diff --git a/tools/proteore_filter_keywords_values/.shed.yml b/tools/proteore_filter_keywords_values/.shed.yml
new file mode 100644
index 000000000..ddaad82e5
--- /dev/null
+++ b/tools/proteore_filter_keywords_values/.shed.yml
@@ -0,0 +1,5 @@
+categories: [Proteomics]
+description: ProteoRE - Filter a file by keywords or values
+long_description: Filter a file by keywords or values
+name: proteore_filter_keywords_values
+owner: proteore
diff --git a/tools/proteore_filter_keywords_values/README.rst b/tools/proteore_filter_keywords_values/README.rst
new file mode 100644
index 000000000..bcf21be0f
--- /dev/null
+++ b/tools/proteore_filter_keywords_values/README.rst
@@ -0,0 +1,102 @@
+Wrapper for Filter by keywords or numerical values Tool
+=======================================================
+
+**Authors**
+
+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
+
+This work has been partially funded through the French National Agency for Research (ANR) IFB project.
+
+Contact support@proteore.org for any questions or concerns about the Galaxy implementation of this tool.
+
+-------------------------------------------------------
+
+This tool allows to remove unneeded data (e.g. contaminants, non-significant values) from a proteomics results file (e.g. MaxQuant or Proline output).
+
+**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.
+
+- If you choose to fill in the field, the keywords should be separated by ";", for example: A8K2U0;Q5TA79;O43175
+
+- If you choose to upload a file in a text format in which each line is a keyword, for example:
+
+REV
+
+TRYP_PIG
+
+ALDOA_RABBIT
+
+**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 removed.
+
+- 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 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 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.
+
+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
+
+-------------------------------------------------------
+
+**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**
+
+The tool will produce 2 output files.
+
+* A text file containing the resulting filtered input file.
+
+* A text file containing the rows removed from the input file.
\ No newline at end of file
diff --git a/tools/proteore_filter_keywords_values/filter_kw_val.py b/tools/proteore_filter_keywords_values/filter_kw_val.py
new file mode 100644
index 000000000..950785ca7
--- /dev/null
+++ b/tools/proteore_filter_keywords_values/filter_kw_val.py
@@ -0,0 +1,458 @@
+import argparse
+import csv
+import re
+import sys
+
+
+def options():
+    """
+    Parse options:
+        -i, --input     Input filename and boolean value if the file contains header ["filename,true/false"]  # noqa 501
+        --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,=/>/>=/</<=/!="]
+        --values_range  range of values to be keep, example : --values_range 5 20 c1 true
+        --operation     'keep' or 'discard' lines concerned by filter(s)
+        --operator      The operator used to filter with several keywords/values : AND or OR
+        --o --output    The output filename
+        --discarded_lines    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("--operation", default="keep", type=str, choices=['keep', 'discard'], help='')  # noqa 501
+    parser.add_argument("--operator", default="OR", type=str, choices=['AND', 'OR'], help='')  # noqa 501
+    parser.add_argument("-o", "--output", default="output.txt")
+    parser.add_argument("--discarded_lines", default="filtered_output.txt")
+    parser.add_argument("-s", "--sort_col", help="")
+
+    args = parser.parse_args()
+
+    filters(args)
+
+
+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.')
+
+
+def proper_ncol(ncol, file):
+    if ncol not in range(len(file[0])):
+        print("Column " + str(ncol + 1) + " not found in input file")
+        # traceback.print_exc(file=sys.stdout)
+        sys.exit(1)
+
+# 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]+$")
+    int_format = re.compile(r"^[-]?[0-9][0-9]*$")
+    scientific_number = re.compile(r"^[-+]?[\d]+\.?[\d]*[Ee](?:[-+]?[\d]+)?$")
+    test = ""
+    if number_format == "int":
+        test = re.match(int_format, n)
+    elif number_format == "float":
+        test = re.match(float_format, n)
+        if test is None:
+            test = re.match(scientific_number, n)
+
+    if test:
+        return True
+    else:
+        return False
+
+# Filter the document
+
+
+def filters(args):
+    filename = args.input.split(",")[0]
+    header = str_to_bool(args.input.split(",")[1])
+    csv_file = blank_to_NA(read_file(filename))
+    results_dict = {}
+    operator_dict = {"Equal": "=", "Higher": ">", "Equal-or-higher": ">=", "Lower": "<", "Equal-or-lower": "<=", "Different": "!="}  # noqa 501
+
+    if args.kw:
+        keywords = args.kw
+        for k in keywords:
+            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:
+            header = str_to_bool(kf[1])
+            ncol = column_from_txt(kf[2], csv_file)
+            keywords = read_keywords_file(kf[0], header, ncol)
+            results_dict = filter_keyword(csv_file, header, results_dict,
+                                          keywords, kf[3], kf[4])
+
+    if args.value:
+        for v in args.value:
+            v[0] = v[0].replace(",", ".")
+            v[2] = operator_dict[v[2]]
+            if is_number("float", v[0]):
+                csv_file = comma_number_to_float(csv_file,
+                                                 column_from_txt(
+                                                     v[1], csv_file), header)
+                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")
+
+    if args.values_range:
+        for vr in args.values_range:
+            vr[:2] = [value.replace(",", ".") for value in vr[:2]]
+            csv_file = comma_number_to_float(csv_file,
+                                             column_from_txt(
+                                                 vr[2], csv_file), header)
+            if (is_number("float", vr[0]) or is_number("int", vr[0])) and (is_number("float", vr[1]) or is_number("int", vr[1])):  # noqa 501
+                results_dict = filter_values_range(csv_file,
+                                                   header, results_dict,
+                                                   vr[0], vr[1], vr[2], vr[3])
+
+    remaining_lines = []
+    filtered_lines = []
+
+    if header is True:
+        remaining_lines.append(csv_file[0])
+        filtered_lines.append(csv_file[0])
+
+    if results_dict == {}:   # no filter used
+        remaining_lines.extend(csv_file[1:])
+    else:
+        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)
+
+                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, csv_file)
+        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)
+
+    # swap lists of lines (files) if 'keep' option selected
+    if args.operation == "keep":
+        swap = remaining_lines, filtered_lines
+        remaining_lines = swap[1]
+        filtered_lines = swap[0]
+
+    # 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.discarded_lines, "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:
+            head = tab[0]
+            tab = tab[1:]
+
+        # list of empty cells in the column to sort
+        unsortable_lines = [i for i, line in enumerate(tab) if (line[sort_col]=='' or line[sort_col] == 'NA')]  # noqa 501
+        unsorted_tab = [tab[i] for i in unsortable_lines]
+        tab = [line for i, line in enumerate(tab) if i not in unsortable_lines]
+
+        if only_number(tab, sort_col) and any_float(tab, sort_col):
+            tab = comma_number_to_float(tab, sort_col, False)
+            tab = sorted(tab, key=lambda row: float(row[sort_col]),
+                         reverse=reverse)
+        elif only_number(tab, sort_col):
+            tab = sorted(tab, key=lambda row: int(row[sort_col]),
+                         reverse=reverse)
+        else:
+            tab = sorted(tab, key=lambda row: row[sort_col], reverse=reverse)
+
+        tab.extend(unsorted_tab)
+        if header is True:
+            tab = [head] + tab
+
+    return tab
+
+
+# replace all blank cells to NA
+
+
+def blank_to_NA(csv_file):
+
+    tmp = []
+    for line in csv_file:
+        line = ["NA" if cell=="" or cell==" " or cell=="NaN" else cell for cell in line ]  # noqa 501
+        tmp.append(line)
+
+    return tmp
+
+# turn into float a column
+
+
+def comma_number_to_float(csv_file, ncol, header):
+    if header:
+        tmp = [csv_file[0]]
+        csv_file = csv_file[1:]
+    else:
+        tmp = []
+
+    for line in csv_file:
+        line[ncol] = line[ncol].replace(",", ".")
+        tmp.append(line)
+
+    return (tmp)
+
+# return True is there is at least one float in the column
+
+
+def any_float(tab, col):
+
+    for line in tab:
+        if is_number("float", line[col].replace(",", ".")):
+            return True
+
+    return False
+
+
+def only_number(tab, col):
+    for line in tab:
+        if not (is_number("float", line[col].replace(",", ".")) or is_number("int", line[col].replace(",", "."))):  # noqa 501
+            return False
+    return True
+
+# Read the keywords file to extract the list of keywords
+
+
+def read_keywords_file(filename, header, ncol):
+    with open(filename, "r") as csv_file:
+        lines = csv.reader(csv_file, delimiter='\t')
+        lines = blank_to_NA(lines)
+        if (len(lines[0])) > 1:
+            keywords = [line[ncol] for line in lines]
+        else:
+            keywords = ["".join(key) for key in lines]
+    if header:
+        keywords = keywords[1:]
+    keywords = list(set(keywords))
+
+    return keywords
+
+# 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 "")
+    # [tab.remove(blank) for blank in tab if blank.isspace() or blank == ""]
+    tab = [line for line in tab if len("".join(line).replace(" ", "")) != 0]  # noqa 501
+
+    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, csv_file)
+    if type(keywords) != list:
+        keywords = keywords.upper().split()  # Split list of filter keyword
+
+    for id_line, line in enumerate(csv_file):
+        if header is True and id_line == 0:
+            continue
+        keyword_inline = line[ncol].replace('"', "").split(";")
+
+        # Perfect match or not
+        if match is True:
+            found_in_line = any(pid.upper() in keywords for pid in keyword_inline)  # noqa 501
+        else:
+            found_in_line = any(ft in pid.upper() for pid in keyword_inline for ft in keywords)  # noqa 501
+
+        # 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, csv_file)
+    nb_string = 0
+
+    for id_line, line in enumerate(csv_file):
+        if header is True and id_line == 0:
+            continue
+        value = line[ncol].replace('"', "").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]
+
+        # impossible to treat (ex : "" instead of a number),
+        # we keep the line by default
+        else:
+            nb_string += 1
+            if id_line in results_dict:
+                results_dict[id_line].append(False)
+            else:
+                results_dict[id_line] = [False]
+
+    # number of lines in the csv file
+    if header:
+        nb_lines = len(csv_file) - 1
+    else:
+        nb_lines = len(csv_file)
+
+    # if there's no numeric value in the column
+    if nb_string == nb_lines:
+        print('No numeric values found in the column ' + str(ncol + 1))
+        print('The filter "'+str(opt)+' '+str(filter_value)+'" can not be applied on the column '+str(ncol+1))  # noqa 501
+
+    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):  # noqa 501
+    inclusive = str_to_bool(inclusive)
+    bottom_value = float(bottom_value)
+    top_value = float(top_value)
+    ncol = column_from_txt(ncol, csv_file)
+    nb_string = 0
+
+    for id_line, line in enumerate(csv_file):
+        if header is True and id_line == 0:
+            continue
+        value = line[ncol].replace('"', "").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]
+
+        # impossible to treat (ex : "" instead of a number),
+        # we keep the line by default
+        else:
+            nb_string += 1
+            if id_line in results_dict:
+                results_dict[id_line].append(False)
+            else:
+                results_dict[id_line] = [False]
+
+    # number of lines in the csv file
+    if header:
+        nb_lines = len(csv_file) - 1
+    else:
+        nb_lines = len(csv_file)
+
+    # if there's no numeric value in the column
+    if nb_string == nb_lines:
+        print('No numeric values found in the column ' + str(ncol + 1))
+        if inclusive:
+            print ('The filter "'+str(bottom_value)+' <= x <= '+str(top_value)+'" can not be applied on the column '+str(ncol+1))  # noqa 501
+        else:
+            print ('The filter "'+str(bottom_value)+' < x < '+str(top_value)+'" can not be applied on the column '+str(ncol+1))  # noqa 501
+
+    return results_dict
+
+
+def column_from_txt(ncol, file):
+    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")
+
+    proper_ncol(ncol, file)
+
+    return ncol
+
+# return True if value is in the determined values, false otherwise
+
+
+def value_compare(value, filter_value, opt):
+    test_value = False
+
+    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
+
+    return test_value
+
+
+if __name__ == "__main__":
+    options()
diff --git a/tools/proteore_filter_keywords_values/filter_kw_val.xml b/tools/proteore_filter_keywords_values/filter_kw_val.xml
new file mode 100644
index 000000000..322a9ebf7
--- /dev/null
+++ b/tools/proteore_filter_keywords_values/filter_kw_val.xml
@@ -0,0 +1,280 @@
+<tool id="proteore mqoutputfilter" name="filter by keywords and/or numerical value" version="2021.06.14" profile="20.01">
+    <description></description>
+    <requirements>
+    </requirements>
+    <command detect_errors="aggressive"><![CDATA[
+        python '$__tool_directory__/filter_kw_val.py'
+        -i '$input1,$header'
+        -o '$kept_lines'
+        --discarded_lines '$discarded_lines'
+        --operation '$operation'
+        --operator '$operator'
+
+        ## Keywords
+        #for $key in $keyword
+            #if $key.k.kw != 'None'
+                #if $key.k.kw == 'text'
+                    --kw '$key.k.txt' '$key.ncol' '$key.match'
+                #else if $key.k.kw == 'file'
+                    --kw_file '$key.k.file' '$key.k.header' '$key.k.ncol' '$key.ncol' '$key.match' 
+                #end if
+            #end if
+        #end for
+
+        ## value to filter
+        #for $val in $value
+            #if $val.value != 'None'
+                --value $val.value $val.ncol $val.operator
+            #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 $sort.sort_bool == 'true'
+            --sort_col '$sort.sort_column,$sort.reversed_sort'
+        #end if
+
+    ]]></command>
+    <inputs>
+        <param type="data" name="input1" format="tabular" label="Input file" />
+        <param name="header" type="boolean" checked="true" truevalue="true" falsevalue="false" label="Does file contain header?" />
+        <param name="operation" type="select" label="Operation" help="keep or discard word(s) or value(s) that match filters ?">
+            <option value="keep">Keep</option>
+            <option value="discard">Discard</option>
+        </param>
+        <param name="operator" type="select" label="Select an operator to combine your filters (if more than one)" 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>
+        
+        <repeat name="keyword" title="Filter by keywords" >
+            <param name="data_column" type="text" value="c1" label="Column number on which to apply the filter" help='For example, fill in "c1" if you want to seek keywords in the first column (and keep or discard them)'>
+                <validator type="regex" message="Please enter a column number, for example: 'c1' for the first column">[c]{0,1}[0-9]+</validator>
+            </param>
+            <param type="boolean" name="match" truevalue="True" label="Search for exact match?" help='Choosing "Yes" will only filter out exact match, see help section' />
+            <conditional name="k" >
+                <param name="kw" type="select" label="Enter keywords" >
+                    <option value="text" selected="true">copy/paste</option>
+                    <option value="file">File containing keywords</option>
+                </param>
+                <when value="text" >
+                    <param name="txt" type="text" label="Copy/paste keywords to find (keep or discard)" help='Keywords must be separated by tab, space or carriage return into the form field, for example: A8K2U0 Q5TA79 O43175' >
+                        <sanitizer>
+                        <valid initial="string.printable">
+                            <remove value="&apos;"/>
+                        </valid>
+                        <mapping initial="none">
+                            <add source="&apos;" target="__sq__"/>
+                        </mapping>
+                        </sanitizer>
+                    </param>
+                </when>
+                <when value="file" >
+                    <param name="file" type="data" format="txt,tabular" label="File containing keywords" />
+                    <param name="ncol" type="text" value="c1" label="Specify the column containing keywords" help='For example, fill in "c1" if keywords are in the first column' />
+                    <param name="header" type="boolean" checked="true" truevalue="true" falsevalue="false" label="Does file contain header?" />
+                </when>
+            </conditional>
+        </repeat>
+        <repeat name="value" title="Filter by numerical value" >
+            <param name="ncol" type="text" value="c1" label="Column number on which to apply the filter" help='For example, fill in "c1" if you want to seek values in the first column (and keep or discard them)'>
+                <validator type="regex" message="Please enter a column number, for example: 'c1' for the first column">[c]{0,1}[0-9]+</validator>
+            </param>
+            <param name="operator" type="select" label="Select operator" >
+                <option value="Equal">=</option>
+                <option value="Higher">&gt;</option>
+                <option value="Equal-or-higher">&gt;=</option>
+                <option value="Lower">&lt;</option>
+                <option value="Equal-or-lower">&lt;=</option>
+                <option value="Different">!=</option>
+            </param>
+            <param name="value" type="float" value="" label="Value"></param>
+        </repeat>
+        <repeat name="values_range" title="Filter by range of numerical values">
+            <param name="ncol" type="text" value="c1" label="Column number on which to apply the filter" help='For example, fill in "c1" if you want to seek values in the first column (and keep or discard them)'>
+                <validator type="regex" message="Please enter a column number, for example: 'c1' for the first column">[c]{0,1}[0-9]+</validator>
+            </param>
+            <param name="bottom_value" type="float" value="" label="Enter the bottom value" />
+            <param name="top_value" type="float" value="" label="Enter the top value" />
+            <param name="inclusive" type="boolean" label="inclusive range ?" checked="false" truevalue="true" falsevalue="false" />
+        </repeat>
+        <conditional name="sort">
+            <param name="sort_bool" type="boolean" label="Sort by column ?" checked="false" truevalue="true" falsevalue="false" />
+            <when value="true">
+                <param name="sort_column" type="text" value="" label="Sort result files by:" help="Fill in 'c1' if you want to sort your result file by the column 1 values">
+                    <validator type="regex" message="Please enter a column number, for example: 'c1' for the first column">[c]{0,1}[0-9]</validator>
+                </param>
+                <param name="reversed_sort" type="boolean" checked="false" truevalue="true" falsevalue="false" label="Sort in descending order ?"/>
+            </when>
+            <when value="false"/>
+        </conditional>
+    </inputs>    
+    <outputs>
+        <data name="discarded_lines" format="tsv" label="Filtered_${input1.name} - discarded_lines" />
+        <data name="kept_lines" format="tsv" label="Filtered_${input1.name}" />
+    </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="operation" value="discard"/>
+            <param name="sort_column" value="c1"/>
+            <conditional name="sort">
+                <param name="sort_bool" value="false"/>
+                <param name="reversed_sort" value="false"/>
+            </conditional>
+            <repeat name="keyword">
+                <param name="ncol" value="c1" />
+                <param name="match" value="True" />
+                <conditional name="k">
+                    <param name="kw" value="text" />
+                    <param name="txt" value="P04264 P35908 P13645 Q5D862 Q5T749 Q8IW75 P81605 P22531 P59666 P78386" />
+                </conditional>
+            </repeat>
+            <repeat name="value">
+                <param name="ncol" value="c3"/>
+                <param name="operator" value="Higher"/>
+                <param name="value" value="20" />
+            </repeat>
+            <output name="kept_lines" file="output.tsv" />
+            <output name="discarded_lines" file="discarded_lines.tsv" />
+        </test>
+    </tests>
+    <help><![CDATA[
+**Description**
+
+This tool allows to keep/discard rows from your dataset according to different filter such as keywords (e.g. a list of contaminants) or numerical values (e.g. intensity measurements below a given threshold).
+A boolean operator "OR/AND" allows to combine different type of filters making this tool very powerful.
+
+-----
+
+**Input**
+
+A table (file in txt, tab, tsv, csv format) of your identification and/or quantification results for example.
+  
+-----
+
+**Parameters**
+
+**Operation**
+
+- **Keep**: only keep lines with keyword(s) and/or value(s) concerned by defined filter(s) 
+- **Discard**: only keep lines with keyword(s) and/or value(s) NOT concerned by defined filter(s)
+
+.. class:: infomark
+
+Two output files are created, one with kept lines and the other one with discarded lines. 
+
+**Select an operator to combine your filters (if more than one)**
+
+Many filters (criteria) can be combined in a single execution making this tool quite powerful; this can be achieved using the following boolean operators: 
+
+- **OR**: only one of filters must be satisfied to keep/discard one row
+- **AND**: all filters must be satisfied to keep/discard one row
+
+-----
+
+**Filter by keyword(s)**
+
+Click on the "Filter by keywords" box to use it. You can either fill in the field (copy/paste) or upload a file which contains the keywords. 
+
+"Column number on which to apply the filter": You must then specify the column number of your input file on wich to apply the filter by keywords.  
+
+- If you choose to fill in the field, the keywords should be separated by tab, space or carriage return into the form field, for example: A8K2U0 Q5TA79 O43175
+
+- If you choose to upload a file in a text format in which each line is a keyword, for example:
+
+TRYP_PIG
+
+ALDOA_RABBIT
+
+LYSO_ECOLI
+
+Lines that contains these keywords will be either removed (if "Discard" selected) or retained (if "Keep" selected) from input file.
+
+"Search for exact match?": 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 removed (i.e. using the "discard" mode).
+
+- If you choose **No**, all the fields containing the keyword will be removed.
+
+For both options, the filter is case insensitive (uppercase and lowercase are treated equally)
+
+Example:
+
+**Yes** option (exact match) selected using the keyword "kinase": only lines which contain exactly the word "kinase" will be removed.
+
+**No** option (partial match) for "kinase": lines which contain "kinase" and lines with "alpha-kinase" (and so on) will be removed.
+
+
+-----
+
+**Filter by numerical values**: You can filter your data by a column of numerical values. 
+
+"Column number on which to apply the filter": you must specify the column number of your input file on wich to apply the filter by numerical value. 
+
+Then select one of the operators in the list :
+
+- = (equal)
+- != (not equal)
+- < (lower than)
+- <= (lower than or equal to)
+- > (greater than)
+- >= (greater than or equal to)
+
+Then enter the numerical threshold to apply by filling the "Value" box. 
+If you choose > 10, each row containing a numerical value (in the chosen column of your input file) that correspond to your settings will be kept or discarded (based on operation parameter).
+
+-----
+
+**Filter by a range of values**: You can also set a range of values to filter your file.
+Conversely to the numeric filter, rows with numerical values within the defined range will be kept while rows with values out of this range will be discarded (or the other way around based on operation parameter).
+
+-----
+
+**Sort by column ?**
+click on the "Yes" button allows to "Sort result files by:" a column number. this can be done in ascending (default value) or descending order by entering the column number on which to sort the data.
+
+-----
+
+**Output**
+
+The tool returns two output files.
+
+* A text file containing the results that satisfy your filters (i.e. "keep" mode).
+
+* A text file containing the rows removed from the input file (i.e. "discard" mode).
+
+-----
+
+.. class:: infomark
+
+**Authors**
+
+David Christiany, Lien Nguyen, 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
+
+This work has been partially funded through the French National Agency for Research (ANR) IFB project.
+
+Help: contact@proteore.org for any questions or concerns about this tool.
+
+    ]]></help>
+   <citations>
+         <citation type="doi">doi:10.1186/1471-2105-15-293</citation>
+         <citation type="bibtex">@book{toolsProteoRE,
+            journal = {GitHub repository},
+            publisher = {GitHub},
+            title = {ProteoRE tools},
+            url = {https://github.com/galaxyproteomics/tools-galaxyp},
+            author = {Lien Nguyen, David Chistiany, Florence Combes,Christophe Caron, Valentin Loux Yves Vandenbrouck},
+            date = {2021},
+            year = {2021},
+        }</citation>
+</tool>
diff --git a/tools/proteore_filter_keywords_values/releases_notes.rst b/tools/proteore_filter_keywords_values/releases_notes.rst
new file mode 100644
index 000000000..4a3d9fee0
--- /dev/null
+++ b/tools/proteore_filter_keywords_values/releases_notes.rst
@@ -0,0 +1,20 @@
+===================================
+Filter by keywords - Releases Notes
+===================================
+
+==================
+2019-03-15 Release
+==================
+
+New
+---
+
+Improvements
+------------
+
+- more intuitive outputs : 
+    1. Filtered
+    2. Filtered - discarded lines
+
+Fixes
+-----
diff --git a/tools/proteore_filter_keywords_values/test-data/Lacombe_et_al_2017_OK.txt b/tools/proteore_filter_keywords_values/test-data/Lacombe_et_al_2017_OK.txt
new file mode 100644
index 000000000..188c01b91
--- /dev/null
+++ b/tools/proteore_filter_keywords_values/test-data/Lacombe_et_al_2017_OK.txt
@@ -0,0 +1,165 @@
+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
+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
+		
+		
+		
diff --git a/tools/proteore_filter_keywords_values/test-data/discarded_lines.tsv b/tools/proteore_filter_keywords_values/test-data/discarded_lines.tsv
new file mode 100644
index 000000000..0bb9d57b2
--- /dev/null
+++ b/tools/proteore_filter_keywords_values/test-data/discarded_lines.tsv
@@ -0,0 +1,21 @@
+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
+P04745	Alpha-amylase 1	23
+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
diff --git a/tools/proteore_filter_keywords_values/test-data/output.tsv b/tools/proteore_filter_keywords_values/test-data/output.tsv
new file mode 100644
index 000000000..8682b80b8
--- /dev/null
+++ b/tools/proteore_filter_keywords_values/test-data/output.tsv
@@ -0,0 +1,142 @@
+Protein accession number (UniProt)	Protein name	Number of peptides (razor + unique)
+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
+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