUCACUG", "struct": ".((((...))))..((((((((.........((((....))))((((((((((.....)))))))...)))...))))))))..........((((..(.((.......)).)..)))).", }, "seq_pgt781_028": { "energy": -41.8, "seq": "UUGACGCUCCGCGCGUGGGGACCCCGCCGCACAUGAAUUCCAGUCCUUAUGCUCAUUGACCUGAGGUGCGUGCUCAAAUGCGAUUGAGACGCAUCACACGGGCGCACCGUCUGUGUCAAU", "struct": "(((((((...(((.(((((...)))))))).(((((..........)))))((((......))))((((((.(((((......)))))))))))...((((.....))))..))))))).", }, "seq_pgt781_029": { "energy": -20.6, "seq": "GCUACAUGACUUGUUCCUACUGAGCCGUUCAGUGCACACUCUCUAAUGCGAGACGCCGAGAAUCUAAGAACCGUCCCUAACUUCUAUCACAUUGUAGCGUUUUGUAGUACUAUGGCGGGC", "struct": ".......................(((((..(((((...........(((((((((((((.......((((...........))))......)))..))))))))))))))))))))....", }, "seq_pgt781_030": { "energy": -24.1, "seq": "GAAGAGACAAUAUGUGAUUGGAGUUGAUCAAUCAGUUGGGACGUGCGACGUUAAACACGGUCGUGCGAGGUGCUAGGGAUCACGCAUAAAAAACGUAGGAAACAACAUGAAUCCGCGAAC", "struct": "..........((((((........(((((..(.(((.....((((((((..........))))))))....))))..))))))))))).....(((.(((..((...))..))))))...", }, "seq_pgt781_031": { "energy": -35.1, "seq": "CGCGCCUAUGCUGGGCAUAGCUACUGGUCGCUCGCUAGUGGGCAAUUACGCGUGCUCGGAAAAUCACGUCAACGCCUCUAAGGGCGGCAAACCCGGAGAAAUACGCGCAGCCACCCGAUG", "struct": ".((((((((((...))))))((((((((.....))))))))........((((...((........))...)))).(((..(((.......)))..))).....))))............", }, "seq_pgt781_032": { "energy": -27.5, "seq": "UUUUCCUGAUUCGCCGGACCAUACUCGGUUGCCGAAACGUCACUAUGCACUGCGGCCAUUAGAUCACAGGCUAGACCCUGCGGCGCUCAAGUCUAGCAGUGUAUACAUCGAGAUAAUCCA", "struct": "......((((....((((((......)))..)))....)))).(((((((((((((.....(.((.((((......)))).)))......)))..))))))))))...............", }, "seq_pgt781_033": { "energy": -28.7, "seq": "UCGCUACUACGGGUAGCGAGGCUACGCGGGUAUGCUGGUCCGUUACCAUUAAACAAUCAAGCUCUUGUAUAGCUAGUCAGUCAUCUAUGAUAAUAUAGACCACGCCAAUUGCGCUUACCU", "struct": "((((((((...))))))))((.((.(((.(...(((((((.((((.(((...((...(.((((.......)))).)...)).....))).))))...))))).))....).))).)))).", }, "seq_pgt781_034": { "energy": -19.3, "seq": "CUACAUUUUUGCAAAUCUAUGCAAGACUUAAGUUUACUCAGGAAUCGCUUCUUAGCAGGCCAUUUAUACAAUGUCACCGAGCCGUUUAAUAAUGGAUGGAACAAGGUAUCCCAAGAAGUU", "struct": "......(((((((......)))))))....................(((((((.(..((......((((..(((..(((..(((((....))))).))).)))..)))))))))))))).", }, "seq_pgt781_035": { "energy": -34.2, "seq": "UUUUGUUUAGGGACUAGUCCGUGGAAUUGCAAACGAGAGGAUUAUUGUCCACACCAACCGGUGUGUCCCAGUAUCGGUCUCCCUGACUUUGUUGCAGUACACAGACUUCCAAACUACGAG", "struct": "..((((....(((..((((.(((..(((((((.(((...(((.((((..((((((....))))))...)))))))((((.....)))))))))))))).))).))))))).....)))).", }, "seq_pgt781_036": { "energy": -27.8, "seq": "UCGCAUAGAAGAACACCCCGCAUCGUCUAAGCGUGCACAACGUAUAGCACCUGUAGUUAAGGAGCAUUGCGUCCUCGUGAUUGAGGAAGUUUCAGCUCUCAAUUUAAGCACUGGCGGUAC", "struct": ".................((((...(((((.((((.....)))).))).))((..((((..(((((...((.((((((....)))))).))....))))).))))..)).....))))...", }, "seq_pgt781_037": { "energy": -25.8, "seq": 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"CGCGGACGUGCGUCGCAAGGAAAGAUUCUUACGACUAAUUUCGGCUUGGAACUAGUUUGCGUACACAUCAUUCGAUGCCAGCCCCGUCUGACGACAUACUGCUGUCAUAUCUCAGCGCGA", "struct": "((((((((.((...(((..(((.(((...((((((((.((((.....)))).))))...))))...))).)))..)))..))..)))))).))......(((((........)))))...", }, "seq_pgt781_086": { "energy": -28.4, "seq": "UUGCCGAUCAAGAGAGCGGCAGGACCAUUUCAAACAGGUACAAUGUGUGGUUUCGUCAGGGCGUUAGUUGCAAAUCCCACCGGUUCGACUUGGGAAAAGCAGAAAUGCGGUAAAAUUACG", "struct": ".(((((.((....)).)))))(((((((.....(((.......)))))))))).......(((((..((((...(((((..(((...))))))))...)))).)))))............", }, "seq_pgt781_087": { "energy": -21.3, "seq": "CUCGGACCACGUCCUCUUAUCCUACGUACAUCACGACCUGCUAGGACAUACCUUUCUGCAGUUAGAACUCAGUAGACGCUCAACAGACGAGUUUUUUUUCUAUUAUCGACACUGGUCGUG", "struct": "...((((...)))).................(((((((....(((.....))).((((((((....)))..))))).((((.......)))).....................)))))))", }, "seq_pgt781_088": { "energy": -22.9, "seq": "CAUAACCCGGAUCCAUUUACGGAAGGCCCACCACAAGUCCAGACAGAAUAGCAAACAGAACGGAUCAGGUGGAGCAGGAUCUCGAAAGUUGUAACAGAAGAGAAAUUUCCUCCGUACAUA", "struct": ".......(((((((......)))..(((((((....((((.....................))))..))))).)).(((((((....((....))....))))....)))))))......", }, "seq_pgt781_089": { "energy": -23.2, "seq": "GAUCUCCGUAGGCACCCGCGGCUCUCGACGGAACCUUAUCGCGGCAAAAAAGCACCGGAUUCCCAAGACCAAACCCUGCCUUUCGCAACUCUAUACCCGCAGGUCAGUCCUGCUUCCUAA", "struct": "......((.(((((.....((.(((....((((.....(((..((......))..))).))))..)))))......)))))..)).............(((((.....))))).......", }, "seq_pgt781_090": { "energy": -33.6, "seq": "UCUGCUAAGGGCAGCGGCUUCAUGGUGGUUUGGCGCGUGACCCUUUUUGUGACGCUUAACCUUUUAGGUUUGCCGCCCGACCUAUUGGCGAUGUGCUUCCUUCCCCAGAAUCGUUGUUAC", "struct": ".........((((((((.(((.(((.((...(((((((.((.......)).))((..(((((...))))).))((((.........))))..))))).....))))))))))))))))..", }, "seq_pgt781_091": { "energy": -19.8, "seq": "GAUCUUUCCAAAAACCUGGCGCCGUAGGAUUACAUACUAACAUGGGGUGAUAAUAGCUUGAAUACGAUCGCACUUGCAUUCUCUUUCCACUAGGGACGGAACAGGCUUAUUGGCAACGCG", "struct": ".......((((.((((((.((((.(((.............((.((.(((((..((.......))..))))).)))).............))).)).))...)))).)).)))).......", }, "seq_pgt781_092": { "energy": -28.7, "seq": "UGGCCAAUACCCCAGCGAGCUUGGUGCUCAGAUCAGUCCGCGCACUGACCAAGGAAGCAUUUUGUGUCUGCAUAUAUGAGUAGUAUCCGUCCUUUGACAGUUCAGGCUGCUUGUGGGUCA", "struct": "........((((((((.((((((((((............))))((((.(.(((((........(((.((((........)))))))...))))).).)))).)))))))).)).))))..", }, "seq_pgt781_093": { "energy": -21.0, "seq": "CGUCCAGCAUUUUCCGCGGAUUGCUACCGGAGGACUACCAAUCACUCUUCGCAAGAAUAAACGCCUUCCUAAUAUGCUGAGGUCGACUCGCAUGAAAGUAAAAACGAGAGCGAAACAUCC", "struct": ".((((.......((((.(........)))))))))............(((((..........((.(((.....((((.(((.....)))))))))).))..........)))))......", }, "seq_pgt781_094": { "energy": -30.5, "seq": "AAACUAACUUCCGCCAUUUUUUCACCGGGCAACCGCCGGUGUUCUCCCAAGAAAGGCUUGAUUGGUGUGUUACACGGUGGGUAACAGCAGGGUUCACAUAAGACCCCGGUCAUCGGUUUA", "struct": "............(((.((((((((((((.......)))))).......)))))))))..((((((((((((((.(....)))))))((.(((.((......)).))).))))))))))..", }, "seq_pgt781_095": { "energy": -39.0, "seq": "AAUUGGUUGGGUCUUACUAGUCGACCUUUAUGAUCAGUGGGUGGGUGCCCGACGCCGCGGUCCUCAUCGUUGCCACUUGUGGCCCACCCGGACUGUGCUUGGUUCUCGGAUCCGUGCUAU", "struct": "...(((((((((((.....(..((((.....(..((((((((((..((((((.((.(((((....))))).))...))).)))))))))..))))..)..))))..)))))))).)))).", }, "seq_pgt781_096": { "energy": -32.0, "seq": "GUGACAGAAGCAUUCGAACAAAUCGGGGCAGGCGAGUGUGGCUUAAUGGAGGAGGAAAUCGUGUCGACCUUCUACUGUUACUGAUAAAUUCUACUUGCGUUAAGCACCGCGUGCACGGCC", "struct": ".............((((.....))))(((.((((.(((..((((((((.(((((((.((((.((.(((........)))))))))...)))).))).))))))))..))).))).).)))", }, "seq_pgt781_097": { "energy": -27.2, "seq": "GGGCGGUCCUACAUUUGUUUAGUUGUAAGUAGCUCCAGGUCCCGUAAAGGCAGUGCCGGUAGCUAUUCUGUGUGCGGUACAUGAACCACGAGAAUCCAAUCUUUUCAUCCCAGCCGGGAA", "struct": "((((((..((((................))))..))..))))(((.....((((((((...((......))...)))))).))....))).................((((....)))).", }, "seq_pgt781_098": { "energy": -26.1, "seq": "CCAUUCUGUUUGCGCUUAUUAUUGAAGGUCCCGGGCACUGUCACGAUAACCUGCUAUCGUUCUAUGGUGUCUCAUUGAGUUGGCUGUCUUAGCGCCUGACCGUACUAACGACGCUGGGCA", "struct": "(((....(((.(((((.........((.((..(((((((((.((((((......))))))...)))))))))....)).)).........)))))..)))(((....)))....)))...", }, "seq_pgt781_099": { "energy": -37.5, "seq": "UGACUGACCCUAAAGCACUGCGAUAUUCCGGUGUCUACUGGCUUUCCGGGCCUCCUAGCGGGCCCAUAGAGCAGUUAUCGCGUAAUGAUCGCCACGAAACGUGAGUGGCAGUACCGACUA", "struct": "..............((((((.((...))))))))..((((.((((..((((((......))))))..))))))))..(((.(((.((.((((((((...)))).)))))).))))))...", }, "seq_pgt781_100": { "energy": -35.6, "seq": "CCCUGCGGGCUGUGCGGACUAAAUACGCCCUUCCAACCACCGCGGAGGUGGAAAAAUUCGUAGCGGAGGCGAGGUAGGUGCUUAGCGGCGCGAUUCCCCGUCCAUUGCCAUGCAAACUAG", "struct": "(((((((((..(.(((.........))).)..))..(((((.....))))).......))))).)).((((.((...(((((....)))))....)).))))..((((...)))).....", }, } # Loop through all sequences in dictionary of sequences for sequence in rnafold.keys(): # Get the RNA sequence of the current iteration RNAseq = rnafold[sequence]["seq"] # Calculate Nussinov scoring matrix without and with stacked basepair scoring nussinov_result = nussinov(RNAseq) nussinov_scored_result = nussinov(RNAseq, use_scoring=True) # Backtrack the Nussinov scoring matrices to get structures in dot-bracket-notation backtrack_nussinov_result = backtrack(nussinov_result, RNAseq) backtrack_nussinov_scored_result = backtrack(nussinov_scored_result, RNAseq) # Save the determined structure in dot-bracket-notation to the dictionary of current iteration sequence rnafold[sequence]["nussinov"] = backtrack_nussinov_result rnafold[sequence]["nussinov_scored"] = backtrack_nussinov_scored_result # Calculate pairwise basepair distances # 1. Nussinov vs RNAfold # 2. Nussinov Scored vs RNAfold # 3. Nussinov Scored vs Nussinov rnafold[sequence]["bpdist_nus_rna"] = basepair_distance(rnafold[sequence]["nussinov"], rnafold[sequence]["struct"]) rnafold[sequence]["bpdist_nus_scr_rna"] = basepair_distance(rnafold[sequence]["nussinov_scored"], rnafold[sequence]["struct"]) rnafold[sequence]["bpdist_nus_scr_nus"] = basepair_distance(rnafold[sequence]["nussinov_scored"], rnafold[sequence]["nussinov"]) # Lists which are going to contain all pairwise basepair distances # 1. Nussinov vs RNAfold # 2. Nussinov Scored vs RNAfold # 3. Nussinov Scored vs Nussinov comparison_nus_rna = [] comparison_nus_scr_rna = [] comparison_nus_scr_nus = [] # Loop through all sequence and append pairwise basepair distances to the lists above # Could be implemented during the loop before, but for simplicity we loop a second time for sequence in rnafold: comparison_nus_rna.append(rnafold[sequence]["bpdist_nus_rna"]) comparison_nus_scr_rna.append(rnafold[sequence]["bpdist_nus_scr_rna"]) comparison_nus_scr_nus.append(rnafold[sequence]["bpdist_nus_scr_nus"]) # Calculate Pearson correlation coefficients for comparisons # 1. Nussinov vs RNAfold and Nussinov Scored vs RNAfold # 2. Nussinov Scored vs RNAfold and Nussinov Scored vs Nussinov # 3. Nussinov Scored vs Nussinov and Nussinov vs RNAfold pearsoncorr_plot_1, _ = pearsonr(comparison_nus_rna, comparison_nus_scr_rna) pearsoncorr_plot_2, _ = pearsonr(comparison_nus_scr_rna, comparison_nus_scr_nus) pearsoncorr_plot_3, _ = pearsonr(comparison_nus_scr_nus, comparison_nus_rna) # Make a figure and set size and spacing plt.figure(1, figsize=(13, 9), layout="tight") plt.subplots_adjust(hspace=0.3) # Scatter plot of basepair distances of Nussinov vs RNAfold and Nussinov Scored vs RNAfold plt.subplot(2, 2, 1) plt.scatter(comparison_nus_rna, comparison_nus_scr_rna) plt.title("Comparison of pairwise base pair distances\nPearson correlation coefficient: %.2f" % pearsoncorr_plot_1) plt.xlabel('Unmodified Nussinov vs RNAfold') plt.ylabel('Modified Nussinov vs RNAfold') # Scatter plot of basepair distances of Nussinov Scored vs RNAfold and Nussinov Scored vs Nussinov plt.subplot(2, 2, 2) plt.scatter(comparison_nus_scr_rna, comparison_nus_scr_nus) plt.title("Comparison of pairwise base pair distances\nPearson correlation coefficient: %.2f" % pearsoncorr_plot_2) plt.xlabel('Modified Nussinov vs RNAfold') plt.ylabel('Modified Nussinov vs Unmodified Nussinov') # Scatter plot of basepair distances of Nussinov Scored vs Nussinov and Nussinov vs RNAfold plt.subplot(2, 2, 3) plt.scatter(comparison_nus_scr_nus, comparison_nus_rna) plt.title("Comparison of pairwise base pair distances\nPearson correlation coefficient: %.2f" % pearsoncorr_plot_3) plt.xlabel('Modified Nussinov vs Unmodified Nussinov') plt.ylabel('Unmodified Nussinov vs RNAfold') # Sort the lists of compairsons comparison_nus_rna = np.sort(comparison_nus_rna) comparison_nus_scr_rna = np.sort(comparison_nus_scr_rna) comparison_nus_scr_nus = np.sort(comparison_nus_scr_nus) # Cumulative distributions functions cdf_nussinov_rnafold = np.cumsum(comparison_nus_rna) / np.sum(comparison_nus_rna) cdf_nussinov_scored_rnafold = np.cumsum(comparison_nus_scr_rna) / np.sum(comparison_nus_scr_rna) cdf_nussinov_scored_nussinov = np.cumsum(comparison_nus_scr_nus) / np.sum(comparison_nus_scr_nus) # Plot of cumulative distribution of basepair distances plt.subplot(2, 2, 4) plt.plot(comparison_nus_rna, cdf_nussinov_rnafold, label="Unmodified Nussinov vs RNAfold") plt.plot(comparison_nus_scr_rna, cdf_nussinov_scored_rnafold, label="Modified Nussinov vs RNAfold") plt.plot(comparison_nus_scr_nus, cdf_nussinov_scored_nussinov, label="Modified Nussinov vs Unmodified Nussinov") plt.title("Cumulative distribution of base pair distances\n") plt.xlabel("Base pair distance") plt.ylabel("p") plt.legend() # Save plots plt.savefig("rna.png")