N. Beerenwinkel

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

LongSom detects somatic variants in LR scRNA-seq data without normal samples, revealing clonal heterogeneity in ovarian cancer.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

CTAT-LR-fusion detects fusion transcripts from long and short read RNA-seq data, improving accuracy in bulk and single cell transcriptomes.

Network-based clustering unveils interconnected landscapes of genomic and clinical features across myeloid malignancies

The paper identifies novel subgroups in myeloid malignancies using a network-based clustering approach that integrates genomic and clinical features, improving prognostic accuracy.

Parallel evolution and enhanced virulence upon in vivo passage of an RNA virus in Drosophila melanogaster

This study shows that RNA virus evolution under host immune pressure leads to parallel mutations in a specific viral protein, increasing virulence and replication efficiency across different host genotypes.

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer

This study uses long-read single-cell RNA sequencing to detect isoforms and genomic alterations in ovarian cancer, revealing novel isoforms, cell type-specific expression patterns, and gene fusions.

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

This study uses single-cell DNA sequencing to test for neutral evolution in cancer and healthy tissues, finding deviations from a clock-like model in many datasets.

Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees

This paper introduces TreeMHN, a probabilistic framework for jointly inferring exclusivity patterns and recurrent trajectories from tumor mutation trees, showing improved performance over existing methods.

Mapping single-cell transcriptomes to copy number evolutionary trees

SCATrEx maps single-cell transcriptomes to copy number evolutionary trees, revealing clone-specific gene expression heterogeneity in tumors.

A statistical test for clonal exclusivity in tumour evolution

This paper introduces a statistical test, GeneAccord, to identify clonally exclusive gene pairs in tumours and applies it to AML data, finding 6 exclusive and 2 co-occurring pairs.

Spatial distribution of private gene mutations in clear cell renal cell carcinoma

The paper analyzes intra-tumour heterogeneity and subclonal mutations in clear cell renal cell carcinoma using multi-regional sequencing, finding that most tumours harbor private mutations and clonally exclusive pathway pairs.

Dynamic changes of genomic and transcriptomic tumor diversity during melanoma progression

This study tracks genomic and transcriptomic changes in melanoma progression using a mouse model, identifying key genes and pathways involved in metastasis.

Drug-induced resistance evolution necessitates less aggressive treatment

This paper shows that aggressive drug treatments can increase resistance evolution by altering mutation rates, and proposes an optimal treatment strategy that minimizes resistance risk.

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

This paper compares mutational pathways to lopinavir resistance in HIV-1 subtypes B and C using a probabilistic model, finding distinct initial mutations and diverse evolutionary pathways.

GATA3 and MDM2 are synthetic lethal in estrogen receptor-positive breast cancers

This study identifies GATA3 and MDM2 as synthetic lethal in ER-positive breast cancers, showing MDM2 inhibition can target GATA3-deficient tumors via the PI3K/Akt/mTOR pathway.

Single-cell copy number calling and event history reconstruction

This paper develops SCICoNE, a statistical model for single-cell copy number analysis from low-depth sequencing data, showing its accuracy on simulated data and application to breast cancer samples.

Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics

This study uses single-cell DNA sequencing to analyze clonal diversity in AML, revealing mutation co-occurrence, mutual exclusivity, and evolutionary patterns linked to treatment resistance and poor prognosis.

Spatial structure governs the mode of tumour evolution

The paper shows that tumour architecture determines evolutionary patterns, identifying four onco-evolutionary modes based on spatial structure and cell dispersal.

Mutational interactions define novel cancer subgroups

This study uses a Bayesian network model to analyze mutational interactions across 8,198 cancer samples, identifying 22 novel clusters that improve survival prediction and suggest drug targets.

A stochastic model of metastatic bottleneck predicts patient outcome and therapy response

The paper develops a stochastic model of metastatic bottleneck to predict patient outcomes and therapy response, showing that interventions targeting the bottleneck are more effective than reducing overall tumor burden.

The geometry of partial fitness orders and an efficient method for detecting genetic interactions

This paper introduces an efficient method to detect genetic interactions from partial fitness orders using geometric and graph-theoretic approaches, improving upon previous methods exponentially.

Context-dependent deposition and regulation of mRNAs in P-bodies

The paper identifies mRNA subsets associated with P-bodies under stress and shows how Puf5p regulates their decay or storage depending on cellular conditions.

Inferring Genetic Interactions From Comparative Fitness Data

This paper develops quantitative tools to infer genetic interactions from fitness rank orders, demonstrating their application across multiple genetic systems.

A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution

This study tests the infinite sites assumption in tumor evolution using single-cell sequencing data and finds recurrent mutations in multiple datasets.