# HG changeset patch
# User proteore
# Date 1527865847 14400
# Node ID 6f32c1e12572fa1c1aeff77bb3863e0d67992c85
# Parent  c6ba1e6f686955126f67e085adeab232604f1357
planemo upload commit 72b345a7df2c87f07a9df71ecee1f252c9355337

diff -r c6ba1e6f6869 -r 6f32c1e12572 README.rst
--- a/README.rst	Fri Apr 20 09:07:23 2018 -0400
+++ b/README.rst	Fri Jun 01 11:10:47 2018 -0400
@@ -3,7 +3,7 @@
 
 **Authors**
 
-T.P. Lien Nguyen, Florence Combes, Yves Vandenbrouck CEA, INSERM, CNRS, Grenoble-Alpes University, BIG Institute, FR
+T.P. Lien Nguyen, David Christiany, Florence Combes, Yves Vandenbrouck CEA, INSERM, CNRS, Grenoble-Alpes University, BIG Institute, FR
 
 Sandra Dérozier, Olivier Rué, Christophe Caron, Valentin Loux INRA, Paris-Saclay University, MAIAGE Unit, Migale Bioinformatics platform
 
@@ -15,9 +15,7 @@
 
 This tool allows to remove unneeded data (e.g. contaminants, non-significant values) from a proteomics results file (e.g. MaxQuant or Proline output).
 
-**For each row, if there are more than one protein IDs/protein names/gene names, only the first one will be considered in the output**
-
-**Filter the file by keywords**
+**Filter by keyword(s)**
 
 Several options can be used. For each option, you can fill in the field or upload a file which contains the keywords.
 
@@ -45,11 +43,55 @@
 
 **No** option (partial match) for "kinase": not only lines which contain "kinase" but also lines with "alpha-kinase" (and so  on) are removed.
 
-**Filter the file by values**
+-------------------------------------------------------
+
+**Filter by values**
+
+You can filter your data by a column of numerical values.
+Enter the column to be use and select one operator in the list :
+
+- "="
+- "!="
+- "<"
+- "<="
+- ">"
+- ">="
+
+Then enter the value to filter and specify the column to apply that option.
+If a row contains a value that correspond to your settings, it will be filtered.
+
+-------------------------------------------------------
+
+**Filter by a range of values**
+
+You can also set a range of values to filter your file.
+In opposition to value filter, rows with values inside of the defined range are kept.
 
-You can choose to use one or more options (e.g. to filter out peptides of low intensity value, by q-value, etc.).
+Rows with values outside of the defined range will be filtered.
+
+-------------------------------------------------------
+
+**AND/OR operator**
+
+Since you can add as many filters as you want, you can choose how filters apply on your data.
+
+AND or OR operator option works on all filters :
+
+- OR : only one filter to be satisfied to remove one row
+- AND : all filters must be satisfied to remove one row
 
-* For each option, you can choose between "=", ">", ">=", "<" and "<=", then enter the value to filter and specify the column to apply that option.
+-------------------------------------------------------
+
+**Sort the results files**
+
+You can sort the result file if you wish, it can help you to check results. 
+
+In order to do so : enter the column to be used, all columns will be sorted according to the one filled in.
+
+Rows stay intact, just in different order like excel.
+You can also choose ascending or descending order, by default descending order is set.
+
+-------------------------------------------------------
 
 **Output**
 
diff -r c6ba1e6f6869 -r 6f32c1e12572 filter_kw_val.py
--- a/filter_kw_val.py	Fri Apr 20 09:07:23 2018 -0400
+++ b/filter_kw_val.py	Fri Jun 01 11:10:47 2018 -0400
@@ -1,38 +1,46 @@
-import argparse
-import re
-
+import argparse, re, csv
 
 def options():
     """
     Parse options:
         -i, --input     Input filename and boolean value if the file contains header ["filename,true/false"]
-        -m, --match     if the keywords should be filtered in exact
         --kw            Keyword to be filtered, the column number where this filter applies, 
                         boolean value if the keyword should be filtered in exact ["keyword,ncol,true/false"].
                         This option can be repeated: --kw "kw1,c1,true" --kw "kw2,c1,false" --kw "kw3,c2,true"
         --kwfile        A file that contains keywords to be filter, the column where this filter applies and 
                         boolean value if the keyword should be filtered in exact ["filename,ncol,true/false"]
         --value         The value to be filtered, the column number where this filter applies and the 
-                        operation symbol ["value,ncol,=/>/>=/</<="]
+                        operation symbol ["value,ncol,=/>/>=/</<=/!="]
+        --values_range  range of values to be keep, example : --values_range 5 20 c1 true 
+        --operator      The operator used to filter with several keywords/values : AND or OR
         --o --output    The output filename
-        --trash_file    The file contains removed lines
+        --filtered_file    The file contains removed lines
+        -s --sort_col   Used column to sort the file, ",true" for reverse sorting, ",false" otherwise example : c1,false
     """
     parser = argparse.ArgumentParser()
     parser.add_argument("-i", "--input", help="Input file", required=True)
     parser.add_argument("--kw", nargs="+", action="append", help="")
     parser.add_argument("--kw_file", nargs="+", action="append", help="")
     parser.add_argument("--value", nargs="+", action="append", help="")
+    parser.add_argument("--values_range", nargs="+", action="append", help="")
+    parser.add_argument("--operator", default="OR", type=str, choices=['AND','OR'],help='')
     parser.add_argument("-o", "--output", default="output.txt")
-    parser.add_argument("--trash_file", default="trash_MQfilter.txt")
+    parser.add_argument("--filtered_file", default="filtered_output.txt")
+    parser.add_argument("-s","--sort_col", help="")
 
     args = parser.parse_args()
-
     filters(args)
 
-def isnumber(number_format, n):
-    """
-    Check if a variable is a float or an integer
-    """
+def str_to_bool(v):
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+#Check if a variable is a float or an integer
+def is_number(number_format, n):
     float_format = re.compile(r"^[-]?[0-9][0-9]*.?[0-9]+$")
     int_format = re.compile(r"^[-]?[0-9][0-9]*$")
     test = ""
@@ -43,157 +51,216 @@
     if test:
         return True
 
+#Filter the document
 def filters(args):
-    """
-    Filter the document
-    """
-    MQfilename = args.input.split(",")[0]
-    header = args.input.split(",")[1]
-    MQfile = readMQ(MQfilename)
-    results = [MQfile, None]
+    filename = args.input.split(",")[0]
+    header = str_to_bool(args.input.split(",")[1])
+    csv_file = read_file(filename)
+    results_dict = {}
 
     if args.kw:
         keywords = args.kw
         for k in keywords:
-            results = filter_keyword(results[0], header, results[1], k[0], k[1], k[2])
+            results_dict=filter_keyword(csv_file, header, results_dict, k[0], k[1], k[2])
+
     if args.kw_file:
         key_files = args.kw_file
         for kf in key_files:
-            ids = readOption(kf[0])
-            results = filter_keyword(results[0], header, results[1], ids, kf[1], kf[2])
+            keywords = read_option(kf[0])
+            results_dict=filter_keyword(csv_file, header, results_dict, keywords, kf[1], kf[2])
+
     if args.value:
         for v in args.value:
-            if isnumber("float", v[0]):
-                results = filter_value(results[0], header, results[1], v[0], v[1], v[2])
+            if is_number("float", v[0]):
+                results_dict = filter_value(csv_file, header, results_dict, v[0], v[1], v[2])
             else:
                 raise ValueError("Please enter a number in filter by value")
 
-    # Write results to output
-    output = open(args.output, "w")
-    output.write("".join(results[0]))
-    output.close()
+    if args.values_range:
+        for vr in args.values_range:
+            if (is_number("float", vr[0]) or is_number("int", vr[0])) and (is_number("float",vr[1]) or is_number("int",vr[1])):
+                results_dict = filter_values_range(csv_file, header, results_dict, vr[0], vr[1], vr[2], vr[3])
+
+    remaining_lines=[]
+    filtered_lines=[]
 
-    # Write deleted lines to trash_file
-    trash = open(args.trash_file, "w")
-    trash.write("".join(results[1]))
-    trash.close()
+    if header is True : 
+        remaining_lines.append(csv_file[0])
+        filtered_lines.append(csv_file[0])
+
+    for id_line,line in enumerate(csv_file) :
+        if id_line in results_dict :   #skip header and empty lines
+            if args.operator == 'OR' :
+                if any(results_dict[id_line]) :
+                    filtered_lines.append(line)
+                else : 
+                    remaining_lines.append(line)
 
-def readOption(filename):
-    # Read the keywords file to extract the list of keywords
-    f = open(filename, "r")
-    file_content = f.read()
-    filter_list = file_content.split("\n")
-    filters = ""
-    for i in filter_list:
-        filters += i + ";"
-    filters = filters[:-1]
+            elif args.operator == "AND" :
+                if all(results_dict[id_line]) :
+                    filtered_lines.append(line)
+                else : 
+                    remaining_lines.append(line)
+    
+    #sort of results by column
+    if args.sort_col :
+        sort_col=args.sort_col.split(",")[0]
+        sort_col=column_from_txt(sort_col)
+        reverse=str_to_bool(args.sort_col.split(",")[1])
+        remaining_lines= sort_by_column(remaining_lines,sort_col,reverse,header)
+        filtered_lines = sort_by_column(filtered_lines,sort_col,reverse,header)
+    
+    # Write results to output
+    with open(args.output,"w") as output :
+        writer = csv.writer(output,delimiter="\t")
+        writer.writerows(remaining_lines)
+
+    # Write filtered lines to filtered_output
+    with open(args.filtered_file,"w") as filtered_output :
+        writer = csv.writer(filtered_output,delimiter="\t")
+        writer.writerows(filtered_lines)
+
+#function to sort the csv_file by value in a specific column
+def sort_by_column(tab,sort_col,reverse,header):
+    
+    if len(tab) > 1 : #if there's more than just a header or 1 row
+        if header is True :
+            head=tab[0]
+            tab=tab[1:]
+
+        if is_number("int",tab[0][sort_col]) :
+            tab = sorted(tab, key=lambda row: int(row[sort_col]), reverse=reverse)
+        elif is_number("float",tab[0][sort_col]) :
+            tab = sorted(tab, key=lambda row: float(row[sort_col]), reverse=reverse)
+        else :
+            tab = sorted(tab, key=lambda row: row[sort_col], reverse=reverse)
+        
+        if header is True : tab = [head]+tab
+
+    return tab
+
+#Read the keywords file to extract the list of keywords
+def read_option(filename):
+    with open(filename, "r") as f:
+        filter_list=f.read().splitlines()
+    filter_list=[key for key in filter_list if len(key.replace(' ',''))!=0]
+    filters=";".join(filter_list)
+
     return filters
 
-def readMQ(MQfilename):
-    # Read input file
-    mqfile = open(MQfilename, "r")
-    mq = mqfile.readlines()
+# Read input file
+def read_file(filename):
+    with open(filename,"r") as f :
+        reader=csv.reader(f,delimiter="\t")
+        tab=list(reader)
+
     # Remove empty lines (contain only space or new line or "")
-    [mq.remove(blank) for blank in mq if blank.isspace() or blank == ""]
-    return mq
+    #[tab.remove(blank) for blank in tab if blank.isspace() or blank == ""]
+    tab=[line for line in tab if len("".join(line).replace(" ","")) !=0 ]
+    
+    return tab
+
+#seek for keywords in rows of csvfile, return a dictionary of boolean (true if keyword found, false otherwise) 
+def filter_keyword(csv_file, header, results_dict, keywords, ncol, match):
+    match=str_to_bool(match)
+    ncol=column_from_txt(ncol)
+
+    keywords = keywords.upper().split(";")                                            # Split list of filter keyword
+    [keywords.remove(blank) for blank in keywords if blank.isspace() or blank == ""]  # Remove blank keywords
+    keywords = [k.strip() for k in keywords]        # Remove space from 2 heads of keywords
+
+    for id_line,line in enumerate(csv_file):
+        if header is True and id_line == 0 : continue
+        #line = line.replace("\n", "")
+        keyword_inline = line[ncol].replace('"', "").split(";")
+        #line = line + "\n"
+
+        #Perfect match or not
+        if match is True :
+            found_in_line = any(pid.upper() in keywords for pid in keyword_inline)
+        else: 
+            found_in_line = any(ft in pid.upper() for pid in keyword_inline for ft in keywords)     
+
+        #if the keyword is found in line
+        if id_line in results_dict : results_dict[id_line].append(found_in_line)
+        else : results_dict[id_line]=[found_in_line]
+
+    return results_dict
+
+#filter ba determined value in rows of csvfile, return a dictionary of boolean (true if value filtered, false otherwise)
+def filter_value(csv_file, header, results_dict, filter_value, ncol, opt):
+
+    filter_value = float(filter_value)
+    ncol=column_from_txt(ncol)
 
-def filter_keyword(MQfile, header, filtered_lines, ids, ncol, match):
-    mq = MQfile
-    if isnumber("int", ncol.replace("c", "")):
-        id_index = int(ncol.replace("c", "")) - 1 
+    for id_line,line in enumerate(csv_file):
+        if header is True and id_line == 0 : continue
+        value = line[ncol].replace('"', "").strip()
+        if value.replace(".", "", 1).isdigit():
+            to_filter=value_compare(value,filter_value,opt)
+            
+            #adding the result to the dictionary
+            if id_line in results_dict : results_dict[id_line].append(to_filter)
+            else : results_dict[id_line]=[to_filter]
+            
+    return results_dict
+
+#filter ba determined value in rows of csvfile, return a dictionary of boolean (true if value filtered, false otherwise)
+def filter_values_range(csv_file, header, results_dict, bottom_value, top_value, ncol, inclusive):
+    inclusive=str_to_bool(inclusive)
+    bottom_value = float(bottom_value)
+    top_value=float(top_value)
+    ncol=column_from_txt(ncol)
+
+    for id_line, line in enumerate(csv_file):
+        if header is True and id_line == 0 : continue
+        value = line[ncol].replace('"', "").strip()
+        if value.replace(".", "", 1).isdigit():
+            value=float(value)
+            if inclusive is True:
+                in_range = not (bottom_value <= value <= top_value)
+            else : 
+                in_range = not (bottom_value < value < top_value)
+
+            #adding the result to the dictionary
+            if id_line in results_dict : results_dict[id_line].append(in_range)
+            else : results_dict[id_line]=[in_range]
+
+    return results_dict 
+
+def column_from_txt(ncol):
+    if is_number("int", ncol.replace("c", "")): 
+        ncol = int(ncol.replace("c", "")) - 1 
     else:
         raise ValueError("Please specify the column where "
                          "you would like to apply the filter "
                          "with valid format")
-
-    # Split list of filter IDs
-    ids = ids.upper().split(";")
-    # Remove blank IDs
-    [ids.remove(blank) for blank in ids if blank.isspace() or blank == ""]
-    # Remove space from 2 heads of IDs
-    ids = [id.strip() for id in ids]
-
-
-    if header == "true":
-        header = mq[0]
-        content = mq[1:]
-    else:
-        header = ""
-        content = mq[:]
-
-    if not filtered_lines: # In case there is already some filtered lines from other filters
-        filtered_lines = []
-        if header != "":
-            filtered_lines.append(header)
+    return ncol
 
-    for line in content:
-        line = line.replace("\n", "")
-        id_inline = line.split("\t")[id_index].replace('"', "").split(";")
-        # Take only first IDs
-        #one_id_line = line.replace(line.split("\t")[id_index], id_inline[0]) 
-        line = line + "\n"
-
-        if match != "false":
-            # Filter protein IDs
-            if any(pid.upper() in ids for pid in id_inline):
-                filtered_lines.append(line)
-                mq.remove(line)
-            #else:
-            #    mq[mq.index(line)] = one_id_line
-        else:
-            if any(ft in pid.upper() for pid in id_inline for ft in ids):
-                filtered_lines.append(line)
-                mq.remove(line)
-            #else:
-            #    mq[mq.index(line)] = one_id_line
-    return mq, filtered_lines
+#return True if value is in the determined values, false otherwise
+def value_compare(value,filter_value,opt):
+    test_value=False
 
-def filter_value(MQfile, header, filtered_prots, filter_value, ncol, opt):
-    mq = MQfile
-    if ncol and isnumber("int", ncol.replace("c", "")): 
-        index = int(ncol.replace("c", "")) - 1 
-    else:
-        raise ValueError("Please specify the column where "
-                         "you would like to apply the filter "
-                         "with valid format")
-    if header == "true":
-        header = mq[0]
-        content = mq[1:]
-    else:
-        header = ""
-        content = mq[:]
-    if not filtered_prots: # In case there is already some filtered lines from other filters
-        filtered_prots = []
-        if header != "":
-            filtered_prots.append(header)
+    if opt == "<":
+        if float(value) < filter_value:
+            test_value = True
+    elif opt == "<=":
+        if float(value) <= filter_value:
+            test_value = True
+    elif opt == ">":
+        if float(value) > filter_value:
+            test_value = True
+    elif opt == ">=":
+        if float(value) >= filter_value:
+            test_value = True
+    elif opt == "=":
+        if float(value) == filter_value:
+            test_value = True
+    elif opt == "!=": 
+        if float(value) != filter_value:
+            test_value = True
 
-    for line in content:
-        prot = line.replace("\n","")
-        filter_value = float(filter_value)
-        pep = prot.split("\t")[index].replace('"', "")
-        if pep.replace(".", "", 1).isdigit():
-            if opt == "<":
-                if float(pep) >= filter_value:
-                    filtered_prots.append(line)
-                    mq.remove(line)
-            elif opt == "<=":
-                if float(pep) > filter_value:
-                    filtered_prots.append(line)
-                    mq.remove(line)
-            elif opt == ">":
-            #print(prot.number_of_prots, filter_value, int(prot.number_of_prots) > filter_value)
-                if float(pep) <= filter_value:
-                    filtered_prots.append(line)
-                    mq.remove(line)
-            elif opt == ">=":
-                if float(pep) < filter_value:
-                    filtered_prots.append(line)
-                    mq.remove(line)
-            else:
-                if float(pep) != filter_value:
-                    filtered_prots.append(line)
-                    mq.remove(line)
-    return mq, filtered_prots
+    return test_value
 
 if __name__ == "__main__":
     options()
diff -r c6ba1e6f6869 -r 6f32c1e12572 filter_kw_val.xml
--- a/filter_kw_val.xml	Fri Apr 20 09:07:23 2018 -0400
+++ b/filter_kw_val.xml	Fri Jun 01 11:10:47 2018 -0400
@@ -9,10 +9,11 @@
         python $__tool_directory__/filter_kw_val.py
         -i "$input1,$header"
         -o "$output1"
-        --trash_file "$trash_file"
+        --filtered_file "$filtered_file"
+        --operator "$operator"
 
         ## Keywords
-        #for $i, $key in enumerate($keyword)
+        #for $key in $keyword
             #if $key.k.kw != "None"
                 #if $key.k.kw == "text"
                     --kw "$key.k.txt" "$key.ncol" "$key.match"
@@ -22,8 +23,8 @@
             #end if
         #end for
 
-        ## Number of proteins
-        #for $i, $val in enumerate($value)
+        ## value to filter
+        #for $val in $value
             #if $val.v.val != "None"
                 --value
                 #if $val.v.val == "Equal"
@@ -34,16 +35,35 @@
                     $val.v.equal_higher "$val.ncol" ">="
                 #else if $val.v.val == "Lower"
                     $val.v.lower "$val.ncol" "<"
-                #else
+                #else if $val.v.val == "Equal or lower"
                     $val.v.equal_lower "$val.ncol" "<="
+                #else 
+                    $val.v.different "$val.ncol" "!="
                 #end if
             #end if
         #end for
 
+        ##range of values to keep
+        #for $vr in $values_range
+            #if vr 
+                --values_range $vr.bottom_value $vr.top_value $vr.ncol $vr.inclusive
+            #end if
+        #end for
+
+        #if $sort_column != ""
+            --sort_col "$sort_column,$reversed_sort"
+        #end if
+
     ]]></command>
     <inputs>
         <param type="data" name="input1" format="txt,tabular" label="Input file" help="Input file is a tab-delimited file containing proteomics identification and/or quantitative results" />
         <param name="header" type="boolean" checked="true" truevalue="true" falsevalue="false" label="Does your input file contain header?" />
+        <param name="operator" type="select" label="Please select your operator for your filters" help="OR : only one filter must be satisfied to filter a row, AND : all your filters must be satisfied to filter a row" >
+            <option value="OR" selected="True">OR</option>
+            <option value="AND">AND</option>
+        </param>
+        <param name="sort_column" type="text" value="" label="If you want to sort the result files by values from a column, please enter a column number" help="For example : fill in 'c1' if you want to sort your result file by the column 1 values." />
+        <param name="reversed_sort" type="boolean" checked="false" truevalue="true" falsevalue="false" label="Sort in descending order ?"/>        
         <repeat name="keyword" title="Filter by keywords" >
             <param name="ncol" type="text" value="c1" label="Please specify the column number of the input file on which you want to apply the filter" help='For example, fill in "c1" if the keywords you want to filter out are listed in the first column' />
             <param type="boolean" name="match" truevalue="True" label="Would you like to search for exact match?" help='Choosing "Yes" will only filter out exact match (i.e. case sensitive), see below for more details' />
@@ -71,7 +91,6 @@
                 </when>
             </conditional>
         </repeat>
-
         <repeat name="value" title="Filter by value" >
             <param name="ncol" type="text" value="c1" label="Please specify the column number of the input file on which you want to apply the filter" help='For example, fill in "c1" if the keywords you want to filter out are listed in the first column' />
             <conditional name="v" >
@@ -82,6 +101,7 @@
                     <option value="Equal or higher">&gt;=</option>
                     <option value="Lower">&lt;</option>
                     <option value="Equal or lower">&lt;=</option>
+                    <option value="Different">!=</option>
                 </param>
                 <when value="None" >
                 </when>
@@ -100,18 +120,29 @@
                 <when value="Equal or lower" >
                     <param type="float" name="equal_lower" value="" label="Value" />
                 </when>
+                <when value="Different">
+                    <param type="float" name="different" value="" label="Value"/>
+                </when>
             </conditional>
         </repeat>
-
+        <repeat name="values_range" title="Filter by range of values">
+            <param name="ncol" type="text" value="c1" label="Please specify the column number of the input file on which you want to apply the filter" help='For example, fill in "c1" if the keywords you want to filter out are listed in the first column' />
+            <param name="bottom_value" type="float" value="" label="Please enter the bottom value" />
+            <param name="top_value" type="float" value="" label="Please enter the top value" />
+            <param name="inclusive" type="boolean" label="inclusive range ?" checked="false" truevalue="true" falsevalue="false" />
+        </repeat>
     </inputs>
     <outputs>
         <data name="output1" format="tabular" label="${tool.name} on ${input1.name}" />
-        <data name="trash_file" format="tabular" label="${tool.name} on ${input1.name} - Filtered lines" />
+        <data name="filtered_file" format="tabular" label="${tool.name} on ${input1.name} - Filtered lines" />
     </outputs>
     <tests>
         <test>
             <param name="input1" value="Lacombe_et_al_2017_OK.txt" />
             <param name="header" value="true" />
+            <param name="operator" value="OR"/>
+            <param name="sort_column" value="c1"/>
+            <param name="reversed_sort" value="false"/>
             <repeat name="keyword">
                 <param name="ncol" value="c1" />
                 <param name="match" value="True" />
@@ -120,16 +151,21 @@
                     <param name="txt" value="P04264;P35908;P13645;Q5D862;Q5T749;Q8IW75;P81605;P22531;P59666;P78386" />
                 </conditional>
             </repeat>
-            <output name="output1" file="FKW_Lacombe_et_al_2017_OK.txt" />
-            <output name="trash_file" file="Trash_FKW_Lacombe_et_al_2017_OK.txt" />
+            <repeat name="value">
+                <param name="ncol" value="c3"/>
+                <conditional name="v">
+                    <param name="val" value="Higher"/>
+                    <param name="higher" value="20" />
+                </conditional>
+            </repeat>
+            <output name="output1" file="output.csv" />
+            <output name="filtered_file" file="filtered_output.csv" />
         </test>
     </tests>
     <help><![CDATA[
-This tool allows to filter out data according to your specific needs (e.g. contaminants, non-significant values or related to a particular annotation) from a proteomics results file (e.g. MaxQuant or Proline output).
+This tool allows to remove unneeded data (e.g. contaminants, non-significant values) from a proteomics results file (e.g. MaxQuant or Proline output).
 
-**For each row, if there are more than one protein IDs/protein names/gene names, only the first one will be considered in the output**
-
-**Filter the file by keywords**
+**Filter by keyword(s)**
 
 Several options can be used. For each option, you can fill in the field or upload a file which contains the keywords.
 
@@ -143,25 +179,69 @@
 
 ALDOA_RABBIT
 
-**The line that contains these keywords will be filtered from input file and provided in a separate file.**
+**The line that contains these keywords will be eliminated from input file.**
 
 **Keywords search can be applied by performing either exact match or partial one by using the following option**
 
-- If you choose **Yes**, only the fields that contains exactly the same content will be filtered.
+- If you choose **Yes**, only the fields that contains exactly the same content will be removed.
 
-- If you choose **No**, all the fields containing the keyword will be filtered.
+- If you choose **No**, all the fields containing the keyword will be removed.
 
 For example:
 
-**Yes** option (exact match) selected using the keyword "kinase": only lines which contain exactly "kinase" is filtered (and not "Kinase").
+**Yes** option (exact match) selected using the keyword "kinase": only lines which contain exactly "kinase" is removed.
 
 **No** option (partial match) for "kinase": not only lines which contain "kinase" but also lines with "alpha-kinase" (and so  on) are removed.
 
-**Filter the file by values**
+-----
+
+**Filter by values**
+
+You can filter your data by a column of numerical values.
+Enter the column to be use and select one operator in the list :
+
+- "="
+- "!="
+- "<"
+- "<="
+- ">"
+- ">="
+
+Then enter the value to filter and specify the column to apply that option.
+If a row contains a value that correspond to your settings, it will be filtered.
+
+-----
+
+**Filter by a range of values**
+
+You can also set a range of values to filter your file.
+In opposition to value filter, rows with values inside of the defined range are kept.
 
-You can choose to use one or more options (e.g. to filter out peptides of low intensity value, by q-value, etc.).
+Rows with values outside of the defined range will be filtered.
+
+-----
+
+**AND/OR operator**
+
+Since you can add as many filters as you want, you can choose how filters apply on your data.
+
+AND or OR operator option works on all filters :
+
+- OR : only one filter to be satisfied to remove one row
+- AND : all filters must be satisfied to remove one row
 
-* For each option, you can choose between "=", ">", ">=", "<" and "<=", then enter the value to filter and specify the column to apply that option.
+-----
+
+**Sort the results files**
+
+You can sort the result file if you wish, it can help you to check results. 
+
+In order to do so : enter the column to be used, all columns will be sorted according to the one filled in.
+
+Rows stay intact, just in different order like excel.
+You can also choose ascending or descending order, by default descending order is set.
+
+-----
 
 **Output**
 
@@ -169,7 +249,7 @@
 
 * A text file containing the resulting filtered input file.
 
-* A text file containing the rows that have been filtered from the input file.
+* A text file containing the rows removed from the input file.
 
 -----
 
@@ -177,7 +257,7 @@
 
 **Authors**
 
-T.P. Lien Nguyen, Florence Combes, Yves Vandenbrouck - CEA, INSERM, CNRS, Grenoble-Alpes University, BIG Institute, FR
+T.P. Lien Nguyen, David Christiany, Florence Combes, Yves Vandenbrouck - CEA, INSERM, CNRS, Grenoble-Alpes University, BIG Institute, FR
 
 Sandra Dérozier, Olivier Rué, Christophe Caron, Valentin Loux - INRA, Paris-Saclay University, MAIAGE Unit, Migale Bioinformatics platform, FR
 
diff -r c6ba1e6f6869 -r 6f32c1e12572 test-data/FKW_Lacombe_et_al_2017_OK.txt
--- a/test-data/FKW_Lacombe_et_al_2017_OK.txt	Fri Apr 20 09:07:23 2018 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,154 +0,0 @@
-Protein accession number (UniProt)	Protein name	Number of peptides (razor + unique)
-
-P15924	Desmoplakin	69
-P02538	Keratin, type II cytoskeletal 6A	53
-P02768	Serum albumin	44
-P08779	Keratin, type I cytoskeletal 16	29
-Q02413	Desmoglein-1	24
-P07355	"Annexin A2;Putative annexin A2-like protein"	22
-P14923	Junction plakoglobin	22
-P02788	Lactotransferrin	21
-Q9HC84	Mucin-5B	21
-P29508	Serpin B3	20
-P63261	Actin, cytoplasmic 2	19
-Q8N1N4	Keratin, type II cytoskeletal 78	18
-Q04695	Keratin, type I cytoskeletal 17	18
-P01876	Ig alpha-1 chain C region	16
-Q01469	Fatty acid-binding protein 5, epidermal	15
-P31944	Caspase-14	15
-P01833	Polymeric immunoglobulin receptor	15
-P06733	Alpha-enolase	15
-P25311	Zinc-alpha-2-glycoprotein	15
-Q15149	Plectin	15
-P19013	Keratin, type II cytoskeletal 4	13
-Q6KB66	Keratin, type II cytoskeletal 80	13
-Q08188	Protein-glutamine gamma-glutamyltransferase E	12
-P13646	Keratin, type I cytoskeletal 13	11
-Q86YZ3	Hornerin	11
-P04259	Keratin, type II cytoskeletal 6B	10
-P02545	"Prelamin-A/C;Lamin-A/C"	10
-P04083	Annexin A1	10
-P11021	78 kDa glucose-regulated protein	10
-P02787	Serotransferrin	9
-P04040	Catalase	9
-P31151	Protein S100-A7	9
-P31947	14-3-3 protein sigma	9
-Q96P63	Serpin B12	9
-P14618	Pyruvate kinase PKM	9
-P60174	Triosephosphate isomerase	9
-Q06830	Peroxiredoxin-1	9
-P01040	Cystatin-A	8
-P05089	Arginase-1	8
-P01834	Ig kappa chain C region	8
-P04406	Glyceraldehyde-3-phosphate dehydrogenase	8
-P0DMV9	Heat shock 70 kDa protein 1B	8
-P13639	Elongation factor 2	8
-P35579	Myosin-9	8
-P68371	Tubulin beta-4B chain	8
-Q8WVV4	Protein POF1B	8
-O75635	Serpin B7	7
-P01857	Ig gamma-1 chain C region	7
-P61626	Lysozyme C	7
-P68363	Tubulin alpha-1B chain	7
-P01009	"Alpha-1-antitrypsin;Short peptide from AAT"	6
-P07900	Heat shock protein HSP 90-alpha	6
-Q9NZH8	Interleukin-36 gamma	6
-O43707	"Alpha-actinin-4;Alpha-actinin-1"	6
-O75223	Gamma-glutamylcyclotransferase	6
-P00338	L-lactate dehydrogenase A chain	6
-P07339	Cathepsin D	6
-P62987	Ubiquitin-60S ribosomal protein L40	6
-P10599	Thioredoxin	6
-Q9UGM3	Deleted in malignant brain tumors 1 protein	6
-Q9UI42	Carboxypeptidase A4	6
-P47929	Galectin-7	5
-Q13867	Bleomycin hydrolase	5
-Q6P4A8	Phospholipase B-like 1	5
-O75369	Filamin-B	5
-P00441	Superoxide dismutase [Cu-Zn]	5
-P04792	Heat shock protein beta-1	5
-P11142	Heat shock cognate 71 kDa protein	5
-P58107	Epiplakin	5
-P60842	Eukaryotic initiation factor 4A-I	5
-P62937	Peptidyl-prolyl cis-trans isomerase A	5
-P63104	14-3-3 protein zeta/delta	5
-Q92820	Gamma-glutamyl hydrolase	5
-O75342	Arachidonate 12-lipoxygenase, 12R-type	4
-P09211	Glutathione S-transferase P	4
-P31025	Lipocalin-1	4
-P48594	Serpin B4	4
-Q14574	Desmocollin-3	4
-Q5T750	Skin-specific protein 32	4
-Q6UWP8	Suprabasin	4
-O60911	Cathepsin L2	4
-P00558	Phosphoglycerate kinase 1	4
-P04075	Fructose-bisphosphate aldolase A	4
-P07384	Calpain-1 catalytic subunit	4
-P0CG05	Ig lambda-2 chain C regions	4
-P18206	Vinculin	4
-P62258	14-3-3 protein epsilon	4
-P68871	Hemoglobin subunit beta	4
-Q9C075	Keratin, type I cytoskeletal 23	4
-A8K2U0	Alpha-2-macroglobulin-like protein 1	3
-P00738	Haptoglobin	3
-P01011	Alpha-1-antichymotrypsin	3
-P02763	Alpha-1-acid glycoprotein 1	3
-P18510	Interleukin-1 receptor antagonist protein	3
-P22528	Cornifin-B	3
-P30740	Leukocyte elastase inhibitor	3
-P80188	Neutrophil gelatinase-associated lipocalin	3
-Q15828	Cystatin-M	3
-Q9HCY8	Protein S100-A14	3
-P01623	Ig kappa chain V-III region	3
-P01877	Ig alpha-2 chain C region	3
-P06396	Gelsolin	3
-P14735	Insulin-degrading enzyme	3
-P20933	N(4)-(beta-N-acetylglucosaminyl)-L-asparaginase	3
-P25788	Proteasome subunit alpha type-3	3
-P26641	Elongation factor 1-gamma	3
-P36952	Serpin B5	3
-P40926	Malate dehydrogenase, mitochondrial	3
-Q9Y6R7	IgGFc-binding protein	3
-O95274	Ly6/PLAUR domain-containing protein 3	2
-P00491	Purine nucleoside phosphorylase	2
-P04080	Cystatin-B	2
-P09972	Fructose-bisphosphate aldolase C	2
-P19012	Keratin, type I cytoskeletal 15	2
-P20930	Filaggrin	2
-Q96FX8	p53 apoptosis effector related to PMP-22	2
-Q9UIV8	Serpin B13	2
-P01625	Ig kappa chain V-IV region Len	2
-P01765	Ig heavy chain V-III region TIL	2
-P01766	Ig heavy chain V-III region BRO	2
-P01860	Ig gamma-3 chain C region	2
-P01871	Ig mu chain C region	2
-P05090	Apolipoprotein D	2
-P06870	Kallikrein-1	2
-P07858	Cathepsin B	2
-P08865	40S ribosomal protein SA	2
-P11279	Lysosome-associated membrane glycoprotein 1	2
-P13473	Lysosome-associated membrane glycoprotein 2	2
-P19971	Thymidine phosphorylase	2
-P23284	Peptidyl-prolyl cis-trans isomerase B	2
-P23396	40S ribosomal protein S3	2
-P25705	ATP synthase subunit alpha, mitochondrial	2
-P27482	Calmodulin-like protein 3	2
-P31949	Protein S100-A11	2
-P40121	Macrophage-capping protein	2
-P42357	Histidine ammonia-lyase	2
-P47756	F-actin-capping protein subunit beta	2
-P48637	Glutathione synthetase	2
-P49720	Proteasome subunit beta type-3	2
-P50395	Rab GDP dissociation inhibitor beta	2
-P59998	Actin-related protein 2/3 complex subunit 4	2
-P61160	Actin-related protein 2	2
-P61916	Epididymal secretory protein E1	2
-P04745	Alpha-amylase 1	23
-Q9NZT1	Calmodulin-like protein 5	8
-P12273	Prolactin-inducible protein	6
-Q96DA0	Zymogen granule protein 16 homolog B	5
-P01036	Cystatin-S	5
-Q8TAX7	Mucin-7	2
-P01037	Cystatin-SN	2
-P09228	Cystatin-SA	2
-		
\ No newline at end of file
diff -r c6ba1e6f6869 -r 6f32c1e12572 test-data/Lacombe_et_al_2017_OK.txt
diff -r c6ba1e6f6869 -r 6f32c1e12572 test-data/Trash_FKW_Lacombe_et_al_2017_OK.txt
--- a/test-data/Trash_FKW_Lacombe_et_al_2017_OK.txt	Fri Apr 20 09:07:23 2018 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
-Protein accession number (UniProt)	Protein name	Number of peptides (razor + unique)
-
-P04264	Keratin, type II cytoskeletal 1	61
-P35908	Keratin, type II cytoskeletal 2 epidermal	40
-P13645	Keratin, type I cytoskeletal 10	40
-Q5D862	Filaggrin-2	14
-Q5T749	Keratinocyte proline-rich protein	13
-Q8IW75	Serpin A12	3
-P81605	Dermcidin	3
-P22531	Small proline-rich protein 2E	3
-P59666	Neutrophil defensin 3	2
-P78386	Keratin, type II cuticular Hb5	2
\ No newline at end of file
diff -r c6ba1e6f6869 -r 6f32c1e12572 test-data/filtered_output.csv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/filtered_output.csv	Fri Jun 01 11:10:47 2018 -0400
@@ -0,0 +1,21 @@
+Protein accession number (UniProt)	Protein name	Number of peptides (razor + unique)
+P02538	Keratin, type II cytoskeletal 6A	53
+P02768	Serum albumin	44
+P02788	Lactotransferrin	21
+P04264	Keratin, type II cytoskeletal 1	61
+P04745	Alpha-amylase 1	23
+P07355	Annexin A2;Putative annexin A2-like protein	22
+P08779	Keratin, type I cytoskeletal 16	29
+P13645	Keratin, type I cytoskeletal 10	40
+P14923	Junction plakoglobin	22
+P15924	Desmoplakin	69
+P22531	Small proline-rich protein 2E	3
+P35908	Keratin, type II cytoskeletal 2 epidermal	40
+P59666	Neutrophil defensin 3	2
+P78386	Keratin, type II cuticular Hb5	2
+P81605	Dermcidin	3
+Q02413	Desmoglein-1	24
+Q5D862	Filaggrin-2	14
+Q5T749	Keratinocyte proline-rich protein	13
+Q8IW75	Serpin A12	3
+Q9HC84	Mucin-5B	21
diff -r c6ba1e6f6869 -r 6f32c1e12572 test-data/output.csv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/output.csv	Fri Jun 01 11:10:47 2018 -0400
@@ -0,0 +1,142 @@
+Protein accession number (UniProt)	Protein name	Number of peptides (razor + unique)
+A8K2U0	Alpha-2-macroglobulin-like protein 1	3
+O43707	Alpha-actinin-4;Alpha-actinin-1	6
+O60911	Cathepsin L2	4
+O75223	Gamma-glutamylcyclotransferase	6
+O75342	Arachidonate 12-lipoxygenase, 12R-type	4
+O75369	Filamin-B	5
+O75635	Serpin B7	7
+O95274	Ly6/PLAUR domain-containing protein 3	2
+P00338	L-lactate dehydrogenase A chain	6
+P00441	Superoxide dismutase [Cu-Zn]	5
+P00491	Purine nucleoside phosphorylase	2
+P00558	Phosphoglycerate kinase 1	4
+P00738	Haptoglobin	3
+P01009	Alpha-1-antitrypsin;Short peptide from AAT	6
+P01011	Alpha-1-antichymotrypsin	3
+P01036	Cystatin-S	5
+P01037	Cystatin-SN	2
+P01040	Cystatin-A	8
+P01623	Ig kappa chain V-III region	3
+P01625	Ig kappa chain V-IV region Len	2
+P01765	Ig heavy chain V-III region TIL	2
+P01766	Ig heavy chain V-III region BRO	2
+P01833	Polymeric immunoglobulin receptor	15
+P01834	Ig kappa chain C region	8
+P01857	Ig gamma-1 chain C region	7
+P01860	Ig gamma-3 chain C region	2
+P01871	Ig mu chain C region	2
+P01876	Ig alpha-1 chain C region	16
+P01877	Ig alpha-2 chain C region	3
+P02545	Prelamin-A/C;Lamin-A/C	10
+P02763	Alpha-1-acid glycoprotein 1	3
+P02787	Serotransferrin	9
+P04040	Catalase	9
+P04075	Fructose-bisphosphate aldolase A	4
+P04080	Cystatin-B	2
+P04083	Annexin A1	10
+P04259	Keratin, type II cytoskeletal 6B	10
+P04406	Glyceraldehyde-3-phosphate dehydrogenase	8
+P04792	Heat shock protein beta-1	5
+P05089	Arginase-1	8
+P05090	Apolipoprotein D	2
+P06396	Gelsolin	3
+P06733	Alpha-enolase	15
+P06870	Kallikrein-1	2
+P07339	Cathepsin D	6
+P07384	Calpain-1 catalytic subunit	4
+P07858	Cathepsin B	2
+P07900	Heat shock protein HSP 90-alpha	6
+P08865	40S ribosomal protein SA	2
+P09211	Glutathione S-transferase P	4
+P09228	Cystatin-SA	2
+P09972	Fructose-bisphosphate aldolase C	2
+P0CG05	Ig lambda-2 chain C regions	4
+P0DMV9	Heat shock 70 kDa protein 1B	8
+P10599	Thioredoxin	6
+P11021	78 kDa glucose-regulated protein	10
+P11142	Heat shock cognate 71 kDa protein	5
+P11279	Lysosome-associated membrane glycoprotein 1	2
+P12273	Prolactin-inducible protein	6
+P13473	Lysosome-associated membrane glycoprotein 2	2
+P13639	Elongation factor 2	8
+P13646	Keratin, type I cytoskeletal 13	11
+P14618	Pyruvate kinase PKM	9
+P14735	Insulin-degrading enzyme	3
+P18206	Vinculin	4
+P18510	Interleukin-1 receptor antagonist protein	3
+P19012	Keratin, type I cytoskeletal 15	2
+P19013	Keratin, type II cytoskeletal 4	13
+P19971	Thymidine phosphorylase	2
+P20930	Filaggrin	2
+P20933	N(4)-(beta-N-acetylglucosaminyl)-L-asparaginase	3
+P22528	Cornifin-B	3
+P23284	Peptidyl-prolyl cis-trans isomerase B	2
+P23396	40S ribosomal protein S3	2
+P25311	Zinc-alpha-2-glycoprotein	15
+P25705	ATP synthase subunit alpha, mitochondrial	2
+P25788	Proteasome subunit alpha type-3	3
+P26641	Elongation factor 1-gamma	3
+P27482	Calmodulin-like protein 3	2
+P29508	Serpin B3	20
+P30740	Leukocyte elastase inhibitor	3
+P31025	Lipocalin-1	4
+P31151	Protein S100-A7	9
+P31944	Caspase-14	15
+P31947	14-3-3 protein sigma	9
+P31949	Protein S100-A11	2
+P35579	Myosin-9	8
+P36952	Serpin B5	3
+P40121	Macrophage-capping protein	2
+P40926	Malate dehydrogenase, mitochondrial	3
+P42357	Histidine ammonia-lyase	2
+P47756	F-actin-capping protein subunit beta	2
+P47929	Galectin-7	5
+P48594	Serpin B4	4
+P48637	Glutathione synthetase	2
+P49720	Proteasome subunit beta type-3	2
+P50395	Rab GDP dissociation inhibitor beta	2
+P58107	Epiplakin	5
+P59998	Actin-related protein 2/3 complex subunit 4	2
+P60174	Triosephosphate isomerase	9
+P60842	Eukaryotic initiation factor 4A-I	5
+P61160	Actin-related protein 2	2
+P61626	Lysozyme C	7
+P61916	Epididymal secretory protein E1	2
+P62258	14-3-3 protein epsilon	4
+P62937	Peptidyl-prolyl cis-trans isomerase A	5
+P62987	Ubiquitin-60S ribosomal protein L40	6
+P63104	14-3-3 protein zeta/delta	5
+P63261	Actin, cytoplasmic 2	19
+P68363	Tubulin alpha-1B chain	7
+P68371	Tubulin beta-4B chain	8
+P68871	Hemoglobin subunit beta	4
+P80188	Neutrophil gelatinase-associated lipocalin	3
+Q01469	Fatty acid-binding protein 5, epidermal	15
+Q04695	Keratin, type I cytoskeletal 17	18
+Q06830	Peroxiredoxin-1	9
+Q08188	Protein-glutamine gamma-glutamyltransferase E	12
+Q13867	Bleomycin hydrolase	5
+Q14574	Desmocollin-3	4
+Q15149	Plectin	15
+Q15828	Cystatin-M	3
+Q5T750	Skin-specific protein 32	4
+Q6KB66	Keratin, type II cytoskeletal 80	13
+Q6P4A8	Phospholipase B-like 1	5
+Q6UWP8	Suprabasin	4
+Q86YZ3	Hornerin	11
+Q8N1N4	Keratin, type II cytoskeletal 78	18
+Q8TAX7	Mucin-7	2
+Q8WVV4	Protein POF1B	8
+Q92820	Gamma-glutamyl hydrolase	5
+Q96DA0	Zymogen granule protein 16 homolog B	5
+Q96FX8	p53 apoptosis effector related to PMP-22	2
+Q96P63	Serpin B12	9
+Q9C075	Keratin, type I cytoskeletal 23	4
+Q9HCY8	Protein S100-A14	3
+Q9NZH8	Interleukin-36 gamma	6
+Q9NZT1	Calmodulin-like protein 5	8
+Q9UGM3	Deleted in malignant brain tumors 1 protein	6
+Q9UI42	Carboxypeptidase A4	6
+Q9UIV8	Serpin B13	2
+Q9Y6R7	IgGFc-binding protein	3