TY - JOUR
T1 - Red panda
T2 - a novel method for detecting variants in single-cell RNA sequencing
AU - Cornish, Adam
AU - Roychoudhury, Shrabasti
AU - Sarma, Krishna
AU - Pramanik, Suravi
AU - Bhakat, Kishor
AU - Dudley, Andrew
AU - Mishra, Nitish K.
AU - Guda, Chittibabu
N1 - Funding Information:
This work was supported by the development funds to CG, and the Office of Graduate Studies fellowship to AC, from the University of Nebraska Medical Center (UNMC) and the NIH award [2P01AG029531] to CG. The University of Nebraska DNA Sequencing Core and the Bioinformatics and Systems Biology core receive partial support from the National Institutes of Health grants [P20GM103427, 1P30GM110768, P30CA036727]. Publication costs are funded by the development funds to CG from UNMC.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Heterozygous variant calling
KW - Human articular chondrocytes
KW - Red panda
KW - Single cell sequencing
KW - Variant calling using scRNAseq
UR - http://www.scopus.com/inward/record.url?scp=85098253947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098253947&partnerID=8YFLogxK
U2 - 10.1186/s12864-020-07224-3
DO - 10.1186/s12864-020-07224-3
M3 - Article
C2 - 33372593
AN - SCOPUS:85098253947
VL - 21
JO - BMC Genomics
JF - BMC Genomics
SN - 1471-2164
M1 - 830
ER -