Transcriptome Analysis Introduction and Examples from the Neuroscien
The goal of this book is to be an accessible guide for undergraduate and graduate students to the new field of data-driven biology. Next-generation sequencing technologies have put genome-scale analysis of gene expression into the standard toolb
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APPUNTI LECTURE NOTES
Alessandro Cellerino Scuola Normale Superiore Piazza dei Cavalieri, 7 56126 Pisa Michele Sanguanini Gonville and Caius College University of Cambridge Trinity Street CB2 1TA, Cambridge Cambridgeshire, United Kingdom Transcriptome Analysis Introduction and Examples from the Neurosciences
Alessandro Cellerino Michele Sanguanini
Transcriptome Analysis Introduction and Examples from the Neurosciences
c 2018 Scuola Normale Superiore Pisa isbn 978-88-7642-642-1 (eBook) DOI 10.1007/978-88-7642-642-1 issn 2532-991X (print) issn 2611-2248 (online)
Contents
Preface
ix
Introduction: why studying transcriptomics?
xi
1 A primer on data distributions and their visualisation 1.1 Stochastic processes and Poisson distribution . . . 1.1.1 Gaussian and t-Student distributions . . . . 1.1.2 Parameters of a distribution . . . . . . . . 1.2 Representation of quantitative biological data . . . 1.2.1 Violin plot . . . . . . . . . . . . . . . . . 1.2.2 Scatter plot . . . . . . . . . . . . . . . . . 1.3 Lists of genes and Venn diagrams . . . . . . . . .
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2 Next-generation RNA sequencing 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Advantages of RNA-seq . . . . . . . . . . . . . . . . . 2.3 RNA purification . . . . . . . . . . . . . . . . . . . . . 2.3.1 RNA quality assessment . . . . . . . . . . . . . 2.3.2 Abundant RNA species . . . . . . . . . . . . . . 2.3.3 Tissue-specific abundant RNA species . . . . . . 2.4 Library preparation . . . . . . . . . . . . . . . . . . . . 2.4.1 RNA fragmentation . . . . . . . . . . . . . . . . 2.4.2 Reverse transcription . . . . . . . . . . . . . . . 2.4.3 Addition of the adapters . . . . . . . . . . . . . 2.4.4 Quality control . . . . . . . . . . . . . . . . . . 2.5 Library sequencing . . . . . . . . . . . . . . . . . . . . 2.5.1 Bridge amplification and sequencing by synthesis 2.5.2 Single-end or paired-end sequencing? . . . . . . 2.5.3 Choosing the right read length . . . . . . . . . . 2.6 Other applications of next-generation RNA sequencing .
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vi Alessandro Cellerino - Michele Sanguanini
3 RNA-seq raw data processing 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3.2 General quality assessment . . . . . . . . . . . . . . . . 3.2.1 The analysis of Kmer levels permits estimation of the presence of artifact sequences . . . . . . . 3.3 Removal of artefacts . . . . . . . . . . . . . . . . . . . 3.4 Mapping the reads to the reference genome . . . . . . . 3.4.1 The Burrows-Wheeler transform . . . . . . . . . 3.4.2 Application of the Burrows-Wheeler transform to genome mapping . . . . . . . . . . . . . . . . 3.4.3 Optimal storage of the suffixes index . . . . . . . 3.4.4 Structure of a SAM file . . . . . . . . . . . . . . 3.5 Complexity and depth of the sequencing . . . . . . . . . 3.5.1 The Negative Binomial distribution is commonly used to estimate the complexity of a library . . . 3.5.2 Marginal value of additional
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