Anchor-Enhanced Bead Design for Reduced Oligonucleotide Synthesis Errors in Single-cell sequencing
Cribbs, A. P.; Sun, J.; Philpott, M.; Loi, D.; Hoffman, G.; Robson, J.; Mehta, N.; Calcutt, E.; Gamble, V.; Brown, T.; Brown, T.; Oppermann, U.
Show abstract
Single-cell transcriptomics, reliant on the incorporation of barcodes and unique molecular identifiers (UMIs) into captured polyA+ mRNA, faces a significant challenge due to synthesis errors in oligonucleotide capture sequences. These inaccuracies, which are especially problematic in long-read sequencing, impair the precise identification of sequences and result in inaccuracies in UMI deduplication. To mitigate this issue, we have modified the oligonucleotide capture design, which integrates an interposed anchor between the barcode and UMI, and a V base anchor adjacent to the polyA capture region. This configuration is devised to ensure compatibility with both short and long-read sequencing technologies, facilitating improved UMI recovery and enhanced feature detection, thereby improving the efficacy of droplet-based sequencing methods.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER 97%
- RoCK and ROI: Single-cell transcriptomics with multiplexed enrichment of selected transcripts and region-specific sequencing 97%
- High throughput, error corrected Nanopore single cell transcriptome sequencing 97%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Highly accurate barcode and UMI error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing 97%
- Lightning Fast and Highly Sensitive Full-Length Single-cell sequencing using FLASH-Seq 97%
- A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples 97%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.