Abstract

MotivationMicroRNA (miRNA) precursor arms give rise to multiple isoforms simultaneously called ‘isomiRs.’ IsomiRs from the same arm typically differ by a few nucleotides at either their 5′ or 3′ termini or both. In humans, the identities and abundances of isomiRs depend on a person’s sex and genetic ancestry as well as on tissue type, tissue state and disease type/subtype. Moreover, nearly half of the time the most abundant isomiR differs from the miRNA sequence found in public databases. Accurate mining of isomiRs from deep sequencing data is thus important.ResultsWe developed isoMiRmap, a fast, standalone, user-friendly mining tool that identifies and quantifies all isomiRs by directly processing short RNA-seq datasets. IsoMiRmap is a portable ‘plug-and-play’ tool, requires minimal setup, has modest computing and storage requirements, and can process an RNA-seq dataset with 50 million reads in just a few minutes on an average laptop. IsoMiRmap deterministically and exhaustively reports all isomiRs in a given deep sequencing dataset and quantifies them accurately (no double-counting). IsoMiRmap comprehensively reports all miRNA precursor locations from which an isomiR may be transcribed, tags as ‘ambiguous’ isomiRs whose sequences exist both inside and outside of the space of known miRNA sequences and reports the public identifiers of common single-nucleotide polymorphisms and documented somatic mutations that may be present in an isomiR. IsoMiRmap also identifies isomiRs with 3’ non-templated post-transcriptional additions. Compared to similar tools, isoMiRmap is the fastest, reports more bona fide isomiRs, and provides the most comprehensive information related to an isomiR’s transcriptional origin.Availability and implementationThe codes for isoMiRmap are freely available at https://cm.jefferson.edu/isoMiRmap/ and https://github.com/TJU-CMC-Org/isoMiRmap/. IsomiR profiles for the datasets of the 1000 Genomes Project, spanning five population groups, and The Cancer Genome Atlas (TCGA), spanning 33 cancer studies, are also available at https://cm.jefferson.edu/isoMiRmap/.Supplementary informationSupplementary data are available at Bioinformatics online.

Highlights

  • IntroductionMicroRNAs (miRNAs) are among the best studied non-coding RNAs in the last 25 years

  • MicroRNAs are among the best studied non-coding RNAs in the last 25 years

  • When presented with deep-sequenced reads from any short-RNA dataset that have been quality-trimmed and from which the adaptors have been removed, isoMiRmap traverses the reads and accesses the accompanying tables to retrieve the information for each read in turn and generates the output for the user

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Summary

Introduction

MicroRNAs (miRNAs) are among the best studied non-coding RNAs in the last 25 years. According to the model of miRNA biogenesis that prevailed for the first nearly 20 years (Bartel, 2004, 2009), a miRNA precursor is processed to give rise to a single mature miRNA from either its left or its right arm. This sequence has been reported by miRBase (Kozomara and Griffiths-Jones, 2014) as the miRNA ‘product’ of the corresponding precursor miRNA. The sequence of the reference miRNA served as the basis for designing quantification assays and synthetic RNAs to be used in experimental work

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