Biology subjects

Ryan, M. C.

Publications and source records attributed to Ryan, M. C..

4 recordsLinked to original sources

bioRxiv2020

OmicPioneer-sc: an integrated, interactive visualization environment for single-cell sequencing data

OmicPioneer-sc is an open-source data visualization/analysis package that integrates dimensionality-reduction plots (DRPs) such as t-SNE and UMAP with Next-Generation Clustered Heat Maps (NGCHMs) and Pathway Visualization Modules (PVMs) in a seamless, highly interactive exploratory environment. It includes fluent zooming and navigation, a statistical toolkit, dozens of link-outs to external public bioinformatic resources, high-resolution graphics that meet the requirements of all major journals, and the ability to store all metadata needed to reproduce the visualizations at a later time. A user-friendly, multi-panel graphical interface enables non-informaticians to interact with the system without programming, asking and answering questions that require navigation among the three types of modules or extension from them to the Gene Ontology or information on therapies. The visual integration can be useful for detective work to identify and annotate cell-types for color-coding of the DRPs, and multiple NGCHMs can be layered on top of each other (with toggling among them) as an aid to multi-omic analysis. The tools are available in containerized form with APIs to facilitate incorporation as a plug-in to other bioinformatic environments. The capabilities of OmicPioneer-sc are illustrated here through application to a single-cell RNA-seq airway dataset pertinent to the biology of both cancer and COVID-19. [Supplemental material is available for this article.]

bioinformatics↗

bioRxiv2020

White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit

We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age ({Delta}age) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. Followed feature selection, a machine learning model was trained with fractional anisotropy values in 34 of 43 tracts on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on {Delta}age and whether this effect was moderated by chronological age using the regression spline model. The results showed that {Delta}age was significantly elevated in the age >30 group in patients (p < 0.001) but not in age [<=] 30 group (p = 0.364). {Delta}age in patients was significantly and negatively associated with both working memory ({beta} = -0.176, p = 0.007) and processing speed ({beta} = -0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the {Delta}age is elevated in SZs and become significantly from middle life stage; the increase of {Delta}age in SZs is associated with the decline neurocognitive performance.

neuroscience↗

bioRxiv2018

Lipid analysis of CO2-rich subsurface aquifers suggests an autotrophy-based deep biosphere with lysolipids enriched in CPR bacteria

Sediment-hosted CO2-rich aquifers deep below the Colorado Plateau (USA) contain a remarkable diversity of uncultivated microorganisms, including Candidate Phyla Radiation (CPR) bacteria that are putative symbionts unable to synthesize membrane lipids. The origin of organic carbon in these ecosystems is unknown and the source of CPR membrane lipids remains elusive. We collected cells from deep groundwater brought to the surface by eruptions of Crystal Geyser, sequenced the community, and analyzed the whole community lipidome over time. Characteristic stable carbon isotopic compositions of microbial lipids suggest that bacterial and archaeal CO2 fixation ongoing in the deep subsurface provides organic carbon for the complex communities that reside there. Coupled lipidomic-metagenomic analysis indicates that CPR bacteria lack complete lipid biosynthesis pathways but still possess regular lipid membranes. These lipids may therefore originate from other community members, which also adapt to high in situ pressure by increasing fatty acid unsaturation. An unusually high abundance of lysolipids attributed to CPR bacteria may represent an adaptation to membrane curvature stress induced by their small cell sizes. Our findings provide new insights into the carbon cycle in the deep subsurface and suggest the redistribution of lipids into putative symbionts within this community.

microbiology↗

bioRxiv2017

CRAVAT 4: Cancer-Related Analysis of Variants Toolkit

Cancer sequencing studies are increasingly comprehensive and well-powered, returning long lists of somatic mutations that can be difficult to sort and interpret. Diligent analysis and quality control can require multiple computational tools of distinct utility and producing disparate output, creating additional challenges for the investigator. The Cancer-Related Analysis of Variants Toolkit (CRAVAT) is an evolving suite of informatics tools for mutation interpretation that includes mutation projecting and quality control, impact prediction and extensive annotation, gene- and mutation-level interpretation including joint prioritization of all nonsilent consequence types, and structural and mechanistic visualization. Results from CRAVAT submissions are explored in an interactive, user-friendly web-environment with dynamic filtering and sorting designed to highlight the most informative mutation, even in the context of very large studies. CRAVAT can be run on a public web-portal, in the cloud, or downloaded for local use, and is easily integrated with other methods for cancer omics analysis.\n\nConflict of interestAll authors declare no potential conflict of interest.

bioinformatics↗