Background: Studies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution, and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or do not explore the full potential of RMS data.
Results: We suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock-community data sets shows the potential to clearly separate between strains even when the 16S is 100% identical and genome-wide dierences is < 0:02, indicating RMS has a very high resolution. From a simulation study we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real data set of infants guts we show that RMS is capable of detecting a strain-diversity gradient for Escherichia coli across time.
Conclusion: We find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain-level. Like shotgun metagenomics, it requires a good database of reference genomes, and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS.