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SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty
Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to group...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337143/ https://www.ncbi.nlm.nih.gov/pubmed/37438847 http://dx.doi.org/10.1186/s13059-023-03003-x |
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author | Wu, Euphy Y. Singh, Noor P. Choi, Kwangbom Zakeri, Mohsen Vincent, Matthew Churchill, Gary A. Ackert-Bicknell, Cheryl L. Patro, Rob Love, Michael I. |
author_facet | Wu, Euphy Y. Singh, Noor P. Choi, Kwangbom Zakeri, Mohsen Vincent, Matthew Churchill, Gary A. Ackert-Bicknell, Cheryl L. Patro, Rob Love, Michael I. |
author_sort | Wu, Euphy Y. |
collection | PubMed |
description | Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03003-x. |
format | Online Article Text |
id | pubmed-10337143 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103371432023-07-13 SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty Wu, Euphy Y. Singh, Noor P. Choi, Kwangbom Zakeri, Mohsen Vincent, Matthew Churchill, Gary A. Ackert-Bicknell, Cheryl L. Patro, Rob Love, Michael I. Genome Biol Software Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03003-x. BioMed Central 2023-07-12 /pmc/articles/PMC10337143/ /pubmed/37438847 http://dx.doi.org/10.1186/s13059-023-03003-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Wu, Euphy Y. Singh, Noor P. Choi, Kwangbom Zakeri, Mohsen Vincent, Matthew Churchill, Gary A. Ackert-Bicknell, Cheryl L. Patro, Rob Love, Michael I. SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty |
title | SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty |
title_full | SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty |
title_fullStr | SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty |
title_full_unstemmed | SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty |
title_short | SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty |
title_sort | seesaw: detecting isoform-level allelic imbalance accounting for inferential uncertainty |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337143/ https://www.ncbi.nlm.nih.gov/pubmed/37438847 http://dx.doi.org/10.1186/s13059-023-03003-x |
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