A joint NCBI and EMBL-EBI transcript set for clinical genomics and research

Subcytoplasmic location of translation controls protein output

The cytoplasm is highly compartmentalized, but the extent and consequences of subcytoplasmic mRNA localization in non-polarized cells are largely unknown. We determined mRNA enrichment in TIS granules (TGs) and the rough endoplasmic reticulum (ER) through particle sorting and isolated cytosolic mRNAs by digitonin extraction. When focusing on genes that encode non-membrane proteins, we observed that 52% have transcripts enriched in specific compartments. Compartment enrichment correlates with a combinatorial code based on mRNA length, exon length, and 3' UTR-bound RNA-binding proteins. Compartment-biased mRNAs differ in the functional classes of their encoded proteins: TG-enriched mRNAs encode low-abundance proteins with strong enrichment of transcription factors, whereas ER-enriched mRNAs encode large and highly expressed proteins. Compartment localization is an important determinant of mRNA and protein abundance, which is supported by reporter experiments showing that redirecting cytosolic mRNAs to the ER increases their protein expression. In summary, the cytoplasm is functionally compartmentalized by local translation environments.

Multi-site validation of a functional assay to adjudicate SCN5A Brugada Syndrome-associated variants

Brugada Syndrome (BrS) is an inheritable arrhythmia condition that is associated with rare, loss-of-function variants in the cardiac sodium channel gene, SCN5A. Interpreting the pathogenicity of SCN5A missense variants is challenging and ~79% of SCN5A missense variants in ClinVar are currently classified as Variants of Uncertain Significance (VUS). An in vitro SCN5A-BrS automated patch clamp assay was generated for high-throughput functional studies of NaV1.5. The assay was independently studied at two separate research sites - Vanderbilt University Medical Center and Victor Chang Cardiac Research Institute - revealing strong correlations, including peak INa density (R2=0.86). The assay was calibrated according to ClinGen Sequence Variant Interpretation recommendations using high-confidence variant controls (n=49). Normal and abnormal ranges of function were established based on the distribution of benign variant assay results. The assay accurately distinguished benign controls (24/25) from pathogenic controls (23/24). Odds of Pathogenicity values derived from the experimental results yielded 0.042 for normal function (BS3 criterion) and 24.0 for abnormal function (PS3 criterion), resulting in up to strong evidence for both ACMG criteria. The calibrated assay was then used to study SCN5A VUS observed in four families with BrS and other arrhythmia phenotypes associated with SCN5A loss-of-function. The assay revealed loss-of-function for three of four variants, enabling reclassification to likely pathogenic. This validated APC assay provides clinical-grade functional evidence for the reclassification of current VUS and will aid future SCN5A-BrS variant classification.

Pharmacogenomic Considerations for Anticoagulant Prescription in Patients with Hereditary Haemorrhagic Telangiectasia

Hereditary haemorrhagic telangiectasia (HHT) is a vascular dysplasia that commonly results in bleeding but with frequent indications for therapeutic anticoagulation. Our aims were to advance the understanding of drug-specific intolerance and evaluate if there was an indication for pharmacogenomic testing. Genes encoding proteins involved in the absorption, distribution, metabolism, and excretion of warfarin, heparin, and direct oral anticoagulants (DOACs) apixaban, rivaroxaban, edoxaban, and dabigatran were identified and examined. Linkage disequilibrium with HHT genes was excluded, before variants within these genes were examined following whole genome sequencing of general and HHT populations. The 44 genes identified included 5/17 actionable pharmacogenes with guidelines. The 76,156 participants in the Genome Aggregation Database v3.1.2 had 28,446 variants, including 9668 missense substitutions and 1076 predicted loss-of-function (frameshift, nonsense, and consensus splice site) variants, i.e., approximately 1 in 7.9 individuals had a missense substitution, and 1 in 71 had a loss-of-function variant. Focusing on the 17 genes relevant to usually preferred DOACs, similar variant profiles were identified in HHT patients. With HHT patients at particular risk of haemorrhage when undergoing anticoagulant treatment, we explore how pre-emptive pharmacogenomic testing, alongside HHT gene testing, may prove beneficial in reducing the risk of bleeding and conclude that HHT patients are well placed to be at the vanguard of personalised prescribing.

Saturation genome editing of DDX3X clarifies pathogenicity of germline and somatic variation

Loss-of-function of DDX3X is a leading cause of neurodevelopmental disorders (NDD) in females. DDX3X is also a somatically mutated cancer driver gene proposed to have tumour promoting and suppressing effects. We perform saturation genome editing of DDX3X, testing in vitro the functional impact of 12,776 nucleotide variants. We identify 3432 functionally abnormal variants, in three distinct classes. We train a machine learning classifier to identify functionally abnormal variants of NDD-relevance. This classifier has at least 97% sensitivity and 99% specificity to detect variants pathogenic for NDD, substantially out-performing in silico predictors, and resolving up to 93% of variants of uncertain significance. Moreover, functionally-abnormal variants can account for almost all of the excess nonsynonymous DDX3X somatic mutations seen in DDX3X-driven cancers. Systematic maps of variant effects generated in experimentally tractable cell types have the potential to transform clinical interpretation of both germline and somatic disease-associated variation.

Transcript assembly and annotations: Bias and adjustment

Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood. We investigate the impact of annotations on transcript assembly. Surprisingly, we observe that opposite conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios.

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

MUTYH-associated polyposis: Is it time to change upper gastrointestinal surveillance? A single-center case series and a literature overview

BACKGROUND

The presence of Spigelman stage (SS) IV duodenal polyposis is considered the most significant risk factor for duodenal cancer in patients with MUTYH-associated polyposis (MAP). However, advanced SS disease is rarely reported in MAP patients, and no clear recommendations on small bowel (SB) surveillance have been proposed in this patient setting.

AIM

To research more because that case reports of duodenal cancers in MAP suggest that they may develop in the absence of advanced benign SS disease and often involve the distal portion of the duodenum.

METHODS

We describe a series of MAP patients followed up at the Regina Elena National Cancer Institute of Rome (Italy). A literature overview on previously reported SB cancers in MAP is also provided.

RESULTS

We identified two (6%) SB adenocarcinomas with no previous history of duodenal polyposis. Our observations, supported by literature evidence, suggest that the formula for staging duodenal polyposis and predicting risk factors for distal duodenum and jejunal cancer may need to be adjusted to take this into account rather than focusing solely on the presence or absence of SS IV disease.

CONCLUSION

Our study emphasizes the need for further studies to define appropriate upper gastrointestinal surveillance programs in MAP patients.

Genome-wide detection of human intronic AG-gain variants located between splicing branchpoints and canonical splice acceptor sites

Human genetic variants that introduce an AG into the intronic region between the branchpoint (BP) and the canonical splice acceptor site (ACC) of protein-coding genes can disrupt pre-mRNA splicing. Using our genome-wide BP database, we delineated the BP-ACC segments of all human introns and found extreme depletion of AG/YAG in the [BP+8, ACC-4] high-risk region. We developed AGAIN as a genome-wide computational approach to systematically and precisely pinpoint intronic AG-gain variants within the BP-ACC regions. AGAIN identified 350 AG-gain variants from the Human Gene Mutation Database, all of which alter splicing and cause disease. Among them, 74% created new acceptor sites, whereas 31% resulted in complete exon skipping. AGAIN also predicts the protein-level products resulting from these two consequences. We performed AGAIN on our exome/genomes database of patients with severe infectious diseases but without known genetic etiology and identified a private homozygous intronic AG-gain variant in the antimycobacterial gene SPPL2A in a patient with mycobacterial disease. AGAIN also predicts a retention of six intronic nucleotides that encode an in-frame stop codon, turning AG-gain into stop-gain. This allele was then confirmed experimentally to lead to loss of function by disrupting splicing. We further showed that AG-gain variants inside the high-risk region led to misspliced products, while those outside the region did not, by two case studies in genes STAT1 and IRF7. We finally evaluated AGAIN on our 14 paired exome-RNAseq samples and found that 82% of AG-gain variants in high-risk regions showed evidence of missplicing. AGAIN is publicly available from https://hgidsoft.rockefeller.edu/AGAIN and https://github.com/casanova-lab/AGAIN.

EASTR: Identifying and eliminating systematic alignment errors in multi-exon genes

Accurate alignment of transcribed RNA to reference genomes is a critical step in the analysis of gene expression, which in turn has broad applications in biomedical research and in the basic sciences. We reveal that widely used splice-aware aligners, such as STAR and HISAT2, can introduce erroneous spliced alignments between repeated sequences, leading to the inclusion of falsely spliced transcripts in RNA-seq experiments. In some cases, the 'phantom' introns resulting from these errors make their way into widely-used genome annotation databases. To address this issue, we present EASTR (Emending Alignments of Spliced Transcript Reads), a software tool that detects and removes falsely spliced alignments or transcripts from alignment and annotation files. EASTR improves the accuracy of spliced alignments across diverse species, including human, maize, and Arabidopsis thaliana, by detecting sequence similarity between intron-flanking regions. We demonstrate that applying EASTR before transcript assembly substantially reduces false positive introns, exons, and transcripts, improving the overall accuracy of assembled transcripts. Additionally, we show that EASTR's application to reference annotation databases can detect and correct likely cases of mis-annotated transcripts.

A deep catalog of protein-coding variation in 985,830 individuals

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

European LeukemiaNet laboratory recommendations for the diagnosis and management of chronic myeloid leukemia

From the laboratory perspective, effective management of patients with chronic myeloid leukemia (CML) requires accurate diagnosis, assessment of prognostic markers, sequential assessment of levels of residual disease and investigation of possible reasons for resistance, relapse or progression. Our scientific and clinical knowledge underpinning these requirements continues to evolve, as do laboratory methods and technologies. The European LeukemiaNet convened an expert panel to critically consider the current status of genetic laboratory approaches to help diagnose and manage CML patients. Our recommendations focus on current best practice and highlight the strengths and pitfalls of commonly used laboratory tests.

CHESS 3: an improved, comprehensive catalog of human genes and transcripts based on large-scale expression data, phylogenetic analysis, and protein structure

CHESS 3 represents an improved human gene catalog based on nearly 10,000 RNA-seq experiments across 54 body sites. It significantly improves current genome annotation by integrating the latest reference data and algorithms, machine learning techniques for noise filtering, and new protein structure prediction methods. CHESS 3 contains 41,356 genes, including 19,839 protein-coding genes and 158,377 transcripts, with 14,863 protein-coding transcripts not in other catalogs. It includes all MANE transcripts and at least one transcript for most RefSeq and GENCODE genes. On the CHM13 human genome, the CHESS 3 catalog contains an additional 129 protein-coding genes. CHESS 3 is available at http://ccb.jhu.edu/chess .

AlphaMissense is better correlated with functional assays of missense impact than earlier prediction algorithms

Missense variants that alter a single amino acid in the encoded protein contribute to many human disorders but pose a substantial challenge in interpretation. Though these variants can be reliably identified through sequencing, distinguishing the clinically significant ones remains difficult, such that "Variants of Unknown Significance" outnumber those classified as "Pathogenic" or "Likely Pathogenic." Numerous in silico approaches have been developed to predict the functional impact of missense variants to inform clinical interpretation, the latest being AlphaMissense, which uses artificial intelligence methods trained on predicted protein structure. To independently assess the performance of AlphaMissense and 38 other predictors of missense severity, we compared predictions to data from multiplexed assays of variant effect (MAVE). MAVE experiments generate almost every possible individual amino acid change in a gene and measure their functional impact using a high-throughput assay. Assessing 17,696 variants across five genes (DDX3X, MSH2, PTEN, KCNQ4, and BRCA1), we find that AlphaMissense is consistently one of the top five algorithms based on correlation with functional impact and is the best-correlated algorithm for two genes. We conclude that AlphaMissense represents the current best-in-class predictor by this metric; however, the improvement over other algorithms is modest. We note that multiple missense predictors, including AlphaMissense, appear to overcall variants as pathogenic despite minimal functional impact and that substantially more high-quality training data, including consistently analyzed patient cohorts and MAVE analyses, are required to improve accuracy.

Reanalysis of Chromosomal Microarray Data Using a Smaller Copy Number Variant Call Threshold Identifies Four Cases with Heterozygous Multiexon Deletions of ARID1B, EHMT1, and FOXP1 Genes

Introduction

Chromosomal microarray (CMA) is a highly accurate and established method for detecting copy number variations (CNVs) in clinical genetic testing. CNVs are important etiological factors for disorders such as intellectual disability, developmental delay, and multiple congenital anomalies. Recently developed analytical methods have facilitated the identification of smaller CNVs. Therefore, reanalyzing CMA data using a smaller CNV calling threshold may yield useful information. However, this method was left to the discretion of each institution.

Methods

We reanalyzed the CMA data of 131 patients using a smaller CNV call threshold: 50 kb 50 probes for gain and 25 kb 25 probes for loss. We interpreted the reanalyzed CNVs based on the most recently available information. In the reanalysis, we filtered the data using the Clinical Genome Resource dosage sensitivity gene list as an index to quickly and efficiently check morbid genes.

Results

The number of copy number loss was approximately 20 times greater, and copy number gain was approximately three times greater compared to those in the previous analysis. We detected new likely pathogenic CNVs in four participants: a 236.5 kb loss within ARID1B, a 50.6 kb loss including EHMT1, a 46.5 kb loss including EHMT1, and an 89.1 kb loss within the FOXP1 gene.

Conclusion

The method employed in this study is simple and effective for CMA data reanalysis using a smaller CNV call threshold. Thus, this method is efficient for both ongoing and repeated analyses. This study may stimulate further discussion of reanalysis methodology in clinical laboratories.

The status of the human gene catalogue

Scientists have been trying to identify every gene in the human genome since the initial draft was published in 2001. In the years since, much progress has been made in identifying protein-coding genes, currently estimated to number fewer than 20,000, with an ever-expanding number of distinct protein-coding isoforms. Here we review the status of the human gene catalogue and the efforts to complete it in recent years. Beside the ongoing annotation of protein-coding genes, their isoforms and pseudogenes, the invention of high-throughput RNA sequencing and other technological breakthroughs have led to a rapid growth in the number of reported non-coding RNA genes. For most of these non-coding RNAs, the functional relevance is currently unclear; we look at recent advances that offer paths forward to identifying their functions and towards eventually completing the human gene catalogue. Finally, we examine the need for a universal annotation standard that includes all medically significant genes and maintains their relationships with different reference genomes for the use of the human gene catalogue in clinical settings.

Systematic identification of disease-causing promoter and untranslated region variants in 8,040 undiagnosed individuals with rare disease

Background

Both promoters and untranslated regions (UTRs) have critical regulatory roles, yet variants in these regions are largely excluded from clinical genetic testing due to difficulty in interpreting pathogenicity. The extent to which these regions may harbour diagnoses for individuals with rare disease is currently unknown.

Methods

We present a framework for the identification and annotation of potentially deleterious proximal promoter and UTR variants in known dominant disease genes. We use this framework to annotate de novo variants (DNVs) in 8,040 undiagnosed individuals in the Genomics England 100,000 genomes project, which were subject to strict region-based filtering, clinical review, and validation studies where possible. In addition, we performed region and variant annotation-based burden testing in 7,862 unrelated probands against matched unaffected controls.

Results

We prioritised eleven DNVs and identified an additional variant overlapping one of the eleven. Ten of these twelve variants (82%) are in genes that are a strong match to the individual's phenotype and six had not previously been identified. Through burden testing, we did not observe a significant enrichment of potentially deleterious promoter and/or UTR variants in individuals with rare disease collectively across any of our region or variant annotations.

Conclusions

Overall, we demonstrate the value of screening promoters and UTRs to uncover additional diagnoses for previously undiagnosed individuals with rare disease and provide a framework for doing so without dramatically increasing interpretation burden.

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

CRISPR base editing screens are powerful tools for studying disease-associated variants at scale. However, the efficiency and precision of base editing perturbations vary, confounding the assessment of variant-induced phenotypic effects. Here, we provide an integrated pipeline that improves the estimation of variant impact in base editing screens. We perform high-throughput ABE8e-SpRY base editing screens with an integrated reporter construct to measure the editing efficiency and outcomes of each gRNA alongside their phenotypic consequences. We introduce BEAN, a Bayesian network that accounts for per-guide editing outcomes and target site chromatin accessibility to estimate variant impacts. We show this pipeline attains superior performance compared to existing tools in variant classification and effect size quantification. We use BEAN to pinpoint common variants that alter LDL uptake, implicating novel genes. Additionally, through saturation base editing of LDLR, we enable accurate quantitative prediction of the effects of missense variants on LDL-C levels, which aligns with measurements in UK Biobank individuals, and identify structural mechanisms underlying variant pathogenicity. This work provides a widely applicable approach to improve the power of base editor screens for disease-associated variant characterization.

A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect prediction

Introduction: Variants in 5' and 3' untranslated regions (UTR) contribute to rare disease. While predictive algorithms to assist in classifying pathogenicity can potentially be highly valuable, the utility of these tools is often unclear, as it depends on carefully selected training and validation conditions. To address this, we developed a high confidence set of pathogenic (P) and likely pathogenic (LP) variants and assessed deep learning (DL) models for predicting their molecular effects. Methods: 3' and 5' UTR variants documented as P or LP (P/LP) were obtained from ClinVar and refined by reviewing the annotated variant effect and reassessing evidence of pathogenicity following published guidelines. Prediction scores from sequence-based DL models were compared between three groups: P/LP variants acting though the mechanism for which the model was designed (model-matched), those operating through other mechanisms (model-mismatched), and putative benign variants. PhyloP was used to compare conservation scores between P/LP and putative benign variants. Results: 295 3' and 188 5' UTR variants were obtained from ClinVar, of which 26 3' and 68 5' UTR variants were classified as P/LP. Predictions by DL models achieved statistically significant differences when comparing modelmatched P/LP variants to both putative benign variants and modelmismatched P/LP variants, as well as when comparing all P/LP variants to putative benign variants. PhyloP conservation scores were significantly higher among P/LP compared to putative benign variants for both the 3' and 5' UTR. Discussion: In conclusion, we present a high-confidence set of P/LP 3' and 5' UTR variants spanning a range of mechanisms and supported by detailed pathogenicity and molecular mechanism evidence curation. Predictions from DL models further substantiate these classifications. These datasets will support further development and validation of DL algorithms designed to predict the functional impact of variants that may be implicated in rare disease.

eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.

EMQN: Recommendations for genetic testing in inherited cardiomyopathies and arrhythmias

Inherited cardiomyopathies and arrhythmias (ICAs) are a prevalent and clinically heterogeneous group of genetic disorders that are associated with increased risk of sudden cardiac death and heart failure. Making a genetic diagnosis can inform the management of patients and their at-risk relatives and, as such, molecular genetic testing is now considered an integral component of the clinical care pathway. However, ICAs are characterised by high genetic and allelic heterogeneity, incomplete / age-related penetrance, and variable expressivity. Therefore, despite our improved understanding of the genetic basis of these conditions, and significant technological advances over the past two decades, identifying and recognising the causative genotype remains challenging. As clinical genetic testing for ICAs becomes more widely available, it is increasingly important for clinical laboratories to consolidate existing knowledge and experience to inform and improve future practice. These recommendations have been compiled to help clinical laboratories navigate the challenges of ICAs and thereby facilitate best practice and consistency in genetic test provision for this group of disorders. General recommendations on internal and external quality control, referral, analysis, result interpretation, and reporting are described. Also included are appendices that provide specific information pertinent to genetic testing for hypertrophic, dilated, and arrhythmogenic right ventricular cardiomyopathies, long QT syndrome, Brugada syndrome, and catecholaminergic polymorphic ventricular tachycardia.

Pan-conserved segment tags identify ultra-conserved sequences across assemblies in the human pangenome

The human pangenome, a new reference sequence, addresses many limitations of the current GRCh38 reference. The first release is based on 94 high-quality haploid assemblies from individuals with diverse backgrounds. We employed a k-mer indexing strategy for comparative analysis across multiple assemblies, including the pangenome reference, GRCh38, and CHM13, a telomere-to-telomere reference assembly. Our k-mer indexing approach enabled us to identify a valuable collection of universally conserved sequences across all assemblies, referred to as "pan-conserved segment tags" (PSTs). By examining intervals between these segments, we discerned highly conserved genomic segments and those with structurally related polymorphisms. We found 60,764 polymorphic intervals with unique geo-ethnic features in the pangenome reference. In this study, we utilized ultra-conserved sequences (PSTs) to forge a link between human pangenome assemblies and reference genomes. This methodology enables the examination of any sequence of interest within the pangenome, using the reference genome as a comparative framework.

Bioinformatic Analysis of the CXCR2 Ligands in Cancer Processes

Human CXCR2 has seven ligands, i.e., CXCL1, CXCL2, CXCL3, CXCL5, CXCL6, CXCL7, and CXCL8/IL-8-chemokines with nearly identical properties. However, no available study has compared the contribution of all CXCR2 ligands to cancer progression. That is why, in this study, we conducted a bioinformatic analysis using the GEPIA, UALCAN, and TIMER2.0 databases to investigate the role of CXCR2 ligands in 31 different types of cancer, including glioblastoma, melanoma, and colon, esophageal, gastric, kidney, liver, lung, ovarian, pancreatic, and prostate cancer. We focused on the differences in the regulation of expression (using the Tfsitescan and miRDB databases) and analyzed mutation types in CXCR2 ligand genes in cancers (using the cBioPortal). The data showed that the effect of CXCR2 ligands on prognosis depends on the type of cancer. CXCR2 ligands were associated with EMT, angiogenesis, recruiting neutrophils to the tumor microenvironment, and the count of M1 macrophages. The regulation of the expression of each CXCR2 ligand was different and, thus, each analyzed chemokine may have a different function in cancer processes. Our findings suggest that each type of cancer has a unique pattern of CXCR2 ligand involvement in cancer progression, with each ligand having a unique regulation of expression.

Long-Read DNA Sequencing: Recent Advances and Remaining Challenges

DNA sequencing has revolutionized medicine over recent decades. However, analysis of large structural variation and repetitive DNA, a hallmark of human genomes, has been limited by short-read technology, with read lengths of 100-300 bp. Long-read sequencing (LRS) permits routine sequencing of human DNA fragments tens to hundreds of kilobase pairs in size, using both real-time sequencing by synthesis and nanopore-based direct electronic sequencing. LRS permits analysis of large structural variation and haplotypic phasing in human genomes and has enabled the discovery and characterization of rare pathogenic structural variants and repeat expansions. It has also recently enabled the assembly of a complete, gapless human genome that includes previously intractable regions, such as highly repetitive centromeres and homologous acrocentric short arms. With the addition of protocols for targeted enrichment, direct epigenetic DNA modification detection, and long-range chromatin profiling, LRS promises to launch a new era of understanding of genetic diversity and pathogenic mutations in human populations.

DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data

DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.

Emergence and influence of sequence bias in evolutionarily malleable, mammalian tandem arrays

BACKGROUND

The radiation of mammals at the extinction of the dinosaurs produced a plethora of new forms-as diverse as bats, dolphins, and elephants-in only 10-20 million years. Behind the scenes, adaptation to new niches is accompanied by extensive innovation in large families of genes that allow animals to contact the environment, including chemosensors, xenobiotic enzymes, and immune and barrier proteins. Genes in these "outward-looking" families are allelically diverse among humans and exhibit tissue-specific and sometimes stochastic expression.

RESULTS

Here, we show that these tandem arrays of outward-looking genes occupy AT-biased isochores and comprise the "tissue-specific" gene class that lack CpG islands in their promoters. Models of mammalian genome evolution have not incorporated the sharply different functions and transcriptional patterns of genes in AT- versus GC-biased regions. To examine the relationship between gene family expansion, sequence content, and allelic diversity, we use population genetic data and comparative analysis. First, we find that AT bias can emerge during evolutionary expansion of gene families in cis. Second, human genes in AT-biased isochores or with GC-poor promoters experience relatively low rates of de novo point mutation today but are enriched for non-synonymous variants. Finally, we find that isochores containing gene clusters exhibit low rates of recombination.

CONCLUSIONS

Our analyses suggest that tolerance of non-synonymous variation and low recombination are two forces that have produced the depletion of GC bases in outward-facing gene arrays. In turn, high AT content exerts a profound effect on their chromatin organization and transcriptional regulation.

Genetic Variants of Adrenoceptors

Adrenoceptors are class A G-protein-coupled receptors grouped into three families (α1-, α2-, and β-adrenoceptors), each one including three members. All nine corresponding adrenoceptor genes display genetic variation in their coding and adjacent non-coding genomic region. Coding variants, i.e., nucleotide exchanges within the transcribed and translated receptor sequence, may result in a difference in amino acid sequence thus altering receptor function and signaling. Such variants have been intensely studied in vitro in overexpression systems and addressed in candidate-gene studies for distinct clinical parameters. In recent years, large cohorts were analyzed in genome-wide association studies (GWAS), where variants are detected as significant in context with specific traits. These studies identified two of the in-depth characterized 18 coding variants in adrenoceptors as repeatedly statistically significant genetic risk factors - p.Arg389Gly in the β1- and p.Thr164Ile in the β2-adrenoceptor, along with 56 variants in the non-coding regions adjacent to the adrenoceptor gene loci, the functional role of which is largely unknown at present. This chapter summarizes current knowledge on the two coding variants in adrenoceptors that have been consistently validated in GWAS and provides a prospective overview on the numerous non-coding variants more recently attributed to adrenoceptor gene loci.

Genome annotation: From human genetics to biodiversity genomics

Within the next decade, the genomes of 1.8 million eukaryotic species will be sequenced. Identifying genes in these sequences is essential to understand the biology of the species. This is challenging due to the transcriptional complexity of eukaryotic genomes, which encode hundreds of thousands of transcripts of multiple types. Among these, a small set of protein-coding mRNAs play a disproportionately large role in defining phenotypes. Due to their sequence conservation, orthology can be established, making it possible to define the universal catalog of eukaryotic protein-coding genes. This catalog should substantially contribute to uncovering the genomic events underlying the emergence of eukaryotic phenotypes. This piece briefly reviews the basics of protein-coding gene prediction, discusses challenges in finalizing annotation of the human genome, and proposes strategies for producing annotations across the eukaryotic Tree of Life. This lays the groundwork for obtaining the catalog of all genes-the Earth's code of life.

UniProt and Mass Spectrometry-Based Proteomics-A 2-Way Working Relationship

The human proteome comprises of all of the proteins produced by the sequences translated from the human genome with additional modifications in both sequence and function caused by nonsynonymous variants and posttranslational modifications including cleavage of the initial transcript into smaller peptides and polypeptides. The UniProtKB database (www.uniprot.org) is the world's leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information and presents a summary of experimentally verified, or computationally predicted, functional information added by our expert biocuration team for each protein in the proteome. Researchers in the field of mass spectrometry-based proteomics both consume and add to the body of data available in UniProtKB, and this review highlights the information we provide to this community and the knowledge we in turn obtain from groups via deposition of large-scale datasets in public domain databases.

Investigating Open Reading Frames in Known and Novel Transcripts using ORFanage

ORFanage is a system designed to assign open reading frames (ORFs) to known and novel gene transcripts while maximizing similarity to annotated proteins. The primary intended use of ORFanage is the identification of ORFs in the assembled results of RNA sequencing experiments, a capability that most transcriptome assembly methods do not have. Our experiments demonstrate how ORFanage can be used to find novel protein variants in RNA-seq datasets, and to improve the annotations of ORFs in tens of thousands of transcript models in the human annotation databases. Through its implementation of a highly accurate and efficient pseudo-alignment algorithm, ORFanage is substantially faster than other ORF annotation methods, enabling its application to very large datasets. When used to analyze transcriptome assemblies, ORFanage can aid in the separation of signal from transcriptional noise and the identification of likely functional transcript variants, ultimately advancing our understanding of biology and medicine.

Splam: a deep-learning-based splice site predictor that improves spliced alignments

The process of splicing messenger RNA to remove introns plays a central role in creating genes and gene variants. Here we describe Splam, a novel method for predicting splice junctions in DNA based on deep residual convolutional neural networks. Unlike some previous models, Splam looks at a relatively limited window of 400 base pairs flanking each splice site, motivated by the observation that the biological process of splicing relies primarily on signals within this window. Additionally, Splam introduces the idea of training the network on donor and acceptor pairs together, based on the principle that the splicing machinery recognizes both ends of each intron at once. We compare Splam's accuracy to recent state-of-the-art splice site prediction methods, particularly SpliceAI, another method that uses deep neural networks. Our results show that Splam is consistently more accurate than SpliceAI, with an overall accuracy of 96% at predicting human splice junctions. Splam generalizes even to non-human species, including distant ones like the flowering plant Arabidopsis thaliana. Finally, we demonstrate the use of Splam on a novel application: processing the spliced alignments of RNA-seq data to identify and eliminate errors. We show that when used in this manner, Splam yields substantial improvements in the accuracy of downstream transcriptome analysis of both poly(A) and ribo-depleted RNA-seq libraries. Overall, Splam offers a faster and more accurate approach to detecting splice junctions, while also providing a reliable and efficient solution for cleaning up erroneous spliced alignments.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Pooled tagging and hydrophobic targeting of endogenous proteins for unbiased mapping of unfolded protein responses

System-level understanding of proteome organization and function requires methods for direct visualization and manipulation of proteins at scale. We developed an approach enabled by high-throughput gene tagging for the generation and analysis of complex cell pools with endogenously tagged proteins. Proteins are tagged with HaloTag to enable visualization or direct perturbation. Fluorescent labeling followed by in situ sequencing and deep learning-based image analysis identifies the localization pattern of each tag, providing a bird's-eye-view of cellular organization. Next, we use a hydrophobic HaloTag ligand to misfold tagged proteins, inducing spatially restricted proteotoxic stress that is read out by single cell RNA sequencing. By integrating optical and perturbation data, we map compartment-specific responses to protein misfolding, revealing inter-compartment organization and direct crosstalk, and assigning proteostasis functions to uncharacterized genes. Altogether, we present a powerful and efficient method for large-scale studies of proteome dynamics, function, and homeostasis.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).

Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: Recommendations from the ClinGen SVI Splicing Subgroup

The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) framework for classifying variants uses six evidence categories related to the splicing potential of variants: PVS1, PS3, PP3, BS3, BP4, and BP7. However, the lack of guidance on how to apply such codes has contributed to variation in the specifications developed by different Clinical Genome Resource (ClinGen) Variant Curation Expert Panels. The ClinGen Sequence Variant Interpretation Splicing Subgroup was established to refine recommendations for applying ACMG/AMP codes relating to splicing data and computational predictions. We utilized empirically derived splicing evidence to (1) determine the evidence weighting of splicing-related data and appropriate criteria code selection for general use, (2) outline a process for integrating splicing-related considerations when developing a gene-specific PVS1 decision tree, and (3) exemplify methodology to calibrate splice prediction tools. We propose repurposing the PVS1_Strength code to capture splicing assay data that provide experimental evidence for variants resulting in RNA transcript(s) with loss of function. Conversely, BP7 may be used to capture RNA results demonstrating no splicing impact for intronic and synonymous variants. We propose that the PS3/BS3 codes are applied only for well-established assays that measure functional impact not directly captured by RNA-splicing assays. We recommend the application of PS1 based on similarity of predicted RNA-splicing effects for a variant under assessment in comparison with a known pathogenic variant. The recommendations and approaches for consideration and evaluation of RNA-assay evidence described aim to help standardize variant pathogenicity classification processes when interpreting splicing-based evidence.

Male breast cancer risk associated with pathogenic variants in genes other than BRCA1/2: an Italian case-control study

BACKGROUND

Germline pathogenic variants (PVs) in BRCA1/2 genes are associated with breast cancer (BC) risk in both women and men. Multigene panel testing is being increasingly used for BC risk assessment, allowing the identification of PVs in genes other than BRCA1/2. While data on actionable PVs in other cancer susceptibility genes are now available in female BC, reliable data are still lacking in male BC (MBC). This study aimed to provide the patterns, prevalence and risk estimates associated with PVs in non-BRCA1/2 genes for MBC in order to improve BC prevention for male patients.

METHODS

We performed a large case-control study in the Italian population, including 767 BRCA1/2-negative MBCs and 1349 male controls, all screened using a custom 50 cancer gene panel.

RESULTS

PVs in genes other than BRCA1/2 were significantly more frequent in MBCs compared with controls (4.8% vs 1.8%, respectively) and associated with a threefold increased MBC risk (OR: 3.48, 95% CI: 1.88-6.44; p < 0.0001). PV carriers were more likely to have personal (p = 0.03) and family (p = 0.02) history of cancers, not limited to BC. PALB2 PVs were associated with a sevenfold increased MBC risk (OR: 7.28, 95% CI: 1.17-45.52; p = 0.034), and ATM PVs with a fivefold increased MBC risk (OR: 4.79, 95% CI: 1.12-20.56; p = 0.035).

CONCLUSIONS

This study highlights the role of PALB2 and ATM PVs in MBC susceptibility and provides risk estimates at population level. These data may help in the implementation of multigene panel testing in MBC patients and inform gender-specific BC risk management and decision making for patients and their families.

Novel variants in established epilepsy genes in focal epilepsy

INTRODUCTION

Next generation sequencing (NGS) has greatly expanded our understanding of genetic contributors in multiple epilepsy syndromes, including focal epilepsy. Describing the genetic architecture of common syndromes promises to facilitate the diagnostic process as well as aid in the identification of patients who stand to benefit from genetic testing, but most studies to date have been limited to examining children or adults with intellectual disability. Our aim was to determine the yield of targeted sequencing of 5 established epilepsy genes (DEPDC5, LGI1, SCN1A, GRIN2A, and PCHD19) in an extensively phenotyped cohort of focal epilepsy patients with normal intellectual function or mild intellectual disability, as well as describe novel variants and determine the characteristics of variant carriers.

PATIENTS AND METHODS

Targeted panel sequencing was performed on 96 patients with a strong clinical suspicion of genetic focal epilepsy. Patients had previously gone through a comprehensive diagnostic epilepsy evaluation in The Neurology Clinic, University Clinical Center of Serbia. Variants of interest (VOI) were classified using the American College of Medical Genetics and the Association for Molecular Pathology criteria.

RESULTS

Six VOI in eight (8/96, 8.3%) patients were found in our cohort. Four likely pathogenic VOI were determined in six (6/96, 6.2%) patients, two DEPDC5 variants in two patients, one SCN1A variant in two patients and one PCDH19 variant in two patients. One variant of unknown significance (VUS) was found in GRIN2A in one (1/96, 1.0%) patient. Only one VOI in GRIN2A was classified as likely benign. No VOI were detected in LGI1.

CONCLUSION

Sequencing of only five known epilepsy genes yielded a diagnostic result in 6.2% of our cohort and revealed multiple novel variants. Further research is necessary for a better understanding of the genetic basis in common epilepsy syndromes in patients with normal intellectual function or mild intellectual disability.

Haack T, Eggermann T. Quality assurance within the context of genome diagnostics (a german perspective)

The rapid and dynamic implementation of Next-Generation Sequencing (NGS)-based assays has revolutionized genetic testing, and in the near future, nearly all molecular alterations of the human genome will be diagnosable via massive parallel sequencing. While this progress will further corroborate the central role of human genetics in the multidisciplinary management of patients with genetic disorders, it must be accompanied by quality assurance measures in order to allow the safe and optimal use of knowledge ascertained from genome diagnostics. To achieve this, several valuable tools and guidelines have been developed to support the quality of genome diagnostics. In this paper, authors with experience in diverse aspects of genomic analysis summarize the current status of quality assurance in genome diagnostics, with the aim of facilitating further standardization and quality improvement in one of the core competencies of the field.

The Rapidly Expanding Genetic Spectrum of Common Variable Immunodeficiency-Like Disorders

The understanding of common variable immunodeficiency disorders (CVID) is in evolution. CVID was previously a diagnosis of exclusion. New diagnostic criteria have allowed the disorder to be identified with greater precision. With the advent of next-generation sequencing (NGS), it has become apparent that an increasing number of patients with a CVID phenotype have a causative genetic variant. If a pathogenic variant is identified, these patients are removed from the overarching diagnosis of CVID and are deemed to have a CVID-like disorder. In populations where consanguinity is more prevalent, the majority of patients with severe primary hypogammaglobulinemia will have an underlying inborn error of immunity, usually an early-onset autosomal recessive disorder. In nonconsanguineous societies, pathogenic variants are identified in approximately 20% to 30% of patients. These are often autosomal dominant mutations with variable penetrance and expressivity. To add to the complexity of CVID and CVID-like disorders, some genetic variants such as those in TNFSF13B (transmembrane activator calcium modulator cyclophilin ligand interactor) predispose to, or enhance, disease severity. These variants are not causative but can have epistatic (synergistic) interactions with more deleterious mutations to worsen disease severity. This review is a description of the current understanding of genes associated with CVID and CVID-like disorders. This information will assist clinicians in interpreting NGS reports when investigating the genetic basis of disease in patients with a CVID phenotype.

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity

The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3' end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3' processing are deployed across human tissues, with nearly half of multi-transcript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

Genetic testing for misclassified monogenic diabetes in Māori and Pacific peoples in Aōtearoa New Zealand with early-onset type 2 diabetes

Aims

Monogenic diabetes accounts for 1-2% of diabetes cases yet is often misdiagnosed as type 2 diabetes. The aim of this study was to examine in Māori and Pacific adults clinically diagnosed with type 2 diabetes within 40 years of age, (a) the prevalence of monogenic diabetes in this population (b) the prevalence of beta-cell autoantibodies and (c) the pre-test probability of monogenic diabetes.

Methods

Targeted sequencing data of 38 known monogenic diabetes genes was analyzed in 199 Māori and Pacific peoples with BMI of 37.9 ± 8.6 kg/m² who had been diagnosed with type 2 diabetes between 3 and 40 years of age. A triple-screen combined autoantibody assay was used to test for GAD, IA-2, and ZnT8. MODY probability calculator score was generated in those with sufficient clinical information (55/199).

Results

No genetic variants curated as likely pathogenic or pathogenic were found. One individual (1/199) tested positive for GAD/IA-2/ZnT8 antibodies. The pre-test probability of monogenic diabetes was calculated in 55 individuals with 17/55 (31%) scoring above the 20% threshold considered for diagnostic testing referral.

Discussion

Our findings suggest that monogenic diabetes is rare in Māori and Pacific people with clinical age, and the MODY probability calculator likely overestimates the likelihood of a monogenic cause for diabetes in this population.

Insights from a genome-wide truth set of tandem repeat variation

Tools for genotyping tandem repeats (TRs) from short read sequencing data have improved significantly over the past decade. Extensive comparisons of these tools to gold standard diagnostic methods like RP-PCR have confirmed their accuracy for tens to hundreds of well-studied loci. However, a scarcity of high-quality orthogonal truth data limited our ability to measure tool accuracy for the millions of other loci throughout the genome. To address this, we developed a TR truth set based on the Synthetic Diploid Benchmark (SynDip). By identifying the subset of insertions and deletions that represent TR expansions or contractions with motifs between 2 and 50 base pairs, we obtained accurate genotypes for 139,795 pure and 6,845 interrupted repeats in a single diploid sample. Our approach did not require running existing genotyping tools on short read or long read sequencing data and provided an alternative, more accurate view of tandem repeat variation. We applied this truth set to compare the strengths and weaknesses of widely-used tools for genotyping TRs, evaluated the completeness of existing genome-wide TR catalogs, and explored the properties of tandem repeat variation throughout the genome. We found that, without filtering, ExpansionHunter had higher accuracy than GangSTR and HipSTR over a wide range of motifs and allele sizes. Also, when errors in allele size occurred, ExpansionHunter tended to overestimate expansion sizes, while GangSTR tended to underestimate them. Additionally, we saw that widely-used TR catalogs miss between 16% and 41% of variant loci in the truth set. These results suggest that genome-wide analyses would benefit from genotyping a larger set of loci as well as further tool development that builds on the strengths of current algorithms. To that end, we developed a new catalog of 2.8 million loci that captures 95% of variant loci in the truth set, and created a modified version of ExpansionHunter that runs 2 to 3x faster than the original while producing the same output.

Benchmarking splice variant prediction algorithms using massively parallel splicing assays

Background

Variants that disrupt mRNA splicing account for a sizable fraction of the pathogenic burden in many genetic disorders, but identifying splice-disruptive variants (SDVs) beyond the essential splice site dinucleotides remains difficult. Computational predictors are often discordant, compounding the challenge of variant interpretation. Because they are primarily validated using clinical variant sets heavily biased to known canonical splice site mutations, it remains unclear how well their performance generalizes.

Results

We benchmarked eight widely used splicing effect prediction algorithms, leveraging massively parallel splicing assays (MPSAs) as a source of experimentally determined ground-truth. MPSAs simultaneously assay many variants to nominate candidate SDVs. We compared experimentally measured splicing outcomes with bioinformatic predictions for 3,616 variants in five genes. Algorithms' concordance with MPSA measurements, and with each other, was lower for exonic than intronic variants, underscoring the difficulty of identifying missense or synonymous SDVs. Deep learning-based predictors trained on gene model annotations achieved the best overall performance at distinguishing disruptive and neutral variants. Controlling for overall call rate genome-wide, SpliceAI and Pangolin also showed superior overall sensitivity for identifying SDVs. Finally, our results highlight two practical considerations when scoring variants genome-wide: finding an optimal score cutoff, and the substantial variability introduced by differences in gene model annotation, and we suggest strategies for optimal splice effect prediction in the face of these issues.

Conclusion

SpliceAI and Pangolin showed the best overall performance among predictors tested, however, improvements in splice effect prediction are still needed especially within exons.

Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma

A hallmark of high-risk childhood medulloblastoma is the dysregulation of RNA translation. Currently, it is unknown whether medulloblastoma dysregulates the translation of putatively oncogenic non-canonical open reading frames. To address this question, we performed ribosome profiling of 32 medulloblastoma tissues and cell lines and observed widespread non-canonical ORF translation. We then developed a step-wise approach to employ multiple CRISPR-Cas9 screens to elucidate functional non-canonical ORFs implicated in medulloblastoma cell survival. We determined that multiple lncRNA-ORFs and upstream open reading frames (uORFs) exhibited selective functionality independent of the main coding sequence. One of these, ASNSD1-uORF or ASDURF, was upregulated, associated with the MYC family oncogenes, and was required for medulloblastoma cell survival through engagement with the prefoldin-like chaperone complex. Our findings underscore the fundamental importance of non-canonical ORF translation in medulloblastoma and provide a rationale to include these ORFs in future cancer genomics studies seeking to define new cancer targets.

SCN1A: bioinformatically informed revised boundaries for promoter and enhancer regions

Pathogenic variations in the sodium voltage-gated channel alpha subunit 1 (SCN1A) gene are responsible for multiple epilepsy phenotypes, including Dravet syndrome, febrile seizures (FS) and genetic epilepsy with FS plus. Phenotypic heterogeneity is a hallmark of SCN1A-related epilepsies, the causes of which are yet to be clarified. Genetic variation in the non-coding regulatory regions of SCN1A could be one potential causal factor. However, a comprehensive understanding of the SCN1A regulatory landscape is currently lacking. Here, we summarized the current state of knowledge of SCN1A regulation, providing details on its promoter and enhancer regions. We then integrated currently available data on SCN1A promoters by extracting information related to the SCN1A locus from genome-wide repositories and clearly defined the promoter and enhancer regions of SCN1A. Further, we explored the cellular specificity of differential SCN1A promoter usage. We also reviewed and integrated the available human brain-derived enhancer databases and mouse-derived data to provide a comprehensive computationally developed summary of SCN1A brain-active enhancers. By querying genome-wide data repositories, extracting SCN1A-specific data and integrating the different types of independent evidence, we created a comprehensive catalogue that better defines the regulatory landscape of SCN1A, which could be used to explore the role of SCN1A regulatory regions in disease.

Transcript Assembly and Annotations: Bias and Adjustment

Motivation

Transcript annotations play a critical role in gene expression analysis as they serve as a reference for quantifying isoform-level expression. The two main sources of annotations are RefSeq and Ensembl/GENCODE, but discrepancies between their methodologies and information resources can lead to significant differences. It has been demonstrated that the choice of annotation can have a significant impact on gene expression analysis. Furthermore, transcript assembly is closely linked to annotations, as assembling large-scale available RNA-seq data is an effective data-driven way to construct annotations, and annotations are often served as benchmarks to evaluate the accuracy of assembly methods. However, the influence of different annotations on transcript assembly is not yet fully understood.

Results

We investigate the impact of annotations on transcript assembly. We observe that conflicting conclusions can arise when evaluating assemblers with different annotations. To understand this striking phenomenon, we compare the structural similarity of annotations at various levels and find that the primary structural difference across annotations occurs at the intron-chain level. Next, we examine the biotypes of annotated and assembled transcripts and uncover a significant bias towards annotating and assembling transcripts with intron retentions, which explains above the contradictory conclusions. We develop a standalone tool, available at https://github.com/Shao-Group/irtool, that can be combined with an assembler to generate an assembly without intron retentions. We evaluate the performance of such a pipeline and offer guidance to select appropriate assembling tools for different application scenarios.

Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci

Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".