Benchmarking RNA Editing Detection Tools
RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identif...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | BioTech |
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| Online Access: | https://www.mdpi.com/2673-6284/12/3/56 |
| _version_ | 1827726931440697344 |
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| author | David Rodríguez Morales Sarah Rennie Shizuka Uchida |
| author_facet | David Rodríguez Morales Sarah Rennie Shizuka Uchida |
| author_sort | David Rodríguez Morales |
| collection | DOAJ |
| description | RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identified: (1) the most frequent, adenosine to inosine (A-to-I); and (2) the less frequent, cysteine to uracil (C-to-U) RNA editing. Unlike other epitranscriptomic marks, RNA editing can be readily detected from RNA sequencing (RNA-seq) data without any chemical conversions of RNA before sequencing library preparation. Furthermore, analyzing RNA editing patterns from transcriptomic data provides an additional layer of information about the epitranscriptome. As the significance of epitranscriptomics, particularly RNA editing, gains recognition in various fields of biology and medicine, there is a growing interest in detecting RNA editing sites (RES) by analyzing RNA-seq data. To cope with this increased interest, several bioinformatic tools are available. However, each tool has its advantages and disadvantages, which makes the choice of the most appropriate tool for bench scientists and clinicians difficult. Here, we have benchmarked bioinformatic tools to detect RES from RNA-seq data. We provide a comprehensive view of each tool and its performance using previously published RNA-seq data to suggest recommendations on the most appropriate for utilization in future studies. |
| first_indexed | 2024-03-10T22:59:20Z |
| format | Article |
| id | doaj.art-67bd5f62f3994a49834d25997ec4973a |
| institution | Directory Open Access Journal |
| issn | 2673-6284 |
| language | English |
| last_indexed | 2024-03-10T22:59:20Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioTech |
| spelling | doaj.art-67bd5f62f3994a49834d25997ec4973a2023-11-19T09:47:57ZengMDPI AGBioTech2673-62842023-08-011235610.3390/biotech12030056Benchmarking RNA Editing Detection ToolsDavid Rodríguez Morales0Sarah Rennie1Shizuka Uchida2Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, DenmarkDepartment of Biology, University of Copenhagen, DK-2200 Copenhagen N, DenmarkCenter for RNA Medicine, Department of Clinical Medicine, Aalborg University, DK-2450 Copenhagen SV, DenmarkRNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identified: (1) the most frequent, adenosine to inosine (A-to-I); and (2) the less frequent, cysteine to uracil (C-to-U) RNA editing. Unlike other epitranscriptomic marks, RNA editing can be readily detected from RNA sequencing (RNA-seq) data without any chemical conversions of RNA before sequencing library preparation. Furthermore, analyzing RNA editing patterns from transcriptomic data provides an additional layer of information about the epitranscriptome. As the significance of epitranscriptomics, particularly RNA editing, gains recognition in various fields of biology and medicine, there is a growing interest in detecting RNA editing sites (RES) by analyzing RNA-seq data. To cope with this increased interest, several bioinformatic tools are available. However, each tool has its advantages and disadvantages, which makes the choice of the most appropriate tool for bench scientists and clinicians difficult. Here, we have benchmarked bioinformatic tools to detect RES from RNA-seq data. We provide a comprehensive view of each tool and its performance using previously published RNA-seq data to suggest recommendations on the most appropriate for utilization in future studies.https://www.mdpi.com/2673-6284/12/3/56databasesepitranscriptomicsRNA editingRNA sequencingtools |
| spellingShingle | David Rodríguez Morales Sarah Rennie Shizuka Uchida Benchmarking RNA Editing Detection Tools BioTech databases epitranscriptomics RNA editing RNA sequencing tools |
| title | Benchmarking RNA Editing Detection Tools |
| title_full | Benchmarking RNA Editing Detection Tools |
| title_fullStr | Benchmarking RNA Editing Detection Tools |
| title_full_unstemmed | Benchmarking RNA Editing Detection Tools |
| title_short | Benchmarking RNA Editing Detection Tools |
| title_sort | benchmarking rna editing detection tools |
| topic | databases epitranscriptomics RNA editing RNA sequencing tools |
| url | https://www.mdpi.com/2673-6284/12/3/56 |
| work_keys_str_mv | AT davidrodriguezmorales benchmarkingrnaeditingdetectiontools AT sarahrennie benchmarkingrnaeditingdetectiontools AT shizukauchida benchmarkingrnaeditingdetectiontools |