Abstract
BackgroundSingle-cell sequencing experiments use short DNA barcode ‘tags’ to identify reads that originate from the same cell. In order to recover single-cell information from such experiments, reads must be grouped based on their barcode tag, a crucial processing step that precedes other computations. However, this step can be difficult due to high rates of mismatch and deletion errors that can afflict barcodes.ResultsHere we present an approach to identify and error-correct barcodes by traversing the de Bruijn graph of circularized barcode k-mers. Our approach is based on the observation that circularizing a barcode sequence can yield error-free k-mers even when the size of k is large relative to the length of the barcode sequence, a regime which is typical single-cell barcoding applications. This allows for assignment of reads to consensus fingerprints constructed from k-mers.ConclusionWe show that for single-cell RNA-Seq circularization improves the recovery of accurate single-cell transcriptome estimates, especially when there are a high number of errors per read. This approach is robust to the type of error (mismatch, insertion, deletion), as well as to the relative abundances of the cells. Sircel, a software package that implements this approach is described and publically available.
Highlights
Single-cell sequencing experiments use short DNA barcode ‘tags’ to identify reads that originate from the same cell
In the Drop-Seq protocol, which is a popular microfluidic-based single-cell experimental platform, DNA barcodes are synthesized on a solid bead support, using split-and-pool DNA synthesis [10], and this approach has been applied to obtain single-cell transcriptome profiles from a number of model- and non-model organisms [3, 6, 13, 16, 19, 21]
We have shown how a de Bruijn graph formulation of the barcode calling problem based on circularization of input sequences is a useful approach to identify and error-correct barcode sequences
Summary
Single-cell sequencing experiments use short DNA barcode ‘tags’ to identify reads that originate from the same cell. In order to recover single-cell information from such experiments, reads must be grouped based on their barcode tag, a crucial processing step that precedes other computations. This step can be difficult due to high rates of mismatch and deletion errors that can afflict barcodes. Tagging of sequencing reads with short DNA barcodes is a common experimental practice that enables a pooled sequencing library to be separated into biologically meaningful partitions This technique is in the cornerstone of many single-cell sequencing experiments, where reads originating from individual cells are tagged with cell-specific barcodes; as such, the first step in any single-cell sequencing experiment involves separating reads by barcode to recover single-cell profiles Some current approaches require that the approximate number of cells in the experiment be known beforehand, and in some experimental contexts such information is not obtained
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