Logo Search packages:      
Sourcecode: python-biopython version File versions  Download package

__init__.py

#!/usr/bin/env python
# Created: Wed May 29 08:07:18 2002
# thomas@cbs.dtu.dk, Cecilia.Alsmark@ebc.uu.se
# Copyright 2001 by Thomas Sicheritz-Ponten and Cecilia Alsmark.
# All rights reserved.
# This code is part of the Biopython distribution and governed by its
# license.  Please see the LICENSE file that should have been included
# as part of this package.

"""Miscellaneous functions for dealing with sequences."""

import re, time
from Bio import SeqIO
from Bio.Seq import Seq
from Bio import Alphabet
from Bio.Alphabet import IUPAC
from Bio.Data import IUPACData, CodonTable


######################################
# DNA
######################
# {{{ 

def reverse(seq):
    """Reverse the sequence. Works on string sequences (DEPRECATED).

    This function is now deprecated, instead use the string's built in slice
    method with a step of minus one:

    >>> "ACGGT"[::-1]
    'TGGCA'
    """
    import warnings
    import Bio
    warnings.warn("Bio.SeqUtils.reverse() is deprecated, use the string's "
                  "slice method with a step of minus one instead.",
                  Bio.BiopythonDeprecationWarning)
    r = list(seq)
    r.reverse()
    return ''.join(r)

def GC(seq):
    """Calculates G+C content, returns the percentage (float between 0 and 100).

    Copes mixed case sequences, and with the ambiguous nucleotide S (G or C)
    when counting the G and C content.  The percentage is calculated against
    the full length, e.g.: 

    >>> from Bio.SeqUtils import GC
    >>> GC("ACTGN")
    40.0

    Note that this will return zero for an empty sequence.
    """
    try:
        gc = sum(map(seq.count,['G','C','g','c','S','s']))
        return gc*100.0/len(seq)
    except ZeroDivisionError:
        return 0.0
        
    
def GC123(seq):
    """Calculates total G+C content plus first, second and third positions.

    Returns a tuple of four floats (percentages between 0 and 100) for the
    entire sequence, and the three codon positions.  e.g.

    >>> from Bio.SeqUtils import GC123
    >>> GC123("ACTGTN")
    (40.0, 50.0, 50.0, 0.0)

    Copes with mixed case sequences, but does NOT deal with ambiguous
    nucleotides.
    """
    d= {}
    for nt in ['A','T','G','C']:
       d[nt] = [0,0,0]

    for i in range(0,len(seq),3):
        codon = seq[i:i+3]
        if len(codon) <3: codon += '  '
        for pos in range(0,3):
            for nt in ['A','T','G','C']:
                if codon[pos] == nt or codon[pos] == nt.lower():
                    d[nt][pos] += 1
    gc = {}
    gcall = 0
    nall = 0
    for i in range(0,3):
        try:
            n = d['G'][i] + d['C'][i] +d['T'][i] + d['A'][i]
            gc[i] = (d['G'][i] + d['C'][i])*100.0/n
        except:
            gc[i] = 0

        gcall = gcall + d['G'][i] + d['C'][i]
        nall = nall + n

    gcall = 100.0*gcall/nall
    return gcall, gc[0], gc[1], gc[2]

def GC_skew(seq, window = 100):
    """Calculates GC skew (G-C)/(G+C) for multuple windows along the sequence.

    Returns a list of ratios (floats), controlled by the length of the sequence
    and the size of the window.

    Does NOT look at any ambiguous nucleotides.
    """
    # 8/19/03: Iddo: added lowercase 
    values = []
    for i in range(0, len(seq), window):
        s = seq[i: i + window]
        g = s.count('G') + s.count('g')
        c = s.count('C') + s.count('c')
        skew = (g-c)/float(g+c)
        values.append(skew)
    return values

from math import pi, sin, cos, log
def xGC_skew(seq, window = 1000, zoom = 100,
                         r = 300, px = 100, py = 100):
    """Calculates and plots normal and accumulated GC skew (GRAPHICS !!!)."""
    from Tkinter import Scrollbar, Canvas, BOTTOM, BOTH, ALL, \
                        VERTICAL, HORIZONTAL, RIGHT, LEFT, X, Y
    yscroll = Scrollbar(orient = VERTICAL)
    xscroll = Scrollbar(orient = HORIZONTAL)
    canvas = Canvas(yscrollcommand = yscroll.set,
                    xscrollcommand = xscroll.set, background = 'white')
    win = canvas.winfo_toplevel()
    win.geometry('700x700')
   
    yscroll.config(command = canvas.yview)
    xscroll.config(command = canvas.xview)
    yscroll.pack(side = RIGHT, fill = Y)
    xscroll.pack(side = BOTTOM, fill = X)
    canvas.pack(fill=BOTH, side = LEFT, expand = 1)
    canvas.update()

    X0, Y0  = r + px, r + py
    x1, x2, y1, y2 = X0 - r, X0 + r, Y0 -r, Y0 + r
   
    ty = Y0
    canvas.create_text(X0, ty, text = '%s...%s (%d nt)' % (seq[:7], seq[-7:], len(seq)))
    ty +=20
    canvas.create_text(X0, ty, text = 'GC %3.2f%%' % (GC(seq)))
    ty +=20
    canvas.create_text(X0, ty, text = 'GC Skew', fill = 'blue')
    ty +=20
    canvas.create_text(X0, ty, text = 'Accumulated GC Skew', fill = 'magenta')
    ty +=20
    canvas.create_oval(x1,y1, x2, y2)

    acc = 0
    start = 0
    for gc in GC_skew(seq, window):
        r1 = r
        acc+=gc
        # GC skew
        alpha = pi - (2*pi*start)/len(seq)
        r2 = r1 - gc*zoom
        x1 = X0 + r1 * sin(alpha)
        y1 = Y0 + r1 * cos(alpha)
        x2 = X0 + r2 * sin(alpha)
        y2 = Y0 + r2 * cos(alpha)
        canvas.create_line(x1,y1,x2,y2, fill = 'blue')
        # accumulated GC skew
        r1 = r - 50
        r2 = r1 - acc
        x1 = X0 + r1 * sin(alpha)
        y1 = Y0 + r1 * cos(alpha)
        x2 = X0 + r2 * sin(alpha)
        y2 = Y0 + r2 * cos(alpha)
        canvas.create_line(x1,y1,x2,y2, fill = 'magenta')

        canvas.update()
        start += window

    canvas.configure(scrollregion = canvas.bbox(ALL))

def molecular_weight(seq):
    """Calculate the molecular weight of a DNA sequence."""
    if type(seq) == type(''): seq = Seq(seq, IUPAC.unambiguous_dna)
    weight_table = IUPACData.unambiguous_dna_weights
    return sum(weight_table[x] for x in seq)

def nt_search(seq, subseq):
    """Search for a DNA subseq in sequence.

    use ambiguous values (like N = A or T or C or G, R = A or G etc.)
    searches only on forward strand
    """
    pattern = ''
    for nt in subseq:
        value = IUPACData.ambiguous_dna_values[nt]
        if len(value) == 1:
            pattern += value
        else:
            pattern += '[%s]' % value

    pos = -1
    result = [pattern]
    l = len(seq)
    while True:
        pos+=1
        s = seq[pos:]
        m = re.search(pattern, s)
        if not m: break
        pos += int(m.start(0))
        result.append(pos)
    return result

# }}}
   
######################################
# Protein
######################
# {{{ 

# temporary hack for exception free translation of "dirty" DNA
# should be moved to ???

00224 class ProteinX(Alphabet.ProteinAlphabet):
    """Variant of the extended IUPAC extended protein alphabet (DEPRECATED)."""
    letters = IUPACData.extended_protein_letters + "X"

#Can't add a deprecation warning to the class due to the following line:
proteinX = ProteinX()

00231 class MissingTable:
    def __init__(self, table):
        self._table = table
        import warnings
        import Bio
        warnings.warn("Function Bio.SeqUtils.makeTableX() and related classes ProteinX "
                      "and MissingTable are deprecated.", Bio.BiopythonDeprecationWarning)
    def get(self, codon, stop_symbol):
        try:
            return self._table.get(codon, stop_symbol)
        except CodonTable.TranslationError:
            return 'X'

def makeTableX(table):
    assert table.protein_alphabet == IUPAC.extended_protein
    return CodonTable.CodonTable(table.nucleotide_alphabet, proteinX,
                                 MissingTable(table.forward_table),
                                 table.back_table, table.start_codons,
                                 table.stop_codons)

# end of hacks

def seq3(seq):
    """Turn a one letter code protein sequence into one with three letter codes.

    The single input argument 'seq' should be a protein sequence using single
    letter codes, either as a python string or as a Seq or MutableSeq object.

    This function returns the amino acid sequence as a string using the three
    letter amino acid codes. Output follows the IUPAC standard (including
    ambiguous characters B for "Asx", J for "Xle" and X for "Xaa", and also U
    for "Sel" and O for "Pyl") plus "Ter" for a terminator given as an asterisk.  Any unknown
    character (including possible gap characters), is changed into 'Xaa'.

    e.g.
    >>> from Bio.SeqUtils import seq3
    >>> seq3("MAIVMGRWKGAR*")
    'MetAlaIleValMetGlyArgTrpLysGlyAlaArgTer'

    This function was inspired by BioPerl's seq3.
    """
    threecode = {'A':'Ala', 'B':'Asx', 'C':'Cys', 'D':'Asp',
                 'E':'Glu', 'F':'Phe', 'G':'Gly', 'H':'His',
                 'I':'Ile', 'K':'Lys', 'L':'Leu', 'M':'Met',
                 'N':'Asn', 'P':'Pro', 'Q':'Gln', 'R':'Arg',
                 'S':'Ser', 'T':'Thr', 'V':'Val', 'W':'Trp',
                 'Y':'Tyr', 'Z':'Glx', 'X':'Xaa', '*':'Ter',
                 'U':'Sel', 'O':'Pyl', 'J':'Xle',
                 }
    #We use a default of 'Xaa' for undefined letters
    #Note this will map '-' to 'Xaa' which may be undesirable!
    return ''.join([threecode.get(aa,'Xaa') for aa in seq])


# }}}

######################################
# Mixed ??? 
######################
# {{{ 

def GC_Frame(seq, genetic_code = 1):
    """Just an alias for six_frame_translations (DEPRECATED).

    Please use six_frame_translations directly, as this function is deprecated."""
    import warnings
    import Bio
    warnings.warn("GC_Frame is deprecated; please use six_frame_translations instead",
                  Bio.BiopythonDeprecationWarning)
    return six_frame_translations(seq, genetic_code)

def six_frame_translations(seq, genetic_code = 1):
    """Formatted string showing the 6 frame translations and GC content.

    nice looking 6 frame translation with GC content - code from xbbtools
    similar to DNA Striders six-frame translation

    e.g.
    from Bio.SeqUtils import six_frame_translations
    print six_frame_translations("AUGGCCAUUGUAAUGGGCCGCUGA")
    """
    from Bio.Seq import reverse_complement, translate
    anti = reverse_complement(seq)
    comp = anti[::-1]
    length = len(seq)
    frames = {}
    for i in range(0,3):
        frames[i+1]  = translate(seq[i:], genetic_code)
        frames[-(i+1)] = reverse(translate(anti[i:], genetic_code))

    # create header
    if length > 20:
        short = '%s ... %s' % (seq[:10], seq[-10:])
    else:
        short = seq
    #TODO? Remove the date as this would spoil any unit test...
    date = time.strftime('%y %b %d, %X', time.localtime(time.time()))
    header = 'GC_Frame: %s, ' % date
    for nt in ['a','t','g','c']:
        header += '%s:%d ' % (nt, seq.count(nt.upper()))
      
    header += '\nSequence: %s, %d nt, %0.2f %%GC\n\n\n' % (short.lower(),length, GC(seq))       
    res = header
   
    for i in range(0,length,60):
        subseq = seq[i:i+60]
        csubseq = comp[i:i+60]
        p = i/3
        res = res + '%d/%d\n' % (i+1, i/3+1)
        res = res + '  ' + '  '.join(map(None,frames[3][p:p+20])) + '\n'
        res = res + ' ' + '  '.join(map(None,frames[2][p:p+20])) + '\n'
        res = res + '  '.join(map(None,frames[1][p:p+20])) + '\n'
        # seq
        res = res + subseq.lower() + '%5d %%\n' % int(GC(subseq))
        res = res + csubseq.lower() + '\n'
        # - frames
        res = res + '  '.join(map(None,frames[-2][p:p+20]))  +' \n'
        res = res + ' ' + '  '.join(map(None,frames[-1][p:p+20])) + '\n'
        res = res + '  ' + '  '.join(map(None,frames[-3][p:p+20])) + '\n\n'
    return res

# }}}

######################################
# FASTA file utilities
######################
# {{{ 

def fasta_uniqids(filename):
    """Checks and changes the name/ID's to be unique identifiers by adding numbers (DEPRECATED).

    file - a FASTA format filename to read in.

    No return value, the output is written to screen.
    """
    import warnings
    import Bio
    warnings.warn("fasta_uniqids is deprecated", Bio.BiopythonDeprecationWarning)

    mydict = {}
    txt = open(filename).read()
    entries = []
    for entry in txt.split('>')[1:]:
        name, seq= entry.split('\n',1)
        name = name.split()[0].split(',')[0]
      
        if name in mydict:
            n = 1
            while 1:
                n = n + 1
                _name = name + str(n)
                if _name not in mydict:
                    name = _name
                    break
            
        mydict[name] = seq

    for name, seq in mydict.iteritems():
        print '>%s\n%s' % (name, seq)

def quick_FASTA_reader(file):
    """Simple FASTA reader, returning a list of string tuples.

    The single argument 'file' should be the filename of a FASTA format file.
    This function will open and read in the entire file, constructing a list
    of all the records, each held as a tuple of strings (the sequence name or
    title, and its sequence).

    This function was originally intended for use on large files, where its
    low overhead makes it very fast.  However, because it returns the data as
    a single in memory list, this can require a lot of RAM on large files.
   
    You are generally encouraged to use Bio.SeqIO.parse(handle, "fasta") which
    allows you to iterate over the records one by one (avoiding having all the
    records in memory at once).  Using Bio.SeqIO also makes it easy to switch
    between different input file formats.  However, please note that rather
    than simple strings, Bio.SeqIO uses SeqRecord objects for each record.
    """
    #Want to split on "\n>" not just ">" in case there are any extra ">"
    #in the name/description.  So, in order to make sure we also split on
    #the first entry, prepend a "\n" to the start of the file.
    handle = open(file)
    txt = "\n" + handle.read()
    handle.close()
    entries = []
    for entry in txt.split('\n>')[1:]:
        name,seq= entry.split('\n',1)
        seq = seq.replace('\n','').replace(' ','').upper()
        entries.append((name, seq))
    return entries

def apply_on_multi_fasta(file, function, *args):
    """Apply a function on each sequence in a multiple FASTA file (DEPRECATED).

    file - filename of a FASTA format file
    function - the function you wish to invoke on each record
    *args - any extra arguments you want passed to the function
   
    This function will iterate over each record in a FASTA file as SeqRecord
    objects, calling your function with the record (and supplied args) as
    arguments.

    This function returns a list.  For those records where your function
    returns a value, this is taken as a sequence and used to construct a
    FASTA format string.  If your function never has a return value, this
    means apply_on_multi_fasta will return an empty list.
    """
    import warnings
    import Bio
    warnings.warn("apply_on_multi_fasta is deprecated", Bio.BiopythonDeprecationWarning)
    try:
        f = globals()[function]
    except:
        raise NotImplementedError("%s not implemented" % function)
   
    handle = open(file, 'r')
    records = SeqIO.parse(handle, "fasta")
    results = []
    for record in records:
        arguments = [record.sequence]
        for arg in args: arguments.append(arg)
        result = f(*arguments)
        if result:
            results.append('>%s\n%s' % (record.name, result))
    handle.close()
    return results
         
def quicker_apply_on_multi_fasta(file, function, *args):
    """Apply a function on each sequence in a multiple FASTA file (DEPRECATED).

    file - filename of a FASTA format file
    function - the function you wish to invoke on each record
    *args - any extra arguments you want passed to the function
   
    This function will use quick_FASTA_reader to load every record in the
    FASTA file into memory as a list of tuples.  For each record, it will
    call your supplied function with the record as a tuple of the name and
    sequence as strings (plus any supplied args).

    This function returns a list.  For those records where your function
    returns a value, this is taken as a sequence and used to construct a
    FASTA format string.  If your function never has a return value, this
    means quicker_apply_on_multi_fasta will return an empty list.
    """
    import warnings
    import Bio
    warnings.warn("quicker_apply_on_multi_fasta is deprecated", Bio.BiopythonDeprecationWarning)
    try:
        f = globals()[function]
    except:
        raise NotImplementedError("%s not implemented" % function)
   
    entries = quick_FASTA_reader(file)
    results = []
    for name, seq in entries:
        arguments = [seq]
        for arg in args: arguments.append(arg)
        result = f(*arguments)
        if result:
            results.append('>%s\n%s' % (name, result))
    file.close()
    return results

# }}}

######################################
# Main
#####################
# {{{ 

if __name__ == '__main__':
   import sys, getopt
   # crude command line options to use most functions directly on a FASTA file
   options = {'apply_on_multi_fasta':0,
              'quick':0,
              'uniq_ids':0,
              }

   optlist, args = getopt.getopt(sys.argv[1:], '', ['describe', 'apply_on_multi_fasta=',
                                                    'help', 'quick', 'uniq_ids', 'search='])
   for arg in optlist:
      if arg[0] in ['-h', '--help']:
         pass
      elif arg[0] in ['--describe']:
         # get all new functions from this file
         mol_funcs = [x[0] for x in locals().items() if type(x[1]) == type(GC)]
         mol_funcs.sort()
         print 'available functions:'
         for f in mol_funcs: print '\t--%s' % f
         print '\n\ne.g.\n./sequtils.py  --apply_on_multi_fasta GC test.fas'

         sys.exit(0)
      elif arg[0] in ['--apply_on_multi_fasta']:
         options['apply_on_multi_fasta'] = arg[1]
      elif arg[0] in ['--search']:
         options['search'] = arg[1]
      else:
         key = re.search('-*(.+)', arg[0]).group(1)
         options[key] = 1

         
   if options.get('apply_on_multi_fasta'):
      file = args[0]
      function = options['apply_on_multi_fasta']
      arguments = []
      if options.get('search'):
         arguments = options['search']
      if function == 'xGC_skew':
         arguments = 1000
      if options.get('quick'):
         results = quicker_apply_on_multi_fasta(file, function, arguments)
      else:
         results = apply_on_multi_fasta(file, function, arguments)
      for result in results: print result
      
   elif options.get('uniq_ids'):
      file = args[0]
      fasta_uniqids(file)

# }}}

def _test():
    """Run the Bio.SeqUtils module's doctests (PRIVATE)."""
    print "Runing doctests..."
    import doctest
    doctest.testmod()
    print "Done"

if __name__ == "__main__":
    _test()

Generated by  Doxygen 1.6.0   Back to index