Biology subjects

Pastinen, T.

Publications and source records attributed to Pastinen, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset

Rare disease research and diagnosis rely on the integration of genomic and phenotypic data generated across diverse clinical sites; however, the absence of widely adopted standards for representing genomic data and associated metadata has limited data interoperability, reuse, and cross-study analysis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was established to investigate challenging rare disease cases and evaluate emerging multi-omic technologies for clinical translation. To support coordinated data integration across distributed research sites, we developed a common Consortium Data Model in partnership with domain experts to standardize the capture of participant-, family-, phenotype- and assay-level metadata, with a particular emphasis on using a modular architecture to support linking of multiple data versions from multiple omic technologies to a single individual and attribution of a genetic finding to the specific technology used for its initial discovery. Adoption of the GREGoR Data Model has enabled continued generation and public release of a harmonized, analysis-ready Consortium Dataset. The most recent release includes phenotypic, family and multi-omic data from 12,292 participants in 5,029 families. Other rare disease data sharing efforts are beginning to adopt this data model which will facilitate cross consortium analyses and empower rare disease research. This work demonstrates that a collaborative, flexible, and scalable data model can enable large-scale rare disease research, facilitate cross-center data harmonization, and enable data interoperability.

genomics↗

bioRxiv2026

Hunting for microsatellite instability in long-read data with Owl

Microsatellite instability (MSI) is a key biomarker of mismatch repair deficiency and response to immunotherapy, yet most existing genomic detection methods are optimized for short-read sequencing and rely on small panels of homopolymer markers, limiting the ability to characterize genome-wide and motif-specific patterns of instability. Here we present Owl, a bioinformatic tool for quantifying MSI from long-read (PacBio) genomic data. Owl leverages a genome-wide marker set of more than 140,000 microsatellite repeats ranging from 1-6 bp in length to measure MSI across a phased genome. Using a wrap-around alignment algorithm, Owl constructs repeat-length distributions at each marker site and flags somatic instability using the coefficient of variation. We applied Owl to screen for markers with stable coverage, phasing, and baseline variation across 131 diverse genomes from the Human Pangenome Reference Consortium, where Owl scores ranged from 1.4% to 5.4% of markers exceeding the instability threshold. When applied to 19 cancer cell lines and one diffuse astrocytoma tumor-normal pair, Owl identified five MSI-high genomes with 15-18% unstable markers and showed close concordance with an Illumina DRAGEN MSI assay for the astrocytoma sample. Motif-level analyses revealed shared enrichment of short homopolymer and dinucleotide (A- and AT-rich) repeats across MSI-high cancers, and additionally uncovered a distinct pattern of elevated GGAA microsatellite instability in Ewing sarcoma cell lines, consistent with the known role of the EWS::FLI1 fusion protein at GGAA-rich regulatory elements. Owl is implemented in Rust and integrated into the PacBio HiFi Somatic workflow, providing a scalable framework for MSI analysis from long-read sequencing focused on repeat instability specifically in tumor samples.

bioinformatics↗

bioRxiv2025

A comprehensive bulk and single-cell transcriptional atlas of pediatric leukemias

Advancements in understanding the molecular factors driving pediatric leukemias have led to an ever-increasing volume and diversity of data being generated. Recent studies are moving beyond DNA-based profiling to incorporate transcriptional data, enhancing the characterization of these cancers and informing clinical decisions. However, many existing datasets focus on specific leukemia subtypes, limiting the extraction of broader clinically relevant insights. Here, we present a comprehensive dataset of bulk and single-cell transcriptional data from 69 pediatric leukemia patients, encompassing eight leukemias. This is one of the most diverse pediatric leukemia datasets published to date. Through comparative analysis and explainable machine learning, we demonstrate the utility of these datasets in improving pediatric leukemia characterization and supporting the integration of transcriptional data into clinical testing.

cancer biology↗

bioRxiv2025

Long-read sequencing reveals telomere inheritance patterns from human trios

Telomeres are essential for maintaining genomic integrity and are associated with cellular aging and disease, yet the factors influencing their inheritance across generations remain poorly understood. Leveraging PacBio HiFi long-read sequencing and 75 parent-offspring trios (n = 225) from the Genomic Answers for Kids program, we analyzed individual telomeres across chromosomes and their inheritance. Telomere length (TL) varied between chromosome arms in a way that was consistent in parents and offsprings, with average values ranging from 5000 to 8000 base pairs. Maternal and paternal TL together were a strong predictor of child TL (R2 = 0.59). Notably, using telomeric variant repeats, we developed a tool that enabled allelic tracing for 53.3% of maternally and 49.9% of paternally inherited telomeres. In the child, paternally transmitted alleles were significantly longer than age-matched maternal ones ({Delta}mean = 409 bp, p = 2.6e-05), particularly when from older parents ({Delta}mean = 698 bp, p = 8.9e-05) and at chromosome arms with shorter average TL ({Delta}mean = 752 bp, p = 1.6e-06). These findings reveal parent-of-origin effects and heritable influences on TL, providing novel insights into telomere dynamics and their potential implications in age-related disease susceptibility.

genetics↗

bioRxiv2025

Altered sperm DNA methylation in overweight men associates with transposable element regulation of paternal origins of risk in children

The occurrence of childhood neurodevelopmental disorders has been steadily increasing for decades yet we have little understanding of modes of inheritance implicated in these diseases. Epidemiology studies have revealed an association between an elevated paternal BMI and an increased risk for autism in children, suggesting non-genetic modes of inheritance may be involved. Epigenetic marks, like DNA methylation at cytosines, are especially susceptible to environmental, diet and lifestyle changes. Yet, whether a mans BMI influences the sperm methylome and potentially impacts offspring development remains unresolved. Using MethylC capture (MCC)-sequencing, we identified over 38 000 differentially methylated CpGs (DMCs) in the sperm of men with an elevated BMI, with many occurring in regions that were enriched for neural gene and disease ontologies. Differentially methylated regions (DMRs) were enriched at transposable elements that can act as active enhancers during human zygotic genome activation, and at gene promoters for early lineage specification. These results suggest that sperm methylome alterations that are linked to an elevated BMI may influence key embryonic transcriptional process, notably those associated with trophectoderm and placental development.

developmental biology↗

bioRxiv2025

STRkit: precise, read-level genotyping of short tandem repeats using long reads and single-nucleotide variation

Variation in short tandem repeats (STRs) is implicated in Mendelian disease and complex traits, but can be difficult to resolve with short-read genome sequencing. We present STRkit, a software package for genotyping STRs using long read sequencing (LRS) that uses nearby single-nucleotide variants to improve genotyping accuracy without a priori haplotype information. We show that STRkit has unique strengths versus other methods: it can use data from both major LRS technologies (Pacific Biosciences HiFi [PB] and Oxford Nanopore [ONT]) to output both allele and read-level copy number and sequence, performs best in benchmarking with F1 scores of 0.9633 and 0.9056 with PB and ONT data respectively, achieves a Mendelian inheritance rate of 97.86% with PB data, and is open source software. STRkits features open up new possibilities for association testing, assessing patterns of STR inheritance, and better understanding the functional effects of these notable repeat elements.

bioinformatics↗

bioRxiv2025

Structural variation, selection, and diversification of the NPIP gene family from the human pangenome

The NPIP (nuclear pore interacting protein) gene family has expanded to high copy number in humans and African apes where it has been subject to an excess of amino acid replacement consistent with positive selection (1). Due to the limitations of short-read sequencing, NPIP human genetic diversity has been poorly understood. Using highly accurate assemblies generated from long-read sequencing as part of the human pangenome, we completely characterize 169 human haplotypes (4,665 NPIP paralogs and alleles). Of the 28 NPIP paralogs, just three (NPIPB2, B11, and B14) are fixed at a single copy, and only a single locus, B2, shows no structural variation. Four NPIP paralogs map to large segmental duplication blocks that mediate polymorphic inversions (355 kbp-1.6 Mbp) corresponding to microdeletions associated with developmental delay and autism. Haplotype-based tests of positive selection and selective sweeps identify two paralogs, B9 and B15, within the top percentile for both tests. Using full-length cDNA data from 101 tissue/cell types, we construct paralog-specific gene models and show that 56% (31/55 most abundant isoforms) have not been previously described in RefSeq. We define six distinct translation start sites and other protein structural features that distinguish paralogs, including a variable number tandem repeat that encodes a beta helix of variable size that emerged [~]3.1 million years ago in human evolution. Among the 28 NPIP paralogs, we identify distinct tissue and developmental patterns of expression with only a few maintaining the ancestral testis-enriched expression. A subset of paralogs (NPIPA1, A5, A6-9, B3-5, and B12/B13) show increased brain expression. Our results suggest ongoing positive selection in the human population and rapid diversification of NPIP gene models.

genomics↗

bioRxiv2024

StarPhase: Comprehensive Phase-Aware Pharmacogenomic Diplotyper for Long-Read Sequencing Data

Pharmacogenomics is central to precision medicine, informing medication safety and efficacy. Phar-macogenomic diplotyping of complex genes requires full-length DNA sequences and detection of structural rearrangements. We introduce StarPhase, a tool that leverages PacBio HiFi sequence data to diplotype 21 CPIC Level A pharmacogenes and provides detailed haplotypes and supporting visualizations for HLA-A, HLA-B, and CYP2D6. StarPhase diplotypes have high concordance with benchmarks where 99.5% are either exact matches or minor discrepancies. Manual inspection of the 0.5% mismatches indicates they were correctly called by StarPhase. With StarPhase, we update or correct 26.2% of GeT-RM pharmacogenomic diplotypes. Population distributions from StarPhase mostly reflect those of the All of Us cohort, while also highlighting gaps in existing pharmacogenomic databases that long-read sequencing can fill. With a single HiFi whole genome sequencing assay, StarPhase enables robust PGx diplotyping even as additional pharmacogenes and haplotypes are discovered.

bioinformatics↗

bioRxiv2024

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

bioinformatics↗

bioRxiv2024

Structural polymorphism and diversity of human segmental duplications

Segmental duplications (SDs) contribute significantly to human disease, evolution, and diversity yet have been difficult to resolve at the sequence level. We present a population genetics survey of SDs by analyzing 170 human genome assemblies where the majority of SDs are fully resolved using long-read sequence assembly. Excluding the acrocentric short arms, we identify 173.2 Mbp of duplicated sequence (47.4 Mbp not present in the telomere-to-telomere reference) distinguishing fixed from structurally polymorphic events. We find that intrachromosomal SDs are among the most variable with rare events mapping near their progenitor sequences. African genomes harbor significantly more intrachromosomal SDs and are more likely to have recently duplicated gene families with higher copy number when compared to non-African samples. A comparison to a resource of 563 million full-length Iso-Seq reads identifies 201 novel, potentially protein-coding genes corresponding to these copy number polymorphic SDs.

genomics↗

bioRxiv2024

Genome-wide profiling of highly similar paralogous genes using HiFi sequencing

Variant calling is hindered in segmental duplications by sequence homology. We developed Paraphase, a HiFi-based informatics method that resolves highly similar genes by phasing all haplotypes of a gene family. We applied Paraphase to 160 long (>10 kb) segmental duplication regions across the human genome with high (>99%) sequence similarity, encoding 316 genes. Analysis across five ancestral populations revealed highly variable copy numbers of these regions. We identified 23 families with exceptionally low within-family diversity, where extensive gene conversion and unequal-crossing over have resulted in highly similar gene copies. Furthermore, our analysis of 36 trios identified 7 de novo SNVs and 4 de novo gene conversion events, 2 of which are non-allelic. Finally, we summarized extensive genetic diversity in 9 medically relevant genes previously considered challenging to genotype. Paraphase provides a framework for resolving gene paralogs, enabling accurate testing in medically relevant genes and population-wide studies of previously inaccessible genes.

genomics↗

bioRxiv2024

Expanded methylome and quantitative trait loci detection by long-read profiling of personal DNA

Structural variants (SVs) are omnipresent in human DNA, yet their genotype and methylation status is rarely characterized due to previous limitations in genome assembly and detection of modified nucleotides. Because of this, the extent to which these regions act as quantitative-trait loci is also largely unknown. Here, we generated a pangenome graph summarizing the SVs in 782 de novo assembled genomes obtained from the Genomic Answers for Kids rare disease cohort, that captures 14.6 million CpGs in DNA segments that are absent from the CHM13v2 assembly (SV-CpGs), expanding their number by 43.6%. Next, using 435 methylomes from the same samples, we genotyped a total of 7.99 million SV-CpGs, of which 5.18 million (64.8%) were found to be methylated (SV-5mCpGs) in at least one sample. To understand the provenance and impact of these novel SV-CpGs, we noted that non-repeat sequences were the leading contributor of SV-CpGs (3.3 x 106), followed by centromeric satellites (1.58 x 106), simple repeats (1.19 x 106), Alus (0.67 x 106), satellites (0.39 x 106), L1s (0.27 x 106), and SVAs (0.19 x 106). Meanwhile, the methylation rate of SV-CpGs was the highest in repeat sequences. Moreover, in contrast to Alus and L1s, centromeric satellites, simple repeats and SVA sequences were overrepresented in SV-5mCpGs compared to reference CpGs. Similarly, we established that non-reference CpGs were more than twice (37% vs. 15%) as likely to be variable, showing intermediate methylation levels in the population. Lastly, to explore if SVs detected in this pangenome are potentially causal for functional variation in population we measured methylation quantitative trait loci (SV-mQTLs) using CHM13v2 as a backbone. This revealed over 230,464 methylation bins within 100 kbp of a common SV (>5% MAF) showing significant association (at 5% FDR) with methylation variation. Finally, we assessed how many of these SVs-mQTLs were the leading QTL variant compared to SNVs and identified 65,659 methylation bins (28.5%) where the leading variant was an SV. In conclusion, our results demonstrate that graph genome references providing full SV structures in combination with the associated methylation variation reveal tens-of-thousands of QTLs that are more accurately mapped in personal genomes, underscoring the importance of assembly-based analyses of human traits.

bioinformatics↗

bioRxiv2024

Independent expansion, selection and hypervariability of the TBC1D3 gene family in humans

TBC1D3 is a primate-specific gene family that has expanded in the human lineage and has been implicated in neuronal progenitor proliferation and expansion of the frontal cortex. The gene family and its expression have been challenging to investigate because it is embedded in high-identity and highly variable segmental duplications. We sequenced and assembled the gene family using long-read sequencing data from 34 humans and 11 nonhuman primate species. Our analysis shows that this particular gene family has independently duplicated in at least five primate lineages, and the duplicated loci are enriched at sites of large-scale chromosomal rearrangements on chromosome 17. We find that most humans vary along two TBC1D3 clusters where human haplotypes are highly variable in copy number, differing by as many as 20 copies, and structure (structural heterozygosity 90%). We also show evidence of positive selection, as well as a significant change in the predicted human TBC1D3 protein sequence. Lastly, we find that, despite multiple duplications, human TBC1D3 expression is limited to a subset of copies and, most notably, from a single paralog group: TBC1D3-CDKL. These observations may help explain why a gene potentially important in cortical development can be so variable in the human population.

genetics↗

bioRxiv2023

EpiVar Browser: advanced exploration of epigenomics data under controlled access

MotivationHuman epigenomic data has been generated by large consortia for thousands of cell types to be used as a reference map of normal and disease chromatin states. Since epigenetic data contains potentially identifiable information, similarly to genetic data, most raw files generated by these consortia are stored in controlled-access databases. It is important to protect identifiable information, but this should not hinder secure sharing of these valuable datasets. ResultsGuided by the Framework for responsible sharing of genomic and health-related data from the Global Alliance for Genomics and Health (GA4GH), we have developed a tool to facilitate the exploration of epigenomics datasets aggregate results, while filtering out identifiable information. Specifically, the EpiVar Browser allows a user to navigate an epigenetic dataset from a cohort of individuals and enables direct exploration of genotype-chromatin phenotype relationships. Because the information about individual genotypes is not accessible and aggregated in the output that is made available, no identifiable data is released, yet the interface allows for dynamic genotype - epigenome interrogation. This approach has the potential to accelerate analyses that would otherwise require a lengthy multi-step approval process and provides a generalisable strategy to facilitate responsible access to sensitive epigenomics data. Availability and implementationOnline portal instance: https://computationalgenomics.ca/tools/epivar Source code: https://github.com/c3g/epivar-browser

genomics↗

bioRxiv2023

A common flanking variant is associated with enhanced meiotic stability of the FGF14-SCA27B locus

The factors driving initiation of pathological expansion of tandem repeats remain largely unknown. Here, we assessed the FGF14-SCA27B (GAA)*(TTC) repeat locus in 2,530 individuals by long-read and Sanger sequencing and identified a 5-flanking 17-bp deletion-insertion in 70.34% of alleles (3,463/4,923). This common sequence variation was present nearly exclusively on alleles with fewer than 30 GAA-pure repeats and was associated with enhanced meiotic stability of the repeat locus.

genetics↗

bioRxiv2023

Resolving the unsolved: Comprehensive assessment of tandem repeats at scale

Tandem repeat (TR) variation is associated with gene expression changes and over 50 rare monogenic diseases. Recent advances in sequencing have enabled accurate, long reads that can characterize the full-length sequence and methylation profile of TRs. However, despite these advances in sequencing technology, computational methods to fully profile tandem repeats across the genome do not exist. To address this gap, we introduce tools for tandem repeat genotyping (TRGT), visualization and an accompanying TR database. TRGT accurately resolves the length and sequence composition of TR regions in the human genome. Assessing 937,122 TRs, TRGT showed a Mendelian concordance of 99.56%, allowing a single repeat unit difference. In six samples with known repeat expansions, TRGT detected all repeat expansions while also identifying methylation signals, mosaicism, and providing finer resolution of repeat length. Additionally, we release a database with allele sequences and methylation levels for 937,122 TRs across 100 genomes.

genomics↗

bioRxiv2023

Single-strand mismatch and damage patterns revealed by single-molecule DNA sequencing

Mutations accumulate in the genome of every cell of the body throughout life, causing cancer and other genetic diseases1-4. Almost all of these mosaic mutations begin as nucleotide mismatches or damage in only one of the two strands of the DNA prior to becoming double-strand mutations if unrepaired or misrepaired5. However, current DNA sequencing technologies cannot resolve these initial single-strand events. Here, we developed a single-molecule, long-read sequencing method that achieves single-molecule fidelity for single-base substitutions when present in either one or both strands of the DNA. It also detects single-strand cytosine deamination events, a common type of DNA damage. We profiled 110 samples from diverse tissues, including from individuals with cancer-predisposition syndromes, and define the first single-strand mismatch and damage signatures. We find correspondences between these single-strand signatures and known double-strand mutational signatures, which resolves the identity of the initiating lesions. Tumors deficient in both mismatch repair and replicative polymerase proofreading show distinct single-strand mismatch patterns compared to samples deficient in only polymerase proofreading. In the mitochondrial genome, our findings support a mutagenic mechanism occurring primarily during replication. Since the double-strand DNA mutations interrogated by prior studies are only the endpoint of the mutation process, our approach to detect the initiating single-strand events at single-molecule resolution will enable new studies of how mutations arise in a variety of contexts, especially in cancer and aging.

genomics↗

bioRxiv2022

Comprehensive SMN1 and SMN2 profiling for spinal muscular atrophy analysis using long-read PacBio HiFi sequencing

Spinal muscular atrophy, a leading cause of early infant death, is caused by biallelic mutations of the SMN1 gene. Sequence analysis of SMN1 is challenging due to high sequence similarity with its paralog SMN2. Both genes have variable copy numbers across populations. Furthermore, without pedigree information, it is impossible to identify silent carriers (2+0) with two copies of SMN1 on one chromosome and zero copies on the other. We developed Paraphase, an informatics method that identifies full-length SMN1 and SMN2 haplotypes, determines the gene copy numbers and calls phased variants using long-read PacBio HiFi data. The SMN1 and SMN2 copy number calls by Paraphase are highly concordant with orthogonal methods (99.2% for SMN1 and 100% for SMN2). We applied Paraphase to 438 samples across five ethnic populations to conduct a population-wide haplotype analysis of these highly homologous genes. We identified major SMN1 and SMN2 haplogroups and characterized their co-segregation through pedigree-based analyses. We identified two SMN1 haplotypes that form a common two-copy SMN1 allele in African populations. Testing positive for these two haplotypes in an individual with two copies of SMN1 gives a silent carrier risk of 88.5%, which is significantly higher than the currently used marker (1.7-3.0%). Extending beyond simple copy number testing, Paraphase can detect pathogenic variants and enable potential haplotype-based screening of silent carriers through statistical phasing of haplotypes into alleles. Future analysis of larger population data will allow identification of more diverse haplotypes and genetic markers for silent carriers.

genetics↗