bioin.pattern_count(pattern, text) |
The number of times that a pattern appears as a substring of text. |
bioin.frequency_map(text, k) |
Find the frequency of all k-mers in a string. |
bioin.frequent_words(text, k) |
Find all the most frequent k-mers in text. |
bioin.reverse(pattern) |
Reverse a string sequence. |
bioin.complement(pattern) |
Compute the complementary string of pattern, with every nucleotide being replaced by its complement. |
bioin.reverse_complement(pattern) |
Find the reverse complement of a DNA string. |
bioin.motif.count_motif(motifs) |
Count the number of nucleotides (4 types: ACGT) column wise from a motifs matrix. |
bioin.motif.profile_motif(motifs) |
The percentage of count number of nucleotides column wise from a motifs matrix. |
bioin.motif.consensus_motif(motifs) |
Compute the consensus string of a set of k-mers motifs. |
bioin.motif.score_motif(motifs) |
Compute the total different occurrences of nucleotides that consensus have with motifs. |
bioin.motif.probability_profile(text, …) |
Calculate the probability of a string (i.e. |
bioin.motif.profile_most_probable_kmer(text, …) |
Find a profile-most probable k-mer in a string. |
bioin.motif.greedy_motif_search(dna, k, t) |
Calculate t k-mers from dna that have the best score (i.e. |
bioin.motif.count_with_pseudocount(motifs) |
Count the number of nucleotides (4 types: ACGT) column wise from a motifs matrix, then add 1 to each position, i.e. |
bioin.motif.profile_with_pseudocount(motifs) |
The percentage of pseudocount number of nucleotides column wise from a motifs matrix. |
bioin.motif.consensus_with_pseudocount(motifs) |
Compute the consensus string of a set of k-mers motifs. |
bioin.motif.score_with_pseudocount(motifs) |
Compute the total different occurrences of nucleotides that consensus_pseudo have with motifs |
bioin.motif.probability_with_pseudocount(…) |
Calculate the probability of a string (i.e. |
bioin.motif.profile_most_probable_kmer_with_pseudocount(…) |
Find a profile-most probable k-mer in a string. |
bioin.motif.greedy_motif_search_with_pseudocount(…) |
Calculate t k-mers from dna that have the best score (i.e. |
bioin.motif.profile_probable_motifs(…) |
Find the profile-most probable k-mers in each string from dna. |
bioin.motif.random_motifs(dna, k, t) |
Return a list of random k-mers from each of t different strings dna. |
bioin.motif.randomized_motif_search(dna, k, t) |
Return a list of best k-mers from each of t different strings dna. |
bioin.motif.normalize_probability(probabilities) |
Rescale a collection of probabilityes so that they sum up to 1. |
bioin.motif.weighted_die(probabilities) |
Return a randomly chosen k-mer key with respect to the values in Probabilities. |
bioin.motif.profile_generated_string(text, …) |
Return a randomly generated k-mer from Text whose probabilities are generated from profile. |
bioin.motif.GibbsSampler(Dna, k, t, N) |
Using GibbsSampler method to return the best motifs of t k-mers in each of the strings in Dna. |
bioin.replication.pattern_matching(pattern, …) |
Find all occurrences of a pattern in a string. |
bioin.replication.symbol_array(genome, symbol) |
Calculate the symbol count in genome. |
bioin.replication.faster_symbol_array(…) |
A faster calculation method for counting a symbol in genome. |
bioin.replication.skew_array(genome) |
Compute the skew array of genome as a list. |
bioin.replication.minimum_skew(genome) |
Find a position in a genome where the skew diagram attains a minimum. |
bioin.replication.hamming_distance(p, q) |
Calculate the HammingDistance for two strings. |
bioin.replication.approximate_pattern_matching(…) |
Find all approximate occurences of a pattern in a string. |
bioin.replication.approximate_pattern_count(…) |
Compute the number of occurrences of pattern in text with at most d mismatches. |