[khmer] Draft: Why you shouldn't use digital normalization

Michael R. Crusoe mcrusoe at msu.edu
Sun Dec 29 13:54:39 PST 2013

This would be great to seed a FAQ with.

Didn't we standardize on not capitalizing the 'k' in khmer project?
On Dec 29, 2013 4:51 PM, "C. Titus Brown" <ctb at msu.edu> wrote:

> Comments solicited on this draft blog post!
> -----
> Over the last year, diginorm has occupied an increasingly privileged
> position: it's a lightweight way to achieve an assembly, one that is
> cheaper than almost anything else you can do; our software works reasonably
> well in practice; sequencing data generation capacity is only increasing;
> and our papers, my blog, and our twitter presence have made diginorm pretty
> visible.
> That having been said, there are lots of situations in which diginorm is
> not good or useful.  I talk to a lot of people about it and many people
> seem a bit hesitant to critique it in front of me.  Worse, many people feel
> pressure to try it.  And, worst of all, people don't always tell me when
> they run into problems, incorrectly believing that I am going to be upset
> at them.
> With all that in mind, I thought I'd write a more critical blog post about
> diginorm.  These are just the problems I've heard about -- if you have had
> bad outcomes from using diginorm, I'd love to hear about it, so please do
> either comment on this post or send me an email.@@
> One caveat: assembly is still a challenging research problem in many
> (most?) circumstances.  So consumers should default to critical evaluation
> of any statements about assembly - both those made by me, and those made by
> others.  "It's probably more complicated than they think" is a great
> starting point!
> 1. You should always try digital normalization.
> If you can achieve an assembly without running diginorm, then please don't!
> We developed diginorm because we could not run extant assemblers on our
> data: the assembler would run out of memory on our transcriptome and
> metagenome date sets.  This was partly because very few assembler authors
> prioritize memory efficiency over specificity, and partly because the
> assembly field was less mature than it is now.  While there are still data
> sets that you can only assemble on commodity computers by using diginorm
> and partitioning, new assemblers and new assembly approaches have been
> developed that are much less resource intensive.  I'd particularly like to
> mention probabilistic graphs (as used in minia), sparse graphs
> (soapdenovo2), and distributed graphs (ray).
> I therefore recommend diginorm in only three circumstances: first, when
> you can't easily complete an assembly because of ram limitations; second,
> when repeat structures or polymorphism break assemblers; and third, when
> you want to do a quick and dirty initial assembly because that's all you
> need.
> If you're doing microbial genomes, use spades or mira or mira. If you're
> working with metagenomes, use idba-ud or ray; if you're assembling
> transcriptomes, use Trinity.  If you can run those for your data sts -ud or
> ray. If you're assembling transcriptomes, use Trinity.  If you can run your
> current datasets with those assemblers on your current hardware, there is
> no very good reason to apply diginorm: it probably will not yield big gains
> in assembly quality.  (If that last sentence is a bit equivocal, it's
> because we occasionally do see significant gains in some aspects of
> assembly after diginorm, but we don't really know why.)
> 2. Diginorm rarely or never results in worse assemblies.
> Diginorm unquestionably results in more fragmented assemblies, and also
> forcibly collapses heterozygosity.  I've heard that it does particularly
> bad things to plant genomes, which are repetitive and polyploid :).  I've
> also heard that it performs poorly on viral and phage metagenomes.
> We've also heard that it works well on viral and phage metagenomes, and in
> some eukaryotic genome assemblies it has been the only thing that worked.
>  It drives me a little bit crazy that we don't know why it works well
> sometimes and poorly other times :).  People who have had bad experiences
> would be doing me a huge service by letting me know about them!
> 3. Diginorm works well with all assemblers.
> There are a number of assemblers that return bad results with diginormed
> data.  In fact, there are only two assemblers with a lot of empirical
> evidence for good performance on diginormed data: velvet and trinity.  I've
> heard that ALLPATHS performs much worse on diginormed data than on raw
> data; I've not heard anything concrete from most other assemblers, although
> Mira uses diginorm-like algorithms internally.
> Why might assemblers do especially badly with diginorm data?  Two reasons.
> First, assemblers are heavily heuristic: many of the gains in assembly
> quality that have been realized over the last few years have come from
> better approximate resolutions of tricky situations observed in real data.
>  Diginorm may modify the signatures of these situations and result in
> misapplication of heuristics. One particularly strong signal in the data is
> coverage, which diginorm systematically modifies; it's no big surprise that
> many assemblers perform badly on whack data.
> Second, diginorm breaks the assembly graph at repeats.  This is a
> straightforward consequence of the basic coverage downsampling algorithm,
> and is one big reason why we are pursuing alternatives to our current
> diginorm approach.
> In some sense, that diginorm works at all is kind of amazing; that it
> breaks some or many assemblers should not be surprising at all.
> ----
> So where does this leave diginorm? Should you even try it?
> A few thoughts.
> First, diginorm is pretty computationally inexpensive, comparatively to
> almost everything else.  If you're not getting a good assembly, it probably
> won't cost you too much time to try it out - especially on transcriptomes
> and metagenomes. See Khmer protocols.@@  but don't expect miracles, and,
> if you suspect a problem that Khmer isn't targeted at, maybe try other
> solutions first.  (Please do feel free to ask, on the Khmer mailing list@@,
> whether Khmer might help.  We will try to be honest and will generally
> suggest other, better tools when we know of them.)
> Second, we love to hear about situations where you tried Khmer and
> diginorm and got horrible results.  Our response might be "oh, did you try
> this other set of options?" but we generally don't expect other people to
> invest huge amounts of time in solving problems with our approach --
>  although of course we' re happy to kibitz if you do want to put in the
> time.  Regardless, in the short term we want to know where, when, and why
> our approach sucks, and trying to spare our feelings may be polite but
> isn't effective in the long run :).
> Third, the field of assembly is still evolving quite fast.  Programs, data
> types, and algorithms are changing all the time.  One thing this means is
> that if you are having trouble with a particular data set, there may well
> be a solution out there, or it may be possible to adapt a solution to
> someone else's problem.  So communicate about your problems!
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