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Computational Methods for Quality Check, Preprocessing and Normalization of RNA-Seq Data for Systems Biology and Analysis

  • Gianluca MazzoniEmail author
  • Haja N. Kadarmideen
Chapter

Abstract

The use of RNA sequencing (RNA-Seq) technologies is increasing mainly due to the development of new next-generation sequencing machines that have reduced the costs and the time needed for data generation.

Nevertheless, microarrays are still the more common choice and one of the reasons is the complexity of the RNA-Seq data analysis. Furthermore, numerous biases can arise from RNA-Seq technology, and these biases have to be identified and removed properly in order to obtain accurate results.

Nowadays, many tools have been developed which allow to perform each step without high-level programming skills. However, each step of the pipeline needs to be performed carefully and requires a good knowledge of both the technology and the algorithms.

In this comprehensive review, we describe the fundamental steps of the pipeline for RNA-Seq analysis to identify differentially expressed genes: raw data quality control, trimming and filtering procedures, alignment, postmapping quality control, counting, normalization and differential expression test.

For each step, we present the most common tools and we give a complete description of their main characteristics and advantages focusing on the statistics that they perform and the assumptions that they make about the data.

The choice of the right tool can have a big impact on the final results. Until now, no gold standard has been established for this type of analysis.

In conclusion, this review can be useful for both educational purposes as well as for less experienced practitioners of animal genomic research. In the absence of a commonly accepted standard procedure, the general overview presented in this review can help to make the best choices during the implementation of an RNA-Seq pipeline.

Keywords

Differential Expression Analysis Gene Count Bioinformatics Pipeline Length Bias Estimate Fold Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Programme Commission on Health, Food and Welfare of the Danish Council for Strategic Research (Innovationsfonden) for financial support within the GIFT project.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Large Animal SciencesUniversity of CopenhagenFrederiksberg CDenmark

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