Repository: mpc-bioinformatics/ProtGraph (language: python)

Issue:
PEPTIDE and PROPEP (CHAIN, maybe even SIGNAL-peptides) may have an unique identifier
Currently these features (except for CHAINs), currently do not provide these IDs in FASTA_Headers. We should expand this!

It may be enough to change the following lines: 

(SIGNAL)
https://github.com/mpc-bioinformatics/ProtGraph/blob/master/protgraph/export/peptides/pep_fasta.py#L103

(PEPTIDE)
https://github.com/mpc-bioinformatics/ProtGraph/blob/master/protgraph/export/peptides/pep_fasta.py#L116

(PROPEP)
https://github.com/mpc-bioinformatics/ProtGraph/blob/master/protgraph/export/peptides/pep_fasta.py#L112
Extend VC-Count in BPCSR Output to also consider not only VARIANTs but user-chosen Features
See title. 

Specifically: we want to parameterize the following line: 

https://github.com/mpc-bioinformatics/ProtGraph/blob/master/protgraph/export/pcsr.py#L82
Extend VC-Count in BPCSR Output to also consider not only VARIANTs but user-chosen Features
See title. 

Specifically: we want to parameterize the following line: 

https://github.com/mpc-bioinformatics/ProtGraph/blob/master/protgraph/export/pcsr.py#L82
Dependency apsw is not provided in PyPI
It looks like apsw is not officially in PyPI.


Last working version in ProtGraph:`pip install apsw==3.8.11.1-r1`
Updating PTMs
Currently we only provide n-terminal (and c-terminal) peptide PTMs as well as PTMs on aminoacids. There are also other and different PTMs which ProtGraph should be able to map


Discussion / hints:



apsw should be instead installed from GitHub directly (using `setup.py`)  This issue will be kept open as long as this package is not provided in PyPI.  If it finally is availabe in PyPI, ProtGraph will be changed to use this dependency


Fix patch:
diff --git a/protgraph/annotate_ptms.py b/protgraph/annotate_ptms.py
index 4a1beed..e48126c 100644
--- a/protgraph/annotate_ptms.py
+++ b/protgraph/annotate_ptms.py
@@ -1,3 +1,6 @@
+from collections import defaultdict
+
+import igraph
 from Bio.SeqFeature import FeatureLocation, UnknownPosition
 from Bio.SwissProt import FeatureTable
 
@@ -18,20 +21,47 @@ def annotate_ptms(graph_entry, var_mods: list, fix_mods: list, mass_factor: int)
     Nodes: "delta_mass" ( -> the already in mass-factor unit given delta mass )
     Edges: "qualifiers" ( -> adds Var/Fixed Modification)
     """
-    var_mods = {x: y for x, y in var_mods} if var_mods else {}
-    fix_mods = {x: y for x, y in fix_mods} if fix_mods else {}
+    d_var_mods, d_fix_mods = defaultdict(list), defaultdict(list)
+    if var_mods is not None:
+        for k, v in var_mods:
+            d_var_mods[k].append(v)
+    if fix_mods is not None:
+        for k, v in fix_mods:
+            d_fix_mods[k].append(v)
 
     # FIXMOD Handling
-    _apply_fixmod(graph_entry, fix_mods, mass_factor)
+    _apply_fixmod(graph_entry, d_fix_mods, mass_factor)
 
     # VARMOD Handling
-    _apply_varmod(graph_entry, var_mods, mass_factor)
+    _apply_varmod(graph_entry, d_var_mods, mass_factor)
+
+
+def _flatten_list(double_list: list):
+    return [x for sl in double_list for x in sl]
+
+
+def _create_feature(start_pos, end_pos, mod_type, amino_acid, delta):
+    ''' A wrapper to return a VARMOD or FIXMOD feature '''
+    return FeatureTable(
+        location=FeatureLocation(start_pos, end_pos), type=mod_type, strand=None, id=None,
+        qualifiers=dict(note="{}:{}".format(amino_acid, delta))
+    )
+
+
+def _append_feature_in_edge(attribute, edge, start_pos, end_pos, mod_type, amino_acid, delta):
+    ''' Wrapper function to append an feature '''
+    if attribute not in edge.attributes() or edge[attribute] is None:
+        edge[attribute] = [_create_feature(start_pos, end_pos, mod_type, amino_acid, delta)]
+    else:
+        edge[attribute].append(_create_feature(start_pos, end_pos, mod_type, amino_acid, delta))
 
 
 def _apply_fixmod(graph_entry, fix_mods, mass_factor):
     # Apply for each aminoa acid fixed modification
     for aa, delta in fix_mods.items():
-        if aa not in ["NTERM", "CTERM"]:
+        delta = delta[0]  # We only take the first fix modification. Only a single fixed modification can be applied
+        if aa not in ["NPEPTERM", "CPEPTERM", "NPROTERM", "CPROTERM"] \
+                and not (aa.startswith("NPEP") or aa.startswith("CPEP")):
             # CASE AAs:
             # Simply search for every aminoacid and set its delta_mass statically!
             graph_entry.vs.select(aminoacid=aa)["delta_mass"] = delta*mass_factor
@@ -42,95 +72,116 @@ def _apply_fixmod(graph_entry, fix_mods, mass_factor):
                     spos, epos = __get_node_pos(n)
 
                     # Append qualifier
-                    if "qualifiers" not in in_edge.attributes() or in_edge["qualifiers"] is None:
-                        in_edge["qualifiers"] = [
-                            FeatureTable(
-                                location=FeatureLocation(spos, epos), type="FIXMOD", strand=None, id=None,
-                                qualifiers=dict(note="{}:{}".format(aa, delta))
-                            )
-                        ]
-                    else:
-                        in_edge["qualifiers"].append(
-                            FeatureTable(
-                                location=FeatureLocation(spos, epos), type="FIXMOD", strand=None, id=None,
-                                qualifiers=dict(note="{}:{}".format(aa, delta)))
-                            )
-        elif aa == "NTERM":
-            # CASE N-TERMINAL:
-            [start_node] = graph_entry.vs.select(aminoacid="__start__")
-
-            new_start = graph_entry.add_vertex()
-            # Copy values to new start node
-            for key, val in start_node.attributes().items():
-                new_start[key] = val
-
-            # Set amino acid of old start node to nothing (dereference it as start_node)
-            start_node["aminoacid"] = ""
-            start_node["delta_mass"] = delta*mass_factor
-
-            # Append qualifier
-            new_edge = graph_entry.add_edge(new_start.index, start_node.index)
-            new_edge["qualifiers"] = [
-                FeatureTable(
-                    location=FeatureLocation(0, 1), type="FIXMOD", strand=None, id=None,
-                    qualifiers=dict(note="{}:{}".format(aa, delta))
-                )
-            ]
-        elif aa == "CTERM":
-            # CASE C-TERMINAL:
-            [end_node] = graph_entry.vs.select(aminoacid="__end__")
-
-            new_end = graph_entry.add_vertex()
-            # Copy values to new end node
-            for key, val in end_node.attributes().items():
-                new_end[key] = val
-
-            # Set amino acid of old end node to nothing (dereference it as end_node)
-            end_node["aminoacid"] = ""
-            end_node["delta_mass"] = delta*mass_factor
-
-            # Append new edge
-            graph_entry.add_edge(end_node.index, new_end.index)
-
-            # For all ingoing edges add this new qualifier
-            for in_edge in end_node.in_edges():
-                if "qualifiers" not in in_edge.attributes() or in_edge["qualifiers"] is None:
-                    in_edge["qualifiers"] = ([
-                        FeatureTable(
-                            location=FeatureLocation(
-                                end_node["position"], end_node["position"]+1), type="FIXMOD", strand=None, id=None,
-                            qualifiers=dict(note="{}:{}".format(aa, delta))
-                        )
-                    ])
-                else:
-                    in_edge["qualifiers"].append(
-                        FeatureTable(
-                            location=FeatureLocation(
-                                end_node["position"], end_node["position"]+1), type="FIXMOD", strand=None, id=None,
-                            qualifiers=dict(note="{}:{}".format(aa, delta))
-                        )
-                    )
+                    _append_feature_in_edge("qualifiers", in_edge, spos, epos, "FIXMOD", aa, delta)
+
+    if "NPEPTERM" in fix_mods:
+        deltas = fix_mods["NPEPTERM"]
+        delta = deltas[0]
+        # CASE N-TERMINAL:
+        [start_node] = graph_entry.vs.select(aminoacid="__start__")
+
+        new_start = graph_entry.add_vertex()
+        # Copy values to new start node
+        for key, val in start_node.attributes().items():
+            new_start[key] = val
+
+        # Set amino acid of old start node to nothing (dereference it as start_node)
+        start_node["aminoacid"] = ""
+        start_node["delta_mass"] = delta*mass_factor
+
+        # Append qualifier
+        new_edge = graph_entry.add_edge(new_start.index, start_node.index)
+        new_edge["qualifiers"] = [_create_feature(0, 1, "FIXMOD", "NPEPTERM", delta)]
+
+    if "CPEPTERM" in fix_mods:
+        deltas = fix_mods["CPEPTERM"]
+        delta = deltas[0]
+        # CASE C-TERMINAL:
+        [end_node] = graph_entry.vs.select(aminoacid="__end__")
+
+        new_end = graph_entry.add_vertex()
+        # Copy values to new end node
+        for key, val in end_node.attributes().items():
+            new_end[key] = val
+
+        # Set amino acid of old end node to nothing (dereference it as end_node)
+        end_node["aminoacid"] = ""
+        end_node["delta_mass"] = delta*mass_factor
+
+        # Append new edge
+        graph_entry.add_edge(end_node.index, new_end.index)
+
+        # For all ingoing edges add this new qualifier
+        for in_edge in end_node.in_edges():
+            _append_feature_in_edge(
+                "qualifiers", in_edge, end_node["position"], end_node["position"] + 1, "FIXMOD", "CPEPTERM", delta
+            )
+
+    if "NPROTERM" in fix_mods:
+        deltas = fix_mods["NPROTERM"]
+        delta = deltas[0]
+        # CASE N-TERMINAL for Protein-N
+
+        # Add new nodes
+        nodes_to_update = get_first_last_nodes_from_protein(graph_entry, start_end_aa="__start__", min_max=min)
+
+        # Updata delta masses
+        for n in nodes_to_update:
+            if "delta_mass" in graph_entry.vs[0].attributes() and graph_entry.vs[n.index]["delta_mass"] is not None:
+                graph_entry.vs[n.index]["delta_mass"] += delta*mass_factor
+            else:
+                graph_entry.vs[n.index]["delta_mass"] = delta*mass_factor
+
+        for n in nodes_to_update:
+            for e in n.in_edges():
+                spos, epos = __get_node_pos(n)
+                for in_edge in n.in_edges():
+                    _append_feature_in_edge("qualifiers", e, spos, epos, "FIXMOD", "NPROTERM", delta)
+
+    if "CPROTERM" in fix_mods:
+        deltas = fix_mods["CPROTERM"]
+        delta = deltas[0]
+        # CASE C-TERMINAL for Protein-C
+
+        # Add new nodes
+        nodes_to_update = get_first_last_nodes_from_protein(graph_entry, start_end_aa="__end__", min_max=max)
+
+        # Updata delta masses
+        for n in nodes_to_update:
+            if "delta_mass" in graph_entry.vs[0].attributes() and graph_entry.vs[n.index]["delta_mass"] is not None:
+                graph_entry.vs[n.index]["delta_mass"] += delta*mass_factor
+            else:
+                graph_entry.vs[n.index]["delta_mass"] = delta*mass_factor
+
+        for n in nodes_to_update:
+            for e in n.in_edges():
+                spos, epos = __get_node_pos(n)
+                for in_edge in n.in_edges():
+                    _append_feature_in_edge("qualifiers", e, spos, epos, "FIXMOD", "CPROTERM", delta)
 
 
 def _apply_varmod(graph_entry, var_mods, mass_factor):
-    for aa, delta in var_mods.items():
-        if aa not in ["NTERM", "CTERM"]:
+    for aa, deltas in var_mods.items():
+        if aa not in ["NPEPTERM", "CPEPTERM", "NPROTERM", "CPROTERM"] \
+                and not (aa.startswith("NPEP") or aa.startswith("CPEP")):
             # Search for every aminoacid and set for the new nodes the delta_mass!
             nodes_to_clone = graph_entry.vs.select(aminoacid=aa)
 
             # Clone Nodes
             vc = graph_entry.vcount()
-            graph_entry.add_vertices(len(nodes_to_clone))
-            graph_entry.vs[vc:]["aminoacid"] = nodes_to_clone["aminoacid"]
-            graph_entry.vs[vc:]["position"] = nodes_to_clone["position"]
-            graph_entry.vs[vc:]["accession"] = nodes_to_clone["accession"]
+            graph_entry.add_vertices(len(nodes_to_clone)*len(deltas))
+            graph_entry.vs[vc:]["aminoacid"] = nodes_to_clone["aminoacid"]*len(deltas)
+            graph_entry.vs[vc:]["position"] = nodes_to_clone["position"]*len(deltas)
+            graph_entry.vs[vc:]["accession"] = nodes_to_clone["accession"]*len(deltas)
             if "isoform_accession" in graph_entry.vs[0].attributes():
-                graph_entry.vs[vc:]["isoform_accession"] = nodes_to_clone["isoform_accession"]
+                graph_entry.vs[vc:]["isoform_accession"] = nodes_to_clone["isoform_accession"]*len(deltas)
             if "isoform_position" in graph_entry.vs[0].attributes():
-                graph_entry.vs[vc:]["isoform_position"] = nodes_to_clone["isoform_position"]
+                graph_entry.vs[vc:]["isoform_position"] = nodes_to_clone["isoform_position"]*len(deltas)
 
             # Add Delta Mass to cloned nodes
-            graph_entry.vs[vc:]["delta_mass"] = delta*mass_factor
+            graph_entry.vs[vc:]["delta_mass"] = _flatten_list(
+                [[delta*mass_factor]*len(nodes_to_clone) for delta in deltas]
+            )
 
             # Clone the edges
             edges_to_add = []
@@ -140,33 +191,22 @@ def _apply_varmod(graph_entry, var_mods, mass_factor):
                 spos, epos = __get_node_pos(node)
 
                 for in_edge in node.in_edges():
-                    edges_to_add.append((in_edge.source, offset + vc))
-                    if "qualifiers" not in in_edge.attributes() or in_edge["qualifiers"] is None:
-                        qualifier_info.append([
-                            FeatureTable(
-                                location=FeatureLocation(spos, epos), type="VARMOD", strand=None, id=None,
-                                qualifiers=dict(note="{}:{}".format(aa, delta))
-                            )
-                        ])
-                    else:
-                        qualifier_info.append(in_edge["qualifiers"].copy())
-                        qualifier_info[-1].append(
-                            FeatureTable(
-                                location=FeatureLocation(spos, epos), type="VARMOD", strand=None, id=None,
-                                qualifiers=dict(note="{}:{}".format(aa, delta))
-                            )
-                        )
-                    if "cleaved" in graph_entry.es[0].attributes():
-                        cleaved_info.append(in_edge["cleaved"])
+                    for delta_idx, delta in enumerate(deltas):
+                        edges_to_add.append((in_edge.source, offset + len(nodes_to_clone)*delta_idx + vc))
+                        qualifier_info.append(_create_feature(spos, epos, "VARMOD", aa, delta))
+
+                        if "cleaved" in graph_entry.es[0].attributes():
+                            cleaved_info.append(in_edge["cleaved"])
 
                 for out_edge in node.out_edges():
-                    edges_to_add.append((offset + vc, out_edge.target))
-                    if "qualifiers" in out_edge.attributes():
-                        qualifier_info.append(out_edge["qualifiers"])
-                    else:
-                        qualifier_info.append(None)
-                    if "cleaved" in graph_entry.es[0].attributes():
-                        cleaved_info.append(out_edge["cleaved"])
+                    for delta_idx, delta in enumerate(deltas):
+                        edges_to_add.append((offset + len(nodes_to_clone)*delta_idx + vc, out_edge.target))
+                        if "qualifiers" in out_edge.attributes():
+                            qualifier_info.append(out_edge["qualifiers"])
+                        else:
+                            qualifier_info.append(None)
+                        if "cleaved" in graph_entry.es[0].attributes():
+                            cleaved_info.append(out_edge["cleaved"])
             # Add edges in bulk
             ec = graph_entry.ecount()
             graph_entry.add_edges(edges_to_add)
@@ -174,49 +214,72 @@ def _apply_varmod(graph_entry, var_mods, mass_factor):
             if "cleaved" in graph_entry.es[0].attributes():
                 graph_entry.es[ec:]["cleaved"] = cleaved_info
 
-        elif aa == "NTERM":
-            # CASE N-TERMINAL:
-            # Clone the start node and create a new start node
+        if aa.startswith("NPEP") or aa.startswith("CPEP"):
+            # CASE AAs on C-Term or N-Term
             [start_node] = graph_entry.vs.select(aminoacid="__start__")
-            cloned_start_node = graph_entry.add_vertex()
-            new_start_node = graph_entry.add_vertex()
+            [end_node] = graph_entry.vs.select(aminoacid="__end__")
 
-            # Copy values to new start node
-            for key, val in start_node.attributes().items():
-                cloned_start_node[key] = val
-                new_start_node[key] = val
+            if aa.startswith("NPEP"):
+                filter_expression_for_cloning = lambda node: any([x["position"] == 0 for x in node.neighbors(mode="in")])  # noqa: E731,E501
+                filter_ingoing_edges = lambda x: x == 0  # x --> position of Node # noqa: E731,E501
+                filter_outgoing_edges = lambda _: True  # No Filtering of outgoing edges # noqa: E731,E501
+            else:
+                filter_expression_for_cloning = lambda node: any([x["position"] == end_node["position"] for x in node.neighbors(mode="out")])  # noqa: E731,E501
+                filter_ingoing_edges = lambda _: True  # No Filtering of ingoing edges # noqa: E731,E501
+                filter_outgoing_edges = lambda x: x == end_node["position"]  # x --> position of Node # noqa: E731,E501
+
+            # Filter Nodes which should be cloned
+            nodes_to_check = graph_entry.vs.select(aminoacid=aa[4:])
+            nodes_to_clone = []
+            for n in nodes_to_check:
+                if filter_expression_for_cloning(n):
+                    nodes_to_clone.append(n)
+            nodes_to_clone = igraph.VertexSeq(graph_entry, [n.index for n in nodes_to_clone])
 
-            # De-reference the old start_node and cloned start_node, set deltamass
-            cloned_start_node["aminoacid"] = ""
-            cloned_start_node["delta_mass"] = delta*mass_factor
-            start_node["aminoacid"] = ""
+            # Clone Nodes
+            vc = graph_entry.vcount()
+            graph_entry.add_vertices(len(nodes_to_clone)*len(deltas))
+            graph_entry.vs[vc:]["aminoacid"] = nodes_to_clone["aminoacid"]*len(deltas)
+            graph_entry.vs[vc:]["position"] = nodes_to_clone["position"]*len(deltas)
+            graph_entry.vs[vc:]["accession"] = nodes_to_clone["accession"]*len(deltas)
+            if "isoform_accession" in graph_entry.vs[0].attributes():
+                graph_entry.vs[vc:]["isoform_accession"] = nodes_to_clone["isoform_accession"]*len(deltas)
+            if "isoform_position" in graph_entry.vs[0].attributes():
+                graph_entry.vs[vc:]["isoform_position"] = nodes_to_clone["isoform_position"]*len(deltas)
 
-            # Clone edges
+            # Add Delta Mass to cloned nodes
+            graph_entry.vs[vc:]["delta_mass"] = _flatten_list(
+                [[delta*mass_factor]*len(nodes_to_clone) for delta in deltas]
+            )
+
+            # Clone the edges
             edges_to_add = []
             qualifier_info = []
             cleaved_info = []
-            for e in start_node.out_edges():
-                edges_to_add.append((cloned_start_node.index, e.target))
-                qualifier_info.append(e["qualifiers"])
-                if "cleaved" in graph_entry.es[0].attributes():
-                    cleaved_info.append(e["cleaved"])
+            for offset, node in enumerate(nodes_to_clone):
+                spos, epos = __get_node_pos(node)
 
-            # connect the new starts together:
-            # new_start --> start_node
-            # new_start --> cloned_start_node
-            edges_to_add.append((new_start_node.index, start_node.index))
-            edges_to_add.append((new_start_node.index, cloned_start_node.index))
-            qualifier_info.extend([
-                None,
-                [
-                    FeatureTable(
-                        location=FeatureLocation(0, 1), type="VARMOD", strand=None, id=None,
-                        qualifiers=dict(note="{}:{}".format(aa, delta))
-                    )
-                ]
-            ])
-            if "cleaved" in graph_entry.es[0].attributes():
-                cleaved_info.extend([None, None])
+                # Only a connection from the "start_node"
+                for in_edge in node.in_edges():
+                    if filter_ingoing_edges(in_edge.source_vertex["position"]):
+                        for delta_idx, delta in enumerate(deltas):
+                            edges_to_add.append((in_edge.source, offset + len(nodes_to_clone)*delta_idx + vc))
+                            qualifier_info.append(_create_feature(spos, epos, "VARMOD", aa, delta))
+
+                            if "cleaved" in graph_entry.es[0].attributes():
+                                cleaved_info.append(in_edge["cleaved"])
+
+                # connection to all outgoing edges
+                for out_edge in node.out_edges():
+                    if filter_outgoing_edges(out_edge.target_vertex["position"]):
+                        for delta_idx, delta in enumerate(deltas):
+                            edges_to_add.append((offset + len(nodes_to_clone)*delta_idx + vc, out_edge.target))
+                            if "qualifiers" in out_edge.attributes():
+                                qualifier_info.append(out_edge["qualifiers"])
+                            else:
+                                qualifier_info.append(None)
+                            if "cleaved" in graph_entry.es[0].attributes():
+                                cleaved_info.append(out_edge["cleaved"])
 
             # Add edges in bulk
             ec = graph_entry.ecount()
@@ -225,62 +288,254 @@ def _apply_varmod(graph_entry, var_mods, mass_factor):
             if "cleaved" in graph_entry.es[0].attributes():
                 graph_entry.es[ec:]["cleaved"] = cleaved_info
 
-        elif aa == "CTERM":
-            # CASE N-TERMINAL:
-            # Clone the end node and create a new end node
-            [end_node] = graph_entry.vs.select(aminoacid="__end__")
-            cloned_end_node = graph_entry.add_vertex()
-            new_end_node = graph_entry.add_vertex()
-
-            # Copy values to new end node
-            for key, val in end_node.attributes().items():
-                cloned_end_node[key] = val
-                new_end_node[key] = val
-
-            # De-reference the old end node and cloned end node, set deltamass
-            cloned_end_node["aminoacid"] = ""
-            cloned_end_node["delta_mass"] = delta*mass_factor
-            end_node["aminoacid"] = ""
-
-            # Clone edges
-            edges_to_add = []
-            qualifier_info = []
-            cleaved_info = []
-            for e in end_node.in_edges():
-                edges_to_add.append((e.source, cloned_end_node.index))
-                if "qualifiers" not in e.attributes() or e["qualifiers"] is None:
-                    qualifier_info.append([
-                        FeatureTable(
-                            location=FeatureLocation(end_node["position"], end_node["position"]+1),
-                            type="VARMOD", strand=None, id=None, qualifiers=dict(note="{}:{}".format(aa, delta)))
-                    ])
-                else:
-                    qualifier_info.append(e["qualifiers"].copy())
-                    qualifier_info[-1].append(
-                        FeatureTable(
-                            location=FeatureLocation(end_node["position"], end_node["position"]+1),
-                            type="VARMOD", strand=None, id=None, qualifiers=dict(note="{}:{}".format(aa, delta)))
-                    )
+    if "NPEPTERM" in var_mods:
+        deltas = var_mods["NPEPTERM"]
+        # CASE N-TERMINAL:
+        # Clone the start node and create new start nodes
+        [start_node] = graph_entry.vs.select(aminoacid="__start__")
+        vc = graph_entry.vcount()
+        graph_entry.add_vertices(len(deltas) + 1)
+
+        # Copy values to new start nodes
+        for key, val in start_node.attributes().items():
+            for offset in range(len(deltas) + 1):
+                graph_entry.vs[vc + offset][key] = val
+
+        # De-reference the old start_node and all variable modification nodes. Set the delta masses
+        start_node["aminoacid"] = ""
+        for offset, delta in enumerate(deltas):
+            graph_entry.vs[vc + offset]["aminoacid"] = ""
+            graph_entry.vs[vc + offset]["delta_mass"] = delta*mass_factor
+
+        # Clone edges
+        edges_to_add = []
+        qualifier_info = []
+        cleaved_info = []
+        for e in start_node.out_edges():
+            for offset in range(len(deltas)):
+                edges_to_add.append((vc + offset, e.target))
+                if "qualifiers" in graph_entry.es[0].attributes():
+                    qualifier_info.append(e["qualifiers"])
                 if "cleaved" in graph_entry.es[0].attributes():
                     cleaved_info.append(e["cleaved"])
 
-            # connect the new ends together:
-            # end_node --> new_end_node
-            # cloned_end_node --> new_end_node
-            edges_to_add.append((end_node.index, new_end_node.index))
-            edges_to_add.append((cloned_end_node.index, new_end_node.index))
-            qualifier_info.extend([
-                None, None
-            ])
+        # connect the new starts together:
+        # new_start --> cloned_start_node
+        # new_start --> second_cloned_start_nodes etc..
+        for offset in range(len(deltas)):
+            edges_to_add.append((vc + len(deltas), vc + offset))
+            qualifier_info.append([_create_feature(0, 1, "VARMOD", "NPEPTERM", delta)])
             if "cleaved" in graph_entry.es[0].attributes():
-                cleaved_info.extend([None, None])
+                cleaved_info.append(None)
+
+        # Also do this for the old start node:
+        # new_start --> start_node
+        edges_to_add.append((vc + len(deltas), start_node.index))
+        qualifier_info.append(None)
+        if "cleaved" in graph_entry.es[0].attributes():
+            cleaved_info.append(None)
+
+        # Add edges in bulk
+        ec = graph_entry.ecount()
+        graph_entry.add_edges(edges_to_add)
+        graph_entry.es[ec:]["qualifiers"] = qualifier_info
+        if "cleaved" in graph_entry.es[0].attributes():
+            graph_entry.es[ec:]["cleaved"] = cleaved_info
+
+    if "CPEPTERM" in var_mods:
+        deltas = var_mods["CPEPTERM"]
+        # CASE N-TERMINAL:
+        # Clone the end node and create a new end node
+        [end_node] = graph_entry.vs.select(aminoacid="__end__")
+        vc = graph_entry.vcount()
+        graph_entry.add_vertices(len(deltas) + 1)
+
+        # Copy values to new end nodes
+        for key, val in end_node.attributes().items():
+            for offset in range(len(deltas) + 1):
+                graph_entry.vs[vc + offset][key] = val
+
+        # De-reference the old end:node and all variable modification nodes. Set the delta masses
+        end_node["aminoacid"] = ""
+        for offset, delta in enumerate(deltas):
+            graph_entry.vs[vc + offset]["aminoacid"] = ""
+            graph_entry.vs[vc + offset]["delta_mass"] = delta*mass_factor
+
+        # Clone edges
+        edges_to_add = []
+        qualifier_info = []
+        cleaved_info = []
+        for e in end_node.in_edges():
+            for offset in range(len(deltas)):
+                edges_to_add.append((e.source, vc + offset))
+                qualifier_info.append(
+                    _create_feature(end_node["position"], end_node["position"]+1, "VARMOD", "CPEPTERM", delta)
+                )
+                if "cleaved" in graph_entry.es[0].attributes():
+                    cleaved_info.append(e["cleaved"])
 
-            # Add edges in bulk
-            ec = graph_entry.ecount()
-            graph_entry.add_edges(edges_to_add)
-            graph_entry.es[ec:]["qualifiers"] = qualifier_info
+        # connect the new ends together:
+        # cloned_end_node --> new_end_node
+        # second_cloned_end_node --> new_end_node
+        # ...
+        for offset in range(len(deltas)):
+            edges_to_add.append((vc + offset, vc + len(deltas)))
+            qualifier_info.append(None)
             if "cleaved" in graph_entry.es[0].attributes():
-                graph_entry.es[ec:]["cleaved"] = cleaved_info
+                cleaved_info.append(None)
+
+        # end_node --> new_end_node
+        edges_to_add.append((end_node.index, vc + len(deltas)))
+        qualifier_info.append(None)
+        if "cleaved" in graph_entry.es[0].attributes():
+            cleaved_info.append(None)
+
+        # Add edges in bulk
+        ec = graph_entry.ecount()
+        graph_entry.add_edges(edges_to_add)
+        graph_entry.es[ec:]["qualifiers"] = qualifier_info
+        if "cleaved" in graph_entry.es[0].attributes():
+            graph_entry.es[ec:]["cleaved"] = cleaved_info
+
+    if "NPROTERM" in var_mods:
+        deltas = var_mods["NPROTERM"]
+        # CASE N-TERMINAL for Protein-N
+
+        # Add new nodes
+        nodes_to_clone = get_first_last_nodes_from_protein(graph_entry, start_end_aa="__start__", min_max=min)
+        vc = graph_entry.vcount()
+        graph_entry.add_vertices(len(nodes_to_clone) * len(deltas))
+
+        # Copy values to new end nodes and update the delta_mass
+        offset = 0
+        for n in nodes_to_clone:
+            for delta in deltas:
+                for key, val in n.attributes().items():
+                    graph_entry.vs[vc + offset][key] = val
+                if "delta_mass" in graph_entry.vs[0].attributes() \
+                        and graph_entry.vs[vc + offset]["delta_mass"] is not None:
+                    graph_entry.vs[vc + offset]["delta_mass"] += delta*mass_factor
+                else:
+                    graph_entry.vs[vc + offset]["delta_mass"] = delta*mass_factor
+                offset += 1
+
+        # Clone edges
+        edges_to_add = []
+        qualifier_info = []
+        cleaved_info = []
+
+        offset = 0
+        for n in nodes_to_clone:
+            for delta in deltas:
+                for e in n.in_edges():
+                    spos, epos = __get_node_pos(n)
+                    for in_edge in n.in_edges():
+                        edges_to_add.append((in_edge.source, vc + offset))
+                        qualifier_info.append([_create_feature(spos, epos, "VARMOD", "NPROTERM", delta)])
+                        if "cleaved" in graph_entry.es[0].attributes():
+                            cleaved_info.append(in_edge["cleaved"])
+
+                    for out_edge in n.out_edges():
+                        edges_to_add.append((vc + offset, out_edge.target))
+                        if "qualifiers" in out_edge.attributes():
+                            qualifier_info.append(out_edge["qualifiers"])
+                        else:
+                            qualifier_info.append(None)
+                        if "cleaved" in graph_entry.es[0].attributes():
+                            cleaved_info.append(out_edge["cleaved"])
+                offset += 1
+
+        # Add edges in bulk
+        ec = graph_entry.ecount()
+        graph_entry.add_edges(edges_to_add)
+        graph_entry.es[ec:]["qualifiers"] = qualifier_info
+        if "cleaved" in graph_entry.es[0].attributes():
+            graph_entry.es[ec:]["cleaved"] = cleaved_info
+
+    if "CPROTERM" in var_mods:
+        deltas = var_mods["CPROTERM"]
+        # CASE C-TERMINAL for Protein-C
+
+        # Add new nodes
+        nodes_to_clone = get_first_last_nodes_from_protein(graph_entry, start_end_aa="__end__", min_max=max)
+        vc = graph_entry.vcount()
+        graph_entry.add_vertices(len(nodes_to_clone) * len(deltas))
+
+        # Copy values to new end nodes and update the delta_mass
+        offset = 0
+        for n in nodes_to_clone:
+            for delta in deltas:
+                for key, val in n.attributes().items():
+                    graph_entry.vs[vc + offset][key] = val
+                if "delta_mass" in graph_entry.vs[0].attributes() \
+                        and graph_entry.vs[vc + offset]["delta_mass"] is not None:
+                    graph_entry.vs[vc + offset]["delta_mass"] += delta*mass_factor
+                else:
+                    graph_entry.vs[vc + offset]["delta_mass"] = delta*mass_factor
+                offset += 1
+
+        # Clone edges
+        edges_to_add = []
+        qualifier_info = []
+        cleaved_info = []
+
+        offset = 0
+        for n in nodes_to_clone:
+            for delta in deltas:
+                for e in n.in_edges():
+                    spos, epos = __get_node_pos(n)
+                    for in_edge in n.in_edges():
+                        edges_to_add.append((in_edge.source, vc + offset))
+                        qualifier_info.append([_create_feature(spos, epos, "VARMOD", "CPROTERM", delta)])
+                        if "cleaved" in graph_entry.es[0].attributes():
+                            cleaved_info.append(in_edge["cleaved"])
+
+                    for out_edge in n.out_edges():
+                        edges_to_add.append((vc + offset, out_edge.target))
+                        if "qualifiers" in out_edge.attributes():
+                            qualifier_info.append(out_edge["qualifiers"])
+                        else:
+                            qualifier_info.append(None)
+                        if "cleaved" in graph_entry.es[0].attributes():
+                            cleaved_info.append(out_edge["cleaved"])
+                offset += 1
+
+        # Add edges in bulk
+        ec = graph_entry.ecount()
+        graph_entry.add_edges(edges_to_add)
+        graph_entry.es[ec:]["qualifiers"] = qualifier_info
+        if "cleaved" in graph_entry.es[0].attributes():
+            graph_entry.es[ec:]["cleaved"] = cleaved_info
+
+
+def get_first_last_nodes_from_protein(graph_entry, start_end_aa="__start__", min_max=min):
+    ''' Get the beginning of the protein '''
+    if "isoform_position" in graph_entry.vs[0].attributes():
+        # Get maximum for each isoform end
+        isos = list(set(graph_entry.vs["isoform_accession"]))
+        nodes = set()
+        for iso in isos:
+            vseq = graph_entry.vs.select(isoform_accession=iso)
+            pos = "position" if iso is None else "isoform_position"  # Decide which position to use
+            highetst_num = min_max(
+                [
+                    x
+                    for x, aa in zip(vseq[pos], vseq["aminoacid"])
+                    if x and aa != "" and aa != start_end_aa
+                ]
+            )
+            nodes.update(vseq.select(**{pos: highetst_num}))
+        return list(nodes)
+    else:
+        # Get maximum position and just report this
+        highetst_num = min_max(
+            [
+                x
+                for x, aa in zip(graph_entry.vs["position"], graph_entry.vs["aminoacid"])
+                if x and aa != "" and aa != start_end_aa
+            ]
+        )
+        return list(graph_entry.vs.select(position=highetst_num))
 
 
 def __get_node_pos(n):
diff --git a/protgraph/cli.py b/protgraph/cli.py
index a326510..7db1c52 100644
--- a/protgraph/cli.py
+++ b/protgraph/cli.py
@@ -1,5 +1,7 @@
 import os
+import string
 from argparse import ArgumentTypeError
+from itertools import product
 
 
 def check_if_file_exists(s: str):
@@ -139,7 +141,10 @@ def _parse_mod(input: str):
         s, t = input.split(":", 1)
 
         s = s.strip().upper()
-        if not (s.isalpha() and (len(s) == 1 or s == "CTERM" or s == "NTERM")):
+        if s not in [
+                    "CPEPTERM", "NPEPTERM", "NPROTERM", "CPROTERM",
+                    *[x+y for x, y in list(product(["NPEP", "CPEP", ""], string.ascii_uppercase))]
+                ]:  # Checks for C-/N-Term for Peptide/Protein as well as for [A-Z], NPEP[A-Z] and CPEP[A-Z]
             raise ArgumentTypeError(
                 "The amino acid which gets replaced can only be set to: [A-Z] (1 letter)! Found: '{}'".format(s)
             )
@@ -156,17 +161,20 @@ def _parse_mod(input: str):
         "--fixed_mod", "-fm", type=_parse_mod, action="append",
         help="Apply a fixed modification on a special aminoacid."
         " You can apply multiple fix modifications BUT only one modification per aminoacid is currently allowed."
-        " The form should be '<AminoAcid>:<DeltaMass>' e.g. \"-fm 'M:15.994915'\" would indicate a"
-        " fixed oxidation of M. Note: modifications on the same aminoacid are superseeded."
-        " For the N- and C-Terminus use \"nterm\" or \"cterm\""
+        " The form should be '<AminoAcid>:<DeltaMass>' e.g. \"-fm 'C:57.021464'\" would indicate a"
+        " fixed carbamidomethylation of C."
+        " Note: for fixed modifications on the same aminoacid, we only consider the first one."
+        " For the N- and C-Terminus (for protein or peptide) use \"npepterm\", \"nproterm\" or \"cpepterm\", "
+        "\"cproterm\""
     )
     group.add_argument(
         "--variable_mod", "-vm", type=_parse_mod, action="append",
         help="Apply a variable modification on a special aminoacid."
         " You can apply multiple variable modifications BUT only one modification per aminoacid is currently allowed."
-        " The form should be '<AminoAcid>:<DeltaMass>' e.g. \"-fm 'M:15.994915'\" would indicate a fixed oxidation"
-        " of M. Note: modifications on the same aminoacid are superseeded."
-        " For the N- and C-Terminus use \"nterm\" or \"cterm\""
+        " The form should be '<AminoAcid>:<DeltaMass>' e.g. \"-vm 'M:15.994915'\" would indicate a variable oxidation"
+        " of M."
+        " For the N- and C-Terminus (for protein or peptide) use \"npepterm\", \"nproterm\" or \"cpepterm\", "
+        "\"cproterm\""
     )
 
     # Flag to check if generated graphs are correctly generated
@@ -231,7 +239,8 @@ def _parse_mod(input: str):
     )
     group.add_argument(
         "--queue_size", "-qs", type=int, default=30000,
-        help="Set the size of the queues, default is 30000"
+        help="Set the size of the queues ('reading of entries'-, 'writing of entries'- and statistics-queue), "
+        "default is 30000"
     )
 
 
@@ -372,6 +381,20 @@ def add_graph_exports(group):
         "--export_large_binary_pcsr_pdb_entries", "-elbpcsr_pdbs", type=int, default=10,
         help="Set the number of pdb entries per node. Defaults to 10"
     )
+    count_features = [
+        "INIT_MET", "VARIANT", "VAR_SEQ", "SIGNAL", "MUTAGEN", "CONFLICT",
+        "PEPTIDE", "PROPEP", "CHAIN", "VARMOD", "FIXMOD"
+    ]
+    group.add_argument(
+        "--pcsr_feature_to_count", "-pcsr_ftc", choices=count_features, type=str.upper, action="append",
+        help="Set the features which should be counted into the VC on a graph for all exportes in the pcsr. The values"
+        " here correspond to the actual FT name provided in SwissProt-EMBL. Default is set to use only 'VARIANT',"
+        " since most proteins are complicated due to many variants. Currently parsable features are: " +
+        ", ".join(count_features) + ". Use it as follows to only select specific ones: '-pcsr_ftc VARIANT -pcsr_ftc"
+        " MUTAGEN' to count mutagens and variants or '-pcsr_ftc PEPTIDE' to count only peptides in VC. This parameter"
+        " can be used for later traversal to limit further exploration on specific nodes. E.G.: Only Paths from s to"
+        " e, with maximum x many features (which can be checked on VC in pcsr)"
+    )
 
 
 def add_redis_graph_export(group):
diff --git a/protgraph/export/binary_pcsr.py b/protgraph/export/binary_pcsr.py
index fe0fb6f..f9c0d59 100644
--- a/protgraph/export/binary_pcsr.py
+++ b/protgraph/export/binary_pcsr.py
@@ -58,7 +58,7 @@ def _pcsr_to_bytes(self, b_list):
                 ])
                 b.extend(uint_bytes)
 
-            elif type(self.ctype_mapping[key]) == int:
+            elif type(self.ctype_mapping[key]) is int:
                 uint_bytes = b"".join([
                     (x).to_bytes(int(self.ctype_mapping[key]/8), byteorder="big", signed=False)
                     if x >= 0
diff --git a/protgraph/export/gremlin.py b/protgraph/export/gremlin.py
index e1f83ed..fbfc556 100644
--- a/protgraph/export/gremlin.py
+++ b/protgraph/export/gremlin.py
@@ -72,7 +72,7 @@ def _set_properties(self, rg, prot_graph_attrs):
             return self._set_properties(rg, prot_graph_attrs).property(key, "")
         if (key == "avrg_weight" or key == "mono_weight" or key == "avrg_weight_to_end" or key == "mono_weight_to_end"):
             # Special case for weights
-            if type(value) == int:
+            if type(value) is int:
                 return self._set_properties(rg, prot_graph_attrs).property(key, LongType(value))
             return self._set_properties(rg, prot_graph_attrs).property(key, FloatType(value))
 
diff --git a/protgraph/export/large_binary_pcsr.py b/protgraph/export/large_binary_pcsr.py
index 09b3cc8..a800714 100644
--- a/protgraph/export/large_binary_pcsr.py
+++ b/protgraph/export/large_binary_pcsr.py
@@ -9,6 +9,7 @@ class LargeBinaryPCSR(AExporter):
 
     def start_up(self, **kwargs):
         self.bpcsr_exporter = BinaryPCSR()
+        self.bpcsr_exporter.start_up(**kwargs)
         self.bpcsr_exporter.pdb_count = kwargs["export_large_binary_pcsr_pdb_entries"]
         self.bpcsr_exporter.ctype_mapping = self.bpcsr_exporter._mapping()
         self.out_folder = kwargs["export_output_folder"]
diff --git a/protgraph/export/large_pcsr.py b/protgraph/export/large_pcsr.py
index d4f7cb5..70f1c01 100644
--- a/protgraph/export/large_pcsr.py
+++ b/protgraph/export/large_pcsr.py
@@ -9,6 +9,7 @@ class LargePCSR(AExporter):
 
     def start_up(self, **kwargs):
         self.pcsr_exporter = PCSR()
+        self.pcsr_exporter.start_up(**kwargs)
         self.pcsr_exporter.pdb_count = kwargs["export_large_pcsr_pdb_entries"]
         self.out_folder = kwargs["export_output_folder"]
         self.out_file = os.path.join(self.out_folder, "database.pcsr")
diff --git a/protgraph/export/pcsr.py b/protgraph/export/pcsr.py
index a462659..a9c88a1 100644
--- a/protgraph/export/pcsr.py
+++ b/protgraph/export/pcsr.py
@@ -16,6 +16,14 @@ def __init__(self):
 
     def start_up(self, **kwargs):
         self.pdb_count = kwargs["export_pcsr_pdb_entries"]
+
+        if kwargs["pcsr_feature_to_count"] is None:
+            self.features_to_check = ["VARIANT"]
+        else:
+            self.features_to_check = []
+            for i in kwargs["pcsr_feature_to_count"]:
+                self.features_to_check.append(i)
+
         super(PCSR, self).start_up(**kwargs)
 
     def write_pcsr(self, pg, path):
@@ -79,7 +87,10 @@ def _build_csr_entry(self, graph):
         CL = ["t" if x else "f" for x in graph.es[TO_EDGES]["cleaved"]]  # Get List of Edges[Cleaved]
 
         if "qualifiers" in graph.es[0].attributes():
-            VC = [_count_feature(x, "VARIANT", min) for x in graph.es[TO_EDGES]["qualifiers"]]
+            VC = [
+                sum([_count_feature(x, ftc, min) for ftc in self.features_to_check])
+                for x in graph.es[TO_EDGES]["qualifiers"]
+            ]
             QU = []  # Get List of Edges[Qualifiers] (simplified as in FASTA)
             for qualifier in graph.es[TO_EDGES]["qualifiers"]:
                 if qualifier is None or len(qualifier) == 0:
diff --git a/protgraph/export/peptides/pep_fasta.py b/protgraph/export/peptides/pep_fasta.py
index 035528a..b597c7f 100644
--- a/protgraph/export/peptides/pep_fasta.py
+++ b/protgraph/export/peptides/pep_fasta.py
@@ -102,24 +102,24 @@ def _map_qualifier_to_string(self, qualifier):
 
             elif f.type == "SIGNAL":
                 str_qualifiers.append(
-                    "SIGNAL[" + self._get_location(f) + "]"
+                    "SIGNAL[" + self._get_location(f) + self._get_pep_prop_chain_sig_id(f) + "]"
                 )
 
             elif f.type == "INIT_MET":
                 str_qualifiers.append(
-                    "INIT_MET[" + self._get_location(f) + "]"
+                    "INIT_MET[" + self._get_location(f) + self._get_pep_prop_chain_sig_id(f) + "]"
                 )
             elif f.type == "PROPEP":
                 str_qualifiers.append(
-                    "PROPEP[" + self._get_location(f) + "]"
+                    "PROPEP[" + self._get_location(f) + self._get_pep_prop_chain_sig_id(f) + "]"
                 )
             elif f.type == "PEPTIDE":
                 str_qualifiers.append(
-                    "PEPTIDE[" + self._get_location(f) + "]"
+                    "PEPTIDE[" + self._get_location(f) + self._get_pep_prop_chain_sig_id(f) + "]"
                 )
             elif f.type == "CHAIN":
                 str_qualifiers.append(
-                    "CHAIN[" + self._get_location(f) + "," + f.id + "]"
+                    "CHAIN[" + self._get_location(f) + self._get_pep_prop_chain_sig_id(f) + "]"
                 )
             elif f.type == "FIXMOD":
                 str_qualifiers.append(
@@ -136,16 +136,22 @@ def _map_qualifier_to_string(self, qualifier):
         return str_qualifiers
 
     def _get_location(self, feature):
-        if type(feature.location.start) == UnknownPosition:
+        if type(feature.location.start) is UnknownPosition:
             spos = "?"
         else:
             spos = str(feature.location.start + 1)
-        if type(feature.location.end) == UnknownPosition:
+        if type(feature.location.end) is UnknownPosition:
             epos = "?"
         else:
             epos = str(feature.location.end)
         return spos + ":" + epos
 
+    def _get_pep_prop_chain_sig_id(self, feature):
+        if feature.id is not None:
+            return "," + feature.id
+        else:
+            return ""
+
     def _get_variant_mutagen_qualifier(self, feature, stop_codon="(", offset=1):
         """ Get x -> y or missing """
         message = feature.qualifiers["note"]
@@ -174,7 +180,7 @@ def _get_position_or_isoform_position(self, node, end=False):
         else:
             val = "?"
 
-        if type(val) != str and end:
+        if type(val) is not str and end:
             return val + len(node["aminoacid"]) - 1
         else:
             return val
diff --git a/protgraph/graph_statistics.py b/protgraph/graph_statistics.py
index cc6d0fe..5614f12 100644
--- a/protgraph/graph_statistics.py
+++ b/protgraph/graph_statistics.py
@@ -95,7 +95,7 @@ def _count_feature_list(graph_entry, feature_type="VARIANT", or_count=min):
 
     def kernel(edge, s_list, edge_list):
         offset = _count_feature(edge["qualifiers"], feature_type, or_count)
-        # if variant then shift by offset!
+        # if feature then shift by offset!
         return _add_lists(s_list, [0]*offset + edge_list)
 
     return _dynamic_programming(graph_entry, kernel)
diff --git a/protgraph/protgraph.py b/protgraph/protgraph.py
index 48a8e6f..6547f15 100644
--- a/protgraph/protgraph.py
+++ b/protgraph/protgraph.py
@@ -32,9 +32,8 @@ def prot_graph(**kwargs):
     if "files" not in kwargs or kwargs["files"] is None:
         raise TypeError("missing argument 'files'")
 
-    queue_size = prot_graph_args["queue_size"]
-
     # Set up queues
+    queue_size = prot_graph_args["queue_size"]
     entry_queue = ctx.Queue(queue_size)
     statistics_queue = ctx.Queue(queue_size)
     common_out_file_queue = ctx.Queue(queue_size)
diff --git a/protgraph/scripts/compact_fasta.py b/protgraph/scripts/compact_fasta.py
index e4e73e0..1c1130c 100644
--- a/protgraph/scripts/compact_fasta.py
+++ b/protgraph/scripts/compact_fasta.py
@@ -10,8 +10,9 @@
 def parse_args():
     """ Parse Arguments """
     parser = argparse.ArgumentParser(
-        description="Small script to remove duplicated entries in a fasta and to summarize them into one"
-        " This script works great in conjunction with the peptide fasta export from ProtGraph."
+        description="Small script to remove duplicated entries in fasta and to summarize them into one."
+        " This script works great in conjunction with the peptide fasta export from ProtGraph and has been designed to"
+        " work with huge FASTA-files (tested on ~200GB)"
     )
 
     # Input Fasta
@@ -32,43 +33,167 @@ def parse_args():
         help="Output fasta file. DEFAULT 'compact.fasta' (NOTE: This file WILL be overwritten)"
     )
 
+    # Maximum number of entries in the dictionary
+    parser.add_argument(
+        "--entries_at_once", "-n", default=10_000_000, type=int,
+        help="Number of entries to be checked at once. If set higher, more memory is consumed, "
+        "but less time is needed. Default: 10 Million, which will need very roughly ~2-25GB memory "
+        "(depending on the number of duplicates and the header sizes in the FASTA)."
+    )
+
     return parser.parse_args()
 
 
+def search_duplicates(
+    dict_to_check: dict, in_file, cur_seek_position: int, initial_header: bytes, initial_header_pos: bytes
+        ):
+    """
+    From the current position in file, look for duplicates for the remainder of this file.
+    We update the dictionary where we check for duplicates. This method also returns the positions where
+    a duplicate occured
+    """
+    # Initialize header and sequence
+    header, sequence = initial_header, b""
+    header_pos = initial_header_pos
+
+    # Set for duplicated entries
+    duplicated_entries = set()
+
+    # Iterate over the remaining entries
+    progress = tqdm(
+        unit="B", total=in_file.seek(-1, os.SEEK_END) + 1 - cur_seek_position, position=1, leave=False, unit_scale=True
+    )
+    in_file.seek(cur_seek_position)
+    line = in_file.readline()
+    while line:
+        progress.update(in_file.tell() - (progress.n + cur_seek_position))
+
+        # On a new header, check if the previous entry is in the dict
+        if line.startswith(b">"):
+            if sequence in dict_to_check:
+                # It is in the dict, include and update duplicated entries
+                dict_to_check[sequence].add(header.split(b"|", 2)[2])
+                duplicated_entries.add(header_pos)
+
+            # Reset sequence and update header for next iteration
+            sequence = b""
+            header = line[:-1]
+            header_pos = in_file.tell() - len(header)
+        else:
+            # Just append sequence
+            sequence += line[:-1]
+
+        # Next line for the next iteration
+        line = in_file.readline()
+
+    # For the last entry we check it seperately
+    progress.update(in_file.tell() - (progress.n + cur_seek_position))
+    if sequence in dict_to_check:
+        # It is in the dict, include and update duplicated entries
+        dict_to_check[sequence].add(header.split(b"|", 2)[2])
+        duplicated_entries.add(header_pos)
+    progress.close()
+
+    # Return postions of duplicates
+    return duplicated_entries
+
+
+def write_dict(dict_to_write: dict, out_file, index_off=0, index_name="ID_"):
+    """ Wrapper, to write  fasta entries of the entries in the dict """
+    for index, (key, val) in tqdm(
+        enumerate(dict_to_write.items()), unit="entries", total=len(dict_to_write), position=1, leave=False
+    ):
+        out_file.write(
+            # Write header
+            b">pg|" + index_name.encode() + str(index+index_off).encode() + b"|" + b",".join(val) + b"\n" +
+            # Write sequence
+            b'\n'.join(key[i:i+60] for i in range(0, len(key), 60)) + b"\n"
+        )
+
+    return index + index_off + 1  # plus one for the next iteration
+
+
 def main():
     # Parse args
     args = parse_args()
     input_fasta = os.path.abspath(args.input_fasta[0])
     identifier = args.identifier
     output_fasta = os.path.abspath(args.output_fasta)
+    entries_at_once = args.entries_at_once
 
-    # Read and Write files
-    with open(input_fasta, "r") as inf, open(output_fasta, "w") as outf:
-        header, sequence = None, ""
+    # Open Read and Write Files
+    with open(input_fasta, "rb") as inf, open(output_fasta, "wb") as outf:
+        # Set initial parameters
+        header, sequence = None, b""
         seq_identifier = defaultdict(set)
+        header_pos = None
+        index_written = 0
+
+        # Set set of entries of FASTA-entries to ignore
+        ignore_duplicates_on = set()
 
-        print("Generating dict of sets (to combine same sequences)...")
-        for line in tqdm(inf, unit="lines"):
+        print("Searching for duplicates...")
+        progress = tqdm(unit="B", total=inf.seek(-1, os.SEEK_END) + 1, position=0, unit_scale=True)
 
-            if line.startswith(">"):
+        # Iterate over each entry
+        inf.seek(0)
+        line = inf.readline()
+        while line:
+            progress.update(inf.tell() - progress.n)
+
+            # If entry starts with header process the previous one
+            if line.startswith(b">"):
                 if header is None:
                     # Edge Case very first entry
                     header = line[:-1]
+                    header_pos = inf.tell() - len(header)
+                    line = inf.readline()
                     continue
-                seq_identifier[sequence].add(header.split("|", 2)[2])
-                sequence = ""
+
+                # Check if we ignore this entry
+                if header_pos not in ignore_duplicates_on:
+                    # If not, just add to the dictionary
+                    seq_identifier[sequence].add(header.split(b"|", 2)[2])
+
+                # Set sequence to none and set the new header (for the next iteration)
+                sequence = b""
                 header = line[:-1]
+                header_pos = inf.tell() - len(header)
+
+                # Check if the dict is already at its limit
+                if len(seq_identifier) >= entries_at_once:
+                    # If so, we go through the remainder of the file and write the dict
+
+                    # Save position where we stopped to continue later
+                    seek_to = inf.tell()  # Header is already saved
+
+                    # Already delete duplicated "which we left behind" to save memory early on
+                    ignore_duplicates_on = {x for x in ignore_duplicates_on if x > seek_to}
+
+                    # Go through the remainder of the file to search for duplicates and save duplicated entries
+                    ignore_duplicates_on.update(
+                        search_duplicates(seq_identifier, inf, seek_to, header, header_pos)
+                    )
+
+                    # Go back to where we stopped
+                    inf.seek(seek_to)
+
+                    # Write entries in the dictionary
+                    index_written = write_dict(seq_identifier, outf, index_written, index_name=identifier)
+
+                    # Resest the dictionary for the next round of writing
+                    seq_identifier.clear()
             else:
+                # Just append the sequence
                 sequence += line[:-1]
 
-        # Edge Case do it for the last entry
-        seq_identifier[sequence].add(header.split("|", 2)[2])
-
-        print("Writing new FASTA-file...")
-        for index, (key, val) in tqdm(enumerate(seq_identifier.items()), unit="entries", total=len(seq_identifier)):
-            outf.write(
-                # Write header
-                ">pg|" + identifier + str(index) + "|" + ",".join(val) + "\n" +
-                # Write sequence
-                '\n'.join(key[i:i+60] for i in range(0, len(key), 60)) + "\n"
-            )
+            # Read the next line for the next iteration
+            line = inf.readline()
+
+        # Edge Case: We simply include the last entry into the dict
+        progress.update(inf.tell() - progress.n)
+        progress.close()
+        seq_identifier[sequence].add(header.split(b"|", 2)[2])
+
+        print("Writing final FASTA-entries...")
+        index_written = write_dict(seq_identifier, outf, index_written)
diff --git a/protgraph/scripts/mprofile_20230623143702.dat b/protgraph/scripts/mprofile_20230623143702.dat
new file mode 100644
index 0000000..2aa92f7
--- /dev/null
+++ b/protgraph/scripts/mprofile_20230623143702.dat
@@ -0,0 +1,8059 @@
+CMDLINE python compact_fasta.py --output_fasta test.fasta all_mgfs_optimized.fasta
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+MEM 5091.000000 1687524630.8505
+MEM 5091.000000 1687524630.9509
+MEM 5092.000000 1687524631.0514
+MEM 5092.000000 1687524631.1518
+MEM 5093.000000 1687524631.2523
+MEM 5093.000000 1687524631.3527
+MEM 5095.000000 1687524631.4531
+MEM 5096.000000 1687524631.5536
+MEM 5096.000000 1687524631.6540
diff --git a/protgraph/scripts/print_sums.py b/protgraph/scripts/print_sums.py
index d207b56..bc553cc 100644
--- a/protgraph/scripts/print_sums.py
+++ b/protgraph/scripts/print_sums.py
@@ -70,23 +70,28 @@ def main():
         headers = next(csv_in)
 
         # Set up summation method depending on type
-        first = next(csv_in)[column_index]
-        if not first:
-            raise Exception("Column '{}' (on index {}) contains no entries".format(headers[column_index], column_index))
         try:
-            parsed_entry = ast.literal_eval(first)
-        except Exception:
-            raise Exception("Column '{}' (on index {}) cannot be evaluated".format(headers[column_index], column_index))
-        if type(parsed_entry) == int:
-            exe_func = add
-            summation = 0
-        elif type(parsed_entry) == list:
-            exe_func = _add_lists
-            summation = []
-        else:
-            raise Exception("Column '{}' (on index {}) cannot be summed, type is not list or int".format(
-                headers[column_index], column_index)
-            )
+            while True:
+                first = next(csv_in)[column_index]
+                if not first:
+                    continue
+                parsed_entry = ast.literal_eval(first)
+                if type(parsed_entry) is int:
+                    exe_func = add
+                    summation = 0
+                    break
+                elif type(parsed_entry) is list:
+                    exe_func = _add_lists
+                    summation = []
+                    break
+                else:
+                    raise Exception("Column '{}' (on index {}) cannot be summed, type is not list or int".format(
+                        headers[column_index], column_index)
+                    )
+        except StopIteration:
+            raise Exception("Column '{}' (on index {}) cannot be summed. All cells are empty!".format(
+                    headers[column_index], column_index)
+                )
 
         # Sum all entries depending on type
         try:
@@ -101,7 +106,7 @@ def main():
             )
 
         # Print results
-        if type(summation) == int:
+        if type(summation) is int:
             summation = [summation]
         idx_len = str(int(math.log10(len(summation)))+1)
         entry_len = str(int(math.log10(max(summation)))+1)
diff --git a/setup.py b/setup.py
index 7715f74..1bd16d7 100644
--- a/setup.py
+++ b/setup.py
@@ -11,7 +11,7 @@
 
 setup(
     name='protgraph',
-    version='0.3.9',
+    version='0.3.10',
     author="Dominik Lux",
     description="ProtGraph, a graph generator for proteins.",
     long_description=long_description,
@@ -41,10 +41,10 @@
     extras_require={
         "postgres": ["psycopg>=3.0"],
         "mysql": ["mysql"],
-        "sqlite": ["apsw"],
+        "sqlite": ["apsw>=3.42.0.0"],
         "cassandra": ["cassandra-driver"],
         "gremlin": ["gremlinpython"],
         "redis": ["redis", "redisgraph"],
-        "full": ["mysql", "psycopg>=3.0", "apsw", "cassandra-driver", "redis", "redisgraph", "gremlinpython"],
+        "full": ["mysql", "psycopg>=3.0", "apsw>=3.42.0.0", "cassandra-driver", "redis", "redisgraph", "gremlinpython"],
     },
 )


Test patch:
diff --git a/.github/workflows/functional_tests.yaml b/.github/workflows/functional_tests.yaml
index 8c96288..aa2e385 100644
--- a/.github/workflows/functional_tests.yaml
+++ b/.github/workflows/functional_tests.yaml
@@ -11,6 +11,8 @@ jobs:
         - '3.8' 
         - '3.9' 
         - '3.10'
+        - '3.11'
+        - '3.12'
 
     steps:
       - name: Check out Repository
diff --git a/tests/functional_test.py b/tests/functional_test.py
index b85b3c7..d27829c 100644
--- a/tests/functional_test.py
+++ b/tests/functional_test.py
@@ -29,20 +29,28 @@ def test_fm_none(self):
         args = protgraph.parse_args(["-ft", "none", "-fm", "c:57.021464"] + self.procs_num + self.example_files)
         protgraph.prot_graph(**args)
 
-    def test_fm_nterm(self):
-        args = protgraph.parse_args(["-fm", "ntErm:57.021464"] + self.procs_num + self.example_files)
+    def test_fm_npepterm(self):
+        args = protgraph.parse_args(["-fm", "nPePtErm:57.021464"] + self.procs_num + self.example_files)
         protgraph.prot_graph(**args)
 
-    def test_fm_none_cterm(self):
-        args = protgraph.parse_args(["-ft", "none", "-fm", "CtERM:57.021464"] + self.procs_num + self.example_files)
+    def test_fm_none_cpepterm(self):
+        args = protgraph.parse_args(["-ft", "none", "-fm", "CPePtERM:57.021464"] + self.procs_num + self.example_files)
         protgraph.prot_graph(**args)
 
-    def test_vm_cterm(self):
-        args = protgraph.parse_args(["-vm", "ctErm:57.021464"] + self.procs_num + self.example_files)
+    def test_fm_none_cprotterm(self):
+        args = protgraph.parse_args(["-ft", "none", "-fm", "CPRotERM:57.021464"] + self.procs_num + self.example_files)
         protgraph.prot_graph(**args)
 
-    def test_vm_none_nterm(self):
-        args = protgraph.parse_args(["-ft", "none", "-vm", "ntERM:57.021464"] + self.procs_num + self.example_files)
+    def test_vm_cpepterm(self):
+        args = protgraph.parse_args(["-vm", "cPEPtErm:57.021464"] + self.procs_num + self.example_files)
+        protgraph.prot_graph(**args)
+
+    def test_vm_cproterm(self):
+        args = protgraph.parse_args(["-vm", "cprotErm:57.021464"] + self.procs_num + self.example_files)
+        protgraph.prot_graph(**args)
+
+    def test_vm_none_npepterm(self):
+        args = protgraph.parse_args(["-ft", "none", "-vm", "nPePtERM:57.021464"] + self.procs_num + self.example_files)
         protgraph.prot_graph(**args)
 
     def test_vm(self):
@@ -53,12 +61,20 @@ def test_vm_none(self):
         args = protgraph.parse_args(["-ft", "none", "-vm", "c:57.021464"] + self.procs_num + self.example_files)
         protgraph.prot_graph(**args)
 
-    def test_vm_nterm(self):
-        args = protgraph.parse_args(["-vm", "ntErm:57.021464"] + self.procs_num + self.example_files)
+    def test_vm_nproterm(self):
+        args = protgraph.parse_args(["-vm", "nPRotErm:57.021464"] + self.procs_num + self.example_files)
+        protgraph.prot_graph(**args)
+
+    def test_vm_npepterm(self):
+        args = protgraph.parse_args(["-vm", "nPePtErm:57.021464"] + self.procs_num + self.example_files)
+        protgraph.prot_graph(**args)
+
+    def test_vm_none_cpepterm(self):
+        args = protgraph.parse_args(["-ft", "none", "-vm", "CprotERM:57.021464"] + self.procs_num + self.example_files)
         protgraph.prot_graph(**args)
 
-    def test_vm_none_cterm(self):
-        args = protgraph.parse_args(["-ft", "none", "-vm", "CtERM:57.021464"] + self.procs_num + self.example_files)
+    def test_vm_none_cproterm(self):
+        args = protgraph.parse_args(["-ft", "none", "-vm", "CpeptERM:57.021464"] + self.procs_num + self.example_files)
         protgraph.prot_graph(**args)
 
     def test_none(self):