diff --git a/entries/tflink/__init__.py b/entries/tflink/__init__.py
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+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 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/>.
+
+import pyarrow
+import pyarrow.csv
+import pyarrow.compute as pc
+
+from .. import *
+
+title = "TFLink transcription factor-target gene interactions (2022)"
+description = r"""Directed networks of the regulation of genes by transcription factors (TFs) in seven model organisms, from TFLink, which integrates the TF-target gene interactions of about 10 databases (e.g. ORegAnno, TRRUST, ReMap, GTRD, ChIP-Atlas, HTRIdb, PAZAR, TFe, Yeastract, modERN, REDfly, ...), from small-scale experiments (e.g. reporter assays, electrophoretic mobility shift assays, curated from the literature) and large-scale experiments (mostly ChIP-seq). An edge goes from a TF to a target gene whose expression it regulates, or to whose regulatory region it binds. The data are those of version 1.0 (April 2022) of TFLink.
+
+There is one network per organism, with all its interactions (small and large scale): `human` (*Homo sapiens*), `mouse` (*Mus musculus*), `rat` (*Rattus norvegicus*), `zebrafish` (*Danio rerio*), `fly` (*Drosophila melanogaster*), `worm` (*Caenorhabditis elegans*) and `yeast` (*Saccharomyces cerevisiae*). The small-scale and large-scale subsets that TFLink also provides are those of the edges with `small_scale` true or false. The binding sites of the TFs are not included.
+
+The networks have the following properties:
+
+- `uniprot`: the UniProt accession of the TF or target gene product.
+- `gene_id`: the NCBI Gene identifier of the gene (-1 if not given).
+- `name`: the name of the gene.
+- `tflink_orthologs`: the UniProt accessions of the orthologs of the node in the other organisms of TFLink, prefixed by the organism (e.g. `Hs:Q15049`), separated by `;` (empty if none).
+- `other_orthologs`: the same, for the orthologs that are not in TFLink.
+- Edge `detection_method`: the methods of detection of the interaction, separated by `;`.
+- Edge `pubmed_ids`: the PubMed identifiers of the publications of the interaction, separated by `;`.
+- Edge `source_databases`: the databases from which the interaction comes, separated by `;`.
+- Edge `small_scale`: whether the interaction is supported by small-scale experiments."""
+tags = ['Biological', 'Gene regulation', 'Unweighted', 'Metadata']
+url = ['https://tflink.net/', 'https://tflink.net/download/']
+citation = [('O. Liska, B. Bohár, A. Hidas, T. Korcsmáros, B. Papp, D. Fazekas, E. Ari, "TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species." Database 2022, baac083 (2022)',
+             'https://doi.org/10.1093/database/baac083')]
+icon_hash = None
+# No license is stated for the data (the article is under CC BY-NC 4.0)
+upstream_license = None
+upstream_prefix = 'https://cdn.netbiol.org/tflink/download_files'
+organisms = {"human": "Homo_sapiens_interactions_All_simpleFormat_v1.0.tsv.gz",
+             "mouse": "Mus_musculus_interactions_All_simpleFormat_v1.0.tsv.gz",
+             "rat": "Rattus_norvegicus_interactions_All_simpleFormat_v1.0.tsv",
+             "zebrafish": "Danio_rerio_interactions_All_simpleFormat_v1.0.tsv",
+             "fly": "Drosophila_melanogaster_interactions_All_simpleFormat_v1.0.tsv",
+             "worm": "Caenorhabditis_elegans_interactions_All_simpleFormat_v1.0.tsv",
+             "yeast": "Saccharomyces_cerevisiae_interactions_All_simpleFormat_v1.0.tsv"}
+files = [(f"TFLink_{fname}", alt, None) for alt, fname in organisms.items()]
+
+def fetch_upstream(force=False):
+    return fetch_upstream_files(__name__.split(".")[-1], upstream_prefix, files,
+                                force)
+
+COLUMNS = ["UniprotID.TF", "UniprotID.Target", "NCBI.GeneID.TF",
+           "NCBI.GeneID.Target", "Name.TF", "Name.Target", "Detection.method",
+           "PubmedID", "Organism", "Source.database", "Small-scale.evidence",
+           "TF.TFLink.ortho", "TF.nonTFLink.ortho", "Target.TFLink.ortho",
+           "Target.nonTFLink.ortho"]
+
+def string_ep(g, values, name):
+    """Store the string column `values` as the string edge property `name`,
+    converted from integer codes (one Python string per distinct value)."""
+    d = values.combine_chunks().dictionary_encode()
+    codes = g.new_ep("int", vals=d.indices.to_numpy())
+    labels = d.dictionary.to_pylist()
+    p = g.new_ep("string")
+    map_property_values(codes, p, lambda i: labels[i])
+    g.ep[name] = p
+
+def dash(x):
+    return "" if x == "-" else x
+
+@cache_network()
+@coerce_props(eforce={"pubmed_ids": "string"})
+@annotate()
+def parse(alts=None):
+    name = __name__.split(".")[-1]
+    for fnames, alt, fmt in files:
+        if alts is not None and alt not in alts:
+            continue
+        with open_upstream_file(name, fnames, "rb") as f:
+            t = pyarrow.csv.read_csv(
+                f,
+                parse_options=pyarrow.csv.ParseOptions(delimiter="\t",
+                                                       quote_char=False),
+                convert_options=pyarrow.csv.ConvertOptions(
+                    column_types={c: pyarrow.string() for c in COLUMNS},
+                    strings_can_be_null=False))
+        assert t.column_names == COLUMNS, t.column_names
+        ids = pc.unique(pyarrow.chunked_array(t.column("UniprotID.TF").chunks +
+                                              t.column("UniprotID.Target").chunks))
+        g = Graph(directed=True)
+        g.add_vertex(len(ids))
+        g.add_edge_list(numpy.column_stack(
+            [pc.index_in(t.column("UniprotID.TF"), value_set=ids).to_numpy(),
+             pc.index_in(t.column("UniprotID.Target"), value_set=ids).to_numpy()]))
+        # node attributes, from the first row in which each node appears
+        vals = {k: [""] * len(ids) for k in ("gene_id", "name", "tflink_orthologs",
+                                             "other_orthologs")}
+        for end in ("Target", "TF"):
+            idx = pc.index_in(ids, value_set=t.column(f"UniprotID.{end}"))
+            mask = pc.is_valid(idx).to_numpy(zero_copy_only=False)
+            rows = pc.drop_null(idx)
+            pos = numpy.nonzero(mask)[0]
+            for k, c in (("gene_id", f"NCBI.GeneID.{end}"), ("name", f"Name.{end}"),
+                         ("tflink_orthologs", f"{end}.TFLink.ortho"),
+                         ("other_orthologs", f"{end}.nonTFLink.ortho")):
+                col = t.column(c).take(rows).to_pylist()
+                for i, x in zip(pos, col):
+                    vals[k][i] = x
+        g.vp.uniprot = g.new_vp("string", vals=ids.to_pylist())
+        g.vp.gene_id = g.new_vp("int64_t", vals=[int(x) if x.isdigit() else -1
+                                                for x in vals["gene_id"]])
+        g.vp.name = g.new_vp("string", vals=[dash(x) for x in vals["name"]])
+        g.vp.tflink_orthologs = g.new_vp("string", vals=[dash(x) for x in vals["tflink_orthologs"]])
+        g.vp.other_orthologs = g.new_vp("string", vals=[dash(x) for x in vals["other_orthologs"]])
+        string_ep(g, t.column("Detection.method"), "detection_method")
+        string_ep(g, t.column("PubmedID"), "pubmed_ids")
+        string_ep(g, t.column("Source.database"), "source_databases")
+        ss = t.column("Small-scale.evidence")
+        assert set(pc.unique(ss).to_pylist()) <= {"Yes", "No"}
+        g.ep.small_scale = g.new_ep("bool", vals=pc.equal(ss, "Yes").to_numpy(zero_copy_only=False))
+        del t
+        yield alt, g