Marc K. Halushka

This study identifies blood-based biomarkers that predict early tuberculosis culture conversion by analyzing mRNA and small RNA changes during treatment.

The paper identifies stage-specific miRNAs involved in human oligodendrocyte precursor cell differentiation and maturation through miRNA profiling of hESC-derived cells.

The paper benchmarks normalization and differential expression methods for miRNA-seq using a realistic dataset, finding RPM normalization and edgeR perform best.

PARiS identifies and removes technical isomiR errors in sRNA-seq data, improving miRNA-level differential expression analysis.

This study uses spatial transcriptomics to show that myocarditis involves widespread molecular changes in cardiomyocytes, not just immune cell areas.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

This study introduces NBSR, a method for differential expression analysis of miRNA-seq data that outperforms existing approaches on simulated and experimental datasets.

The paper introduces Brooklyn plots to detect co-expression dysregulation in scRNA-seq data, identifying altered chromatin patterns in dilated cardiomyopathy.

This study identifies biological and technical sources of expression variation across 49 human tissues using GTEx data, revealing 522 variable transcript clusters with varying confidence in their explanations.

The paper optimizes an expression microdissection method to improve miRNA yield and in vivo cell-type specificity for qPCR analysis.

The paper performs deconvolution on GTEx coronary artery data using CIBERSORT to assess challenges and limitations in estimating tissue composition from gene expression data.

The paper curates a comprehensive reference of miRNA expression across 196 human primary cell types using public datasets and provides access through a UCSC Genome Browser track and R/Bioconductor package.

The paper evaluates newly sequenced T2T genomic regions for paralogous miRNA loci and novel miRNAs, finding no bona fide miRNAs and only questionable ones.

miRge3.0 improves upon previous versions by adding UMI processing, faster speed, and better integration with other tools for small RNA-seq analysis.

The paper analyzes matrisome gene expression patterns across tissues, age, sex, and disease using GTEx and TCGA data, finding that transcriptomics can recapitulate proteomic patterns and identify tissue-specific and sex/age-related expression variations.

The paper integrates proteomic and genomic data from skeletal muscle myocytes to identify gene and protein expression differences between slow and fast-twitch fiber types.

The paper identifies basal contamination in GTEx data, showing that highly expressed tissue-specific genes contaminate samples, affecting eQTL assignments and analysis accuracy.

This study examines how tissue composition affects gene co-expression estimates and shows how deconvolution methods can improve co-expression analysis.

The paper introduces mirGFF3, a standardized format for miRNA/isomiR analysis, and mirtop, a tool to convert outputs into this format, enabling data sharing and comparative analysis.

This study evaluates the genetic architecture of sudden cardiac arrest through GWAS and Mendelian randomization, finding genetic correlations with CAD, height, BMI, and electrical instability traits.

The paper introduces xMD-miRNA-seq to obtain in vivo miRNA expression from colon epithelial cells using FFPE tissues, showing cell-specific miRNA profiles comparable to other epithelial sources.

The paper shows miR-21-5p and miR-30a-5p are identical between human and bovine, and their presence in human blood after milk ingestion is likely due to endogenous regulation or technical issues, not xenomiR uptake.

miRge 2.0 improves miRNA analysis with enhanced detection, A-to-I editing analysis, and better alignment, outperforming existing tools on human datasets.

The paper establishes the landscape of human cell-specific microRNA expression using small RNA-seq data from diverse cell types and tissues, identifying novel microRNA loci and patterns correlated with super-enhancer activity.