Biology subjects

McCall, M. N.

Publications and source records attributed to McCall, M. N..

At least 19 recordsLinked to original sources

bioRxiv2026

An assessment of normalization and differential expression methods for miRNA-seq analysis using a realistic benchmark dataset

MicroRNAs are short noncoding RNAs that regulate gene expression and are commonly profiled by small RNA sequencing (miRNA-seq). Despite the widespread use of miRNA-seq, datasets are often analyzed with RNA-seq method such as DESeq2 or edgeR, which do not take into account the specific characteristics of miRNA-seq data. Here, we present a benchmark study of normalization and differential expression approaches using a realistic ground-truth dataset. By mixing mouse RNA of two organs, we generated expression trends while capturing biological and technical variability. Using monotonicity across the dataset and expected fold changes from the mixture design, we assessed normalization and differential expression methods. Normalization benchmarking showed that within-sample scaling, particularly Read Per Million (RPM), best preserved the expected monotonic trends, outperforming cross-sample methods such as TMM, rlog, and VST. These approaches sometimes recovered apparent monotonicity among abundant miRNAs, but inspection of individual profiles suggested likely over-correction. Regarding differential expression, edgeR consistently ranked among the best-performing methods across several metrics, including log2 fold-change estimation, with performance comparable to miRNA-seq-specific tools such as miRglmm and NBSR. DESeq2, edgeR-v4, and limma-based approaches tended to systematically underestimate log2 fold changes. Applying a common RPM-based normalization substantially improved the performance of cross-sample methods, highlighting the strong influence of normalization on differential expression analysis. Overall, our findings support within-sample scaling methods such as RPM for normalization, and edgeR, miRglmm, or NBSR for differential expression. The dataset has been made publicly available, providing a valuable resource for objective method comparison and future miRNA-seq software development.

bioinformatics↗

bioRxiv2026

PARiS: Probabilistic Assignment and Repartitioning of isomiR Sequences: A data-driven method for denoising isomiR read count data

MicroRNAs (miRNAs) are non-coding RNAs, approximately 18-24 nucleotides in length, with important gene regulatory functions. In small RNA sequencing (sRNA-seq), observed isoforms of miRNA, called isomiRs, arise from my biological and technical processes. Alterations in isomiR expression has been linked to a wide variety of human diseases, from cancers to neurological diseases. However, it is difficult to distinguish between technical and biological isomiRs. We present PARiS, an algorithm for the Probabilistic Assignment and Repartitioning of isomiR Sequences, that identifies technical error isomiRs in sRNA-seq data and reassigns them to their most likely biological source. We assess the ability of PARiS to identify and remove error isomiR sequences in a realistic simulation study. Additionally, we compare PARiS to alternative approaches, focusing on downstream miRNA-level differential expression analysis in a variety of settings, including a set of simulated datasets, an experimental benchmark dataset, and three colorectal adenocarcinoma cell lines.

bioinformatics↗

bioRxiv2024

miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA level differential expression and uncovers variable differential expression between isomiRs

MicroRNA-seq data is produced by aligning small RNA sequencing reads of different miRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression (DE) methods developed for mRNA-seq data. We establish miRglmm, a DE method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current DE methods in estimating DE for miRNA, whether or not there is significant isomiR variability, and simultaneously provides estimates of isomiR-level DE.

bioinformatics↗

bioRxiv2023

Patterns of unwanted biological and technical expression variation across 49 human tissues

All tissue-based gene expression studies are impacted by biological and technical sources of variation. Numerous methods are used to normalize and batch correct these datasets. A more accurate understanding of all causes of variation could further optimize these approaches. We used 17,282 samples from 49 tissues in the Genotype Tissue Expression (GTEx) dataset (v8) to investigate patterns and causes of expression variation. Transcript expression was normalized to Z-scores and only the most variable 2% of transcripts were evaluated and clustered based on co-expression patterns. Clustered gene sets were solved to different biological or technical causes related to metadata elements and histologic images. We identified 522 variable transcript clusters (median 11 per tissue) across the samples. Of these, 64% were confidently explained, 15% were likely explained, 7% were low confidence explanations and 14% had no clear cause. Common causes included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), muscle atrophy, diabetes status, and menopause. Technical causes included brain pH and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens dataset of single cell expression. This is the largest exploration of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression. These identified sources of variation will inform which metadata to acquire with tissue harvesting and can be used to improve normalization, batch correction, and analysis of both bulk and single cell RNA-seq data.

bioinformatics↗

bioRxiv2023

A Model-Based Hierarchical Bayesian Approach to Sholl Analysis

SO_SCPLOWUMMARYC_SCPLOWDue to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still the gold standard for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. To fill this longstanding gap, we introduce a fully parametric model-based approach for analyzing Sholl data. We generalize our model to a hierarchical Bayesian framework so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to three real data examples and perform simulation studies comparing the proposed method with a popular alternative.

neuroscience↗

bioRxiv2022

Considerations for Deconvolution: A Case Study with GTEx Coronary Artery Tissues

Differential expression analyses are ubiquitous in the realm of statistical genomics, used to estimate functional differences between genomes of groups of subjects. However, differences in tissue composition between groups may contribute to changes in gene expression, potentially obscuring the detection of functionally significant genes of interest. Deconvolution techniques allow researchers to estimate the abundance of each cell type assumed to be in a tissue. While deconvolution is a useful tool to estimate composition, several crucial considerations must be made when setting up and employing such a workflow in an analysis. We perform a deconvolution on GTEx coronary artery data using CIBERSORT and discuss the challenges and limitations in order to highlight future areas of improvement in the deconvolution framework.

bioinformatics↗

bioRxiv2022

A curated human cellular microRNAome based on 196 primary cell types

BackgroundAn incomplete picture of the expression distribution of microRNAs (miRNAs) across human cell types has long hindered our understanding of this important regulatory class of RNA. With the continued increase in available public small RNA sequencing datasets, there is an opportunity to more fully understand the general distribution of miRNAs at the cell level. ResultsFrom the NCBI Sequence Read Archive, we obtained 6,054 human primary cell datasets and processed 4,184 of them through the miRge3.0 small RNA-seq alignment software. This dataset was curated down, through shared miRNA expression patterns, to 2,077 samples from 196 unique cell types derived from 175 separate studies. Of 2,731 putative miRNAs listed in miRBase (v22.1), 2,452 (89.8%) were detected. Among reasonably expressed miRNAs, 108 were designated as cell specific/near specific, 59 as infrequent, 52 as frequent, 54 as near ubiquitous and 50 as ubiquitous. The complexity of cellular microRNA expression estimates recapitulates tissue expression patterns and informs on the miRNA composition of plasma. ConclusionsThis study represents the most complete reference, to date, of miRNA expression patterns by primary cell type. The data is available through the human cellular microRNAome track at the UCSC Genome Browser (https://genome.ucsc.edu/cgi-bin/hgHubConnect) and an R/Bioconductor package (https://bioconductor.org/packages/microRNAome/).

genomics↗

bioRxiv2021

PCRedux: A Data Mining and Machine Learning Toolkit for qPCR Experiments

MotivationQuantitative Real-time PCR (qPCR) is a widely used -omics method for the precise quantification of nucleic acids, in which the result is associated with the presence/absence or quantity of a specific nucleic acid sequence. As the amount of qPCR data increases worldwide, the manual assessment of results becomes challenging and difficult to reproduce. To overcome this, some automatable characteristics of amplification curves have been described in the literature, often with an appropriate "rule of thumb". ResultsWe developed PCRedux to analyze and calculate 90 numerical qPCR amplification curve descriptors ( features") from large datasets of qPCR amplification curves that are aimed for interpretable machine learning and development of decision support systems. In a case study of a diverse dataset with 3181 positive, negative and ambiguous amplification curves, as assessed by three human raters, we demonstrate a sensitivity >99 % and specificity >97 % in detecting positive and negative amplification. PCRedux is unique as it goes beyond traditional qPCR analysis to capture curvature properties that improve the characterization and classification of amplification curves. The calculation of the features is reproducible and objective, since R is used as a controllable working environment. PCRedux is not a black box, but open source software following on the principle of mathematically interpretable features. These can be combined with user-defined labels for automatic multi-category classification and regression in machine learning. Availabilityhttps://cran.r-project.org/package=PCRedux. Web server: http://shtest.evrogen.net/PCRedux-app/. Documentation: https://PCRuniversum.github.io/PCRedux/.

bioinformatics↗

bioRxiv2021

Sex, age, tissue, and disease patterns of matrisome expression in GTEx transcriptome data

The extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally recapitulated proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 388 genes varied by age and 222 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis matrix changes. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.

molecular biology↗

bioRxiv2020

A systems genomics approach uncovers molecular associates of RSV severity

Globally, RSV infection results in millions of hospitalizations and thousands of deaths each year. Specific factors that contribute to disease severity, such as premature birth and certain comorbidities, are well established. Additionally, genetic variants resulting in alterations in the adaptive and innate immune response appear to be associated with RSV severity. While previous studies focused on a single aspect of the disease, we jointly modeled the association of disparate data modalities with RSV severity. To investigate the host response to respiratory syncytial virus (RSV) infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. Specifically, we developed and employed a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation. Our novel approach enabled us to identify cell type specific and shared cellular pathways associated with RSV severity. Of particular interest was the association between RSV severity, activation of pathways controlling Th17 and acute phase response signaling, and inhibition of B cell receptor signaling, which were present in both airway and immune cells. These data identify specific aspects of dysregulation between the humoral and mucosal response to RSV that may play a critical role in determining illness severity.

genomics↗

bioRxiv2020

Gene network modeling via TopNet reveals robust epistatic interactions between functionally diverse tumor critical mediator genes

Malignant cell transformation and the underlying genomic scale reprogramming of gene expression require cooperation of multiple oncogenic mutations. Notably, this cooperation is reflected in the synergistic regulation of downstream genes, so-called cooperation response genes (CRGs). CRGs impact diverse hallmark features of cancer cells and are not known to be functionally connected. Yet, they act as critical mediators of the cancer phenotype at an unexpectedly high frequency of >50%, as indicated by genetic perturbations. Here we demonstrate that CRGs function within a network of strong genetic interdependencies that are critical to the robustness of the malignant state. Our approach, termed TopNet, utilizes attractor-based ternary network modeling that takes the novel approach of incorporating uncertainty in the underlying gene perturbation data and is capable of identifying non-linear gene interactions. TopNet reveals topological gene network architecture that effectively predicts previously unknown, functionally relevant epistatic gene interactions, and thus, among a broad range of applications, has utility for identification of non-mutant targets for cancer intervention.

cancer biology↗

bioRxiv2020

Proteogenomic single cell analysis of skeletal muscle myocytes

Skeletal muscle myocytes have evolved into slow and fast-twitch types. These types are functionally distinct as a result of differential gene and protein expression. However, an understanding of the complexity of gene and protein variation between myofibers is unknown. We performed deep, whole cell, single cell RNA-seq on intact and fragments of skeletal myocytes from the mouse flexor digitorum brevis muscle. We compared the genomic expression data of 171 of these cells with two human proteomic datasets. The first was a spatial proteomics survey of mosaic patterns of protein expression utilizing the Human Protein Atlas (HPA) and the HPASubC tool. The second was a mass-spectrometry (MS) derived proteomic dataset of single human muscle fibers. Immunohistochemistry and RNA-ISH were used to understand variable expression. scRNA-seq identified three distinct clusters of myocytes (a slow/fast 2A cluster and two fast 2X clusters). Utilizing 1,605 mosaic patterned proteins from visual proteomics, and 596 differentially expressed proteins by MS methods, we explore this fast 2X division. Only 36 genes/proteins had variable expression across all three studies, of which nine are newly described as variable between fast/slow twitch myofibers. An additional 414 genes/proteins were identified as variable by two methods. Immunohistochemistry and RNA-ISH generally validated variable expression across methods presumably due to species-related differences. In this first integrated proteogenomic analysis of mature skeletal muscle myocytes we confirm the main fiber types and greatly expand the known repertoire of twitch-type specific genes/proteins. We also demonstrate the importance of integrating genomic and proteomic datasets.

genomics↗

bioRxiv2019

Auto-regressive modeling and diagnostics for qPCR amplification

Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly-used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of qPCR fluorescence measurements. We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals.

bioinformatics↗

bioRxiv2019

Insufficiency in airway interferon activation defines clinical severity to infant RSV infection

Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. Other than age at the time of infection, the causes and correlates of severe illness in infants lacking known risk factors are poorly defined. We recruited a cohort of confirmed RSV-infected infants and simultaneously assayed the presence of resident airway microbiota and the molecular status of their airways using a novel method. Rigorous statistical analyses identified a molecular airway gene expression signature of severe illness dominated by excessive chemokine expression. Global 16S rRNA sequencing confirmed an association between H. influenzae and clinical severity. Interestingly, adjusting for H. influenzae in our gene expression analysis revealed an association between severity and airway lymphocyte accumulation. Exploring the relationship between airway gene expression and the time of onset of clinical symptoms revealed a robust, acute activation of interferon (IFN) signaling, which was absent in subjects with severe illness. Finally, we explored the relationship between IFN activity, airway gene expression and productive RSV infection using a novel in vitro model of bona fide pediatric human airway epithelial cells. Interestingly, blocking IFN signaling, but not IFN ligand production, in these cells leads to increased viral infection. Our data reveal that acute airway interferon responses are physiologically relevant in the context of infant RSV infection and may be a target for therapeutic intervention. Additionally, the airway gene expression signature we define may be useful as a biomarker for efficacy of intervention responses.

genomics↗

bioRxiv2019

Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants

BackgroundA substantial number of infants infected with RSV develop severe symptoms requiring hospitalization. We currently lack accurate biomarkers that are associated with severe illness. MethodWe defined airway gene expression profiles based on RNA sequencing from nasal brush samples from 106 full-tem previously healthy RSV infected subjects during acute infection (day 1-10 of illness) and convalescence stage (day 28 of illness). All subjects were assigned a clinical illness severity score (GRSS). Using AIC-based model selection, we built a sparse linear correlate of GRSS based on 41 genes (NGSS1). We also built an alternate model based upon 13 genes associated with severe infection acutely but displaying stable expression over time (NGSS2). ResultsNGSS1 is strongly correlated with the disease severity, demonstrating a naive correlation ({rho}) of {rho}=0.935 and cross-validated correlation of 0.813. As a binary classifier (mild versus severe), NGSS1 correctly classifies disease severity in 89.6% of the subjects following cross-validation. NGSS2 has slightly less, but comparable, accuracy with a cross-validated correlation of 0.741 and classification accuracy of 84.0%. ConclusionAirway gene expression patterns, obtained following a minimally-invasive procedure, have potential utility for development of clinically useful biomarkers that correlate with disease severity in primary RSV infection.

immunology↗

bioRxiv2019

Basal Contamination of Bulk Sequencing: Lessons from the GTEx dataset

One of the challenges of next generation sequencing (NGS) is read contamination. We used the Genotype-Tissue Expression (GTEx) project, a large, diverse, and robustly generated dataset, to understand the factors that contribute to contamination. We obtained GTEx datasets and technical metadata and validating RNA-Seq from other studies. Of 48 analyzed tissues in GTEx, 26 had variant co-expression clusters of four known highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicated contamination. Sample contamination by non-native genes was associated with a sample being sequenced on the same day as a tissue that natively expressed those genes. This was highly significant for pancreas and esophagus genes (linear model, p=9.5e-237 and p=5e-260 respectively). Nine SNPs in four genes shown to contaminate non-native tissues demonstrated allelic differences between DNA-based genotypes and contaminated sample RNA-based genotypes, validating the contamination. Low-level contamination affected 4,497 (39.6%) samples (defined as 10 PRSS1 TPM). It also led [≥] to eQTL assignments in inappropriate tissues among these 18 genes. We note this type of contamination occurs widely, impacting bulk and single cell data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses. Awareness of this process is necessary to avoid assigning inaccurate importance to low-level gene expression in inappropriate tissues and cells.

genomics↗

bioRxiv2018

The effect of tissue composition on gene co-expression

Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell type specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. Finally, we provide a Shiny application for users to interactively explore the effect of cell type composition on correlation-based co-expression estimation for any cell types of interest.

bioinformatics↗

bioRxiv2018

Cardioprotection by the Mitochondrial Unfolded Protein Response is Mediated by ATF5

The mitochondrial unfolded protein response (UPRmt)1 is a cytoprotective signaling pathway triggered by mitochondrial dysfunction. Activation of the UPRmt upregulates nuclear-encoded mitochondrial genes, including those for chaperones, proteases, and antioxidants, as well as glycolysis, to restore proteostasis and cell energetics. Activating transcription factor 5 (ATF5), a protein with both mitochondrial and nuclear targeting sequences, is proposed to mediate mammalian UPRmt signaling. Since proteostasis and bioenergetics are important in the response of organs such as the heart to injury, we hypothesized that pharmacologic UPRmt activation may be cardioprotective against ischemia-reperfusion (IR) injury and that such protection would require ATF5. Using a perfused heart IR injury model in wild-type and global Atf5-/- mice, we found that in-vivo administration of the UPRmt inducers oligomycin or doxycycline 6 h prior to ex-vivo IR injury was cardioprotective. Such protection was absent in hearts from Atf5-/- mice, and no protection was observed with acute ex-vivo cardiac administration of doxycycline. Loss of ATF5 also did not alter baseline IR injury (without UPRmt induction). Cardiac gene expression analysis by RNA-Seq revealed mild induction of numerous genes in an ATF5-dependent manner, which may be important for cardioprotection. Analysis of hearts by qPCR showed that oligomycin at 6 h significantly induced genes encoding ATF5 and several known UPRmt-linked proteins. We conclude that ATF5 is required for cardioprotection induced by drugs that activate the UPRmt.

cell biology↗