Biology subjects

Flight, R. M.

Publications and source records attributed to Flight, R. M..

9 recordsLinked to original sources

bioRxiv2026

The recount3 Python package for programmatic access to uniformly processed RNA-seq data

The recount3 online resource provides tens of thousands of uniformly processed RNA-seq samples across human and mouse from major sequencing repositories like the Sequence Read Archive. While access to these datasets has traditionally been centered in the R/Bioconductor ecosystem, the growing prominence of Python in bioinformatics and machine learning necessitates native, efficient tooling for Python users. Therefore, we present the recount3 Python package with robust application programming interface (API) and command-line interface (CLI) for discovering, downloading, and materializing recount3 resources. The software orchestrates uniform resource locator (URL) resolution, persistent on-disk caching, and the automatic parsing of data into analysis-ready data structures, including Pandas DataFrames and BiocPy RangedSummarizedExperiment objects. The recount3 Python package drastically lowers the barrier to entry for large-scale utilization of RNA-seq data in Python-based computational pipelines, bridging the gap between massive public transcriptomic data and modern machine learning ecosystems.

bioinformatics↗

bioRxiv2025

High-Quality Predicted Pathway Annotations Greatly Improve Pathway Enrichment Analysis of Metabolomics Datasets

Background/ObjectivesMetabolism-level interpretation of metabolomics datasets requires aggregation analyses across metabolites. One highlyused aggregation analysis is pathway enrichment analysis (PEA), which involves detecting pathways enriched with metabolites that are differential between experimental groups. Annotating metabolites with pathway associations is a prerequisite for PEA. While several knowledgebases define pathways and include metabolite-pathway annotations, these definitions are often partially or even grossly incomplete due to limitations in current metabolic knowledge and its curation, which greatly limits the effectiveness of PEA. MethodsIn this work, we used a novel multitask classification, graph convolutional-like neural network to generate high-quality metabolite-pathway annotations for pathways defined across KEGG, MetaCyc, and Reactome. We then included these predicted metabolite-pathway annotations when performing PEA on 990 datasets deposited in Metabolomics Workbench. ResultsWe demonstrate an 8-fold increase in the median number of enriched pathways detected across these datasets compared to using only knowledgebase-derived annotations. ConclusionsThe significant increase in enriched pathways substantially improves the biological and biomedical interpretability of metabolomics datasets.

systems biology↗

bioRxiv2025

PLK1-mediated phosphorylation of PHGDH reprograms serine metabolism in advanced prostate cancer

Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to meet their increased biosynthetic and energetic demands. While cells possess the capacity for de novo serine biosynthesis, most transformed cancer cells heavily depend on exogenous serine uptake to sustain their growth, yet the regulatory mechanisms driving this metabolic dependency remain poorly understood. Here, we uncover a novel mechanism by which Polo-like kinase 1 (PLK1), often overexpressed in prostate cancer, orchestrates a metabolic shift in serine and lipid metabolism through the phosphorylation of phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme of the serine synthesis pathway (SSP). We demonstrate that PLK1 phosphorylates PHGDH at three specific sites (S512, S513, S517), leading to a marked reduction in its protein level and enzymatic activity. This downregulation of SSP forces cancer cells to increase their reliance on exogenous serine uptake via the ASCT2 transporter, which, in turn, fuels the biosynthesis of lipids, including sphingolipids essential for tumor growth and survival. Targeting the SSP, serine uptake, or downstream lipid biosynthetic pathways may offer promising therapeutic avenues in PLK1-high advanced cancers.

cancer biology↗

bioRxiv2022

Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments

We introduce a novel, scan-centric method for characterizing peaks from direct injection multi-scan Fourier transform mass spectra of complex samples that utilizes frequency values derived directly from the spacing of raw m/z points in spectral scans. Our peak characterization method utilizes intensity independent noise removal and normalization of scan-level data to provide a much better fit of relative intensity to natural abundance probabilities for low abundance isotopologues that are not present in all of the acquired scans. Moreover, our method calculates both peak- and scan-specific statistics incorporated within a series of quality control steps that are designed to robustly derive peak centers, intensities and intensity ratios with their scan-level variances. These cross-scan characterized peaks are suitable for use in our previously published peak assignment methodology, Small Molecule Isotope Resolved Formula Enumeration (SMIRFE).

bioinformatics↗

bioRxiv2022

Information-Content-Informed Kendall-tau Correlation: Utilizing Missing Values

BackgroundAlmost all correlation measures currently available are unable to directly handle missing values. Typically, missing values are either ignored completely by removing them or are imputed and used in the calculation of the correlation coefficient. In either case, the correlation value will be impacted based on a perspective that the missing data represents no useful information. However, missing values occur in real data sets for a variety of reasons. In metabolomics data sets, a major reason for missing values is that a specific measurable phenomenon falls below the detection limits of the analytical instrumentation (left-censored values). These missing data are not missing at random, but represent potentially useful information by virtue of their "missingness" at one end of the data distribution. MethodsTo include this information due to left-censorsed missingness, we propose the information-content-informed Kendall-tau (ICI-Kt) methodology. We developed a statistical test and then show that most missing values in metabolomics datasets are the result of left-censorship. Next, we show how left-censored missing values can be included within the definition of the Kendall-tau correlation coefficient, and how that inclusion leads to an interpretation of information being added to the correlation. We also implement calculations for additional measures of theoretical maxima and pairwise completeness that add further layers of information interpretation in the methodology. ResultsUsing both simulated and over 700 experimental data sets from The Metabolomics Workbench, we demonstrate that the ICI-Kt methodology allows for the inclusion of left-censored missing data values as interpretable information, enabling both improved determination of outlier samples and improved feature-feature network construction. ConclusionsWe provide explicitly parallel implementations in both R and Python that allow fast calculations of all the variables used when applying the ICI-Kt methodology on large numbers of samples. The ICI-Kt methods are available as an R package and Python module on GitHub at https://github.com/moseleyBioinformaticsLab/ICIKendallTau and https://github.com/moseleyBioinformaticsLab/icikt, respectively.

bioinformatics↗

bioRxiv2018

Advances in Gene Ontology Utilization Improve Statistical Power of Annotation Enrichment

Gene-annotation enrichment is a common method for utilizing ontology-based annotations in these gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through the ontology through edges in the ontological graph, referred to as relations. However, some relations are semantically problematic with respect to scope, necessitating their omission lest erroneous term mappings occur. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. Here, we demonstrate the improvements in annotation enrichment by re-interpreting edges that would otherwise be omitted by traditional ancestor path-tracing methods.\n\nWe demonstrate that GOcats unique handling of relations improves enrichment over conventional methods in the analysis of two different gene-expression datasets: a breast cancer microarray dataset and several horse cartilage development RNAseq datasets. With the breast cancer microarray dataset, we observed significant improvement (one-sided binomial test p-value=1.86E-25) in 182 of 217 significantly enriched GO terms identified from the conventional path traversal method when GOcats path traversal was used. We also found new significantly enriched terms using GOcats, whose biological relevancy has been experimentally demonstrated elsewhere. Likewise, on the horse RNAseq datasets, we observed a significant improvement in GO term enrichment when using GOcats path traversal: one-sided binomial test p-values range from 1.32E-03 to 2.58E-44.

systems biology↗

bioRxiv2018

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts

Gene Ontology is used extensively in scientific knowledgebases and repositories to organize the wealth of available biological information. However, interpreting annotations derived from differential gene lists is difficult without manually sorting into higher-order categories. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. We tested GOcats performance using subcellular location categories to mine annotations from GO-utilizing knowledgebases and evaluating their accuracy against immunohistochemistry datasets in the Human Protein Atlas (HPA).\n\nIn comparison to mappings generated from UniProts controlled vocabulary and from GO slims via OWLTools Map2Slim, GOcats outperforms these methods without reliance on a human-curated set of GO terms. By identifying and properly defining relations with respect to semantic scope, GOcats can use traditionally problematic relations without encountering erroneous term mapping. We applied GOcats in the comparison of HPA-sourced knowledgebase annotations to experimentally-derived annotations provided by HPA directly. During the comparison, GOcats improved correspondence between the annotation sources by adjusting semantic granularity. Utilized in this way, GOcats can perform an accurate knowledgebase-level evaluation of curated HPA-based annotations.

systems biology↗

bioRxiv2017

Elucidation of dose-dependent transcriptional events immediately following ionizing radiation exposure

Long duration space missions expose astronauts to ionizing radiation events associated with highly energetic and charged heavy particles. Such exposure can result in chromosomal aberrations increasing the likelihood of the development of cancer. Early detection and mitigation of these events is critical in providing positive outcomes. In order to aid in the development of portable devices used to measure radiation exposure, we constructed a genome-wide screen to detect transcriptional changes in peripheral blood lymphocytes shortly after (approximately 1 hour) radiation exposure at low (0.3 Gy), medium (1.5 Gy) and high (3.0 Gy) doses compared to control (0.0 Gy) using Affymetrix(R) Human Gene 1.0 ST v1 microarrays. Our results indicate a number of sensitive and specific transcriptional profiles induced by radiation exposure that can potentially be implemented as biomarkers for radiation exposure as well as dose effect. For overall immediate radiation exposure, KDELC1, MRPS30, RARS, and HEXIM1 were determined to be effective biomarkers while PRDM9, CHST4, and SLC26A10 were determined to be biomarkers specific to 0.3 Gy exposure; RPH, CCDC96, WDYHV1, and IFNA16 were identified for 1.5 Gy exposure; and CWC15, CHCHD7, and DNAAF2 were determined to be sensitive and specific to 3.0 Gy exposure. The resulting raw and analyzed data are publicly available through NCBI's Gene Expression Ominibus via accession GSE64375.

bioinformatics↗

bioRxiv2017

High Peak Density Artifacts in Fourier Transform Mass Spectra and their Effects on Data Analysis

Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolites. Observed spectral features (peaks) that are not isotopologues do not directly correspond to known compounds and cannot be placed into existing metabolic networks. Spectral artifacts account for many of these unidentified peaks, and misassignments made to these artifact peaks can create large interpretative errors. Without accurate identification of artifactual features and correct assignment of real features, discerning their roles within living systems is effectively impossible.\n\nWe have observed three types of artifacts unique to FT-MS that often result in regions of abnormally high peak density (HPD), which we collectively refer to as HPD artifacts: i) fuzzy sites representing small regions of m/z space with a fuzzy appearance due to the extremely high number of peaks present; ii) ringing due to a very intense peak producing side bands of decreasing intensity that are symmetrically distributed around the main peak; and iii) partial ringing where only a subset of the side bands are observed for an intense peak. Fuzzy sites and partial ringing appear to be novel artifacts previously unreported in the literature and we hypothesize that all three artifact types derive from Fourier transformation-based issues. In some spectra, these artifacts account for roughly a third of the peaks present in the given spectrum. We have developed a set of tools to detect these artifacts and approaches to mitigate their effects on downstream analyses.

systems biology↗