Respiratory diseases such as asthma, chronic obstructive pulmonary disease and lung cancer represent a critical area for medical research as millions of people are affected globally. The development of new strategies for treatment and/or prevention, and the identification of biomarkers for patient stratification and early detection of disease inception are essential to reducing the impact of lung diseases. The successful translation of research into clinical practice requires a detailed understanding of the underlying biology. In this regard, the advent of next-generation sequencing and mass spectrometry has led to the generation of an unprecedented amount of data spanning multiple layers of biological regulation (genome, epigenome, transcriptome, proteome, metabolome and microbiome). Dealing with this wealth of data requires sophisticated bioinformatics and statistical tools. Here, we review the basic concepts in bioinformatics and genomic data analysis and illustrate the application of these tools to further our understanding of lung diseases. We also highlight the potential for data integration of multi-omic profiles and computational drug repurposing to define disease subphenotypes and match them to targeted therapies, paving the way for personalized medicine.