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Transcriptomics and RNA-Seq Data Analysis

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Bioinformatics and the Cell

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Abstract

High-throughput sequencing data (HTS) has been used in detecting not only differential gene expression, but also alternative splicing events and different transcription start and termination sites. It has also been used in ribosome profiling for characterizing translation efficiency and Hi-C method for constructing genome 3-D architecture. There are two main difficulties in analyzing HTS data: the large file size, often in terabytes, and the allocation of reads to paralogous genes which impacts the accuracy of computed RPKM values, especially for multicellular eukaryotes with many paralogues. This chapter provides a conceptual framework for analyzing HTS data and offers numerical illustrations of solutions to both problems mentioned above. It includes examples from real data on how to compare performance of different software packages.

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  1. University of Ottawa CAREG and Biology Department, Ottawa, ON, Canada

    Xuhua Xia

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Postscript

Postscript

The most frequent use of RNA-Seq data is to identify differentially expressed genes between a patient and a normal person (between an animal disease model and a normal animal). However, the identified differences may often lead to wrong prescriptions instead of a good cure.

In the mid-1970s, the United Nations noted, correctly, that many differences in people between poor regions and wealthy regions could be attributed to malnutrition. Consequently, it made a valiant effort to ship milk powder to poor regions in Africa and Asia to alleviate the perennial problem of malnutrition, without knowing the problem of lactose intolerance shared by many in these poor regions. The result was unsatisfactory and exemplifies the evil of one-dimensional thinking.

Alcohol consumption in a city in North America is almost perfectly correlated with number of churches in a city. Is it because people are happier and party more with more churches? Most people would immediately recognize that the correlation is due to the fact that both alcohol consumption and number of churches depend on population size of the city. The larger the city, the more the people and consequently the more churches and the more alcohol consumption.

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Xia, X. (2018). Transcriptomics and RNA-Seq Data Analysis. In: Bioinformatics and the Cell. Springer, Cham. https://doi.org/10.1007/978-3-319-90684-3_5

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