DiTing: A pipeline to infer and compare biogeochemical pathways from metagenomic and metatranscriptomic data

Metagenomics and metatranscriptomics are powerful methods to uncover key micro-organisms and processes driving biogeochemical cycling in natural ecosystems. Databases dedicated to depicting biogeochemical pathways (for example, metabolism of dimethylsulfoniopropionate (DMSP), which is an abundant organosulfur compound) from metagenomic/metatranscriptomic data are rarely seen. Additionally, a recognized normalization model to estimate the relative abundance and environmental importance of pathways from metagenomic and metatranscriptomic data has not been organized to date. These limitations impact the ability to accurately relate key microbial-driven biogeochemical processes to differences in environmental conditions. Thus, an easy-to-use, specialized tool that infers and visually compares the potential for biogeochemical processes, including DMSP cycling, is urgently required. To solve these issues, we developed DiTing, a tool wrapper to infer and compare biogeochemical pathways among a set of given metagenomic or metatranscriptomic reads in one step, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and a manually created DMSP cycling gene database. Accurate and specific formulae for over 100 pathways were developed to calculate their relative abundance. Output reports detail the relative abundance of biogeochemical pathways in both text and graphical format. DiTing was applied to simulated metagenomic data and resulted in consistent genetic features of simulated benchmark genomic data. Subsequently, when applied to natural metagenomic and metatranscriptomic data from hydrothermal vents and the Tara Ocean project, the functional profiles predicted by DiTing were correlated with environmental condition changes. DiTing can now be confidently applied to wider metagenomic and metatranscriptomic datasets, and it is available at https://github.com/xuechunxu/DiTing.