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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

The distribution of the total reads to different categories of known RNA sequences after removing adaptor-only and poor quality reads.
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Figure 4: The distribution of the total reads to different categories of known RNA sequences after removing adaptor-only and poor quality reads.

Mentions: We loaded ∼20 blood and ∼20 gastrocnemius samples onto the sequencer, ∼20 samples per lane. From our library sample preparation of the Gastrocnemius muscle, we had an average of 6,565,492 initial reads per sample (median 4,557,315; range 1,604,969–24,170,583). This wide range in the initial sequences is due to the variability listed above. There was an average of 5,634,555 post-clipped reads per sample (post-clipped median 4,007,170; range 1,306,238–18,587,936). Post-clipped reads = adaptor was trimmed, adaptor-only reads and reads that were too short were removed. The number of mapped reads that align to mature miRNA sequences from the post-clipped reads depends in part on the analysis software used: miRNAKey, miRDeep2, or miRExpress, and the tissue type (Figures 2 and 3). And in large part to the other categories of RNA sequenced in our samples (Figure 4).


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)

The distribution of the total reads to different categories of known RNA sequences after removing adaptor-only and poor quality reads.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The distribution of the total reads to different categories of known RNA sequences after removing adaptor-only and poor quality reads.
Mentions: We loaded ∼20 blood and ∼20 gastrocnemius samples onto the sequencer, ∼20 samples per lane. From our library sample preparation of the Gastrocnemius muscle, we had an average of 6,565,492 initial reads per sample (median 4,557,315; range 1,604,969–24,170,583). This wide range in the initial sequences is due to the variability listed above. There was an average of 5,634,555 post-clipped reads per sample (post-clipped median 4,007,170; range 1,306,238–18,587,936). Post-clipped reads = adaptor was trimmed, adaptor-only reads and reads that were too short were removed. The number of mapped reads that align to mature miRNA sequences from the post-clipped reads depends in part on the analysis software used: miRNAKey, miRDeep2, or miRExpress, and the tissue type (Figures 2 and 3). And in large part to the other categories of RNA sequenced in our samples (Figure 4).

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