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Red panda: a novel method for detecting variants in single-cell RNA sequencing

BACKGROUND: Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and delet...

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Autores principales: Cornish, Adam, Roychoudhury, Shrabasti, Sarma, Krishna, Pramanik, Suravi, Bhakat, Kishor, Dudley, Andrew, Mishra, Nitish K., Guda, Chittibabu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771073/
https://www.ncbi.nlm.nih.gov/pubmed/33372593
http://dx.doi.org/10.1186/s12864-020-07224-3
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author Cornish, Adam
Roychoudhury, Shrabasti
Sarma, Krishna
Pramanik, Suravi
Bhakat, Kishor
Dudley, Andrew
Mishra, Nitish K.
Guda, Chittibabu
author_facet Cornish, Adam
Roychoudhury, Shrabasti
Sarma, Krishna
Pramanik, Suravi
Bhakat, Kishor
Dudley, Andrew
Mishra, Nitish K.
Guda, Chittibabu
author_sort Cornish, Adam
collection PubMed
description BACKGROUND: Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. RESULTS: In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools—FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus—ranged from 5.8–41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. CONCLUSIONS: We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently existing software. However, methods for identification of genomic variants using scRNA-seq data can be still improved. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-020-07224-3.
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spelling pubmed-77710732020-12-30 Red panda: a novel method for detecting variants in single-cell RNA sequencing Cornish, Adam Roychoudhury, Shrabasti Sarma, Krishna Pramanik, Suravi Bhakat, Kishor Dudley, Andrew Mishra, Nitish K. Guda, Chittibabu BMC Genomics Research BACKGROUND: Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50 bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. RESULTS: In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools—FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus—ranged from 5.8–41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. CONCLUSIONS: We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently existing software. However, methods for identification of genomic variants using scRNA-seq data can be still improved. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-020-07224-3. BioMed Central 2020-12-29 /pmc/articles/PMC7771073/ /pubmed/33372593 http://dx.doi.org/10.1186/s12864-020-07224-3 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Research
Cornish, Adam
Roychoudhury, Shrabasti
Sarma, Krishna
Pramanik, Suravi
Bhakat, Kishor
Dudley, Andrew
Mishra, Nitish K.
Guda, Chittibabu
Red panda: a novel method for detecting variants in single-cell RNA sequencing
title Red panda: a novel method for detecting variants in single-cell RNA sequencing
title_full Red panda: a novel method for detecting variants in single-cell RNA sequencing
title_fullStr Red panda: a novel method for detecting variants in single-cell RNA sequencing
title_full_unstemmed Red panda: a novel method for detecting variants in single-cell RNA sequencing
title_short Red panda: a novel method for detecting variants in single-cell RNA sequencing
title_sort red panda: a novel method for detecting variants in single-cell rna sequencing
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771073/
https://www.ncbi.nlm.nih.gov/pubmed/33372593
http://dx.doi.org/10.1186/s12864-020-07224-3
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