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Added SOLiD tool section with QC tools.
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Guruprasad Anada
@@ -281,6 +281,10 @@
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<tool file="emboss_5/emboss_wordmatch.xml" />
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</section>
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-->
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<section name="SOLiD Data Analysis" id="solid_tools">
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<tool file="solid_tools/solid_qual_stats.xml" />
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<tool file="solid_tools/solid_qual_boxplot.xml" />
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</section>
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<section name="FASTA manipulation" id="fasta_manipulation">
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<tool file="fasta_tools/fasta_compute_length.xml" />
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<tool file="fasta_tools/fasta_filter_by_length.xml" />
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@@ -0,0 +1,36 @@
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<tool id="solid_qual_boxplot" name="Quality Boxplot" version="1.0.0">
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<description>for SOLiD data</description>
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<command>qualsolid_boxplot_graph.sh -t '$input.name' -i $input -o $output</command>
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<inputs>
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<param format="txt" name="input" type="data" label="Statistics report file (output of 'Quality Statistics for SOLiD data' tool)" />
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</inputs>
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<outputs>
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<data format="png" name="output" metadata_source="input" />
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</outputs>
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<help>
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**What it does**
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Creates a boxplot graph for the quality scores in the library.
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.. class:: infomark
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**TIP:** Use the **Quality Statistics for SOLiD data** tool to generate the report file needed for this tool.
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-----
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**Output Example**
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* Black horizontal lines are medians
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* Rectangular red boxes show the Inter-quartile Range (IQR) (top value is Q3, bottom value is Q1)
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* Whiskers show outlier at max. 1.5*IQR
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.. image:: ../static/images/solid_qual.png
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</help>
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</tool>
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@@ -0,0 +1,140 @@
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#! /usr/bin/python
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#Guruprasad Ananda
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import sys, os, zipfile, tempfile
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QUAL_UPPER_BOUND = 41
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QUAL_LOWER_BOUND = 1
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def stop_err( msg ):
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sys.stderr.write( "%s\n" % msg )
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sys.exit()
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def unzip( filename ):
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zip_file = zipfile.ZipFile( filename, 'r' )
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tmpfilename = tempfile.NamedTemporaryFile().name
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for name in zip_file.namelist():
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file( tmpfilename, 'a' ).write( zip_file.read( name ) )
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zip_file.close()
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return tmpfilename
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def __main__():
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infile_score_name = sys.argv[1].strip()
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fout = open(sys.argv[2].strip(),'r+w')
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infile_is_zipped = False
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if zipfile.is_zipfile( infile_score_name ):
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infile_is_zipped = True
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infile_name = unzip( infile_score_name )
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else:
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infile_name = infile_score_name
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readlen = None
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j = 0
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for line in file( infile_name ):
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line = line.strip()
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if not(line) or line.startswith("#") or line.startswith(">"):
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continue
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elems = line.split()
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try:
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for item in elems:
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assert int(item)
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if not(readlen):
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readlen = len(elems)
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if len(elems) != readlen:
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print "Note: Reads in the input dataset are of variable lengths."
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j += 1
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except:
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invalid_lines += 1
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if j > 10:
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break
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invalid_lines = 0
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position_dict = {}
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print >>fout, "column\tcount\tmin\tmax\tsum\tmean\tQ1\tmed\tQ3\tIQR\tlW\trW"
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for k,line in enumerate(file( infile_name )):
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line = line.strip()
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if not(line) or line.startswith("#") or line.startswith(">"):
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continue
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elems = line.split()
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if position_dict == {}:
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for pos in range(readlen):
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position_dict[pos] = [0]*QUAL_UPPER_BOUND
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if len(elems) != readlen:
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invalid_lines += 1
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continue
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for ind,item in enumerate(elems):
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try:
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item = int(item)
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position_dict[ind][item]+=1
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except:
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pass
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invalid_positions = 0
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for pos in position_dict:
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carr = position_dict[pos] #count array for position pos
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total = sum(carr) #number of bases found in this column.
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med_elem = int(round(total/2.0))
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lowest = None #Lowest quality score value found in this column.
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highest = None #Highest quality score value found in this column.
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median = None #Median quality score value found in this column.
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qsum = 0.0 #Sum of quality score values for this column.
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q1 = None #1st quartile quality score.
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q3 = None #3rd quartile quality score.
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q1_elem = int(round((total+1)/4.0))
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q3_elem = int(round((total+1)*3/4.0))
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try:
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for ind,cnt in enumerate(carr):
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qsum += ind*cnt
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if cnt!=0:
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highest = ind
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if lowest==None and cnt!=0: #first non-zero count
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lowest = ind
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if q1==None:
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if sum(carr[:ind+1]) >= q1_elem:
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q1 = ind
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if median==None:
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if sum(carr[:ind+1]) < med_elem:
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continue
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median = ind
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if total%2 == 0: #even number of elements
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median2 = median
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if sum(carr[:ind+1]) < med_elem+1:
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for ind2,elem in enumerate(carr[ind+1:]):
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if elem != 0:
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median2 = ind+ind2+1
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break
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median = (median + median2)/2.0
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if q3==None:
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if sum(carr[:ind+1]) >= q3_elem:
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q3 = ind
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mean = qsum/total #Mean quality score value for this column.
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iqr = q3-q1
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left_whisker = max(q1 - 1.5*iqr,lowest)
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right_whisker = min(q3 + 1.5*iqr,highest)
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print >>fout,"%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" %(pos+1,total,lowest,highest,qsum,mean,q1,median,q3,iqr,left_whisker,right_whisker)
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except:
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invalid_positions += 1
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nullvals = ['NA']*11
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print >>fout,"%s\t%s" %(pos+1,'\t'.join(nullvals))
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if invalid_lines:
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print "Skipped %d reads as invalid." %invalid_lines
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if invalid_positions:
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print "Skipped stats computation for %d read postions." %invalid_positions
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if __name__=="__main__":
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__main__()
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@@ -0,0 +1,65 @@
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<tool id="solid_qual_stats" name="Quality Statistics" version="1.0.0">
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<description>for SOLiD data</description>
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<command interpreter="python">solid_qual_stats.py $input $output1</command>
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<inputs>
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<param format="qualsolid, txtseq.zip" name="input" type="data" label="SOLiD qual file" help="If your dataset doesn't show up in the menu, click the pencil icon next to your dataset and set the datatype to 'qualsolid'" />
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</inputs>
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<outputs>
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<data format="txt" name="output1" metadata_source="input" />
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</outputs>
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<tests>
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<test>
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<param name="input" value="qualscores.qualsolid" />
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<output name="output1" file="qualsolid.stats" />
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</test>
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</tests>
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<help>
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**What it does**
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Creates quality statistics report for the given SOLiD quality score file.
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.. class:: infomark
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**TIP:** This statistics report can be used as input for **Quality Boxplot for SOLiD data** tool.
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-----
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**The output file will contain the following fields:**
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* column = column number (position on the read)
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* count = number of bases found in this column.
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* min = Lowest quality score value found in this column.
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* max = Highest quality score value found in this column.
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* sum = Sum of quality score values for this column.
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* mean = Mean quality score value for this column.
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* Q1 = 1st quartile quality score.
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* med = Median quality score.
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* Q3 = 3rd quartile quality score.
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* IQR = Inter-Quartile range (Q3-Q1).
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* lW = 'Left-Whisker' value (for boxplotting).
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* rW = 'Right-Whisker' value (for boxplotting).
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**Output Example**::
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column count min max sum mean Q1 med Q3 IQR lW rW
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1 6362991 2 32 250734117 20.41 5 9 28 23 2 31
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2 6362991 2 32 250531036 21.37 10 26 30 20 5 31
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3 6362991 2 34 248722469 19.09 10 26 30 20 5 31
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4 6362991 2 34 247654797 18.92 10 26 30 20 5 31
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.
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.
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32 6362991 2 31 143436943 16.54 3 10 25 22 2 31
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33 6362991 2 32 114269843 16.96 3 10 25 22 2 31
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34 6362991 2 29 140638447 12.10 3 10 25 22 2 29
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35 6362991 2 29 138910532 11.83 3 10 25 22 2 29
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</help>
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</tool>
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