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

Adam Cornish,Kishor Bhakat,Andrew Dudley,Shrabasti Roychoudhury,Suravi Pramanik,Krishna Sarma,Chittibabu Guda,Nitish K Mishra

doi:10.1186/s12864-020-07224-3

Abstract

BackgroundSingle-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.ResultsIn 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%.ConclusionsWe 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.

Highlights

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
This study introduces a novel method, Red Panda, that is designed to identify variants in single-cell RNA sequencing and tests how it compares to currently-available variant callers: FreeBayes [20], GATK HaplotypeCaller [21], GATK UnifiedGenotyper [22], Platypus [23], and Monovar [18]
Comparison of different tools using human articular chondrocytes Alignment files generated for each of the 22 chondrocyte scRNA-seq samples were used as input for FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, Platypus, and Red Panda

Summary

Introduction

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. Multiple recent studies using SCS have shown that tumors are genetically diverse and produce subclones that contribute to the pathogenicity of the disease by conferring chemotherapy resistance and metastatic capabilities to the tumor [6, 7]. This technology has proven useful by aiding in characterizing somatic mutations in neurons [8], identifying rare intestinal cell types [9], and discriminating cell types in healthy tissues [10, 11]. An effort has been made to apply best practices for identifying variants in RNA-seq to scRNA-seq datasets [8, 19], but they do not take advantage of the unique nature of the data produced by the scRNA-seq platform

Methods

Results

Discussion

Conclusion