Biology subjects

Borgsmüller, N.

Publications and source records attributed to Borgsmüller, N..

6 recordsLinked to original sources

bioRxiv2024

De novo detection of somatic variants in long-read single-cell RNA sequencing data

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variants calling, LongSom re-annotates marker gene based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variants detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

cancer biology↗

bioRxiv2023

DelSIEVE: joint inference of single-nucleotide variants, somatic deletions, and cell phylogeny from single-cell DNA sequencing data

With rapid advancements in single-cell DNA sequencing (scDNA-seq), various computational methods have been developed to study evolution and call variants on single-cell level. However, modeling deletions remains challenging because they affect total coverage in ways that are difficult to distinguish from technical artifacts. We present DelSIEVE, a statistical method that infers cell phylogeny and single-nucleotide variants, accounting for deletions, from scDNA-seq data. DelSIEVE distinguishes deletions from mutations and artifacts, detecting more evolutionary events than previous methods. Simulations show high performance, and application to cancer samples reveals varying amounts of deletions and double mutants in different tumors.

bioinformatics↗

bioRxiv2022

Single-cell phylogenies reveal deviations from clock-like, neutral evolution in cancer and healthy tissues

How tumors evolve affects cancer progression, therapy response, and relapse. However, whether tumor evolution is driven primarily by selectively advantageous or neutral mutations remains under debate. Resolving this controversy has so far been limited by the use of bulk sequencing data. Here, we leverage the high resolution of single-cell DNA sequencing (scDNA-seq) to test for clock-like, neutral evolution. Under neutrality, different cell lineages evolve at a similar rate, accumulating mutations according to a molecular clock. We developed and benchmarked a test of the somatic clock based on single-cell phylogenies and applied it to 22 scDNA-seq datasets. We rejected the clock in 10/13 cancer and 5/9 healthy datasets. The clock rejection in seven cancer datasets could be related to known driver mutations. Our findings demonstrate the power of scDNA-seq for studying somatic evolution and suggest that some cancer and healthy cell populations are driven by selection while others seem to evolve under neutrality.

evolutionary biology↗

bioRxiv2022

SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data

Single-cell DNA sequencing (scDNA-seq) has enabled the identification of single nucleotide somatic variants and the reconstruction of cell phylogenies. However, statistical phylogenetic models for cell phylogeny reconstruction from raw sequencing data are still in their infancy. Here we present SIEVE (SIngle-cell EVolution Explorer), a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from scDNA-seq reads. SIEVE leverages raw read counts for all nucleotides at candidate variant sites, and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods both in phylogenetic accuracy and variant calling accuracy. We apply SIEVE to three scDNA-seq datasets, for colorectal (CRC) and triple-negative breast cancer (TNBC), one of them generated by us. On simulated data, SIEVE reliably infers homo-and heterozygous somatic variants. The analysis of real data uncovers that double mutant genotypes are rare in CRC but unexpectedly frequent in TNBC samples.

bioinformatics↗

bioRxiv2020

Within-patient genetic diversity of SARS-CoV-2

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. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

genomics↗

bioRxiv2019

WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

Lack of reliable peak detection impedes automated analysis of large scale GC-MS 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. Comparing and contrasting between algorithms is thus difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP 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. Medium and low quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.

bioinformatics↗