Harnessing the Power of Scientific Python to Investigate Biogeochemistry and Metaproteomes of the Central Pacific Ocean

Oceanographic expeditions commonly generate millions of data points for various chemical, biological, and physical features, all in different formats. Scientific Python tools are extremely useful for synthesizing this data to make sense of major trends in the changing ocean environment. In this paper, we present our application of scientific Python to investigate metaproteome data from the oxygen-depleted Central Pacific Ocean. The microbial proteins of this region are major drivers of biogeochemical cycles, and represent a living proxy of the ancient anoxic ocean. They also provide a look into the trajectory of the ocean in the face of rising temperatures, which cause deoxygenation. We assessed 103 metaproteome samples collected in the Central Pacific Ocean on the 2016 ProteOMZ cruise. This data represents ~60,000 identified proteins and over 6 million datapoints, in addition to over 6,600 corresponding chemical, physical, and biological metadata points.

An interactive data analysis tool which enables the scientific user to visualize and interrogate patterns in these large metaproteomic datasets in conjunction with hydrographic features was not previously available. Bench scientists who would like to use this oceanographic data to gain insight into marine biogeochemical cycles were at a disadvantage as no tool existed to query these complex datasets in a visually meaningful way. Our goal was to provide a graphical visualization tool to enhance the exploration of these complex dataset; specifically, using interactive tools to enable users the ability to filter and automatically generate plots from slices of large metaproteomic and hydrographic datasets. We developed a Bokeh application for data exploration which allows the user to hone in on proteins of interest using widgets. The user can then explore relationships between protein abundance and water column depth, hydrographic data, and taxonomic origin. The result is a complete and interactive visualization tool for interrogating a multivariate oceanographic dataset, which helped us to demonstrate a strong relationship between chemical, physical, and biological variables and the microbial proteins expressed. Because it was impossible to display all the proteins at once in the Bokeh application, we additionally describe an application of Holoviews/Datashader to this data, which further highlights the extreme differences between oxygen rich surface waters and the oxygen poor mesopelagic. This application can be easily adapted to new datasets, and is already proving to be a useful tool for exploring patterns in ocean protein abundance.