Biology subjects

Lynch, B. J.

Publications and source records attributed to Lynch, B. J..

3 recordsLinked to original sources

bioRxiv2026

Utanos: A general-purpose shallow whole-genome sequencing analysis workflow identifies interpretable copy number signatures

SummaryA modular FASTQ-to-figures solution for analyzing low-depth or shallow whole-genome sequencing (sWGS) data. Shallow WGS can be used to detect copy number (CN) aberrations, Homologous Recombination Deficiency (HRD), and to detect and create CN signatures. Growing in popularity, this sequencing type is used for neonatal diagnostics and studying cancer. One of the major benefits is the reduced cost compared with deeper sequencing modalities such as Whole Genome Sequencing (WGS). With just 15 million reads often targeted, and sample substrate options like Formalin-Fixed, Paraffin-Embedded (FFPE) blocks widely available, this approach enables an affordable study to be performed at scale. Our pipeline and R package are an end-to-end solution implemented with reusability and modularity in mind. It makes entry and exit from the ecosystem easy, providing regular standardized output formats throughout execution. The pipeline is written in the well-supported and cross-platform Nextflow framework and has been submitted for inclusion in nf-core. Additionally, a Docker image for the utanos R package has been created to improve modularity. Availability and ImplementationThe latest version of all software is freely available on GitHub. For the full processing pipeline, visit: https://github.com/Huntsmanlab/swgs-processing-pipeline. For just the utanos R package, visit: https://github.com/Huntsmanlab/utanos.

bioinformatics↗

bioRxiv2024

Spatial single cell transcriptomic analysis of a novel DICER1 Syndrome GEMM informs the cellular origin and developmental hierarchy of associated sarcomas

DICER1 syndrome predisposes children and young adults to tumor development across various organs. Many of these cancers are sarcomas, which uniquely express the RNase IIIb domain-deficient form of DICER1 and exhibit consistent histological and molecular similarities regardless of their anatomical origins. To uncover their cellular origin and developmental hierarchy, we established a lineage-traceable genetically engineered mouse model that allows for precise activation of Dicer1 mutations in Hic1+ mesenchymal stromal cells. This model resulted in the development of renal tumors closely mirroring human DICER1 sarcoma histologically and molecularly. Single-cell transcriptomics coupled with targeted spatial gene expression analysis revealed a Hic1+ progenitor population marked by Pdgfra, Dpt, and Mfap4, corresponding to universal fibroblasts of steady-state kidneys. These fibroblastic progenitors exhibit the capacity to undergo rhabdomyoblastic differentiation or transition to highly proliferative anaplastic sarcoma. Investigation of patient samples identified analogous cell states. This study uncovers a fibroblastic origin for DICER1 sarcoma and provides a faithful model for mechanistic investigation and therapeutic development for tumors within the rhabdomyosarcoma spectrum.

cancer biology↗

bioRxiv2022

Motion Impact Score for Detecting Spurious Brain-Behavior Associations

Between-participant differences in head motion introduce systematic bias to resting state fMRI brain-wide association studies (BWAS) that is not completely removed by denoising algorithms. Researchers who study traits, or phenotypes associated with in-scanner head motion (e.g. psychiatric disorders) need to know if trait-functional connectivity (FC) effects are biased by residual motion artifact in order to avoid reporting false positive results. We devised an adaptable method, Split Half Analysis of Motion Associated Networks (SHAMAN), to assign a motion impact score to specific trait-FC effects. The SHAMAN approach distinguishes between motion artifact causing overestimation or underestimation of trait-FC effects. SHAMAN was > 95% specific at sample sizes of n = 100 and above. SHAMAN was powered to detect motion overestimation scores 80% of the time at sample sizes of n = 5,000 but could detect motion underestimation scores only 50% of the time at n = 5000, making it most useful for researchers seeking to avoid overestimating trait-FC effects in large BWAS. We computed motion impact scores for trait-FC effect with 45 demographic, biophysical, cognitive, and personality traits from n = 7,270 participants in the Adolescent Brain Cognitive Development (ABCD) Study. After standard denoising with ABCD-BIDS and without motion censoring, 42% (19/45) of traits had significant (p < 0.05) motion overestimation scores and 38% (17/45) of traits had significant motion underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced the proportion of traits with significant motion overestimation scores from 42% to 2% (1/45) but did not decrease the number of traits with significant motion underestimation scores.

neuroscience↗