MiRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data

David Hendrix; Michael Levine; Weiyang Shi

doi:10.1186/gb-2010-11-4-r39

MiRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data

David Hendrix, Michael Levine, Weiyang Shi

Research output: Contribution to journal › Article › peer-review

84 Scopus citations

Abstract

MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci. This program was applied to the simple chordate Ciona intestinalis and identified nearly 400 putative miR loci.

Original language	English (US)
Article number	r39
Journal	Genome biology
Volume	11
Issue number	4
DOIs	https://doi.org/10.1186/gb-2010-11-4-r39
State	Published - Apr 6 2010
Externally published	Yes

All Science Journal Classification (ASJC) codes

Genetics
Ecology, Evolution, Behavior and Systematics
Cell Biology

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10.1186/gb-2010-11-4-r39

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title = "MiRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data",

abstract = "MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci. This program was applied to the simple chordate Ciona intestinalis and identified nearly 400 putative miR loci.",

author = "David Hendrix and Michael Levine and Weiyang Shi",

note = "Funding Information: We thank Benjamin Haley for sharing his extensive knowledge of microRNAs and help with interpretations. Additionally, we thank Daniel S Rokhsar and Shu Shenqiang for thoughtful discussions and help performing the alignment of reads. This work was supported by NIH grant 5R24GM075049-04 to ML.",

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doi = "10.1186/gb-2010-11-4-r39",

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AU - Hendrix, David

AU - Levine, Michael

AU - Shi, Weiyang

N1 - Funding Information: We thank Benjamin Haley for sharing his extensive knowledge of microRNAs and help with interpretations. Additionally, we thank Daniel S Rokhsar and Shu Shenqiang for thoughtful discussions and help performing the alignment of reads. This work was supported by NIH grant 5R24GM075049-04 to ML.

PY - 2010/4/6

Y1 - 2010/4/6

N2 - MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci. This program was applied to the simple chordate Ciona intestinalis and identified nearly 400 putative miR loci.

AB - MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci. This program was applied to the simple chordate Ciona intestinalis and identified nearly 400 putative miR loci.

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MiRTRAP, a computational method for the systematic identification of miRNAs from high throughput sequencing data

Abstract

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