Evidence map›Paper›PMID 39283450›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Step-by-Step Protocol to Generate Hi-C Contact Maps Using the rfy_hic2 Pipeline.

Hideki Tanizawa, Claire Yik-Lok Chung, Shun-Ichiro Fuse, Tomomi Hayashi, Peter Weisel, Ken-Ichi Noma

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Hideki TanizawaDivision of Genome Biology, Institute for Genetic Medicine, Hokkaido University, Sapporo, Japan. tanizawa@igm.hokudai.ac.jp.
Claire Yik-Lok ChungDivision of Genome Biology, Institute for Genetic Medicine, Hokkaido University, Sapporo, Japan.
Shun-Ichiro FuseDivision of Genome Biology, Institute for Genetic Medicine, Hokkaido University, Sapporo, Japan.
Tomomi HayashiInstitute of Molecular Biology, University of Oregon, Eugene, OR, USA.
Peter WeiselInstitute of Molecular Biology, University of Oregon, Eugene, OR, USA.
Ken-Ichi NomaDivision of Genome Biology, Institute for Genetic Medicine, Hokkaido University, Sapporo, Japan. noma@uoregon.edu.

Funding

Condensin connects 3D genome organization, chromosomal segregation, andgene regulation via its interactions with transcription-related factors.R01GM124195 · NIGMS · WISTAR INSTITUTE · PI NOMA, KENICHI · 2017 to 2020
$1.8M
NIGMS NIH HHS R01 GM124195
6 · The paper itself

Abstract

The Hi-C method has emerged as an indispensable tool for analyzing the 3D organization of the genome, becoming increasingly accessible and frequently utilized in chromatin research. To effectively leverage 3D genomics data obtained through advanced technologies, it is crucial to understand what processes are undertaken and what aspects require special attention within the bioinformatics pipeline. This protocol aims to demystify the Hi-C data analysis process for field newcomers. In a step-by-step manner, we describe how to process Hi-C data, from the initial sequencing of the Hi-C library to the final visualization of Hi-C contact data as heatmaps. Each step of the analysis is clearly explained to ensure an understanding of the procedures and their objectives. By the end of this chapter, readers will be equipped with the knowledge to transform raw Hi-C reads into informative visual representations, facilitating a deeper comprehension of the spatial genomic structures critical to cellular functions.

Indexed as

ChromatinComputational BiologyGenomicsSoftwareHigh-Throughput Nucleotide SequencingHumansChromatin3D genome structureHi-CSoftware pipelines

Identifiers

PMID39283450

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.