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Nanni A, Titus-McQuillan J, Bankole KS, Pardo-Palacios F, Signor S, Vlaho S, Moskalenko O, Morse A, Rogers RL, Conesa A, McIntyre LM. Nucleotide-level distance metrics to quantify alternative splicing implemented in TranD. Nucleic Acids Res 2024; 52:e28. [PMID: 38340337 PMCID: PMC10954468 DOI: 10.1093/nar/gkae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/29/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024] Open
Abstract
Advances in affordable transcriptome sequencing combined with better exon and gene prediction has motivated many to compare transcription across the tree of life. We develop a mathematical framework to calculate complexity and compare transcript models. Structural features, i.e. intron retention (IR), donor/acceptor site variation, alternative exon cassettes, alternative 5'/3' UTRs, are compared and the distance between transcript models is calculated with nucleotide level precision. All metrics are implemented in a PyPi package, TranD and output can be used to summarize splicing patterns for a transcriptome (1GTF) and between transcriptomes (2GTF). TranD output enables quantitative comparisons between: annotations augmented by empirical RNA-seq data and the original transcript models; transcript model prediction tools for longread RNA-seq (e.g. FLAIR versus Isoseq3); alternate annotations for a species (e.g. RefSeq vs Ensembl); and between closely related species. In C. elegans, Z. mays, D. melanogaster, D. simulans and H. sapiens, alternative exons were observed more frequently in combination with an alternative donor/acceptor than alone. Transcript models in RefSeq and Ensembl are linked and both have unique transcript models with empirical support. D. melanogaster and D. simulans, share many transcript models and long-read RNAseq data suggests that both species are under-annotated. We recommend combined references.
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Affiliation(s)
- Adalena Nanni
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, Gainesville, FL 32611, USA
| | - James Titus-McQuillan
- University of North Carolina at Charlotte Department of Bioinformatics and Genomics Charlotte, NC, USA
| | - Kinfeosioluwa S Bankole
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, Gainesville, FL 32611, USA
| | | | - Sarah Signor
- Department of Biological Sciences, North Dakota State University, Fargo, ND, USA
| | - Srna Vlaho
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Oleksandr Moskalenko
- University of Florida Research Computing, University of Florida, Gainesville, FL 32611, USA
| | - Alison M Morse
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, Gainesville, FL 32611, USA
| | - Rebekah L Rogers
- University of North Carolina at Charlotte Department of Bioinformatics and Genomics Charlotte, NC, USA
| | - Ana Conesa
- Institute for Integrative Systems Biology. Spanish National Research Council, Paterna, Spain
| | - Lauren M McIntyre
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL 32611, USA
- University of Florida Genetics Institute, University of Florida, Gainesville, FL 32611, USA
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Mestre-Tomás J, Liu T, Pardo-Palacios F, Conesa A. SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark. Genome Biol 2023; 24:286. [PMID: 38082294 PMCID: PMC10712166 DOI: 10.1186/s13059-023-03127-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Long-read RNA sequencing has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile tool that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field.
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Affiliation(s)
- Jorge Mestre-Tomás
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedrátic Agustín Escardino Benlloch, Paterna, 46980, Spain
- Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Camino de Vera, Valencia, 46022, Spain
| | - Tianyuan Liu
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedrátic Agustín Escardino Benlloch, Paterna, 46980, Spain
| | - Francisco Pardo-Palacios
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedrátic Agustín Escardino Benlloch, Paterna, 46980, Spain
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedrátic Agustín Escardino Benlloch, Paterna, 46980, Spain.
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Mestre-Tomás J, Liu T, Pardo-Palacios F, Conesa A. SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark. bioRxiv 2023:2023.08.23.554392. [PMID: 37662216 PMCID: PMC10473693 DOI: 10.1101/2023.08.23.554392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Long-read RNA-seq has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile utility that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field. We demonstrate the effectiveness of SQANTI-SIM by benchmarking five transcriptome reconstruction pipelines using the simulated data.
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Affiliation(s)
- Jorge Mestre-Tomás
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedràtic Agustín Escardino Benlloch, Paterna, 46980, Spain
| | - Tianyuan Liu
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedràtic Agustín Escardino Benlloch, Paterna, 46980, Spain
| | - Francisco Pardo-Palacios
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedràtic Agustín Escardino Benlloch, Paterna, 46980, Spain
| | - Ana Conesa
- Institute for Integrative Systems Biology, Spanish National Research Council, Catedràtic Agustín Escardino Benlloch, Paterna, 46980, Spain
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