Ten simple rules for developing visualization tools in genomics

Le résumé fourni par la source

Visualization is key for expanding and communicating knowledge to both specialized and broad audiences-after all, "a picture is worth a thousand words," right?That much has become clear during the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) outbreak when "Flatten the curve!" [1] turned into a catchier watchword than the usual "Wash your hands!," by referring to the related graphics rather than the health discourses themselves.Coronavirus Disease 2019 (COVID-19) visualizations, good and bad [2], became omnipresent in the public debates, with interactive and dynamic visualization platforms like Nextstrain [3] gaining a lot of popularity.We are now used to seeing graphs and charts in our everyday lives and creating them for scientific papers as part of research.Unfortunately, most of the time, classic visual representations are insufficient to effectively communicate data complexity, and as O'Donoghue puts it, "often dedicated communication approaches need to be developed to address specific data challenges, especially when conveying complex or unfamiliar ideas" [4].Given the gap between creating static figures and building a visualization tool from the ground up, it can be easy to get lost in this nontrivial journey.Our following 10 simple rules are dedicated to biologists and bioinformaticians who, while already being at the crossroads of many fields, want to venture further into the land of Data Visualization ("datavis" or "dataviz" for short).They combine tips and advice that we would have wanted when we first started our own journeys, gathered from our experiences in building genomic and/or datavis tools, and the time spent with related communities.Additionally, they address current challenges in computational biology and the needs of the community.We aim these rules at bioinfo-to-datavis novices looking for guidelines, particularly regarding genomics visualization tools, but experienced practitioners may find it useful to see them gathered in one place too.For better reading comfort, we organized the rules chronologically depending on when they would matter the most during a visualization tool's life cycle, starting with the design (Rules 1 to 5) then development (Rules 5 to 8-ish) phases until it is shared with the rest of the world.Please note, however, that they are still relevant during the whole gestation and that creating a visualization tool is rarely a fully linear process-it does not even end after the initial release, as continued development and support is a cyclic process to be sustained over the years.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.