This quarter’s publications point to a shared theme. Whether researchers are searching for clearer answers in rare disease, mapping cell identity across the human body, or interpreting variants linked to neurodevelopmental conditions, the answers increasingly sit in parts of the genome and transcriptome that short reads cannot fully resolve.
In this quarterly edition of Powered by PacBio, we highlight five new long-read sequencing publications from July to September. These papers include a multi-center study measuring the added detection of attributable variants of HiFi whole genome sequencing in short-read negative rare disease cases, a cross-tissue single-cell isoform atlas from the Tabula Sapiens Consortium, a new computational method for calling transcripts across large long-read RNA cohorts, and two Kinnex RNA studies showing how full-length transcripts sharpen insights into rare disease and autism.
Keep reading for a closer look at these featured publications from July through September 2026.
Jump to topic:Rare disease | Single-cell transcriptomics | Bioinformatics | Disease transcriptomics
Rare disease
Yield of long-read genome sequencing for rare disease diagnosis in short-read genome negative cases
In this multi-center study, researchers from UC Irvine, Ambry Genetics (now Tempus AI), Children’s National Hospital, Harvard, Boston Children’s, and GREGoR show how HiFi genome sequencing is “expanding the range of genomic and epigenomic mechanisms accessible to a single sequencing assay.”
Key highlights:
- HiFi WGS on 144 families (107 unsolved, total 371 individuals) identified “a diagnosis in 13 … out of 107 unsolved cases (12.1%),” including five that “could not have been detected with SR-GS” even upon re-analysis, corresponding to “an incremental diagnostic yield of 4.7%.”
- HiFi diagnoses “by virtue of distinct capabilities: detection of complex SVs, detection of variants in regions unmappable with SRs, detection of de novo variants without sequencing of both biological parents, and analysis of methylation patterns”. In addition, “no previously detected diagnostic variants were missed by LR-GS.”
- Cost: This study “illustrates the potential for long-read platforms to replace multiple other testing platforms … Therefore, when considering the cost of diagnostic LR-GS, it should be compared to the cumulative financial and emotional cost of successive genetic tests.”
- Methylation value: HiFi methylation data evaluated against published (array-based) episignatures to assist in four cases: 1) diagnosis of Chung-Jansen syndrome; 2) pathogenicity of a variant in Tatton-Brown-Rahman syndrome (DNMT3A); 3) reclassification of a VUS as likely pathogenic in Sifrim-Hitz-Weiss syndrome (CHD4); and 4) confirmation of clinical history of prenatal valproate exposure. “LR-GS simultaneously detects sequence and DNA methylation variation on the same DNA molecule. Analyzing these data together can accelerate the diagnostic process, using the established episignatures for several neurodevelopmental disorders to identify disorder-specific methylation patterns”.
Conclusion:
This study is the latest example of the growing body of evidence that PacBio is a more accurate solution for rare disease, consistently explaining significantly more cases and thereby having the potential to end the diagnostic odyssey for many patients and their families.
Single-cell transcriptomics
A comprehensive view matters just as much at the RNA level, where the full-length structure of each transcript can change what a cell does.
Single-cell splice isoform usage reveals distinct axes of cellular identity and senescence
In this preprint, researchers from Stanford, CZ Biohub, and the Tabula Sapiens Consortium present “a cross-tissue single-cell long-read isoform atlas spanning 26 human tissues.”
Key highlights:
- Kinnex on 10x Genomics 3′ scRNA-seq for 60 distinct samples spanning 26 tissues from 12 donors
- “We identify hundreds of thousands of novel isoforms along with their cell-type-specific usage, and discover that over one-third of expressed isoforms are absent from existing reference databases”
- “isoform usage is a structured, measurable axis of cellular identity that is distinct from gene expression”
- Applied to senescence: CDKN2A encodes two major splice products (p16INK4a and p14ARF), which have distinct effector pathways. “we … uncover cell-type-dependent isoform remodeling associated with the p16INK4a senescence program.” “Isoform-level resolution is therefore essential for distinguishing a senescence-associated p16INK4a program from a p53-mediated stress response.”
Conclusion:
A cell atlas is incomplete if built from short-read RNA sequencing, which only measures gene-level expression. But the gene is not the most relevant unit to understand biology and disease. 95% of all human genes are alternatively spliced, and on average, 7 different proteins are made from each human gene, each with different (sometimes opposite!) biological functions. It is often precisely those isoforms that give cells their different identities and functions. This new preprint powerfully highlights that full-length RNA sequencing is needed for cell atlases as a foundation to understand cell-resolved biology.
Bioinformatics
As long-read RNA datasets grow, calling transcripts consistently across a whole cohort becomes essential. A new method from PacBio and collaborators is built for exactly that.
Isocall enables scalable transcript identification from long-read RNA-sequencing data
In this preprint, researchers from PacBio, the University of Virginia, Brandeis, and Baylor introduce Isocall, “a scalable and deterministic computational method for jointly calling transcripts from multiple PacBio long-read RNA sequencing samples.”
Key highlights:
- Isocall works “to identify annotated transcripts present in the dataset, discover novel transcripts, and filter likely artifacts”, thereby producing “a common set of transcript definitions across the cohort while preserving the sample-specific read support for each transcript”.
- Applied to 206 HPRC samples (joint calling entire 3.5 bn read dataset in 25 min).
- In GIAB samples, Isocall “recovered 337 polymorphic splice sites, including a de novo donor site in BTN3A1 that corresponds to a complete isoform switch on the mutant allele”.
Conclusion:
Isocall gives researchers a practical way to bring cohort-scale consistency to long-read transcriptomics, enabling scalable joint isoform calling and characterization of isoform diversity across large PacBio datasets. By processing billions of reads in minutes while keeping sample-level detail intact, it makes population-scale Kinnex RNA studies faster to analyze and easier to compare.
Disease transcriptomics
Kinnex RNA data provide exceptional insights in health and disease research, and two publications this quarter show what that looks like in practice, from rare disease trios to neuronal development and autism.
In this preprint, researchers from the Broad Institute and Boston Children’s report that “lrRNA-seq from whole blood in a rare disease trio cohort yielded a high-quality isoform landscape, and superior detection for transcriptome-wide splicing dysregulation compared to paired srRNA-seq.”
Key highlights:
- 60 Kinnex RNA samples (20 individuals with rare diseases and their unaffected biological parents), average 13.4M full-length reads
- Kinnex data “yields more uniform coverage across transcripts compared to srRNA-seq, and 20.2% of long-read transcripts are greater than 10 kb versus less than 5% from paired srRNA-seq. From lrRNA-seq we identify a mean of 24,439 isoforms of which 18.5% are unannotated in GENCODE. Of these unannotated isoforms, 74.3% are in disease-associated genes.”
- “lrRNA-seq superior to srRNA-seq in demonstrating alternative 5′ donor usage in an individual with ReNU syndrome,” “when we applied the same framework to paired srRNA-seq in the same cohort, this individual was not an outlier.”
- Study highlights that the “lack of comprehensive reference datasets for lrRNA-seq constrains the interpretation of novel isoforms” and “underscoring the need for a coordinated consortium effort to build population-scale lrRNA-seq reference datasets to fully realize the diagnostic potential”
Moving from blood to the developing brain, a second study applies the same full-length approach to neurodevelopment.
In this Nature Communications paper, researchers from the University of Toronto and SickKids in Canada offer “an isoform-centric view for interpreting pathogenic variation in neurodevelopment.”
Key highlights:
- “deep long-read [Kinnex] RNA sequencing and proteomics in iPSC-derived cortical neurons to generate a high-resolution proteogenomic atlas of human neuron development”
- “We identify 182,371 mRNA isoforms (over half previously unknown) and provide direct peptide evidence for the translation of hundreds of novel protein-coding sequences”
- Applied to autism spectrum disorder (ASD): “we observe that ASD risk genes undergo dynamic isoform switching … that remodel key protein domains and regulatory regions, … we uncover widespread, long-range coordination between splicing and polyadenylation”, and “our atlas enables variant reinterpretation in ASD”
Conclusion:
Kinnex RNA data highlight in study after study how much is missed by short-read RNA-seq, and how these missed transcripts play important roles in understanding many diseases. The full potential of such comprehensive information will only get stronger in the future when more comprehensive databases will be built for healthy and disease cohorts. These can only be built with more HiFi data.
Ready to make discoveries of your own?
This quarter’s studies show how much more comes into view when genomes and transcriptomes are read in full. HiFi genome sequencing is solving rare disease cases that short reads left open, while full-length RNA data reveal the isoforms behind cell identity and disease risk. New tools like Isocall make that information easier to analyze at scale.
We’ll be back early next year with another collection of standout publications showcasing how researchers around the world are putting PacBio technology to work.
Ready to see how you can use HiFi sequencing for your next project? Let’s get started.