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Related Concept Videos

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Local sequence and sequencing depth dependent accuracy of RNA-seq reads.

Guoshuai Cai1,2, Shoudan Liang3, Xiaofeng Zheng3

  • 1Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA. Guoshuai.Cai@dartmouth.edu.

BMC Bioinformatics
|August 11, 2017
PubMed
Summary
This summary is machine-generated.

RNA sequencing (RNA-seq) data exhibits biases. Increasing sequencing depth reduces base-level overdispersion, improving RNA-seq accuracy and enabling better statistical modeling for downstream analyses.

Keywords:
Base-level modelingBeta-binomialBiasDifferential expression analysisNon-uniformityOverdispersionRNA-seq

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • RNA sequencing (RNA-seq) technology is prone to biases and spurious effects.
  • These issues lead to non-uniform sequencing read counts across gene base positions.
  • Accurate modeling requires addressing base-level non-uniformity and leveraging read count properties for precise mean and variance estimation.

Purpose of the Study:

  • To investigate the impact of various factors on RNA-seq accuracy.
  • To develop improved RNA-seq read modeling strategies.
  • To compare different modeling approaches for RNA-seq data.

Main Methods:

  • Analysis of base-level overdispersion rates in RNA-seq data.
  • Investigating the relationship between sequencing depth and overdispersion.
  • Assessing the influence of local sequences on overdispersion.
  • Developing a beta-binomial model with a dynamic overdispersion rate.

Main Results:

  • Overdispersion rate decreases with increased sequencing depth at the base level.
  • Local sequence significantly influences overdispersion, but this effect diminishes after accounting for sequencing depth.
  • A beta-binomial model with a dynamic overdispersion rate was developed for modeling RNA-seq read counts.

Conclusions:

  • The study offers insights into position-level overdispersion in RNA-seq.
  • Key factors influencing overdispersion include sequencing depth, local sequence, and preparation protocols.
  • Findings can enhance RNA-seq quality control and statistical downstream analysis methods.