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AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data
Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the meth...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408950/ https://www.ncbi.nlm.nih.gov/pubmed/34465366 http://dx.doi.org/10.1186/s13059-021-02469-x |
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author | Thibodeau, Asa Eroglu, Alper McGinnis, Christopher S. Lawlor, Nathan Nehar-Belaid, Djamel Kursawe, Romy Marches, Radu Conrad, Daniel N. Kuchel, George A. Gartner, Zev J. Banchereau, Jacques Stitzel, Michael L. Cicek, A. Ercument Ucar, Duygu |
author_facet | Thibodeau, Asa Eroglu, Alper McGinnis, Christopher S. Lawlor, Nathan Nehar-Belaid, Djamel Kursawe, Romy Marches, Radu Conrad, Daniel N. Kuchel, George A. Gartner, Zev J. Banchereau, Jacques Stitzel, Michael L. Cicek, A. Ercument Ucar, Duygu |
author_sort | Thibodeau, Asa |
collection | PubMed |
description | Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02469-x. |
format | Online Article Text |
id | pubmed-8408950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84089502021-09-01 AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data Thibodeau, Asa Eroglu, Alper McGinnis, Christopher S. Lawlor, Nathan Nehar-Belaid, Djamel Kursawe, Romy Marches, Radu Conrad, Daniel N. Kuchel, George A. Gartner, Zev J. Banchereau, Jacques Stitzel, Michael L. Cicek, A. Ercument Ucar, Duygu Genome Biol Method Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-021-02469-x. BioMed Central 2021-09-01 /pmc/articles/PMC8408950/ /pubmed/34465366 http://dx.doi.org/10.1186/s13059-021-02469-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Method Thibodeau, Asa Eroglu, Alper McGinnis, Christopher S. Lawlor, Nathan Nehar-Belaid, Djamel Kursawe, Romy Marches, Radu Conrad, Daniel N. Kuchel, George A. Gartner, Zev J. Banchereau, Jacques Stitzel, Michael L. Cicek, A. Ercument Ucar, Duygu AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data |
title | AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data |
title_full | AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data |
title_fullStr | AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data |
title_full_unstemmed | AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data |
title_short | AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data |
title_sort | amulet: a novel read count-based method for effective multiplet detection from single nucleus atac-seq data |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408950/ https://www.ncbi.nlm.nih.gov/pubmed/34465366 http://dx.doi.org/10.1186/s13059-021-02469-x |
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