HiFi Long-Read RNA Sequencing Enhances Clinical Diagnostics in Rare Disorders

HiFi Long-Read RNA Sequencing Enhances Clinical Diagnostics in Rare Disorders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article HiFi Long-Read RNA Sequencing Enhances Clinical Diagnostics in Rare Disorders Carolina Jaramillo Oquendo, Federico Ferraro, Htoo Wai, Heather Ferrao, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7046889/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in European Journal of Human Genetics → Version 1 posted 12 You are reading this latest preprint version Abstract Splice-disrupting variants are estimated to account for one-third of disease-causing variants, yet many remain underrepresented in clinical databases due to limitations in detecting splicing changes beyond canonical splice sites. Short-read RNA sequencing (RNA-seq) has proved to be a valuable complement in clinical practice to address this gap, however, the added value of long-read RNA-seq is unclear. Here, we aimed to assess the clinical utility of PacBio long-read RNA-seq to characterise pathogenic aberrant splicing in rare disorders compared to short-read RNA-seq. Participants from the UK and the Netherlands with suspected splice-altering variants underwent long-read RNA-seq. 28 blood samples and four fibroblast cell lines were sequenced following the Kinnex full-length RNA protocol. Detection of disease genes (OMIM and PanelApp) was comparable with short reads, with fibroblast capturing more transcripts overall. Novel isoforms accounted for ~ 14% of detected transcripts in both tissues, increasing following cycloheximide treatment in fibroblasts and decreasing following goblin depletion in blood. Long-read RNA-seq detected events missed by short-reads including intron retention, multiple exon skipping, differential transcript usage, leaky splicing and variant phasing. In one case long reads revealed that a splice region variant in RPS7 skewed expression toward an unannotated intron 6-retained transcript, likely leading to protein deficiency explaining previous ambiguous results in patient with Diamond-Blackfan anaemia. In another case, we identified a retrotransposon-induced isoform switch in TCOF1 causing Treacher Collins syndrome. Both examples unresolved by short reads. Thereby long-read RNAseq has the potential to improve the detection of clinically relevant transcripts when used in a clinical setting. Health sciences/Medical research/Genetics research Biological sciences/Genetics/RNA splicing Biological sciences/Genetics/Clinical genetics/Genetic testing Biological sciences/Biotechnology/Sequencing/RNA sequencing Biological sciences/Biological techniques/Sequencing/Next-generation sequencing RNAseq HiFi long read diagnostics splicing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction It is estimated that a third of disease-causing variants can disrupt mRNA splicing[ 1 , 2 ]. Splice-affecting variants are often missed in clinical detection and are under-ascertained in clinical variant databases[ 3 ] as these are not limited to canonical splice sites[ 4 , 5 ]. RNA testing is now considered a complementary tool to DNA testing both in terms of providing functional evidence but also in identifying new events missed by traditional methods[ 6 – 14 ]. Within the UK or the Netherlands some healthcare providers offer specialised RNA studies conducted via targeted reverse transcription PCR (RT-PCR) or RNA-sequencing (RNA-seq). RT-PCR is useful in the assessment of genes with low expression (< 1 transcripts per million [TPM]) and aberrant splicing events at low levels[ 15 ]. However, RT-PCR is a bespoke test for each patient, and it is inherently limited by gene annotation choice, PCR amplicon lengths and assumptions on expected splicing abnormalities. On the contrary, RNA-seq is independent of the individual patient, it is agnostic to the resulting abnormally spliced transcript and can aid in identifying a variety of events without making a priori assumptions. Most RNA-seq studies rely on short-read RNA-seq (SR RNA-seq), which although it has its advantages over RT-PCR, is still unable to produce full-length transcripts, resolve complex regions, and identify certain types of aberrant splicing events such as long stretches of intron retention[ 16 , 17 ]. Full-length transcripts allow better assessment of the effects on splicing and quantification of transcript abundance, particularly when the gene in question has multiple isoforms. Additionally, longer reads have sufficient genomic context to accurately map to regions that are challenging due repetition, high polymorphism, or low nucleotide diversity, therefore increasing coverage of genes that standard SR sequencing struggles to capture. As SR RNA-seq is integrated into clinical practice, it is essential to assess the potential benefits of long-read sequencing in this context. Long-read sequencing platforms including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) are now on par with short-reads in terms of throughput and accuracy[ 18 ]. PacBio’s Revio system claims 99.95% (Q33) read accuracy with read lengths of 15-20kb, a yield 3-4x higher, and a 15x higher throughput, than their previous Sequel Ile system. With regards to RNA-seq specifically, PacBio’s Kinnex kit based on the MAS-seq method concatenates smaller amplicons into larger fragment libraries for a higher throughput of full-length RNA, single-cell RNA and 16S rRNA. Although not always documented within publications, data storage requirements for long reads tend to be much higher than short reads for the same yield in gigabytes. Between PacBio and ONT, ONT has historically been associated with larger data storage requirements (especially for raw data) compared to PacBio for similar yields. Long-read RNA-seq (LR RNA-seq) is a relatively new technology and rapidly evolving technology. Due to its novelty, only a limited number of cases that demonstrate its ability to uncover pathogenic splicing events that were missed by SR RNA-seq[ 19 – 21 ]. The aim of this study is to assess the clinical utility of PacBio Hifi sequencing of RNA/cDNA in identifying new and previously known aberrant splicing events in patients with rare disorders. Findings will also be compared to SR Illumina sequencing. Materials and Methods Patient Cohort Participants were enrolled into the University of Southampton's Splicing and Disease study with appropriate ethical approval (REC 11/SC/0269, IRAS 49685, ERGO 23056). The sub-cohort used herein is comprised of 22 individuals with a suspected Mendelian disorder assessed by UK clinical genetics services in whom a candidate variant of uncertain significance (VUS) had been identified through conventional DNA-based testing. SR RNA-seq results for six of the 22 individuals have been previously published[ 14 ]. PacBio Kinnex data was also generated with RNA extracted from fibroblasts from two patients examined at Erasmus MC for whom diagnostic SR RNA-seq was performed. Participants had been examined at the Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, the Netherlands and genetic analyses were performed in a clinical setting. Informed consent was obtained and all individuals or their legal guardians provided written consent to share anonymized clinical and analysis data. Use of genome-wide technologies for diagnostic purposes was previously approved (Institutional-review-board MEC-2012-387). For each variant/patient we report the genotype and observed phenotype (Table 1 ). Table 1 Description of genetic variants, genotypes (GT) and observed phenotypes. AL: acceptor loss, DL: donor loss, AG: acceptor gain, DG: donor gain. * Indicates samples for which short read RNA-seq or RT-PCR results have been previously published. ID Gene Variant(s) GT Phenotype Tissue SpliceAI Δ score AL|DL|AG|DG P01* UBR4 NM_020765.3:c.8488 + 3A > G HET Cerebellar ataxia, nystagmus Blood 0.04|0.26|0.03|0.13 P02 KLHL7 NM_001031710.3:c.936 + 3_936 + 22del HOM Perching syndrome Blood 0.68|0.97|0.0|0.01 P03* NF1 NM_000267.3:c.1168_1179del, p.(Asn390_His393del) HET Neurofibromatosis type 1 Blood 0.03|0.04|0.0|0.0 P04* PTEN NM_000314.8:c.553C > G, p.(His185Asp) HET Callouses palms and soles, prominent bleeding gums, macrocephaly. Blood 0.0|0.0|0.0|0.0 P05 KLHL7 NM_001031710.3:c.936 + 3_936 + 22del HET Unaffected carrier Blood 0.68|0.97|0.0|0.01 P06 NF2 NM_000268.4:c.885 + 5G > A HET Intra-medullary ependymoma. Sibling with ependymoma Blood 0.53|0.52|0.0|0.0 P07 COX7B NM_001866.3:c.40 + 5G > A HET Pupil asymmetry, cerebellar hypoplasia,Ligamentous laxity, cataplexy, Learning difficulities, Microcephaly, right foot neruopathy. Blood 0.0|0.02|0.01|0.01 P08* RPS7 NM_001011.4:c.507 + 3A > G HET Diamond Blackfan Syndrome Blood 0.0|0.03|0.0|0.08 P09 PUF60 NM_078480.3:c.560T > A, p.(Leu187*) HET PUF60-related developmental disorder Blood 0.0|0.01|0.01|0.01 P10* PHF8 NM_015107.3:c.784-2A > G HEMI Global developmental delay, epilepsy and hypotonia Blood 0.99|0.59|0.27|0.0 P11 COL9A2 NM_001852.4:c.1792 + 5G > A HET Stickler syndrome Blood 0.20|0.75|0.0|0.37 P12* PNKP NM_007254.4:c.1029 + 2T > C HET Global developmental delay Blood 0.69|0.93|0.03|0.06 P13 WDR45B NM_019613.4:c.143-5T > A HOM Structural brain abnormality and profound developmental delay Blood 0.82|0.75|0.0|0.0 P14 ITPR1 NM_001378452.1:c.1712A > G HET Ataxic cerebral palsy, global developmental delay Blood 0.01|0.08|0.0|0.55 P15 KIAA0825 NM_001145678.3:c.3451_3456 + 13del NM_001145678.3:c.2020T > A, p.(Tyr674Asn) HET Post axial polydactyly left hand and both feet. Normal development. Mild ear dysplasia and dysmorphism Blood 0.53|0.80|0.0|0.09, 0.0|0.0|0.03|0.03 P16 EFTUD2 NM_004247.4:c.1393A > G, p.(Met465Val) HET Left Kidney Agenesis, Klippelfeil, scoliosis, arachnoid cyst Blood 0.0|0.06|0.0|0.99 P17 ZMYM2 NM_197968.4:c.3301 + 5G > A HET low muscle tone, developmental delay,problems with fine motor skills, squint. Low set ears Blood 0.66|0.98|0.01|0.02 P18 SETD5 NM_001080517.3:c.-177 + 1G > A HET Talipes,Hemihypertrophy, short stature,Speech delay. Blood 0.0|0.98|0.0|0.16 P19 MLH1 NM_000249.4:c.704A > G, p.(Asp235Gly) HET Transverse colon cancer Blood 0.29|0.29|00|0.0 P20 BAP1 NM_004656.4:c.581G > A, p.(Gly194Glu) HET BAP1-inactivated melanocytic tumour Blood 0.0|0.0|0.26|0.0 P21 LMNA NM_170707.4:c.1381-5G > A HET dilated cardiomyopathy Blood 0.01|0.01|0.97|0.0 P22 PTEN NM_000314.8:c.634 + 3A > C HET Macrocephaly,DD, oropharynx-haematoma, Blood 0.92|0.98|0.0|0.0 F01 TCOF1 NM_001371623.1(TCOF1):c.2860–3215_2860-3214insN[3396] HET Treacher Collins syndrome Fibroblast NA F02 YY1 NM_152333.4:c.-120-994_*23708del HET Gabriele-de Vries syndrome Fibroblast NA Short-read RNA-seq and analysis Blood RNA extraction and sequencing had previously been reported[ 14 ]. SR RNA-seq was generated at NovoGene (Hong Kong) with a minimum of 70 million reads. FASTQ files were aligned to the human genome reference (GRCh38) with annotations from GENCODE[ 22 ] release 38 using STAR aligner[ 23 ] v2.6.1c. Fibroblast cell culturing, cycloheximide (CHX) treatment, RNA extraction and sequencing had previously been reported[ 13 ]. SR RNA-seq was generated at GenomeScan, Leiden, the Netherlands, with a minimum of 40 million reads generated per sample. Trimmed reads were aligned to the human genome reference GRCh38 with HISAT2 v2.2.1. Transcript per million were extracted using Stringtie v2.2.3 and annotations from GENCODE release 38. LR sequencing and analysis The PacBio Kinnex method for RNA-seq sample preparation is based on the MAS-Seq method[ 24 ], which concatenates smaller amplicons into larger fragment libraries for throughput increase. Kinnex RNA-seq data from RNA extracted from blood was generated using a Revio system in two batches. The first batch of 12 samples was sequenced at PacBio (Menlo Park, CA), while the second batch of 10 samples was sequenced at the PacBio (EMEA headquarters in London). Kinnex data were processed using the IsoSeq3 pipeline, mapped to GRCh38, and visualised using IGV. Run 1 (n = 16 libraries) was split across four pools (SMRTcell) with 12 unique bio samples. Pools 1–3 consisted of four samples per SMRTcell and were globin depleted. Pool 4 sequenced the same four samples in Pool 3 but with no globin depletion. Comparison of transcriptome profiles between depleted and non-depleted batches in Run 1 led to the decision to omit the depletion step for samples in run 2 (n = 12 libraries) as depletion negatively impacted the transcript diversity ( Supplementary Fig. 1 ). To compensate for the reads that would be taken up by globin genes, the 12 unique samples in run 2 were split across three SMRTcells instead of four. Splicing and expression results for 22 of the 24 biological samples are reported. One sample was excluded due to of lack of VUS, and the other was excluded as it is a biological replicate that was sequenced in both Run 1 and Run 2. HiFi reads from the Revio instrument were processed using the Read Segmentation and Iso-Seq workflow available in SMRT Link version 13.1. Kinnex arrays were segmented into their constituent cDNA reads using skera v1.2.0. Lima v2.10.0 was used to remove barcoded cDNA primers, demultiplexing the pools. The Iso-Seq bioinformatics toolkit v4.1.2 was then used to remove polyA tails, identify artefactual concatamers and cluster sequences, which are then mapped against GRCh38_no_alt_analysis_set using pbmm2 1.14.0 in its specialised IsoSeq mode. Remaining reads were collapsed using the PacBio Iso-Seq toolkit and then classified and filtered using Pigeon 1.2.0. Fibroblast Kinnex RNA-seq data was generated using a Revio system at the Leiden Genome Technology Center (LGTC) in collaboration with GenomeScan (Leiden, the Netherlands), with a minimum of 10 million reads per sample. Two libraries were generated per sequenced individual, one RNA sample from fibroblasts treated with CHX + and one CHX-, for a total of four libraries across one SMRTcell. Sequencing reads were processed with the IsoSeq pipeline, specifically IsoSeq v4.2.0, pbmm2 v1.16.0, and Pigeon v1.3.0, with the same reference and annotation used for the SR data. Assessment of aberrant splicing in blood To determine the functional consequence at a transcript level for each variant, both SR and LR RNA-seq data was loaded into the Integrative Genomics Viewer (IGV)[ 25 ] and each variant was visually inspected to search for splicing abnormalities as detailed previously[ 14 ]. Aberrant splicing events (intron retention, exon skipping, novel acceptor and donor, cryptic exon) were identified using SR RNA-seq (n = 19), and PacBio RNA-seq (n = 22). These events were then collated into a comprehensive list. Each event on this list was cross-checked to determine whether it was detected by the other platform. This approach allowed for a direct comparison of the sensitivity of each technology in identifying splicing events. Results Quality assessment of PacBio Kinnex LR RNA-seq data Blood Sequencing depth varied across pools and runs, where Run 1 (n = 16 samples; 4 samples per SMRTcell) had higher variability compared to Run 2 (n = 12 samples; 3 samples per SMRTcell) (Fig. 1 A). On average each sample had 13.2 million full length non-chimeric (FLNC) reads with a minimum and maximum of 4.9 and 24.3 million respectively. Transcript length distributions appeared uniform across runs and samples with a peak right below 2000 bp (Fig. 1 B). Globin depletion has been shown to enhance the detection of transcripts with lower expression in SR RNA-seq data, as it reduces the representation of globin mRNA (~ 30% of transcripts), freeing up sequencing reads for more relevant transcripts[ 26 – 28 ]. We assessed the utility of this procedure on Kinnex data and sequenced a pool of the same biological samples (n = 4) with and without globin depletion. Along with depletion of globin genes, genome-wide transcript diversity was impacted negatively by this procedure. In undepleted samples at least 10,000 additional transcripts (full-splice match and incomplete splice match) were identified across all four samples ( Supplementary Fig. 1 ). Fibroblasts Libraries derived from 2 patient samples were sequenced on a single SMRTcell (two libraries per sample CHX+/CHX-) and obtained an average 11.7 million FLNC, with a minimum of 11 and a maximum of 12.6 million. Detected transcript-length distributions appeared uniform across the four samples with a peak around 2000 bp (Fig. 1 C), comparable to that observed for blood (Fig. 1 D). An additional peak was observed between 7,500 and 9,000 bps in both cycloheximide (CHX) treated and untreated cells constituted primarily by FN1 transcript, a highly expressed gene in cultured fibroblasts and lowly expressed in blood. Gene detection in blood LR RNA-seq is comparable to SR RNA-seq Across the 22 blood samples the Iso-seq pipeline detected a minimum of 12,271 genes annotated in GENCODE and a maximum of 16,132. To evaluate the potential diagnostic relevance of the long-read RNA-seq, disease gene pick-up rate —entries in OMIM and PanelApp—were also assessed[ 29 ]. On average 8,315 out of 16,630 OMIM genes (50%) were detected with a minimum and maximum of 5,775 and 10,855 respectively. Similarly, an average of 1,822 (min 1,128 max 2,515) out of 3,643 PanelApp genes were detected. The detection rate for both OMIM and PanelApp genes is comparable to the detection rate with SR RNA-seq ( Supplementary Fig. 2 ). A subset of approximately 200 genes, detected by SR RNA-seq (median TPM ≥ 5), were not detected by the LR RNA-seq, showing a median TPM of 0. When the TPM threshold for short reads is lowered to TPM ≥ 1, the number of undetected genes increases to ~ 1,000. CHX treatment increases diversity of genes and transcripts detected in fibroblasts The Iso-seq pipeline detected a minimum of 14,495 genes annotated in GENCODE in untreated fibroblasts and this number increased in CHX-treated fibroblasts, where a minimum of 15,994 genes was detected ( Supplementary Fig. 3 ). We observed that 64.6% of the genes detected in one untreated cell line were also detected in the other cell line, and that this percentage increased to 69.4% in CHX + cells ( Supplementary Fig. 3 ). The most striking difference was in the number of discovered transcripts between CHX- and CHX + fibroblasts with > 65,000 identified in the latter. Importantly, the number of genes and transcripts discoverable appeared to be already saturated at the achieved read depth ( Supplementary Fig. 3 ). Of 16,630 genes included in OMIM, LR RNA-seq detected a minimum of 10,841 (65%) in CHX- fibroblasts, and 10,887 (65%) in CHX + fibroblasts. Similarly, of 3,643 PanelApp genes, a minimum of 2,596 (71%) were detected in CHX- cells and 2,608 (72%) were detected in CHX + cells. This is in concordance with previous observations that RNA-seq in fibroblasts encompass a greater number of disease-relevant genes compared to blood[ 13 ]. Of the genes well detected by SR RNA-seq (TPM ≥ 5) and present in either OMIM or PanelApp, 198 genes were not detected in LR RNA-seq sequencing, and at the lower threshold of TPM ≥ 1 in SRS, the number of genes not detected increased to 858. Assessment of transcripts identified Blood Approximately 40% of detected genes were represented by a single isoform, 20% by 2–5 isoforms and 40% were represented by six or more isoforms. When split by structural categories, most transcripts were categorised as incomplete splice matches, followed by full splice matches and novel isoforms in and not in catalogue (Fig. 2 A-B). Fibroblasts Across the 4 fibroblast cell lines, we observed that ~ 40% of the detected genes presented with ≥ 6 distinct isoforms. In contrast, only about 20% of the genes found in fibroblasts were represented by a single isoform (Fig. 2 C). This proportion was higher in blood, suggesting that fibroblasts express multiple isoforms for a greater number of genes compared to blood. Pigeon-classification of isoforms detected in the CHX- fibroblasts assigned most transcripts to the incomplete splice and full splice match categories, similar to blood (Fig. 2 D). Interestingly, the CHX treatment allowed for a higher number of transcripts classified as novel not in catalogue, i.e. transcripts that use novel donors and/or acceptors not present in the GENCODE annotation. LR RNA-seq identifies known events and provides higher resolution in a subset of cases affecting their clinical care Using both SR and LR RNA-seq, a total of 30 aberrant splicing events were identified across the blood 22 samples. LRS confidently captured 21 aberrant splicing events linked to a variant, three events with low confidence and six events were not captured (Table 2 ). Five of the six events missed by LRS were due to low or no coverage of the whole gene ( PHF8, COX7B and KIAA0825 ). The remaining event was not detected as it was a low-level splicing abnormality requiring higher read depth. In instances where there was good gene coverage, LRS was able to confirm aberrant splicing events found previously, and in five cases, LRS detected additional effects on transcripts, enhancing interpretation of the variants and either helping resolve pathogenicity or providing additional biological insights the into variant’s effect. This included identification of intron retention events with more confidence (i.e. did not need to validate with RT-PCR or intronic reads not present in other samples), phasing variants of interest, and quantifying both known and novel transcripts. Illustrative examples where LR RNA-seq enhanced biological insights NM_001011.4( RPS7 ):c.507 + 3A > G skews expression toward unannotated intron retained transcript : RPS7 encodes a ribosomal protein essential for ribosome biogenesis and function, mutations in this gene have been associated with Diamond-Blackfan anaemia[ 30 , 31 ], consistent with the patient’s phenotype. Proband P08 previously underwent RT-PCR, which yielded normal results[ 15 ]. SR sequencing suggested potential intron 6 retention, however, due presence of intronic reads in controls, this result was inconclusive. Salmon was used to quantify the transcript abundance of ENST00000645674.2 (NM_001011.4) in this sample [short reads] and when compared to 87 unrelated samples the MANE select transcript appeared to be upregulated (Fig. 3 A). LRS confirmed that while this mutation caused increased intron 6 retention in patients, intron 6 retention was also present in controls, explaining RT-PCR results. In both patient and controls two transcript isoforms were identified: one with intron 6 retention and the MANE select transcript (Fig. 3 B-C). In controls the ratio was roughly 1:1, whereas in the patient, the intron retained transcripts was ~ 40x times more abundant. This significant shift in transcript ratio could lead to RPS7 protein deficiency, likely due to competition or degradation of the aberrant isoform. Homozygous NM_001031710.3( KLHL7 ):c.936 + 3_936 + 22del variant causes leaky splicing : Biallelic mutations in KLHL7 are known to cause PERCHING syndrome a rare multisystemic developmental disorder[ 32 , 33 ]. Previous RT-PCR assay identified exon 7 skipping caused by NM_001031710.3:c.936 + 3_936 + 22del variant, evidence which was subsequently used to reclassify the variant and offer carrier testing to family members. LRS confirmed these results, but also identified a complete splice match to the normal MANE select transcript (NM_001031710.3). While a low-level event, no normal transcripts were expected as this was a homozygous mutation, indicating leaky splicing. This patient has most of the constituent features of PERCHING syndrome, detailed phenotype described in previous publication[ 33 ] (patient 6), not consistent with an attenuated phenotype. However, this patient is ambulant and still living at age 17 and perhaps not as profoundly disabled as some of the more severe cases. LRS facilitates variant phasing : The NM_001378452.1( ITPR1 ):c.1712A > G variant was initially referred for splicing assessment. Both SRS and LRS confirmed the presence of and alternative donor site in intron 17. The initial request did not include information about additional variants. However, after receiving initial results, the referring team noted the mention of LRS and subsequently contacted us to investigate phasing for a second variant. LRS successfully provided phasing information, revealing that the second variant was in trans with the initial c.1712A > G variant. Retrotransposon-induced isoform switch in TCOF1 Loss-of-function variants in TCOF1 are the most common cause of Treacher-Collins syndrome[ 34 ] and recently a retrotransposon insertion as novel pathogenic mechanism in this gene was reported[ 35 ]. Outlier-analysis with short-read RNA-Seq identified this pathogenic event, and it suggested the presence of an isoform switch from the canonical TCOF1 transcript to a shorter TCOF1 isoform, lacking a nucleolar localization signal and expected to impair ribosome biosynthesis[ 36 ]. Further characterization showed this to be due to the insertion of a SINE-VNTR-Alu (SVA) retrotransposon into TCOF1 intron 17, that is partially exonized and induces an early termination codon leading to nonsense-mediated mRNA decay. To assess the potential of Kinnex to resolve and characterize this event, we performed LR RNA-seq in fibroblasts from this patient. Manual inspection of the FLNC of CHX- fibroblasts from this patient and phasing of the reads by heterozygous SNVs, showed the isoform switch induced by the SVA: while the reference allele produced mostly long TCOF1 isoforms (supporting reads ratio 70:18), the allele carrying the SVA produced mostly shorter TCOF1 isoforms (supporting reads ratio 8:43). This observation was also confirmed by the data from the CHX + fibroblast RNA (reference allele long:short TCOF1 ratio 87:11; SVA-allele 16:42) and the partial exonization of the SVA in 12 of the 16 reads supporting a long isoform were also detected (Fig. 4 A). Notably, no reconstructed isoform spanning the SVA was identified using the IsoSeq collapse step run with the default settings. LR RNA-seq therefore completely resolved and phased full-length transcripts that included the SVA insertion (4,978 bp) that went undetected using SR RNA-seq. Deletion of YY1 distal exons induces transcript readthrough Loss-of-function YY1 variants cause autosomal dominant Gabriele-de Vries syndrome[ 37 ]. Short-read RNA-seq identified an expression outlier for YY1 in an individual with global developmental delay, visual impairment, white matter abnormalities, and feeding difficulties. Manual inspection of the SRS data indicated read-through transcripts involving the locus of two genes downstream of YY1, i.e. SLC25A29 and SLC25A47. Reanalysis of exome data identified a heterozygous deletion of the last 3 exons of YY1 (NM_003404.5) and exon 2 and 3 of the neighbouring gene SLC25A29 ( NM_001039355.3 ). Examination of FLNC from Kinnex sequencing in CHX + and CHX- fibroblasts from this patient, confirmed the presence of YY1 transcripts including portions of one of the SLC genes or both (Fig. 4 B). The default Iso-Seq pipeline also reconstructed a read-through YY1 product into the SLC25A29 locus in both samples but failed to detect isoforms involving the SLC25A47 locus as these appear to be present in less than 2 FLCN . Discussion We applied PacBio full-length RNA-seq with the Kinnex system in blood (± globin depletion) and fibroblasts (± cycloheximide) to assess throughput, compare it to short-read technology, and evaluate its utility for clinical interpretation of variants referred for RNA-seq studies. Kinnex RNA-seq confirmed findings from previous analyses of SR RNA-seq both in blood and fibroblast RNA. This new technology successfully captured events missed by SR such as intron retention, multiple exon skipping, differential transcript usage and phasing of transcripts, serving as a valuable complement to short-read RNA-seq in a clinical setting. Our study demonstrates the potential of PacBio LR RNA-seq for variant interpretation, but several limitations should be considered. While there is good correlation between short and long reads, coverage was lower for a subset of genes. Pathway analysis showed that these genes were immune-related, including T-cell receptor variable and immunoglobulin genes. Within our cohort, PHF8 and COX7B had TPM values of 7.5 and 4.4 respectively in SR data, compared to 3.2 and 0.3 in blood LR data, impairing assessment of the two variants within these genes. Alternatively, using a different tissue such as fibroblast might provide a better resolution for some genes, e.g. COX7B and PHF8 reached median TPM of 30 and 27, respectively in SRS, compared to 8 and 11 in LRS data. Interestingly, in both assessed tissues we observed discrepancy in the predicted expression values. The discrepancy could be due to read depth, suggesting higher depths might be required to fully capture all genes expressed in blood. Finally, current pipelines used to identify novel events in undiagnosed patients are not yet optimised for LR RNA-seq data or have not been sufficiently adapted to handle this type of data[ 38 ]. Given the large number of novel events detected, distinguishing disease-relevant events from those arising from transcript diversity, yet to be characterised, remains a challenge. Although the assessment of globin depletion and cycloheximide treatment is based on eight and four libraries respectively, our findings provide valuable insights into optimising experimental design, such as selecting appropriate library prep, tissue and determining the number of samples per SMRTcell to best adapt to the need of the study. Strikingly, while globin is depletion in blood is associated with higher transcript diversity[ 26 , 39 ] in SR RNA-seq, the opposite was observed when this treatment was included into the Kinnex protocol. It is important to note that there was noticeable variability in the coverage of samples in the first run and although we cannot attribute the variability to the depletion process, the pool without depletion performed better. In fibroblasts, in line with previous observations[ 13 ], our data confirm that cycloheximide treatment can be beneficial to improve transcript and gene diversity and shows that this can be also detected with Kinnex. Despite its limitations, LR RNA-seq successfully identified additional events and provided a clearer understanding of aberrant splicing. Compared to SRS, LR RNA-seq can better detect intron retention events with less noise and events that span multiple exons. Moreover, LR RNA-seq can detect full-length transcripts, quantify transcript diversity within a sample, and assign them to specific alleles using heterozygous variants. At least 10% of transcripts we detected were novel not in catalogue and this could have significant implications when standard annotations are used to quantify gene expression. This was exemplified with the quantification of RPS7 by Salmon in proband P08, where no annotated transcripts include the retention of intron 6. The quantification of ENST00000645674.2 (NM_001031710.3) in short reads would lead us to believe that there was a higher or normal expression of this transcript. While short reads were able to pick up the potential intron retention, the difference in isoform usage was only made clear with the long-read data. Without LRS, leaky splicing in KLHL7 gene would not have been identified. While this finding doesn’t change the clinical interpretation, as the initial report issued helped reclassify the variant, leaky splicing could help explain variability across disorders caused by mutations in the same gene. Additionally, having full length transcripts, or at least very long reads, enables transcript phasing. This approach therefore broadens access to testing for patients where parental DNA/RNA is unavailable. For these patients we can still assess whether variants are expressed in cis or trans , if variants are not spliced out. We view LR RNA-seq as a valuable tool for detecting aberrant splicing and gene expression in rare disease patients. While throughput has increased, particularly with the Kinnex kit, costs remain higher than short read sequencing, for which there are well-established pipelines. For cases where a VUS is present in a gene well-expressed in blood (TPM ≥ 5) and not located in repetitive regions, SR RNA-seq is likely the best initial test. However, LR RNA-seq could serve as an effective follow-up test, helping to validate or refine SR RNA-seq results. Alternatively, targeted deep long-read sequencing could be applied specifically to genes with a known VUS, instead of whole-transcriptome sequencing[ 40 ], but in this scenario, throughput becomes a limiting factor. In the UK, SR RNA-seq is only just being introduced into clinical practice, so it will take time before LR RNA-seq becomes part of the NHS diagnostic framework. Similarly, in the Netherlands diagnostic SR RNA-seq is available in clinical practice in only few centres. The next step is to develop methods in LRS for identifying relevant events in patients without a variant of uncertain significance (VUS), already being done with SR RNA-seq[ 6 – 8 , 13 , 14 ], helping to determine which patients and samples would benefit most from this technology. While LRS has clear potential in rare disease diagnostics, its successful integration into clinical practice remains an open challenge, requiring further research and optimisation. Declarations Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Code availability We made use of the publicly available listed in the methods with default parameters. We recommend using the latest update of these tools. Acknowledgements The authors thank all patients and families taking part in this research and all clinicians involved. We thank all staff from regional genetics services who recruited patients: SanSan Htun, Tessy Thomas, Andrew Douglas, Claire G Salter (https://orcid.org/0000-0002-2494-1644), Lucy Side (https://orcid.org/0000-0003-4476-8735), Mary O’Driscoll (https://orcid.org/0000-0002-7119-7571), Mark Hamilton, Dr Nayana Lahiri, Sahar Mansour, Stephanie Grenville-Heygate (https://orcid.org/0000-0003-1516-3016), M Suri, Ed Blair, Nicola Foulds (https://orcid.org/0000-0002-5779-0096), Jessica Radley (https://orcid.org/0000-0002-0776-0091), Helen Stewart, Caroline Pottinger, Vivienne McConnell, Ajoy Sarkar. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. Preparation of this manuscript was supported by C.J.O attending the University of Southampton Faculty of Medicine/Faculty of Environmental and Life Sciences Writing Retreat July 2024. Author contributions Conceptualization: D.B.,T.V.H.; Data curation: C.J.O., F.F.; Formal analysis: C.J.O., F.F.; Funding acquisition: D.B.,T.V.H.; Investigation: C.J.O., F.F., H.W., H.F., H.V.L., E.K.,H.D., S.H., D.J.B.; Methodology: C.J.O., F.F.; Project Administration: D.B.,T.V.H.; Resources: H.F., H.V.L, L.T., D.J.B., L.D.K., M.V.D., J.Z.; Software: C.J.O., F.F.; Supervision: L.T., S.E., J.W.H., T.V.H., D.B.; Validation: H.W; Visualisation: C.J.O., F.F.; Writing-original draft: C.J.O., F.F.; Writing-review & editing: C.J.O., F.F., S.E., J.W.H., T.V.H., D.B. Funding The DB Laboratory is supported by National Institute for Health and Care Research Professorship (RP-2016-07-011) and NIHR Senior investigator award NIHR303895. Ethical Approval Informed consent was obtained and all individuals or their legal guardians provided written consent to share anonymized clinical and analysis data. 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Sequencing depth per sample and across each pool/SMRTcell in blood, measured in full-length non-chimeric reads. \u003cstrong\u003eB.\u003c/strong\u003e Distribution of read lengths across pools/SMRTcell in blood. \u003cstrong\u003eC.\u003c/strong\u003e Distribution of read lengths per sample in fibroblasts. \u003cstrong\u003eD.\u003c/strong\u003e Read lengths versus transcripts per million (TPM) in CHX- and CHX+ fibroblasts. R1P1= run 1 Pool 1, CHX = cycloheximide.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7046889/v1/17f6e99aa5a8bc64afe03abf.png"},{"id":87552995,"identity":"2798c959-69c4-4d6c-b1b1-043b8c74a28b","added_by":"auto","created_at":"2025-07-25 06:34:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177241,"visible":true,"origin":"","legend":"\u003cp\u003eIsoform characteristics. \u003cstrong\u003eA.\u003c/strong\u003e Isoforms distribution across structural categories in blood samples. \u003cstrong\u003eB\u003c/strong\u003e. Isoform count per gene in blood samples. \u003cstrong\u003eC.\u003c/strong\u003e Distribution of isoforms across structural categories in CHX- and CHX+ fibroblasts. \u003cstrong\u003eD.\u003c/strong\u003e Isoform count per gene in CHX- and CHX+ fibroblasts. CHX = cycloheximide.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7046889/v1/cbd9d305d065d4e536be1b66.png"},{"id":87554473,"identity":"8a7972f6-2adc-4174-85e7-d9daaa8d5b3f","added_by":"auto","created_at":"2025-07-25 06:42:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149050,"visible":true,"origin":"","legend":"\u003cp\u003eRPS7 isoform identification. \u003cstrong\u003eA\u003c/strong\u003e. TPM values of ENST00000645674.2 (\u003cem\u003eRPS7\u003c/em\u003e) calculated by Salmon in short read data across 88 samples. Transcripts per million (TPM) value for proband highlighted in red. \u003cstrong\u003eB.\u003c/strong\u003e Comparison of transcripts identified by the Iso-seq pipelines in proband (P08) and three controls. \u003cstrong\u003eC.\u003c/strong\u003eVisualisation of transcripts identified in proband (red), P20 as control (green) and annotated reference (blue).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7046889/v1/1ac2c84edaa1973eea937575.png"},{"id":87552998,"identity":"5907547c-73c8-4b0d-bb14-2d4a0b38e603","added_by":"auto","created_at":"2025-07-25 06:34:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":177888,"visible":true,"origin":"","legend":"\u003cp\u003eDetection of full abnormal transcripts due to \u003cem\u003eTCOF1\u003c/em\u003e SVA insertion and \u003cem\u003eYY1\u003c/em\u003e deletion of 2 distal exons\u003cem\u003e. \u003c/em\u003e\u003cstrong\u003eA.\u003c/strong\u003eRepresentative sequence reads indicating differently sized \u0026gt;600 bp insertions in \u003cem\u003eTCOF1\u003c/em\u003e transcript. \u003cstrong\u003eB.\u003c/strong\u003e Sashimi plot showing YY1 half of sequence reads skip two distal exons and splice into the \u003cem\u003eSLC25A29\u003c/em\u003e and \u003cem\u003eSLC25A47\u003c/em\u003e loci (CHX+) and only few of these reads remain in RNA from untreated fibroblasts\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7046889/v1/dcf53fd9a026fafc9d7bf847.png"},{"id":104379281,"identity":"d8988a50-25aa-4755-81ec-5836fe879a0b","added_by":"auto","created_at":"2026-03-11 07:11:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1948012,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7046889/v1/efb2d3cb-be60-4c9c-99ce-8a44e8a4b191.pdf"},{"id":87552993,"identity":"272549f5-0b1d-4f47-a24b-2822eb5bd9a7","added_by":"auto","created_at":"2025-07-25 06:34:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28918,"visible":true,"origin":"","legend":"Table 2","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7046889/v1/aa7a8df61468040ed511103b.docx"},{"id":87553002,"identity":"e9b03770-9865-4994-ac5d-9565fcbf5a2a","added_by":"auto","created_at":"2025-07-25 06:34:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":586751,"visible":true,"origin":"","legend":"Supplemental Material","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7046889/v1/19728b6c153a85c477a8a4bc.docx"}],"financialInterests":"There is a duality of interest\nThis has been addressed in the competing interests within the manuscript","formattedTitle":"HiFi Long-Read RNA Sequencing Enhances Clinical Diagnostics in Rare Disorders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIt is estimated that a third of disease-causing variants can disrupt mRNA splicing[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Splice-affecting variants are often missed in clinical detection and are under-ascertained in clinical variant databases[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] as these are not limited to canonical splice sites[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. RNA testing is now considered a complementary tool to DNA testing both in terms of providing functional evidence but also in identifying new events missed by traditional methods[\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12 CR13\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Within the UK or the Netherlands some healthcare providers offer specialised RNA studies conducted via targeted reverse transcription PCR (RT-PCR) or RNA-sequencing (RNA-seq). RT-PCR is useful in the assessment of genes with low expression (\u0026lt;\u0026thinsp;1 transcripts per million [TPM]) and aberrant splicing events at low levels[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, RT-PCR is a bespoke test for each patient, and it is inherently limited by gene annotation choice, PCR amplicon lengths and assumptions on expected splicing abnormalities. On the contrary, RNA-seq is independent of the individual patient, it is agnostic to the resulting abnormally spliced transcript and can aid in identifying a variety of events without making a priori assumptions. Most RNA-seq studies rely on short-read RNA-seq (SR RNA-seq), which although it has its advantages over RT-PCR, is still unable to produce full-length transcripts, resolve complex regions, and identify certain types of aberrant splicing events such as long stretches of intron retention[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Full-length transcripts allow better assessment of the effects on splicing and quantification of transcript abundance, particularly when the gene in question has multiple isoforms. Additionally, longer reads have sufficient genomic context to accurately map to regions that are challenging due repetition, high polymorphism, or low nucleotide diversity, therefore increasing coverage of genes that standard SR sequencing struggles to capture.\u003c/p\u003e\u003cp\u003eAs SR RNA-seq is integrated into clinical practice, it is essential to assess the potential benefits of long-read sequencing in this context. Long-read sequencing platforms including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) are now on par with short-reads in terms of throughput and accuracy[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. PacBio\u0026rsquo;s Revio system claims 99.95% (Q33) read accuracy with read lengths of 15-20kb, a yield 3-4x higher, and a 15x higher throughput, than their previous Sequel Ile system. With regards to RNA-seq specifically, PacBio\u0026rsquo;s Kinnex kit based on the MAS-seq method concatenates smaller amplicons into larger fragment libraries for a higher throughput of full-length RNA, single-cell RNA and 16S rRNA. Although not always documented within publications, data storage requirements for long reads tend to be much higher than short reads for the same yield in gigabytes. Between PacBio and ONT, ONT has historically been associated with larger data storage requirements (especially for raw data) compared to PacBio for similar yields. Long-read RNA-seq (LR RNA-seq) is a relatively new technology and rapidly evolving technology. Due to its novelty, only a limited number of cases that demonstrate its ability to uncover pathogenic splicing events that were missed by SR RNA-seq[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe aim of this study is to assess the clinical utility of PacBio Hifi sequencing of RNA/cDNA in identifying new and previously known aberrant splicing events in patients with rare disorders. Findings will also be compared to SR Illumina sequencing.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003ePatient Cohort\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Participants were enrolled into the University of Southampton's Splicing and Disease study with appropriate ethical approval (REC 11/SC/0269, IRAS 49685, ERGO 23056). The sub-cohort used herein is comprised of 22 individuals with a suspected Mendelian disorder assessed by UK clinical genetics services in whom a candidate variant of uncertain significance (VUS) had been identified through conventional DNA-based testing. SR RNA-seq results for six of the 22 individuals have been previously published[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. PacBio Kinnex data was also generated with RNA extracted from fibroblasts from two patients examined at Erasmus MC for whom diagnostic SR RNA-seq was performed. Participants had been examined at the Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, the Netherlands and genetic analyses were performed in a clinical setting. Informed consent was obtained and all individuals or their legal guardians provided written consent to share anonymized clinical and analysis data. Use of genome-wide technologies for diagnostic purposes was previously approved (Institutional-review-board MEC-2012-387). For each variant/patient we report the genotype and observed phenotype (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of genetic variants, genotypes (GT) and observed phenotypes. AL: acceptor loss, DL: donor loss, AG: acceptor gain, DG: donor gain. * Indicates samples for which short read RNA-seq or RT-PCR results have been previously published.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariant(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTissue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpliceAI Δ score AL|DL|AG|DG\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP01*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUBR4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_020765.3:c.8488\u0026thinsp;+\u0026thinsp;3A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCerebellar ataxia, nystagmus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.04|0.26|0.03|0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKLHL7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001031710.3:c.936\u0026thinsp;+\u0026thinsp;3_936\u0026thinsp;+\u0026thinsp;22del\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePerching syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.68|0.97|0.0|0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP03*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNF1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_000267.3:c.1168_1179del, p.(Asn390_His393del)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNeurofibromatosis type 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.03|0.04|0.0|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP04*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_000314.8:c.553C\u0026thinsp;\u0026gt;\u0026thinsp;G, p.(His185Asp)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCallouses palms and soles, prominent bleeding gums, macrocephaly.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0|0.0|0.0|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKLHL7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001031710.3:c.936\u0026thinsp;+\u0026thinsp;3_936\u0026thinsp;+\u0026thinsp;22del\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUnaffected carrier\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.68|0.97|0.0|0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNF2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_000268.4:c.885\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIntra-medullary ependymoma. Sibling with ependymoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53|0.52|0.0|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCOX7B\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001866.3:c.40\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePupil asymmetry, cerebellar hypoplasia,Ligamentous laxity, cataplexy, Learning difficulities, Microcephaly, right foot neruopathy.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0|0.02|0.01|0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP08*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRPS7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001011.4:c.507\u0026thinsp;+\u0026thinsp;3A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDiamond Blackfan Syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0|0.03|0.0|0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePUF60\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_078480.3:c.560T\u0026thinsp;\u0026gt;\u0026thinsp;A, p.(Leu187*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePUF60-related developmental disorder\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0|0.01|0.01|0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP10*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePHF8\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_015107.3:c.784-2A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHEMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlobal developmental delay, epilepsy and hypotonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.99|0.59|0.27|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCOL9A2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001852.4:c.1792\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStickler syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.20|0.75|0.0|0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePNKP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_007254.4:c.1029\u0026thinsp;+\u0026thinsp;2T\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGlobal developmental delay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.69|0.93|0.03|0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eWDR45B\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_019613.4:c.143-5T\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStructural brain abnormality and profound developmental delay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.82|0.75|0.0|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eITPR1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001378452.1:c.1712A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAtaxic cerebral palsy, global developmental delay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01|0.08|0.0|0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKIAA0825\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001145678.3:c.3451_3456\u0026thinsp;+\u0026thinsp;13del\u003c/p\u003e\u003cp\u003eNM_001145678.3:c.2020T\u0026thinsp;\u0026gt;\u0026thinsp;A, p.(Tyr674Asn)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePost axial polydactyly left hand and both feet. Normal development. Mild ear dysplasia and dysmorphism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.53|0.80|0.0|0.09, \u003c/p\u003e\u003cp\u003e0.0|0.0|0.03|0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEFTUD2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_004247.4:c.1393A\u0026thinsp;\u0026gt;\u0026thinsp;G, p.(Met465Val)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeft Kidney Agenesis, Klippelfeil, scoliosis, arachnoid cyst\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0|0.06|0.0|0.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eZMYM2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_197968.4:c.3301\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003elow muscle tone, developmental delay,problems with fine motor skills, squint. Low set ears\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66|0.98|0.01|0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eSETD5\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001080517.3:c.-177\u0026thinsp;+\u0026thinsp;1G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTalipes,Hemihypertrophy, short stature,Speech delay.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0|0.98|0.0|0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_000249.4:c.704A\u0026thinsp;\u0026gt;\u0026thinsp;G, p.(Asp235Gly)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTransverse colon cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.29|0.29|00|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBAP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_004656.4:c.581G\u0026thinsp;\u0026gt;\u0026thinsp;A, p.(Gly194Glu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBAP1-inactivated melanocytic tumour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0|0.0|0.26|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLMNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_170707.4:c.1381-5G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003edilated cardiomyopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01|0.01|0.97|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_000314.8:c.634\u0026thinsp;+\u0026thinsp;3A\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMacrocephaly,DD, oropharynx-haematoma,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBlood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.92|0.98|0.0|0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eTCOF1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_001371623.1(TCOF1):c.2860\u0026ndash;3215_2860-3214insN[3396]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTreacher Collins syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFibroblast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eYY1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNM_152333.4:c.-120-994_*23708del\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGabriele-de Vries syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFibroblast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eShort-read RNA-seq and analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBlood RNA extraction and sequencing had previously been reported[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. SR RNA-seq was generated at NovoGene (Hong Kong) with a minimum of 70\u0026nbsp;million reads. FASTQ files were aligned to the human genome reference (GRCh38) with annotations from GENCODE[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] release 38 using STAR aligner[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] v2.6.1c.\u003c/p\u003e\u003cp\u003eFibroblast cell culturing, cycloheximide (CHX) treatment, RNA extraction and sequencing had previously been reported[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. SR RNA-seq was generated at GenomeScan, Leiden, the Netherlands, with a minimum of 40\u0026nbsp;million reads generated per sample. Trimmed reads were aligned to the human genome reference GRCh38 with HISAT2 v2.2.1. Transcript per million were extracted using Stringtie v2.2.3 and annotations from GENCODE release 38.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLR sequencing and analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe PacBio Kinnex method for RNA-seq sample preparation is based on the MAS-Seq method[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which concatenates smaller amplicons into larger fragment libraries for throughput increase. Kinnex RNA-seq data from RNA extracted from blood was generated using a Revio system in two batches. The first batch of 12 samples was sequenced at PacBio (Menlo Park, CA), while the second batch of 10 samples was sequenced at the PacBio (EMEA headquarters in London). Kinnex data were processed using the IsoSeq3 pipeline, mapped to GRCh38, and visualised using IGV.\u003c/p\u003e\u003cp\u003eRun 1 (n\u0026thinsp;=\u0026thinsp;16 libraries) was split across four pools (SMRTcell) with 12 unique bio samples. Pools 1\u0026ndash;3 consisted of four samples per SMRTcell and were globin depleted. Pool 4 sequenced the same four samples in Pool 3 but with no globin depletion. Comparison of transcriptome profiles between depleted and non-depleted batches in Run 1 led to the decision to omit the depletion step for samples in run 2 (n\u0026thinsp;=\u0026thinsp;12 libraries) as depletion negatively impacted the transcript diversity (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). To compensate for the reads that would be taken up by globin genes, the 12 unique samples in run 2 were split across three SMRTcells instead of four. Splicing and expression results for 22 of the 24 biological samples are reported. One sample was excluded due to of lack of VUS, and the other was excluded as it is a biological replicate that was sequenced in both Run 1 and Run 2.\u003c/p\u003e\u003cp\u003eHiFi reads from the Revio instrument were processed using the Read Segmentation and Iso-Seq workflow available in SMRT Link version 13.1. Kinnex arrays were segmented into their constituent cDNA reads using skera v1.2.0. Lima v2.10.0 was used to remove barcoded cDNA primers, demultiplexing the pools. The Iso-Seq bioinformatics toolkit v4.1.2 was then used to remove polyA tails, identify artefactual concatamers and cluster sequences, which are then mapped against GRCh38_no_alt_analysis_set using pbmm2 1.14.0 in its specialised IsoSeq mode. Remaining reads were collapsed using the PacBio Iso-Seq toolkit and then classified and filtered using Pigeon 1.2.0.\u003c/p\u003e\u003cp\u003eFibroblast Kinnex RNA-seq data was generated using a Revio system at the Leiden Genome Technology Center (LGTC) in collaboration with GenomeScan (Leiden, the Netherlands), with a minimum of 10\u0026nbsp;million reads per sample. Two libraries were generated per sequenced individual, one RNA sample from fibroblasts treated with CHX\u0026thinsp;+\u0026thinsp;and one CHX-, for a total of four libraries across one SMRTcell. Sequencing reads were processed with the IsoSeq pipeline, specifically IsoSeq v4.2.0, pbmm2 v1.16.0, and Pigeon v1.3.0, with the same reference and annotation used for the SR data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of aberrant splicing in blood\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo determine the functional consequence at a transcript level for each variant, both SR and LR RNA-seq data was loaded into the Integrative Genomics Viewer (IGV)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and each variant was visually inspected to search for splicing abnormalities as detailed previously[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Aberrant splicing events (intron retention, exon skipping, novel acceptor and donor, cryptic exon) were identified using SR RNA-seq (n\u0026thinsp;=\u0026thinsp;19), and PacBio RNA-seq (n\u0026thinsp;=\u0026thinsp;22). These events were then collated into a comprehensive list. Each event on this list was cross-checked to determine whether it was detected by the other platform. This approach allowed for a direct comparison of the sensitivity of each technology in identifying splicing events.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eQuality assessment of PacBio Kinnex LR RNA-seq data\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eBlood\u003c/strong\u003e\u003cp\u003eSequencing depth varied across pools and runs, where Run 1 (n\u0026thinsp;=\u0026thinsp;16 samples; 4 samples per SMRTcell) had higher variability compared to Run 2 (n\u0026thinsp;=\u0026thinsp;12 samples; 3 samples per SMRTcell) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). On average each sample had 13.2\u0026nbsp;million full length non-chimeric (FLNC) reads with a minimum and maximum of 4.9 and 24.3\u0026nbsp;million respectively. Transcript length distributions appeared uniform across runs and samples with a peak right below 2000 bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eGlobin depletion has been shown to enhance the detection of transcripts with lower expression in SR RNA-seq data, as it reduces the representation of globin mRNA (~\u0026thinsp;30% of transcripts), freeing up sequencing reads for more relevant transcripts[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We assessed the utility of this procedure on Kinnex data and sequenced a pool of the same biological samples (n\u0026thinsp;=\u0026thinsp;4) with and without globin depletion. Along with depletion of globin genes, genome-wide transcript diversity was impacted negatively by this procedure. In undepleted samples at least 10,000 additional transcripts (full-splice match and incomplete splice match) were identified across all four samples (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFibroblasts\u003c/strong\u003e\u003cp\u003eLibraries derived from 2 patient samples were sequenced on a single SMRTcell (two libraries per sample CHX+/CHX-) and obtained an average 11.7\u0026nbsp;million FLNC, with a minimum of 11 and a maximum of 12.6\u0026nbsp;million. Detected transcript-length distributions appeared uniform across the four samples with a peak around 2000 bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), comparable to that observed for blood (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). An additional peak was observed between 7,500 and 9,000 bps in both cycloheximide (CHX) treated and untreated cells constituted primarily by \u003cem\u003eFN1\u003c/em\u003e transcript, a highly expressed gene in cultured fibroblasts and lowly expressed in blood.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene detection in blood LR RNA-seq is comparable to SR RNA-seq\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAcross the 22 blood samples the Iso-seq pipeline detected a minimum of 12,271 genes annotated in GENCODE and a maximum of 16,132. To evaluate the potential diagnostic relevance of the long-read RNA-seq, disease gene pick-up rate \u0026mdash;entries in OMIM and PanelApp\u0026mdash;were also assessed[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. On average 8,315 out of 16,630 OMIM genes (50%) were detected with a minimum and maximum of 5,775 and 10,855 respectively. Similarly, an average of 1,822 (min 1,128 max 2,515) out of 3,643 PanelApp genes were detected. The detection rate for both OMIM and PanelApp genes is comparable to the detection rate with SR RNA-seq (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). A subset of approximately 200 genes, detected by SR RNA-seq (median TPM\u0026thinsp;\u0026ge;\u0026thinsp;5), were not detected by the LR RNA-seq, showing a median TPM of 0. When the TPM threshold for short reads is lowered to TPM\u0026thinsp;\u0026ge;\u0026thinsp;1, the number of undetected genes increases to ~\u0026thinsp;1,000.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCHX treatment increases diversity of genes and transcripts detected in fibroblasts\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Iso-seq pipeline detected a minimum of 14,495 genes annotated in GENCODE in untreated fibroblasts and this number increased in CHX-treated fibroblasts, where a minimum of 15,994 genes was detected (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). We observed that 64.6% of the genes detected in one untreated cell line were also detected in the other cell line, and that this percentage increased to 69.4% in CHX\u0026thinsp;+\u0026thinsp;cells (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). The most striking difference was in the number of discovered transcripts between CHX- and CHX\u0026thinsp;+\u0026thinsp;fibroblasts with \u0026gt;\u0026thinsp;65,000 identified in the latter. Importantly, the number of genes and transcripts discoverable appeared to be already saturated at the achieved read depth (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eOf 16,630 genes included in OMIM, LR RNA-seq detected a minimum of 10,841 (65%) in CHX- fibroblasts, and 10,887 (65%) in CHX\u0026thinsp;+\u0026thinsp;fibroblasts. Similarly, of 3,643 PanelApp genes, a minimum of 2,596 (71%) were detected in CHX- cells and 2,608 (72%) were detected in CHX\u0026thinsp;+\u0026thinsp;cells. This is in concordance with previous observations that RNA-seq in fibroblasts encompass a greater number of disease-relevant genes compared to blood[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOf the genes well detected by SR RNA-seq (TPM\u0026thinsp;\u0026ge;\u0026thinsp;5) and present in either OMIM or PanelApp, 198 genes were not detected in LR RNA-seq sequencing, and at the lower threshold of TPM\u0026thinsp;\u0026ge;\u0026thinsp;1 in SRS, the number of genes not detected increased to 858.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of transcripts identified\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eBlood\u003c/strong\u003e\u003cp\u003eApproximately 40% of detected genes were represented by a single isoform, 20% by 2\u0026ndash;5 isoforms and 40% were represented by six or more isoforms. When split by structural categories, most transcripts were categorised as incomplete splice matches, followed by full splice matches and novel isoforms in and not in catalogue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFibroblasts\u003c/strong\u003e\u003cp\u003eAcross the 4 fibroblast cell lines, we observed that ~\u0026thinsp;40% of the detected genes presented with \u0026ge;\u0026thinsp;6 distinct isoforms. In contrast, only about 20% of the genes found in fibroblasts were represented by a single isoform (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This proportion was higher in blood, suggesting that fibroblasts express multiple isoforms for a greater number of genes compared to blood. Pigeon-classification of isoforms detected in the CHX- fibroblasts assigned most transcripts to the incomplete splice and full splice match categories, similar to blood (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Interestingly, the CHX treatment allowed for a higher number of transcripts classified as novel not in catalogue, i.e. transcripts that use novel donors and/or acceptors not present in the GENCODE annotation.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLR RNA-seq identifies known events and provides higher resolution in a subset of cases affecting their clinical care\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing both SR and LR RNA-seq, a total of 30 aberrant splicing events were identified across the blood 22 samples. LRS confidently captured 21 aberrant splicing events linked to a variant, three events with low confidence and six events were not captured (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Five of the six events missed by LRS were due to low or no coverage of the whole gene (\u003cem\u003ePHF8, COX7B\u003c/em\u003e and \u003cem\u003eKIAA0825\u003c/em\u003e). The remaining event was not detected as it was a low-level splicing abnormality requiring higher read depth. In instances where there was good gene coverage, LRS was able to confirm aberrant splicing events found previously, and in five cases, LRS detected additional effects on transcripts, enhancing interpretation of the variants and either helping resolve pathogenicity or providing additional biological insights the into variant\u0026rsquo;s effect. This included identification of intron retention events with more confidence (i.e. did not need to validate with RT-PCR or intronic reads not present in other samples), phasing variants of interest, and quantifying both known and novel transcripts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIllustrative examples where LR RNA-seq enhanced biological insights\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eNM_001011.4(\u003c/b\u003e\u003cb\u003eRPS7\u003c/b\u003e\u003cb\u003e):c.507\u0026thinsp;+\u0026thinsp;3A\u0026thinsp;\u0026gt;\u0026thinsp;G skews expression toward unannotated intron retained transcript\u003c/b\u003e: \u003cem\u003eRPS7\u003c/em\u003e encodes a ribosomal protein essential for ribosome biogenesis and function, mutations in this gene have been associated with Diamond-Blackfan anaemia[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], consistent with the patient\u0026rsquo;s phenotype. Proband P08 previously underwent RT-PCR, which yielded normal results[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. SR sequencing suggested potential intron 6 retention, however, due presence of intronic reads in controls, this result was inconclusive. Salmon was used to quantify the transcript abundance of ENST00000645674.2 (NM_001011.4) in this sample [short reads] and when compared to 87 unrelated samples the MANE select transcript appeared to be upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). LRS confirmed that while this mutation caused increased intron 6 retention in patients, intron 6 retention was also present in controls, explaining RT-PCR results. In both patient and controls two transcript isoforms were identified: one with intron 6 retention and the MANE select transcript (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C). In controls the ratio was roughly 1:1, whereas in the patient, the intron retained transcripts was ~\u0026thinsp;40x times more abundant. This significant shift in transcript ratio could lead to RPS7 protein deficiency, likely due to competition or degradation of the aberrant isoform.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHomozygous NM_001031710.3(\u003c/b\u003e\u003cb\u003eKLHL7\u003c/b\u003e\u003cb\u003e):c.936\u0026thinsp;+\u0026thinsp;3_936\u0026thinsp;+\u0026thinsp;22del variant causes leaky splicing\u003c/b\u003e: Biallelic mutations in \u003cem\u003eKLHL7\u003c/em\u003e are known to cause PERCHING syndrome a rare multisystemic developmental disorder[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Previous RT-PCR assay identified exon 7 skipping caused by NM_001031710.3:c.936\u0026thinsp;+\u0026thinsp;3_936\u0026thinsp;+\u0026thinsp;22del variant, evidence which was subsequently used to reclassify the variant and offer carrier testing to family members. LRS confirmed these results, but also identified a complete splice match to the normal MANE select transcript (NM_001031710.3). While a low-level event, no normal transcripts were expected as this was a homozygous mutation, indicating leaky splicing. This patient has most of the constituent features of PERCHING syndrome, detailed phenotype described in previous publication[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (patient 6), not consistent with an attenuated phenotype. However, this patient is ambulant and still living at age 17 and perhaps not as profoundly disabled as some of the more severe cases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLRS facilitates variant phasing\u003c/b\u003e: The NM_001378452.1(\u003cem\u003eITPR1\u003c/em\u003e):c.1712A\u0026thinsp;\u0026gt;\u0026thinsp;G variant was initially referred for splicing assessment. Both SRS and LRS confirmed the presence of and alternative donor site in intron 17. The initial request did not include information about additional variants. However, after receiving initial results, the referring team noted the mention of LRS and subsequently contacted us to investigate phasing for a second variant. LRS successfully provided phasing information, revealing that the second variant was in \u003cem\u003etrans\u003c/em\u003e with the initial c.1712A\u0026thinsp;\u0026gt;\u0026thinsp;G variant.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRetrotransposon-induced isoform switch in \u003cem\u003eTCOF1\u003c/em\u003e\u003c/strong\u003e\u003cp\u003eLoss-of-function variants in \u003cem\u003eTCOF1\u003c/em\u003e are the most common cause of Treacher-Collins syndrome[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and recently a retrotransposon insertion as novel pathogenic mechanism in this gene was reported[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Outlier-analysis with short-read RNA-Seq identified this pathogenic event, and it suggested the presence of an isoform switch from the canonical \u003cem\u003eTCOF1\u003c/em\u003e transcript to a shorter \u003cem\u003eTCOF1\u003c/em\u003e isoform, lacking a nucleolar localization signal and expected to impair ribosome biosynthesis[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Further characterization showed this to be due to the insertion of a SINE-VNTR-Alu (SVA) retrotransposon into \u003cem\u003eTCOF1\u003c/em\u003e intron 17, that is partially exonized and induces an early termination codon leading to nonsense-mediated mRNA decay.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eTo assess the potential of Kinnex to resolve and characterize this event, we performed LR RNA-seq in fibroblasts from this patient. Manual inspection of the FLNC of CHX- fibroblasts from this patient and phasing of the reads by heterozygous SNVs, showed the isoform switch induced by the SVA: while the reference allele produced mostly long \u003cem\u003eTCOF1\u003c/em\u003e isoforms (supporting reads ratio 70:18), the allele carrying the SVA produced mostly shorter \u003cem\u003eTCOF1\u003c/em\u003e isoforms (supporting reads ratio 8:43). This observation was also confirmed by the data from the CHX\u0026thinsp;+\u0026thinsp;fibroblast RNA (reference allele long:short \u003cem\u003eTCOF1\u003c/em\u003e ratio 87:11; SVA-allele 16:42) and the partial exonization of the SVA in 12 of the 16 reads supporting a long isoform were also detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Notably, no reconstructed isoform spanning the SVA was identified using the \u003cem\u003eIsoSeq collapse\u003c/em\u003e step run with the default settings. LR RNA-seq therefore completely resolved and phased full-length transcripts that included the SVA insertion (4,978 bp) that went undetected using SR RNA-seq.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDeletion of YY1 distal exons induces transcript readthrough\u003c/strong\u003e\u003cp\u003eLoss-of-function \u003cem\u003eYY1\u003c/em\u003e variants cause autosomal dominant Gabriele-de Vries syndrome[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Short-read RNA-seq identified an expression outlier for \u003cem\u003eYY1\u003c/em\u003e in an individual with global developmental delay, visual impairment, white matter abnormalities, and feeding difficulties. Manual inspection of the SRS data indicated read-through transcripts involving the locus of two genes downstream of \u003cem\u003eYY1, i.e. SLC25A29\u003c/em\u003e and \u003cem\u003eSLC25A47.\u003c/em\u003e Reanalysis of exome data identified a heterozygous deletion of the last 3 exons of \u003cem\u003eYY1 (NM_003404.5)\u003c/em\u003e and exon 2 and 3 of the neighbouring gene \u003cem\u003eSLC25A29\u003c/em\u003e (\u003cem\u003eNM_001039355.3\u003c/em\u003e). Examination of \u003cem\u003eFLNC\u003c/em\u003e from Kinnex sequencing in CHX\u0026thinsp;+\u0026thinsp;and CHX- fibroblasts from this patient, confirmed the presence of \u003cem\u003eYY1\u003c/em\u003e transcripts including portions of one of the \u003cem\u003eSLC\u003c/em\u003e genes or both (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The default Iso-Seq pipeline also reconstructed a read-through \u003cem\u003eYY1\u003c/em\u003e product into the \u003cem\u003eSLC25A29\u003c/em\u003e locus in both samples but failed to detect isoforms involving the \u003cem\u003eSLC25A47\u003c/em\u003e locus as these appear to be present in less than 2 \u003cem\u003eFLCN\u003c/em\u003e.\u003c/p\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe applied PacBio full-length RNA-seq with the Kinnex system in blood (\u0026plusmn;\u0026thinsp;globin depletion) and fibroblasts (\u0026plusmn;\u0026thinsp;cycloheximide) to assess throughput, compare it to short-read technology, and evaluate its utility for clinical interpretation of variants referred for RNA-seq studies. Kinnex RNA-seq confirmed findings from previous analyses of SR RNA-seq both in blood and fibroblast RNA. This new technology successfully captured events missed by SR such as intron retention, multiple exon skipping, differential transcript usage and phasing of transcripts, serving as a valuable complement to short-read RNA-seq in a clinical setting.\u003c/p\u003e\u003cp\u003eOur study demonstrates the potential of PacBio LR RNA-seq for variant interpretation, but several limitations should be considered. While there is good correlation between short and long reads, coverage was lower for a subset of genes. Pathway analysis showed that these genes were immune-related, including T-cell receptor variable and immunoglobulin genes. Within our cohort, \u003cem\u003ePHF8\u003c/em\u003e and \u003cem\u003eCOX7B\u003c/em\u003e had TPM values of 7.5 and 4.4 respectively in SR data, compared to 3.2 and 0.3 in blood LR data, impairing assessment of the two variants within these genes. Alternatively, using a different tissue such as fibroblast might provide a better resolution for some genes, e.g. \u003cem\u003eCOX7B\u003c/em\u003e and \u003cem\u003ePHF8\u003c/em\u003e reached median TPM of 30 and 27, respectively in SRS, compared to 8 and 11 in LRS data. Interestingly, in both assessed tissues we observed discrepancy in the predicted expression values. The discrepancy could be due to read depth, suggesting higher depths might be required to fully capture all genes expressed in blood. Finally, current pipelines used to identify novel events in undiagnosed patients are not yet optimised for LR RNA-seq data or have not been sufficiently adapted to handle this type of data[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Given the large number of novel events detected, distinguishing disease-relevant events from those arising from transcript diversity, yet to be characterised, remains a challenge.\u003c/p\u003e\u003cp\u003eAlthough the assessment of globin depletion and cycloheximide treatment is based on eight and four libraries respectively, our findings provide valuable insights into optimising experimental design, such as selecting appropriate library prep, tissue and determining the number of samples per SMRTcell to best adapt to the need of the study. Strikingly, while globin is depletion in blood is associated with higher transcript diversity[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] in SR RNA-seq, the opposite was observed when this treatment was included into the Kinnex protocol. It is important to note that there was noticeable variability in the coverage of samples in the first run and although we cannot attribute the variability to the depletion process, the pool without depletion performed better. In fibroblasts, in line with previous observations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], our data confirm that cycloheximide treatment can be beneficial to improve transcript and gene diversity and shows that this can be also detected with Kinnex.\u003c/p\u003e\u003cp\u003eDespite its limitations, LR RNA-seq successfully identified additional events and provided a clearer understanding of aberrant splicing. Compared to SRS, LR RNA-seq can better detect intron retention events with less noise and events that span multiple exons. Moreover, LR RNA-seq can detect full-length transcripts, quantify transcript diversity within a sample, and assign them to specific alleles using heterozygous variants.\u003c/p\u003e\u003cp\u003eAt least 10% of transcripts we detected were novel not in catalogue and this could have significant implications when standard annotations are used to quantify gene expression. This was exemplified with the quantification of \u003cem\u003eRPS7\u003c/em\u003e by Salmon in proband P08, where no annotated transcripts include the retention of intron 6. The quantification of ENST00000645674.2 (NM_001031710.3) in short reads would lead us to believe that there was a higher or normal expression of this transcript. While short reads were able to pick up the potential intron retention, the difference in isoform usage was only made clear with the long-read data.\u003c/p\u003e\u003cp\u003eWithout LRS, leaky splicing in \u003cem\u003eKLHL7\u003c/em\u003e gene would not have been identified. While this finding doesn\u0026rsquo;t change the clinical interpretation, as the initial report issued helped reclassify the variant, leaky splicing could help explain variability across disorders caused by mutations in the same gene. Additionally, having full length transcripts, or at least very long reads, enables transcript phasing. This approach therefore broadens access to testing for patients where parental DNA/RNA is unavailable. For these patients we can still assess whether variants are expressed in \u003cem\u003ecis\u003c/em\u003e or \u003cem\u003etrans\u003c/em\u003e, if variants are not spliced out.\u003c/p\u003e\u003cp\u003eWe view LR RNA-seq as a valuable tool for detecting aberrant splicing and gene expression in rare disease patients. While throughput has increased, particularly with the Kinnex kit, costs remain higher than short read sequencing, for which there are well-established pipelines. For cases where a VUS is present in a gene well-expressed in blood (TPM\u0026thinsp;\u0026ge;\u0026thinsp;5) and not located in repetitive regions, SR RNA-seq is likely the best initial test. However, LR RNA-seq could serve as an effective follow-up test, helping to validate or refine SR RNA-seq results. Alternatively, targeted deep long-read sequencing could be applied specifically to genes with a known VUS, instead of whole-transcriptome sequencing[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], but in this scenario, throughput becomes a limiting factor.\u003c/p\u003e\u003cp\u003eIn the UK, SR RNA-seq is only just being introduced into clinical practice, so it will take time before LR RNA-seq becomes part of the NHS diagnostic framework. Similarly, in the Netherlands diagnostic SR RNA-seq is available in clinical practice in only few centres. The next step is to develop methods in LRS for identifying relevant events in patients without a variant of uncertain significance (VUS), already being done with SR RNA-seq[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], helping to determine which patients and samples would benefit most from this technology. While LRS has clear potential in rare disease diagnostics, its successful integration into clinical practice remains an open challenge, requiring further research and optimisation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe made use of the publicly available listed in the methods with default parameters. We recommend using the latest update of these tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all patients and families taking part in this research and all clinicians involved. We thank all staff from regional genetics services who recruited patients: SanSan Htun, Tessy Thomas, \u0026nbsp;Andrew Douglas, Claire G Salter (https://orcid.org/0000-0002-2494-1644), Lucy Side (https://orcid.org/0000-0003-4476-8735), Mary O\u0026rsquo;Driscoll (https://orcid.org/0000-0002-7119-7571), Mark Hamilton, Dr Nayana Lahiri, Sahar Mansour, Stephanie Grenville-Heygate (https://orcid.org/0000-0003-1516-3016), M Suri, Ed Blair, Nicola Foulds (https://orcid.org/0000-0002-5779-0096), Jessica Radley (https://orcid.org/0000-0002-0776-0091), Helen Stewart, Caroline Pottinger, Vivienne McConnell, Ajoy Sarkar.\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. Preparation of this manuscript was supported by C.J.O attending the University of Southampton Faculty of Medicine/Faculty of Environmental and Life Sciences Writing Retreat July 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: D.B.,T.V.H.; Data curation: C.J.O., F.F.; Formal analysis: C.J.O., F.F.; Funding acquisition: D.B.,T.V.H.; Investigation: C.J.O., F.F., H.W., H.F., H.V.L., E.K.,H.D., S.H., D.J.B.; Methodology: C.J.O., F.F.; Project Administration: D.B.,T.V.H.; Resources: H.F., H.V.L, L.T., D.J.B., L.D.K., M.V.D., J.Z.; Software: C.J.O., F.F.; Supervision: L.T., S.E., J.W.H., T.V.H., D.B.; Validation: H.W; Visualisation: C.J.O., F.F.; Writing-original draft: C.J.O., F.F.; Writing-review \u0026amp; editing: C.J.O., F.F., S.E., J.W.H., T.V.H., D.B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DB Laboratory is supported by National Institute for Health and Care Research Professorship (RP-2016-07-011) and NIHR Senior investigator award NIHR303895.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained and all individuals or their legal guardians provided written consent to share anonymized clinical and analysis data.\u003c/p\u003e\n\u003cp\u003eParticipants were enrolled into the University of Southampton\u0026apos;s Splicing and Disease study with appropriate ethical approval (REC 11/SC/0269, IRAS 49685, ERGO 23056).\u003c/p\u003e\n\u003cp\u003eUse of genome-wide technologies for diagnostic purposes was previously approved (Institutional-review-board MEC-2012-387).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLis Tseng, Harsharan Dhillon, Sam Holt, and Jeff Zhou are employees at Pacific Biosciences.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaralle D, Lucassen A, Buratti E. 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Pediatr Blood Cancer 2015;62:1597\u0026ndash;600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pbc.25534\u003c/span\u003e\u003cspan address=\"10.1002/pbc.25534\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAngius A, Uva P, Buers I, Oppo M, Puddu A, Onano S, et al. Bi-allelic Mutations in KLHL7 Cause a Crisponi/CISS1-like Phenotype Associated with Early-Onset Retinitis Pigmentosa. The American Journal of Human Genetics 2016;99:236\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ajhg.2016.05.026\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2016.05.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBruel A-L, Bigoni S, Kennedy J, Whiteford M, Buxton C, Parmeggiani G, et al. 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CRISPR-Cas9/long-read sequencing approach to identify cryptic mutations in BRCA1 and other tumour suppressor genes. J Med Genet 2021;58:850\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jmedgenet-2020-107320\u003c/span\u003e\u003cspan address=\"10.1136/jmedgenet-2020-107320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"RNAseq, HiFi, long read, diagnostics, splicing","lastPublishedDoi":"10.21203/rs.3.rs-7046889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7046889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSplice-disrupting variants are estimated to account for one-third of disease-causing variants, yet many remain underrepresented in clinical databases due to limitations in detecting splicing changes beyond canonical splice sites. Short-read RNA sequencing (RNA-seq) has proved to be a valuable complement in clinical practice to address this gap, however, the added value of long-read RNA-seq is unclear. Here, we aimed to assess the clinical utility of PacBio long-read RNA-seq to characterise pathogenic aberrant splicing in rare disorders compared to short-read RNA-seq.\u0026nbsp;Participants from the UK and the Netherlands with suspected splice-altering variants underwent long-read RNA-seq.\u0026nbsp;28 blood samples and four fibroblast cell lines were sequenced following the Kinnex full-length RNA protocol. Detection of disease genes (OMIM and PanelApp) was comparable with short reads, with fibroblast capturing more transcripts overall. Novel isoforms accounted for ~\u0026thinsp;14% of detected transcripts in both tissues, increasing following cycloheximide treatment in fibroblasts and decreasing following goblin depletion in blood. Long-read RNA-seq detected events missed by short-reads including intron retention, multiple exon skipping, differential transcript usage, leaky splicing and variant phasing. In one case long reads revealed that a splice region variant in \u003cem\u003eRPS7\u003c/em\u003e skewed expression toward an unannotated intron 6-retained transcript, likely leading to protein deficiency explaining previous ambiguous results in patient with Diamond-Blackfan anaemia. In another case, we identified a retrotransposon-induced isoform switch in \u003cem\u003eTCOF1\u003c/em\u003e causing Treacher Collins syndrome. Both examples unresolved by short reads. Thereby long-read RNAseq has the potential to improve the detection of clinically relevant transcripts when used in a clinical setting.\u003c/p\u003e","manuscriptTitle":"HiFi Long-Read RNA Sequencing Enhances Clinical Diagnostics in Rare Disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 06:34:29","doi":"10.21203/rs.3.rs-7046889/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-09-08T14:16:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-08-25T15:52:29+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-08-21T09:42:55+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-28T04:55:52+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-28T01:15:29+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-23T23:20:12+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-22T22:53:24+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-22T16:44:22+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-07-22T13:39:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T10:37:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-04T12:40:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Human Genetics","date":"2025-07-04T12:40:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-human-genetics","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejhg","sideBox":"Learn more about [European Journal of Human Genetics](http://www.nature.com/ejhg/)","snPcode":"41431","submissionUrl":"https://mts-ejhg.nature.com/cgi-bin/main.plex","title":"European Journal of Human Genetics","twitterHandle":"@ejhg_journal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"344d8d3f-d888-41ce-b212-2ebe6c977275","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51937591,"name":"Health sciences/Medical research/Genetics research"},{"id":51937592,"name":"Biological sciences/Genetics/RNA splicing"},{"id":51937593,"name":"Biological sciences/Genetics/Clinical genetics/Genetic testing"},{"id":51937594,"name":"Biological sciences/Biotechnology/Sequencing/RNA sequencing"},{"id":51937595,"name":"Biological sciences/Biological techniques/Sequencing/Next-generation sequencing"}],"tags":[],"updatedAt":"2026-03-11T07:10:50+00:00","versionOfRecord":{"articleIdentity":"rs-7046889","link":"https://doi.org/10.1038/s41431-026-02042-9","journal":{"identity":"european-journal-of-human-genetics","isVorOnly":false,"title":"European Journal of Human Genetics"},"publishedOn":"2026-03-10 04:00:00","publishedOnDateReadable":"March 10th, 2026"},"versionCreatedAt":"2025-07-25 06:34:29","video":"","vorDoi":"10.1038/s41431-026-02042-9","vorDoiUrl":"https://doi.org/10.1038/s41431-026-02042-9","workflowStages":[]},"version":"v1","identity":"rs-7046889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7046889","identity":"rs-7046889","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}