Biology subjects

Baras, A. S.

Publications and source records attributed to Baras, A. S..

2 recordsLinked to original sources

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↗

bioRxiv2017

Towards the human cellular microRNAome

microRNAs are short RNAs that serve as master regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell autonomously and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data has caused considerable confusion and comprehensive cell-level data does not yet exist. Here we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent super-enhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 2,632 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder suggesting Drosha and Dicer-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to deconvolute variable cellular composition of adipose tissue samples highlighting one use of this cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

genomics↗