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利用神经挤压模型比较 miRNA 高通量测序分析工具。

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

机构信息

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

出版信息

Front Genet. 2013 Mar 1;4:20. doi: 10.3389/fgene.2013.00020. eCollection 2013.

DOI:10.3389/fgene.2013.00020
PMID:23459507
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3585423/
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.

摘要

近年来,下一代测序的样品制备和分析技术取得了进展,使得高通量分析和发现新的 miRNA 成为可能。在神经疾病和损伤的情况下,这些类型的实验受到了更多的限制。可能是因为大脑和脊髓等组织无法直接在活体患者中进行采样,而血液和脑脊液的间接采样受到 RNA 含量低的影响。我们使用小鼠模型来研究急性神经挤压对 miRNA 表达的变化。我们从肌肉组织和血浆中检测 miRNA。我们检查了在每种样本类型中,覆盖深度(映射读取的数量)如何改变可检测 miRNA 的数量。我们还发现,起始 RNA 量非常低的样本(小鼠血浆)使得难以获得高成熟 miRNA 覆盖深度。必须针对每种组织评估所需的覆盖深度,以充分检测差异表达,同时权衡对该样本进行测序的成本是否足以达到所需的深度。我们探索了三种不同的软件包(miRDeep2、miRNAKey 和 miRExpress)和两种不同的分析软件包(DESeq 和 EdgeR)生成的总映射读取量和差异表达结果的变化。我们还检查了使用 miRDeep2 预测新的 miRNA 并随后使用 qRT-PCR 在样本中检测它们的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/fccdcbb126a5/fgene-04-00020-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/3ac71f1d4ce4/fgene-04-00020-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/97b01f9f4469/fgene-04-00020-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/e72dd74c94b0/fgene-04-00020-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/5345fd732e6a/fgene-04-00020-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/194e1171bfc9/fgene-04-00020-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/fccdcbb126a5/fgene-04-00020-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/3ac71f1d4ce4/fgene-04-00020-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/97b01f9f4469/fgene-04-00020-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/e72dd74c94b0/fgene-04-00020-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/5345fd732e6a/fgene-04-00020-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/194e1171bfc9/fgene-04-00020-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd81/3585423/fccdcbb126a5/fgene-04-00020-g006.jpg

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