ó w YTc @s—ddlZddlZd„Zd„Zd„Zd„Zd„Zd„Zdd„Z d „Z d d „Z de e edd d de d„ ZdS(iÿÿÿÿNcCsYg}t|dƒ}x!|D]}|j|jƒƒqWg|D]}tt|ƒ^q@S(s` Loads a matrix from a file given. Returns matrix as an array of arrays of numbers. tr(topentappendtsplittmaptfloat(tfilenametarraytfiletlineti((s./MOODS/__init__.pyt load_matrixs  cCsCg|D] }|^q}|jƒx|D]}|jƒq+W|S(s- Creates a reverse complement of PWM (treverse(tmatrixtrowRtme((s./MOODS/__init__.pytreverse_complements   cCs`g}xStt|dƒƒD];}g}x|D]}|j||ƒq0W|j|ƒqW|S(s- Creates a transpose of matrix array i(trangetlenR(R tresR ttmptg((s./MOODS/__init__.pyt transposes cCstttt|ƒƒƒS(s. Calculates a maximum score of matrix (tsumRtmaxR(R ((s./MOODS/__init__.pyt max_score%scCstjt|ƒ|ƒS(sw Estimates the background distribution of nucleotides from seq. The pseudocount ps is added to all counts. (t_cmodulet_bg_from_sequencetstr(tseqtps((s./MOODS/__init__.pytbg_from_sequence+scCstj|||ƒS(s Calculates an absolute threshold from a probability value. Returns: A threshold value T such that the probability that the distribution bg generates a sequence scoring at least T on the input matrix is p. (Rt_threshold_from_p(R tbgtp((s./MOODS/__init__.pytthreshold_from_p2scCs[tj|||ƒ}|rWg|D],}g|D]}|tj|ƒ^q/^q"}n|S(s< Calculates a log-odds matrix from a position frequency matrix Returns: The input PWM matrix transformed to log-odds scores. The score for nucleotide N and position i is log ( matrix[N][i] + ps * bg[N] ) / C[i] - log ( bg[N] ), where C[i] is the sum of terms matrix[N][i] + ps * bg[N] for all nucleotides N. Parameters: Obligatory: matrix Input PWM as a float or integer matrix. bg Normalised background distribution given as a list of four floats. ps Multiplier for pseudocounts. If matrix is a frequency matrix, you may want to set this to zero. Optional: log_base Base for logarithms. Defaults to natural logarithm if None is given. (Rt_count_log_oddstmathtlog(R R!Rtlog_basetretRtval((s./MOODS/__init__.pytcount_log_odds<s<cCsttd„|ƒƒS(s/ Calculates a total number of matches. cSs t|ƒS(N(R(tx((s./MOODS/__init__.pytfs(RR(t matchArray((s./MOODS/__init__.pyt total_matchesbsicCs!gt|ƒD]}d|^q S(s6 Creates a flat background distribution table gð?(R(tsizeR ((s./MOODS/__init__.pytflatbghsitlfic  Cst|ƒ} t|ƒ} |s0t||ƒ}n=t|ƒttƒƒks]t|ƒdkrmtdƒ‚dSt|ƒttƒƒkr¤g|D] }|^q}n"t|ƒ| ksÆtdƒ‚dS|rü|g|D]}t|ƒ^qÖ}d|}n|r-g|D]}t||||ƒ^q }n|rrgtt|ƒƒD]#}t j |||||ƒ^qF}nt j t |ƒ|||| | | ƒ}|r|| }g|| D]/}g|D]\}}|| |f^qÁ^q´}gt||ƒD]\}}||^qù}n|S(sH Finds position weight matrix matches in DNA sequence. Returns: An array of references to result arrays. There is one result array for each matrix, in the same order as the input matrices. Each result array is a list of tuples of position and score given as: [(pos1, score1), (pos2, score2) ...] Parameters: Obligatory: sequence DNA sequence as python string object, containing characters acgtACGT. matrices An array of matrices, each represented as a list of four lists of equal length. These lists correspond the frequencies or scores of the nucleotides A, C, G and T, respectively. thresholds A number or a list of numbers, used as threshold values for matrix scanning. If a single number is given, it is used for all matrices; otherwise, there should be as many threshold values as there are matrices. Optional: bg Background distribution as an array of four doubles, corresponding to the frequencies of A, C, G and T, respectively. By default the background is estimated from the sequence. convert_log_odds If True, assumes that the input matrices are frequency or count matrices, and converts them to log-odds scoring matrices using function count_log_odds; otherwise, treat them as scoring matrices. Default True. threshold_from_p If True, assumes that thresholds are p-values and computes the corresponding absolute threshold based on the matrix using function threshold_from_p; otherwise the threshold is used as a hard cut-off. Default True. log_base Base for logarithms used in log-odds computations. Relevant if using convert_log_odds=True and threshold_from_p=False. Defaults to natural logarithm if None is given. pseudocount Pseudocount used in log-odds conversion and added to sequence symbol counts when estimating the background from sequence. Default 1. both_strands Scans against reverse complement sequence in addition to the input sequence. Hits on reverse complement are reported at position [position - sequence_length], which is always negative. The actual hit site for any hit is always seq[pos, pos + matrix_length]. Default False. Tuning parameters: (Optional, do not affect the results, but can give minor speed-ups in some cases. You can pretty much ignore these.) algorithm Selects the algorithm to use for scanning "naive" naive algorithm "pla" permutated lookahead algorithm "supera" super alphabet algorithm. - Good for long matrices (> 20) "lf" lookahead filtration algorithm. - Default algorithm in most cases. - Sequence can be searched with multiple matrices simultaneously. q An integer, used for fine-tuning "supera" and "lf" algorithms. The default value 7 should be ok pretty much always, but can be tuned to possibly slightly increase performance. combine True or False, determines whether "lf" algorithm combines all matrices to a single scanning pass. Default True. is1Background does not seem to be a list of length 4s:Number of thresholds does not match the number of matricesiN(RRttypetlistt RuntimeErrortNoneRR*txrangetMOODSR#Rt_searchRtzip(tsequencetmatricest thresholdsR!tconvert_log_oddsR#t both_strandsR't pseudocountt algorithmtqtcombinetntmR R R(t main_hitsthitstpostscoret reverse_hitstmainR ((s./MOODS/__init__.pytsearchns2N  -  # +?' @/(tMOODS._cmoduleR7R%R RRRRR#R5R*R.R0tTruetFalseRK(((s./MOODS/__init__.pyts     &