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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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A priori estimation of sequencing effort in complex microbial metatranscriptomes.

Toni Monleon-Getino1,2, Jorge Frias-Lopez3

  • 1Section of Statistics (Department of Genetics, Microbiology, and Statistics) University of Barcelona Barcelona Spain.

Ecology and Evolution
|December 11, 2020
PubMed
Summary

Determining sequencing depth for metatranscriptomic analysis is crucial for microbial community studies. This method uses rarefaction curves to estimate optimal sequencing effort, ensuring robust gene expression profiling.

Keywords:
NGSmachine learningmetagenomicsmetatranscriptomicsrarefaction curvesample sizesequencing effortsimulation

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Metatranscriptomic analysis of microbial communities involves complex gene expression from numerous organisms.
  • Limited sequencing budgets pose challenges in achieving sufficient library depth for comprehensive analysis.
  • Accurate estimation of sequencing depth is vital for reliable metatranscriptomic results and avoiding data saturation.

Purpose of the Study:

  • To develop a method for calculating optimal sequencing effort in metatranscriptomic studies.
  • To provide a tool for estimating sequencing depth requirements for microbial community RNA-seq experiments.
  • To enable efficient and cost-effective characterization of complex microbial ecosystems.

Main Methods:

  • Utilized simulated metatranscriptomic/metagenomic matrices for method development.
  • Employed an extrapolation rarefaction curve approach.
  • Applied a Weibull growth model to estimate gene richness as a function of sequencing depth.

Main Results:

  • Developed a method to compute sequencing effort at various confidence intervals.
  • Enabled the estimation of a priori sequencing effort based on initial sequence fractions.
  • Demonstrated the ability to predict the number of observed genes relative to sequencing depth.

Conclusions:

  • The presented analytical pipeline offers a robust approach for determining sequencing effort in metatranscriptomics.
  • This tool aids in the time-effective and in-depth characterization of complex microbial communities.
  • Facilitates efficient resource allocation for microbiome research, improving data quality and analysis.