Biology subjects

Halushka, M. K.

Publications and source records attributed to Halushka, M. K..

24 records · Page 2Linked to original sources

bioRxiv2018

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API

BackgroundMicroRNAs (miRNAs) are small RNA molecules ([~]22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, a lack of consensus on miRNA/isomiR analyses exists, and the resulting diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods.\n\nFindingsTo overcome this situation, we present here a community-based project, miRTOP (miRNA Transcriptomic Open Project) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream analysis tools that are compatible with any existing detection and quantification tool. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, miRge2.0, isomiR-SEA, sRNAbench, and Prost!, as well as BAM files. Its open architecture enables any tool or pipeline to output results in mirGFF3.\n\nConclusionsCollectively a comprehensive isomiR categorization system, along with the accompanying mirGFF3 and mirtop API provide a complete solution for the standardization of miRNA and isomiR analysis, enabling data sharing, reporting, comparative analyses, and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps to miRNA detection, annotation, and quantification.

bioinformatics↗

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

xMD-miRNA-seq to generate near in vivo miRNA expression estimates in colon epithelial cells

Accurate, RNA-seq based, microRNA (miRNA) expression estimates from primary cells have recently been described. However, this in vitro data is mainly obtained from cell culture, which is known to alter cell maturity/differentiation status, significantly changing miRNA levels. What is needed is a robust method to obtain in vivo miRNA expression values directly from cells. We introduce expression microdissection miRNA small RNA sequencing (xMD-miRNA-seq), a method to isolate cells directly from formalin fixed paraffin-embedded (FFPE) tissues. xMD-miRNA-seq is a low-cost, high-throughput, immunohistochemistry-based method to capture any cell type of interest. As a proof-of-concept, we isolated colon epithelial cells from two specimens and performed low-input small RNA-seq. We generated up to 600,000 miRNA reads from the samples. Isolated epithelial cells, had abundant epithelial-enriched miRNA expression (miR-192; miR-194; miR-200b; miR-200c; miR-215; miR-375) and overall similar miRNA expression patterns to other epithelial cell populations (colonic enteroids and flow-isolated colon epithelium). xMD-derived epithelial cells were generally not contaminated by other adjacent cells of the colon as noted by t-SNE analysis. xMD-miRNA-seq allows for simple, economical, and efficient identification of cell-specific miRNA expression estimates. Further development will enhance rapid identification of cell-specific miRNA expression estimates in health and disease for nearly any cell type using archival FFPE material.

molecular biology↗

bioRxiv2018

miR-21-5p and miR-30a-5p are identical in human and bovine, have similar isomiR distribution, and cannot be used to identify xenomiR uptake from cow milk

microRNAs (miRNAs) are often highly conserved across species, but species-specific sequences are known. In addition, miRNA \"isomiRs\" arise from the same precursor molecule but differ in post-processing length and modification, usually at the 3 end. A recently published feeding study reported the intriguing result that two bovine milk-specific miRNAs were taken up into human circulation after ingestion of bovine milk. Unfortunately, this interpretation is based on annotation errors in a public microRNA database. Reanalysis using databses including the MirGeneDB database reveals that the miRNAs in question, miR-21-5p and miR-30a-5p, arise from 100% identical 5 precursor sequences in human and bovine, and the putative bovine-specific isomiRs appear to be depleted, not enriched, in bovine milk. Thus, enrichment of these isomiRs in human blood is inconsistent with uptake of xenomiRs and likely betrays endogenous miRNA regulation in response to diet or technical artifact.

molecular biology↗

bioRxiv2018

Cell-type specific expression of oncogenic and tumor suppressive microRNAs in the human prostate and prostate cancer

MiR-1 and miR-143 are frequently reduced in human prostate cancer (PCa), while miR-141 and miR-21 are frequently elevated. Consequently, these miRNAs have been studied as cell-autonomous tumor suppressors and oncogenes. However, the cell-type specificity of these miRNAs is not well defined in prostate tissue. Through two different microdissection techniques, and droplet digital RT-PCR, we quantified these miRNAs in the stroma and epithelium of radical prostatectomy specimens. In contrast to their purported roles as cell-autonomous tumor suppressors, we found miR-1 and miR-143 expression to be predominantly stromal. Conversely, miR-141 was predominantly epithelial. MiR-21 was detected in both stroma and epithelium. Strikingly, the levels of miR-1 and miR-143 were significantly reduced in tumor-associated stroma, but not tumor epithelium. Gene expression analyses in human cell lines, tissues, and prostate-derived stromal cultures support the cell-type selective expression of miR-1, miR-141, and miR-143. Analyses of the PCa Genome Atlas (TCGA-PRAD) showed a strong positive correlation between stromal markers and miR-1 and miR-143, and a strong negative correlation between stromal markers and miR-141. In these tumors, loss of miR-1 and gain of miR-21 was highly associated with biochemical recurrence. These data shed new light on stromal and epithelial miRNA expression in the PCa tumor microenvironment.

cancer biology↗

bioRxiv2018

miRge 2.0: An updated tool to comprehensively analyze microRNA sequencing data

miRNAs play important roles in the regulation of gene expression. The rapidly developing field of microRNA sequencing (miRNA-seq; small RNA-seq) needs comprehensive bioinformatics tools to analyze these large datasets. We present the second iteration of miRge, miRge 2.0, with multiple enhancements. miRge 2.0 adds new functionality including novel miRNA detection, A-to-I editing analysis, better output files, and improved alignment to miRNAs. Our novel miRNA detection method is the first to use both miRNA hairpin sequence structure and composition of isomiRs resulting in a more specific capture of potential miRNAs. Using known miRNA data, our support vector machine (SVM) model predicted miRNAs with an average Matthews correlation coefficient (MCC) of 0.939 over 32 human cell datasets and outperformed miRDeep2 and miRAnalyzer regarding phylogenetic conservation. The A-to-I editing analysis implementation strongly correlated with a reference datasets prior analysis with adjusted R2 = 0.96. miRge 2.0 comes with alignment libraries to both miRBase v21 and MirGeneDB for 6 species: human, mouse, rat, fruit fly, nematode and zebrafish; and has a tool to create custom libraries. With the redevelopment of the tool in Python, it is now incorporated into bcbio-nextgen and implementable through Bioconda. miRge 2.0 is freely available at: https://github.com/mhalushka/miRge.

bioinformatics↗