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Lysenkova Wiklander M, Arvidsson G, Bunikis I, Lundmark A, Raine A, Marincevic-Zuniga Y, Gezelius H, Bremer A, Feuk L, Ameur A, Nordlund J. A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing. Life Sci Alliance 2024; 7:e202302481. [PMID: 38777370 PMCID: PMC11111970 DOI: 10.26508/lsa.202302481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 05/02/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024] Open
Abstract
The B-cell acute lymphoblastic leukemia (ALL) cell line REH, with the t(12;21) ETV6::RUNX1 translocation, is known to have a complex karyotype defined by a series of large-scale chromosomal rearrangements. Taken from a 15-yr-old at relapse, the cell line offers a practical model for the study of pediatric B-ALL. In recent years, short- and long-read DNA and RNA sequencing have emerged as a complement to karyotyping techniques in the resolution of structural variants in an oncological context. Here, we explore the integration of long-read PacBio and Oxford Nanopore whole-genome sequencing, IsoSeq RNA sequencing, and short-read Illumina sequencing to create a detailed genomic and transcriptomic characterization of the REH cell line. Whole-genome sequencing clarified the molecular traits of disrupted ALL-associated genes including CDKN2A, PAX5, BTG1, VPREB1, and TBL1XR1, as well as the glucocorticoid receptor NR3C1 Meanwhile, transcriptome sequencing identified seven fusion genes within the genomic breakpoints. Together, our extensive whole-genome investigation makes high-quality open-source data available to the leukemia genomics community.
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Affiliation(s)
- Mariya Lysenkova Wiklander
- https://ror.org/048a87296 Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
| | - Gustav Arvidsson
- https://ror.org/048a87296 Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
| | - Ignas Bunikis
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 National Genomics Infrastructure, Uppsala University, Uppsala, Sweden
| | - Anders Lundmark
- https://ror.org/048a87296 Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
| | - Amanda Raine
- https://ror.org/048a87296 Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 National Genomics Infrastructure, Uppsala University, Uppsala, Sweden
| | - Yanara Marincevic-Zuniga
- https://ror.org/048a87296 Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 National Genomics Infrastructure, Uppsala University, Uppsala, Sweden
| | - Henrik Gezelius
- https://ror.org/048a87296 Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 National Genomics Infrastructure, Uppsala University, Uppsala, Sweden
| | - Anna Bremer
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- https://ror.org/01apvbh93 Department of Clinical Genetics, Uppsala University Hospital, Uppsala, Sweden
| | - Lars Feuk
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 National Genomics Infrastructure, Uppsala University, Uppsala, Sweden
| | - Adam Ameur
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 National Genomics Infrastructure, Uppsala University, Uppsala, Sweden
| | - Jessica Nordlund
- https://ror.org/048a87296 Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 SciLifeLab, Uppsala University, Uppsala, Sweden
- https://ror.org/048a87296 National Genomics Infrastructure, Uppsala University, Uppsala, Sweden
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2
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Shelton WJ, Zandpazandi S, Nix JS, Gokden M, Bauer M, Ryan KR, Wardell CP, Vaske OM, Rodriguez A. Long-read sequencing for brain tumors. Front Oncol 2024; 14:1395985. [PMID: 38915364 PMCID: PMC11194609 DOI: 10.3389/fonc.2024.1395985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/27/2024] [Indexed: 06/26/2024] Open
Abstract
Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.
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Affiliation(s)
- William J. Shelton
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Sara Zandpazandi
- Department of Neurosurgery, Medical University of South Carolina, Charleston, SC, United States
| | - J Stephen Nix
- Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Murat Gokden
- Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Michael Bauer
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Katie Rose Ryan
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Christopher P. Wardell
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Olena Morozova Vaske
- Department of Molecular, Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, United States
| | - Analiz Rodriguez
- Department of Neurosurgery, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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3
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Schloissnig S, Pani S, Rodriguez-Martin B, Ebler J, Hain C, Tsapalou V, Söylev A, Hüther P, Ashraf H, Prodanov T, Asparuhova M, Hunt S, Rausch T, Marschall T, Korbel JO. Long-read sequencing and structural variant characterization in 1,019 samples from the 1000 Genomes Project. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590093. [PMID: 38659906 PMCID: PMC11042266 DOI: 10.1101/2024.04.18.590093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Structural variants (SVs) contribute significantly to human genetic diversity and disease 1-4 . Previously, SVs have remained incompletely resolved by population genomics, with short-read sequencing facing limitations in capturing the whole spectrum of SVs at nucleotide resolution 5-7 . Here we leveraged nanopore sequencing 8 to construct an intermediate coverage resource of 1,019 long-read genomes sampled within 26 human populations from the 1000 Genomes Project. By integrating linear and graph-based approaches for SV analysis via pangenome graph-augmentation, we uncover 167,291 sequence-resolved SVs in these samples, considerably advancing SV characterization compared to population-wide short-read sequencing studies 3,4 . Our analysis details diverse SV classes-deletions, duplications, insertions, and inversions-at population-scale. LINE-1 and SVA retrotransposition activities frequently mediate transductions 9,10 of unique sequences, with both mobile element classes transducing sequences at either the 3'- or 5'-end, depending on the source element locus. Furthermore, analyses of SV breakpoint junctions suggest a continuum of homology-mediated rearrangement processes are integral to SV formation, and highlight evidence for SV recurrence involving repeat sequences. Our open-access dataset underscores the transformative impact of long-read sequencing in advancing the characterisation of polymorphic genomic architectures, and provides a resource for guiding variant prioritisation in future long-read sequencing-based disease studies.
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4
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Keskus A, Bryant A, Ahmad T, Yoo B, Aganezov S, Goretsky A, Donmez A, Lansdon LA, Rodriguez I, Park J, Liu Y, Cui X, Gardner J, McNulty B, Sacco S, Shetty J, Zhao Y, Tran B, Narzisi G, Helland A, Cook DE, Chang PC, Kolesnikov A, Carroll A, Molloy EK, Pushel I, Guest E, Pastinen T, Shafin K, Miga KH, Malikic S, Day CP, Robine N, Sahinalp C, Dean M, Farooqi MS, Paten B, Kolmogorov M. Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.22.24304756. [PMID: 38585974 PMCID: PMC10996739 DOI: 10.1101/2024.03.22.24304756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate detection of different types of somatic SVs using a phased breakpoint graph approach. To benchmark various short- and long-read SV detection methods, we sequenced five tumor/normal cell line pairs with Illumina, Nanopore, and PacBio sequencing platforms; on this benchmark Severus showed the highest F1 scores (harmonic mean of the precision and recall) as compared to long-read and short-read methods. We then applied Severus to three clinical cases of pediatric cancer, demonstrating concordance with known genetic findings as well as revealing clinically relevant cryptic rearrangements missed by standard genomic panels.
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Affiliation(s)
- Ayse Keskus
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Asher Bryant
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Tanveer Ahmad
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Byunggil Yoo
- Children’s Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA
| | | | - Anton Goretsky
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Ataberk Donmez
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Lisa A. Lansdon
- Children’s Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA
| | - Isabel Rodriguez
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, MD, USA
| | - Jimin Park
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Yuelin Liu
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Xiwen Cui
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | | | | | - Samuel Sacco
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Jyoti Shetty
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yongmei Zhao
- Sequencing Facility Bioinformatics Group, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Bao Tran
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | | | | | | | | | | | | | - Erin K. Molloy
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Irina Pushel
- Children’s Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA
| | - Erin Guest
- Children’s Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA
| | - Tomi Pastinen
- Children’s Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA
| | - Kishwar Shafin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, MD, USA
| | - Karen H. Miga
- UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA
| | - Salem Malikic
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Chi-Ping Day
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | | | - Cenk Sahinalp
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Michael Dean
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, MD, USA
| | - Midhat S. Farooqi
- Children’s Mercy Hospital, University of Missouri-Kansas City School of Medicine, Kansas City, MO, USA
| | | | - Mikhail Kolmogorov
- Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
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5
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Krupina K, Goginashvili A, Cleveland DW. Scrambling the genome in cancer: causes and consequences of complex chromosome rearrangements. Nat Rev Genet 2024; 25:196-210. [PMID: 37938738 PMCID: PMC10922386 DOI: 10.1038/s41576-023-00663-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 11/09/2023]
Abstract
Complex chromosome rearrangements, known as chromoanagenesis, are widespread in cancer. Based on large-scale DNA sequencing of human tumours, the most frequent type of complex chromosome rearrangement is chromothripsis, a massive, localized and clustered rearrangement of one (or a few) chromosomes seemingly acquired in a single event. Chromothripsis can be initiated by mitotic errors that produce a micronucleus encapsulating a single chromosome or chromosomal fragment. Rupture of the unstable micronuclear envelope exposes its chromatin to cytosolic nucleases and induces chromothriptic shattering. Found in up to half of tumours included in pan-cancer genomic analyses, chromothriptic rearrangements can contribute to tumorigenesis through inactivation of tumour suppressor genes, activation of proto-oncogenes, or gene amplification through the production of self-propagating extrachromosomal circular DNAs encoding oncogenes or genes conferring anticancer drug resistance. Here, we discuss what has been learned about the mechanisms that enable these complex genomic rearrangements and their consequences in cancer.
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Affiliation(s)
- Ksenia Krupina
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA, USA
| | - Alexander Goginashvili
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA, USA
| | - Don W Cleveland
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA, USA.
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6
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Audano PA, Beck CR. Small polymorphisms are a source of ancestral bias in structural variant breakpoint placement. Genome Res 2024; 34:7-19. [PMID: 38176712 PMCID: PMC10904011 DOI: 10.1101/gr.278203.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 01/02/2024] [Indexed: 01/06/2024]
Abstract
High-quality genome assemblies and sophisticated algorithms have increased sensitivity for a wide range of variant types, and breakpoint accuracy for structural variants (SVs, ≥50 bp) has improved to near base pair precision. Despite these advances, many SV breakpoint locations are subject to systematic bias affecting variant representation. To understand why SV breakpoints are inconsistent across samples, we reanalyzed 64 phased haplotypes constructed from long-read assemblies released by the Human Genome Structural Variation Consortium (HGSVC). We identify 882 SV insertions and 180 SV deletions with variable breakpoints not anchored in tandem repeats (TRs) or segmental duplications (SDs). SVs called from aligned sequencing reads increase breakpoint disagreements by 2×-16×. Sequence accuracy had a minimal impact on breakpoints, but we observe a strong effect of ancestry. We confirm that SNP and indel polymorphisms are enriched at shifted breakpoints and are also absent from variant callsets. Breakpoint homology increases the likelihood of imprecise SV calls and the distance they are shifted, and tandem duplications are the most heavily affected SVs. Because graph genome methods normalize SV calls across samples, we investigated graphs generated by two different methods and find the resulting breakpoints are subject to other technical biases affecting breakpoint accuracy. The breakpoint inconsistencies we characterize affect ∼5% of the SVs called in a human genome and can impact variant interpretation and annotation. These limitations underscore a need for algorithm development to improve SV databases, mitigate the impact of ancestry on breakpoints, and increase the value of callsets for investigating breakpoint features.
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Affiliation(s)
- Peter A Audano
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA
| | - Christine R Beck
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA;
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
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7
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Lysenkova Wiklander M, Övernäs E, Lagensjö J, Raine A, Petri A, Wiman AC, Ramsell J, Marincevic-Zuniga Y, Gezelius H, Martin T, Bunikis I, Ekberg S, Erlandsson R, Larsson P, Mosbech MB, Häggqvist S, Hellstedt Kerje S, Feuk L, Ameur A, Liljedahl U, Nordlund J. Genomic, transcriptomic and epigenomic sequencing data of the B-cell leukemia cell line REH. BMC Res Notes 2023; 16:265. [PMID: 37817248 PMCID: PMC10566058 DOI: 10.1186/s13104-023-06537-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Abstract
OBJECTIVES The aim of this data paper is to describe a collection of 33 genomic, transcriptomic and epigenomic sequencing datasets of the B-cell acute lymphoblastic leukemia (ALL) cell line REH. REH is one of the most frequently used cell lines for functional studies of pediatric ALL, and these data provide a multi-faceted characterization of its molecular features. The datasets described herein, generated with short- and long-read sequencing technologies, can both provide insights into the complex aberrant karyotype of REH, and be used as reference datasets for sequencing data quality assessment or for methods development. DATA DESCRIPTION This paper describes 33 datasets corresponding to 867 gigabases of raw sequencing data generated from the REH cell line. These datasets include five different approaches for whole genome sequencing (WGS) on four sequencing platforms, two RNA sequencing (RNA-seq) techniques on two different sequencing platforms, DNA methylation sequencing, and single-cell ATAC-sequencing.
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Affiliation(s)
- Mariya Lysenkova Wiklander
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Elin Övernäs
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Johanna Lagensjö
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Amanda Raine
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Anna Petri
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ann-Christin Wiman
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Jon Ramsell
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Yanara Marincevic-Zuniga
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Henrik Gezelius
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Tom Martin
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Ignas Bunikis
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Sara Ekberg
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Rikard Erlandsson
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Pontus Larsson
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Mai-Britt Mosbech
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Susana Häggqvist
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Susanne Hellstedt Kerje
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Lars Feuk
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Adam Ameur
- Department of Immunology, Genetics and Pathology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ulrika Liljedahl
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden
| | - Jessica Nordlund
- Department of Medical Sciences and Science for Life Laboratory, Uppsala University, Box 1432, Uppsala, SE-751 44, Sweden.
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8
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Yang W, Ma W, Huang J, Cai Y, Peng X, Zhao F, Zhang D, Zou Z, Sun H, Qi X, Ge M. Beijing Children's Hospital guidelines on the design and conduction of the first standardized database for medulloblastoma. Metab Brain Dis 2023; 38:2393-2400. [PMID: 37261631 DOI: 10.1007/s11011-023-01233-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 05/09/2023] [Indexed: 06/02/2023]
Abstract
Medulloblastoma (MB) is one of the most common malignant childhood brain tumors (WHO grade IV). Its high degree of malignancy leads to an unsatisfactory prognosis, requiring more precise and personalized treatment in the near future. Multi-omics and artificial intelligence have been playing a significant role in precise medical research, but their implementation needs a large amount of clinical information and biomaterials. For these reasons, it is urgent for current MB researchers to establish a large sample-size database of MB that contains complete clinical data and sufficient biomaterials such as blood, cerebrospinal fluid (CSF), cancer tissue, and urine. Unfortunately, there are few biobanks of pediatric central nervous system (CNS) tumors throughout the world for limited specimens, scarce funds, different standards collecting methods and et cl. Even though, China falls behind western countries in this area. The present research set up a standard workflow to construct the Beijing Children's Hospital Medulloblastoma (BCH-MB) biobank. Clinical data from children with MB and for collecting and storing biomaterials, along with regular follow-up has been collected and recorded in this database. In the future, the BCH-MB biobank could make it possible to validate the promising biomarkers already identified, discover unrevealed MB biomarkers, develop novel therapies, and establish personalized prognostic models for children with MB upon the support of its sufficient data and biomaterials, laying the foundation for individualized therapies of children with MB.
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Affiliation(s)
- Wei Yang
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Wenping Ma
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Jiansong Huang
- Department of Neurosurgery, Peking University International Hospital, Peking University Health Science Center, Peking University, Beijing, 102200, China
| | - Yingjie Cai
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Xiaojiao Peng
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Fengmao Zhao
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Di Zhang
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Zhewei Zou
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Hailang Sun
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
| | - Xiang Qi
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
| | - Ming Ge
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
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9
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Calvo-Roitberg E, Daniels RF, Pai AA. Challenges in identifying mRNA transcript starts and ends from long-read sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.26.550536. [PMID: 37546743 PMCID: PMC10402045 DOI: 10.1101/2023.07.26.550536] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Long-read sequencing (LRS) technologies have the potential to revolutionize scientific discoveries in RNA biology, especially by enabling the comprehensive identification and quantification of full length mRNA isoforms. However, inherently high error rates make the analysis of long-read sequencing data challenging. While these error rates have been characterized for sequence and splice site identification, it is still unclear how accurately LRS reads represent transcript start and end sites. Here, we systematically assess the variability and accuracy of mRNA terminal ends identified by LRS reads across multiple sequencing platforms. We find substantial inconsistencies in both the start and end coordinates of LRS reads spanning a gene, such that LRS reads often fail to accurately recapitulate annotated or empirically derived terminal ends of mRNA molecules. To address this challenge, we introduce an approach to condition reads based on empirically derived terminal ends and identified a subset of reads that are more likely to represent full-length transcripts. Our approach can improve transcriptome analyses by enhancing the fidelity of transcript terminal end identification, but may result in lower power to quantify genes or discover novel isoforms. Thus, it is necessary to be cautious when selecting sequencing approaches and/or interpreting data from long-read RNA sequencing.
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Affiliation(s)
| | - Rachel F Daniels
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA
| | - Athma A Pai
- RNA Therapeutics Institute, University of Massachusetts Chan Medical School, Worcester, MA
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Audano PA, Beck CR. Small allelic variants are a source of ancestral bias in structural variant breakpoint placement. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.25.546295. [PMID: 37425850 PMCID: PMC10327140 DOI: 10.1101/2023.06.25.546295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
High-quality genome assemblies and sophisticated algorithms have increased sensitivity for a wide range of variant types, and breakpoint accuracy for structural variants (SVs, ≥ 50 bp) has improved to near basepair precision. Despite these advances, many SVs in unique regions of the genome are subject to systematic bias that affects breakpoint location. This ambiguity leads to less accurate variant comparisons across samples, and it obscures true breakpoint features needed for mechanistic inferences. To understand why SVs are not consistently placed, we reanalyzed 64 phased haplotypes constructed from long-read assemblies released by the Human Genome Structural Variation Consortium (HGSVC). We identified variable breakpoints for 882 SV insertions and 180 SV deletions not anchored in tandem repeats (TRs) or segmental duplications (SDs). While this is unexpectedly high for genome assemblies in unique loci, we find read-based callsets from the same sequencing data yielded 1,566 insertions and 986 deletions with inconsistent breakpoints also not anchored in TRs or SDs. When we investigated causes for breakpoint inaccuracy, we found sequence and assembly errors had minimal impact, but we observed a strong effect of ancestry. We confirmed that polymorphic mismatches and small indels are enriched at shifted breakpoints and that these polymorphisms are generally lost when breakpoints shift. Long tracts of homology, such as SVs mediated by transposable elements, increase the likelihood of imprecise SV calls and the distance they are shifted. Tandem Duplication (TD) breakpoints are the most heavily affected SV class with 14% of TDs placed at different locations across haplotypes. While graph genome methods normalize SV calls across many samples, the resulting breakpoints are sometimes incorrect, highlighting a need to tune graph methods for breakpoint accuracy. The breakpoint inconsistencies we characterize collectively affect ~5% of the SVs called in a human genome and underscore a need for algorithm development to improve SV databases, mitigate the impact of ancestry on breakpoint placement, and increase the value of callsets for investigating mutational processes.
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Affiliation(s)
- Peter A Audano
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Christine R Beck
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, CT, USA
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