Metacontextify: Automated Environmental Annotation of Marine Metagenomic Samples
By Maarten Langen & Vera van Noort
Why did we do this study?
In metagenomics, researchers sequence DNA taken directly from the environment to find out which microbes live where and which proteins they produce. This information holds untapped potential for new discoveries, both in fundamental biology and in industry, for example in the search for new enzymes. To understand these microbes and predict which proteins are useful under which conditions, we need to know the environment they live in. However, for many samples, conditions such as temperature or salinity were never recorded or are no longer available.
“To understand these microbes and predict which proteins are useful under which conditions, we need to know the environment they live in.”
What did we do?
The EU’s Copernicus program provides decades of modeled ocean data based on satellite observations. Yet this data is rarely used in the biological sciences. To change that, we developed Metacontextify, a Python tool that retrieves environmental properties from Copernicus for relevant biological identifiers (MGnify/ENA).
We validated the tool on samples with measured environmental conditions. To show its value, we used it to compare the environments in which cold-shock proteins and antifreeze proteins occur.
We found that the number of cold-shock proteins encoded by an organism is related to its genome size, which in turn is related to the environmental temperature.
Finally, we retrieved environmental properties for MGnify marine prokaryote genomes and MGnify proteins, so that other researchers can use them directly.
“To change that, we developed Metacontextify, a Python tool that retrieves environmental properties from Copernicus for relevant biological identifiers (MGnify/ENA).”
Key findings
For samples with measured conditions, the temperatures from Metacontextify closely matched the measured values.
The number of samples with environmental data increased 1.45- to 10.85-fold compared to the original metadata, depending on the property.
We retrieved environmental properties for 684 million marine proteins.
How did the VSC contribute?
Developing the tool meant working with large datasets: 30 years of environmental data for the world’s oceans, combined with tens of thousands of sample locations. The VSC allowed us to match each location to its environmental conditions efficiently.
The VSC allowed us to match each location to its environmental conditions efficiently.
Publication
This Research Showcase presents a tailored overview prepared by the authors based on their original publication, “Metacontextify: automated environmental annotation of marine metagenomic samples.” Read the original publication.
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