Biology subjects

Shireman, J.

Publications and source records attributed to Shireman, J..

4 recordsLinked to original sources

bioRxiv2026

Agentic Discovery Transfer Enables Cross-Cancer Discovery at Scale

Cancer research generates tens of thousands of studies each year, yet whether a published finding holds in another cancer type or patient population is rarely tested, because doing so is labor-intensive and difficult to scale. We define this task as discovery transfer and present SMART, a multi-agent large language model framework for discovery transfer in single-cell RNA-sequencing (scRNA-seq) studies. Given a source manuscript and a target scRNA-seq dataset, SMART extracts major clinical and mechanistic findings, generates and executes analyses for the testable findings, and reports which findings transfer and suggests follow-up. Applied to 121 studies across six cancer types, SMART identified findings that transfer across cancers, including a lung cancer prognostic signature that we confirmed to be associated with survival in liver cancer, pancreatic cancer and pleural mesothelioma. SMART also generated new mechanistic hypotheses, demonstrating how agentic discovery transfer can extend published findings beyond their original setting.

bioinformatics↗

bioRxiv2026

AutoZyme: An Autonomous Agentic Framework to Optimize Bioinformatics Software

Performance bottlenecks in widely used genomics and bioinformatics software present a substantial and growing burden as biological datasets continue to increase in size and number. Relieving these bottlenecks relies largely on expert manual optimization and therefore remains difficult to scale. Here we present AutoZyme, an agentic framework for scientific software optimization. Given a target function, AutoZyme builds benchmarks, identifies bottlenecks, and iteratively tests code changes, retaining only those that improve runtime while preserving output. We evaluated AutoZyme on 45 functions, improving runtime without substantial memory increases in over 95% of cases considered. Across 38 functions from Seurat, Scanpy and related packages in genomics and bioinformatics, AutoZyme reduced runtime by a median of 8.52-fold, with the largest reductions exceeding 676-fold. The optimized functions are distributed through AutoZyme-Library as drop-in replacements for existing analysis pipelines. We also release AutoZyme as a reusable framework for optimizing additional user-specified packages and functions.

bioinformatics↗

bioRxiv2024

CASSIA allows for robust, automated cell annotation in single-cell RNA-sequencing data

Cell type annotation is an essential step in single-cell RNA-sequencing analysis, and numerous annotation methods are available. Most require a combination of computational and domain-specific expertise, and they frequently yield inconsistent results that can be challenging to interpret. Large language models have the potential to expand accessibility while reducing manual input and improving accuracy, but existing approaches suffer from hyperconfidence, hallucinations, and lack of reasoning. To address these limitations, we developed CASSIA for automated, accurate, and interpretable cell annotation of single-cell RNA-sequencing data. As demonstrated in analyses of over 970 cell types, CASSIA improves annotation accuracy in benchmark datasets as well as complex and rare cell populations, and also provides users with reasoning and quality assessment to ensure interpretability, guard against hallucinations, and calibrate confidence.

bioinformatics↗

bioRxiv2021

CRISPR-Cas9 Screen Reveals PSMB3 Contributes to Gliomagenesis Through Proteasome-Dependent and Independent Mechanisms

Glioblastoma (GBM) is the most common adult malignant brain tumor, with a median survival of 21 months and a 100% recurrence rate. Even though many of the critical oncogenic drivers for GBM have been identified, the basis of gliomagenesis is still under investigation. To identify novel genes that contribute to GBM progression, we performed a genome-wide CRISPR-Cas9 knockout screen. We identified four previously unstudied genes - PSMB3, CHCHD4, SPDYE5, HSPA1 - which had elevated expression in cancer and demonstrated a significant positive correlation with respect to GBM growth and patient survival in vivo and patient datasets. Furthermore, overexpression of PSMB3 and HSPA5 in neural stem cells resulted in transformation to a cancer phenotype. Further investigation of PSMB3, a subunit of the proteasome, allowed us to identify both ubiquitin-mediated and non-ubiquitin-mediated mechanisms of oncogenesis. Ultimately, the data from our CRISPR screens suggests that these genes drive tumor progression, making them promising therapeutic targets for GBM.

cancer biology↗