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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

Screen shot of miRExpress output. Example of mature miRNA and isomiR alignment to a precursor miRNA.
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Figure 1: Screen shot of miRExpress output. Example of mature miRNA and isomiR alignment to a precursor miRNA.

Mentions: An interesting and easy to use feature of the miRExpress package identifies where some of the unmapped reads go. In the results file, the miRNA reads are displayed as they align to the precursor sequence. This data reveals a category called “others” that is not counted or presented in the standard miRNA output. These are sequences that are slightly different from the mature miRNA sequence in miRBase. For instance, the 3′ end of miRNAs are often modified or shortened (Aravin and Tuschl, 2005; Landgraf et al., 2007; Westholm et al., 2011; Juvvuna et al., 2012). The sequences that match the reference miRNA but are a few nucleotides shorter or longer or mismatched by more than our one allowable SNV, are aligned and exhibited in this result folder. These slightly variable reads are called isomiRs. Figure 1 is a screen shot depicting the alignment of mmu-mir-1249-5p and 3p and mmu-mir-671-5p and 3p. The reads aligned under the mature sequence are the reads that were detected in the sample and the read counts. Under the line marked “Others” it displays sequences that were too short or too variable to be counted in the output. In the example in Figure 1, there are more miRNAs tallied in the “Others” category for mmu-miR-671-5p than align to the mature miRNA sequence. Therefore, the output to the results file is 6, rather than 51. Approximately 1–12% of the reads end up in the “others” category for Gastrocnemius muscle and are not counted as mapped reads in the output. It would be interesting to assess what the biological significance of these 3′ modifications are and whether or not they are tissue or disease specific.


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)

Screen shot of miRExpress output. Example of mature miRNA and isomiR alignment to a precursor miRNA.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Screen shot of miRExpress output. Example of mature miRNA and isomiR alignment to a precursor miRNA.
Mentions: An interesting and easy to use feature of the miRExpress package identifies where some of the unmapped reads go. In the results file, the miRNA reads are displayed as they align to the precursor sequence. This data reveals a category called “others” that is not counted or presented in the standard miRNA output. These are sequences that are slightly different from the mature miRNA sequence in miRBase. For instance, the 3′ end of miRNAs are often modified or shortened (Aravin and Tuschl, 2005; Landgraf et al., 2007; Westholm et al., 2011; Juvvuna et al., 2012). The sequences that match the reference miRNA but are a few nucleotides shorter or longer or mismatched by more than our one allowable SNV, are aligned and exhibited in this result folder. These slightly variable reads are called isomiRs. Figure 1 is a screen shot depicting the alignment of mmu-mir-1249-5p and 3p and mmu-mir-671-5p and 3p. The reads aligned under the mature sequence are the reads that were detected in the sample and the read counts. Under the line marked “Others” it displays sequences that were too short or too variable to be counted in the output. In the example in Figure 1, there are more miRNAs tallied in the “Others” category for mmu-miR-671-5p than align to the mature miRNA sequence. Therefore, the output to the results file is 6, rather than 51. Approximately 1–12% of the reads end up in the “others” category for Gastrocnemius muscle and are not counted as mapped reads in the output. It would be interesting to assess what the biological significance of these 3′ modifications are and whether or not they are tissue or disease specific.

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