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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

New miRNA detection rate. (A) As a million reads at a time are added to the sequencing depth of the Gastrocnemius Muscle, the number of newly detectable miRNAs is reduced. Displayed are the number of newly detected miRNAs with at least 1, 3, 5, 10, or 50 reads. (B) As a million reads at a time are added to the sequencing depth of the Plasma, the number of newly detectable miRNAs is reduced. Displayed are the number of newly detected miRNAs with at least 1, 3, 5, 10, or 50 reads.
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Figure 5: New miRNA detection rate. (A) As a million reads at a time are added to the sequencing depth of the Gastrocnemius Muscle, the number of newly detectable miRNAs is reduced. Displayed are the number of newly detected miRNAs with at least 1, 3, 5, 10, or 50 reads. (B) As a million reads at a time are added to the sequencing depth of the Plasma, the number of newly detectable miRNAs is reduced. Displayed are the number of newly detected miRNAs with at least 1, 3, 5, 10, or 50 reads.

Mentions: We began with 500,000 randomly chosen post-clipped reads and mapped them to miRBase using miRDeep2. We display the number of detected miRNAs and their corresponding coverage: 1 mapped read only, at least 3 mapped reads, at least 5 mapped reads, 10 mapped reads, and 50 mapped reads. We increased the number of post-clipped reads going into the alignment incrementally and calculated the number of new miRNAs that were detected with each addition of reads (Figure 5). Not surprisingly, we found that the more we increased the input of reads, the more miRNAs we slowly included. The number of additional miRNAs detected vs. the increased number of reads required has to be weighed for each experiment. At the start of any experiment, we recommend sequencing representative control samples for each tissue type to a significant depth to examine the number of miRNAs that can be detected and determine what number of detectable miRNAs is suitable for your experiments.


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)

New miRNA detection rate. (A) As a million reads at a time are added to the sequencing depth of the Gastrocnemius Muscle, the number of newly detectable miRNAs is reduced. Displayed are the number of newly detected miRNAs with at least 1, 3, 5, 10, or 50 reads. (B) As a million reads at a time are added to the sequencing depth of the Plasma, the number of newly detectable miRNAs is reduced. Displayed are the number of newly detected miRNAs with at least 1, 3, 5, 10, or 50 reads.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: New miRNA detection rate. (A) As a million reads at a time are added to the sequencing depth of the Gastrocnemius Muscle, the number of newly detectable miRNAs is reduced. Displayed are the number of newly detected miRNAs with at least 1, 3, 5, 10, or 50 reads. (B) As a million reads at a time are added to the sequencing depth of the Plasma, the number of newly detectable miRNAs is reduced. Displayed are the number of newly detected miRNAs with at least 1, 3, 5, 10, or 50 reads.
Mentions: We began with 500,000 randomly chosen post-clipped reads and mapped them to miRBase using miRDeep2. We display the number of detected miRNAs and their corresponding coverage: 1 mapped read only, at least 3 mapped reads, at least 5 mapped reads, 10 mapped reads, and 50 mapped reads. We increased the number of post-clipped reads going into the alignment incrementally and calculated the number of new miRNAs that were detected with each addition of reads (Figure 5). Not surprisingly, we found that the more we increased the input of reads, the more miRNAs we slowly included. The number of additional miRNAs detected vs. the increased number of reads required has to be weighed for each experiment. At the start of any experiment, we recommend sequencing representative control samples for each tissue type to a significant depth to examine the number of miRNAs that can be detected and determine what number of detectable miRNAs is suitable for your experiments.

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