Functions_documentation.md

Bioinformatics-toolspy Code Documentation

Introduction

This document provides documentation for the bioinformatics code snippets provided. These snippets cover various bioinformatics algorithms and functions for sequence analysis, alignment, and motif finding.

Functions

levenshtein_distance(string1, string2)

Calculates the Levenshtein distance between two input strings.

Example:

import bioinformatics
print(bioinformatics.levenshtein_distance("kitten","sitting"))  # Output: 3

sequence_alignment(sequence1, sequence2, gap_penalty, mismatch_penalty)

Performs sequence alignment between two sequences with specified gap and mismatch penalties.

Example:

import bioinformatics
print(bioinformatics.sequence_alignment("AGGGCT", "AGGCA", 3, 2))  # Output: (3, 'AGGGCT-', '-AGGCA')

longest_common_subsequences(sequence_list)

Finds the longest common subsequence among a list of input sequences.

Example:

import bioinformatics
print(bioinformatics.longest_common_subsequences(["ACCGAAGG","ACCGAACC","CCACCGAAGG","GGACCGAACC"]))  # Output: 'ACCGA'

longest_common_subsequence(sequence1, sequence2)

Finds the longest common subsequence between two input sequences.

Example:

import bioinformatics
print(bioinformatics.longest_common_subsequence("ACCGAAGG", "ACCGAACC"))  # Output: 'ACCGAA'

commun_patterns(pattern_list)

Finds common patterns among a list of input patterns.

Example:

import bioinformatics
print(bioinformatics.commun_patterns(["XAaXV","XAsXV","XAcXV"]))  # Output: 'XAXV'

reconstruct_from_kmers(k, kmers)

Reconstructs a string from a collection of k-mers.

Example:

import bioinformatics
print(bioinformatics.reconstruct_from_kmers(3,["AAT","ATG", "TGC", "GCT", "CTA"]))  # Output: 'AATGCTA'

translate_rna_to_aminoacid(rna_sequence)

Translates an RNA sequence into an amino acid sequence.

Example:

import bioinformatics
print(bioinformatics.translate_rna_to_aminoacid("AUGGCCAUGGCGCCCAGAACUGAGAUCAAUAGUACCCGUAUUAACGGGUGA"))  # Output: 'MAAMGSST*'

de_bruijn_collection(kmers, prefix_func, suffix_func)

Constructs a de Bruijn graph from a collection of k-mers.

Example:

import bioinformatics
print(bioinformatics.de_bruijn_collection(["ATG", "ATG", "TGT", "TGG", "CAT", "GGA", "GAT", "AGA"], lambda kmer: kmer[:-1], lambda kmer: kmer[1:]))  # Output: {'AT': ['TG', 'TG', 'TG'], 'TG': ['GT', 'GG'], 'GT': ['TG'], 'GG': ['GA'], 'GA': ['AT'], 'CA': ['AT']}

find_eulerian_cycle(graph)

Finds an Eulerian cycle in a graph.

Example:

import bioinformatics
print(bioinformatics.find_eulerian_cycle({"AAT": ["ATG"],"ATG": ["TGC"],"TGC": ["GCT"],"GCT": ["CTA"],"CTA": ["TAC"],"TAC": ["ACG"],"ACG": ["CGA"],"CGA": ["GAT"],"GAT": ["ATG"]}))  # Output: ['AAT', 'ATG', 'TGC', 'GCT', 'CTA', 'TAC', 'ACG', 'CGA', 'GAT', 'ATG']

de_bruijn(k, sequence)

Constructs a de Bruijn graph from a sequence.

Example:

import bioinformatics
print(bioinformatics.de_bruijn(3, "ATGATCAAG"))  # Output: {'ATG': ['TGA'], 'TGA': ['GAT'], 'GAT': ['ATC'], 'ATC': ['TCA'], 'TCA': ['CAA', 'CAG'], 'CAA': ['AAG'], 'AAG': ['AG']}

grph_kmers(kmers)

Constructs a graph from a collection of k-mers.

Example:

import bioinformatics
print(bioinformatics.grph_kmers(["ACCGA", "CCGAA", "CGAAG", "GAAGC", "AAGCT"]))  # Output: {'ACCGA': ['CCGAA'], 'CCGAA': ['CGAAG'], 'CGAAG': ['GAAGC'], 'GAAGC': ['AAGCT']}

reconstruct(kmers)

Reconstructs a string from a collection of overlapping k-mers.

Example:

import bioinformatics
print(bioinformatics.reconstruct(["ACCGA", "CCGAA", "CGAAG", "GAAGC", "AAGCT"]))  # Output: 'ACCGAAGCT'

kmer_composition(k, sequence)

Finds the k-mer composition of a sequence.

Example:

import bioinformatics
print(bioinformatics.kmer_composition(2,'CAATCCAAC'))  # Output: ['CA', 'AA', 'AT', 'TT', 'TC', 'CC', 'CA', 'AA', 'AC']

distance_between_pattern_and_strings(pattern, string_list)

Calculates the total Hamming distance between a pattern and a list of strings.

Example:

import bioinformatics
print(bioinformatics.distance_between_pattern_and_strings("AA",['AAATTGACGCAT','GACGAAAAACGTT','CGTCAGCGCCTG''GCTGAGCAAAGG','AGTACGGGACAG']))  # Output: 14

gibbs(k, t, n, dna, iterations)

Performs Gibbs sampling for motif finding.

Example:

import bioinformatics
print(bioinformatics.gibbs(4, 5, 10,["GGCGTTCAGGCA", "AAGAATCAGTCA", "CAAGGAGTTCGC", "CACGTCAATCAC", "CAATAATATTCG"],1000))  # Output: ['TCAG', 'TCAG', 'TCAG', 'TCAG', 'TCAG']

randomized_motif_search(k, t, dna, iterations)

Performs randomized motif search for motif finding.

Example:

import bioinformatics
print(bioinformatics.randomized

_motif_search(3,5,["GGCGTTCAGGCA", "AAGAATCAGTCA", "CAAGGAGTTCGC", "CACGTCAATCAC", "CAATAATATTCG"],1))  # Output: ['TTC', 'TTC', 'TTC', 'TTC', 'TTC']

greedy_motif_search(k, t, dna)

Performs greedy motif search for motif finding.

Example:

import bioinformatics
print(bioinformatics.greedy_motif_search(3,5,["GGCGTTCAGGCA", "AAGAATCAGTCA", "CAAGGAGTTCGC", "CACGTCAATCAC", "CAATAATATTCG"]))  # Output: ['TTC', 'TTC', 'TTC', 'TTC', 'TTC']

most_probable(dna_string, k, profile_matrix)

Finds the most probable k-mer in a DNA string given a profile matrix.

Example:

import bioinformatics
print(bioinformatics.most_probable("ACCTGTTTATTGCCTAAGTTCCGAACAAACCCAATATAGCCCGAGGGCCT",5,[[0.2, 0.2, 0.3, 0.2, 0.3],[0.4, 0.3, 0.1, 0.5, 0.1],[0.3, 0.3, 0.5, 0.2, 0.4],[0.1, 0.2, 0.1, 0.1, 0.2]]))  # Output: 'CCGAG'

median_string(dna, k)

Finds the median string in a collection of DNA strings.

Example:

import bioinformatics
print(bioinformatics.median_string(['AAATTGACGCAT','GACGACCACGTT','CGTCAGCGCCTG''GCTGAGCACCGG','AGTACGGGACAG'],6))  # Output: 'GACGAC'

enumerate_motifs(dna, k, d)

Enumerates all motifs of length k with at most d mismatches in a collection of DNA strings.

Example:

import bioinformatics
print(bioinformatics.enumerate_motifs(["ATTTGGC","TGCCTTA","CGGTATC","GAAAATT"],3,1))  # Output: ['ATA', 'ATT', 'GTT', 'TTA', 'TTG']

pattern_to_number(pattern)

Converts a DNA pattern to its corresponding integer.

Example:

import bioinformatics
print(bioinformatics.pattern_to_number("CC"))  # Output: 13

number_to_pattern(number, k)

Converts an integer to its corresponding DNA pattern of length k.

Example:

import bioinformatics
print(bioinformatics.number_to_pattern(5,2))  # Output: 'GG'

generate_frequency_array(dna_string, k)

Generates the frequency array of k-mers in a DNA string.

Example:

import bioinformatics
print(bioinformatics.generate_frequency_array("AAACAGATCACCCGCTGAGCGGGTTATCTGTT",1))  # Output: [5, 4, 2, 1, 0, 0, 2, 2, 1, 2]

reverse_complement(dna_string)

Finds the reverse complement of a DNA string.

Example:

import bioinformatics
print(bioinformatics.reverse_complement("AAAAAGCATAAACATTAAAGAG"))  # Output: 'CTCTTTAATGTTTATGCTTTTT'

frequent_words_mismatch(dna_string, k, d)

Finds the most frequent k-mers with at most d mismatches in a DNA string.

Example:

import bioinformatics
print(bioinformatics.frequent_words_mismatch("ACGTTGCATGTCGCATGATGCATGAGAGCT",4,1))  # Output: ['ATGT', 'GATG', 'ATGC']

approximate_pattern_matching(pattern, text, d)

Finds all approximate occurrences of a pattern in a text with at most d mismatches.

Example:

import bioinformatics
print(bioinformatics.approximate_pattern_matching("AAAAAGCATAAACATTAAAGAG","AAAAA",0))  # Output: [0, 1, 2, 3, 4, 5, 6, 21]

approximate_pattern_count(pattern, text, d)

Counts the number of approximate occurrences of a pattern in a text with at most d mismatches.

Example:

import bioinformatics
print(bioinformatics.approximate_pattern_count("AAAAAGCATAAACATTAAAGAG","AAAAA",0))  # Output: 8

min_skew(genome)

Finds the positions in a genome where the skew diagram attains its minimum value.

Example:

import bioinformatics
print(bioinformatics.min_skew("CATGGGCATCGGCCATACGCC"))  # Output: [1, 6]

clump_finding(genome, k, L, t)

Finds patterns forming clumps in a genome.

Example:

import bioinformatics
print(bioinformatics.clump_finding("CGGACTCGACAGATGTGAAGAACGACAATGTGAAGACTCGACACGACAGAGTGAAGAGAAGAG",5,50,4))  # Output: ['CGACA']

pattern_count(text, pattern)

Counts the occurrences of a pattern in a text.

Example:

import bioinformatics
print(bioinformatics.pattern_count("cgatatatccatag","ata"))  # Output: 3

frequent_words(text, k)

Finds the most frequent k-mers in a text.

Example:

import bioinformatics
print(bioinformatics.frequent_words("actgactcccaccccc",3))  # Output: ['ccc']

pattern_count_positions(text, pattern)

Finds the positions of all occurrences of a pattern in a text.

Example:

import bioinformatics
print(bioinformatics.pattern_count_positions("cgatatatccatag","ata"))  # Output: [2, 4, 10]

hamming_distances(pattern, sequence)

Calculates the Hamming distances between a pattern and a sequence.

Example:

import bioinformatics
print(bioinformatics.hamming_distances("cgatatatccatag","ata"))  # Output: [3, 2, 1, 2, 2, 1, 2, 1, 0, 1, 2, 3, 1, 2]