Red panda: a novel method for detecting variants in single-cell RNA sequencing
| Autor: | Andrew T. Dudley, Shrabasti Roychoudhury, Kishor K. Bhakat, Chittibabu Guda, Nitish K. Mishra, Krishna Sarma, Adam Cornish, Suravi Pramanik |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:QH426-470
lcsh:Biotechnology Cell Computational biology Biology Proteomics Polymorphism Single Nucleotide 03 medical and health sciences Mice 0302 clinical medicine lcsh:TP248.13-248.65 Red panda Exome Sequencing Genetics medicine Animals 030304 developmental biology 0303 health sciences Single cell sequencing Human articular chondrocytes Variant calling using scRNAseq Sequence Analysis RNA Research RNA High-Throughput Nucleotide Sequencing Fibroblasts Predictive value 3. Good health lcsh:Genetics medicine.anatomical_structure Simulated data DNA microarray Single-Cell Analysis Heterozygous variant calling 030217 neurology & neurosurgery Software Biotechnology |
| Zdroj: | BMC Genomics BMC Genomics, Vol 21, Iss S11, Pp 1-14 (2020) |
| ISSN: | 1471-2164 |
| Popis: | 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 (1-50bp) 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. 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%. We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently-existing software.AvailabilitySource code freely available under the MIT License at https://github.com/adambioi/red_panda, and is supported on Linux |
| Databáze: | OpenAIRE |
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