Whole exomes from single cells: Fludigm C1 update

This was not a planned post but it follows on nicely from today's other one about exomes. This time I'm writing about Fluidigm's new single-cell exome-seq protocol. Yup that's right, whole exomes from 96 single cells! The C1 is an amazing piece of kit (wish I had one) and we've used it a little bit for mRNA-seq. The ability to sequence single-cell genomes and exomes means you can pretty much do whatever you want with a single-cell now. So how do the exomes look?

More on exomes

I've been finding out more about exomes: specifically QC analysis using HS Metrics in Picard. There's loads of useful metrics and I'm hoping to get to a point that I can explain these to users here and also look at the results to try and troubleshoot an experiment. I'm also trying to understand what sort of read length we should be using for exome analysis. An earlier post discussed my thoughts around moving to PE125 or switching to SE125 and running more lanes. In a follow up post (watch this space) I'll try to consider the impact of different run modes: will users/reviewers accept any kind of read for an exome or will they baulk at seeing something different from the paired-end norm?

PS: Your comments on this would be greatly appreciated!

Are PCR-free exomes the answer

I'm continuing my exome posts with a quick observation. There have been several talks recently that I've seen where people present genome and exome data and highlight the drop-out of genomic regions primarily due to PCR amplification and hybridisation artefacts. They make a compelling case for avoiding PCR when possible, and for sequencing a genome to get the very best quality exome.

A flaw with this is that we often want to sequence an exome not simply to reduce the costs of sequencing, but more importantly to increase the coverage to a level that would not be economical for a genome, even on an X Ten! For studies of heterogeneous cancer we may want to sequence the exome to 100x or even 1000x coverage to look for rare mutant alleles. Unfortunately this is exactly the kind of analysis that might be messed up by those same PCR artefact's, namely PCR duplication (introducing allele bias) and base misincorporation (introducing artifactual variants).

PCR free exomes: In my lab we are running Illumina's rapid exomes so PCR is a requirement to complete the Nextera library prep. But if we were to use another method then in theory PCR-free exomes would be possible. Even if we stick to Nextera (or Agilent QXT) then we could aim for very low-cycle PCR libraries. The amount of exome library we are getting is huge, often 100's of nanomoles, when we only need picomoles for sequencing.

Something we might try testing is a PCR-free or PCR-lite (pardon the American spelling) exome to see if we can reduce exome artefacts and improve variant calling. If anyone else is doing this please let me know how you are getting along and how far we can push this.

Sequencing exomes: what sort of read to use?

What's the right way to sequence an exome? We've been looking at Illumina's v4 chemistry for HiSeq 2500 and wondering whether we should jump to PE125bp or not, or should we try to reconfigure our exome capture for shorter or longer fragments.

Exome-seq: Exomes have been a big hit, there are currently over 3000 publications in PubMed with the search term "exome". Given that the first in-solution exome paper was only published in 2009 that's pretty amazing, but then again the exome is an amazing research tool.

Note to readers: This post started out as a writing down my thoughts about whether we should move to longer reads for exomes. But it has become a bit more rambling as I started to find out I need to do some mroe digging. I may well come back to this post with an update or version two...

There are many ways to prepare an exome for sequencing and in my lab we're currently using Illumina's rapid exome kit. We're also about to compare this to Agilent's new SureSelect QXT kit which is a direct competitor to Illumina's Nextera-based offering. But we've never tried Nimblegen or AmpliSeq, however this post is more about how to sequence the exome than prepare it so enough of kit comparisons.

The standard exome: Their are two things you need to consider when sequencing exomes: read depth and read-length. I'm not going to worry about depth in this post, and instead I'm going to focus on read-length. Today most labs appear to be running exomes at PE75bp, a standard which I am not sure has ever been agreed by anyone, but it has been accepted as being good-enough for most projects (Illumina recommend PE75-100). I know of some groups that moved over to PE100 to simplify lab logistics as much as anything else, but I am not clear that there are significant benefits to increasing length so we've stuck at PE75 for the time being.

Are longer reads better: With the advent of v4 chemistry on HiSeq 2500 we should be able to generate high-quality paired-end 125bp reads, albeit with a slightly higher error rate at the end of the read. At first glance this additional data seems too good to ignore, especially when Illumina do not sell a 150cycle SBS kit, and 3x50cycle SBS would not be that much cheaper (and more hassle for my lab staff!) By my reckoning PE75 costs £900 per lane whilst PE125 is £1200, or £300 for an extra 100bp of coverage. So if cost does not prevent us using PE125, should we simply switch?

Insert size vs read-length: As you can see below the average distribution of exome fragments size spans the read-length of the sequencer. The solid black line indicates 150bp (PE75): everything to the left of this will be fragments sequenced with an overlapping reads (opes), whilst everything to the right is sequenced with non-overlapping reads (nopes). As read-length increases the percentage of fragments sequenced with an overlap also increases, at PE100 (dashed line)  this is over 50% of reads, and at PE125 (dotted line) it's about 75% of all fragments. An overlapping read creates some issues as the two reads are not independent, tools need to take the overlap into account when calculating on-target coverage, etc; but it also offers the opportunity to increase variant calling quality by increasing Q-scores in the overlap region.

Exome libraries may not be the best size for sequencing: If a non-overlapping read is the best kind to generate then we may need to reconfigure library prep in the light of v4 chemistry. An interesting comparison can be made to the Agilent Bioanalyser trace below the computed insert size distribution. If you overlay and rescale the two images, then the Agilent trace appears to be peak-shifted to larger fragments, and the right-hand fragment distribution is much broader. This appears to demonstrate the preference of clustering:sequencing for shorter fragments.

Exome libraries are probably the best size for capturing exons: The average exon length in the Human genome is 170 bp with 80–85% exons less than 200bp (Zhu et al & Sakharkar et al) so the 185bp average fragment length seems almost ideal.

Table reproduced from Shkharkar et al 2004

So what's the sweetspot for Exome capture and sequencing: The simple answer is I don't know, and several factors are likely to affect this. As we increase read-length we'll get more fragments with overlapping reads that could be wasteful; the same happens if we decrease fragment size so longer reads give us more and more overlap with higher quality. But unless there are tools to make use of this the data are redundant. So fragments should not be longer than reads.

But fragments are captured by probes of 95bp so we should probably not make fragments shorter than probes.

Exome capture kits contain blocking oligos to prevent adapter:adapter hybridisation and off-target pull-down. As fragment length increases then the amount of near-target sequence captured may increase meaning we should not make fragments too long. A long fragment risks too much off-target enrichment by the secondary capture of off-target fragments.

Lastly (for now) we'd like to be able to use independent fragments for our analysis so read-pairs might be better replaced with longer single-reads, but twice as many. So perhaps the answer is probes that efficiently capture exons with little or no fragment:fragment hybridisation, coupled to single-end 185bp sequencing with low error-rate across the reads.

Dr Evil's exome seqeuncing services

Sequencing service providers are popping up everywhere and offering some great deals on genomes, exomes and RNA-seq. How can this sequencing be so cheap I hear some of my users saying? The costs are usually dependent on a certain volume of work and are likely to bring in lots of the same sort of sequencing at once, while the promotion is on. Running RNA-seq in 96-well plates and across multiple short-read flowcells is very efficient so big savings can be made this way and providers are keen to pass those savings onto you to get your business and hopefully keep it. I think we’ve almost reached the point where NGS reads are a commodity and people just want them as fast and cheaply as possible, just like Sanger sequencing.

Thinking about this made an idea pop into my head, not one I’m going to pursue but one I thought readers of this blog would like to hear about.

Welcome to Dr Evil’s Sequencing Centre:

Targeted RNA-seq methods are here

Illumina and Life Technologies are both launching targeted RNA-seq applications which are likely to become standard tools for many labs; if the price is right.

The ability to target a portion of the genome has revolutionised next-generation sequencing experiments. The analysis of exomes has exploded, custom panels for exome-style pull-down are being used to great effect in 1000’s of samples and amplicon analysis is making it possible to run 10,000’s of samples in a single experiment (we’ve run a HiSeq flowcell with 12288 samples on it, 1536 per lane using Fluidigm – currently unpublished).

The next frontier looks like it could be using the same techniques to target a portion of the transcriptome, again allowing many 1000’s or 10,000’s of samples to be analysed in a single experiment. These technologies are likely to replace real-time PCR for mid- to high-plex studies. Anyone that has tried to run a few 100 TaqMan or SYBR assays on their 96-well qPCR machine will see the potential. And users with BioMark, TLDA, Wafergen, and other high-throughput qPCR systems will see the potential of using just one analysis method (NGS) as their primary data collection tool.

New York Times piece on personalised medicine

The New York Times recently published an article on Foundation Medicine and their approach to personalised cancer genomics (targeted sequencing of 236 cancer genes). The test costs $5800 and is covered by some insurers in the USA. And the reports from the test have caused doctors to change patient therapy. 

So far so good. A personalised test a cancer patient might be able to afford (and they may not have to pay anyway). 

The article also refers to the My Cancer Genome web site, which offers a gene-by-gene portal into Cancer genomics. The site lists different diseases and points users to some of the literature supporting the impact of a particular gene on cancer. For examples a user can search for melanoma and BRAF to find mutations that might affect prognosis. At the same time they can check for clinical trials that might be ongoing.


Targeted sequencing is getting loser and closer to reality in the clinic. The technology we are using in my lab (and others like it) is becoming pretty robust. All that is needed is a good way to make doctors aware of what's possible and get the results into their hands in a way that means they can focus on medicine and not bioinformatics.

ctExome-seq: Circulating tumour exomes as a non-invasive method to track tumour evolution during treatment

Monitoring of cancer patients is an important part of their treatment and this is traditionally done with tests like Computed Tomography imaging (CT) or biomarker analysis. NGS of cancer amplicons, exomes and/or genomes is being discussed as a realistic addition, and possibly an alternative, to these traditional methods. A major hurdle to their use is the fact that analysis of cancers can be hugely complicated by tumour heterogeneity and that serial biopsy of patients to obtain material for sequencing is not a realistic option for many patients. Although the analysis of circulating tumour cells has received a lot of interest, collecting the cells is no easy task (there have been some great papers that show what might be possible from CTC’s, the MALBAC method published in Science is a current favourite of mine). Nitzan Rosenfelds group at the Cambridge Institute has been pioneering the use of circulating tumour DNA (ctDNA) sequencing as a more amenable method to understand cancer genomics.

Next-Gen Sequencing pg’s of DNA with Thruplex (and other methods)

Sequencing from small amounts of nucleic acid is opening up new areas of research. Whilst microarray analysis of single cells has been performed in the past analysing the genome from such a small amount of DNA has been an almost impossible task. Equally, analysis of fragmented DNA in FFPE sections has proven to be technically challenging.

HuID-seq blog

There has been lots of recent activity around using NGS gene resequencing in the clinic. Although clinical DNA sequencing has been an important tool for several decades the explosion in NGS methods for amplicon resequencing has made it feasible for just about any lab to do. Previous posts on this blog have discussed NGS amplicon methods and some of the tools needed to design amplicons.

It is not easy to say clear which technologie(s) will dominate in the clinical space, nor whether small targeted panels will be preferred over more comprehensive and larger panels, medical exomes or even whole genomes. But it does seem pretty clear that amplicon-sequencing is going to be a very important clinical tool.

Why is patient ID important: As we are more easily able to sequence not just multiple genes but also multiple patients in s single NGS run it becomes very important to make sure results are not assigned to the wrong patient. Clinical molecular labs spend a huge amount of time and effort on making sure results don’t get mixed up, but I thought the tests themselves could be improved to determine which patient results came from at the same time as the clinical results are being generated. Just add a large enough number of SNP loci to allow patient identification by comparison to a simple blood-based genotyping assay.

SNP-seq for patient ID: I have been discussing using additional content in amplicon (and other) tests for a year or so, but have never found the time to get in the lab and demonstrate the idea. I asked our Tech-Transfer people about it and they said whilst it was a nice idea there was little that could be protected from an IP perspective. As I am not going to get time to work on it, and as it can’t be protected easily I am hoping this blog will help stimulate discussion and someone will take the idea on board for their research. I call the method HuID-seq.

Comparison to STR profiling: There is already a gold-standard for Human Identification in the STR profiling used in forensic applications. Unfortunately the tests cannot be simply added to an NGS assay. What we need is a level of discrimination so that results cannot be sent to the wrong patient, in theory the HuID-seq could be set at a level significantly lower than forensic STRs. Today 13 STR loci are used in the United States Combined DNA Index System (CODIS) forensic kits. SNPs have lower resolving power and more are likely to be needed but before I get onto that a recent paper deserves a mention. In Biotechniques Bornman et al published an NGS based method that reproduces STR data very nicely (see Short-read, high-throughput sequencing technology for STR genotyping). Potentially this could be added to current tests but I prefer SNPs for a number of reasons.

Why SNPs, which ones and how many: SNPs are a good choice because they are easily assayed by PCR amplification of by in-solution capture methods. SNPs are also already assayed by current NGS methods and perhaps most importantly if coding SNPs are used then RNA-seq data can also be used for HuID-seq. This will be important if array-based gene expression signatures are ported over to NGS. It also means that the HuID-seq method could be used to very good effect in research projects whatever the source of data. It is often important to quality control large experimental datasets to remove duplicate samples or wrongly assigned samples. In the supplementary information for the Nature paper The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups, the authors presented a novel eQTL-like approach to check that the same patients samples were used for whole genome gene-expression and SNP genotyping arrays. They termed the method BeadArray Diagnostic for Genotype and Expression Relationships (BADGER).

Which ones: There were around 250,000 SNPs present on both Affymetrix and Illumina arrays. 2000 could be used for identification, 100 for ethnicity and 200 for gender. All SNPs would allow mapping of samples onto other SNP or sequence based data, e.g. SNP arrays, ChIP-seq, RNA-seq and exomes or genomes. We looked carefully at which of these SNPs might be used in a HuID-seq method and came up with some requirements.

• MAF 0.5 (0.4-0.6)
• Present on most widely used genotyping arrays.
• Coding SNPs
• Ability to predict identity
• Ability to predict ethnicity
• Ability to predict gender

How many: The number of SNPs is going to be higher than the STR loci, but it need not be very high and the “real-estate” taken up by such a HuID-seq panel need not be large in comparison to an NGS clinical test. SNPs need to be present very broadly in the population to be of any use, ideally using SNPs with MAF0.5 gives any individual a 50:50 chance of being homozygous for one allele or heterozygous. If this 50:50 chance holds true for all SNPs, and if the assay used is perfect then (i.e. no errors in genotyping) then just 4 SNPs give a >90% chance of uniquely identifying an individual. Increasing this to 8 results in a 99.5% and 20 SNPs gives a 99.9999% chance. Choosing a set of SNPS such that there is at least one per chromosome arm leads to a set of about 48. The final number of SNPs used could be determined by the requirement for unique identification, cost or complexity of PCR.

Community cohesion: It makes sense that if this approach is going to be used then everyone should use the same set of SNPs. The best way to do this would be to get people from different clinical and research backgrounds in a room to discuss the why’s and wherefores’ of different SNPs and to recommend a set to the community. If Life Tech, Illumina, Roche, or others get marketing too early on then we are likely to see multiple standards. This is exactly what we have in STR kits with the US and Europe using different sets of loci.

There are already some SNPs being used to QC data by the Broad but the GATK pages have been updated and the older page is now a dead link! LifeTech are launching an 8 SNP Ampliseq based Sample ID kit giving a resolution of 1:5000, you spike the SNP targeting reagents into your assay and go. Illumina are aslo collaborating on forensic products, Dr. Bruce Budowle at the Institute of Applied Genetics, University of North Texas discusses forensic NGS applications on You Tube with the IlluminaInc channel.

It probably has not escaped your notice that a HuID-seq panel could be used for cell line authentication as well. This is something that often gets attention but is easily forgotten by PhD students and post-docs until too late. Making a test part of their ChIP-seq or RNA-seq experiment and comparing back to a reference database would be a simple ad-on.

The exome is only a small portion of the genome

Ricki Lewis (Genetic linkage blog) wrote a great guest post for Scientific American on what exome sequencing can’t do. It seems timely considering the explosion of interest in exome sequencing and exome arrays. Not so long ago most people I knew still talked about junk DNA, exome sequencing and exome arrays essentially allows users to ignore the junk to get on with real science. As Ricki points out exome analysis is a phenomenally useful tool but users need to understand what they can’t do to get the most from their studies.

Ricki listed 10 things exomes are not so good for, my list is a lot shorter at just 4.

1. Regulatory sequence is missing (although this is being added, e.g. Illumina).
2. Not all exons are included.
3. Structural variants (CNV, InDel, Inv, Trans, etc) are not easily assayed with current exome products.
4. No two exome products are the same.

Exome analysis has had a real impact, especially on Mendelian diseases that remain undiagnosed. However users need to remember they are only looking at a very small portion of the genome. Ricki puts it this way “the exome, including only exons, is to the genome what a Wikipedia entry about a book is to the actual book”.

I posted a month or so ago about choosing between exome-chip and exome-seq. The explosion in exome-chips has been an even bigger surprise then exome-seq. Illumina admitted that they had been overwhelmed with demand for their array products. It appeared to be pretty clear that exome-seq would take off as soon as the cost came down to something reasonable. However according to Illumina over 1M samples have now been run on exome chips!

Of course analysis of an exome is allowing studies to happen that would never get off the ground if whole genome sequencing were the only option. The cost and relative ease of analysis makes the technology accessible to almost anyone. As the methods and content improve over the next coupe of years this is going to get even easier.

The simplest thing for users to remember is that they are restricting analysis to a subset of the genome. This means that just because you don’t find a variant does not mean one is not lurking outside the exome; absence of evidence is not evidence of absence as statisticians would put it.

It is also helpful to remember that not all exomes are created equal. Commercial products are designed with a price and user in mind. Academic input is usually limited to a few groups and there are always other bits that could be added in. Illumina have done a great job including some of the regulome in their product but the commercial products are in a similar arms race to the one faced by microarray vendors a decade ago. Just because a product targets a bigger exome does not mean it is better for your study.

Exomes are well and truly here to stay. We'll probably see an exome journal soon enough as there is so much interest.

AmpliSeq 2

Ion Torrent released AmpliSeq 2.0 a little while ago. The biggest change is an increase to 3072 amplicons per pool. I saw a Life Tech slide deck which had up to 6144 amplicons pre pool so there looks like more room for improvment.

Who knows when we might see an multiplex PCR exome!

PS: see a previous post about AmpliSeq for more general details.

AmpliSeq design tool demo

I have previously blogged about amplicon sequencing (here, here and here) because I think it is one of the most exciting developments for clinical application of next-generation sequencing. Anyone can access the technology but there is a lot of choice about which system to use that can be off-putting. I'd certainly like to encourage more people to give it a go with their favourite genes, or start collaborating with other groups to design a panel you all might use.

I was asked if I'd like to play with Life Tech's new AmpliSeq design tool before general release and below you'll find out what I thought of it. All-in-all it looks good, is simple to use and the results include primer sequences.

Primer design history: many of you may well have sat down and tried to design primers by eye in a 1000bp stretch of DNA. I know my first experiences back in my undergraduate third year were not great but there were no tools available at the time.

The huge change for me was the release of Primer3, it was an update of a previously unreleased programme called simply Primer written by Mark Daly and Steve Lincoln in Eric Lander's group in the very early '90s. Primer3 was released in 2000, published in the Methods in Molecular Biology series. It is the basis of most primer design software I have ever used. But I don't know if it is used in the AmpliSeq designer.

What is AmpliSeq: AmpliSeq is LifeTechnologie's amplicon sequencing application. It allows up to 1536 amplicons in a single-tube PCR based assay. It is a highly-multiplexed PCR system rather than a droplet, microfluidic, hybridisation or Extension:Ligation based system. Ampliseq requires as little as 10ng of DNA and produces 200 or 150bp amplicons which should amplify FFPE samples very well.

AmpliSeq Designer: LifeTech are about to release a new product for custom panel creation the AmpliSeq Designer. This tool allows creation of custom AmpliSeq panels based on your input of genes (in multiple formats). They say once designed a panel can be ordered and delivered in weeks. Simply log into your Ion Community account, agree to a license (I'd suggest reading it as you don't want your gene list being scanned for interesting panels without getting something back yourself) and get designing!

I uploaded a list of 23 Cosmic genes (here is my list) as gene names and only got a couple of easily fixed errors. You can choose between 150bp and 200bp amplicons. There is currently a 5bp "padding" at the ends of exons but this will become user tuneable later on. Obviously you would not want the primers to be in the exons or you would ever "sequence" the ends, rather just resequence your oligos.

This will be the subject of another post soon to be written about choosing oligo providers.

My first design consisted of 23 target genes, 124KB of sequence, 1081 amplicons and 90.89% coverage of targeted bases.

After hitting "submit targets" I got a message an an automated email saying the results would be emailed soon. They suggest 48 hours for design but I got mine back in just under 4 hours. The results download included four files (available as a zip file), a Bed file, a data sheet, and coverage details and summary files. It looks like there is a very strong correlation between the exon length and the final coverage, with short exons being almost always successfully targeted but longer ones being less so.
There was an odd discrepancy between the number of ampiconsl reported in the web page (1081) and the summary files (1143) however the data sheet containing primer sequences has 1081 pairs. I guess a few targets have droped out completely and primer sequences are never reported.

Primer sequences: A big congratulations to LifeTech for releasing these back to users. However as I did not read the license in any detail there may be restrictions on how these sequences are used.

There is a note in the data sheet file saying that Ampliseq primer sequences contain proprietary modifications. Whether this makes them unsuitable for other applications (I am immediately thinking of many) is not clear but I am sure some users will give it a go. I'd certainly be calling for the other companies to release sequences rather than just send back panels of amplicons.

Barcoding: From the Ampliseq data sheet it looks like only 32 barcodes are available at this time. It is also probable that these are single barcodes at one end of molecule. I very much favour the two barcode strategy where the combination allows fewer primers to be used in a secondary PCR against tag sequences. Just 16 forward and 24 reverse oligos allow a single 384well plate to be amplified and sequenced in one sequencing run.

Of course on Ion Torrent 384 samples would not get the required coverage for 1536 amplicons. But the Proton should be able to work with numbers like these and getting the tools and kits built today will save changing things later on.

The BED file: Included in the zip file is a BED file of my panel. This was very simple to upload to UCSC and visualise coverage across the genes targeted. I have zoomed into TP53 for the example below.

UCSC TP53 BED file

Coverage of my targets: Only Kit achieved 100% coverage of all 5358 target bases in the design. Amplicons for FGFR3 and AKT1 were the worst with only 75% coverage.

It will be very important for amplicon-sequencing users to understand what bases they are likely to see in the final sequence data before they run an experiment on their samples. It would probably help if the community thought a bit more about what the important metrics to report are for this kind of panel. Obviously most user will want to get 100% of all target bases covered. But this is unlikely to be possible with most panels as they increase in size and we should consider how much stringency of coverage can be relaxed.

Coverage is reported for target regions as a percentage. This is the percentage of bases targeted by AmpliSeq and it would be important to monitor final coverage in case there is dropout of particular amplicons in the final sequence data.

One thing that might help in design tools like the AmpliSeq one, Agilent's HaloPlex Design Wizard, Fluidigm's Access Array designer and Illumina's TruSeq Custom Amplicon Design Studio is the ability for users to specify bases that must be covered. TP53 for instance has several hot-spots a user would not want to miss, whereas other Cancer genes are mutated more or less randomly along their sequence. It may be important to specify particular bases for TP53, but 85-95% coverage for non-hot-spot genes may be OK if large numbers of patients are being screened and coverage drop out is random. If coverage drop out is not random then careful review of the literature before finalising a design might be warranted.

Sequencing results: Unfortunately I can't report back on the quality of the final panel. My lab does not have an Ion Torrent, of course if LifeTech want to leave one in the lab for a few days with the reagents I'd happily give you all an update.

Other considerations: I'd like to see tools like this develop and ideally see an open source variant, perhaps in Galaxy? I am sure a lot of people would use such a tool so whoever puts it out can expect a good citation record! The things we are considering are flexibility in amplicon length rather than specific cutoffs, an ability to add tags for any sequencing platform, higher degrees of multiplexing, an ability to target SNPS by very short read sequencing(>10bp), etc.

Please feel free to comment on what you would like to see from Amplicon-sequencing tools.

Choosing between exome-arrays and exome-seq

Illumina and Affymetrix both recently released Exome microarrays.

The "death" of Gene expression microarrays: This is an interesting development considering there has been a lot of debate over the “death of arrays” in the last year or so.

It does looks like 3’ expression arrays are fading fast due to the cost and quality of RNA-seq data and improvements in RNA-seq analysis. Although we’ve not quite stopped using GX arrays yet.

SV-seq vs snpCGH: The relative merits of SV-seq vs snpCGH for copy number and LOH are less clear. 1M (or more) high-quality genotype calls can be obtained cheaply and quickly using arrays and the intensity information allows sensitive copy number detection. Whilst sequencing is ultimately more sensitive to structural-variation as we assay the DNA structure directly, there are real limitations in per-genotype quality with low to medium coverage. It is this that holds sequencing back and I think keeps a market open for snpCGH arrays. The costs and time for medium to high coverage SV-seq can’t yet compete with arrays where you can run 1000 samples in a couple of days for a few hundred dollars each.

Exome-arrays vs Exome-seq: Now exome arrays are vying to compete with exome-seq and to be honest I was quite surprised that array companies are bothering. Especially when one of them happens to be the leading next-gen sequencing company!

GenomeWeb covered the announcement of the products at last years ASHG.

Affy and Illumina aim to offer a fast and economical method to assess exomic variants. They suggest we might use these to follow up exome-seq or to complement GWAS in large cohorts to achieve statistical power in exome centred studies. There has been a huge amount of content generated for microarrays over the past few years of sequencing. And variants in the exome are likely to be functionally relevant (although we should make sure regulatory regions are equally well assayed).

Affy’s exome array targets around 320,000 snps, InDels and other variants. It is being offered at $70 to customers.

Illumina’s exome array comes in three versions, the HumanExome targets around 250,000 snps, the OmniExpressExome targets 950,000 and the Omni5Exome targets 4.75M snps. According to the companies press release the 250,000 exonic SNPs were identified from an analysis of over 12,000 sequenced genomes. It was being offered at $45 to early customers. The HumanExome chip is being offered at $80.

A difference of 70,000 exonic SNPs sounds quite large but I’m not sure what the real impact will be on research projects. Whilst Affymetrix have not revealed the scale of interest Illumina have said they are expecting to process over 1M samples on their Exome-chips.

The costs quoted appear to be chip only prices and one of the issues facing researchers as chips drop in cost is the relatively static price of the processing. It looks like Illumina’s OmniExpressExome for instance will cost about £150-200 to run once service costs are incorporated.

At around £200-250, Exome-seq is slightly more expensive but currently much more labour intensive. Illumina recently dropped the price of an exome to $50, if you include a TruSeq library prep at $50 and a one-sixth of a lane of PE75bp sequencing the total Exome-seq cost is £200-250. The major limitation compared to arrays may not be cost but is more likely to be time as it takes about 7 days to sequence 96 exomes on HiSeq 2000.

Which might you use? I think the arrays are going to fill a real need for researchers who have large sample collections and want to access exome content. Many more samples can be run in a given time for a given budget on arrays than on a sequencer. I think smaller projects are likely to be run on a sequencer though. And improvements to the Exome-seq workflows might make chips seem clunky to use.

Can we get rid of a two day hyb and still generate high quality exome data?

When might we start using newer library prep technologies like Nextera to access low DNA samples for Exome-seq?

Can we analyse FFPE exomes? Both chips and sequencing suffer here but this is a huge potential market.

Arrays RIP? My career in genomics started over ten years ago when I set up an Affy lab processing expression arrays with just 8000 probes. I have run lots of arrays since then including one project of 2500 HT12’s which we completed in just five weeks, running this number of samples on a sequencer is tough. Whether arrays will completely disappear or not will be a discussion for a while yet I think.