Wiz regulates clustered protocadherin genes by restricting CTCF/cohesin loop extrusion in a genomic-distance biased manner.

Code ↔ Paper

14 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 14 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Materials and methods › Artificial intelligence (AI) model › Benchmarking. ↔ paper/benchmark.py, lines 88–125 · score 0.87 · passive aggressive classifier, support vector machine, decision tree, LightGBM, random forest, perceptron
[2] § Materials and methods › Artificial intelligence (AI) model › Benchmarking. ↔ paper/draw_pCBS.py, the whole file · a weak match · score 0.86 · passive aggressive classifier, support vector machine, decision tree, LightGBM, random forest, perceptron
[3] § Materials and methods › Artificial intelligence (AI) model › Tokenization of input sequences. ↔ AI/preprocess/data_collator.py, the whole file · a weak match · score 0.86 · undefined amino acids, padding token, 0–25, end token, start token, protein sequences
[4] § Materials and methods › Artificial intelligence (AI) model › Benchmarking. ↔ paper/benchmark.py, lines 88–125 · score 0.84 · passive aggressive classifier, DeepZF, LightGBM, Random Forest, benchmarked, boosting
[5] § Materials and methods › Artificial intelligence (AI) model › Benchmarking. ↔ scripts/formal/test.sh, lines 13–57 · score 0.82 · passive aggressive classifier, DeepZF, LightGBM, Random Forest, boosting, perceptron
[6] § Materials and methods › Artificial intelligence (AI) model › COP architecture. ↔ AI/preprocess/COP/encoder.py, lines 13–147 · score 0.78 · rotary positional embeddings, Multi head attention, RMS normalization, residual, dropout, module
[7] § Materials and methods › Artificial intelligence (AI) model › COP architecture. ↔ AI/preprocess/COP/model.py, lines 27–144 · score 0.72 · ProteinBert, attention layer, RMS normalized, C2H2, token, embedding
[8] § Materials and methods › Artificial intelligence (AI) model › COP architecture. ↔ AI/preprocess/COP/encoder.py, lines 238–291 · score 0.71 · DNA protein cross, RMS normalized, cross attention, protein sequences, module, token
[9] § Materials and methods › Artificial intelligence (AI) model › Tokenization of input sequences. ↔ AI/preprocess/data_collator.py, the whole file · a weak match · score 0.71 · padding token, 0–6, tokenized, C2H2, nucleotides, masking
[10] § Materials and methods › Artificial intelligence (AI) model › COP architecture. ↔ AI/preprocess/COP/encoder.py, lines 293–367 · score 0.62 · RMSNorm, cross attention, GELU, FFN, dropout, head
[11] § Materials and methods › Artificial intelligence (AI) model › COP architecture. ↔ AI/preprocess/COP/encoder.py, lines 369–413 · score 0.60 · feedforward network, cross attention, FFN, embeddings, COP, protein
[12] § Materials and methods › Artificial intelligence (AI) model › Processing of mouse C2H2-ZFP data. ↔ AI/dataset/run.sh, lines 27–32 · score 0.59 · ft_zn_fing, organism, C2H2, UniProt, protein
[13] § Materials and methods › Artificial intelligence (AI) model › Processing of mouse C2H2-ZFP data. ↔ AI/dataset/scripts/download_uniprot_C2H2_protein_table.py, lines 15–45 · score 0.59 · ft_zn_fing, UniProt, organism, id, C2H2, protein
[14] § Materials and methods › Artificial intelligence (AI) model › COP architecture. ↔ AI/preprocess/COP/model.py, lines 27–144 · score 0.59 · RMSNorm, logits, GELU, FFN, dropout, head

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 413 lines · 14 KB · MIT · 4 matches

import torch
from common_ai.non_causality_hyena import HyenaOperator
from common_ai.utils import Residual
from einops import einsum, repeat
from einops.layers.torch import EinMix
from torch import nn
# torch does not import opt_einsum as backend by default. import opt_einsum manually will enable it.
from torch.backends import opt_einsum
from torchtune.modules import MultiHeadAttention, RotaryPositionalEmbeddings
class SecondEncoder(nn.Module):
def __init__(
self,
vocab: int,
max_num_tokens: int,
dim_emb: int,
dim_head: int,
heads: int,
depth: int,
dim_ffn: int,
dropout: float,
use_hyena: bool,
hyena_order: int,
hyena_filter_order: int,
) -> None:
super().__init__()
self.depth = depth
self.use_hyena = use_hyena
self.embed = nn.Sequential(
nn.Embedding(
vocab,
dim_emb,
),
nn.Dropout(dropout),
)
self.rms_norms = nn.ModuleList([nn.RMSNorm(dim_emb) for _ in range(depth)])
self.self_attentions = nn.ModuleList(
[
(
MultiHeadAttention(
embed_dim=dim_emb,
num_heads=heads,
num_kv_heads=heads,
head_dim=dim_head,
q_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
k_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
v_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
output_proj=EinMix(
"b s nhd -> b s d",
weight_shape="nhd d",
bias_shape="d",
d=dim_emb,
nhd=heads * dim_head,
),
pos_embeddings=RotaryPositionalEmbeddings(
dim=dim_head, max_seq_len=max_num_tokens
),
max_seq_len=max_num_tokens,
is_causal=False,
attn_dropout=dropout,
)
if not use_hyena
else HyenaOperator(
d_model=dim_emb,
l_max=max_num_tokens,
order=hyena_order,
filter_order=hyena_filter_order,
dropout=dropout,
filter_dropout=dropout,
)
)
for _ in range(depth)
]
)
self.ffns = nn.ModuleList(
[
Residual(
nn.Sequential(
nn.RMSNorm(dim_emb),
EinMix(
"b s d -> b s d_f",
weight_shape="d d_f",
bias_shape="d_f",
d=dim_emb,
d_f=dim_ffn,
),
nn.GELU(),
EinMix(
"b s d_f -> b s d",
weight_shape="d_f d",
bias_shape="d",
d=dim_emb,
d_f=dim_ffn,
),
nn.Dropout(dropout),
)
)
for _ in range(depth)
]
)
self.last_rms_norm = nn.RMSNorm(dim_emb)
def forward(self, second_ids: torch.Tensor) -> torch.Tensor:
mask = second_ids != 0
embs = self.embed(second_ids)
for i in range(self.depth):
embs_rms = self.rms_norms[i](embs)
if not self.use_hyena:
embs = embs + self.self_attentions[i](
x=embs_rms,
y=embs_rms,
mask=einsum(mask, mask, "b s1, b s2 -> b s1 s2"),
)
else:
# self_attentions[i] is actually hyena
embs = embs + self.self_attentions[i](embs_rms)
embs = self.ffns[i](embs)
embs = self.last_rms_norm(embs)
return embs, mask
class DNAEncoder(nn.Module):
def __init__(
self,
vocab: int,
max_num_tokens: int,
dim_emb: int,
dim_head: int,
heads: int,
depth: int,
dim_ffn: int,
dropout: float,
use_hyena: bool,
hyena_order: int,
hyena_filter_order: int,
) -> None:
super().__init__()
self.depth = depth
self.use_hyena = use_hyena
self.embed = nn.Sequential(
nn.Embedding(
vocab,
dim_emb,
),
nn.Dropout(dropout),
)
self.rms_norms = nn.ModuleList([nn.RMSNorm(dim_emb) for _ in range(depth)])
self.self_attentions = nn.ModuleList(
[
(
MultiHeadAttention(
embed_dim=dim_emb,
num_heads=heads,
num_kv_heads=heads,
head_dim=dim_head,
q_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
k_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
v_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
output_proj=EinMix(
"b s nhd -> b s d",
weight_shape="nhd d",
bias_shape="d",
d=dim_emb,
nhd=heads * dim_head,
),
# DNA starts with CLS token, so increase max_seq_len by 1
pos_embeddings=RotaryPositionalEmbeddings(
dim=dim_head, max_seq_len=max_num_tokens + 1
),
max_seq_len=max_num_tokens + 1,
is_causal=False,
attn_dropout=dropout,
)
if not use_hyena
else HyenaOperator(
d_model=dim_emb,
l_max=max_num_tokens + 1,
order=hyena_order,
filter_order=hyena_filter_order,
dropout=dropout,
filter_dropout=dropout,
)
)
for _ in range(depth)
]
)
self.dna_protein_rms_norms = nn.ModuleList(
[nn.RMSNorm(dim_emb) for _ in range(depth)]
)
self.dna_protein_cross_attentions = nn.ModuleList(
[
MultiHeadAttention(
embed_dim=dim_emb,
num_heads=heads,
num_kv_heads=heads,
head_dim=dim_head,
q_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
k_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
v_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
output_proj=EinMix(
"b s nhd -> b s d",
weight_shape="nhd d",
bias_shape="d",
d=dim_emb,
nhd=heads * dim_head,
),
# protein bert clip the protein sequence by <start> and <end> tokens, so increase max_seq_len by 2
pos_embeddings=RotaryPositionalEmbeddings(
dim=dim_head, max_seq_len=max_num_tokens + 2
),
max_seq_len=max_num_tokens + 2,
is_causal=False,
attn_dropout=dropout,
)
for _ in range(depth)
]
)
self.dna_second_rms_norms = nn.ModuleList(
[nn.RMSNorm(dim_emb) for _ in range(depth)]
)
self.dna_second_cross_attentions = nn.ModuleList(
[
MultiHeadAttention(
embed_dim=dim_emb,
num_heads=heads,
num_kv_heads=heads,
head_dim=dim_head,
q_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
k_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
v_proj=EinMix(
"b s d -> b s nhd",
weight_shape="d nhd",
bias_shape="nhd",
d=dim_emb,
nhd=heads * dim_head,
),
output_proj=EinMix(
"b s nhd -> b s d",
weight_shape="nhd d",
bias_shape="d",
d=dim_emb,
nhd=heads * dim_head,
),
# DNA starts with CLS token, so increase max_seq_len by 1
pos_embeddings=RotaryPositionalEmbeddings(
dim=dim_head, max_seq_len=max_num_tokens + 1
),
max_seq_len=max_num_tokens + 1,
is_causal=False,
attn_dropout=dropout,
)
for _ in range(depth)
]
)
self.ffns = nn.ModuleList(
[
Residual(
nn.Sequential(
nn.RMSNorm(dim_emb),
EinMix(
"b s d -> b s d_f",
weight_shape="d d_f",
bias_shape="d_f",
d=dim_emb,
d_f=dim_ffn,
),
nn.GELU(),
EinMix(
"b s d_f -> b s d",
weight_shape="d_f d",
bias_shape="d",
d=dim_emb,
d_f=dim_ffn,
),
nn.Dropout(dropout),
)
)
for _ in range(depth)
]
)
self.last_rms_norm = nn.RMSNorm(dim_emb)
def forward(
self,
dna_ids: torch.Tensor,
protein_embs: torch.Tensor,
second_embs: torch.Tensor,
second_mask: torch.Tensor,
) -> torch.Tensor:
dna_mask = dna_ids != 0
dna_embs = self.embed(dna_ids)
for i in range(self.depth):
dna_embs_rms = self.rms_norms[i](dna_embs)
if not self.use_hyena:
dna_embs = dna_embs + self.self_attentions[i](
x=dna_embs_rms,
y=dna_embs_rms,
mask=einsum(dna_mask, dna_mask, "b s1, b s2 -> b s1 s2"),
)
else:
# self_attentions[i] is actually hyena
dna_embs = dna_embs + self.self_attentions[i](dna_embs_rms)
# DNA and protein cross-attention
dna_embs_rms = self.dna_protein_rms_norms[i](dna_embs)
dna_embs = dna_embs + self.dna_protein_cross_attentions[i](
x=dna_embs_rms,
y=protein_embs,
mask=repeat(dna_mask, "b s1 -> b s1 s2", s2=protein_embs.shape[1]),
)
# DNA and secondary structure cross-attention
dna_embs_rms = self.dna_second_rms_norms[i](dna_embs)
dna_embs = dna_embs + self.dna_second_cross_attentions[i](
x=dna_embs_rms,
y=second_embs,
mask=einsum(dna_mask, second_mask, "b s1, b s2 -> b s1 s2"),
)
# DNA feedforward network
dna_embs = self.ffns[i](dna_embs)
# only use the embedding for CLS token
dna_embs = dna_embs[:, 0, :]
dna_embs = self.last_rms_norm(dna_embs)
return dna_embs

encoder.py at commit 353997b, under MIT · at the source

Overview

Authors: Tianjie Li^1,2, Jingwei Li^1,2, Leyang Wang^1,2, Haiyan Huang^1,2, Qiang Wu^1,2

ORCID iDs: Tianjie Li, Jingwei Li, Leyang Wang, Haiyan Huang, Qiang Wu

Institutions: Shanghai Jiao Tong University (China); Ruijin Hospital (China); State Key Laboratory of Medical Genomics

Journal: PLoS genetics, volume 22, issue 7, article e1012242

Dates: received 12 February 2026; accepted 6 July 2026; published online 16 July 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pgen.1012242 · PMID 42461945 · PMCID PMC13395409 · OpenAlex W7168814918

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: mouse (organism), cellular / molecular (subfield)

Methods: Connectivity, Statistics, Machine learning

MeSH: Cadherins*, CCCTC-Binding Factor*, Cell Cycle Proteins*, Chromosomal Proteins, Non-Histone*, Animals, Binding Sites, Cohesins, Enhancer Elements, Genetic, Gene Expression Regulation, Mice, Promoter Regions, Genetic, Protocadherins, Transcription Factors, Zinc Fingers (* major topic)

Topic: Genomics and Chromatin Dynamics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: National Key R&D Program of China (2022YFC3400200); National Natural Science Foundation of China (32330016)

Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Zinc finger proteins (ZFPs or ZNFs) constitute the largest family of transcription factors in mammals; however, their regulatory mechanism remains largely elusive. Here we propose COP (C2H2-ZFP occupancy predictor), a deep learning-based heuristic screening tool that integrates DNA sequence with protein primary and secondary features to assess ZFP genomic enrichments. Applying COP to the mouse clustered protocadherin (cPcdh) gene locus, we identified dozens of C2H2-ZFPs potentially involved in CTCF-mediated gene regulation with Wiz (widely interspaced zinc finger-containing protein) having the highest number of 12 ZFs. We confirmed Wiz enrichments at all of the CTCF-binding site (CBS) elements across the three Pcdh clusters by Myc-tagging the endogenous Wiz gene. Genetic experiments revealed significant increases of expression levels of the cPcdh genes upon Wiz deletion in both neuronal cells in vitro and in mouse brain in vivo. Finally, integrated ChIP-seq, RNA-seq, and 4C-seq analyses demonstrated that Wiz regulates CTCF/cohesin occupancy and long-range enhancer-promoter contacts in a genomic-distance biased manner. Together, these findings reveal a key role for Wiz in coupling cohesin occupancy to long-range cPcdh regulation and highlight important functions of C2H2-ZFPs in enhancer-promoter interactions.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

ljw20180420/COP

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 353997b21fa1b80fc71654e48a3c406c70572702, 7 September 2026

Languages: Python (66), Shell (10), Jupyter (2)

Size: 157 files, 78 scripts

Software Heritage: not archived

Found in: “Data Availability”

Holds: README, license file, environment (environment.yml, requirements_space.txt, requirements_torch_pascal.txt, DeepZF/environment.yml), tests, 2 notebooks

Not found: CITATION.cff, continuous integration, documentation

Tools: pandas (35 files), NumPy (20 files), scikit-learn (17 files), PyTorch (11 files), TensorFlow (8 files), Keras (6 files), SciPy (4 files), Matplotlib (2 files), BEDTools (1 file), LightGBM (1 file), seaborn (1 file), XGBoost (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

80 files

AI/__init__.py, Python, 1 line
AI/dataset.py, Python, 31 lines
AI/dataset/extra/scripts/transform_protein_feature.py, Python, 77 lines
AI/dataset/run.sh, Shell, 382 lines, 1 match
AI/dataset/scripts/assess_SRR.py, Python, 34 lines
AI/dataset/scripts/assess_peak_width.py, Python, 19 lines
AI/dataset/scripts/balance_data.py, Python, 43 lines
AI/dataset/scripts/download_alphafoldDB_mmcif.py, Python, 44 lines
AI/dataset/scripts/download_uniprot_C2H2_protein_table.py, Python, 45 lines, 1 match
AI/dataset/scripts/generate_unittest_data.py, Python, 15 lines
AI/dataset/scripts/get_pCBS.py, Python, 63 lines
AI/dataset/scripts/parse_protein_feature.py, Python, 60 lines
AI/dataset/scripts/sample_data.py, Python, 23 lines
AI/dataset/scripts/select_SRR.py, Python, 22 lines
AI/dataset/scripts/select_seed.py, Python, 27 lines
AI/dataset/scripts/split_data.py, Python, 22 lines
AI/gradio_fn.py, Python, 58 lines
AI/inference.py, Python, 44 lines
AI/metric.py, Python, 326 lines
AI/metric/accuracy.py, Python, 106 lines
AI/metric/brier_score.py, Python, 134 lines
AI/metric/confusion_matrix.py, Python, 88 lines
AI/metric/f1.py, Python, 130 lines
AI/metric/matthews_correlation.py, Python, 140 lines
AI/metric/precision.py, Python, 145 lines
AI/metric/recall.py, Python, 135 lines
AI/metric/roc_auc.py, Python, 191 lines
AI/preprocess/COP/__init__.py, Python, 1 line
AI/preprocess/COP/encoder.py, Python, 413 lines, 4 matches
AI/preprocess/COP/model.py, Python, 235 lines, 2 matches
AI/preprocess/DeepZF/__init__.py, Python, 1 line
AI/preprocess/DeepZF/data_collator.py, Python, 74 lines
AI/preprocess/DeepZF/model.py, Python, 337 lines
AI/preprocess/LightGBM/__init__.py, Python, 1 line
AI/preprocess/LightGBM/model.py, Python, 205 lines
AI/preprocess/Scikit/__init__.py, Python, 1 line
AI/preprocess/Scikit/model.py, Python, 346 lines
AI/preprocess/XGBoost/__init__.py, Python, 1 line
AI/preprocess/XGBoost/model.py, Python, 306 lines
AI/preprocess/__init__.py, Python, 1 line
AI/preprocess/data_collator.py, Python, 139 lines, 2 matches
AI/preprocess/model.py, Python, 71 lines
DeepZF/BindZFPredictor.ipynb, Jupyter, 30 lines
DeepZF/BindZF_predictor/code/create_zf_pred_df_and_cal_auc.py, Python, 54 lines
DeepZF/BindZF_predictor/code/finetuning.py, Python, 249 lines
DeepZF/BindZF_predictor/code/main_bindzfpredictor_predict.py, Python, 140 lines
DeepZF/BindZF_predictor/code/main_loo_bindzfpredictor.py, Python, 94 lines
DeepZF/PWMpredictor.ipynb, Jupyter, 31 lines
DeepZF/PWMpredictor/code/create_b1h_input_and_label_data.py, Python, 83 lines
DeepZF/PWMpredictor/code/create_c_rc_input_and_label.py, Python, 39 lines
DeepZF/PWMpredictor/code/functions.py, Python, 321 lines
DeepZF/PWMpredictor/code/main_PWMpredictor.py, Python, 83 lines
DeepZF/PWMpredictor/code/main_loo_PWMpredictor.py, Python, 71 lines
DeepZF/PWMpredictor/code/models_PWMpredictor.py, Python, 16 lines
DeepZF/PWMpredictor/code/models_loo_PWMpredictor.py, Python, 12 lines
DeepZF/PWMpredictor/code/utiles_loo_PWMpredictor.py, Python, 19 lines
DeepZF/PWMpredictor/code/utiles_models_PWMpredictor.py, Python, 152 lines
DeepZF/PWMpredictor/code/utiles_models_loo_PWMpredictor.py, Python, 97 lines
DeepZF/PWMpredictor/result_analysis/create_mosbat_input_protein_bert.py, Python, 114 lines
DeepZF/PWMpredictor/result_analysis/eval_DeepZF.sh, Shell, 27 lines
DeepZF/PWMpredictor/result_analysis/eval_PWMpredictor.py, Python, 51 lines
DeepZF/PWMpredictor/result_analysis/eval_mosbat.py, Python, 49 lines
DeepZF/PWMpredictor/result_analysis/pearson_cor_per_pos.py, Python, 27 lines
app_space.py, Python, 12 lines
paper/benchmark.py, Python, 125 lines, 2 matches
paper/draw_pCBS.py, Python, 50 lines, 1 match
paper/extract_ctcf_binding_site.py, Python, 50 lines
paper/pCBS.py, Python, 111 lines
run.py, Python, 72 lines
scripts/formal/app.sh, Shell, 10 lines
scripts/formal/hpo.sh, Shell, 43 lines
scripts/formal/infer.sh, Shell, 45 lines
scripts/formal/space.py, Python, 53 lines
scripts/formal/test.sh, Shell, 57 lines, 1 match
scripts/formal/train.sh, Shell, 103 lines
scripts/formal/upload.sh, Shell, 29 lines
scripts/formal/upload_dataset.sh, Shell, 19 lines
scripts/unittest/hpo.sh, Shell, 41 lines
LICENSE.md, License, 9 lines
README.md, Text, 72 lines

At the source: github.com/ljw20180420/COP

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
78 scripts, each with its path and the digest of its content;
14 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

The raw data of RNA-seq, ChIP-seq, and QHR-4C generated in this study were deposited in the Genome Sequence Archive (GSA) at the National Genomics Data Center (NGDC), China National Center for Bioinformation (CNCB), under accession number CRA037522. The source code of the COP has been uploaded to GitHub (https://github.com/ljw20180420/COP). The underlying numerical data for all of graphs and summary statistics have been provided in spreadsheet form as Supporting information.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 14 MeSH terms, 2 funders, 78 references.

Cite

This paper

Li, T., Li, J., Wang, L., Huang, H., & Wu, Q. (2026). Wiz regulates clustered protocadherin genes by restricting CTCF/cohesin loop extrusion in a genomic-distance biased manner. PLoS genetics, 22(7), e1012242. https://doi.org/10.1371/journal.pgen.1012242

BibTeX

@article{li2026wiz, author = {Li, Tianjie and Li, Jingwei and Wang, Leyang and Huang, Haiyan and Wu, Qiang}, title = {{Wiz regulates clustered protocadherin genes by restricting CTCF/cohesin loop extrusion in a genomic-distance biased manner}}, journal = {PLoS genetics}, year = {2026}, month = jul, volume = {22}, number = {7}, pages = {e1012242}, publisher = {PLOS}, issn = {1553-7390}, doi = {10.1371/journal.pgen.1012242}, url = {https://doi.org/10.1371/journal.pgen.1012242}, pmid = {42461945}, pmcid = {PMC13395409} }

RIS

TY - JOUR AU - Li, Tianjie AU - Li, Jingwei AU - Wang, Leyang AU - Huang, Haiyan AU - Wu, Qiang TI - Wiz regulates clustered protocadherin genes by restricting CTCF/cohesin loop extrusion in a genomic-distance biased manner T2 - PLoS genetics J2 - PLoS Genet PY - 2026 DA - 2026/07/16 VL - 22 IS - 7 SP - e1012242 SN - 1553-7390 PB - PLOS DO - 10.1371/journal.pgen.1012242 UR - https://doi.org/10.1371/journal.pgen.1012242 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pgen.1012242", "type": "article-journal", "title": "Wiz regulates clustered protocadherin genes by restricting CTCF/cohesin loop extrusion in a genomic-distance biased manner", "container-title": "PLoS genetics", "author": [ { "family": "Li", "given": "Tianjie" }, { "family": "Li", "given": "Jingwei" }, { "family": "Wang", "given": "Leyang" }, { "family": "Huang", "given": "Haiyan" }, { "family": "Wu", "given": "Qiang" } ], "container-title-short": "PLoS Genet", "volume": "22", "issue": "7", "page": "e1012242", "DOI": "10.1371/journal.pgen.1012242", "PMID": "42461945", "PMCID": "PMC13395409", "ISSN": "1553-7390", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pgen.1012242", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 16 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3389/fgene.2026.1807347 [code]: Epigenetic regulators are preferentially coordinated with protocadherin gene expression across the human brain: a genome-wide co-expression analysis.
Journal: Frontiers in genetics
In common: pandas, SciPy, Matplotlib, 1 other tool, cellular / molecular, 9 references
[2] doi:10.3389/fpsyg.2026.1774068 [code]: Analysis of cognitive mechanisms in phoneme perception and pronunciation errors among Korean language learners.
Journal: Frontiers in psychology
In common: LightGBM, XGBoost, Keras, 8 other tools
[3] doi:10.1038/s41592-026-03057-2 [code]: CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.
Journal: Nature methods
In common: BEDTools, Keras, TensorFlow, 7 other tools, mouse, 1 reference
[4] doi:10.1038/s41467-026-75700-7 [code]: Gene regulatory innovations from transposable elements in primate cerebellum development.
Journal: Nature communications
In common: BEDTools, Keras, TensorFlow, 6 other tools, mouse, cellular / molecular, 1 reference
[5] doi:10.7554/elife.110588 [code]: Opening the black box toward a modular approach to spike sorting.
Journal: eLife
In common: LightGBM, XGBoost, TensorFlow, 7 other tools, mouse
[6] doi:10.1038/s42003-026-10957-8 [code]: Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: XGBoost, Keras, TensorFlow, 7 other tools, mouse, cellular / molecular
[7] doi:10.1038/s41598-026-48613-0 [code]: An snRNA-seq aging clock for the fruit fly head sheds light on sex-biased aging.
Journal: Scientific reports
In common: XGBoost, Keras, TensorFlow, 7 other tools, cellular / molecular
[8] doi:10.1371/journal.pcbi.1014615 [code]: Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains.
Journal: PLoS computational biology
In common: XGBoost, Keras, TensorFlow, 7 other tools
[9] doi:10.1093/bib/bbag118 [code]: Drug screening for α-synuclein aggregation inhibitors via multimodal graph neural network.
Journal: Briefings in bioinformatics
In common: LightGBM, XGBoost, PyTorch, 6 other tools, cellular / molecular
[10] doi:10.1126/sciadv.aed3650 [code]: Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactive <i>m/z</i> mapping and exploration.
Journal: Science advances
In common: XGBoost, Keras, TensorFlow, 6 other tools, mouse

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.