Enhancing miRNA annotation confidence in miRBase by continuous cross dataset analysis

Thomas Birkballe Hansen; Jørgen Kjems; Jesper B Bramsen

Enhancing miRNA annotation confidence in miRBase by continuous cross dataset analysis

Thomas Birkballe Hansen, Jørgen Kjems, Jesper B Bramsen

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

32 Citations (Scopus)

Abstract

The immaculate annotation of all microRNAs (miRNAs) is a prerequisite to study their biological function on a genome-wide scale. However, the original criteria for proper miRNA annotation seem unsuited for the automated analysis of the immense number of small RNA reads available in next generation sequencing (NGS) datasets. Here we analyze the confidence of past miRNA annotation in miRBase by cross-analyzing publicly available NGS datasets using strengthened annotation requirements. Our analysis highlights that a large number of annotated human miRNAs in miRBase seems to require more experimental validation to be confidently annotated. Notably, our dataset analysis also identified almost 300 currently non-annotated miRNA*s and 28 novel miRNAs. These observations hereby greatly increase the confidence of past miRNA annotation in miRBase but also illustrate the usefulness of continuous re-evaluating NGS datasets in the identification of novel miRNAs.

Original language	English
Journal	RNA Biology
Volume	8
Issue	3
Pages (from-to)	378-383
Number of pages	6
ISSN	1547-6286
Publication status	Published - Jun 2011

Keywords

miRNA
miRNA annotation
miRNA prediction
miRBase
small
RNA sequencing

Cite this

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title = "Enhancing miRNA annotation confidence in miRBase by continuous cross dataset analysis",

abstract = "The immaculate annotation of all microRNAs (miRNAs) is a prerequisite to study their biological function on a genome-wide scale. However, the original criteria for proper miRNA annotation seem unsuited for the automated analysis of the immense number of small RNA reads available in next generation sequencing (NGS) datasets. Here we analyze the confidence of past miRNA annotation in miRBase by cross-analyzing publicly available NGS datasets using strengthened annotation requirements. Our analysis highlights that a large number of annotated human miRNAs in miRBase seems to require more experimental validation to be confidently annotated. Notably, our dataset analysis also identified almost 300 currently non-annotated miRNA*s and 28 novel miRNAs. These observations hereby greatly increase the confidence of past miRNA annotation in miRBase but also illustrate the usefulness of continuous re-evaluating NGS datasets in the identification of novel miRNAs.",

keywords = "miRNA, miRNA annotation, miRNA prediction, miRBase, small, RNA sequencing",

author = "Hansen, {Thomas Birkballe} and J{\o}rgen Kjems and Bramsen, {Jesper B}",

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T1 - Enhancing miRNA annotation confidence in miRBase by continuous cross dataset analysis

AU - Hansen, Thomas Birkballe

AU - Kjems, Jørgen

AU - Bramsen, Jesper B

PY - 2011/6

Y1 - 2011/6

N2 - The immaculate annotation of all microRNAs (miRNAs) is a prerequisite to study their biological function on a genome-wide scale. However, the original criteria for proper miRNA annotation seem unsuited for the automated analysis of the immense number of small RNA reads available in next generation sequencing (NGS) datasets. Here we analyze the confidence of past miRNA annotation in miRBase by cross-analyzing publicly available NGS datasets using strengthened annotation requirements. Our analysis highlights that a large number of annotated human miRNAs in miRBase seems to require more experimental validation to be confidently annotated. Notably, our dataset analysis also identified almost 300 currently non-annotated miRNA*s and 28 novel miRNAs. These observations hereby greatly increase the confidence of past miRNA annotation in miRBase but also illustrate the usefulness of continuous re-evaluating NGS datasets in the identification of novel miRNAs.

AB - The immaculate annotation of all microRNAs (miRNAs) is a prerequisite to study their biological function on a genome-wide scale. However, the original criteria for proper miRNA annotation seem unsuited for the automated analysis of the immense number of small RNA reads available in next generation sequencing (NGS) datasets. Here we analyze the confidence of past miRNA annotation in miRBase by cross-analyzing publicly available NGS datasets using strengthened annotation requirements. Our analysis highlights that a large number of annotated human miRNAs in miRBase seems to require more experimental validation to be confidently annotated. Notably, our dataset analysis also identified almost 300 currently non-annotated miRNA*s and 28 novel miRNAs. These observations hereby greatly increase the confidence of past miRNA annotation in miRBase but also illustrate the usefulness of continuous re-evaluating NGS datasets in the identification of novel miRNAs.

KW - miRNA

KW - miRNA annotation

KW - miRNA prediction

KW - miRBase

KW - small

KW - RNA sequencing

M3 - Journal article

C2 - 21558790

SN - 1547-6286

VL - 8

SP - 378

EP - 383

JO - RNA Biology

JF - RNA Biology

IS - 3

ER -

Enhancing miRNA annotation confidence in miRBase by continuous cross dataset analysis

Abstract

Keywords

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