Computational Prediction of miRNA Genes from Small RNA Sequencing Data

SummarySmall non-coding RNAs, in particular microRNAs, are critical for normal physiology and are candidate biomarkers, regulators, and therapeutic targets for a wide variety of diseases. There is an ever-growing interest in the comprehensive and accurate annotation of microRNAs across diverse cell types, conditions, species, and disease states. Highthroughput sequencing technology has emerged as the method of choice for profiling microRNAs. Specialized bioinformatic strategies are required to mine as much meaningful information as possible from the sequencing data to provide a comprehensive view of the microRNA landscape. Here we present miRquant 2.0, an expanded bioinformatics tool for accurate annotation and quantification of microRNAs and their isoforms (termed isomiRs) from small RNA-sequencing data. We anticipate that miRquant 2.0 will be useful for researchers interested not only in quantifying known microRNAs but also mining the rich well of additional information embedded in small RNA-sequencing data.

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Evidence for human microRNA-offset RNAs in small RNA sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btp419 ◽

2009 ◽

Vol 25 (18) ◽

pp. 2298-2301 ◽

Cited By ~ 86

Author(s):

D. Langenberger ◽

C. Bermudez-Santana ◽

J. Hertel ◽

S. Hoffmann ◽

P. Khaitovich ◽

...

Keyword(s):

Rna Sequencing ◽

Small Rna ◽

Small Rna Sequencing ◽

Sequencing Data

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Mining small RNA sequencing data: a new approach to identify small nucleolar RNAs in Arabidopsis

Nucleic Acids Research ◽

10.1093/nar/gkp225 ◽

2009 ◽

Vol 37 (9) ◽

pp. e69-e69 ◽

Cited By ~ 15

Author(s):

Ho-Ming Chen ◽

Shu-Hsing Wu

Keyword(s):

Rna Sequencing ◽

Small Rna ◽

Small Rna Sequencing ◽

Small Nucleolar Rnas ◽

Sequencing Data ◽

New Approach ◽

Nucleolar Rnas

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In silico genome wide mining of conserved and novel miRNAs in the brain and pineal gland of Danio rerio using small RNA sequencing data

Genomics Data ◽

10.1016/j.gdata.2015.11.013 ◽

2016 ◽

Vol 7 ◽

pp. 46-53 ◽

Cited By ~ 3

Author(s):

Suyash Agarwal ◽

Naresh Sahebrao Nagpure ◽

Prachi Srivastava ◽

Basdeo Kushwaha ◽

Ravindra Kumar ◽

...

Keyword(s):

Pineal Gland ◽

Rna Sequencing ◽

Danio Rerio ◽

Small Rna ◽

In Silico ◽

Small Rna Sequencing ◽

Sequencing Data ◽

Genome Wide ◽

The Brain

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Traces of post-transcriptional RNA modifications in deep sequencing data

Biological Chemistry ◽

10.1515/bc.2011.043 ◽

2011 ◽

Vol 392 (4) ◽

Cited By ~ 44

Author(s):

Sven Findeiß ◽

David Langenberger ◽

Peter F. Stadler ◽

Steve Hoffmann

Keyword(s):

Rna Sequencing ◽

Small Rna ◽

Small Rna Sequencing ◽

Data Sets ◽

Sequencing Data ◽

Rna Modifications ◽

Deep Sequencing Data ◽

Complex Processes ◽

Mature Micrornas ◽

Rna Maturation

Abstract Many aspects of the RNA maturation leave traces in RNA sequencing data in the form of deviations from the reference genomic DNA. This includes, in particular, genomically non-encoded nucleotides and chemical modifications. The latter leave their signatures in the form of mismatches and conspicuous patterns of sequencing reads. Modified mapping procedures focusing on particular types of deviations can help to unravel post-transcriptional modification, maturation and degradation processes. Here, we focus on small RNA sequencing data that is produced in large quantities aimed at the analysis of microRNA expression. Starting from the recovery of many well known modified sites in tRNAs, we provide evidence that modified nucleotides are a pervasive phenomenon in these data sets. Regarding non-encoded nucleotides we concentrate on CCA tails, which surprisingly can be found in a diverse collection of transcripts including sub-populations of mature microRNAs. Although small RNA sequencing libraries alone are insufficient to obtain a complete picture, they can inform on many aspects of the complex processes of RNA maturation.

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Comprehensive processing of high-throughput small RNA sequencing data including quality checking, normalization, and differential expression analysis using the UEA sRNA Workbench

RNA ◽

10.1261/rna.059360.116 ◽

2017 ◽

Vol 23 (6) ◽

pp. 823-835 ◽

Cited By ~ 21

Author(s):

Matthew Beckers ◽

Irina Mohorianu ◽

Matthew Stocks ◽

Christopher Applegate ◽

Tamas Dalmay ◽

...

Keyword(s):

Differential Expression ◽

Rna Sequencing ◽

Expression Analysis ◽

High Throughput ◽

Small Rna ◽

Differential Expression Analysis ◽

Small Rna Sequencing ◽

Sequencing Data ◽

Comprehensive Processing

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Paired-end small RNA sequencing reveals a possible overestimation in the isomiR sequence repertoire previously reported from conventional single read data analysis

10.21203/rs.3.rs-351479/v1 ◽

2021 ◽

Author(s):

Jose Francisco Sanchez-Herrero ◽

Raquel Pluvinet ◽

Antonio Luna-de Haro ◽

Lauro Sumoy

Keyword(s):

Data Analysis ◽

Rna Sequencing ◽

Small Rna ◽

Small Rna Sequencing ◽

Sequencing Data ◽

Technical Noise ◽

Sequencing Errors ◽

Abundance And Diversity ◽

Template Dna ◽

Generation Sequencing

Abstract Background Next generation sequencing has allowed the discovery of miRNA isoforms, termed isomirs. Some isomirs are derived from imprecise processing of pre-miRNA precursors, leading to length variants. Additional variability is introduced by non-templated addition of bases at the ends or editing of internal bases, resulting in base differences relative to the template DNA sequence. We hypothesized that some component of the isomir variation reported so far could be due to systematic technical noise and not real. Results We have developed the XICRA pipeline to analyze small RNA sequencing data at the isomir level. We exploited its ability to use single or merged reads to compare isomir results derived from paired-end (PE) reads with those from single reads (SR) to address whether detectable sequence differences relative to canonical miRNAs found in isomirs are true biological variations or the result of errors in sequencing. We have detected non-negligible systematic differences between SR and PE data which primarily affect putative internally edited isomirs, and at a much smaller frequency terminal length changing isomirs. This is relevant for the identification of true isomirs in small RNA sequencing datasets. Conclusions We conclude that potential artifacts derived from sequencing errors and/or data processing could result in an overestimation of abundance and diversity of miRNA isoforms. Efforts in annotating the isomirnome should take this into account.

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