Biology subjects

Weber, L. L.

Publications and source records attributed to Weber, L. L..

4 recordsLinked to original sources

bioRxiv2026

Arborist: Prioritizing Bulk DNA Inferred Tumor Phylogenies via Low-pass Single-cell DNA Sequencing Data

Cancer arises from an evolutionary process that can be reconstructed from DNA sequencing and modeled by tumor phylogenies. High coverage bulk DNA sequencing (bulk DNA-seq) is widely available, but tumor phylogeny inference requires deconvolution, often resulting in non-uniqueness in the solution space. Single-cell DNA sequencing (scDNA-seq) holds potential to yield higher resolution tumor phylogenies, but the sparsity of emerging low-pass sequencing technologies poses challenges for the study of single-nucleotide variants. Increasing availability of data sequenced with both modalities provides an opportunity to capitalize on the advantages of these technologies. While inference methods exist for bulk DNA-seq and for low-pass scDNA-seq, no joint inference methods currently exist. As a first step, we propose a method named ARBORIST that prioritizes tumor phylogenies inferred via bulk DNA-seq using low-pass scDNA-seq data. ARBORIST takes as input a candidate set of trees with corresponding SNV clustering, along with variant and total read count data from scDNA-seq and uses variational inference to approximate a lower bound on the marginal likelihood of each tree in the candidate set. On simulated data, matching characteristics of current scDNA-seq data, ARBORIST outperforms both bulk and low-pass single-cell reconstruction methods. On a biological dataset, ARBORIST conclusively resolves the evolutionary relationship between different SNV clusters on a malignant peripheral nerve sheath tumor, which is supported by orthogonal validation via a proxy for copy number. ARBORIST provides a principled framework for integrating bulk DNA-seq and low-pass scDNA-seq data, improving confidence in tumor phylogeny reconstruction. Availabilityhttps://github.com/VanLoo-lab/Arborist

bioinformatics↗

bioRxiv2024

Pharming: Joint Clonal Tree Reconstruction of SNV and CNAEvolution from Single-cell DNA Sequencing of Tumors

Cancer arises through an evolutionary process in which somatic mutations, including single nucleotide variants (SNVs) and copy number aberrations (CNAs), drive the development of a malignant, heterogeneous tumor. Reconstructing this evolutionary history from sequencing data is critical for understanding the order in which mutations are acquired and the dynamic interplay between different types of alterations. Advances in modern whole genome single-cell sequencing now enable the accurate inference of copy number profiles in individual cells. However, the low sequencing coverage of these low pass sequencing technologies poses a challenge for reliably inferring the presence or absence of SNVs within tumor cells, limiting the ability to simultaneously study the evolutionary relationships between SNVs and CNAs. In this work, we introduce a novel tumor phylogeny inference method, PO_SCPLOWHARMINGC_SCPLOW, that jointly infers the evolutionary histories of SNVs and CNAs. Our key insight is to leverage the high accuracy of copy number inference methods and the fact that SNVs co-occur in regions with CNAs in order to enable more precise tumor phylogeny reconstruction for both alteration types. We demonstrate via simulations that PO_SCPLOWHARMINGC_SCPLOW outperforms state-of-the-art single-modality tumor phylogeny inference methods. Additionally, we apply PO_SCPLOWHARMINGC_SCPLOW to a triple-negative breast cancer case, achieving high-resolution, joint reconstruction of CNA and SNV evolution, including the de novo detection of a clonal whole-genome duplication event. Thus, PO_SCPLOWHARMINGC_SCPLOW offers the potential for more comprehensive and detailed tumor phylogeny inference for high-throughput, low-coverage single-cell DNA sequencing technologies compared to existing approaches. Availabilityhttps://github.com/elkebir-group/Pharming

bioinformatics↗

bioRxiv2023

TRIBAL: Tree Inference of B cell Clonal Lineages

B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies. AvailabilityTRIBAL is available at https://github.com/elkebir-group/tribal

immunology↗

bioRxiv2022

Phertilizer: growing a clonal tree from ultra-low coverage single-cell DNA sequencing data of tumors

Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We present PO_SCPLOWHERTILIZERC_SCPLOW, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of PO_SCPLOWHERTILIZERC_SCPLOW on simulated data as well as on two real datasets, finding that PO_SCPLOWHERTILIZERC_SCPLOW effectively utilizes the copynumber signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods. Availabilityhttps://github.com/elkebir-group/phertilizer

bioinformatics↗