About the Authors
Dr. Altuna Akalin organized the book structure, wrote most of the book and edited the rest. He is a bioinformatics scientist and the head of Bioinformatics and Omics Data Science Platform at the Berlin Institute for Medical Systems Biology, Max Delbrück Center in Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He is interested in using machine learning and statistics to uncover patterns related to important biological variables such as disease state and type. He lived in the USA, Norway, Turkey, Japan and Switzerland in order to pursue research work and education related to computational genomics. The underlying aim of his current work is utilizing complex molecular signatures to provide decision support systems for disease diagnostics and biomarker discovery. In addition to the research efforts and the managing of a scientific lab, since 2015, he has been organizing and teaching at computational genomics courses in Berlin with participants from across the world. This book is mostly a result of material developed for those and previous teaching efforts at Weill Cornell Medical College in New York and Friedrich Miescher Institute in Basel, Switzerland.
Dr. Akalin and the following contributing authors have decades of combined experience in data analysis for genomics. They are developers of Bioconductor packages such as methylKit, genomation, RCAS and netSmooth. In addition, they have played key roles in developing end-to-end genomics data analysis pipelines for RNA-seq, ChIP-seq, Bisulfite-seq, and single cell RNA-seq called PiGx.
Dr. Bora Uyar contributed Chapter 8, “RNA-seq Analysis”. He started his bioinformatics training in Sabanci University (Istanbul/Turkey), from which he got his undergraduate degree. Later, he obtained an MSc from Simon Fraser University (Vancouver/Canada), then a PhD from the European Molecular Biology Laboratory in Heidelberg/Germany. Since 2015, he has been working as a bioinformatics scientist at the Bioinformatics Platform and Omics Data Science Platform at the Berlin Institute for Medical Systems Biology. He has been contributing to the bioinformatics platform through research, collaborations, services and data analysis method development. His current primary research interest is the integration of multiple types of omics datasets to discover prognostic/diagnostic biomarkers of cancers.
Dr. Vedran Franke contributed Chapter 9, “ChIP-seq Analysis”. He received his PhD from the University of Zagreb. His work focused on the biogenesis and function of small RNA molecules during early embryogenesis, and establishment of pluripotency. Prior to his PhD, he worked as a scientific researcher under Boris Lenhard at the University of Bergen, Norway, focusing on principles of gene enhancer functions. He continues his research in the Bioinformatics and Omics Data Science Platform at the Berlin Institute for Medical System Biology. He develops tools for multi-omics data integration, focusing on single-cell RNA sequencing, and epigenomics. His integrated knowledge of cellular physiology along with his proficiency in data analysis enable him to find creative solutions to difficult biological problems.
Dr. Jonathan Ronen contributed Chapter 11, “Multi-omics Analysis”. Dr. Ronen got his MSc in control engineering from the Norwegian University of Science and Technology in 2010. He then worked as a software developer in Oslo, Brussels, and Munich. During that time, he was also on the founding team of www.holderdeord.no, a website that links votes in the Norwegian parliament to pledges made in party manifestos. In 2014–2015, He worked as a data scientist in New York University’s Social Media and Political Participation lab. During that time, he also launched www.lahadam.co.il, a website which tracked Israeli politicians’ Facebook posts. He obtained a PhD in computational biology in 2020, where he has published tools for imputation for single cell RNA-seq using priors, and integrative analysis of multi-omics data using deep learning.