Systems Biology (Methods in Molecular Biology)

Chapter 14

A Method for Cross-Species Visualization and Analysis

of RNA-Sequence Data

Stephen A. Ramsey

Abstract

In this methods article, I describe a computational workflow for cross-species visualization and comparison
of mRNA-seq transcriptome profiling data. The workflow is based on gene set variation analysis (GSVA)
and is illustrated using commands in the R programming language. I provide a complete step-by-step
procedure for the workflow using mRNA-seq data sets from dog and human bladder cancer as an example.

Key wordsmRNA-seq, Cross-species, Transcriptome, Bioinformatics, Gene function

1 Introduction

Transcriptome profiling by high-throughput short-read

end-sequencing of cDNA fragments, i.e., mRNA-seq, has become

a standard systems biology research modality due to its specificity

for transcript detection, large dynamic range, and ability to quantify

the entire polyadenylated transcriptome in one assay [1, 2]. How-

ever, applications such as comparative oncology, comparative

immunology, or evolutionary functional genomics involve compar-

ing and contrasting transcriptome responses across tissues indiffer-

ent species, for example between cancer and normal tissue for

neoplasms that occur in humans and domestic dogs [3–5], between

human and mouse in various immune cell types [6, 7], or in various

organs or tissues in various species in the context of evolutionary

studies [8–12]. While for simple two-sample mRNA-seq study

designs it is possible to use scatter plots of gene expression ratios

between pairs of orthologs [3], such an approach does not naturally

extend to more complicated (e.g., multi-factor or time-course)

study designs and it does not enable cross-species analysis that is

unsupervised by the sample types. Alternatively, normalized mea-

sures of gene expression such as fragments per kilobase of transcript

per million reads (FPKM) can be used to compare expression for

Mariano Bizzarri (ed.),Systems Biology, Methods in Molecular Biology, vol. 1702,
https://doi.org/10.1007/978-1-4939-7456-6_14,©Springer Science+Business Media LLC 2018

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