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Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Long-read single-cell RNA sequencing (scRNA-seq), capturing full-length transcripts, lacked the depth to provide this information so far. Here, we increased the PacBio sequencing depth to 12,000 reads per cell, leveraging multiple strategies, including artifact removal and transcript concatenation, and applied the technology to samples from three human ovarian cancer patients. Our approach captured 152,000 isoforms, of which over 52,000 were novel, detected cell type- and cell-specific isoform usage, and revealed differential isoform expression in tumor and mesothelial cells. Furthermore, we identified gene fusions, including a novel scDNA sequencing-validated IGF2BP2::TESPA1 fusion, which was misclassified as high TESPA1 expression in matched short-read data, and called somatic and germline mutations, confirming targeted NGS cancer gene panel results. With multiple new opportunities, especially for cancer biology, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine., bioRxiv

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity., bioRxiv

Methodological & Technological developments Methodological & Technological developmentsMethodological & Technological developments; IntroductionLack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GCMS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult.Technological and methodological innovationWe present part of the work published in [1] and implemented in our workflow for improved peak picking (WiPP),focusing on the use of machine learning-based classification to optimize and improve different steps of the common GC-MS metabolomics data processing workflow. Our approach evaluates the quality of detected peaks using a machine learning based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis.Results and impactWe benchmarked our workflow to standard compound mixes and a complex biological dataset, demonstrating that peak detection is improved. Furthermore, the approach can provide an impartial performance comparison of different peak picking algorithms. We also discuss the applicability of the approach to liquid chromatography-mass spectrometry data.References[1] Gloaguen, Y.; Borgsmüller, N. et al. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-MassSpectrometry (GC-MS) Data. Metabolites 2019, 9, 171.

Introduction Lack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GCMS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult. Technological and methodological innovation We present part of the work published in [1] and implemented in our workflow for improved peak picking (WiPP), focusing on the use of machine learning-based classification to optimize and improve different steps of the common GC-MS metabolomics data processing workflow. Our approach evaluates the quality of detected peaks using a machine learning based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis. Results and impact We benchmarked our workflow to standard compound mixes and a complex biological dataset, demonstrating that peak detection is improved. Furthermore, the approach can provide an impartial performance comparison of different peak picking algorithms. We also discuss the applicability of the approach to liquid chromatography-mass spectrometry data. References [1] Gloaguen, Y.; Borgsmüller, N. et al. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data. Metabolites 2019, 9, 171.