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

ABSTRACT:

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 high

SUBMITTER: Cornish A 

PROVIDER: S-EPMC7771073 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature