diff --git a/entries/collectri/__init__.py b/entries/collectri/__init__.py
index d12874f..c2e2cfc 100644
--- a/entries/collectri/__init__.py
+++ b/entries/collectri/__init__.py
@@ -41,7 +41,7 @@ The networks have the following properties:
 tags = ['Biological', 'Gene regulation', 'Signed', 'Unweighted', 'Metadata']
 url = ['https://github.com/saezlab/CollecTRI', 'https://doi.org/10.5281/zenodo.8192729']
 citation = [('S. Müller-Dott, E. Tsirvouli, M. Vazquez, R. O. Ramirez Flores, P. Badia-i-Mompel, R. Fallegger, D. Türei, A. Lægreid, J. Saez-Rodriguez, "Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities." Nucleic Acids Research 51, 10934-10949 (2023)', 'https://doi.org/10.1093/nar/gkad841')]
-icon_hash = None
+date_added = '2026-10-27'
 upstream_license = ("CC BY 4.0", "https://creativecommons.org/licenses/by/4.0/")
 # Zenodo record 8192729 (version 2.0)
 upstream_prefix = 'https://zenodo.org/api/records/8192729/files'

diff --git a/entries/collectri/__init__.py b/entries/collectri/__init__.py
index ef94de2..d12874f 100644
--- a/entries/collectri/__init__.py
+++ b/entries/collectri/__init__.py
@@ -32,12 +32,12 @@ Self-loops are autoregulation. The regulons for mouse and rat distributed with t
 
 The networks have the following properties:
 
-- `name`: the gene symbol of the gene (e.g. `MYC`).
-- `sign` (edges, `regulons` only): the mode of regulation, +1 for activation and -1 for repression.
-- `sign_decision` (edges, `regulons` only): how the sign was decided (`PMID`: from the literature evidence in the resources; `regulon`: from the role of the TF; `default activation`).
-- `resources` (edges): the resources in which the interaction is found, separated by `;`.
-- `references` (edges, `regulons` only): the PubMed identifiers of the publications supporting the interaction, separated by `;`.
-- `extri_confidence` (edges, `source` only): the confidence of the text-mined interaction in ExTRI (`High` or `Low`); empty if not in ExTRI."""
+- (Node) `name`: the gene symbol of the gene (e.g. `MYC`).
+- (Edge) `sign` (edges, `regulons` only): the mode of regulation, +1 for activation and -1 for repression.
+- (Edge) `sign_decision` (edges, `regulons` only): how the sign was decided (`PMID`: from the literature evidence in the resources; `regulon`: from the role of the TF; `default activation`).
+- (Edge) `resources` (edges): the resources in which the interaction is found, separated by `;`.
+- (Edge) `references` (edges, `regulons` only): the PubMed identifiers of the publications supporting the interaction, separated by `;`.
+- (Edge) `extri_confidence` (edges, `source` only): the confidence of the text-mined interaction in ExTRI (`High` or `Low`); empty if not in ExTRI."""
 tags = ['Biological', 'Gene regulation', 'Signed', 'Unweighted', 'Metadata']
 url = ['https://github.com/saezlab/CollecTRI', 'https://doi.org/10.5281/zenodo.8192729']
 citation = [('S. Müller-Dott, E. Tsirvouli, M. Vazquez, R. O. Ramirez Flores, P. Badia-i-Mompel, R. Fallegger, D. Türei, A. Lægreid, J. Saez-Rodriguez, "Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities." Nucleic Acids Research 51, 10934-10949 (2023)', 'https://doi.org/10.1093/nar/gkad841')]

diff --git a/entries/collectri/__init__.py b/entries/collectri/__init__.py
index 0029c50..ef94de2 100644
--- a/entries/collectri/__init__.py
+++ b/entries/collectri/__init__.py
@@ -72,7 +72,6 @@ def parse(alts=None):
                 tgt = "Target Gene (Associated Gene Name)"
         g = Graph()
         vs = {}
-        ids = []
         for x in pd.concat([d[src], d[tgt]]):
             if x not in vs:
                 vs[x] = len(vs)

diff --git a/entries/collectri/__init__.py b/entries/collectri/__init__.py
new file mode 100644
index 0000000..0029c50
--- /dev/null
+++ b/entries/collectri/__init__.py
@@ -0,0 +1,96 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2020-2026 Tiago de Paula Peixoto <tiago@skewed.de>
+#
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Affero General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE.  See the GNU Affero General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Affero General Public License along
+# with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+from .. import *
+import numpy as np
+import pandas as pd
+
+title = "CollecTRI human transcriptional regulation (2023)"
+description = r"""The human gene regulatory network of CollecTRI ("Collection of Transcriptional Regulatory Interactions"), compiled by Müller-Dott et al. from twelve resources of transcription factor (TF) to target gene interactions: the text-mining resource ExTRI, the curated databases HTRI, TRRUST, TFactS, GOA, IntAct, SIGNOR, CytReg and GEREDB, the curations of NTNU and of Pavlidis et al. (2021), and the highest-confidence interactions of DoRothEA. Nodes are genes (identified by their gene symbols), and a directed edge $(i, j)$ indicates that the transcription factor $i$ regulates the expression of gene $j$.
+
+There are two networks:
+
+- `regulons`: the authors' signed regulons (43,175 interactions), in which each interaction is an activation or a repression. The sign was taken from the resources when available, otherwise from the TF's known role as activator or repressor, and otherwise set to activation by default. Two sources (`AP1` and `NFKB`) are protein complexes rather than single TFs.
+- `source`: all TF-target pairs of the integrated resources (48,681 pairs), from the authors' source table, before the filtering and the assignment of signs.
+
+Self-loops are autoregulation. The regulons for mouse and rat distributed with the tools of the authors are translated from the human ones by orthology, and are not included here.
+
+The networks have the following properties:
+
+- `name`: the gene symbol of the gene (e.g. `MYC`).
+- `sign` (edges, `regulons` only): the mode of regulation, +1 for activation and -1 for repression.
+- `sign_decision` (edges, `regulons` only): how the sign was decided (`PMID`: from the literature evidence in the resources; `regulon`: from the role of the TF; `default activation`).
+- `resources` (edges): the resources in which the interaction is found, separated by `;`.
+- `references` (edges, `regulons` only): the PubMed identifiers of the publications supporting the interaction, separated by `;`.
+- `extri_confidence` (edges, `source` only): the confidence of the text-mined interaction in ExTRI (`High` or `Low`); empty if not in ExTRI."""
+tags = ['Biological', 'Gene regulation', 'Signed', 'Unweighted', 'Metadata']
+url = ['https://github.com/saezlab/CollecTRI', 'https://doi.org/10.5281/zenodo.8192729']
+citation = [('S. Müller-Dott, E. Tsirvouli, M. Vazquez, R. O. Ramirez Flores, P. Badia-i-Mompel, R. Fallegger, D. Türei, A. Lægreid, J. Saez-Rodriguez, "Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities." Nucleic Acids Research 51, 10934-10949 (2023)', 'https://doi.org/10.1093/nar/gkad841')]
+icon_hash = None
+upstream_license = ("CC BY 4.0", "https://creativecommons.org/licenses/by/4.0/")
+# Zenodo record 8192729 (version 2.0)
+upstream_prefix = 'https://zenodo.org/api/records/8192729/files'
+files = [("CollecTRI_regulons.csv/content", "regulons", None),
+         ("CollecTRI_source.tsv/content", "source", None)]
+
+def fetch_upstream(force=False):
+    return fetch_upstream_files(__name__.split(".")[-1], upstream_prefix, files,
+                                force)
+
+@cache_network()
+@coerce_props()
+@annotate()
+def parse(alts=None):
+    name = __name__.split(".")[-1]
+    for fname, alt, fmt in files:
+        if alts is not None and alt not in alts:
+            continue
+        with open_upstream_file(name, fname, "rb") as f:
+            if alt == "regulons":
+                d = pd.read_csv(f, dtype=str, keep_default_na=False)
+                src, tgt = "source", "target"
+            else:
+                # the first line is a comment of the authors' workflow
+                d = pd.read_csv(f, sep="\t", skiprows=1, dtype=str,
+                                keep_default_na=False)
+                src = "Transcription Factor (Associated Gene Name)"
+                tgt = "Target Gene (Associated Gene Name)"
+        g = Graph()
+        vs = {}
+        ids = []
+        for x in pd.concat([d[src], d[tgt]]):
+            if x not in vs:
+                vs[x] = len(vs)
+        g.add_vertex(len(vs))
+        g.vp.name = g.new_vp("string", vals=list(vs))
+        g.add_edge_list(np.column_stack((d[src].map(vs).to_numpy(),
+                                         d[tgt].map(vs).to_numpy())))
+        if alt == "regulons":
+            g.ep.sign = g.new_ep("int", vals=d["weight"].astype(float).astype(int).to_numpy())
+            g.ep.sign_decision = g.new_ep("string", vals=d["sign_decision"].tolist())
+            res = d["resources"].str.split(";").map(
+                lambda l: ";".join(x.removesuffix("_CollecTRI") for x in l if x != "CollecTRI"))
+            g.ep.resources = g.new_ep("string", vals=res.tolist())
+            refs = d["references"].str.replace("CollecTRI:", "")
+            g.ep.references = g.new_ep("string", vals=refs.tolist())
+        else:
+            pres = [c for c in d.columns if c.endswith(" present")]
+            res = d[pres].apply(lambda r: ";".join(x for x in r if x), axis=1)
+            g.ep.resources = g.new_ep("string", vals=res.tolist())
+            g.ep.extri_confidence = g.new_ep("string", vals=d["[ExTRI] Confidence"].tolist())
+        yield alt, g