Hi all,

I have 27 soil WGS metagenome datasets and I am trying to assemble them into contigs that are at least 1000-2000 kb long. Each dataset on its own is 20-30 Gigabytes of paired-end read fastq files.

I first tried the [Ray Meta assembler][1] because it's supposed to run well in parallel. I was able to do that for most datasets but have gotten very short contigs (most are <500 kb). Then I found [this paper][2] that suggests it does better for low-complexity datasets.

I also took a look at [Concoct][3] and I think the strategy sounds like it makes sense, but the code on their github pages is woefully outdated and I'm not sure how much of it is still maintained. Also, it suggests combining all datasets into one and then trying to assemble it (a "coassembly") and using that for the downstream analysis, but that approach will be computationally challenging since my datasets are so large.

If anyone has any experience with assembling complex soil datasets, I'd love to do some brain storming, so please reach out! 

Thanks!


  [1]: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2012-13-12-r122
  [2]: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169662
  [3]: http://www.nature.com/nmeth/journal/v11/n11/full/nmeth.3103.html
The best approach for contiguity is generally to coassemble if you have sufficient resources.  Some assemblers (HipMer/MetaHipMer, Ray, and Omega/Disco) can distribute that to spread the memory use across multiple nodes...  since you've tried Ray, you might try the others too and see if they give better results.  For a single node, we've found Megahit gives the best results with the lowest resource consumption.

You can also try approaches such as binning (using e.g. Metabat) and then assembling just reads that map to each individual bin.  Normalization, error-correction, and/or discarding low-depth reads can also improve assemblies.  With Ray and Disco, both error-correction and merging paired reads prior to assembly increases continuity.

But in general it's not a solved problem, so you'll have to experiment a lot!  Don't expect great continuity, though; complex metagenomes often yield an L50 (length) of 200bp or less.

Note that you may be able to bin the raw reads using a binning tool based on depth covariance if the 27 samples are different (different conditions, location, time, etc).