Biology subjects

Grossman, R. L.

Publications and source records attributed to Grossman, R. L..

9 recordsLinked to original sources

bioRxiv2025

Query Augmented Generation (QAG) from the Genomic DataCommons for Accurate Variant Statistics

In precision oncology, researchers often use public knowledgebases to check somatic variant frequencies against their cohort data. Large language models (LLMs) can quickly answer questions on somatic variant frequencies, but often hallucinate and give inaccurate results for factual data. Using synthetic queries, we show that somatic variant frequencies in baseline LLM responses are underestimated compared to the Genomic Data Commons (GDC), the worlds largest data commons for cancer research. We present a modular architecture called Query Augmented Generation (QAG) for integrating LLMs with high-quality data from a third party data source such as a data commons, knowledgebase or database. We apply QAG to the GDC to help researchers obtain accurate frequencies for somatic variants, copy number variants, and MSI status--even for complex queries requiring multiple steps in the GDC portal and API. Our software is deployed as a model context protocol (MCP) server on Hugging Face and available on GitHub.

bioinformatics↗

bioRxiv2023

ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning

Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies. AvailabilityATAT is available at: https://github.com/StevenSong/tissue-alignment

bioinformatics↗

bioRxiv2023

Latent transcriptional programs reveal histology-encoded tumor features spanning tissue origins

Precision medicine in cancer treatment depends on deciphering tumor phenotypes to reveal the underlying biological processes. Molecular profiles, including transcriptomics, provide an information-rich tumor view, but their high-dimensional features and assay costs can be prohibitive for clinical translation at scale. Recent studies have suggested jointly leveraging histology and genomics as a strategy for developing practical clinical biomarkers. Here, we use machine learning techniques to identify de novo latent transcriptional processes in squamous cell carcinomas (SCCs) and to accurately predict their activity levels directly from tumor histology images. In contrast to analyses focusing on pre-specified, individual genes or sample groups, our latent space analysis reveals sets of genes associated with both histologically detectable features and clinically relevant processes, including immune response, collagen remodeling, and fibrosis. The results demonstrate an approach for discovering clinically interpretable histological features that indicate complex, potentially treatment-informing biological processes.

cancer biology↗

bioRxiv2022

Towards Self-Describing and FAIR Bulk Formats for Biomedical Data

We introduce a self-describing serialized format for bulk biomedical data called the Portable Format for Biomedical (PFB) data. The Portable Format for Biomedical data is based upon Avro and encapsulates a data model, a data dictionary, the data itself, and pointers to third party controlled vocabularies. In general, each data element in the data dictionary is associated with a third party controlled vocabulary to make it easier for applications to harmonize two or more PFB files. We describe experimental studies showing the performance improvements when importing and exporting bulk biomedical data in the PFB format versus using JSON and SQL formats.

bioinformatics↗

bioRxiv2021

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

bioinformatics↗

bioRxiv2020

HeartBioPortal2.0: new developments and updates for genetic ancestry and cardiometabolic quantitative traits in diverse human populations

Cardiovascular disease (CVD) is the leading cause of death worldwide for both genders and across most racial and ethnic groups. However, different races and ethnicities exhibit different rates of cardiovascular disease and its related cardiorenal and metabolic co-morbidities, suggesting differences in genetic predisposition and risk of onset, as well as socioeconomic and lifestyle factors (diet, exercise, etc.) that act upon an individuals unique underlying genetic background. Here we present HeartBioPortal2.0, a major update to HeartBioPortal, the worlds largest CVD genetics data precision medicine platform for harmonized CVD-relevant genetic variants, which now enables search and analysis of human genetic information related to heart disease across ethnically diverse populations and cardiovascular/renal/metabolic quantitative traits pertinent to CVD pathophysiology. HeartBioPortal2.0 is structured as a cloud-based computing platform and knowledge portal that consolidates a multitude of CVD-relevant next-generation sequencing data modalities into a single powerful query and browsing interface between data and user via a user-friendly web application publicly available to the scientific research community. Since its initial release, HeartBioPortal2.0 has added new cardiovascular/renal/metabolic disease relevant gene expression data as well as genetic association data from numerous large-scale genome-wide association study (GWAS) consortiums such as CARDIoGRAMplusC4D, TOPMed, FinnGen, AFGen, MESA, MEGASTROKE, UK Biobank, CHARGE, Biobank Japan, MyCode, among other studies. In addition, HeartBioPortal2.0 now includes support for quantitative traits and ethnically diverse populations, allowing users to investigate the shared genetic architecture of any gene or its variants across the continuous cardiometabolic spectrum from health (e.g., blood pressure traits) to disease (hypertension), facilitating the understanding of CVD trait genetics that inform health-to-disease transitions and endophenotypes. Custom visualizations in the new and improved user interface (UI), including performance enhancements and new security features such as user authentication collectively re-imagine HeartBioPortals user experience and provide a data commons that co-locates data, storage and computing infrastructure in the context of studying the genetic basis behind the leading cause of global mortality. Database URLhttps://www.heartbioportal.com/

bioinformatics↗

bioRxiv2019

Uniform Genomic Data Analysis in the NCI Genomic Data Commons

The goal of the National Cancer Institute (NCI) Genomic Data Commons (GDC) is to provide the cancer research community with a data repository of uniformly processed genomic and associated clinical data that enables data sharing and collaborative analysis in the support of precision medicine. The initial GDC dataset include genomic, epigenomic, proteomic, clinical and other data from the NCI TCGA and TARGET programs. Data production for the GDC started in June, 2015 using an OpenStack-based private cloud. By June of 2016, the GDC had analyzed more than 50,000 raw sequencing data inputs, as well as multiple other data types. Using the latest human genome reference build GRCh38, the GDC generated a variety of data types from aligned reads to somatic mutations, gene expression, miRNA expression, DNA methylation status, and copy number variation. In this paper, we describe the pipelines and workflows used to process and harmonize the data in the GDC. The generated data, as well as the original input files from TCGA and TARGET, are available for download and exploratory analysis at the GDC Data Portal and Legacy Archive (https://gdc.cancer.gov/).

bioinformatics↗

bioRxiv2018

Cancer eQTLs can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we demonstrate a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one-third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response.

genomics↗

bioRxiv2017

Large-Scale Uniform Analysis of Cancer Whole Genomes in Multiple Computing Environments

The International Cancer Genome Consortium (ICGC)s Pan-Cancer Analysis of Whole Genomes (PCAWG) project aimed to categorize somatic and germline variations in both coding and non-coding regions in over 2,800 cancer patients. To provide this dataset to the research working groups for downstream analysis, the PCAWG Technical Working Group marshalled ~800TB of sequencing data from distributed geographical locations; developed portable software for uniform alignment, variant calling, artifact filtering and variant merging; performed the analysis in a geographically and technologically disparate collection of compute environments; and disseminated high-quality validated consensus variants to the working groups. The PCAWG dataset has been mirrored to multiple repositories and can be located using the ICGC Data Portal. The PCAWG workflows are also available as Docker images through Dockstore enabling researchers to replicate our analysis on their own data.

genomics↗