Correcting PCR amplification errors in unique molecular identifiers to generate absolute numbers of sequencing molecules.
Sun, J.; Philpott, M.; Loi, D.; Li, S.; Monteagudo-Mesas, P.; Hoffman, G.; Robson, J.; Mehta, N.; Gamble, V.; Brown, T.; Brown, T.; Canzar, S.; Oppermann, U.; Cribbs, A. P.
Show abstract
Unique Molecular Identifiers (UMIs) are random oligonucleotide sequences that remove PCR amplification biases. However, the impact that PCR associated sequencing errors have on the accuracy of generating absolute counts of RNA molecules is underappreciated. We show that PCR errors are the main source of inaccuracy in both bulk and single-cell sequencing data, and synthesizing UMIs using homotrimeric nucleotide blocks provides an error correcting solution, that allows absolute counting of sequenced molecules.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- RoCK and ROI: Single-cell transcriptomics with multiplexed enrichment of selected transcripts and region-specific sequencing 97%
- RNA modifications detection by comparative Nanopore direct RNA sequencing 97%
- Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER 97%
Similar papers in this journal
- Comprehensive Benchmarking of CITE-seq versus DOGMA-seq Single Cell Multimodal Omics 97%
- RATTLE: Reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing 97%
- DEMINERS enables clinical metagenomics and comparative transcriptomic analysis by increasing throughput and accuracy of nanopore direct RNA sequencing 96%
Similar papers in this journal
- Scalable co-sequencing of RNA and DNA from individual nuclei 97%
- A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines 96%
- Nano3P-seq: transcriptome-wide analysis of gene expression and tail dynamics using end-capture nanopore cDNA sequencing 96%
"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.