BenLangmead

class DeBruijnGraph:
    """ A de Bruijn multigraph built from a collection of strings.
        User supplies strings and k-mer length k.  Nodes of the de
        Bruijn graph are k-1-mers and edges correspond to the k-mer
        that joins a left k-1-mer to a right k-1-mer. """

    @staticmethod
    def chop(st, k):
        """ Chop a string up into k mers of given length """
        for i in xrange(0, len(st)-(k-1)):

BenLangmead

def neighbors1mm(kmer, alpha):
    """ Generate all neighbors at Hamming distance 1 from kmer """
    for j in xrange(len(kmer)-1, -1, -1):
        for c in alpha:
            if c == kmer[j]: continue
            yield kmer[:j] + c + kmer[j+1:]

def correct1mm(read, k, kmerhist, alpha, thresh):
    """ Return an error-corrected version of read.  k = k-mer length
        for k-mer count profile.  kmerhist maps distinct kmers to their

BenLangmead

import re
import sys
import string
import numpy
import math

def fastaKmerParser(fns, k):
    """ Parse all (overlapping) k-mers of length k from all the FASTA
        filenames provided. """
    for fn in fns:

BenLangmead

import numpy

class HMM(object):
    """ Simple Hidden Markov Model implementation.  User provides
        transition, emission and initial probabilities as dictionaries.
        Transition and emission probabilities are expressed as a dict
        mapping a 2-character code onto the floating-point probability
        for that table entry.  States and emissions are represented
        with single characters.  """
    

BenLangmead

"""
gtf_flux_fix.py

Author: Ben Langmead (langmea@cs.jhu.edu)
Date: Sept 2, 2013

Remove GTF records for sequence IDs that don't appear in any of the specified
FASTA files.  Move the transcript_id attribute into the first attribute
position.  Output transformed GTF to stdout.
"""

BenLangmead

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BenLangmead

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BenLangmead

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BenLangmead

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BenLangmead

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