CD Genomics

06/04/2026

The most common Hi-C budget mistake: optimizing for resolution, not for your question.

When researchers ask "how much Hi-C depth do I need?", the instinct is to anchor on the highest resolution tier — 5 kb, 1 kb, whatever the benchmark paper used. That instinct is often wrong.

Resolution is an output. Your biological question is the input. Planning depth in the wrong order wastes budget and, worse, produces a dataset that can't actually support the conclusion you need.

Dr. Yang H., Senior Scientist at CD Genomics, breaks down the reasoning in our latest resource article. Three principles stood out:

1. There is no universal correct depth. The same read count that comfortably calls A/B compartments will severely underpow­er loop detection. "High resolution Hi-C" is not one experiment — it's a family of experiments with very different cost structures.

2. "Maximum resolution" is often the wrong budget target. If your question is genome-wide structural comparison, TAD-level context mapping, or a scaffold for targeted follow-up, a moderate depth is entirely defensible — and often better than overcounting a small region.

3. Phased planning reduces financial risk. Phase 1: Generate enough data for QC, complexity assessment, and signal review at your target scale. Decision gate: Will deeper sequencing materially change the conclusion? Phase 2: Only proceed if the evidence supports it.

The piece also covers why library complexity is the real ceiling on depth (additional sequencing past saturation only adds duplicates, not resolution), and when switching to Capture Hi-C or Micro-C is more cost-efficient than simply sequencing deeper.

Worth bookmarking if you're designing a 3D genomics project or advising collaborators on scope.

👉 Full article by Dr. Yang H.: https://www.cd-genomics.com/3d-genomics/resource/hi-c-budget-planning-sequencing-depth.html

06/04/2026

A common mistake in HiChIP study design: choosing an antibody target because it's familiar, not because it matches the regulatory question.

The result is technically clean data that can't answer the hypothesis. That's not a sequencing failure — it's an anchor selection failure.

The anchor is the lens. If the lens doesn't match the mechanism you're testing, HiChIP can return a large, well-QC'd dataset with almost no decision value.

Our senior scientist Dr. Yang H. breaks down why this happens and how to avoid it in a new resource article. The short version:

Three things a biologically strong HiChIP anchor needs to satisfy:

Mechanism match — Are you testing enhancer-driven activation or promoter-centric connectivity? Those require different anchors. Using a broadly enriched mark when your hypothesis is enhancer-specific gives you interpretable-looking output for the wrong layer.

Meaningful simplification — The right anchor should help you prioritize interactions that are plausible for your model, not merely abundant. A richer interaction map is not always a more informative one.

Defensible validation path — Can you sketch in advance what a "convincing" result would look like, and what orthogonal data would support it? If not, the anchor may not be specific enough for the question.

The five traps most HiChIP projects fall into:

Selecting the target before defining the regulatory claim
Assuming stronger enrichment = stronger evidence
Expecting a single anchor to answer every regulatory question
Under-planning how the output connects to downstream validation
Defaulting to the most-cited target without checking mechanism fit
When the anchor is wrong, the deliverable isn't just suboptimal — it's actively harder to interpret than a simpler experiment would have been.

Read the full article: https://www.cd-genomics.com/3d-genomics/resource/hichip-target-selection-wrong-anchor-weakens-study.html

06/04/2026

Hi-C can tell you that locus A contacts locus B, and that locus B contacts locus C.

What it cannot tell you is whether A, B, and C are simultaneously in the same physical complex — or simply sharing a neighborhood at different moments. That distinction is the difference between an enhancer hub and a coincidence. Standard paired-end sequencing is structurally incapable of answering it.

HiPore-C resolves this directly.

By sequencing intact concatemers on Oxford Nanopore PromethION, each read encodes a complete multi-way contact event — three or more restriction fragments from a single ligation molecule, captured simultaneously. There's no inference. There's no statistical reconstruction. The co-occupancy is in the read.

What HiPore-C delivers that paired Hi-C cannot:

→ Higher-order contacts (≥3 loci per read) — enhancer hubs, LCR multi-gene contacts, and multi-way regulatory complexes captured as single molecules → Complex SV resolution — N50 >15 kb reads cross repetitive regions, centromeres, and SV breakpoints that short reads cannot span → Allele-specific interactions — heterozygous SNPs across a single long molecule enable haplotype-resolved chromatin contacts without computational phasing overhead → Direct V2G evidence — physically linking a risk variant, its enhancer, and its target promoter in one molecule; not a statistical prediction

The benchmark: HiPore-C at the β-globin locus (Nat Commun. 2023) — the locus control region (LCR) was found to simultaneously contact ≥2 globin genes in a single molecule, confirming a true enhancer hub rather than sequential pairwise interactions. Multi-way contact frequency exceeded pairwise model predictions, demonstrating that Hi-C structurally underestimates hub complexity.

Recommended depth: 50–100 Gb per sample. Analysis via PPL-Toolbox (Pore-C Pipeline-Toolbox), delivering contact matrices compatible with standard Hi-C viewers plus dedicated higher-order interaction tables.

Best fit for: V2G prioritization in drug target discovery, cancer structural variant 3D impact mapping, imprinting and X-inactivation allele-resolved studies, complex genome scaffolding.

Service details: https://www.cd-genomics.com/3d-genomics/services/hipore-c.html

06/04/2026

The resolution floor of standard Hi-C is set by its restriction enzyme.

Cut with DpnII, and your smallest detectable fragment is ~4 kb. That means any chromatin loop shorter than that — the short-range enhancer-promoter contacts that drive transcription in the 1–20 kb window — sits below the detection threshold. You're looking at chromosomal architecture with a map that can't read street-level.

Micro-C replaces the restriction enzyme with MNase.

Micrococcal nuclease digests exposed linker DNA uniformly, generating fragments centered on single nucleosomes (~150 bp) and di-nucleosomes (~300 bp). There's no sequence motif bias, no AT-rich deserts left uncut, no genomic blind spots. Every ligation product captured reflects contacts between adjacent nucleosomes — not between restriction fragments 4 kb apart.

What this unlocks:

→ ~150 bp resolution — one order of magnitude finer than standard Hi-C → Short-range loops (

06/04/2026

Every Hi-C experiment gives you pairwise contacts. Two loci per read, every time.

That's been the fundamental limitation of standard chromatin conformation capture for 20 years: you can see whether locus A is near locus B, but you can't determine whether A, B, and C are simultaneously in the same physical complex — or just sharing a neighborhood at different times.

Pore-C changes the unit of measurement.

By sequencing intact concatemers on Oxford Nanopore — without fragmenting the ligation product — each read directly encodes multi-way contacts: three, four, sometimes more restriction fragments captured in a single molecule. The difference matters enormously when you're asking regulatory questions. Whether a risk variant, an enhancer, and a promoter form a co-occupying hub is not answerable from pairwise data alone.

What the platform delivers that standard Hi-C cannot:

→ Higher-order contacts — multi-way interaction tables alongside standard paired format (Juicebox / HiGlass compatible) → Native CpG methylation — base modification detected simultaneously from the same raw signal, no bisulfite treatment needed → Long-range scaffolding — reads averaging 15–30 kb (some exceeding 100 kb) bridge repetitive regions, centromeres, and telomeres that short reads cannot resolve → Chromosome-scale haplotype phasing — heterozygous SNPs + methylation imprints across a single long read enable allele-resolved assembly

A 2025 study using Pore-C with C-Phasing assembled an ultra-complex polyploid plant genome — previously intractable with PacBio contigs + short-read Hi-C — into chromosome-scale, haplotype-resolved scaffolds with >99% accuracy. Multi-way contacts bridged centromeric repeats that had left contigs isolated.

Standard deliverables: Raw .fastq with methylation tags, sorted .bam, .hic / .mcool contact matrices, multi-way .pairs tables, QC PDF with N50, concatemericity, cis/trans ratio, methylation detection rate.

If your project involves complex genome assembly, structural variant validation, enhancer-promoter hub analysis, or allele-resolved chromatin mapping — this is worth a closer look.

Service details: https://www.cd-genomics.com/3d-genomics/services/pore-c.html

05/28/2026

Knockdown efficiency? That's the easy part. The harder question: what else did your A*O or siRNA change in the transcriptome?

For oligonucleotide therapeutics, potent target knockdown doesn't guarantee safety or selectivity. Off-target effects — seed region mismatches, RNase H-dependent cleavage, immune pathway activation — can derail programs that looked clean by qPCR alone.

CD Genomics' A*O and siRNA Drug Development Omics Solution goes beyond single-gene readouts with a modular multi-omics approach:

→ RNA-seq — knockdown potency and pathway-level transcriptional response → Small RNA-seq — miRNA-like off-target regulation, seed region effects → Targeted RNA-seq — deep candidate validation → Custom bioinformatics — SeedMatchR off-target detection, immune/inflammatory pathway safety signals, GO/KEGG enrichment

A*Os and siRNAs have fundamentally different mechanisms (RNase H vs RISC) and different off-target risks. Our analysis accounts for modality-specific biology — not a one-size pipeline.

A SeedMatchR demonstration detected significant seed-matched expression shift (P = 7.74 × 10⁻⁸); glycol nucleic acid seed modification abolished the signal — validating detection and rescue in one analysis.

Transcriptome-wide evidence, not just a qPCR band.

Explore the service: https://www.cd-genomics.com/aso-sirna-drug-development-omics-solution.html

*O

05/28/2026

Your CRISPR-edited iPSC clone looks correct by Sanger sequencing. But does it really have the genotype you think?

A 2022 Stem Cell Reports study evaluated 27 edited iPSC clones that passed standard PCR + Sanger QC. Deeper analysis revealed 33% harbored large on-target defects — allele copy number losses and LOH — completely invisible to standard methods.

CD Genomics' iPSC Gene Editing and Quality Control Solution provides layered, project-specific QC that matches the value of your clones:

→ Target verification — Sanger to targeted sequencing with allele-level resolution → Junction confirmation — ligation PCR + sequencing for KI and reporter lines → Clone-level comparison — structured genotype tables across clones, editing patterns, QC flags → Genome-wide QC — Illumina, PacBio, or Nanopore WGS for CNV/SNV/SV review

An exploratory KO needs different QC than a disease model clone entering a year-long differentiation protocol. We help define the right level upfront.

Deliverables include allele maps, clone comparison tables, off-target tables, and full bioinformatics reports — structured evidence, not raw sequencing files.

Explore the service: https://www.cd-genomics.com/ipsc-gene-editing-quality-control-solution.html

05/21/2026

Gel Purification Is the Most Annoying Step in RNA-Seq. This New Method Gets Rid of It Completely.

If you've ever prepared an RNA-seq library, you know the drill: run a gel, cut out the band, elute overnight, precipitate. It's slow, manual, and you lose sample at every step.

The reason labs do it: primer dimer. Excess primers form useless artifacts that dominate your sequencing data.

A new study in Communications Biology presents a simple fix: add exonuclease I after reverse transcription. It digests leftover primers without touching your cDNA. No gel needed.

But that's not all:

🧬 They also used a high-processivity reverse transcriptase that reads through modified bases and secondary structures — a major problem for small RNAs like tRNAs, which are densely modified.

📊 Result: >10-fold sensitivity improvement. More useful reads per run. Less wasted data.

🔬 The workflow is now automatable. No gel means liquid handlers can do the work — higher throughput, less variability, lower input requirements.

This is a practical improvement that any RNA-seq lab can adopt. For small RNA researchers, epitranscriptomics groups, and anyone working with limited RNA — this matters.

Full article: https://www.nature.com/articles/s42003-026-10136-9

A method is presented for library preparation for absolute quantification RNA sequencing (AQRNA-seq), offering increased sensitivity and removes primer dimer without the need for gel-purification steps.

05/21/2026

Tau Protein Builds Up in the Brain. But Why Does It Cause Different Diseases in Different People? The Answer May Be in the Glia.

Here's a puzzle in neurodegeneration research: Alzheimer's disease, Pick's disease, and progressive supranuclear palsy all involve tau protein accumulation. But they affect different brain regions and produce different symptoms. Why?

A new study in Nature Communications used advanced single-nucleus technology to find out.

Researchers analyzed chromatin accessibility and gene expression from the same individual nuclei across three tauopathies. The key finding: glial cells — not just neurons — are driving the disease-specific differences.

What they discovered:

🧬 Glia dominate disease-specific gene regulation. Microglia and astrocytes show the most pronounced regulatory changes, and those changes are different in each disease.

🔬 Genetic risk variants act through glia. Non-coding risk variants converge on specific functional modules in microglia — immune response, lipid metabolism, and more.

🎯 In Pick's disease, sphingomyelin regulation is specifically affected. This points to a potentially druggable pathway.

The takeaway: glia are not passive responders in tauopathies. They are active drivers of disease-specific pathology. And single-nucleus epigenomics is the tool that revealed it.

Full article: nature.com/articles/s41467-026-73007-1

Accumulation of abnormal tau protein selectively affects specific brain cell populations in tau-related dementias. Here, the authors use single nuclei data to show how genetic risk is linked to regional vulnerability across tau dementias.

05/19/2026

The Poly(A) Tail Is Not What You Think. 17% of mRNAs Have Non-A Bases Buried Inside — and That Changes Everything.

Here's a fact that surprises most RNA biologists: the poly(A) tail is not just a string of As. A 2019 Nature Communications study using PAIso-seq found that 17% of mRNAs contain U, G, or C residues embedded within the tail.

Those non-A residues carry regulatory information. And different splice isoforms of the same gene can have completely different tail profiles — different lengths, different compositions, different regulation.

The problem: most methods can't see any of this. Gel-based methods give you an average. Short-read NGS caps out around 230 nt and can't link tails to specific isoforms.

CD Genomics' PolyA Length Analysis Solution uses long-read sequencing (PacBio HiFi and Nanopore) to capture the full picture.

What we can measure:

🧬 Exact tail length per transcript isoform — not a population average 🔬 Internal non-A residues — U, G, and C positions within the tail 📊 APA site usage — which polyadenylation signals are being used 🧪 Single-cell sensitivity — PAIso-seq validated down to 0.5 ng total RNA

Platform options:

→ PAIso-seq (PacBio HiFi) — highest precision, lowest input → TAIL Iso-seq (Nanopore) — full-length transcripts + tail quantification → Nano 3P-seq — unbiased detection, no oligo(dT) bias → FLEP-seq / FLEP-seq2 — highest multi-parameter density per molecule

Used in developmental biology, mRNA therapy design, plant stress research, and single-cell transcriptomics.

Learn more: cd-genomics.com/polya-length-analysis-solution.html