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To read a Hi-C map, first identify what each matrix cell represents: the observed or normalized contact signal between two genomic bins. Then read from broad to fine scale—distance-decay and the diagonal, A/B compartments, contact domains, loops and stripes—while checking resolution, filtering, normalization and sample metadata. Bright colors indicate relatively more contact under that display; they are not a direct ruler of nanometres, a photograph of a chromosome or proof that one locus regulates another.
What a Hi-C map actually measures
Hi-C uses DNA-DNA proximity ligation to detect long-range chromatin contacts across the genome. DNA fragments that are close enough in the cell are joined, sequenced and assigned to genomic positions. The resulting pairs are grouped into a matrix: after you choose a bin size, each cell summarizes contacts between two genomic intervals.
ENCODE describes the assay as “Hi-C is an assay that uses DNA-DNA proximity ligation to identify long range chromatin contacts in an unbiased, genome-wide fashion.” The map is therefore a statistical summary of many molecules and cells, not a single three-dimensional structure.
How to read the axes and cells
Both axes represent genomic coordinates. A cell at coordinates x and y reports how much contact signal was observed between the bin around x and the bin around y. Depending on the file, that signal may be raw contact counts or a normalized value. The diagonal represents contacts between nearby or identical genomic intervals; off-diagonal cells represent contacts between more separated intervals.
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A bright pixel or block means relatively more signal than the surrounding cells under the selected normalization and color scale. It does not state that the two bins are a particular distance apart in nanometres. Contact frequency is influenced by genomic separation, chromatin state, assay chemistry, sequencing depth, mapping and normalization.
Why the same biology can look different
Changing the bin size, color range, balancing method or filters can make a feature appear sharper, weaker or absent. A map at 5-kilobase bins is not directly comparable by eye with one at 100-kilobase bins. Treat the display as an analysis product whose settings must be recorded, not as an unchanging image.
Establish the experiment before interpreting a pattern
Metadata determines what a visible pattern can support. Record these items before drawing biological conclusions.
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Sample and biological context
- Organism and reference genome build.
- Tissue or cell type, treatment or condition, developmental state and other relevant sample details.
- Replicate identity and whether you are viewing one replicate, a merged map or an aggregate.
- Assay protocol, such as the type of Hi-C preparation, because protocols can produce different coverage and contact profiles.
A difference between two maps is interpretable only if the samples and reference coordinates are aligned. A different genome build, cell mixture or treatment can create apparent structural changes that are not caused by the feature you are investigating.
Construction and display settings
- Bin size or nominal resolution.
- Mapping-quality threshold and handling of multimapping reads.
- Duplicate-removal rules and the number of usable contact pairs.
- Matrix normalization, including whether balancing was applied.
- Color scale, clipping and whether the panel uses raw, expected-normalized or another transformed signal.
ENCODE notes that its contact matrices use matrix balancing to account for nonuniformities such as restriction-site number and accessibility to cutting. Balancing changes the values used for visual comparison; it does not create new contacts. Check the metadata for the exact matrix and annotation file rather than assuming that every map uses the same convention.
A practical sequence for reading a map
- Confirm the sample. Read the organism, genome build, cell type, condition, replicate and protocol from the record or figure legend.
- Confirm how the matrix was made. Note the bin size, mapping-quality filter, duplicate handling, coverage and normalization. Verify whether the file is a contact matrix, a called-feature track or a transformed comparison.
- Read the diagonal and distance decay. Nearby bins usually have the strongest local signal. Use that expected decay as the background against which domains and focal enrichments are judged.
- Scan broad structure. At a suitable coarse resolution, look for alternating regions with similar interaction preferences—the visual basis for A/B compartment analysis.
- Move to local structure. At finer resolution, inspect contact domains and their boundaries, then focal loop-like enrichments and any stripes. Do not demand a feature at a resolution or coverage level that cannot support it.
- Check confidence and independent evidence. Compare replicates and feature-calling results. For functional or mechanistic claims, examine expression, accessibility, histone-mark or protein-focused interaction data alongside Hi-C.
This order prevents a vivid pixel from dominating interpretation before you have established whether it is real, reproducible and appropriate to the biological scale of the question.
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Recognize the main patterns
| Pattern | Visual scale | Interpretation | Important qualification |
|---|---|---|---|
| A/B compartments | Broad genomic intervals | Regions with different interaction preferences, commonly associated with segregated open and closed chromatin | Sign and color conventions are pipeline-specific |
| Contact domains or TAD-like regions | Local blocks along the diagonal | More contacts within an interval than across its boundaries | Boundaries and calls vary with resolution and algorithm |
| Loops | Focal enriched pixels or small clusters | Localized enrichment between two loci | A statistical map feature, not proof of regulatory causation |
| Stripes | Elongated signal from one locus across an interval | Contacts extending along a region rather than concentrating on one partner | Visibility depends strongly on coverage, scale and display |
A/B compartments
Compartment structure is a broad-scale pattern in which genomic regions preferentially interact with other regions of the same interaction class. ENCODE describes this organization as spatial segregation of open and closed chromatin. A compartment track is often displayed beside the matrix or derived from a matrix transformation.
Do not assume that “A” always has a positive score, or that a particular color always means open chromatin. ENCODE’s listed bigWig output uses positive values for the A compartment, but that sign convention belongs to that output and is not universal. Inspect the track metadata and, where appropriate, compare it with accessibility or chromatin-mark data.
Contact domains and TAD-like regions
A domain-like block appears when contacts inside an interval are stronger than contacts crossing its edges. Boundaries can look like interruptions or corners along the diagonal. The term “TAD” is an interpretation of a domain pattern, not a permanently fixed object that every algorithm, resolution or cell type will call identically.
When a boundary shifts between conditions, first ask whether both maps have comparable depth and resolution and whether the same calling method was used. A boundary that disappears at coarse resolution may still be present at a finer scale, while a noisy fine-resolution map can produce unstable boundaries.
Loops
A loop is a localized enrichment between two loci, often seen as a bright dot against a distance-adjusted background. Loop callers test whether the enrichment is stronger than expected; the result is a statistical feature call rather than a direct observation of a molecular tether.
Use loop coordinates, false-discovery or confidence information supplied by the calling method, replicate support and nearby genomic annotations together. A loop alone does not establish that one enhancer regulates a named gene. Expression, accessibility, histone marks or a protein-focused interaction assay can provide the independent evidence needed for a mechanistic hypothesis.
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Stripes
A stripe is an elongated enrichment extending from one locus across a genomic interval. It differs from a loop because signal is distributed along a range of partners rather than concentrated at one point. Stripes can be easy to miss when the color scale emphasizes focal peaks or when coverage is sparse, so inspect a compatible display and scale.
Resolution, coverage and quality control
Hi-C processing is a chain: reads are mapped and filtered, valid contact pairs are formed, pairs are assigned to bins, the matrix is normalized, features are called and the result is visualized. A problem early in that chain can look like an unusual biological pattern later. Reviews of computational analysis also treat confidence estimation as part of sound interpretation.
Resolution is a trade-off
Smaller bins can reveal finer domains, loops or stripes only when coverage is sufficient for stable estimates. With sparse data, individual cells fluctuate and apparent dots or boundaries may be sampling noise. Larger bins smooth noise but can merge nearby features. Choose the finest resolution that the usable contact count and replicate consistency can support, not the smallest bin size offered by a viewer.
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ENCODE provides matrices at multiple resolutions and outputs with different bin sizes and mapping-quality thresholds. Its page marks mapQ30 files as preferred for the relevant map and feature outputs, with preferred mapQ30 annotations in several output categories. Confirm the selected file and its metadata before interpreting a figure.
ENCODE release criteria (updated September 2022)
The following are ENCODE’s listed release standards for the stated categories. They are not universal biological cutoffs and do not replace dataset-specific quality assessment.
| Metric | Listed ENCODE criterion | How to use it |
|---|---|---|
| Total reads for intact Hi-C experiments | 2 billion reads minimum | A release standard for the relevant ENCODE category, not a guarantee that every feature is detectable |
| Unique reads in Hi-C contacts | 50% minimum; over 60% described as ideal | Indicates how much sequenced material contributes unique contacts |
| Unique reads in long-range contacts | 15% minimum | Helps assess the portion of contacts informative for longer-range organization |
| Maximum unique total duplicates for in situ Hi-C | 40% | A stated ENCODE release limit for that experiment category |
Always report the number of usable pairs, filtering choices and replicate behavior with a feature call. A map can meet a release criterion and still be underpowered for a particular fine-scale comparison.
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Normalization and feature calling
Why normalization matters
Raw contact counts reflect more than chromatin organization. Restriction-site number, cutting accessibility, mappability, sequencing depth and other technical factors vary across bins. Matrix balancing and related normalization methods reduce some of these nonuniformities so that cells can be compared more fairly.
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Calls are model-dependent
Compartment, domain, loop and stripe callers use different statistical definitions. The same matrix can yield different boundaries or loop lists when resolution, expected background, thresholds or algorithms change. Treat a call as “a feature under this method and these settings,” then test whether it is supported by replicates or an independent assay.
How to compare Hi-C maps between samples
- Align references. Use the same genome build and coordinate system, or perform an explicit, documented conversion.
- Match processing. Apply comparable mapping-quality filters, duplicate handling, binning, normalization and color scales. Compare like with like before inspecting biological differences.
- Compare at the same scale. Examine compartments at broad resolution, domains at local resolution and loops only where coverage supports focal calls.
- Use replicates. Ask whether a boundary, compartment switch or loop is reproducible rather than relying on one panel.
- Separate signal from display. Recheck a suspected change with the underlying values, a distance-aware background and the feature caller’s confidence output.
- Bring in orthogonal data. If the claim concerns activity or mechanism, compare with expression, accessibility, histone marks or a protein-focused interaction assay.
A useful comparison asks a specific question: did broad compartment preference change, did a domain boundary move, or did a focal interaction gain strength? “The map looks brighter” is not a biological result unless depth, normalization and color limits are controlled.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Hi-C compared with other 3D-genome data
| Data type | What it contributes | When it is complementary | What it cannot establish alone |
|---|---|---|---|
| Hi-C | Genome-wide contact frequencies across the sampled population and condition | Broad architecture, local domains and candidate focal contacts | A unique 3D structure or causal regulation |
| ChIA-PET | Interactions enriched for a protein of interest | Testing whether a protein-associated interaction coincides with genome-wide architecture | All contacts in the genome; it is targeted rather than an unbiased survey |
| Expression, accessibility or histone-mark assays | Functional or chromatin-state context | Assessing whether a structural pattern is associated with activity or chromatin state | Direct physical contact by themselves |
| 3D structural models | A visual or mathematical reconstruction based on contact data and assumptions | Communicating a hypothesis about organization | A uniquely determined chromosome conformation from one contact map |
ChIA-PET and Hi-C answer overlapping but distinct questions: Hi-C surveys contacts genome-wide, whereas ChIA-PET enriches interactions involving a selected protein. Choose the assay by the biological question, then compare mapping, filtering, normalization, replicates and confidence procedures before comparing their outputs.
What a Hi-C map can—and cannot—prove
- It can support a structural hypothesis: for example, that two loci show reproducibly enriched contact under a specified condition.
- It can reveal organization at several scales: broad compartments, local contact domains, focal loops and elongated stripes, subject to resolution and coverage.
- It cannot provide a literal distance scale: contact frequency is not a nanometre measurement.
- It cannot by itself prove regulatory causation: a contact does not demonstrate that one locus controls a gene.
- It does not uniquely determine a 3D structure: many conformations can produce similar population-averaged contact frequencies.
- It describes the sampled population: a map averages cells and may hide cell-to-cell heterogeneity.
Phrase conclusions at the level supported by the data: “these loci show increased normalized contact in this condition” is defensible; “this enhancer physically activates this gene” requires perturbation or other mechanistic evidence beyond the contact map.
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Troubleshooting misleading or missing features
The map is noisy at fine resolution
Increase the bin size, inspect usable contact counts and compare replicates. If the pattern stabilizes only after coarsening, report the coarser scale rather than presenting unstable fine-bin pixels.
A bright dot appears in only one display
Check color limits, balancing, expected-contact correction, mapping-quality filters and duplicate handling. Reopen the same region with matched settings before calling a loop.
Compartment signs look reversed
Read the track metadata. Positive values indicate the A compartment in the described ENCODE bigWig output, but sign conventions differ among pipelines; a negative value is not universally “A” or “B.”
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Verify genome build, bin size, depth, normalization and caller. Test replicate support and ask whether the difference persists at a common resolution. A caller-dependent shift is not automatically a biological boundary movement.
A suspected interaction is absent
Absence of a visible pixel is not proof of no contact. The feature may be below coverage, blurred by binning, affected by filtering or present only in a cell subset. Check power, replicate consistency and an assay suited to the specific interaction or protein question.
A reporting checklist
- Name the organism, genome build, sample, condition, replicate and protocol.
- State bin size, mapping-quality threshold, duplicate policy, usable contact depth and normalization.
- Describe the color scale and any distance-normalized or transformed display.
- Identify the feature-calling method, resolution and confidence criteria.
- Distinguish matrix observations from called loops, domains or compartment tracks.
- Use cautious language for causation and include orthogonal evidence for functional claims.
- Record whether a convention—such as positive values for compartment A—belongs to a particular output rather than to Hi-C universally.
Following this checklist turns a visually compelling heat map into a reproducible, appropriately qualified interpretation.
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