Summary
What I am saying in this post is there approximates to 3 "generations" of in silico gap-closers/polishes:
- GapFiller - amongst other "early" generations (2012);
- medaka/racon/Nanopolish/Pilon (2014 - 2018 inclusively)
- Nextpolish/PEPPER/Apollo/Homopolish/NeuralPolish (2020 onwards).
The trendy approach is combining 2. and 3. I can't guarantee it will work for cyanobacteria gap-closing, but the shear volume of algorithms dedicated to this purpose, infers they must be delivering results.
Background
Whats the issue with a reference genome? The key thing about many bacteria is their ability to "dump" genes and then regain them via plasmid associated recombination. This can occur extensively within a single species. This makes reference associated assembly essentially impossible in many bacteria - particularly against the official NCBI reference genome. Hence, the official NCBI reference gene, is not like a human genome reference - which is a gold standard. With due to respect to those who have dedicated time to presenting the official bacterial reference genomes are not very useful.
This BioStars post here demonstrates that even Mycobacteria tuberculosis would not assemble via a reference genome and TB in context is one of the most conserved genomes in bacteria, the SNP behaviour is famously limited.
If attempting a reference genome it is worth considering the following:
- Any amount of genetic divergence can result genetic island phenomena and non-contiguous genomes that will make assembly difficult, therefore a complete genome as close to the target genome as possible is a good reference.
- For example, simply Blasting sections of you genome to look for a consistent reference that is close to yours. A custom database of complete genomes for your species my be useful, but just sending bits of your target onto the NCBI website will be useful. The idea is to get lots of bits of the genome matching the closest strain.
- QUAST was going to be my suggestion too.
- Mapping via e.g. Mummer4 against the reference genome as the "scaffold".
Key question:
The same strain also has 1-2 very fragmented genomes (100s of pieces) available in NCBI at scaffold level. The reads themselves are not available, so I can't just add them to the assembly.
I'm not sure what "not available" means.
Key suggestion: Polishing
The other approach are recent de novo assemblers and in silico "polishing". What I'm recommending is to first try "polishing". This doesn't exclude subsequent reference genome assemblies - not at all - but it's a way to improve the de novo assemblies and if there are still issues then consider a reference genome.
Firstly I would read someone who is really up to date, e.g.
Comparative evaluation of Nanopore polishing tools for microbial genome assembly and polishing strategies for downstream analysis
They are medaka fans and are using this as part of a combination approach. medaka was part of the established stable of "polishers"
- GapFiller - I don't think this is useful.
- racon is specifically for long reads
- Nanopolish here
The authors prefer medaka then goes on to describe
Nextpolish, PEPPER, Apollo, Homopolish, and NeuralPolish
Much more recent from 2020 ... thats a lot of polishing tools. Note, PEPPER and Apollo are often used for algorithms names - make sure its 2020 onwards, with PEPPER being 2021. They suggest medaka x PEPPER.
I really think this combination polishing approach with an established (medaka) and one the very latest polishing algorithms, such as PEPPER (2021) is really worth trying. If it doesn't work for the specific purpose it will certainly improve the contig quality. However, given the shear volume of work dedicated to this area then there has to be good reason.
By the same token that
Money doesnt give happiness, but sure gives a better standard of misery.
Then ...
Polishing might not give gap closure, but sure gives a better standard of quality to the same old bunch of miserable contigs
de novo Assembly
Maybe Miniasm? I'd look at "polishing" the de novo assembly you have.
RagTag as a SPAdes alternative is interesting @MaximilianPress
Spades
would do this and that is likely a long way from the optimal approach. First question, which genome? Secondly, this is an impressive data set - long read, short read why not simply use advanced gap closing algorithms and the latest de novo assemblers? $\endgroup$