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Comparison of Analysis Tools for miRNA High Throughput Sequencing Using Nerve Crush as a Model.
Metpally, Raghu Prasad Rao; Nasser, Sara; Malenica, Ivana; Courtright, Amanda; Carlson, Elizabeth; Ghaffari, Layla; Villa, Stephen; Tembe, Waibhav; Van Keuren-Jensen, Kendall.
Afiliação
  • Metpally RP; Collaborative Bioinformatics Center, Translational Genomics Research Institute Phoenix, AZ, USA.
Front Genet ; 4: 20, 2013.
Article em En | MEDLINE | ID: mdl-23459507
ABSTRACT
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2013 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2013 Tipo de documento: Article