Mercurial > repos > devteam > microsats_mutability
changeset 0:4aa1ee5d8510 draft
Imported from capsule None
| author | devteam |
|---|---|
| date | Tue, 01 Apr 2014 10:52:29 -0400 |
| parents | |
| children | 372b2f5668f3 |
| files | microsats_mutability.py microsats_mutability.xml test-data/ortho_ms.tab test-data/ortho_ms_mut.tab tool_dependencies.xml |
| diffstat | 5 files changed, 854 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/microsats_mutability.py Tue Apr 01 10:52:29 2014 -0400 @@ -0,0 +1,494 @@ +#!/usr/bin/env python +#Guruprasad Ananda +""" +This tool computes microsatellite mutability for the orthologous microsatellites fetched from 'Extract Orthologous Microsatellites from pair-wise alignments' tool. +""" +import fileinput +import string +import sys +import tempfile +from galaxy.tools.util.galaxyops import * +from bx.intervals.io import * +from bx.intervals.operations import quicksect + +fout = open(sys.argv[2],'w') +p_group = int(sys.argv[3]) #primary "group-by" feature +p_bin_size = int(sys.argv[4]) +s_group = int(sys.argv[5]) #sub-group by feature +s_bin_size = int(sys.argv[6]) +mono_threshold = 9 +non_mono_threshold = 4 +p_group_cols = [p_group, p_group+7] +s_group_cols = [s_group, s_group+7] +num_generations = int(sys.argv[7]) +region = sys.argv[8] +int_file = sys.argv[9] +if int_file != "None": #User has specified an interval file + try: + fint = open(int_file, 'r') + dbkey_i = sys.argv[10] + chr_col_i, start_col_i, end_col_i, strand_col_i = parse_cols_arg( sys.argv[11] ) + except: + stop_err("Unable to open input Interval file") + + +def stop_err(msg): + sys.stderr.write(msg) + sys.exit() + + +def reverse_complement(text): + DNA_COMP = string.maketrans( "ACGTacgt", "TGCAtgca" ) + comp = [ch for ch in text.translate(DNA_COMP)] + comp.reverse() + return "".join(comp) + + +def get_unique_elems(elems): + seen = set() + return[x for x in elems if x not in seen and not seen.add(x)] + + +def get_binned_lists(uniqlist, binsize): + binnedlist = [] + uniqlist.sort() + start = int(uniqlist[0]) + bin_ind = 0 + l_ind = 0 + binnedlist.append([]) + while l_ind < len(uniqlist): + elem = int(uniqlist[l_ind]) + if elem in range(start, start+binsize): + binnedlist[bin_ind].append(elem) + else: + start += binsize + bin_ind += 1 + binnedlist.append([]) + binnedlist[bin_ind].append(elem) + l_ind += 1 + return binnedlist + + +def fetch_weight(H, C, t): + if (H-(C-H)) < t: + return 2.0 + else: + return 1.0 + + +def mutabilityEstimator(repeats1, repeats2, thresholds): + mut_num = 0.0 #Mutability Numerator + mut_den = 0.0 #Mutability denominator + for ind, H in enumerate(repeats1): + C = repeats2[ind] + t = thresholds[ind] + w = fetch_weight(H, C, t) + mut_num += ((H-C)*(H-C)*w) + mut_den += w + return [mut_num, mut_den] + + +def output_writer(blk, blk_lines): + global winspecies, speciesind + all_elems_1 = [] + all_elems_2 = [] + all_s_elems_1 = [] + all_s_elems_2 = [] + for bline in blk_lines: + if not(bline): + continue + items = bline.split('\t') + seq1 = items[1] + seq2 = items[8] + if p_group_cols[0] == 6: + items[p_group_cols[0]] = int(items[p_group_cols[0]]) + items[p_group_cols[1]] = int(items[p_group_cols[1]]) + if s_group_cols[0] == 6: + items[s_group_cols[0]] = int(items[s_group_cols[0]]) + items[s_group_cols[1]] = int(items[s_group_cols[1]]) + all_elems_1.append(items[p_group_cols[0]]) #primary col elements for species 1 + all_elems_2.append(items[p_group_cols[1]]) #primary col elements for species 2 + if s_group_cols[0] != -1: #sub-group is not None + all_s_elems_1.append(items[s_group_cols[0]]) #secondary col elements for species 1 + all_s_elems_2.append(items[s_group_cols[1]]) #secondary col elements for species 2 + uniq_elems_1 = get_unique_elems(all_elems_1) + uniq_elems_2 = get_unique_elems(all_elems_2) + if s_group_cols[0] != -1: + uniq_s_elems_1 = get_unique_elems(all_s_elems_1) + uniq_s_elems_2 = get_unique_elems(all_s_elems_2) + mut1 = {} + mut2 = {} + count1 = {} + count2 = {} + """ + if p_group_cols[0] == 7: #i.e. the option chosen is group-by unit(AG, GTC, etc) + uniq_elems_1 = get_unique_units(j.sort(lambda x, y: len(x)-len(y))) + """ + if p_group_cols[0] == 6: #i.e. the option chosen is group-by repeat number. + uniq_elems_1 = get_binned_lists( uniq_elems_1, p_bin_size ) + uniq_elems_2 = get_binned_lists( uniq_elems_2, p_bin_size ) + + if s_group_cols[0] == 6: #i.e. the option chosen is subgroup-by repeat number. + uniq_s_elems_1 = get_binned_lists( uniq_s_elems_1, s_bin_size ) + uniq_s_elems_2 = get_binned_lists( uniq_s_elems_2, s_bin_size ) + + for pitem1 in uniq_elems_1: + #repeats1 = [] + #repeats2 = [] + thresholds = [] + if s_group_cols[0] != -1: #Sub-group by feature is not None + for sitem1 in uniq_s_elems_1: + repeats1 = [] + repeats2 = [] + if type(sitem1) == type(''): + sitem1 = sitem1.strip() + for bline in blk_lines: + belems = bline.split('\t') + if type(pitem1) == list: + if p_group_cols[0] == 6: + belems[p_group_cols[0]] = int(belems[p_group_cols[0]]) + if belems[p_group_cols[0]] in pitem1: + if belems[s_group_cols[0]] == sitem1: + repeats1.append(int(belems[6])) + repeats2.append(int(belems[13])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + mut1[str(pitem1)+'\t'+str(sitem1)] = mutabilityEstimator( repeats1, repeats2, thresholds ) + if region == 'align': + count1[str(pitem1)+'\t'+str(sitem1)] = min( sum(repeats1), sum(repeats2) ) + else: + if winspecies == 1: + count1["%s\t%s" % ( pitem1, sitem1 )] = sum(repeats1) + elif winspecies == 2: + count1["%s\t%s" % ( pitem1, sitem1 )] = sum(repeats2) + else: + if type(sitem1) == list: + if s_group_cols[0] == 6: + belems[s_group_cols[0]] = int(belems[s_group_cols[0]]) + if belems[p_group_cols[0]] == pitem1 and belems[s_group_cols[0]] in sitem1: + repeats1.append(int(belems[6])) + repeats2.append(int(belems[13])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + mut1["%s\t%s" % ( pitem1, sitem1 )] = mutabilityEstimator( repeats1, repeats2, thresholds ) + if region == 'align': + count1[str(pitem1)+'\t'+str(sitem1)] = min( sum(repeats1), sum(repeats2) ) + else: + if winspecies == 1: + count1[str(pitem1)+'\t'+str(sitem1)] = sum(repeats1) + elif winspecies == 2: + count1[str(pitem1)+'\t'+str(sitem1)] = sum(repeats2) + else: + if belems[p_group_cols[0]] == pitem1 and belems[s_group_cols[0]] == sitem1: + repeats1.append(int(belems[6])) + repeats2.append(int(belems[13])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + mut1["%s\t%s" % ( pitem1, sitem1 )] = mutabilityEstimator( repeats1, repeats2, thresholds ) + if region == 'align': + count1[str(pitem1)+'\t'+str(sitem1)] = min( sum(repeats1), sum(repeats2) ) + else: + if winspecies == 1: + count1["%s\t%s" % ( pitem1, sitem1 )] = sum(repeats1) + elif winspecies == 2: + count1["%s\t%s" % ( pitem1, sitem1 )] = sum(repeats2) + else: #Sub-group by feature is None + for bline in blk_lines: + belems = bline.split('\t') + if type(pitem1) == list: + #print >> sys.stderr, "item: " + str(item1) + if p_group_cols[0] == 6: + belems[p_group_cols[0]] = int(belems[p_group_cols[0]]) + if belems[p_group_cols[0]] in pitem1: + repeats1.append(int(belems[6])) + repeats2.append(int(belems[13])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + else: + if belems[p_group_cols[0]] == pitem1: + repeats1.append(int(belems[6])) + repeats2.append(int(belems[13])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + mut1["%s" % (pitem1)] = mutabilityEstimator( repeats1, repeats2, thresholds ) + if region == 'align': + count1["%s" % (pitem1)] = min( sum(repeats1), sum(repeats2) ) + else: + if winspecies == 1: + count1[str(pitem1)] = sum(repeats1) + elif winspecies == 2: + count1[str(pitem1)] = sum(repeats2) + + for pitem2 in uniq_elems_2: + #repeats1 = [] + #repeats2 = [] + thresholds = [] + if s_group_cols[0] != -1: #Sub-group by feature is not None + for sitem2 in uniq_s_elems_2: + repeats1 = [] + repeats2 = [] + if type(sitem2)==type(''): + sitem2 = sitem2.strip() + for bline in blk_lines: + belems = bline.split('\t') + if type(pitem2) == list: + if p_group_cols[0] == 6: + belems[p_group_cols[1]] = int(belems[p_group_cols[1]]) + if belems[p_group_cols[1]] in pitem2 and belems[s_group_cols[1]] == sitem2: + repeats2.append(int(belems[13])) + repeats1.append(int(belems[6])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + mut2["%s\t%s" % ( pitem2, sitem2 )] = mutabilityEstimator( repeats2, repeats1, thresholds ) + #count2[str(pitem2)+'\t'+str(sitem2)]=len(repeats2) + if region == 'align': + count2["%s\t%s" % ( pitem2, sitem2 )] = min( sum(repeats1), sum(repeats2) ) + else: + if winspecies == 1: + count2["%s\t%s" % ( pitem2, sitem2 )] = len(repeats2) + elif winspecies == 2: + count2["%s\t%s" % ( pitem2, sitem2 )] = len(repeats1) + else: + if type(sitem2) == list: + if s_group_cols[0] == 6: + belems[s_group_cols[1]] = int(belems[s_group_cols[1]]) + if belems[p_group_cols[1]] == pitem2 and belems[s_group_cols[1]] in sitem2: + repeats2.append(int(belems[13])) + repeats1.append(int(belems[6])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + mut2["%s\t%s" % ( pitem2, sitem2 )] = mutabilityEstimator( repeats2, repeats1, thresholds ) + if region == 'align': + count2["%s\t%s" % ( pitem2, sitem2 )] = min( sum(repeats1), sum(repeats2) ) + else: + if winspecies == 1: + count2["%s\t%s" % ( pitem2, sitem2 )] = len(repeats2) + elif winspecies == 2: + count2["%s\t%s" % ( pitem2, sitem2 )] = len(repeats1) + else: + if belems[p_group_cols[1]] == pitem2 and belems[s_group_cols[1]] == sitem2: + repeats1.append(int(belems[13])) + repeats2.append(int(belems[6])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + mut2["%s\t%s" % ( pitem2, sitem2 )] = mutabilityEstimator( repeats2, repeats1, thresholds ) + if region == 'align': + count2["%s\t%s" % ( pitem2, sitem2 )] = min( sum(repeats1), sum(repeats2) ) + else: + if winspecies == 1: + count2["%s\t%s" % ( pitem2, sitem2 )] = len(repeats2) + elif winspecies == 2: + count2["%s\t%s" % ( pitem2, sitem2 )] = len(repeats1) + else: #Sub-group by feature is None + for bline in blk_lines: + belems = bline.split('\t') + if type(pitem2) == list: + if p_group_cols[0] == 6: + belems[p_group_cols[1]] = int(belems[p_group_cols[1]]) + if belems[p_group_cols[1]] in pitem2: + repeats2.append(int(belems[13])) + repeats1.append(int(belems[6])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + else: + if belems[p_group_cols[1]] == pitem2: + repeats2.append(int(belems[13])) + repeats1.append(int(belems[6])) + if belems[4] == 'mononucleotide': + thresholds.append(mono_threshold) + else: + thresholds.append(non_mono_threshold) + mut2["%s" % (pitem2)] = mutabilityEstimator( repeats2, repeats1, thresholds ) + if region == 'align': + count2["%s" % (pitem2)] = min( sum(repeats1), sum(repeats2) ) + else: + if winspecies == 1: + count2["%s" % (pitem2)] = sum(repeats2) + elif winspecies == 2: + count2["%s" % (pitem2)] = sum(repeats1) + for key in mut1.keys(): + if key in mut2.keys(): + mut = (mut1[key][0]+mut2[key][0])/(mut1[key][1]+mut2[key][1]) + count = count1[key] + del mut2[key] + else: + unit_found = False + if p_group_cols[0] == 7 or s_group_cols[0] == 7: #if it is Repeat Unit (AG, GCT etc.) check for reverse-complements too + if p_group_cols[0] == 7: + this, other = 0, 1 + else: + this, other = 1, 0 + groups1 = key.split('\t') + mutn = mut1[key][0] + mutd = mut1[key][1] + count = 0 + for key2 in mut2.keys(): + groups2 = key2.split('\t') + if groups1[other] == groups2[other]: + if groups1[this] in groups2[this]*2 or reverse_complement(groups1[this]) in groups2[this]*2: + #mut = (mut1[key][0]+mut2[key2][0])/(mut1[key][1]+mut2[key2][1]) + mutn += mut2[key2][0] + mutd += mut2[key2][1] + count += int(count2[key2]) + unit_found = True + del mut2[key2] + #break + if unit_found: + mut = mutn/mutd + else: + mut = mut1[key][0]/mut1[key][1] + count = count1[key] + mut = "%.2e" % (mut/num_generations) + if region == 'align': + print >> fout, str(blk) + '\t'+seq1 + '\t' + seq2 + '\t' +key.strip()+ '\t'+str(mut) + '\t'+ str(count) + elif region == 'win': + fout.write("%s\t%s\t%s\t%s\n" % ( blk, key.strip(), mut, count )) + fout.flush() + + #catch any remaining repeats, for instance if the orthologous position contained different repeat units + for remaining_key in mut2.keys(): + mut = mut2[remaining_key][0]/mut2[remaining_key][1] + mut = "%.2e" % (mut/num_generations) + count = count2[remaining_key] + if region == 'align': + print >> fout, str(blk) + '\t'+seq1 + '\t'+seq2 + '\t'+remaining_key.strip()+ '\t'+str(mut)+ '\t'+ str(count) + elif region == 'win': + fout.write("%s\t%s\t%s\t%s\n" % ( blk, remaining_key.strip(), mut, count )) + fout.flush() + #print >> fout, blk + '\t'+remaining_key.strip()+ '\t'+str(mut)+ '\t'+ str(count) + + +def counter(node, start, end, report_func): + if start <= node.start < end and start < node.end <= end: + report_func(node) + if node.right: + counter(node.right, start, end, report_func) + if node.left: + counter(node.left, start, end, report_func) + elif node.start < start and node.right: + counter(node.right, start, end, report_func) + elif node.start >= end and node.left and node.left.maxend > start: + counter(node.left, start, end, report_func) + + +def main(): + infile = sys.argv[1] + + for i, line in enumerate( file ( infile )): + line = line.rstrip('\r\n') + if len( line )>0 and not line.startswith( '#' ): + elems = line.split( '\t' ) + break + if i == 30: + break # Hopefully we'll never get here... + + if len( elems ) != 15: + stop_err( "This tool only works on tabular data output by 'Extract Orthologous Microsatellites from pair-wise alignments' tool. The data in your input dataset is either missing or not formatted properly." ) + global winspecies, speciesind + if region == 'win': + if dbkey_i in elems[1]: + winspecies = 1 + speciesind = 1 + elif dbkey_i in elems[8]: + winspecies = 2 + speciesind = 8 + else: + stop_err("The species build corresponding to your interval file is not present in the Microsatellite file.") + + fin = open(infile, 'r') + skipped = 0 + linestr = "" + + if region == 'win': + msats = NiceReaderWrapper( fileinput.FileInput( infile ), + chrom_col = speciesind, + start_col = speciesind+1, + end_col = speciesind+2, + strand_col = -1, + fix_strand = True) + msatTree = quicksect.IntervalTree() + for item in msats: + if type( item ) is GenomicInterval: + msatTree.insert( item, msats.linenum, item.fields ) + + for iline in fint: + try: + iline = iline.rstrip('\r\n') + if not(iline) or iline == "": + continue + ielems = iline.strip("\r\n").split('\t') + ichr = ielems[chr_col_i] + istart = int(ielems[start_col_i]) + iend = int(ielems[end_col_i]) + isrc = "%s.%s" % ( dbkey_i, ichr ) + if isrc not in msatTree.chroms: + continue + result = [] + root = msatTree.chroms[isrc] #root node for the chrom + counter(root, istart, iend, lambda node: result.append( node )) + if not(result): + continue + tmpfile1 = tempfile.NamedTemporaryFile('wb+') + for node in result: + tmpfile1.write("%s\n" % "\t".join( node.other )) + + tmpfile1.seek(0) + output_writer(iline, tmpfile1.readlines()) + except: + skipped += 1 + if skipped: + print "Skipped %d intervals as invalid." % (skipped) + elif region == 'align': + if s_group_cols[0] != -1: + print >> fout, "#Window\tSpecies_1\tSpecies_2\tGroupby_Feature\tSubGroupby_Feature\tMutability\tCount" + else: + print >> fout, "#Window\tSpecies_1\tWindow_Start\tWindow_End\tSpecies_2\tGroupby_Feature\tMutability\tCount" + prev_bnum = -1 + try: + for line in fin: + line = line.strip("\r\n") + if not(line) or line == "": + continue + elems = line.split('\t') + try: + assert int(elems[0]) + assert len(elems) == 15 + except: + continue + new_bnum = int(elems[0]) + if new_bnum != prev_bnum: + if prev_bnum != -1: + output_writer(prev_bnum, linestr.strip().replace('\r','\n').split('\n')) + linestr = line + "\n" + else: + linestr += line + linestr += "\n" + prev_bnum = new_bnum + output_writer(prev_bnum, linestr.strip().replace('\r','\n').split('\n')) + except Exception, ea: + print >> sys.stderr, ea + skipped += 1 + if skipped: + print "Skipped %d lines as invalid." % (skipped) + + +if __name__ == "__main__": + main()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/microsats_mutability.xml Tue Apr 01 10:52:29 2014 -0400 @@ -0,0 +1,125 @@ +<tool id="microsats_mutability1" name="Estimate microsatellite mutability" version="1.1.0"> + <description>by specified attributes</description> + <requirements> + <requirement type="package" version="0.7.1">bx-python</requirement> + <requirement type="package" version="1.0.0">galaxy-ops</requirement> + </requirements> + <command interpreter="python"> + microsats_mutability.py + $input1 + $out_file1 + ${pri_condition.primary_group} + #if $pri_condition.primary_group == "6": + ${pri_condition.binsize} ${pri_condition.subgroup} -1 + #else: + 0 ${pri_condition.sub_condition.subgroup} + #if $pri_condition.sub_condition.subgroup == "6": + ${pri_condition.sub_condition.s_binsize} + #else: + -1 + #end if + #end if + $gens + ${region.type} + #if $region.type == "win": + ${region.input2} $input2.dbkey $input2.metadata.chromCol,$input2.metadata.startCol,$input2.metadata.endCol,$input2.metadata.strandCol + #else: + "None" + #end if + </command> + <inputs> + <page> + <param name="input1" type="data" format="tabular" label="Select dataset containing Orthologous microsatellites"/> + <conditional name="region"> + <param name="type" type="select" label="Estimate rates corresponding to" multiple="false"> + <option value="align">Alignment block</option> + <option value="win">Intervals in your history</option> + </param> + <when value="win"> + <param format="interval" name="input2" type="data" label="Choose intervals"> + <validator type="unspecified_build" /> + </param> + </when> + <when value="align" /> + </conditional> + <param name="gens" size="10" type="integer" value="1" label="Number of generations between the two species in input file"/> + <conditional name="pri_condition"> + <param name="primary_group" type="select" label="Group by" multiple="false"> + <option value="4">Motif type (mono/di/tri etc.)</option> + <option value="7">Repeat Unit (AG, GCT etc.)</option> + <option value="6">Repeat Number </option> + </param> + <when value="6"> + <param name="binsize" size="10" type="integer" value="1" label="Bin-size" help="Bin-size denotes the number of repeat numbers to be considered as a group. Bin-size of 5 will group every 5 consecutive repeat numbers into a group."/> + <param name="subgroup" type="select" label="Sub-group by" multiple="false"> + <option value="-1">None</option> + <option value="4">Motif type (mono/di/tri etc.)</option> + <option value="7">Repeat Unit (AG, GCT etc.)</option> + </param> + </when> + <when value="7"> + <conditional name="sub_condition"> + <param name="subgroup" type="select" label="Sub-group by" multiple="false"> + <option value="-1">None</option> + <option value="4">Motif type (mono/di/tri etc.)</option> + <option value="6">Repeat Number </option> + </param> + <when value="-1"></when> + <when value="4"></when> + <when value="6"> + <param name="s_binsize" size="10" type="integer" value="1" label="Bin size" help="Bin-size denotes the number of repeat numbers to be considered as a group. Bin-size of 5 will group every 5 consecutive repeat numbers into a group."/> + </when> + </conditional> + </when> + <when value="4"> + <conditional name="sub_condition"> + <param name="subgroup" type="select" label="Sub-group by" multiple="false"> + <option value="-1">None</option> + <option value="7">Repeat Unit (AG, GCT etc.)</option> + <option value="6">Repeat Number </option> + </param> + <when value="-1"></when> + <when value="7"></when> + <when value="6"> + <param name="s_binsize" size="10" type="integer" value="1" label="Bin size" help="Bin-size denotes the number of repeat numbers to be considered as a group. Bin-size of 5 will group every 5 consecutive repeat numbers into a group."/> + </when> + </conditional> + </when> + </conditional> + </page> + </inputs> + <outputs> + <data format="tabular" name="out_file1" /> + </outputs> + <!-- + <tests> + <test> + <param name="input1" value="ortho_ms.tab"/> + <param name="type" value="align"/> + <param name="gens" value="1"/> + <param name="primary_group" value="4"/> + <param name="sub_condition|subgroup" value="7"/> + <output name="out_file1" file="ortho_ms_mut.tab"/> + </test> + </tests> + --> +<help> +.. class:: infomark + +**What it does** + +This tool computes microsatellite mutability for the orthologous microsatellites fetched from 'Extract Orthologous Microsatellites from pair-wise alignments' tool. + +Mutability is computed according to the method described in the following paper: + +*Webster et al., Microsatellite evolution inferred from human-chimpanzee genomic sequence alignments, Proc Natl Acad Sci 2002 June 25; 99(13): 8748-8753* + +----- + +.. class:: warningmark + +**Note** + +The user selected group and subgroup by features, the computed mutability and the count of the number of repeats used to compute that mutability are added as columns to the output. +</help> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ortho_ms.tab Tue Apr 01 10:52:29 2014 -0400 @@ -0,0 +1,151 @@ +#Block Seq1_Name Seq1_Start Seq1_End Seq1_Type Seq1_Length Seq1_RepeatNumber Seq1_Unit Seq2_Name Seq2_Start Seq2_End Seq2_Type Seq2_Length Seq2_RepeatNumber Seq2_Unit +5 hg18.chr1 6483 6496 trinucleotide 13 4 GCT panTro2.chr15 100042575 100042588 trinucleotide 13 4 GCT +5 hg18.chr1 7111 7119 dinucleotide 8 4 CT panTro2.chr15 100043212 100043220 dinucleotide 8 4 CT +5 hg18.chr1 6483 6496 trinucleotide 13 4 GCT panTro2.chr15 100042575 100042588 trinucleotide 13 4 GCT +5 hg18.chr1 7111 7119 dinucleotide 8 4 CT panTro2.chr15 100043212 100043220 dinucleotide 8 4 CT +9 hg18.chr1 11258 11267 dinucleotide 9 4 CT panTro2.chr9_random 4677431 4677440 dinucleotide 9 4 CT +10 hg18.chr1 11881 11889 dinucleotide 8 4 TC panTro2.chr15 100031465 100031473 dinucleotide 8 4 TC +10 hg18.chr1 18275 18284 mononucleotide 9 9 A panTro2.chr15 100037854 100037863 mononucleotide 9 9 A +10 hg18.chr1 11881 11889 dinucleotide 8 4 TC panTro2.chr15 100031465 100031473 dinucleotide 8 4 TC +10 hg18.chr1 16317 16329 dinucleotide 12 6 GT panTro2.chr15 100035910 100035921 dinucleotide 11 5 TG +10 hg18.chr1 18275 18284 mononucleotide 9 9 A panTro2.chr15 100037854 100037863 mononucleotide 9 9 A +10 hg18.chr1 18452 18467 mononucleotide 15 15 T panTro2.chr15 100038029 100038042 mononucleotide 13 13 T +12 hg18.chr1 20736 20756 dinucleotide 20 10 TC panTro2.chr15_random 1091184 1091198 dinucleotide 14 7 TC +13 hg18.chr1 20799 20812 dinucleotide 13 6 TC panTro2.chrUn 1510811 1510831 dinucleotide 20 10 TC +13 hg18.chr1 21563 21572 dinucleotide 9 4 AG panTro2.chrUn 1511572 1511581 dinucleotide 9 4 AG +13 hg18.chr1 21671 21681 dinucleotide 10 5 AC panTro2.chrUn 1511678 1511688 dinucleotide 10 5 AC +14 hg18.chr1 23313 23328 mononucleotide 15 15 A panTro2.chrUn 1508926 1508942 mononucleotide 16 16 A +16 hg18.chr1 26215 26228 mononucleotide 13 13 A panTro2.chrUn 135175 135189 mononucleotide 14 14 A +16 hg18.chr1 26657 26667 dinucleotide 10 5 TG panTro2.chrUn 135618 135628 dinucleotide 10 5 TG +16 hg18.chr1 27104 27114 mononucleotide 10 10 T panTro2.chrUn 136064 136073 mononucleotide 9 9 T +16 hg18.chr1 27291 27299 dinucleotide 8 4 CT panTro2.chrUn 136251 136259 dinucleotide 8 4 CT +16 hg18.chr1 30483 30495 trinucleotide 12 4 TTC panTro2.chrUn 139435 139447 trinucleotide 12 4 TTC +16 hg18.chr1 30503 30522 mononucleotide 19 19 T panTro2.chrUn 139457 139482 mononucleotide 25 25 T +16 hg18.chr1 26657 26667 dinucleotide 10 5 TG panTro2.chrUn 135618 135628 dinucleotide 10 5 TG +16 hg18.chr1 27104 27114 mononucleotide 10 10 T panTro2.chrUn 136064 136073 mononucleotide 9 9 T +16 hg18.chr1 27291 27299 dinucleotide 8 4 CT panTro2.chrUn 136251 136259 dinucleotide 8 4 CT +16 hg18.chr1 30483 30495 trinucleotide 12 4 TTC panTro2.chrUn 139435 139447 trinucleotide 12 4 TTC +17 hg18.chr1 33660 33676 mononucleotide 16 16 A panTro2.chrUn 9698149 9698162 mononucleotide 13 13 A +17 hg18.chr1 33660 33676 mononucleotide 16 16 A panTro2.chrUn 9698149 9698162 mononucleotide 13 13 A +19 hg18.chr1 35586 35595 dinucleotide 9 4 AT panTro2.chrUn 9700318 9700327 dinucleotide 9 4 AT +19 hg18.chr1 36427 36435 dinucleotide 8 4 GT panTro2.chrUn 9701160 9701168 dinucleotide 8 4 GT +19 hg18.chr1 37182 37192 mononucleotide 10 10 A panTro2.chrUn 9701917 9701928 mononucleotide 11 11 A +19 hg18.chr1 37620 37628 dinucleotide 8 4 TA panTro2.chrUn 9702357 9702365 dinucleotide 8 4 TA +19 hg18.chr1 41199 41207 dinucleotide 8 4 CT panTro2.chrUn 9705949 9705957 dinucleotide 8 4 CT +19 hg18.chr1 41728 41741 mononucleotide 13 13 A panTro2.chrUn 9706479 9706495 mononucleotide 16 16 A +19 hg18.chr1 35586 35595 dinucleotide 9 4 AT panTro2.chrUn 9700318 9700327 dinucleotide 9 4 AT +19 hg18.chr1 36427 36435 dinucleotide 8 4 GT panTro2.chrUn 9701160 9701168 dinucleotide 8 4 GT +19 hg18.chr1 37182 37192 mononucleotide 10 10 A panTro2.chrUn 9701917 9701928 mononucleotide 11 11 A +19 hg18.chr1 37620 37628 dinucleotide 8 4 TA panTro2.chrUn 9702357 9702365 dinucleotide 8 4 TA +19 hg18.chr1 41199 41207 dinucleotide 8 4 CT panTro2.chrUn 9705949 9705957 dinucleotide 8 4 CT +19 hg18.chr1 41728 41741 mononucleotide 13 13 A panTro2.chrUn 9706479 9706495 mononucleotide 16 16 A +21 hg18.chr1 47418 47426 dinucleotide 8 4 TA panTro2.chrUn 9713027 9713035 dinucleotide 8 4 TA +21 hg18.chr1 47950 47959 dinucleotide 9 4 TC panTro2.chrUn 9713559 9713568 dinucleotide 9 4 TC +21 hg18.chr1 47418 47426 dinucleotide 8 4 TA panTro2.chrUn 9713027 9713035 dinucleotide 8 4 TA +21 hg18.chr1 47950 47959 dinucleotide 9 4 TC panTro2.chrUn 9713559 9713568 dinucleotide 9 4 TC +25 hg18.chr1 56744 56752 dinucleotide 8 4 GA panTro2.chrUn 9723923 9723931 dinucleotide 8 4 GA +25 hg18.chr1 60215 60224 mononucleotide 9 9 A panTro2.chrUn 9727396 9727405 mononucleotide 9 9 A +25 hg18.chr1 56744 56752 dinucleotide 8 4 GA panTro2.chrUn 9723923 9723931 dinucleotide 8 4 GA +25 hg18.chr1 60215 60224 mononucleotide 9 9 A panTro2.chrUn 9727396 9727405 mononucleotide 9 9 A +25 hg18.chr1 61039 61050 mononucleotide 11 11 A panTro2.chrUn 9728220 9728230 mononucleotide 10 10 A +25 hg18.chr1 61710 61725 mononucleotide 15 15 T panTro2.chrUn 9728890 9728903 mononucleotide 13 13 T +26 hg18.chr1 67953 67961 dinucleotide 8 4 AT panTro2.chrUn 9735669 9735677 dinucleotide 8 4 AT +26 hg18.chr1 68606 68614 dinucleotide 8 4 AT panTro2.chrUn 9736324 9736332 dinucleotide 8 4 AT +26 hg18.chr1 67038 67059 mononucleotide 21 21 A panTro2.chrUn 9734760 9734777 mononucleotide 17 17 A +26 hg18.chr1 67953 67961 dinucleotide 8 4 AT panTro2.chrUn 9735669 9735677 dinucleotide 8 4 AT +26 hg18.chr1 68606 68614 dinucleotide 8 4 AT panTro2.chrUn 9736324 9736332 dinucleotide 8 4 AT +27 hg18.chr1 72077 72088 dinucleotide 11 5 AC panTro2.chrUn 9742946 9742957 dinucleotide 11 5 AC +32 hg18.chr1 81043 81052 dinucleotide 9 4 GA panTro2.chrUn 1797450 1797459 dinucleotide 9 4 GA +39 hg18.chr1 87721 87729 dinucleotide 8 4 AG panTro2.chr1_random 7074333 7074341 dinucleotide 8 4 AG +39 hg18.chr1 88697 88707 dinucleotide 10 5 TG panTro2.chr1_random 7075309 7075319 dinucleotide 10 5 TG +39 hg18.chr1 87721 87729 dinucleotide 8 4 AG panTro2.chr1_random 7074333 7074341 dinucleotide 8 4 AG +39 hg18.chr1 88697 88707 dinucleotide 10 5 TG panTro2.chr1_random 7075309 7075319 dinucleotide 10 5 TG +40 hg18.chr1 91199 91212 mononucleotide 13 13 A panTro2.chr1_random 7089889 7089900 mononucleotide 11 11 A +42 hg18.chr1 93838 93847 dinucleotide 9 4 CT panTro2.chr1_random 7090757 7090768 dinucleotide 11 5 CT +42 hg18.chr1 93838 93847 dinucleotide 9 4 CT panTro2.chr1_random 7090757 7090768 dinucleotide 11 5 CT +43 hg18.chr1 97391 97399 dinucleotide 8 4 AG panTro2.chr1 244578 244586 dinucleotide 8 4 AG +44 hg18.chr1 98409 98425 mononucleotide 16 16 A panTro2.chr1_random 7095323 7095342 mononucleotide 19 19 A +44 hg18.chr1 98409 98425 mononucleotide 16 16 A panTro2.chr1_random 7095323 7095342 mononucleotide 19 19 A +46 hg18.chr1 101851 101859 dinucleotide 8 4 CA panTro2.chr1_random 7109861 7109869 dinucleotide 8 4 CA +47 hg18.chr1 103743 103753 dinucleotide 10 5 CT panTro2.chr1 250633 250643 dinucleotide 10 5 CT +47 hg18.chr1 104174 104182 dinucleotide 8 4 GA panTro2.chr1 251064 251072 dinucleotide 8 4 GA +47 hg18.chr1 107676 107685 dinucleotide 9 4 AG panTro2.chr1 254594 254603 dinucleotide 9 4 AG +47 hg18.chr1 107735 107743 dinucleotide 8 4 AG panTro2.chr1 254653 254661 dinucleotide 8 4 AG +47 hg18.chr1 108222 108231 dinucleotide 9 4 AT panTro2.chr1 255140 255148 dinucleotide 8 4 AT +47 hg18.chr1 108253 108261 dinucleotide 8 4 AT panTro2.chr1 255171 255179 dinucleotide 8 4 AT +47 hg18.chr1 103743 103753 dinucleotide 10 5 CT panTro2.chr1 250633 250643 dinucleotide 10 5 CT +47 hg18.chr1 104174 104182 dinucleotide 8 4 GA panTro2.chr1 251064 251072 dinucleotide 8 4 GA +47 hg18.chr1 107676 107685 dinucleotide 9 4 AG panTro2.chr1 254594 254603 dinucleotide 9 4 AG +47 hg18.chr1 107735 107743 dinucleotide 8 4 AG panTro2.chr1 254653 254661 dinucleotide 8 4 AG +47 hg18.chr1 108222 108231 dinucleotide 9 4 AT panTro2.chr1 255140 255148 dinucleotide 8 4 AT +47 hg18.chr1 108253 108261 dinucleotide 8 4 AT panTro2.chr1 255171 255179 dinucleotide 8 4 AT +48 hg18.chr1 110072 110081 dinucleotide 9 4 TG panTro2.chr1 258778 258787 dinucleotide 9 4 TG +48 hg18.chr1 110072 110081 dinucleotide 9 4 TG panTro2.chr1 258778 258787 dinucleotide 9 4 TG +49 hg18.chr1 111133 111148 mononucleotide 15 15 T panTro2.chr1 260455 260473 mononucleotide 18 18 T +49 hg18.chr1 111503 111515 trinucleotide 12 4 TAA panTro2.chr1 260828 260846 trinucleotide 18 6 TAA +51 hg18.chr1 113280 113290 dinucleotide 10 5 AT panTro2.chr1_random 8247726 8247736 dinucleotide 10 5 AT +55 hg18.chr1 114709 114728 mononucleotide 19 19 A panTro2.chr1 262958 262973 mononucleotide 15 15 A +55 hg18.chr1 115520 115529 mononucleotide 9 9 A panTro2.chr1 263765 263775 mononucleotide 10 10 A +55 hg18.chr1 114709 114728 mononucleotide 19 19 A panTro2.chr1 262958 262973 mononucleotide 15 15 A +56 hg18.chr1 118460 118475 mononucleotide 15 15 T panTro2.chr1 267602 267617 mononucleotide 15 15 T +56 hg18.chr1 119541 119556 mononucleotide 15 15 T panTro2.chr1 268682 268695 mononucleotide 13 13 T +56 hg18.chr1 119775 119783 dinucleotide 8 4 GA panTro2.chr1 268914 268922 dinucleotide 8 4 GA +56 hg18.chr1 123929 123958 mononucleotide 29 29 T panTro2.chr1 273095 273130 mononucleotide 35 35 T +72 hg18.chr1 134751 134763 mononucleotide 12 12 A panTro2.chr1_random 7189181 7189197 mononucleotide 16 16 A +72 hg18.chr1 134994 135006 trinucleotide 12 4 GTG panTro2.chr1_random 7189427 7189439 trinucleotide 12 4 GTG +72 hg18.chr1 135805 135813 dinucleotide 8 4 GT panTro2.chr1_random 7190238 7190246 dinucleotide 8 4 GT +72 hg18.chr1 136433 136442 dinucleotide 9 4 TC panTro2.chr1_random 7190866 7190875 dinucleotide 9 4 TC +72 hg18.chr1 137771 137781 mononucleotide 10 10 A panTro2.chr1_random 7192213 7192224 mononucleotide 11 11 A +72 hg18.chr1 138639 138652 trinucleotide 13 4 AAT panTro2.chr1_random 7193082 7193095 trinucleotide 13 4 AAT +72 hg18.chr1 140196 140205 dinucleotide 9 4 AG panTro2.chr1_random 7194642 7194651 dinucleotide 9 4 AG +72 hg18.chr1 141348 141361 mononucleotide 13 13 T panTro2.chr1_random 7195790 7195814 mononucleotide 24 24 T +72 hg18.chr1 134994 135006 trinucleotide 12 4 GTG panTro2.chr1_random 7189427 7189439 trinucleotide 12 4 GTG +72 hg18.chr1 135805 135813 dinucleotide 8 4 GT panTro2.chr1_random 7190238 7190246 dinucleotide 8 4 GT +72 hg18.chr1 136433 136442 dinucleotide 9 4 TC panTro2.chr1_random 7190866 7190875 dinucleotide 9 4 TC +72 hg18.chr1 138639 138652 trinucleotide 13 4 AAT panTro2.chr1_random 7193082 7193095 trinucleotide 13 4 AAT +72 hg18.chr1 140196 140205 dinucleotide 9 4 AG panTro2.chr1_random 7194642 7194651 dinucleotide 9 4 AG +72 hg18.chr1 142689 142699 mononucleotide 10 10 T panTro2.chr1_random 7197149 7197158 mononucleotide 9 9 T +73 hg18.chr1 145718 145733 mononucleotide 15 15 A panTro2.chr1_random 7202121 7202135 mononucleotide 14 14 A +74 hg18.chr1 146143 146155 mononucleotide 12 12 T panTro2.chr1_random 7208622 7208632 mononucleotide 10 10 T +74 hg18.chr1 146971 146985 mononucleotide 14 14 A panTro2.chr1_random 7209449 7209463 mononucleotide 14 14 A +75 hg18.chr1 150335 150351 mononucleotide 16 16 T panTro2.chr1_random 7213029 7213047 mononucleotide 18 18 T +75 hg18.chr1 151431 151440 mononucleotide 9 9 A panTro2.chr1_random 7214127 7214137 mononucleotide 10 10 A +75 hg18.chr1 153993 154017 pentanucleotide 24 4 AAAAC panTro2.chr1_random 7216686 7216711 pentanucleotide 25 5 AAAAC +75 hg18.chr1 155151 155174 mononucleotide 23 23 A panTro2.chr1_random 7217843 7217867 mononucleotide 24 24 A +75 hg18.chr1 156454 156463 mononucleotide 9 9 A panTro2.chr1_random 7219148 7219157 mononucleotide 9 9 A +75 hg18.chr1 156998 157017 tetranucleotide 19 4 TTTA panTro2.chr1_random 7219691 7219710 tetranucleotide 19 4 TTTA +75 hg18.chr1 151431 151440 mononucleotide 9 9 A panTro2.chr1_random 7214127 7214137 mononucleotide 10 10 A +75 hg18.chr1 153993 154017 pentanucleotide 24 4 AAAAC panTro2.chr1_random 7216686 7216711 pentanucleotide 25 5 AAAAC +75 hg18.chr1 154537 154559 mononucleotide 22 22 A panTro2.chr1_random 7217231 7217251 mononucleotide 20 20 A +75 hg18.chr1 156454 156463 mononucleotide 9 9 A panTro2.chr1_random 7219148 7219157 mononucleotide 9 9 A +75 hg18.chr1 156998 157017 tetranucleotide 19 4 TTTA panTro2.chr1_random 7219691 7219710 tetranucleotide 19 4 TTTA +76 hg18.chr1 159723 159736 mononucleotide 13 13 T panTro2.chr1 224074251 224074269 mononucleotide 18 18 T +76 hg18.chr1 160798 160818 pentanucleotide 20 4 GTTTT panTro2.chr1 224075335 224075355 pentanucleotide 20 4 GTTTT +76 hg18.chr1 159723 159736 mononucleotide 13 13 T panTro2.chr1 224074251 224074269 mononucleotide 18 18 T +76 hg18.chr1 160798 160818 pentanucleotide 20 4 GTTTT panTro2.chr1 224075335 224075355 pentanucleotide 20 4 GTTTT +77 hg18.chr1 163076 163084 dinucleotide 8 4 AT panTro2.chr1_random 7293459 7293467 dinucleotide 8 4 AT +77 hg18.chr1 165179 165187 dinucleotide 8 4 GC panTro2.chr1_random 7295617 7295625 dinucleotide 8 4 GC +77 hg18.chr1 165310 165322 mononucleotide 12 12 A panTro2.chr1_random 7295748 7295770 mononucleotide 22 22 A +77 hg18.chr1 163076 163084 dinucleotide 8 4 AT panTro2.chr1_random 7293459 7293467 dinucleotide 8 4 AT +77 hg18.chr1 165179 165187 dinucleotide 8 4 GC panTro2.chr1_random 7295617 7295625 dinucleotide 8 4 GC +77 hg18.chr1 165310 165322 mononucleotide 12 12 A panTro2.chr1_random 7295748 7295770 mononucleotide 22 22 A +83 hg18.chr1 219668 219689 tetranucleotide 21 5 TAAA panTro2.chr3 77587413 77587435 tetranucleotide 22 5 TAAA +83 hg18.chr1 219668 219689 tetranucleotide 21 5 TAAA panTro2.chr3 77587413 77587435 tetranucleotide 22 5 TAAA +87 hg18.chr1 222298 222309 mononucleotide 11 11 T panTro2.chrUn 1781936 1781946 mononucleotide 10 10 T +87 hg18.chr1 222298 222309 mononucleotide 11 11 T panTro2.chrUn 1781936 1781946 mononucleotide 10 10 T +89 hg18.chr1 224906 224915 mononucleotide 9 9 T panTro2.chrUn 1780175 1780184 mononucleotide 9 9 T +89 hg18.chr1 224906 224915 mononucleotide 9 9 T panTro2.chrUn 1780175 1780184 mononucleotide 9 9 T +93 hg18.chr1 227371 227380 dinucleotide 9 4 GA panTro2.chr1_random 7325572 7325581 dinucleotide 9 4 GA +93 hg18.chr1 227392 227408 mononucleotide 16 16 A panTro2.chr1_random 7325593 7325616 mononucleotide 23 23 A +93 hg18.chr1 227371 227380 dinucleotide 9 4 GA panTro2.chr1_random 7325572 7325581 dinucleotide 9 4 GA +96 hg18.chr1 234056 234064 dinucleotide 8 4 AG panTro2.chr1_random 8589985 8589993 dinucleotide 8 4 AG +97 hg18.chr1 235032 235042 dinucleotide 10 5 TG panTro2.chr1_random 7331012 7331022 dinucleotide 10 5 TG +97 hg18.chr1 237516 237529 mononucleotide 13 13 A panTro2.chr1_random 7333512 7333527 mononucleotide 15 15 A +99 hg18.chr1 240155 240164 dinucleotide 9 4 CT panTro2.chr1_random 7334380 7334389 dinucleotide 9 4 CT +99 hg18.chr1 243706 243714 dinucleotide 8 4 AG panTro2.chr1_random 7337953 7337961 dinucleotide 8 4 AG +99 hg18.chr1 244724 244746 mononucleotide 22 22 A panTro2.chr1_random 7338970 7338987 mononucleotide 17 17 A +99 hg18.chr1 240155 240164 dinucleotide 9 4 CT panTro2.chr1_random 7334380 7334389 dinucleotide 9 4 CT +99 hg18.chr1 243706 243714 dinucleotide 8 4 AG panTro2.chr1_random 7337953 7337961 dinucleotide 8 4 AG +99 hg18.chr1 244724 244746 mononucleotide 22 22 A panTro2.chr1_random 7338970 7338987 mononucleotide 17 17 A +100 hg18.chr1 248168 248176 dinucleotide 8 4 CA panTro2.chr1_random 7109861 7109869 dinucleotide 8 4 CA +100 hg18.chr1 248168 248176 dinucleotide 8 4 CA panTro2.chr1_random 7109861 7109869 dinucleotide 8 4 CA
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ortho_ms_mut.tab Tue Apr 01 10:52:29 2014 -0400 @@ -0,0 +1,75 @@ +#Window Species_1 Window_Start Window_End Species_2 Groupby_Feature SubGroupby_Feature Mutability Count +5 hg18.chr1 6483 6496 panTro2.chr15 100042575 100042588 trinucleotide GCT 0.00e+00 4 +10 hg18.chr1 18452 18467 panTro2.chr15 100038029 100038042 mononucleotide T 4.00e+00 13 +10 hg18.chr1 18452 18467 panTro2.chr15 100038029 100038042 dinucleotide GT 1.00e+00 5 +12 hg18.chr1 20736 20756 panTro2.chr15_random 1091184 1091198 dinucleotide TC 9.00e+00 7 +13 hg18.chr1 20799 20812 panTro2.chrUn 1510811 1510831 dinucleotide TC 1.10e+01 24 +14 hg18.chr1 23384 23405 panTro2.chrUn 1508999 1509025 mononucleotide A 1.00e+00 15 +14 hg18.chr1 23384 23405 panTro2.chrUn 1508999 1509025 mononucleotide T 1.30e+01 36 +15 hg18.chr1 25352 25371 panTro2.chr15_random 1087905 1087924 tetranucleotide AAAT 0.00e+00 4 +16 hg18.chr1 30503 30522 panTro2.chrUn 139457 139482 trinucleotide TTC 0.00e+00 4 +16 hg18.chr1 30503 30522 panTro2.chrUn 139457 139482 mononucleotide A 1.00e+00 13 +16 hg18.chr1 30503 30522 panTro2.chrUn 139457 139482 mononucleotide T 1.85e+01 32 +17 hg18.chr1 34037 34047 panTro2.chrUn 9698527 9698537 mononucleotide A 4.50e+00 23 +19 hg18.chr1 41728 41741 panTro2.chrUn 9706479 9706495 mononucleotide A 5.00e+00 23 +19 hg18.chr1 41728 41741 panTro2.chrUn 9706479 9706495 dinucleotide GT 0.00e+00 16 +20 hg18.chr1 44654 44681 panTro2.chrUn 9709915 9709942 tetranucleotide TTTC 4.50e+00 21 +22 hg18.chr1 52103 52120 panTro2.chrUn 9718011 9718024 mononucleotide T 1.00e+00 18 +22 hg18.chr1 52103 52120 panTro2.chrUn 9718011 9718024 dinucleotide AC 4.00e+00 6 +25 hg18.chr1 63706 63720 panTro2.chrUn 9730896 9730914 mononucleotide T 1.60e+01 14 +25 hg18.chr1 63706 63720 panTro2.chrUn 9730896 9730914 dinucleotide TA 0.00e+00 6 +25 hg18.chr1 63706 63720 panTro2.chrUn 9730896 9730914 tetranucleotide ATAC 9.00e+00 5 +26 hg18.chr1 67038 67059 panTro2.chrUn 9734760 9734777 mononucleotide A 1.60e+01 17 +27 hg18.chr1 72077 72088 panTro2.chrUn 9742946 9742957 dinucleotide AC 0.00e+00 5 +28 hg18.chr1 73838 73906 panTro2.chr15 99975357 99975380 tetranucleotide AAAG 1.44e+02 10 +32 hg18.chr1 81064 81077 panTro2.chrUn 1797471 1797489 mononucleotide A 2.50e+01 13 +35 hg18.chr1 82527 82541 panTro2.chr1_random 7070707 7070721 mononucleotide A 0.00e+00 14 +40 hg18.chr1 91199 91212 panTro2.chr1_random 7089889 7089900 mononucleotide A 4.00e+00 11 +41 hg18.chr1 91538 91554 panTro2.chr1 223998154 223998167 mononucleotide A 9.00e+00 13 +42 hg18.chr1 95472 95491 panTro2.chr1_random 7092383 7092404 dinucleotide AT 1.85e+01 22 +42 hg18.chr1 95472 95491 panTro2.chr1_random 7092383 7092404 dinucleotide AC 3.60e+01 12 +43 hg18.chr1 96802 96815 panTro2.chr1 243988 244001 dinucleotide TC 0.00e+00 6 +44 hg18.chr1 98409 98425 panTro2.chr1_random 7095323 7095342 mononucleotide A 9.00e+00 16 +45 hg18.chr1 101230 101250 panTro2.chr1 248453 248467 mononucleotide A 1.85e+01 25 +45 hg18.chr1 101230 101250 panTro2.chr1 248453 248467 dinucleotide GT 4.00e+00 17 +49 hg18.chr1 112974 112995 panTro2.chr1 262299 262320 trinucleotide TAA 4.00e+00 4 +49 hg18.chr1 112974 112995 panTro2.chr1 262299 262320 dinucleotide TA 5.00e-01 15 +49 hg18.chr1 112974 112995 panTro2.chr1 262299 262320 mononucleotide T 9.00e+00 15 +49 hg18.chr1 112974 112995 panTro2.chr1 262299 262320 dinucleotide CA 1.00e+00 5 +55 hg18.chr1 114709 114728 panTro2.chr1 262958 262973 mononucleotide A 1.60e+01 30 +56 hg18.chr1 120154 120164 panTro2.chr1 269287 269300 mononucleotide A 8.89e+00 109 +56 hg18.chr1 120154 120164 panTro2.chr1 269287 269300 mononucleotide T 8.80e+00 89 +72 hg18.chr1 141348 141361 panTro2.chr1_random 7195790 7195814 mononucleotide T 3.65e+01 65 +72 hg18.chr1 141348 141361 panTro2.chr1_random 7195790 7195814 trinucleotide GTG 0.00e+00 8 +72 hg18.chr1 141348 141361 panTro2.chr1_random 7195790 7195814 trinucleotide AAC 0.00e+00 21 +72 hg18.chr1 141348 141361 panTro2.chr1_random 7195790 7195814 mononucleotide A 8.33e+00 52 +72 hg18.chr1 141348 141361 panTro2.chr1_random 7195790 7195814 trinucleotide TTA 0.00e+00 4 +72 hg18.chr1 141348 141361 panTro2.chr1_random 7195790 7195814 trinucleotide AAT 0.00e+00 25 +73 hg18.chr1 145718 145733 panTro2.chr1_random 7202121 7202135 mononucleotide A 1.00e+00 14 +74 hg18.chr1 146971 146985 panTro2.chr1_random 7209449 7209463 mononucleotide A 2.00e+00 24 +74 hg18.chr1 146971 146985 panTro2.chr1_random 7209449 7209463 mononucleotide T 4.00e+00 10 +75 hg18.chr1 156998 157017 panTro2.chr1_random 7219691 7219710 mononucleotide T 4.00e+00 16 +75 hg18.chr1 156998 157017 panTro2.chr1_random 7219691 7219710 tetranucleotide GGAG 0.00e+00 16 +75 hg18.chr1 156998 157017 panTro2.chr1_random 7219691 7219710 tetranucleotide TTTA 0.00e+00 24 +75 hg18.chr1 156998 157017 panTro2.chr1_random 7219691 7219710 pentanucleotide AAAAC 1.00e+00 8 +75 hg18.chr1 156998 157017 panTro2.chr1_random 7219691 7219710 mononucleotide A 3.00e+00 61 +75 hg18.chr1 156998 157017 panTro2.chr1_random 7219691 7219710 dinucleotide AC 0.00e+00 34 +76 hg18.chr1 160798 160818 panTro2.chr1 224075335 224075355 mononucleotide T 2.50e+01 13 +76 hg18.chr1 160798 160818 panTro2.chr1 224075335 224075355 pentanucleotide GTTTT 0.00e+00 4 +77 hg18.chr1 165310 165322 panTro2.chr1_random 7295748 7295770 mononucleotide A 1.00e+02 24 +77 hg18.chr1 165310 165322 panTro2.chr1_random 7295748 7295770 dinucleotide CA 0.00e+00 10 +78 hg18.chr1 166066 166095 panTro2.chr1 224080667 224080691 pentanucleotide AAAAC 1.00e+00 4 +83 hg18.chr1 219668 219689 panTro2.chr3 77587413 77587435 tetranucleotide TAAA 0.00e+00 5 +87 hg18.chr1 222298 222309 panTro2.chrUn 1781936 1781946 mononucleotide T 1.00e+00 20 +93 hg18.chr1 228854 228869 panTro2.chr1_random 7327066 7327078 mononucleotide A 2.90e+01 31 +94 hg18.chr1 231193 231209 panTro2.chr1 223990552 223990572 mononucleotide T 1.60e+01 16 +97 hg18.chr1 237516 237529 panTro2.chr1_random 7333512 7333527 mononucleotide A 1.00e+01 28 +97 hg18.chr1 237516 237529 panTro2.chr1_random 7333512 7333527 mononucleotide T 1.60e+01 15 +97 hg18.chr1 237516 237529 panTro2.chr1_random 7333512 7333527 tetranucleotide TTTA 9.00e+00 7 +98 hg18.chr1 237855 237870 panTro2.chr1 223998154 223998167 mononucleotide A 4.00e+00 13 +99 hg18.chr1 245760 245793 panTro2.chr1_random 7340006 7340026 dinucleotide AT 2.50e+01 52 +99 hg18.chr1 245760 245793 panTro2.chr1_random 7340006 7340026 dinucleotide TC 1.67e+01 64 +99 hg18.chr1 245760 245793 panTro2.chr1_random 7340006 7340026 mononucleotide A 2.50e+01 34 +99 hg18.chr1 245760 245793 panTro2.chr1_random 7340006 7340026 dinucleotide GT 2.31e+01 114 +99 hg18.chr1 245760 245793 panTro2.chr1_random 7340006 7340026 dinucleotide AC 4.90e+01 34 +100 hg18.chr1 247545 247565 panTro2.chr1_random 7109243 7109259 mononucleotide A 8.00e+00 26
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_dependencies.xml Tue Apr 01 10:52:29 2014 -0400 @@ -0,0 +1,9 @@ +<?xml version="1.0"?> +<tool_dependency> + <package name="bx-python" version="0.7.1"> + <repository changeset_revision="41eb9d9f667d" name="package_bx_python_0_7" owner="devteam" prior_installation_required="False" toolshed="http://toolshed.g2.bx.psu.edu" /> + </package> + <package name="galaxy-ops" version="1.0.0"> + <repository changeset_revision="4e39032e4ec6" name="package_galaxy_ops_1_0_0" owner="devteam" prior_installation_required="False" toolshed="http://toolshed.g2.bx.psu.edu" /> + </package> +</tool_dependency>