Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE.

Long-read single-cell RNA sequencing (scRNA-seq) enables the quantification of RNA isoforms in individual cells. However, long-read scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes. We introduce BLAZE, which accurately and efficiently identifies 10x cell barcodes using only nanopore long-read scRNA-seq data. BLAZE outperforms the existing tools and provides an accurate representation of the cells present in long-read scRNA-seq when compared to matched short reads. BLAZE simplifies long-read scRNA-seq while improving the results, is compatible with downstream tools accepting a cell barcode file, and is available at https://github.com/shimlab/BLAZE .

NanoSplicer: accurate identification of splice junctions using Oxford Nanopore sequencing.

MOTIVATION: Long-read sequencing methods have considerable advantages for characterizing RNA isoforms. Oxford Nanopore sequencing records changes in electrical current when nucleic acid traverses through a pore. However, basecalling of this raw signal (known as a squiggle) is error prone, making it challenging to accurately identify splice junctions. Existing strategies include utilizing matched short-read data and/or annotated splice junctions to correct nanopore reads but add expense or limit junctions to known (incomplete) annotations. Therefore, a method that could accurately identify splice junctions solely from nanopore data would have numerous advantages. RESULTS: We developed 'NanoSplicer' to identify splice junctions using raw nanopore signal (squiggles). For each splice junction, the observed squiggle is compared to candidate squiggles representing potential junctions to identify the correct candidate. Measuring squiggle similarity enables us to compute the probability of each candidate junction and find the most likely one. We tested our method using (i) synthetic mRNAs with known splice junctions and (ii) biological mRNAs from a lung-cancer cell-line. The results from both datasets demonstrate NanoSplicer improves splice junction identification, especially when the basecalling error rate near the splice junction is elevated. AVAILABILITY AND IMPLEMENTATION: NanoSplicer is available at https://github.com/shimlab/NanoSplicer and archived at https://doi.org/10.5281/zenodo.6403849. Data is available from ENA: ERS7273757 and ERS7273453. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A comparison of software for the evaluation of complex DNA profiles.

We compare two open-source programs for the evaluation of evidential weight arising from complex DNA profiles recovered in a crime investigation. Here, "complex" means one or more of: low-template, degraded and mixed-source. Although software for complex DNA profile analysis has made great strides in recent years, the ability of courts to effectively scrutinise and challenge the reliability of the resulting evidence remains problematic. One key step is to compare different software on the same evidence, but there are currently few published comparisons in part because of the problems posed by restricted access to commercial software. We present here an extensive comparison between two open-source software, LikeLTD and EuroForMix. We find that despite different modelling assumptions the two programs generate similar results. The differences that we do identify can inform future improvements and can provide a benchmark for acceptable discrepancies between alternative programs.