Biology subjects

Marth, G.

Publications and source records attributed to Marth, G..

4 recordsLinked to original sources

bioRxiv2026

DS5: An Open-Source Framework for Standardized High-Throughput Drug Screening Data Storage, Analysis, and Drug Prioritization

Background. High-throughput drug screening (HTS) is increasingly used in precision oncology across cell lines, patient-derived models such as organoids (PDO) and xenograft organoids (PDxO), and ex vivo patient samples, with multiple active clinical trials incorporating HTS for individualizing treatment. Yet HTS efforts predominantly rely on ad hoc, spreadsheet-based workflows, with no standardized infrastructure for storing, analyzing, and comparing screens across patients, cohorts, or institutions, limiting reproducibility and slowing clinical data exchange. Existing tools address isolated pieces of the workflow, handling either data management or analysis and visualization, but none integrate standardized storage, reproducible dose-response analysis, individualized drug prioritization, and clinician and molecular tumor board friendly reporting in a single open-source framework. Results. We present DS5, an open-source Python framework that operates on an HDF5-based DS5 file format that unifies standardized storage, quality control, dose-response analysis, cohort-level drug prioritization, and automated reporting in a single lightweight package. DS5 enforces raw-data immutability, integrates RxNorm-based drug name standardization, and scales from small clinical cohorts to large pharmacogenomics datasets on a standard laptop. We validated DS5 against two public benchmarks spanning over 428,000 drug, cell line pairs, recovering published LN IC50 values from GDSC2 (Pearson r = 0.973) and Emax values from CTRP (r = 0.961). Using a published breast cancer PDxO dataset (45 compounds by 16 models), we reproduced GR-based profiles via a custom-metric extension and used DS5's cohort-level prioritization to predict in vivo tumor response with ROC AUC = 0.907 across 30 drug PDX pairs. Finally, we demonstrate the utilities of DS5 in an ongoing pediatric brain tumor precision oncology initiative, where its cohort-normalized drug rankings and automated reports directly support molecular tumor board discussions. Conclusion. DS5 provides the cancer basic and translational research community with a standardized file format and reproducible computational analysis methods to support functional drug screening experiments; and its reporting features facilitate bridging raw drug screening measurements to translational decisions. DS5 is released open-source under the permissible MIT license.

bioinformatics↗

bioRxiv2022

Variable RNA sampling biases mediate concordance of single-cell and nucleus sequencing across cell types

A mechanistic understanding of the biological and technical factors that impact cell and nuclear transcript measurements is essential to designing, analyzing, and interpreting single-cell and single-nucleus RNA sequencing experiments. RNA sampling in nuclei and cells is fundamentally different as nuclei contain the same pre-mRNA population as cells, yet contain a small subset of the largely-cytoplasmic mRNAs. Nonetheless, early studies argued that including pre-mRNA in single-nucleus analysis led to results comparable to cellular samples. However, typical bioinformatic workflows do not distinguish between pre-mRNA and mRNA when analyzing gene expression, and variation in the relative abundance of pre-mRNA and mRNA across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA in routine gene expression analysis is now commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public datasets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling in single-cell and nucleus RNA-seq. We disentangle the roles of bioinformatic processing, assay choice, and biological variability on measured gene expression and marker gene selection. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias within and between assays and limits the generalizability of a recently-published normalization method intended to correct for this bias. As an alternative solution, we demonstrate the applicability of an existing post hoc gene length-based correction method developed for conventional RNA-seq gene set enrichment analysis. Finally, we show that the inclusion of pre-mRNA in bioinformatic processing can impart a larger effect on gene expression estimates than the choice of cell versus nuclear assay, which is pivotal to the effective reuse of existing data. Broadly, these analyses advance our understanding of the biological and technical factors underlying variation in single-cell and single-nucleus RNA-seq experiments to promote more informed choices in experimental design, data analysis, and data sharing and reuse.

bioinformatics↗

bioRxiv2020

Mobile Element Insertions and Associated Structural Variants in Longitudinal Breast Cancer Samples

While mobile elements are largely inactive in healthy somatic tissues, increased activity has been found in cancer tissues, with significant variation among different cancer types. In addition to insertion events, mobile elements have also been found to mediate many structural variation events in the genome. Here, to better understand the timing and impact of mobile element insertions and structural variants involving existing mobile elements in cancer, we examined their activity in longitudinal samples of four metastatic breast cancer patients. With whole-genome sequencing data from multiple timepoints through tumor progression, we used mobile element detection software followed by visual confirmation of the insertions. We identified 11 mobile element insertions or structural variants involving existing elements and found that the majority of these occurred early in tumor progression. Two of the identified insertions were SVA elements, which have rarely been found in previous cancer studies. Most of the variants appear to impact intergenic regions; however, we identified a translocation interrupting MAP2K4 involving Alu elements and a deletion in YTHDF2 involving mobile elements that likely inactivate reported tumor suppressor genes. The high variant allele fraction of the MAP2K4 translocation, the loss of the other copy of MAP2K4, the recurrent loss-of-function mutations found in this gene in other human cancers, and the important function of MAP2K4 indicate that this translocation is potentially a driver mutation. Overall, using a unique longitudinal dataset, we find that most variants are likely passenger mutations in the four patients we examined, but some variants impact tumor progression.

genomics↗

bioRxiv2019

Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches

When interpreting sequencing data from multiple spatial or longitudinal biopsies, detecting sample mix-ups is essential yet more difficult than in studies of germline variation. In most genomic studies of tumors, genetic variation is frequently detected through pairwise comparisons of the tumor and a matched normal tissue from the sample donor, and in many cases, only somatic variants are reported. The disjoint genotype information that results hinders the use of existing tools that detect sample swaps solely based on genotypes of germline variants. To address this problem, we have developed somalier, which can operate directly on the alignments, so as not to require jointly-called germline variants. Instead, somalier extracts a small sketch of informative genetic variation for each sample. Sketches from hundreds of biopsies and normal tissues can then be compared in under a second. This speed also makes it useful for checking relatedness in large cohorts of germline samples. Somalier produces both text output and an interactive visual report that facilitates the detection and correction of sample swaps using multiple relatedness metrics. We introduce the tool and demonstrate its utility on a cohort of five glioma samples each with a normal, tumor, and cell-free DNA sample. Applying somalier to high-coverage sequence data from the 1000 Genomes Project also identifies several related samples. Somalier can be applied to diverse sequencing data types and genome builds, and is freely available for academic use at github.com/brentp/somalier.

bioinformatics↗