Mercurial > repos > chmaramis > testirprofiler
changeset 12:cdf95051bc55 draft default tip
Uploaded 2 tools
| author | chmaramis |
|---|---|
| date | Sun, 18 Mar 2018 07:11:06 -0400 |
| parents | 14896ea6e180 |
| children | |
| files | cmpb2016/top10_CDR3_inexact_pairing.py cmpb2016/top10_CDR3_inexact_pairing.xml comp_clono_CDR3.py comp_clono_CDR3.xml ngs_filtering.py ngs_filtering.xml |
| diffstat | 6 files changed, 688 insertions(+), 75 deletions(-) [+] |
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--- a/cmpb2016/top10_CDR3_inexact_pairing.py Sun Mar 18 07:07:39 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Apr 18 11:37:40 2016 - -@author: chmaramis -""" - -# -*- coding: utf-8 -*- -""" -Created on Mon Apr 18 09:48:00 2016 - -@author: chmaramis -""" - -import pandas as pd -import numpy as np -import sys -import functools as ft - -def maxHam1(s1, s2): - if len(s1) != len(s2): - return False - else: - return sum(c1 != c2 for c1, c2 in zip(s1, s2)) <= 1 - - -if __name__ == "__main__": - - clonosFN = sys.argv[1] - outFN = sys.argv[2] - - Cl = pd.read_csv(clonosFN,sep='\t',index_col=0) - T10 = Cl[:10].copy() - - aa_junction = np.array(T10['AA JUNCTION']) - geneCol = [x for x in T10.columns if x.upper().endswith('GENE')][0] - - F = np.zeros((2,20)) - - for i in range(0,10): - taa = T10['AA JUNCTION'][i+1] - gene = T10[geneCol][i+1] - S1 = Cl['AA JUNCTION'].apply(ft.partial(maxHam1, s2=taa)) - S2 = Cl[geneCol] == gene - S1[i+1] = False - F[0,2*i] = (S1 & S2).sum() - F[0,2*i+1] = Cl['Frequency %'][S1 & S2].sum() - F[1,2*i] = (S1 & ~S2).sum() - F[1,2*i+1] = Cl['Frequency %'][S1 & ~S2].sum() - - - K = list(aa_junction+' Nr. Clonos') - L = list(aa_junction+' Freq. %') - columns = [val for pair in zip(K,L) for val in pair] - - D = pd.DataFrame(F,columns=columns, index=['same gene', 'different gene']) - D.to_csv(outFN,sep='\t') -
--- a/cmpb2016/top10_CDR3_inexact_pairing.xml Sun Mar 18 07:07:39 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,17 +0,0 @@ -<tool id="top10CDR3inexPair" name="Top10 CDR3 Inexact Pairing" version="0.9"> -<description>Pair CDR3 of top10 clonotypes with similar CDR3</description> -<command interpreter="python"> -top10_CDR3_inexact_pairing.py "$clonos" "$out" -</command> -<inputs> -<param format="txt" name="clonos" type="data" label="Full List of Clonotypes"/> -</inputs> - -<outputs> -<data format="tabular" name="out" label="${clonos.name}_top10CDR3_inexactPairing"/> -</outputs> -<help> -Provides the number of clonotypes with AA Junction (CDR3) similar to that of the top-10 clonotypes (maximum 1 AA difference). Distinguishes between clonotypes using the same gene and different gene. Works for both V-Gene and J-Gene clonotypes. -</help> -</tool> -
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/comp_clono_CDR3.py Sun Mar 18 07:11:06 2018 -0400 @@ -0,0 +1,78 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Jun 19 17:33:34 2014 + +@author: chmaramis +""" + +from __future__ import division +import numpy as np +from pandas import * +import functools as ft +import sys +import time + +frm = lambda x,y: '{r}/{l}'.format(r=x,l=y) + + +def cdr3Computation(inp_name, out1, t10n, fname): + + frame = DataFrame() + tp = read_csv(inp_name, iterator=True, chunksize=5000,sep='\t', index_col=0 ) + frame = concat([chunk for chunk in tp]) + + grouped = frame.groupby(['AA JUNCTION']) + x=grouped.size() + x1=DataFrame(x.index, columns=['AA JUNCTION']) + x1['Reads']=x.values + total = sum(x1['Reads']) + #x1['Reads/Total'] = ['{r}/{l}'.format(r=pr , l = total) for pr in x1['Reads']] + x1['Reads/Total'] = x1['Reads'].map(ft.partial(frm, y=total)) + x1['Frequency %'] = (100*x1['Reads']/total).map('{:.4f}'.format) + + final = x1.sort_values(by = ['Reads'] , ascending = False) + + final.index=range(1,len(final)+1) + final.to_csv(out1 , sep = '\t') + + numofclono = len(final) + clust = len(final[final['Reads'] > 1]) + sing = len (final[final['Reads'] == 1]) + top10 = final[['AA JUNCTION','Frequency %']].head(10) + top10.to_csv(t10n , sep = '\t') + + summary = [[str(top10['AA JUNCTION'].values[0])]] + summary.append([top10['Frequency %'].values[0]]) + summary.append([numofclono]) + summary.append([clust,'{:.4f}'.format(100*clust/numofclono)]) + summary.append([sing,'{:.4f}'.format(100*sing/numofclono)]) + + ind = ['Dominant Clonotype (CDR3)', 'Frequency', 'Number of Clonotypes' , 'Expanding Clonotypes','Singletons'] + spl = fname.split('_') + col = [spl[0],'%'] + + frsum = DataFrame(summary,index = ind, columns = col) + + return frsum + +if __name__ == '__main__': + + start=time.time() + + # Parse input arguments + inp_name = sys.argv[1] + out1 = sys.argv[2] + t10n = sys.argv[3] + sname = sys.argv[4] + fname = sys.argv[5] + + # Execute basic function + frsum = cdr3Computation(inp_name,out1,t10n,fname) + + # Save output to CSV files + if not frsum.empty: + frsum.to_csv(sname, sep = '\t') + + # Print execution time + stop=time.time() + print('Runtime:' + str(stop-start))
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/comp_clono_CDR3.xml Sun Mar 18 07:11:06 2018 -0400 @@ -0,0 +1,19 @@ +<tool id="compClonoCDR3" name="CDR3 Clonotypes Computation" version="0.9"> + <description>Compute CDR3 clonotypes</description> + <command interpreter="python">comp_clono_CDR3.py $input $clonos $topcl $summ ${input.name}</command> + <inputs> + <param format="tabular" name="input" type="data" label="Filtered-in File"/> + </inputs> + +<outputs> + <data name="clonos" format="tabular" label="${input.name}_CDR3"/> + <data name="topcl" format="tabular" label="${input.name}_top10CDR3"/> + <data name="summ" format="tabular" label="${input.name}_Summary3"/> +</outputs> + + + <help> +This tool computes the CDR3 clonotypes. + </help> + +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ngs_filtering.py Sun Mar 18 07:11:06 2018 -0400 @@ -0,0 +1,452 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Sep 4 18:41:42 2013 + +@author: chmaramis +""" + +from __future__ import division +import string as strpy +import numpy as np +from pandas import * +from numpy import nan as NA +import time +import sys + + +def filter_condition_AAjunction(x): + x= x.strip() + if ' ' in x: + return x.split(' ')[0] + else: + return x + +#-----------frame creation--------------------- +def filtering(inp,cells,psorf,con,prod,CF,Vper,Vgene,laa1,laa2,conaa,Jgene,Dgene,fname): + + try: + path=inp + frame = DataFrame() + seqlen = [] + head = [] + tp = read_csv(path, iterator=True, chunksize=5000,sep='\t', index_col=0 ) + frame = concat([chunk for chunk in tp]) + + frcol = list(frame.columns) + #print frcol[-1] + if 'Unnamed' in frcol[-1]: + del frcol[-1] + frame=frame[frcol] + + frame.index = range(1,len(frame)+1) + + head.append('Total reads of raw data') + seqlen.append(len(frame)) + + #------------drop nulls-------------------- + filtered = DataFrame() + filtall = DataFrame() + summ_df = DataFrame() + filtered = frame[isnull(frame['AA JUNCTION']) | isnull(frame['V-GENE and allele'])] + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = "NoResults" + frame = frame[frame['AA JUNCTION'].notnull()] + frame = frame[frame['V-GENE and allele'].notnull()] + + head.append('Not Null CDR3/V') + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + filtered = DataFrame() + + if psorf.startswith('y') or psorf.startswith('Y'): + + cc0=np.array(frame['V-GENE and allele'].unique()) + + + for x in cc0: + x1=x.split('*') + try: + if (x1[1].find('P')>-1) or (x1[1].find('ORF')>-1): + filtered = filtered.append(frame[frame['V-GENE and allele'] == x]) + frame['V-GENE and allele']=frame['V-GENE and allele'].replace(x,NA) + elif x.find('or')>-1: + posa=x.count('or') + x2=x.split('or') + x4='' + genelist=[] + for cnt in range(0, posa+1): + x3=x2[cnt].split('*') + x3[0]=x3[0].strip()#kobei ta space + k=x3[0].split(' ')# holds only TRBV + if cnt==0: + genelist.append(k[1]) + x4+=k[1] + elif ((str(k[1]) in genelist) == False) & (x3[1].find('P')==-1):# check for P in x3 + genelist.append(k[1]) + x4+=' or ' + x4+=k[1] + x3=None + k1=None + genelist=None + + frame['V-GENE and allele']=frame['V-GENE and allele'].replace(x,x4) + + else: + s=x1[0].split(' ') + frame['V-GENE and allele']=frame['V-GENE and allele'].replace(x,s[1]) + except IndexError as e: + print('V-gene is already been formed') + continue + + x=None + x1=None + s=None + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'P or ORF' + frame = frame[frame['V-GENE and allele'].notnull()] + + head.append('Functional TRBV') + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + filtered = DataFrame() + + + + #------------FILTERING for data quality-------------------- + if con.startswith('y') or con.startswith('Y'): + filtered = frame [frame['AA JUNCTION'].str.contains('X') | + frame['AA JUNCTION'].str.contains('#') | + frame['AA JUNCTION'].str.contains('[*]')] + + + + frame = frame [~frame['AA JUNCTION'].str.contains('X') & + ~frame['AA JUNCTION'].str.contains('#') & + ~frame['AA JUNCTION'].str.contains('[*]') ] + + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'X,#,*' + head.append('Not Containing X,#,*') + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + filtered = DataFrame() + + # Set label of functionality column, taking into account current & past IMGT Summary column label + functionality_label = 'Functionality' + if 'V-DOMAIN Functionality' in frame.columns: + functionality_label = 'V-DOMAIN Functionality' + + if prod.startswith('y') or prod.startswith('Y'): + filtered = frame[~frame[functionality_label].str.startswith('productive')] + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'not productive' + + + frame=frame[frame[functionality_label].str.startswith('productive')] + + head.append('Productive') + head.append('filter out') + seqlen.append(len(frame)) + + seqlen.append(len(filtered)) + + + frame['AA JUNCTION'] = frame['AA JUNCTION'].map(filter_condition_AAjunction) + + if CF.startswith('y') or CF.startswith('Y'): + if cells == 'TCR': + filtered = DataFrame() + filtered = frame[~frame['AA JUNCTION'].str.startswith('C') | + ~frame['AA JUNCTION'].str.endswith('F')] + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'Not C..F' + + frame = frame[frame['AA JUNCTION'].str.startswith('C') & + frame['AA JUNCTION'].str.endswith('F')] + + head.append('CDR3 landmarks C-F') + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + filtered = DataFrame() + elif cells == 'BCR': + filtered = DataFrame() + filtered = frame[~frame['AA JUNCTION'].str.startswith('C') | + ~frame['AA JUNCTION'].str.endswith('W')] + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'Not C..W' + + frame = frame[frame['AA JUNCTION'].str.startswith('C') & + frame['AA JUNCTION'].str.endswith('W')] + + head.append('CDR3 landmarks C-W') + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + filtered = DataFrame() + else: + print('TCR or BCR type') + + + filtered = DataFrame() + + filtered = frame[frame['V-REGION identity %'] < Vper] + + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'identity < {iden}%'.format(iden = Vper) + + frame=frame[frame['V-REGION identity %']>= Vper] + head.append('Identity >= {iden}%'.format(iden = Vper)) + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + + head.append('Total filter out A') + head.append('Total filter in A') + seqlen.append(len(filtall)) + seqlen.append(len(frame)) + + ############################### + if Vgene != 'null': + + filtered = DataFrame() + + filtered = frame[frame['V-GENE and allele'] != Vgene] + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'V-GENE != {} '.format(Vgene) + + + frame = frame[frame['V-GENE and allele'] == Vgene] + + + + head.append('V-GENE = {} '.format(Vgene)) + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + + + + ############################### + if (laa1 != 'null') or (laa2 != 'null'): + if int(laa2) == 0: + low = int(laa1) + high = 100 + elif int(laa1) > int(laa2): + low = int(laa2) + high = int(laa1) + else: + low = int(laa1) + high = int(laa2) + + filtered = DataFrame() + criteria = frame['AA JUNCTION'].apply(lambda row: (len(row)-2) < low) + criteria2 = frame['AA JUNCTION'].apply(lambda row: (len(row)-2) > high) + filtered = frame[criteria | criteria2] + + filtall = filtall.append(filtered) + if int(laa2)==0: + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'CDR3 length not bigger than {}'.format(low) + else: + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'CDR3 length not from {} to {}'.format(low,high) + + criteria3 = frame['AA JUNCTION'].apply(lambda row: (len(row)-2) >= low) + criteria4 = frame['AA JUNCTION'].apply(lambda row: (len(row)-2) <= high) + frame = frame[criteria3 & criteria4] + + if int(laa2)==0: + head.append('CDR3 length bigger than {}'.format(low)) + else: + head.append('CDR3 length from {} to {} '.format(low,high)) + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + + ############################### + if conaa != 'null': + if conaa.islower(): + conaa = conaa.upper() + filtered = DataFrame() + + filtered = frame[~frame['AA JUNCTION'].str.contains(conaa)] + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'CDR3 not containing {}'.format(conaa) + + frame = frame[frame['AA JUNCTION'].str.contains(conaa) ] + + head.append('CDR3 containing {}'.format(conaa)) + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + + + + + #####------------keep the small J gene name-------------------- + #frame['J-GENE and allele'] = frame['J-GENE and allele'].map(filter_condition_Jgene) + cc2=np.array(frame['J-GENE and allele'].unique()) + + for x in cc2: + try: + if notnull(x): + x1=x.split('*') + # print(x) + # print (x1[0]) + trbj=x1[0].split(' ') + frame['J-GENE and allele']=frame['J-GENE and allele'].replace(x,trbj[1]) + except IndexError as e: + print('J-Gene has been formed') + + + + x=None + x1=None + + + #------------keep the small D gene name-------------------- + cc1=np.array(frame['D-GENE and allele'].unique()) + for x in cc1: + try: + if notnull(x): + x1=x.split('*') + trbd=x1[0].split(' ') + frame['D-GENE and allele']=frame['D-GENE and allele'].replace(x,trbd[1]) + else: + frame['D-GENE and allele']=frame['D-GENE and allele'].replace(x,'none') + except IndexError as e: + print('D-gene has been formed') + + + x=None + x1=None + + + if Jgene != 'null': + + filtered = DataFrame() + + filtered = frame[frame['J-GENE and allele'] != Jgene] + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'J-GENE not {} '.format(Jgene) + + + frame = frame[frame['J-GENE and allele'] == Jgene] + + + + head.append('J-GENE = {} '.format(Jgene)) + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + + + + if Dgene != 'null': + + filtered = DataFrame() + + filtered = frame[frame['D-GENE and allele'] != Dgene] + + filtall = filtall.append(filtered) + if len(filtall) > 0: + filtall.loc[filtered.index,'Reason'] = 'D-GENE not {} '.format(Dgene) + + + frame = frame[frame['D-GENE and allele'] == Dgene] + + + + head.append('D-GENE = {} '.format(Dgene)) + head.append('filter out') + seqlen.append(len(frame)) + seqlen.append(len(filtered)) + + + head.append('Total filter out') + head.append('Total filter in') + seqlen.append(len(filtall)) + seqlen.append(len(frame)) + summ_df = DataFrame(index = head) + col = fname + + summ_df[col] = seqlen + frame=frame.rename(columns = {'V-GENE and allele':'V-GENE', + 'J-GENE and allele':'J-GENE','D-GENE and allele':'D-GENE'}) + + + frcol.append('Reason') + + filtall = filtall[frcol] + + #--------------out CSV--------------------------- + frame.index = range(1,len(frame)+1) + if not summ_df.empty: + summ_df['%'] = (100*summ_df[summ_df.columns[0]]/summ_df[summ_df.columns[0]][summ_df.index[0]]).map(('{:.4f}'.format)) + return(frame,filtall,summ_df) + except KeyError as e: + print('This file has no ' + str(e) + ' column') + return(frame,filtall,summ_df) + + +if __name__ == '__main__': + + start=time.time() + + # Parse input arguments + inp = sys.argv[1] + cells = sys.argv[2] + psorf = sys.argv[3] + con = sys.argv[4] + prod = sys.argv[5] + CF = sys.argv[6] + Vper = float(sys.argv[7]) + Vgene = sys.argv[8] + laa1 = sys.argv[9] + conaa = sys.argv[10] + filterin = sys.argv[11] + filterout = sys.argv[12] + Sum_table = sys.argv[13] + Jgene = sys.argv[14] + Dgene = sys.argv[15] + laa2 = sys.argv[16] + fname = sys.argv[17] + + # Execute basic function + fin,fout,summ = filtering(inp,cells,psorf,con,prod,CF,Vper,Vgene,laa1,laa2,conaa,Jgene,Dgene,fname) + + # Save output to CSV files + if not summ.empty: + summ.to_csv(Sum_table, sep = '\t') + if not fin.empty: + fin.to_csv(filterin , sep = '\t') + if not fout.empty: + fout.to_csv(filterout, sep= '\t') + + # Print execution time + stop=time.time() + print('Runtime:' + str(stop-start)) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ngs_filtering.xml Sun Mar 18 07:11:06 2018 -0400 @@ -0,0 +1,139 @@ +<tool id="ngsFilt" name="IMGT Report Filtering" version="0.9" profile="15.10"> + <description>Filter IMGT Summary Data</description> + <command interpreter="python">ngs_filtering.py $input $TCR_or_BCR $Vfun $spChar $prod $delCF $threshold $Vg.Vgid $clen.cdr3len1 $cdp.cdr3part $filtin $filtout $summ $Jg.Jgid $Dg.Dgid $clen.cdr3len2 $process_id + + </command> + <inputs> + <param format="txt" name="input" type="data" label="IMGT Summary Output"/> + <param format="txt" name="process_id" type="text" label="Process ID"/> + <param name="TCR_or_BCR" type="select" label="T-cell or B-cell option"> + <option value="TCR">T-cell</option> + <option value="BCR">B-cell</option> + </param> + <param name="Vfun" type="select" label="Only Take Into Account Fuctional V-GENE? "> + <option value="y">yes</option> + <option value="n">no</option> + </param> + + <param name="spChar" type="select" label="Only Take Into Account CDR3 with no Special Characters (X,*,#)? "> + <option value="y">yes</option> + <option value="n">no</option> + </param> + + <param name="prod" type="select" label="Only Take Into Account Productive Sequences? "> + <option value="y">yes</option> + <option value="n">no</option> + </param> + + <param name="delCF" type="select" label="Only Take Into Account CDR3 with valid start/end landmarks? "> + <option value="y">yes</option> + <option value="n">no</option> + </param> + + <param name="threshold" type="float" size="3" value="0" min="0" max="100" label="V-REGION identity %" /> + + <conditional name="Vg"> + + <param name="Vg_select" type="select" label="Select Specific V gene?"> + <option value="y">Yes</option> + <option value="n" selected="true">No</option> + </param> + + <when value="y"> + <param format="txt" name="Vgid" type="text" label="Type V gene"/> + </when> + + <when value="n"> + <param name="Vgid" type="hidden" value="null" /> + </when> + + </conditional> + + <conditional name="Jg"> + + <param name="Jg_select" type="select" label="Select Specific J gene?"> + <option value="y">Yes</option> + <option value="n" selected="true">No</option> + </param> + + <when value="y"> + <param format="txt" name="Jgid" type="text" label="Type J gene"/> + </when> + + <when value="n"> + <param name="Jgid" type="hidden" value="null" /> + </when> + + </conditional> + + <conditional name="Dg"> + + <param name="Dg_select" type="select" label="Select Specific D gene?"> + <option value="y">Yes</option> + <option value="n" selected="true">No</option> + </param> + + <when value="y"> + <param format="txt" name="Dgid" type="text" label="Type D gene"/> + </when> + + <when value="n"> + <param name="Dgid" type="hidden" value="null" /> + </when> + + </conditional> + + + + <conditional name="clen"> + + <param name="clen_select" type="select" label="Select CDR3 length range?"> + <option value="y">Yes</option> + <option value="n" selected="true">No</option> + </param> + + <when value="y"> + <param name="cdr3len1" type="integer" size="3" value="0" min="0" max="100" label="CDR3 Length Lower Threshold" /> + <param name="cdr3len2" type="integer" size="3" value="0" min="0" max="100" label="CDR3 Length Upper Threshold" /> + </when> + + <when value="n"> + <param name="cdr3len1" type="hidden" value="null" /> + <param name="cdr3len2" type="hidden" value="null" /> + </when> + + </conditional> + + <conditional name="cdp"> + + <param name="cdp_select" type="select" label="Only select CDR3 containing specific amino-acid sequence?"> + <option value="y">Yes</option> + <option value="n" selected="true">No</option> + </param> + + <when value="y"> + <param format="txt" name="cdr3part" type="text" label="Type specific amino-acid sequence"/> + </when> + + <when value="n"> + <param name="cdr3part" type="hidden" value="null" /> + </when> + </conditional> + + </inputs> + + + <outputs> + <data name="filtin" format="tabular" label="${process_id}_filterin"/> + <data name="filtout" format="tabular" label="${process_id}_filterout"/> + <data name="summ" format="tabular" label="${process_id}_Summary"/> + + + </outputs> + + + <help> +This tool filters IMGT Summary Data based on a combination of criteria. + </help> + +</tool>