Alessandro Cellerino received a PhD in Neurobiology from Scuola Normale Superiore, Pisa, Italy. After continuing his research in various institutions, including Max-Planck Institut für Psychiatrie, Martinsried, Germany; University of Tübingen, Forschungsstelle für experimentelle Ophthalmologie; and CNR, Institute of Neurophysiology, Pisa, he returned to Scuola Normale Superiore as assistant professor, was visiting scientist and junior group leader at the Leibniz Institute on Aging, Jena and was later promoted to associate professor at Scuola Normale. He is also leader of a cooperation research group between Scuola Normale Superiore and the Leibniz Institute on Aging, Jena.

Alessandro Cellerino's scientific interests relate to neuroscience, biology of aging and functional genomics. He has published a monograph and numerous articles within these fields and he teaches neurogenomics and biology of aging.

 
Michele Sanguanini is a member of Gonville & Caius College, a PhD candidate at the Centre for Misfolding Diseases (Department of Chemistry, University of Cambridge), and a Scuola Normale Superiore graduate. His research involves the systems biology and biophysics underlying neurodegenerative processes and ageing, with a focus on protein aggregation in Alzheimer's disease.

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 toolbox of experimental biologists. Yet, biological interpretation of high-dimensional data is made difficult by the lack of a common language between experimental and data scientists. By combining theory with practical examples of how specific tools were used to obtain novel insights in biology, particularly in the neurosciences, the book intends to teach students how to design, analyse, and extract biological knowledge from transcriptome sequencing experiments.

Undergraduate and graduate students in biomedical and quantitative sciences will benefit from this text as well as academics untrained in the subject.

Preface.- Introduction: why study transcriptomics?.- 1. Data distribution and visualisation.- 2. Next-generation RNA sequencing.- 3. RNA-seq raw data processing.- 4. Differentially expressed gene detection & analysis.- 5. Unbiased clustering methods.- 6. Knowledge-based clustering methods.- 7. Network analysis.- 8. Mesoscale transcriptome analysis.- 9. Microscale transcriptome analysis.- Bibliography.- Index.