Bastien
Chevreux
bach@chevreux.org

> Solomon Short

> Reliable information lets you say 'I don't know' with real confidence.

# Some advice when going into a sequencing project

## Talk to your sequencing provider(s) before sequencing

Well, duh! But it's interesting what kind of mails I sometimes get. Like in:

> We've sequenced a one gigabase, diploid eukaryote with Solexa 36bp paired-end with 200bp insert size at 25x coverage. Could you please tell us how to assemble this data set de-novo to get a finished genome?

A situation like the above should have never happened. Good sequencing providers are interested in keeping customers long term and will therefore try to find out what exactly your needs are. These folks generally know their stuff (they're making a living out of it) and most of the time propose you a strategy that fulfills your needs for a near minimum amount of money.

Listen to them.

If you think they try to rip you off or are overselling their competences (which most providers I know won't even think of trying, but there are some), ask a quote from a couple of other providers. You'll see pretty quickly if there are some things not being right.

As a matter of fact, a rule which has saved me time and again for finding sequencing providers is not to go for the cheapest provider, especially if their price is far below quotes from other providers. They're cutting corners somewhere others don't cut for a reason.

## Choosing a sequencing provider

This is a slightly reworked version of a post I made on the MIRA talk mailing list. The question "Could you please recommend me a sequencing provider?" arrives every now and then in my private inbox, often enough for me decide to make a collage of the responses I gave in the past and post it to MIRA talk.

This response got, errrr, a little longer, but allow me to note that I will not give you names. The reasons are manyfold:

- once upon a time I worked for a sequencing company
- the company I am currently employed with is not in the sequencing provider business, but the company uses more than one sequencing provider on a regular base and I get to see quite some data
- due to my development on MIRA in my free time, I'm getting insight into a number of highs and lows of sequencing technologies at different sequencing providers which I would not get if I were to expose them publicly ... I do not want to jeopardise these relationships.

That being said, there are a number of general considerations which could help you. Excuse me in case the detours I am going to make are obvious to you, but I'm writing this also for future references. Also, please bear with me if I look at "sequencing" a bit differently than you might be accustomed to from academia, but I have worked for quite some time now in industry ... and there cost-effectiveness respectively "probability of success" of a project as whole is paramount to everything else. I'll come back to that further down.

There's one -- and only one -- question which you, as sequencing customer, need to be able to answer ... if necessary in every excruciating detail, but you must know the answer. The question is:

### WHAT DO YOU WANT?!

#### Detour - Sequencing -

For me, every "sequencing project", be it genomic or transcriptomic, really consists of four major phases:

1. data generation: This can be broadly seen as everything to get the DNA/RNA ready to be sent off to sequencing (usually something the client does), the library prep at the sequencing provider and finally the sequencing itself (including base calling). An area of thousand pitfalls where each step (and the communication) is crucial and even one slight inadvertence can make the difference between a "simple" project and a "hard" project. E.g.: taking DNA from growing cells (especially bacteria in exponential growing phase) might not be a good idea ... it makes assembly more difficult. Some DNA extraction methods generate more junk than good fragments etc.pp

   The reason I am emphasizing this is simple: nowadays, the "sequencing" itself is not the most expensive part of a sequencing project, the next two steps are (most of the time anyway).

2. assembly & finishing: Still a hard problem. Even a "simple" bacterium can present weeks of effort to get right if its riddled with phages, prophages, transposon elements, genetically engineered repeats etc.pp. And starting with eukaryotes the real fun starts: ploidy, retrotransposons etc. make for an unbelievable genome plasticity and almost always have their own surprises. I've seen "simple" Saccharomyces cerevisiae - where biologist swore to high heaven they were "close to the publicly sequenced strains" - being *very* different from what they were expected to be, both on the DNA level and the genome organisation level.

   Getting eukaryotes right "down to the last base" might cost quite some money, especially when looping back to step 1 (data generation) to tackle difficult areas.

3. annotation: Something many people forget: give the sequence a meaning. Here too, things can get quite costly if done "right", i.e., with hand curation. Especially on organism which are not part of the more commonly sequenced species or are generally more complex.

   Annotation of a de-novo transcriptome assembly is also not for the faint of heart, especially if done on short, unpaired read assemblies.

4. using the sequencing data: ... for whatever it was generated for.

The above makes it clear that, depending on what you are really interested in within your project and what you expect to be able to do with the sequencing data, one can cut corners and reduce cost here and there (but not everywhere). And therefore, the above question "What do you want?" is one which - after the initial chit-chat of "hi, hello, nice to meet you, a pleasure to be here, etc." - every good representative of respectable sequencing providers I have met so far will ask as very first question. Usually in the form of "what do you want to sequence and what will you want to use the data for (and what not)?"

### WHAT DO YOU NEED?!

... difference between "want" and "need" ...

Every other question - like where to sequence, which sequencing technology to use, how to process the sequencing data afterwards - is incidental and subordinated to your answer(s) to the question of "what do you want?!" But often sequencing customers get their priorities wrong by putting forward another question:

### WHAT WILL IT COST ME?

And its inevitable companion question "Can you make it cheaper?"

#### Detour - Putting things into perspective -

Come to think of it, people sometimes have very interesting ideas regarding costs. Interesting as in "outright silly." It may be because they do not really know what they want or feel unsure on a terrain unbeknownst to them, and often instead focus their energy on single aspects of a wider project because they feel more at home there. And suddenly the focus lies on haggling and bartering for some prices because, after all, this is something everyone knows how to do, right?

As I hinted earlier, the pure sequencing costs are nowadays probably not the biggest factor in any sequencing project: 454, Illumina, IonTorrent and other technology providers have seen to that. E.g., in 20043/2004 it still cost somewhere between 150 - 200 k€ to get an 8x Sanger coverage of a moderately sized bacterium (4 to 5 mb). Nowadays, for the same organism, you get coverages in the dozens (going with 454) for a few thousand Euro ... or coverages in the hundreds or even thousands (going with Illumina) for a few hundred Euro.

Cost for assembly, finishing and annotation have not followed the same decrease. Yes, advances in algorithms have made things easier in some parts, but not really on the same scale. Furthermore, the "short read" technologies have more than made up for algorithmical complexity when compared to the old Sanger reads. Maybe that "(ultra)long read" technologies will alleviate the problem, but I would not hold my breath for them to really work well.

One thing however has almost not changed at all: your costs of actually doing followup experiments and data interpretation! Remember that sequencing in itself is most of the time not the ultimate goal, you actually want to gain something out of it. Be it abstract knowledge for a paper or concrete hints for producing some compounds or whatever, chances are that you will actually devote a substantial amount of your resources (time, manpower, mental health) into followup activities (lab experiments, genetic engineering, writing papers) to turn the abstract act of sequencing into something tangible, be it papers, fame, new products, money, or whatever you want to achieve.

And this is the place where it pays to stop and think: "what do I want? what are my strengths and where are my weaknesses? where are my priorities?" The English have a nice saying: "Being penny-wise and pound-foolish is not wise." I may add: Especially not if you are basing man months / years of lab work and your career on the outcome of something like sequencing. Maybe I'm spoiled because I have left academia for quite some time now, but in sequencing I always prefer to throw a bit more money at the sequencing process itself to minimise risks of the later stages.

### WHERE TO SEQUENCE?

There's one last detour I'd like to make, and that is the question of "where to sequence?"

#### Detour - Public or private, old-timers or young-timers ? -

Choosing a sequencing provider is highly dependent on your answer to "what do you want?" In case you want to keep the sequencing data (or the very act of sequencing) secret (even only for some time) will probably lead you to commercial sequencing companies. There you more or less have complete control on the data. Paranoid people might perhaps argue that you can have that only with own sequencing equipment and personnel, but I have the feeling that only a minority is able to cough-up the necessary money for purchasing sequencing equipment for a small one-time project.

Instead of companies you could however also look whether one of the existing sequencing centers in the world might be a good cooperation candidate. Especially if you are doing this project within the scope of your university. Note however that there might be a number of gotchas lurking there, beside the obvious "the data is not really secret anymore": sometimes the raw sequencing data needs to be publicly released, maybe earlier than you would like; or the sequencing center imposes that each and every paper you publish with that data as basis has them as (co-)first author.

A related problem is "whom do I trust to deliver good work?" Intuition says that institutes with a long sequencing history have amassed quite some knowledge in this field, making them experts in all three aspects (data generation, assembly & finishing, annotation) of a sequencing project ... and intuition probably isn't wrong there. The same thing is probably true for sequencing companies which have existed for more than just a couple of years, though from what I have seen so far is that - due to size - sequencing companies sometimes really focus on the data generation and rely on partner companies for "assembly" and "annotation". This is not to say that younger companies are bad. Incidentally, it is my belief that in this field, people are still more important than technology ... and every once in a while good people split off a well known institute (or company) to try their luck in an own company. Always look for references there.

The following statement is a personal opinion (and you can call me biased for that): Personally, I am however quite wary of sequencing done at locations where a sequencer exists because someone got a grant to buy one (because it was chic & en-vogue to get a shiny new toy) but where the instrument then slowly starts to collect dust after the initial flurry ... and because people often do not calculate chemistry costs which arise in case they'd really thought of using the machine 24/7. I want to know that technicians actually work with those things every day, that they know the ins and outs of the work, the protocols, the chemistry, the moods of the machine (even an instrument can have a bad day). I honestly do not believe that one can build up enough expertise when operating these things "every once in a while".

### Summary of all the above

All of the above means that depending on what I need the data for, I have the freedom choose among different providers. In case I just need masses of raw data and potential savings are substantial, I might go with the cheapest whom I know to generate good data. If I want good service and second round of data in case I am not 110% satisfied with the first round (somehow people have stopped questioning me there), this is usually not the cheapest provider ... but the additional costs are not really high. If I wanted my data really really quick, I'd search for a provider with Ion Torrent, or MiSeq (I am actually looking for one with a MiSeq, so if anyone knows a good one, preferably in Europe -> mail me). Though I already did transcriptomics on eukaryotes, in case I needed larger eukaryotes assembled de-novo & also annotated, I would probably look for the help of a larger sequencing center as this starts to get dangerously near the fringe of my field of expertise.

In closing this part, here are a couple of guidelines which have not failed me so far for choosing sequencing providers:

- Building a good relationship helps. In case your institute / university already has good (or OK) experience with a provider, ask there first.
- It is a lot easier to build a good relationship with someone who speaks your language ... or a good(!) English.
- I will not haggle for a couple of hundred Euros in a single project, I'll certainly reconsider this when savings are in the tens of thousands.
- Managing expectations: some sequencing projects are high risk from the start, for lots of possible reasons (underfunded, bad starting material, unclear organism). This is *sometimes* (!) OK as long as everyone involved knows and acknowledges this. However, you should always have a clear target ("what am I looking for?") and preferably know in advance how to treat the data to get there.
- Errors occur, stay friendly at first. In case the expectations were clear (see above), the material and organism are not at fault but the data quality somehow is bad, it is not too difficult to have the sequencing provider acknowledge this and get additional sequencing for no added cost.

Regarding the technologies you can use ... it really depends on what you want to do :-) And note that I base my answers on technologies available today without bigger problems: PacBio, Illumina, with IonTorrent as Joker for quick projects. 454 can still be considered, but probably not for too long anymore as Roche stopped development of the technology and thus PacBio takes over the part for long reads. Oxford Nanopore might become a game changer, but they are not just yet

## Specific advice

Here's how I see things as of now (January 2014), which might not necessarily be how others see them.

### Technologies

#### Sanger

Use for: checking assemblies; closing gaps by PCR; checking for a couple of genes with known sequence (i.e., where you can design oligos for).

Do not use for: anything else. In particular, if you find yourself designing oligos for a 96 well plate destined for Sanger sequencing of a single bacterial DNA sample, you (probably) are doing something wrong.

#### Pacific Biosciences

Use for: de-novo of bacteria and lower eukaryotes (or higher eukaryotes if you have the money). PacBio should be seen as the technology to use when getting the best assemblies with least number of contigs is important to you. Also, resequencing of variants of known organisms with lots of genomic reorganisation flexibility due to high numbers of transposons (where short reads will not help in getting the chromosome assembled/mapped correctly).

Do not use for: resequencing of "dull" organisms (where the only differences will be simple SNPs or simple insertion/deletions or simple contig reorganisations at non-repetitive places). Illumina will do a much better and cost effective job there.

As of January 2014: aim for at least 100x coverage of raw data, better 130x to 150x as pre-processing (quality clip, removal of adapters and other sequencing artefacts) will take its toll and reduce the data by up to 1/3. After that, the error correction/self-correction of raw reads into corrected reads will again reduce the data considerably.

It's really a numbers game: the more data you have, the more likely you will also get many of those really long reads in the 5 to 30 Kb range which are extremely useful to get over those nasty repeats.

MIRA will most probably give you longer contigs with corrected PacBio reads than you get with the HGAP pipeline, but the number of indel errors will currently be higher. Either use Quiver on the results of MIRA ... or simply polish the assembly with a cheap Illumina data set. The latter approach will also give you better results than a Quiver approach.

For non-haploid organisms, you might need more coverage to get enough data at ploidy sites to get the reads correctly out of error correction.

Preparation of your DNA sample is not trivial as many methods will break your DNA into "small" chunks which are good enough for Sanger, 454, Illumina or Ion Torrents, but not for PacBio.

#### Illumina

Use for: general resequencing jobs (finding SNPs, indel locations of any size, copy number variations etc.); gene expression analysis; cheap test sequencing of unknown organisms to assess complexity; de-novo sequencing if you are OK with getting hundreds / thousands of contigs (depending on organism, some bacteria get only a few dozen).

Careful with high GC organisms, starting with 60% to 65% GC Illumina reads contain more errors: SNP detection may be less reliable if extreme care is not taken to perform good read clipping. Especially the dreaded GGCxG motif often leads to problems in Illumina reads.

For de-novo assemblies, do NOT (never ever at all and under no circumstances) use the Nextera kit, take TruSeq. The non-random fragmentation behaviour of Nextera leads to all sorts of problems for assemblers (not only MIRA) which try to use kmer frequencies as a criterion for repetitiveness of a given sequence.

#### Ion Torrent

Use for: like Illumina. With three notable exceptions: 1) SNP detection is not as good as with Illumina (more false positives and false negatives) 2) de-novo assemblies will contain more single-base indels and 3) Ion having problems with homopolymers, that technology is not as well suited as complimentary hybrid technology for PacBio as is Illumina (except for high-GC perhaps).

Ion has a speed advantage on Illumina: if you have your own machine, getting from your sample to data takes less time than with Illumina.

Also, it looks like as if Ion has less problems with GC content or sequence motifs than Illumina.

#### Roche 454

That technology is on the way out, but there may be two reasons to not completely dismiss 454: 1) the average read length of 700 bp can be seen as a plus when compared to Illumina or Ion ... but then there's PacBio to take care of read length. 2) the large read-pair libraries work better with 454 than Illumina mate-pair libraries, something which might be important for scaffolding data where even PacBio could not completely resolve long repeats.

### Sequencing de-novo

- On a cheap gene fishing expedition? Probably Illumina HiSeq, at least 100bp, 150 to 250bp or 300bp if your provider supports it well. Paired-end definitely a plus. As alternative: Ion Torrent for small organism (maybe up to 100Mb) and when you need results quickly without caring for possible frameshifts.
- Want some larger contigs? PacBio. Add in cheap Illumina 100bp paired-end (150 to 300bp if provider supports it) to get rid of those last frameshifts which may remain.
- Maybe scaffolding of contigs above? PacBio + Illumina 100bp + a large paired-end library (e.g. 454 20kb)
- Have some good friends at Oxford Nanopore who can give you some MinIon engineering samples? Man, I'd kill for some bacterial test sets with those (especially Bacillus subtilis 168)

### Re-sequencing / mapping

There is a reason why Illumina currently dominates the market as it does: a cheap Illumina run (preferably paired-end) will answer most of your questions in 99% of the cases. Things will get difficult for organisms with high numbers of repeats and/or frequent genome re-arrangements. Then using longer read technologies and/or Illumina mate-pair may be required.

## A word or two on coverage ...

### Low coverage isn't worth it

There's one thing to be said about coverage and de-novo assembly: especially for bacteria, getting more than 'decent' coverage is cheap with any current day technology. Every assembler I know will be happy to assemble de-novo genomes with coverages of 25x, 30x, 40x ... and the number of contigs will still drop dramatically between a 15x Ion Torrent and a 30x Ion Torrent project.

In any case, do some calculations: if the coverage you expect to get reaches 50x (e.g. 200MB raw sequence for a 4MB genome), then you (respectively the assembler) can still throw away the worst 20% of the sequence (with lots of sequencing errors) and concentrate on the really, really good parts of the sequences to get you nice contigs.

Other example: the price for 1 gigabase Illumina paired-end of a single DNA prep is way, way below USD 1000, even with commercial providers. Then you just need to do the math: is it worth to invest 10, 20, 30 or more days of wet lab work, designing primers, doing PCR sequencing etc. and trying to close remaining gaps or hunt down sequencing errors when you went for a 'low' coverage or a non-hybrid sequencing strategy? Or do you invest a few bucks more to get some additional coverage and considerably reduce the uncertainties and gaps which remain?

Remember, you probably want to do research on your bug and not research on how to best assemble and close genomes. So even if you put (PhD) students on the job, it's costing you time and money if you wanted to save money earlier in the sequencing. Penny-wise and pound-foolish is almost never a good strategy :-)

I do agree that with eukaryotes, things start to get a bit more interesting from the financial point of view.

### Catch-22: too high coverage

There is, however, a catch-22 situation with coverage: too much coverage isn't good either. Without going into details: sequencing errors sometimes interfere heavily when coverage exceeds ~60x to 80x for 454 & IonTorrent and approximately 150x to 200x for Solexa/Illumina.

In those cases, do yourself a favour: there's more than enough data for your project ... just cut it down to some reasonable amount: 40x to 50x for 454 & IonTorrent, 100x for Solexa/Illumina.

## A word of caution regarding your DNA in hybrid sequencing projects

So, you have decided that sequencing your bug with PacBio and Illumina (or PacBio and Ion Torrent or whatever) may be a viable way to get the best bang for your buck. Then please follow this advice: prepare enough DNA in one go for the sequencing provider so that they can sequence it with all the technologies you chose without you having to prepare another batch ... or even grow another culture!

The reason for that is that as soon as you do that, the probability that there is a mutation somewhere that your first batch did not have is not negligible. And if there is a mutation, even if it is only one base, there is a >95% chance that MIRA will find it and thinks it is some repetitive sequence (like a duplicated gene with a mutation in it) and splits contigs at those places.

Now, there are times when you cannot completely be sure that different sequencing runs did not use slightly different batches (or even strains).

One example: the SFF files for SRA000156 and SRA001028 from the NCBI short trace archive should both contain E.coli K12 MG-16650 (two unpaired half plates and a paired-end plate). However, they contain DNA from different cultures. Furthermore, the DNA was prepared by different labs. The net effect is that the sequences in the paired-end library contain a few distinct mutations from the sequences in the two unpaired half-plates. Furthermore, the paired-end sequences contain sequences from phages that are not present in the unpaired sequences.

In those cases, provide strain information to the reads so that MIRA can discern possible repeats from possible SNPs.

## Advice for bacteria

### Do not sample DNA from bacteria in exponential growth phase!

The reason is simple: some bacteria grow so fast that they start replicating themselves even before having finished the first replication cycle. This leads to more DNA around the origin of replication being present in cells, which in turn fools assemblers and mappers into believing that those areas are either repeats or that there are copy number changes.

Sample. In. Stationary. Phase!

For de-novo assemblies, MIRA will warn you if it detects data which points at exponential phase. In mapping assemblies, look at the coverage profile of your genome: if you see a smile shape (or V shape), you have a problem.

### Beware of (high copy number) plasmids!

This is a source of interesting problems and furthermore gets people wondering why MIRA sometimes creates more contigs than other assemblers when it usually creates less.

Here's the short story: there are data sets which include one ore several high-copy plasmid(s). Here's a particularly ugly example: SRA001028 from the NCBI short read archive which contains a plate of paired-end reads for Ecoli K12 MG1655-G ().

The genome is sequenced at ~10x coverage, but during the assembly, three intermediate contigs with ~2kb attain a silly maximum coverage of ~1800x each. This means that there were ~540 copies of this plasmid (or these plasmids) in the sequencing.

When using the uniform read distribution algorithm - which is switched on by default when using "--job=" and the quality level of 'accurate' - MIRA will find out about the average coverage of the genome to be at ~10x. Subsequently this leads MIRA to dutifully create ~500 additional contigs (plus a number of contig debris) with various incarnations of that plasmid at an average of ~10x, because it thought that these were repetitive sites within the genome that needed to be disentangled.

Things get even more interesting when some of the plasmid / phage copies are slightly different from each other. These too will be split apart and when looking through the results later on and trying to join the copies back into one contig, one will see that this should not be done because there are real differences.

DON'T PANIC!

The only effect this has on your assembly is that the number of contigs goes up. This in turn leads to a number of questions in my mailbox why MIRA is sometimes producing more contigs than Newbler (or other assemblers), but that is another story (hint: Newbler either collapses repeats or leaves them completely out of the picture by not assembling repetitive reads).

What you can do is the following:

1. either you assemble everything together and the join the plasmid contigs manually after assembly, e.g. in gap4 (drawback: on really high copy numbers, MIRA will work quite a bit longer ... and you will have a lot of fun joining the contigs afterwards)

2. or, after you found out about the plasmid(s) and know the sequence, you filter out reads in the input data which contain this sequence (you can use `mirabait` for this) and assemble the remaining reads.