diff small_rna_maps.py @ 0:6d48150495e3 draft

planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/small_rna_maps commit d4d8106d66b65679a1a685ab94bfcf99cdb7b959

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/small_rna_maps.py	Mon Jul 24 06:28:45 2017 -0400
@@ -0,0 +1,204 @@
+import argparse
+from collections import defaultdict
+
+import numpy
+
+import pysam
+
+
+def Parser():
+    the_parser = argparse.ArgumentParser()
+    the_parser.add_argument('--input', dest='input', required=True,
+                            nargs='+', help='input BAM files')
+    the_parser.add_argument('--sample_name', dest='sample_name',
+                            required=True, nargs='+', help='sample name')
+    the_parser.add_argument('--output', action='store',
+                            type=str, help='output tabular file')
+    the_parser.add_argument('-S', '--sizes', action='store',
+                            help='use to output read sizes dataframe')
+    args = the_parser.parse_args()
+    return args
+
+
+class Map:
+
+    def __init__(self, bam_file, sample, computeSize=False):
+        self.sample_name = sample
+        self.bam_object = pysam.AlignmentFile(bam_file, 'rb')
+        self.chromosomes = dict(zip(self.bam_object.references,
+                                self.bam_object.lengths))
+        self.map_dict = self.create_map(self.bam_object)
+        self.max = self.compute_max(self.map_dict)
+        self.mean = self.compute_mean(self.map_dict)
+        self.median = self.compute_median(self.map_dict)
+        self.coverage = self.compute_coverage(self.map_dict)
+        if computeSize:
+            self.size = self.compute_size(self.map_dict)
+
+    def create_map(self, bam_object):
+        '''
+        Returns a map_dictionary {(chromosome,read_position,polarity):
+                                                    [read_length, ...]}
+        '''
+        map_dictionary = defaultdict(list)
+        # get empty value for start and end of each chromosome
+        for chrom in self.chromosomes:
+            map_dictionary[(chrom, 1, 'F')] = []
+            map_dictionary[(chrom, self.chromosomes[chrom], 'F')] = []
+        for chrom in self.chromosomes:
+            for read in bam_object.fetch(chrom):
+                positions = read.positions  # a list of covered positions
+                for pos in positions:
+                    if not map_dictionary[(chrom, pos+1, 'F')]:
+                        map_dictionary[(chrom, pos+1, 'F')] = []
+                    if not map_dictionary[(chrom, pos+1, 'R')]:
+                        map_dictionary[(chrom, pos+1, 'R')] = []
+                if read.is_reverse:
+                    map_dictionary[(chrom, positions[-1]+1,
+                                    'R')].append(read.query_alignment_length)
+                else:
+                    map_dictionary[(chrom, positions[0]+1,
+                                    'F')].append(read.query_alignment_length)
+        return map_dictionary
+
+    def compute_max(self, map_dictionary):
+        '''
+        takes a map_dictionary as input and returns
+        a max_dictionary {(chromosome,read_position,polarity):
+                              max_of_number_of_read_at_any_position}
+        '''
+        merge_keylist = [(i[0], 0) for i in map_dictionary.keys()]
+        max_dictionary = dict(merge_keylist)
+        for key in map_dictionary:
+            if len(map_dictionary[key]) > max_dictionary[key[0]]:
+                max_dictionary[key[0]] = len(map_dictionary[key])
+        return max_dictionary
+
+    def compute_mean(self, map_dictionary):
+        '''
+        takes a map_dictionary as input and returns
+        a mean_dictionary {(chromosome,read_position,polarity):
+                                                mean_value_of_reads}
+        '''
+        mean_dictionary = dict()
+        for key in map_dictionary:
+            if len(map_dictionary[key]) == 0:
+                mean_dictionary[key] = 0
+            else:
+                mean_dictionary[key] = round(numpy.mean(map_dictionary[key]),
+                                             1)
+        return mean_dictionary
+
+    def compute_median(self, map_dictionary):
+        '''
+        takes a map_dictionary as input and returns
+        a mean_dictionary {(chromosome,read_position,polarity):
+                                                    mean_value_of_reads}
+        '''
+        median_dictionary = dict()
+        for key in map_dictionary:
+            if len(map_dictionary[key]) == 0:
+                median_dictionary[key] = 0
+            else:
+                median_dictionary[key] = numpy.median(map_dictionary[key])
+        return median_dictionary
+
+    def compute_coverage(self, map_dictionary, quality=10):
+        '''
+        takes a map_dictionary as input and returns
+        a coverage_dictionary {(chromosome,read_position,polarity):
+                                                coverage}
+        '''
+        coverage_dictionary = dict()
+        for chrom in self.chromosomes:
+            coverage_dictionary[(chrom, 1, 'F')] = 0
+            coverage_dictionary[(chrom, self.chromosomes[chrom], 'F')] = 0
+
+        for key in map_dictionary:
+            coverage = self.bam_object.count_coverage(
+                                                reference=key[0],
+                                                start=key[1]-1,
+                                                end=key[1],
+                                                quality_threshold=quality)
+            """ Add the 4 coverage values """
+            coverage = [sum(x) for x in zip(*coverage)]
+            coverage_dictionary[key] = coverage[0]
+            # coverage_dictionary[(key[0], key[1], 'R')] = coverage
+        return coverage_dictionary
+
+    def compute_size(self, map_dictionary):
+        '''
+        Takes a map_dictionary and returns a dictionary of sizes:
+        {chrom: {polarity: {size: nbre of reads}}}
+        '''
+        size_dictionary = defaultdict(lambda: defaultdict(
+                                      lambda: defaultdict( int )))
+        #  to track empty chromosomes
+        for chrom in self.chromosomes:
+            if self.bam_object.count(chrom) == 0:
+                size_dictionary[chrom]['F'][10] = 0
+        for key in map_dictionary:
+            for size in map_dictionary[key]:
+                size_dictionary[key[0]][key[2]][size] += 1
+        return size_dictionary
+
+    def write_size_table(self, out):
+        '''
+        Dataset, Chromosome, Polarity, Size, Nbr_reads
+        out is an *open* file handler
+        '''
+        for chrom in sorted(self.size):
+            sizes = self.size[chrom]['F'].keys()
+            sizes.extend(self.size[chrom]['R'].keys())
+            for polarity in sorted(self.size[chrom]):
+                for size in range(min(sizes), max(sizes)+1):
+                    try:
+                        line = [self.sample_name, chrom, polarity, size,
+                                self.size[chrom][polarity][size]]
+                    except KeyError:
+                        line = [self.sample_name, chrom, polarity, size, 0]
+                    line = [str(i) for i in line]
+                    out.write('\t'.join(line) + '\n')
+
+    def write_table(self, out):
+        '''
+        Dataset, Chromosome, Chrom_length, Coordinate, Nbr_reads
+        Polarity, Max, Mean, Median, Coverage
+        out is an *open* file handler
+        '''
+        for key in sorted(self.map_dict):
+            line = [self.sample_name, key[0], self.chromosomes[key[0]],
+                    key[1], len(self.map_dict[key]), key[2], self.max[key[0]],
+                    self.mean[key], self.median[key], self.coverage[key]]
+            line = [str(i) for i in line]
+            out.write('\t'.join(line) + '\n')
+
+
+def main(inputs, samples, file_out, size_file_out=''):
+    F = open(file_out, 'w')
+    header = ["Dataset", "Chromosome", "Chrom_length", "Coordinate",
+              "Nbr_reads", "Polarity", "Max", "Mean", "Median", "Coverage"]
+    F.write('\t'.join(header) + '\n')
+    if size_file_out:
+        Fs = open(size_file_out, 'w')
+        header = ["Dataset", "Chromosome", "Polarity", "Size", "Nbr_reads"]
+        Fs.write('\t'.join(header) + '\n')
+        for file, sample in zip(inputs, samples):
+            mapobj = Map(file, sample, computeSize=True)
+            mapobj.write_table(F)
+            mapobj.write_size_table(Fs)
+        Fs.close()
+    else:
+        for file, sample in zip(inputs, samples):
+            mapobj = Map(file, sample, computeSize=False)
+            mapobj.write_table(F)
+        F.close()
+
+
+if __name__ == "__main__":
+    args = Parser()
+    # if identical sample names
+    if len(set(args.sample_name)) != len(args.sample_name):
+        args.sample_name = [name + '_' + str(i) for
+                            i, name in enumerate(args.sample_name)]
+    main(args.input, args.sample_name, args.output, args.sizes)