Cystic fibrosis (CF) is an autosomal recessive disease caused by more than 2,000 mutations in the CF transmembrane conductance regulator (CFTR) gene, generating some variability in symptoms and disease severity among individuals sharing the same CFTR genotype. Although CF data are increasing and the role of modifier genes is largely acknowledged in the field as contributing to such variability, it is still unclear how different cellular pathways are affected by such diversity of mutations and vice versa, how different pathways affect the severity of mutations. Our goal is to apply bioinformatic and systems biology tools to collect, integrate and visualize existing CF data so as to extract their biological significance and find novel drug targets and therapeutic strategies. Here, we present the CyF-MAP, a disease map integrating most molecular mechanisms and pathways so far described in CF, with a focus on CFTR and its variants. CyF-MAP represents the first step into a specific CF knowledge resource consisting in accurate pathways retrieved from selected data encompassing literature manual curation and participation of experts from different CF domains. Furthermore, CyF-MAP is divided into two submaps representing wild-type CFTR (wt-CFTR) and the most common mutation F508del-CFTR. Inclusion of the F508del-CFTR map allows comparison of the underlying mechanisms involved in CF with those occurring under physiological conditions. Indeed, the major pathways involved in wt-CFTR and F508del-CFTR are represented, including protein biosynthesis, ER retention/degradation or export, activation/ inactivation at the PM and recycling/degradation after endocytosis. CyF-MAP represents a crowdsourced repository of CF knowledge accessible and exchangeable to researchers enabling easy access to accurately depicted CFTR molecular mechanisms. Accessing CyF-MAP allows interpretation of biological significance of CF data and analysis of the different molecular, functional, and physiological levels of CF. This will be an open-access web resource, aiming at constituting a reference CF data repository with comprehensive user input capabilities for continuous updating. Work supported by UIDB/04046/2020 and UIDP/04046/2020 centre grants from FCT, Portugal (to BioISI). CP is recipient of a BioSys PhD programme fellowship (PD/BD/131405/2017) from FCT (Portugal). The authors are grateful to Carlos Farinha, Michael Gray and Karl Kunzelmann for their expert advice in the construction of CyF-MAP.