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Empirical insights into the stochasticity of small RNA sequencing.

ABSTRACT: The choice of stochasticity distribution for modeling the noise distribution is a fundamental assumption for the analysis of sequencing data and consequently is critical for the accurate assessment of biological heterogeneity and differential expression. The stochasticity of RNA sequencing has been assumed to follow Poisson distributions. We collected microRNA sequencing data and observed that its stochasticity is better approximated by gamma distributions, likely because of the stochastic nature of exponential PCR amplification. We validated our findings with two independent datasets, one for microRNA sequencing and another for RNA sequencing. Motivated by the gamma distributed stochasticity, we provided a simple method for the analysis of RNA sequencing data and showed its superiority to three existing methods for differential expression analysis using three data examples of technical replicate data and biological replicate data.

SUBMITTER: Qin LX

PROVIDER: S-EPMC4823707 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

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Empirical insights into the stochasticity of small RNA sequencing.

Qin Li-Xuan LX Tuschl Thomas T Singer Samuel S

Scientific reports 20160407

The choice of stochasticity distribution for modeling the noise distribution is a fundamental assumption for the analysis of sequencing data and consequently is critical for the accurate assessment of biological heterogeneity and differential expression. The stochasticity of RNA sequencing has been assumed to follow Poisson distributions. We collected microRNA sequencing data and observed that its stochasticity is better approximated by gamma distributions, likely because of the stochastic natur ...[more]

PMID: 27052356

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Empirical insights into the stochasticity of small RNA sequencing.

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Empirical insights into the stochasticity of small RNA sequencing.

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