PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing
Abstract Background Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. There are grow...
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2024-11-01
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| Online Access: | https://doi.org/10.1186/s12859-024-05982-5 |
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| author | Quinlan Furumo Michelle M. Meyer |
| author_facet | Quinlan Furumo Michelle M. Meyer |
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| description | Abstract Background Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. There are growing amounts of bacterial 3′-end sequencing data, yet there is currently no commonly accepted analysis methodology for this datatype. Most data analysis approaches are somewhat ad hoc and, despite the presence of substantial signal within annotated genes, focus on genomic regions outside the annotated genes (e.g. 3′ or 5′ UTRs). Furthermore, the lack of consistent systematic analysis approaches, as well as the absence of genome-wide ground truth data, make it impossible to compare conclusions generated by different labs, using different organisms. Results We present PIPETS, (Poisson Identification of PEaks from Term-Seq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. Across two different datasets from two different organisms, PIPETS identified significant 3'-end termination signal across a wider range of annotated genomic contexts than existing analysis approaches, suggesting that existing approaches may miss biologically relevant signal. Furthermore, assessment of the previously called 3′-end positions not captured by PIPETS showed that they were uniformly very low coverage. Conclusions PIPETS provides a broadly applicable platform to explore and analyze 3′-end sequencing data sets from across different organisms. It requires only the 3′-end sequencing data, and is broadly accessible to non-expert users. |
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| language | English |
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| spelling | doaj-art-2a8c68a77d174ba5bbec126bce5437702025-08-20T02:33:05ZengBMCBMC Bioinformatics1471-21052024-11-0125111710.1186/s12859-024-05982-5PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencingQuinlan Furumo0Michelle M. Meyer1Department of Biology, Boston CollegeDepartment of Biology, Boston CollegeAbstract Background Over the last decade the drop in short-read sequencing costs has allowed experimental techniques utilizing sequencing to address specific biological questions to proliferate, oftentimes outpacing standardized or effective analysis approaches for the data generated. There are growing amounts of bacterial 3′-end sequencing data, yet there is currently no commonly accepted analysis methodology for this datatype. Most data analysis approaches are somewhat ad hoc and, despite the presence of substantial signal within annotated genes, focus on genomic regions outside the annotated genes (e.g. 3′ or 5′ UTRs). Furthermore, the lack of consistent systematic analysis approaches, as well as the absence of genome-wide ground truth data, make it impossible to compare conclusions generated by different labs, using different organisms. Results We present PIPETS, (Poisson Identification of PEaks from Term-Seq data), an R package available on Bioconductor that provides a novel analysis method for 3'-end sequencing data. PIPETS is a statistically informed, gene-annotation agnostic methodology. Across two different datasets from two different organisms, PIPETS identified significant 3'-end termination signal across a wider range of annotated genomic contexts than existing analysis approaches, suggesting that existing approaches may miss biologically relevant signal. Furthermore, assessment of the previously called 3′-end positions not captured by PIPETS showed that they were uniformly very low coverage. Conclusions PIPETS provides a broadly applicable platform to explore and analyze 3′-end sequencing data sets from across different organisms. It requires only the 3′-end sequencing data, and is broadly accessible to non-expert users.https://doi.org/10.1186/s12859-024-05982-5Bacterial 3′-sequencingTranscription terminationR-package |
| spellingShingle | Quinlan Furumo Michelle M. Meyer PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing BMC Bioinformatics Bacterial 3′-sequencing Transcription termination R-package |
| title | PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing |
| title_full | PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing |
| title_fullStr | PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing |
| title_full_unstemmed | PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing |
| title_short | PIPETS: a statistically informed, gene-annotation agnostic analysis method to study bacterial termination using 3′-end sequencing |
| title_sort | pipets a statistically informed gene annotation agnostic analysis method to study bacterial termination using 3 end sequencing |
| topic | Bacterial 3′-sequencing Transcription termination R-package |
| url | https://doi.org/10.1186/s12859-024-05982-5 |
| work_keys_str_mv | AT quinlanfurumo pipetsastatisticallyinformedgeneannotationagnosticanalysismethodtostudybacterialterminationusing3endsequencing AT michellemmeyer pipetsastatisticallyinformedgeneannotationagnosticanalysismethodtostudybacterialterminationusing3endsequencing |