Biology subjects

Firman, T.

Publications and source records attributed to Firman, T..

3 recordsLinked to original sources

bioRxiv2024

Rapid, Reliable, and Interpretable CNV Curation Visualizations for Diagnostic Settings with SeeNV

Copy number variants (CNVs), structural alterations in the genome involving duplication or deletion of DNA segments, are implicated in various health conditions. Despite their clinical significance, accurate identification and interpretation of CNVs remain challenging, especially in the context of whole exome sequencing (WES), which is commonly used in clinical diagnostic laboratories. While WES offers economic advantages over whole genome sequencing (WGS), it struggles with CNV detection due to technical noise introduced by laboratory and analytic processes. Manual curation of CNV calls generated by these tools is labor-intensive and error-prone. To address this, we introduce SeeNV, a command-line tool designed to aid manual curation of CNVs at scale. SeeNV is one solution to these issues developed in collaboration with and used by the Precision Diagnostics Laboratory at Childrens Hospital Colorado. SeeNV generates static infographics for each CNV, incorporating sample and cohort sequencing coverage statistics, CNV population frequency, and more, facilitating rapid and precise assessment. Using CNVs calls identified in publicly available WES and WGS samples, we show users can rapidly and reliably curate CNV calls, needing only 4.3 seconds to curate a call, achieving 0.93 precision and 0.72 recall. SeeNV is freely available for download on GitHub: https://github.com/MSBradshaw/SeeNV.

genomics↗

bioRxiv2023

Hypothesis Generation For Rare and Undiagnosed Diseases Through Clustering and Classifying Time-Versioned Biological Ontologies

Rare diseases affect 1-in-10 people in the United States and despite increased genetic testing, up to half never receive a diagnosis. Even when using advanced genome sequencing platforms to discover variants, if there is no connection between the variants found in the patients genome and their phe-notypes in the literature, then the patient will remain undiagnosed. When a direct variant-phenotype connection is not known, putting a patients information in the larger context of phenotype relation-ships and protein-protein-interactions may provide an opportunity to find an indirect explanation. Databases such as STRING contain millions of protein-protein-interactions and HPO contains the relations of thousands of phenotypes. By integrating these networks and clustering the entities within we can potentially discover latent gene-to-phenotype connections. The historical records for STRING and HPO provide a unique opportunity to create a network time series for evaluating the cluster sig-nificance. Most excitingly, working with Childrens Hospital Colorado we provide promising hy-potheses about latent gene-to-phenotype connections for 38 patients with undiagnosed diseases. We also provide potential answers for 14 patients listed on MyGene2. Clusters our tool finds significant harbor 2.35 to 8.72 times as many gene-to-phenotypes edges inferred from known drug interactions than clusters find to be insignificant. Our tool, BOCC, is available as a web app and command line tool.

bioinformatics↗

bioRxiv2018

Maximum Caliber can build and infer models of oscillation in a three-gene feedback network

Single-cell protein expression time trajectories provide rich temporal data quantifying cellular variability and its role in dictating fitness. However, theoretical models to analyze and fully extract information from these measurements remain limited for three reasons: i) gene expression profiles are noisy, rendering models of averages inapplicable, ii) experiments typically measure only a few protein species while leaving other molecular actors - necessary to build traditional bottom-up models - unnoticed, and iii) measured data is in fluorescence, not particle number. We have recently addressed these challenges in an alternate top-down approach using the principle of Maximum Caliber (MaxCal) to model genetic switches with one and two protein species. In the present work we address scalability and broader applicability of MaxCal by extending to a three-gene (A, B, C) feedback network that exhibits oscillation, commonly known as the repressilator. We test MaxCals inferential power by using synthetic data of noisy protein number time traces - serving as a proxy for experimental data - generated from a known underlying model. We notice that the minimal MaxCal model - accounting for production, degradation, and only one type of symmetric coupling between all three species - reasonably infers several underlying features of the circuit such as the effective production rate, degradation rate, frequency of oscillation, and protein number distribution. Next, we build models of higher complexity including different levels of coupling between A, B, and C and rigorously assess their relative performance. While the minimal model (with four parameters) performs remarkably well, we note that the most complex model (with six parameters) allowing all possible forms of crosstalk between A, B, and C slightly improves prediction of rates, but avoids ad-hoc assumption of all the other models. It is also the model of choice based on Bayesian Information Criteria. We further analyzed time trajectories in arbitrary fluorescence (using synthetic trajectories) to mimic realistic data. We conclude that even with a three-protein system including both fluorescence noise and intrinsic gene expression fluctuations, MaxCal can faithfully infer underlying details of the network, opening up future directions to model other network motifs with many species.

synthetic biology↗