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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...

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Autores principales: Wu, Euphy Y., Singh, Noor P., Choi, Kwangbom, Zakeri, Mohsen, Vincent, Matthew, Churchill, Gary A., Ackert-Bicknell, Cheryl L., Patro, Rob, Love, Michael I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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.
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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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