1
|
Budurlean L, Tukaramrao DB, Zhang L, Dovat S, Broach J. Integrating Optical Genome Mapping and Whole Genome Sequencing in Somatic Structural Variant Detection. J Pers Med 2024; 14:291. [PMID: 38541033 PMCID: PMC10971281 DOI: 10.3390/jpm14030291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 04/10/2024] Open
Abstract
Structural variants drive tumorigenesis by disrupting normal gene function through insertions, inversions, translocations, and copy number changes, including deletions and duplications. Detecting structural variants is crucial for revealing their roles in tumor development, clinical outcomes, and personalized therapy. Presently, most studies rely on short-read data from next-generation sequencing that aligns back to a reference genome to determine if and, if so, where a structural variant occurs. However, structural variant discovery by short-read sequencing is challenging, primarily because of the difficulty in mapping regions of repetitive sequences. Optical genome mapping (OGM) is a recent technology used for imaging and assembling long DNA strands to detect structural variations. To capture the structural variant landscape more thoroughly in the human genome, we developed an integrated pipeline that combines Bionano OGM and Illumina whole-genome sequencing and applied it to samples from 29 pediatric B-ALL patients. The addition of OGM allowed us to identify 511 deletions, 506 insertions, 93 duplications/gains, and 145 translocations that were otherwise missed in the short-read data. Moreover, we identified several novel gene fusions, the expression of which was confirmed by RNA sequencing. Our results highlight the benefit of integrating OGM and short-read detection methods to obtain a comprehensive analysis of genetic variation that can aid in clinical diagnosis, provide new therapeutic targets, and improve personalized medicine in cancers driven by structural variation.
Collapse
Affiliation(s)
- Laura Budurlean
- Department of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA 17033, USA
| | | | - Lijun Zhang
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Sinisa Dovat
- Department of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA 17033, USA
- Department of Pediatrics, Penn State Cancer Institute, Hershey, PA 17033, USA
| | - James Broach
- Department of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA 17033, USA
| |
Collapse
|
2
|
Chen C, Qin F, Singh S, Tang Y, Li H. CTNNBIP1-CLSTN1 functions as a housekeeping chimeric RNA and regulates cell proliferation through SERPINE2. Cell Death Discov 2023; 9:369. [PMID: 37805599 PMCID: PMC10560238 DOI: 10.1038/s41420-023-01668-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/13/2023] [Accepted: 09/25/2023] [Indexed: 10/09/2023] Open
Abstract
The conventional understanding that chimeric RNAs are unique to carcinoma and are the products of chromosomal rearrangement is being challenged. However, experimental evidence supporting the function of chimeric RNAs in normal physiology is scarce. We decided to focus on one particular chimeric RNA, CTNNBIP1-CLSTN1. We examined its expression in various tissues and cell types and compared it quantitatively among cancer and noncancer cells. We further investigated its role in a panel of noncancer cells and investigated the functional mechanism. We found that this fusion transcript is expressed in almost all tissues and a wide range of cell types, including fibroblasts, epithelial cells, stem cells, vascular endothelial cells, and hepatocytes. In addition, the CTNNBIP1-CLSTN1 expression level in noncancerous cell lines was not evidently different from that in cancer cell lines. Furthermore, in at least three cell types, silencing CTNNBIP1-CLSTN1 significantly reduced the cell proliferation rate by inducing G2/M arrest and apoptosis. Importantly, rescue experiments confirmed that cell cycle arrest was restored by exogenous expression of the chimera but not the wild-type parental gene. Further evidence is provided that CTNNBIP1-CLSTN1 regulates cell proliferation through SERPINE2. Thus, CTNNBIP1-CLSTN1 is an example of a new class of fusion RNAs, dubbed "housekeeping chimeric RNAs".
Collapse
Affiliation(s)
- Chen Chen
- School of Basic Medical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450001, Henan, China
- Department of Clinical Laboratory, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, 253000, Shandong, China
| | - Fujun Qin
- School of Basic Medical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450001, Henan, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA
- ICMR-Center for Research, Management and Control of Haemoglobinopathies (Unit of ICMR-National Institute of Immunohaematology, Mumbai), Chandrapur, Maharashtra, 442406, India
| | - Yue Tang
- School of Basic Medical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, 450001, Henan, China.
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, 22908, USA.
| |
Collapse
|
3
|
Schimmelpfennig C, Rade M, Füssel S, Löffler D, Blumert C, Bertram C, Borkowetz A, Otto DJ, Puppel SH, Hönscheid P, Sommer U, Baretton GB, Köhl U, Wirth M, Thomas C, Horn F, Kreuz M, Reiche K. Characterization and evaluation of gene fusions as a measure of genetic instability and disease prognosis in prostate cancer. BMC Cancer 2023; 23:575. [PMID: 37349736 DOI: 10.1186/s12885-023-11019-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 05/27/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND Prostate cancer (PCa) is one of the most prevalent cancers worldwide. The clinical manifestations and molecular characteristics of PCa are highly variable. Aggressive types require radical treatment, whereas indolent ones may be suitable for active surveillance or organ-preserving focal therapies. Patient stratification by clinical or pathological risk categories still lacks sufficient precision. Incorporating molecular biomarkers, such as transcriptome-wide expression signatures, improves patient stratification but so far excludes chromosomal rearrangements. In this study, we investigated gene fusions in PCa, characterized potential novel candidates, and explored their role as prognostic markers for PCa progression. METHODS We analyzed 630 patients in four cohorts with varying traits regarding sequencing protocols, sample conservation, and PCa risk group. The datasets included transcriptome-wide expression and matched clinical follow-up data to detect and characterize gene fusions in PCa. With the fusion calling software Arriba, we computationally predicted gene fusions. Following detection, we annotated the gene fusions using published databases for gene fusions in cancer. To relate the occurrence of gene fusions to Gleason Grading Groups and disease prognosis, we performed survival analyses using the Kaplan-Meier estimator, log-rank test, and Cox regression. RESULTS Our analyses identified two potential novel gene fusions, MBTTPS2,L0XNC01::SMS and AMACR::AMACR. These fusions were detected in all four studied cohorts, providing compelling evidence for the validity of these fusions and their relevance in PCa. We also found that the number of gene fusions detected in a patient sample was significantly associated with the time to biochemical recurrence in two of the four cohorts (log-rank test, p-value < 0.05 for both cohorts). This was also confirmed after adjusting the prognostic model for Gleason Grading Groups (Cox regression, p-values < 0.05). CONCLUSIONS Our gene fusion characterization workflow revealed two potential novel fusions specific for PCa. We found evidence that the number of gene fusions was associated with the prognosis of PCa. However, as the quantitative correlations were only moderately strong, further validation and assessment of clinical value is required before potential application.
Collapse
Affiliation(s)
- Carolin Schimmelpfennig
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Michael Rade
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Susanne Füssel
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Dennis Löffler
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Conny Blumert
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Catharina Bertram
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Angelika Borkowetz
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Dominik J Otto
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Sven-Holger Puppel
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Pia Hönscheid
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ulrich Sommer
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Gustavo B Baretton
- Institute of Pathology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Ulrike Köhl
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
- Institute of Clinical Immunology, Medical Faculty, University Hospital, University of Leipzig, Leipzig, Germany
| | - Manfred Wirth
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Christian Thomas
- Department of Urology, University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Friedemann Horn
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Markus Kreuz
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany.
- Institute of Clinical Immunology, Medical Faculty, University Hospital, University of Leipzig, Leipzig, Germany.
| |
Collapse
|
4
|
Singh S, Shi X, Ahmad SB, Manley T, Piczak C, Phung C, Sun Y, Lynch S, Sharma A, Li H. RTCpredictor: Identification of Read-Through Chimeric RNAs from RNA Sequencing Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.02.526869. [PMID: 36778443 PMCID: PMC9915620 DOI: 10.1101/2023.02.02.526869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Read-through chimeric RNAs are gaining attention in cancer and other research fields, yet current tools often fail in predicting them. We have thus developed the first read-through chimeric RNA specific prediction method, RTCpredictor, utilizing a fast ripgrep algorithm to search for all possible exon-exon combinations of parental gene pairs. Compared with other ten popular tools, RTCpredictor achieved top performance on both simulated and real datasets. We randomly selected up to 30 candidate read-through chimeras predicted from each software method and experimentally validated a total of 109 read-throughs and on this set, RTCpredictor outperformed all the other methods. In addition, RTCpredictor ( https://github.com/sandybioteck/RTCpredictor ) has less memory requirements and faster execution time.
Collapse
|
5
|
Detroja R, Gorohovski A, Giwa O, Baum G, Frenkel-Morgenstern M. ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data. NAR Genom Bioinform 2021; 3:lqab112. [PMID: 34859212 PMCID: PMC8633610 DOI: 10.1093/nargab/lqab112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 10/21/2021] [Accepted: 11/22/2021] [Indexed: 12/16/2022] Open
Abstract
Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.
Collapse
Affiliation(s)
- Rajesh Detroja
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Alessandro Gorohovski
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Olawumi Giwa
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Gideon Baum
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Milana Frenkel-Morgenstern
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| |
Collapse
|
6
|
The Fusion of CLEC12A and MIR223HG Arises from a trans-Splicing Event in Normal and Transformed Human Cells. Int J Mol Sci 2021; 22:ijms222212178. [PMID: 34830054 PMCID: PMC8625150 DOI: 10.3390/ijms222212178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/30/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022] Open
Abstract
Chimeric RNAs are often associated with chromosomal rearrangements in cancer. In addition, they are also widely detected in normal tissues, contributing to transcriptomic complexity. Despite their prevalence, little is known about the characteristics and functions of chimeric RNAs. Here, we examine the genetic structure and biological roles of CLEC12A-MIR223HG, a novel chimeric transcript produced by the fusion of the cell surface receptor CLEC12A and the miRNA-223 host gene (MIR223HG), first identified in chronic myeloid leukemia (CML) patients. Surprisingly, we observed that CLEC12A-MIR223HG is not just expressed in CML, but also in a variety of normal tissues and cell lines. CLEC12A-MIR223HG expression is elevated in pro-monocytic cells resistant to chemotherapy and during monocyte-to-macrophage differentiation. We observed that CLEC12A-MIR223HG is a product of trans-splicing rather than a chromosomal rearrangement and that transcriptional activation of CLEC12A with the CRISPR/Cas9 Synergistic Activation Mediator (SAM) system increases CLEC12A-MIR223HG expression. CLEC12A-MIR223HG translates into a chimeric protein, which largely resembles CLEC12A but harbours an altered C-type lectin domain altering key disulphide bonds. These alterations result in differences in post-translational modifications, cellular localization, and protein-protein interactions. Taken together, our observations support a possible involvement of CLEC12A-MIR223HG in the regulation of CLEC12A function. Our workflow also serves as a template to study other uncharacterized chimeric RNAs.
Collapse
|
7
|
Mukherjee S, Detroja R, Balamurali D, Matveishina E, Medvedeva Y, Valencia A, Gorohovski A, Frenkel-Morgenstern M. Computational analysis of sense-antisense chimeric transcripts reveals their potential regulatory features and the landscape of expression in human cells. NAR Genom Bioinform 2021; 3:lqab074. [PMID: 34458728 PMCID: PMC8386243 DOI: 10.1093/nargab/lqab074] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 07/02/2021] [Accepted: 08/20/2021] [Indexed: 12/11/2022] Open
Abstract
Many human genes are transcribed from both strands and produce sense-antisense gene pairs. Sense-antisense (SAS) chimeric transcripts are produced upon the coalescing of exons/introns from both sense and antisense transcripts of the same gene. SAS chimera was first reported in prostate cancer cells. Subsequently, numerous SAS chimeras have been reported in the ChiTaRS-2.1 database. However, the landscape of their expression in human cells and functional aspects are still unknown. We found that longer palindromic sequences are a unique feature of SAS chimeras. Structural analysis indicates that a long hairpin-like structure formed by many consecutive Watson-Crick base pairs appears because of these long palindromic sequences, which possibly play a similar role as double-stranded RNA (dsRNA), interfering with gene expression. RNA-RNA interaction analysis suggested that SAS chimeras could significantly interact with their parental mRNAs, indicating their potential regulatory features. Here, 267 SAS chimeras were mapped in RNA-seq data from 16 healthy human tissues, revealing their expression in normal cells. Evolutionary analysis suggested the positive selection favoring sense-antisense fusions that significantly impacted the evolution of their function and structure. Overall, our study provides detailed insight into the expression landscape of SAS chimeras in human cells and identifies potential regulatory features.
Collapse
Affiliation(s)
- Sumit Mukherjee
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Rajesh Detroja
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Deepak Balamurali
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Elena Matveishina
- Department of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow 119234, Russian Federation
- Institute of Bioengineering, Research Centre of Biotechnology, Russian Academy of Sciences, Moscow 117312, Russian Federation
| | - Yulia A Medvedeva
- Institute of Bioengineering, Research Centre of Biotechnology, Russian Academy of Sciences, Moscow 117312, Russian Federation
- Department of Biomedical Physics, Moscow Institute of Technology, Dolgoprudny 141701, Russian Federation
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), C/ Jordi Girona 29, 08034, Barcelona, Spain
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain
| | - Alessandro Gorohovski
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| | - Milana Frenkel-Morgenstern
- Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
| |
Collapse
|
8
|
Wang Y, Zou Q, Li F, Zhao W, Xu H, Zhang W, Deng H, Yang X. Identification of the cross-strand chimeric RNAs generated by fusions of bi-directional transcripts. Nat Commun 2021; 12:4645. [PMID: 34330918 PMCID: PMC8324879 DOI: 10.1038/s41467-021-24910-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 07/14/2021] [Indexed: 12/22/2022] Open
Abstract
A major part of the transcriptome complexity is attributed to multiple types of DNA or RNA fusion events, which take place within a gene such as alternative splicing or between different genes such as DNA rearrangement and trans-splicing. In the present study, using the RNA deep sequencing data, we systematically survey a type of non-canonical fusions between the RNA transcripts from the two opposite DNA strands. We name the products of such fusion events cross-strand chimeric RNA (cscRNA). Hundreds to thousands of cscRNAs can be found in human normal tissues, primary cells, and cancerous cells, and in other species as well. Although cscRNAs exhibit strong tissue-specificity, our analysis identifies thousands of recurrent cscRNAs found in multiple different samples. cscRNAs are mostly originated from convergent transcriptions of the annotated genes and their anti-sense DNA. The machinery of cscRNA biogenesis is unclear, but the cross-strand junction events show some features related to RNA splicing. The present study is a comprehensive survey of the non-canonical cross-strand RNA junction events, a resource for further characterization of the originations and functions of the cscRNAs.
Collapse
Affiliation(s)
- Yuting Wang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Beijing, China
| | - Qin Zou
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Fajin Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.,Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Beijing, China
| | - Wenwei Zhao
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Hui Xu
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Wenhao Zhang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Haiteng Deng
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.
| |
Collapse
|
9
|
Singh S, Li H. Comparative study of bioinformatic tools for the identification of chimeric RNAs from RNA Sequencing. RNA Biol 2021; 18:254-267. [PMID: 34142643 DOI: 10.1080/15476286.2021.1940047] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Chimeric RNAs are gaining more and more attention as they have broad implications in both cancer and normal physiology. To date, over 40 chimeric RNA prediction methods have been developed to facilitate their identification from RNA sequencing data. However, a limited number of studies have been conducted to compare the performance of these tools; additionally, previous studies have become outdated as more software tools have been developed within the last three years. In this study, we benchmarked 16 chimeric RNA prediction software, including seven top performers in previous benchmarking studies, and nine that were recently developed. We used two simulated and two real RNA-Seq datasets, compared the 16 tools for their sensitivity, positive prediction value (PPV), F-measure, and also documented the computational requirements (time and memory). We noticed that none of the tools are inclusive, and their performance varies depending on the dataset and objects. To increase the detection of true positive events, we also evaluated the pair-wise combination of these methods to suggest the best combination for sensitivity and F-measure. In addition, we compared the performance of the tools for the identification of three classes (read-through, inter-chromosomal and intra-others) of chimeric RNAs. Finally, we performed TOPSIS analyses and ranked the weighted performance of the 16 tools.
Collapse
Affiliation(s)
- Sandeep Singh
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Hui Li
- Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA, USA.,Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA, USA
| |
Collapse
|
10
|
Landscape of Chimeric RNAs in Non-Cancerous Cells. Genes (Basel) 2021; 12:genes12040466. [PMID: 33805149 PMCID: PMC8064075 DOI: 10.3390/genes12040466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/18/2021] [Accepted: 03/19/2021] [Indexed: 11/21/2022] Open
Abstract
Gene fusions and their products (RNA and protein) have been traditionally recognized as unique features of cancer cells and are used as ideal biomarkers and drug targets for multiple cancer types. However, recent studies have demonstrated that chimeric RNAs generated by intergenic alternative splicing can also be found in normal cells and tissues. In this study, we aim to identify chimeric RNAs in different non-neoplastic cell lines and investigate the landscape and expression of these novel candidate chimeric RNAs. To do so, we used HEK-293T, HUVEC, and LO2 cell lines as models, performed paired-end RNA sequencing, and conducted analyses for chimeric RNA profiles. Several filtering criteria were applied, and the landscape of chimeric RNAs was characterized at multiple levels and from various angles. Further, we experimentally validated 17 chimeric RNAs from different classifications. Finally, we examined a number of validated chimeric RNAs in different cancer and non-cancer cells, including blood from healthy donors, and demonstrated their ubiquitous expression pattern.
Collapse
|
11
|
Tworowski D, Gorohovski A, Mukherjee S, Carmi G, Levy E, Detroja R, Mukherjee SB, Frenkel-Morgenstern M. COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics. Nucleic Acids Res 2021; 49:D1113-D1121. [PMID: 33166390 PMCID: PMC7778969 DOI: 10.1093/nar/gkaa969] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 10/07/2020] [Accepted: 11/04/2020] [Indexed: 12/12/2022] Open
Abstract
The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.
Collapse
Affiliation(s)
- Dmitry Tworowski
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel
| | - Alessandro Gorohovski
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel
| | - Sumit Mukherjee
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel
| | - Gon Carmi
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel
| | - Eliad Levy
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel
| | - Rajesh Detroja
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel
| | - Sunanda Biswas Mukherjee
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel
| | - Milana Frenkel-Morgenstern
- Laboratory of Cancer Genomics and Biocomputing of Complex Diseases, Azrieli Faculty of Medicine, Bar-Ilan University, Henrietta Szold 8, Safed 13195, Israel
| |
Collapse
|
12
|
Abstract
Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.
Collapse
Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, Sweden
| |
Collapse
|