Comparative analysis of bioinformatics tools to characterize SARS-CoV-2 subgenomic RNAs Article Swipe
Denise Lavezzari
,
Antonio Mori
,
Elena Pomari
,
Michela Deiana
,
Antonio Fadda
,
Luca Bertoli
,
Alessandro Sinigaglia
,
Silvia Riccetti
,
Luisa Barzon
,
Chiara Piubelli
,
Massimo Delledonne
,
Maria Rosaria Capobianchi
,
Concetta Castilletti
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26508/lsa.202302017
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26508/lsa.202302017
During the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), positive-sense genomic RNA and subgenomic RNAs (sgRNAs) are synthesized by a discontinuous process of transcription characterized by a template switch, regulated by transcription-regulating sequences (TRS). Although poorly known about makeup and dynamics of sgRNAs population and function of its constituents, next-generation sequencing approaches with the help of bioinformatics tools have made a significant contribution to expand the knowledge of sgRNAs in SARS-CoV-2. For this scope to date, Periscope, LeTRS, sgDI-tector, and CORONATATOR have been developed. However, limited number of studies are available to compare the performance of such tools. To this purpose, we compared Periscope, LeTRS, and sgDI-tector in the identification of canonical (c-) and noncanonical (nc-) sgRNA species in the data obtained with the Illumina ARTIC sequencing protocol applied to SARS-CoV-2–infected Caco-2 cells, sampled at different time points. The three software showed a high concordance rate in the identification and in the quantification of c-sgRNA, whereas more differences were observed in nc-sgRNA. Overall, LeTRS and sgDI-tector result to be adequate alternatives to Periscope to analyze Fastq data from sequencing platforms other than Nanopore.
Related Topics
Concepts
Subgenomic mRNA
Computational biology
Guide RNA
Biology
DNA sequencing
Nanopore sequencing
Population
Genetics
Bioinformatics
RNA
Gene
Genome
Cas9
Medicine
Environmental health
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26508/lsa.202302017
- https://www.life-science-alliance.org/content/lsa/6/12/e202302017.full.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387013790
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387013790Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26508/lsa.202302017Digital Object Identifier
- Title
-
Comparative analysis of bioinformatics tools to characterize SARS-CoV-2 subgenomic RNAsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-25Full publication date if available
- Authors
-
Denise Lavezzari, Antonio Mori, Elena Pomari, Michela Deiana, Antonio Fadda, Luca Bertoli, Alessandro Sinigaglia, Silvia Riccetti, Luisa Barzon, Chiara Piubelli, Massimo Delledonne, Maria Rosaria Capobianchi, Concetta CastillettiList of authors in order
- Landing page
-
https://doi.org/10.26508/lsa.202302017Publisher landing page
- PDF URL
-
https://www.life-science-alliance.org/content/lsa/6/12/e202302017.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.life-science-alliance.org/content/lsa/6/12/e202302017.full.pdfDirect OA link when available
- Concepts
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Subgenomic mRNA, Computational biology, Guide RNA, Biology, DNA sequencing, Nanopore sequencing, Population, Genetics, Bioinformatics, RNA, Gene, Genome, Cas9, Medicine, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.cells, | 134 |
| abstract_inverted_index.expand | 66 |
| abstract_inverted_index.makeup | 40 |
| abstract_inverted_index.number | 88 |
| abstract_inverted_index.poorly | 37 |
| abstract_inverted_index.result | 168 |
| abstract_inverted_index.severe | 4 |
| abstract_inverted_index.sgRNAs | 44, 70 |
| abstract_inverted_index.showed | 143 |
| abstract_inverted_index.tools. | 99 |
| abstract_inverted_index.analyze | 176 |
| abstract_inverted_index.applied | 130 |
| abstract_inverted_index.compare | 94 |
| abstract_inverted_index.genomic | 12 |
| abstract_inverted_index.limited | 87 |
| abstract_inverted_index.points. | 139 |
| abstract_inverted_index.process | 23 |
| abstract_inverted_index.sampled | 135 |
| abstract_inverted_index.species | 119 |
| abstract_inverted_index.studies | 90 |
| abstract_inverted_index.switch, | 30 |
| abstract_inverted_index.whereas | 157 |
| abstract_inverted_index.(sgRNAs) | 17 |
| abstract_inverted_index.Although | 36 |
| abstract_inverted_index.However, | 86 |
| abstract_inverted_index.Illumina | 126 |
| abstract_inverted_index.Overall, | 164 |
| abstract_inverted_index.adequate | 171 |
| abstract_inverted_index.c-sgRNA, | 156 |
| abstract_inverted_index.compared | 104 |
| abstract_inverted_index.dynamics | 42 |
| abstract_inverted_index.function | 47 |
| abstract_inverted_index.observed | 161 |
| abstract_inverted_index.obtained | 123 |
| abstract_inverted_index.protocol | 129 |
| abstract_inverted_index.purpose, | 102 |
| abstract_inverted_index.software | 142 |
| abstract_inverted_index.syndrome | 7 |
| abstract_inverted_index.template | 29 |
| abstract_inverted_index.Nanopore. | 184 |
| abstract_inverted_index.Periscope | 174 |
| abstract_inverted_index.available | 92 |
| abstract_inverted_index.canonical | 113 |
| abstract_inverted_index.different | 137 |
| abstract_inverted_index.knowledge | 68 |
| abstract_inverted_index.nc-sgRNA. | 163 |
| abstract_inverted_index.platforms | 181 |
| abstract_inverted_index.regulated | 31 |
| abstract_inverted_index.sequences | 34 |
| abstract_inverted_index.Periscope, | 78, 105 |
| abstract_inverted_index.approaches | 53 |
| abstract_inverted_index.developed. | 85 |
| abstract_inverted_index.population | 45 |
| abstract_inverted_index.sequencing | 52, 128, 180 |
| abstract_inverted_index.subgenomic | 15 |
| abstract_inverted_index.CORONATATOR | 82 |
| abstract_inverted_index.SARS-CoV-2. | 72 |
| abstract_inverted_index.concordance | 146 |
| abstract_inverted_index.coronavirus | 8 |
| abstract_inverted_index.differences | 159 |
| abstract_inverted_index.performance | 96 |
| abstract_inverted_index.replication | 2 |
| abstract_inverted_index.respiratory | 6 |
| abstract_inverted_index.sgDI-tector | 108, 167 |
| abstract_inverted_index.significant | 63 |
| abstract_inverted_index.synthesized | 19 |
| abstract_inverted_index.alternatives | 172 |
| abstract_inverted_index.contribution | 64 |
| abstract_inverted_index.noncanonical | 116 |
| abstract_inverted_index.sgDI-tector, | 80 |
| abstract_inverted_index.(SARS-CoV-2), | 10 |
| abstract_inverted_index.characterized | 26 |
| abstract_inverted_index.constituents, | 50 |
| abstract_inverted_index.discontinuous | 22 |
| abstract_inverted_index.transcription | 25 |
| abstract_inverted_index.bioinformatics | 58 |
| abstract_inverted_index.identification | 111, 150 |
| abstract_inverted_index.positive-sense | 11 |
| abstract_inverted_index.quantification | 154 |
| abstract_inverted_index.next-generation | 51 |
| abstract_inverted_index.SARS-CoV-2–infected | 132 |
| abstract_inverted_index.transcription-regulating | 33 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5028198430 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I4210101944, https://openalex.org/I4210153126 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.541836 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |