Those of us working in bacterial genomics are all to familiar with de novo genome assembly. One of the first accessible and practical tools for bacterial genome assembly was Velvet. My group use Velvet a lot, and wrote the popular VelvetOptimiser software.
Since then, many alternatives to Velvet have appeared, including ABYSS, SOAPdenovo, ALLPATH-LG, SGA, Ray, and many others. The motivation for some of these alternatives was to improve performance and decrease RAM usage when assembling large, polyploid organisms which Velvet was not really designed to handle.
Despite these alternatives, Velvet has still thrived due to it having a strong user community, and still giving good, usable assemblies. But there is always room for improvement and new ideas, and I believe an excellent option for bacterial assemblies currently is SPAdes. It recently ranked very well in the GAGE-B assessment and in this post I will explain its relationship to Velvet in broad terms.
What's the same?
SPAdes is a de Bruijn graph based assembler, just like most short read assemblers, including Velvet. It breaks reads into fixed-size k-mers, builds a graph, cleans the graph, then finds paths through the graph. These paths end up as contigs.
SPAdes was originally intended for assembling MDA data. This is data that comes from single-cell sequencing using the multiple displacement amplification method for tiny amounts of input DNA. This produces wildly varying genome coverage, something which existing assemblers were not able to deal with well. But SPAdes by default now works with regular data, but it is neat that it can support MDA when required.
The target data source for SPAdes is Illumina reads. Like all de Bruijn graph based assemblers, they work best with shorter, high quality reads where indels are rare. For PGM and 454 data I would look elsewhere.
The authors would argue, and the GAGE-B assessment supports the argument, that SPAdes does a better job than Velvet and other assemblers on microbial genome data. I have not had extensive experience with it yet, but have used it enough to now recommend it to others and trust it on my own data sets (well, as much as I trust any assembler!).
But there is a good reason SPAdes does better. It is really multiple tools in one. This integrated approach makes things much simpler to incorporate in pipelines. Here are the key steps SPAdes makes, as best I understand them:
- Read error correction based on k-mer frequencies using BayesHammer
- De Bruijn graph assembly at multiple k-mer sizes, not just a single fixed one.
- Merging of different k-mer assemblies (good for varying coverage)
- Scaffolding of contigs from PE/MP reads
- Repeat resolution from PE/MP data using rectangle graphs
- Contig error correction based on aligning the original reads with BWA back to contigs
Just like Velvet, it can use multiple threads for some parts of the algorithm. SPAdes produces a final "contigs.fasta" and "scaffolds.fasta" file, and a detailed log file so you can reconstruct your results. I think it is using more sophisticated dynamic methods for estimating k-mer coverage and cutoffs. Of course it takes longer to run than Velvet, but it is doing a lot more than Velvet does.
The SPAdes software is easy to install, has a nice clean interface, and follows my minimum standards for bioinformatics software. The authors are actively developing it, and respond to bug reports and questions. The results are good, and the computational requirements are reasonable. It is well worth trying on your own microbial data. So go and download it, try it out, and email your feedback today.