Search bioRxiv⌕ Search

Biology subjects

Aharon, R.

Publications and source records attributed to Aharon, R..

2 recordsLinked to original sources

Long read sequencing reveals changes in measured isoform structure and abundance due to RNA processing

Oxford Nanopore Technology enables direct RNA sequencing without reverse transcription (RT) and cDNA sequencing without PCR amplification, allowing investigation of how RNA preparation protocols bias sequencing data. PCR amplification is known to introduce biases related to expression levels, transcript lengths, and GC content. However, biases introduced by RT have been described more recently and their quantification remains more elusive. Using seven cell line datasets from SG-NEx, we compared nanopore sequencing of direct RNA, direct cDNA, and PCR-amplified cDNA. We identified hundreds of differentially expressed genes and isoforms between protocols, indicating significant changes in observed expression. Differentially expressed isoforms displayed expected amplification biases, and further we observed decreased PCR counts associated with higher m6A methylation levels on native RNA. We also found differential transcript usage in hundreds of genes across cell lines, showing substantial changes in measured isoform proportions between protocols. Analysis of structural differences between isoforms that switch dominance revealed that while some events involve simple RNA fragmentation, most involve complex structural differences including novel junctions (e.g. exon skipping, intron retention). Compared to non-DTU genes, inserted introns (exitrons) were significantly more common after RT and amplification. Furthermore, visualisation of raw coverage data proved crucial for qualitative validation of experimental findings.

bioinformatics↗

More cells, more doublets in highly multiplexed single-cell data

Withdrawal statementThe authors have withdrawn this manuscript. In our initial submission of this paper, we proposed a combinatoric model for the probability of finding doublets in sample-barcoded single-cell RNA-sequencing data. This model predicts a doublet rate higher than the rate included in the documentation for the 10X Flex protocol. This was motivated by our use of doublet-finding software, scDblFinder, on experimental data using the Flex protocol, which identified several times more doublets than we expected based on the documentation. Our original model produced better agreement with the results of scDblFinder in 9 of our own data sets as well as one public dataset made available by 10X. During revisions, however, we performed Monte Carlo simulations of doublet formation that were inconsistent with the results of our model, and much closer to the predictions from the documentation. This prompted us to reanalyse the assumptions of our model. A longer version of this withdrawal statement that explains in detail the errors in the model and includes updated figures is available at https://github.com/Oshlack/flex-doublets, but in brief, our initial assumptions over-predicted the doublet fraction, and our revised model is now consistent with both the Monte Carlo simulations and the predictions from the Flex documentation. We therefore speculate that the unexpectedly high doublet fractions observed in the experimental data might be due to some combination of experimental conditions and software performance. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please ontact the corresponding author.

bioinformatics↗