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Comparison of Analysis Tools for miRNA High Throughput Sequencing Using Nerve Crush as a Model.

Metpally RP, Nasser S, Malenica I, Courtright A, Carlson E, Ghaffari L, Villa S, Tembe W, Van Keuren-Jensen K - Front Genet (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Collaborative Bioinformatics Center, Translational Genomics Research Institute Phoenix, AZ, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Display of the different numbers of detectable mature miRNAs in each tissue type. (A) miRNAs detected in Gastrocnemius Muscle and Plasma by miRExpress. (B) miRNAs detected in Gastrocnemius Muscle and Plasma by miRKey. (C) miRNAs detected in Gastrocnemius Muscle and Plasma by miRDeep2.
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Figure 3: Display of the different numbers of detectable mature miRNAs in each tissue type. (A) miRNAs detected in Gastrocnemius Muscle and Plasma by miRExpress. (B) miRNAs detected in Gastrocnemius Muscle and Plasma by miRKey. (C) miRNAs detected in Gastrocnemius Muscle and Plasma by miRDeep2.

Mentions: We were also interested in the common overlapping miRNAs and the distinct miRNAs expressed in each sample type we tested, Gastrocnemius muscle and plasma. Those results are displayed in Figure 3. There are ∼100 miRNAs expressed in the muscle that are not detected in the blood and ∼50 distinct miRNAs are identified in the plasma samples.


Comparison of Analysis Tools for miRNA High Throughput Sequencing Using Nerve Crush as a Model.

Metpally RP, Nasser S, Malenica I, Courtright A, Carlson E, Ghaffari L, Villa S, Tembe W, Van Keuren-Jensen K - Front Genet (2013)

Display of the different numbers of detectable mature miRNAs in each tissue type. (A) miRNAs detected in Gastrocnemius Muscle and Plasma by miRExpress. (B) miRNAs detected in Gastrocnemius Muscle and Plasma by miRKey. (C) miRNAs detected in Gastrocnemius Muscle and Plasma by miRDeep2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585423&req=5

Figure 3: Display of the different numbers of detectable mature miRNAs in each tissue type. (A) miRNAs detected in Gastrocnemius Muscle and Plasma by miRExpress. (B) miRNAs detected in Gastrocnemius Muscle and Plasma by miRKey. (C) miRNAs detected in Gastrocnemius Muscle and Plasma by miRDeep2.
Mentions: We were also interested in the common overlapping miRNAs and the distinct miRNAs expressed in each sample type we tested, Gastrocnemius muscle and plasma. Those results are displayed in Figure 3. There are ∼100 miRNAs expressed in the muscle that are not detected in the blood and ∼50 distinct miRNAs are identified in the plasma samples.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Collaborative Bioinformatics Center, Translational Genomics Research Institute Phoenix, AZ, USA.

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.

No MeSH data available.


Related in: MedlinePlus