Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model

Recent advances in sample preparation and analysis for next generation sequencing have made it possible to profile and discover new miRNAs in a high throughput manner. In the case of neurological disease and injury, these types of experiments have been more limited. Possibly because tissues such as...

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Main Authors: Raghu Prasad Rao Metpally, Sara eNasser, Ivana eMalenica, Amanda eCourtright, Elizabeth eCarlson, Layla eGhaffari, Stephen eVilla, Waibhav eTembe, Kendall eVan Keuren-Jensen
Format: Article
Language:English
Published: Frontiers Media S.A. 2013-03-01
Series:Frontiers in Genetics
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00020/full
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author Raghu Prasad Rao Metpally
Sara eNasser
Ivana eMalenica
Amanda eCourtright
Elizabeth eCarlson
Layla eGhaffari
Stephen eVilla
Waibhav eTembe
Kendall eVan Keuren-Jensen
author_facet Raghu Prasad Rao Metpally
Sara eNasser
Ivana eMalenica
Amanda eCourtright
Elizabeth eCarlson
Layla eGhaffari
Stephen eVilla
Waibhav eTembe
Kendall eVan Keuren-Jensen
author_sort Raghu Prasad Rao Metpally
collection DOAJ
description Recent advances in sample preparation and analysis for next generation sequencing have made it possible to profile and discover new miRNAs in a high throughput manner. In the case of neurological disease and injury, these types of experiments have been more limited. Possibly because tissues such as the brain and spinal cord are inaccessible for direct sampling in living patients, and indirect sampling of blood and cerebrospinal fluid are affected by low amounts of RNA. We used a mouse model to examine changes in miRNA expression in response to acute nerve crush. We assayed miRNA from both muscle tissue and blood plasma. We examined how the depth of coverage (the number of mapped reads) changed the number of detectable miRNAs in each sample type. We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain. Each tissue must be assessed independently for the depth of coverage required to adequately power detection of differential expression, weighed against the cost of sequencing that sample to the adequate depth. We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR. We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.
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spelling doaj.art-58ec8b18dbd14accaa9566e89d8e5b682022-12-22T01:27:28ZengFrontiers Media S.A.Frontiers in Genetics1664-80212013-03-01410.3389/fgene.2013.0002039300Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a modelRaghu Prasad Rao Metpally0Sara eNasser1Ivana eMalenica2Amanda eCourtright3Elizabeth eCarlson4Layla eGhaffari5Stephen eVilla6Waibhav eTembe7Kendall eVan Keuren-Jensen8Translational Genomics Research InstituteTranslational Genomics Research InstituteTranslational Genomics Research InstituteTranslational Genomics Research InstituteTranslational Genomics Research InstituteTranslational Genomics Research InstituteUniversity of San FranciscoTranslational Genomics Research InstituteTranslational Genomics Research InstituteRecent advances in sample preparation and analysis for next generation sequencing have made it possible to profile and discover new miRNAs in a high throughput manner. In the case of neurological disease and injury, these types of experiments have been more limited. Possibly because tissues such as the brain and spinal cord are inaccessible for direct sampling in living patients, and indirect sampling of blood and cerebrospinal fluid are affected by low amounts of RNA. We used a mouse model to examine changes in miRNA expression in response to acute nerve crush. We assayed miRNA from both muscle tissue and blood plasma. We examined how the depth of coverage (the number of mapped reads) changed the number of detectable miRNAs in each sample type. We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain. Each tissue must be assessed independently for the depth of coverage required to adequately power detection of differential expression, weighed against the cost of sequencing that sample to the adequate depth. We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR. We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00020/fullmiRNAsmall RNANerve injurynext generation sequencingdifferential expression
spellingShingle Raghu Prasad Rao Metpally
Sara eNasser
Ivana eMalenica
Amanda eCourtright
Elizabeth eCarlson
Layla eGhaffari
Stephen eVilla
Waibhav eTembe
Kendall eVan Keuren-Jensen
Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model
Frontiers in Genetics
miRNA
small RNA
Nerve injury
next generation sequencing
differential expression
title Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model
title_full Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model
title_fullStr Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model
title_full_unstemmed Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model
title_short Comparison of analysis tools for miRNA high throughput sequencing using nerve crush as a model
title_sort comparison of analysis tools for mirna high throughput sequencing using nerve crush as a model
topic miRNA
small RNA
Nerve injury
next generation sequencing
differential expression
url http://journal.frontiersin.org/Journal/10.3389/fgene.2013.00020/full
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