I posted last week about the emerging field of Immunogenomics. Today I’ve taken a brief look at what is happening in Radiogenomics. Whilst this field is not using NGS in such a comprehensive way I think it can only be a matter of time before it ramps up.
Radiotherapy is an important tool in treating cancer and the impact of genomics on the field was recognised by researchers in Cambridge and Manchester in 2004. Those researchers started the Radiogenomics: Assessment of Polymorphisms for Predicting the Effects of Radiotherapy study (RAPPER) and also helped found the International Radiogenomics Consortium.
The ultimate aim of this consortium is to individualise radiation dose prescription for patients maximising the impact on the tumour whilst minimising normal tissue damage for the patient. They aim to find genomic variants that can help predict how patients will respond to radiotherapy and allow tailoring of treatment. This is somewhat similar to pharmacogenomics approaches used for drugs like Warfarin where SNP genotyping can help establish the correct dose for individual patients. The consortium should make it easier to collect samples for genomic studies and also spur development of methods for radiogenomic research.
The consortium is likely to also learn a lot more about the biology of radiation-induced tissue and DNA damage. Whilst understanding how individuals may respond to radiotherapy is a primary goal, hopefully a better understanding of biology may lead to a list of genes that might be mutated in tumours making them more susceptible to radiotherapy as well.
The Radiogenomics Consotium conducted a GWAS in radiotherapy patients (Independent validation of genes and polymorphisms reported to be associated with radiation toxicity: a prospective analysis study. Lancet Oncol. 2012) to address concerns over how underpowered previous research on late side-effects had been. Late side-effects can have serious impacts on patients and their treatment. This prospective study genotyped 92 SNPs (selected from previous studies) in 1600 breast and prostate cancer patients using the Fluidigm 96.96 Dynamic Arrays. None of the SNPs previously reported to have a significant associations with radiation sensitivity were confirmed. The consortium suggested that the previous associations were “dominated by false-positive associations due to small sample sizes, multiple testing, and the absence of rigorous independent validation attempts in the original studies”.
As the costs of sequencing continue to fall and as associations are found it is likely that NGS will become a more important tool for the consortium. Longitudinal studies of cancer patients can be incredibly revealing and comparison of cancer genome and normal genome with radiotherapy follow up data is likely to yield interesting results.
Wednesday, 12 September 2012
Thursday, 6 September 2012
Immunogenomics is coming
The immune system is becoming easier to investigate as new methods based on nextgen sequencing are published. I am not an immunologist and the complexities of the immune system for me are stuck back in the days of my undergraduate training. And that was in the 90’s!
Nature and the HudsonAlpha Institute are hosting the first Immunogenomics conference next month bringing together scientists from many disciplines to learn about large-scale immune sequencing projects; epigenetics and the immune system and many other topics. Immunogenomics looks like it is going to make headlines next year.
There have been several papers describing HLA typing (e.g. Gabreil 2009 & Bentley 2009 using 454 and more recently Wang 2011 using HiSeq) and many groups are working on using next-gen methods to replace older tests.
A new product from Sequenta is aiming to make this kind of analysis simple to do for any user. The Lymphosight platform uses a multiplex PCR to amplify the IgH, IgK, TRB, TRG, TRD immune cell receptor loci, allowing each T or B cell to be characterised and counted. Immune cell proliferation in response to disease and other studies might be far easier to carry out using this new kit. With 100’s of millions of reads coming from HiSeq, and eventually Proton, even fairly rare immune cells should be detectable in a high background.
The company discuss a test they ran where sequences associated with a B cell tumour were diluted into a normal background at 1:1,000,000. They got very reproducible, quantitative results and a useful dynamic range that compares well to flow cytometry methods currently being used. They expect Lymphosight to be useful in monitoring of minimal residual disease.
David Haussler Director, Centre for Biomolecular Science and Engineering at UCSC said 'We can read genomes from your immune cells. They adapt throughout your lifetime so they can protect you from diseases. Reading those genomes will be important, and you’re going to hear a lot about them next year.'
I recently visited TRON, a spin out from Mainz University Medical Center, where they are conducting translational research in the field of oncology and immunology. One of their aims is to take personalised immunogenomic markers and turn these into personalised Cancer vaccines. The head of TRON, Ugur Sahin just published a very interesting article in OncoImmunology where they describe using NGS to demonstrate a proof-of-concept for identification of immunogenic tumour mutations that are targetable by individualised vaccines. They analysed a melanoma cell line and found over 500 non-synonymous expressed somatic mutations, one third of which were immunogenic. From these they made long peptides of 27aa length and tested these for immune response. 11 of these immunogenic tumour-specific peptides effectively immunised mice against the Tumorigenic cell line (see figure 1 from their paper below).
I am sure Sequenta are hoping groups like these will be using Lymphosight to do perform their analysis of the Immune repertoire.
Nature and the HudsonAlpha Institute are hosting the first Immunogenomics conference next month bringing together scientists from many disciplines to learn about large-scale immune sequencing projects; epigenetics and the immune system and many other topics. Immunogenomics looks like it is going to make headlines next year.
There have been several papers describing HLA typing (e.g. Gabreil 2009 & Bentley 2009 using 454 and more recently Wang 2011 using HiSeq) and many groups are working on using next-gen methods to replace older tests.
A new product from Sequenta is aiming to make this kind of analysis simple to do for any user. The Lymphosight platform uses a multiplex PCR to amplify the IgH, IgK, TRB, TRG, TRD immune cell receptor loci, allowing each T or B cell to be characterised and counted. Immune cell proliferation in response to disease and other studies might be far easier to carry out using this new kit. With 100’s of millions of reads coming from HiSeq, and eventually Proton, even fairly rare immune cells should be detectable in a high background.
 |
| Lymphosight workflow from Sequenta website |
The company discuss a test they ran where sequences associated with a B cell tumour were diluted into a normal background at 1:1,000,000. They got very reproducible, quantitative results and a useful dynamic range that compares well to flow cytometry methods currently being used. They expect Lymphosight to be useful in monitoring of minimal residual disease.
David Haussler Director, Centre for Biomolecular Science and Engineering at UCSC said 'We can read genomes from your immune cells. They adapt throughout your lifetime so they can protect you from diseases. Reading those genomes will be important, and you’re going to hear a lot about them next year.'
I recently visited TRON, a spin out from Mainz University Medical Center, where they are conducting translational research in the field of oncology and immunology. One of their aims is to take personalised immunogenomic markers and turn these into personalised Cancer vaccines. The head of TRON, Ugur Sahin just published a very interesting article in OncoImmunology where they describe using NGS to demonstrate a proof-of-concept for identification of immunogenic tumour mutations that are targetable by individualised vaccines. They analysed a melanoma cell line and found over 500 non-synonymous expressed somatic mutations, one third of which were immunogenic. From these they made long peptides of 27aa length and tested these for immune response. 11 of these immunogenic tumour-specific peptides effectively immunised mice against the Tumorigenic cell line (see figure 1 from their paper below).
I am sure Sequenta are hoping groups like these will be using Lymphosight to do perform their analysis of the Immune repertoire.
Monday, 3 September 2012
How to make Outlook "out-of-office" work for you
Apologies to readers who might have been hoping for some posts over the past few weeks but I have been offline whilst holidaying in France and Spain.
One of my "to-do's" before I left was to respond to the "change your password or you'll be locked out" email from our IT manager. This was one thing that got missed at the end of the frantic Friday afternoon before leaving and subsequently I could not log on, even through webmail.
However, this made for a lovely and uninterrupted holiday and I am sure made it easier to forget about work as there was little point to even try and get online. As a result though I have come back to over 800 emails.
This got me wondering about what I might be writing in my out of office message next time I go away. I think it should go something like this...
"I am currently out-of-the-office on holiday until the 1st of September and will not have any access to email whilst away. If your message really is important then please send it again on the 2nd of September as I will be deleting every email I receive between now and my return to work. Sorry for any inconvenience."
This would certainly make the task I now face much easier!
PS: Normal posting will resume shortly.
One of my "to-do's" before I left was to respond to the "change your password or you'll be locked out" email from our IT manager. This was one thing that got missed at the end of the frantic Friday afternoon before leaving and subsequently I could not log on, even through webmail.
However, this made for a lovely and uninterrupted holiday and I am sure made it easier to forget about work as there was little point to even try and get online. As a result though I have come back to over 800 emails.
This got me wondering about what I might be writing in my out of office message next time I go away. I think it should go something like this...
"I am currently out-of-the-office on holiday until the 1st of September and will not have any access to email whilst away. If your message really is important then please send it again on the 2nd of September as I will be deleting every email I receive between now and my return to work. Sorry for any inconvenience."
This would certainly make the task I now face much easier!
PS: Normal posting will resume shortly.
Friday, 10 August 2012
Battle of the benchtops part II (you'll need a strong bench for one of these!)
Comparison papers help.
As do beers at AGBT!
The latest sequencer comparison paper: Mike Quails group at the Sanger published a comparison of PGM, MiSeq and PacBio (interesting choice of the third platform). They sequenced several small genomes that varied massively in GC content. It was interesting to me that these genomes are the routine test genomes for Mikes group, most of us would shudder if a user asked us to sequence something with 20% GC on HiSeq!
Table 1 is excellent reading and should help people in making purchasing decisions. Collecting all this information together needs to be done by each individual institute as prices can vary quite widely. But the table as it stands should allow anyone to make basic comparisons and also see what is missing that they might need to put greater effort into. In the paper they say that although the raw error rate is significantly different for the instruments compared, the affect on SNP calling is negligible given sufficient coverage. 15x appeared fine for the genomes tested. I’d prefer to have seen this in the table as well, to act as a counter to claims around error rates from sales people! They compared most of the things you would want to when deciding what to buy (see the table for everything). The sequencing costs differ significantly per Gb at $500, $1000 and $2000 for MiSeq, PGM 318 and PacBio respectively. This compares to about $50 per GB on HiSeq.
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| Table 1 from the paper |
How did they do the comparison: Genomes sequenced included Bordetella pertussis (68% GC), Salmonella Pullorum (52% GC), Staphylococcus aureus (33% GC) and Plasmodium falciparum (19% GC). They made PCR-free or PCR amplified libraries for MiSeq PE150bp runs, or HiSeq PE75bp lanes allowing a direct comparison of the impact of PCR. Additionally they prepared Nextera libraries from three of the genomes sequenced (Bp, Sa & Pf) and whilst two produced “remarkably even” data the Pf genome was very biased. They made PGM libraries using physical shearing and “Fragmentase” digestion using the Ion Xpress kits and showed both to be comparable. These were run on 316 chips for 65 cycles, generating mean read lengths of 120 base pairs. Standard PacBio libraries were prepared and sequenced using C1 chemistry on multiple SMRT-cells how many?
What did they find: PGM struggled with the very AT rich Pf genome, and the bias appeared to be partly in the library-prep. By tweaking the protocol and swapping the polymerase for a better one they demonstrated a significant improvement in results. Why don’t all companies do this kind of testing before releasing products on us users, using the best polymerase or ligase available can make a huge difference.
Error rates were best for MiSeq, no surprise to Illumina users there. But there was no impact on true-SNP calling with PGM doing best at 15x genome coverage although it did produce more incorrect SNP calls. PGM and MiSeq correctly called 82% and 76% of SNPs and produced 1800 and 1300 incorrect SNP calls respectively. For Illumina MiSeq made more correct SNP calls than HiSeq or GAIIx and Nextera library prep worked as well as the standard protocol. Both MiSeq and PGM’s built-in variant calling was inadequate; MiSeq reporter called 7% and Torrent suite called 1.5% of variants. SNP calling for PacBio was hampered by a lack of tools as most are designed for short-read data.
A word of caution: The paper is out-dated as are all comparisons and the authors are happy to acknowledge this. It takes time to perform an experiment like this, analyse it and finally write it up. C2 chemistry was used for PacBio and a new method has been described for magnetic loading of chips. MiSeq now has 500bp kits available and even more reads. PGM has error rate has improved. MiSeq has an upgrade being rolled out now for more and longer reads. To be fair to the non-Illumina platforms MiSeq is based on a pretty mature technology whilst Ion and PacBio should be given some time to catch-up (and perhaps overtake), some of the issues with the PGM and PacBio might be resolved by evolution.
GenomeWeb had comments from Ion, Illumina and PacBio. Ion and Illumina both said the comparison was fair. Ion clarified this by saying that the data showed what was possible in 2011 but that error rate was now just 0.4%. Whilst IlluminaLoman et al presented.
Mike also spoke to GenomeWeb and said that the same test genomes are still being run and that the results were as valid today as back in 2011. Significant improvements had come from PGM 200 cycle kits and the C2 chemistry for PacBio.
I am confident there will be more of these comparisons in the next few months. Expect at least one AGBT presentation and lots more discussion over beers.
See you at the bar perhaps?
What do celebrities think about science and what do scientists think of celebrity genomes?
Sense About Science is an organisation that tries to provide expert advice on scientific matters to whoever needs it. They monitor the papers and news and produces an annual “Celebrities and Science” round up of the best and worst comments from people in the public eye. The organisation behind Sense About Science has come under some criticism for being pro-GM and a bit radical and certainly not everyone is a fan. But I enjoyed reading through their annual reviews and wanted to share a few of my favourite comments. The best for me was from Nicole ‘Snooki’ Polizzi who said “the oceans were salty because of all the whale sperm”! See the bottom of this post for a selection from the last three years round-ups.
Sense About Science scan many publications looking for comment; of course celebs and politicians don’t always get it wrong but it is far easier to pick up on the crazies out there. There appear to be fewer celebrities who deny evolution or suggest “fossil fuels” aren’t running out, whilst some politicians careers appear to be built on such claims.
Jeff chaired the debate and before starting the room was evenly split between those who agreed, disagreed or were undecided on the statement “celebrity genomes are a useful contribution to science and society”.
The debate focused around how useful genomes from celebrities were in creating a dialogue between scientists and the public. Paul argued that celebrity genomes are no more important than non-celebrity genomes, so what makes celebrities qualified to speak about genomics? Ewan argued that celebrity genomes have contributed to science, even if only a little. At the end of the debate Jeff asked the audience to judge what impact they thought celebrity genomes had on science, 33% said positive, 62% said negative and 4% were undecided. He also asked the audience if they thought celebrity genomes had had an impact on society, 41% positive, 51% negative and 8% were undecided.
During the debate Paul talked about the impact celebrities can have as patient advocates using Michael J Fox and Parkinsons as an example. Celebrities have as much chance of developing cancer as any of us and as they get their cancer genomes sequenced and see a benefit from the “treatment” they are uniquely placed to talk about the impact in a way that is going to get across to more people than coverage of a Nature paper on the BBC six o’clock news will ever do.
We should be trying to engage with this as much as possible, shouldn’t we?
PS: If you are a celebrity (why wouldn’t they be reading my blog?) and need some advice then help is just a phone call away, call sense about science on +44(0)20 7478 4380. I can’t promise they can say how many reads you’ll need for your next exome sequencing experiment!
PPS: If you want your celebrity genome sequenced there are plenty of labs in LA.
My pick of the best and worst from the annual round-ups.
Sense About Science scan many publications looking for comment; of course celebs and politicians don’t always get it wrong but it is far easier to pick up on the crazies out there. There appear to be fewer celebrities who deny evolution or suggest “fossil fuels” aren’t running out, whilst some politicians careers appear to be built on such claims.
What do Scientists think of celebrity genomes? Jeff Barrett's web page at the Sanger has coverage of a debate on the value of celebrity genomes between Ewan Birney and Paul Flicek. This was part of a series of events at the Sanger institute looking at the relationship between society and personal genomics.
Jeff chaired the debate and before starting the room was evenly split between those who agreed, disagreed or were undecided on the statement “celebrity genomes are a useful contribution to science and society”.
The debate focused around how useful genomes from celebrities were in creating a dialogue between scientists and the public. Paul argued that celebrity genomes are no more important than non-celebrity genomes, so what makes celebrities qualified to speak about genomics? Ewan argued that celebrity genomes have contributed to science, even if only a little. At the end of the debate Jeff asked the audience to judge what impact they thought celebrity genomes had on science, 33% said positive, 62% said negative and 4% were undecided. He also asked the audience if they thought celebrity genomes had had an impact on society, 41% positive, 51% negative and 8% were undecided.
During the debate Paul talked about the impact celebrities can have as patient advocates using Michael J Fox and Parkinsons as an example. Celebrities have as much chance of developing cancer as any of us and as they get their cancer genomes sequenced and see a benefit from the “treatment” they are uniquely placed to talk about the impact in a way that is going to get across to more people than coverage of a Nature paper on the BBC six o’clock news will ever do.
We should be trying to engage with this as much as possible, shouldn’t we?
PPS: If you want your celebrity genome sequenced there are plenty of labs in LA.
My pick of the best and worst from the annual round-ups.
Positive:
Bonnie Tyler when questioned about trying acupuncture said “I lost some weight but I was also on a more sensible diet at the same time which, if I’m cynical, is more likely the reason for the weight loss.” And Natascha McElhone’s comments about tetanus after a visit to Angola: “It’s completely preventable if you’re inoculated against it.”
Negative:
Heather Mills “meat sits in your colon for 40 years and putrefies, and eventually gives you the illness you die of. And that is a fact.”
Roger Moore “eating foie gras can lead to Alzheimer’s, diabetes and rheumatoid arthritis. In short, eating foie gras is a tasty way of getting terminally ill.” I don’t eat foie gras on compassionate grounds but it is unlikely to be the cause of so many diseases, and I am not sure any of those Sir Patrick listed are actually terminal?
Alex Reid gave out a horrible message about unprotected sex saying “it’s actually very good for a man to have unprotected sex as long as he doesn’t ejaculate” and “semen has a lot of nutrition. A tablespoon of semen has your equivalent of steak eggs, lemons and oranges.” Irresponsible nutter if you ask me!
Julia Sawalha doesn’t get inoculated or take anti-malarials but uses “ homeopathic alternatives, called ‘nosodes’” and said “I’m the only one who never goes down with anything.”
Joanna Lumley, her AbFab co-star put the increase in cancer down to “the growth hormones in the food we eat, that try to make all the chickens, sheep and cows, more productive”.
Sarah Palin who’s autobiography “Going Rogue” says that she “didn’t believe in the theory that human beings — thinking, loving beings — originated from fish that sprouted legs and crawled out of the sea or from monkeys who eventually swung down from the trees.” Yikes how can such strong anti-evolution views be held by someone who (from a UK news coverage perspective) holds some power in the USA?
Michelle Bachman, member of the US House of Representatives and Republican Presidential Candidate, told journalists that a woman had told him her daughter suffered mental retardation after receiving the HPV vaccine, and that this vaccination program has dangerous consequences. What is the likelihood she is a right-wing, pro-christian, pro-guns, anti-abortion Republican?
These last two particularly disturb me. The first highlights how nuts some politicians are. The second because as the UK MMR scare showed, bad science can become mainstream fact and affect us all in a very negative way.
We shouldn’t believe everything we hear in the press, but politicians surely have an obligation to be careful about what they say.
Bonnie Tyler when questioned about trying acupuncture said “I lost some weight but I was also on a more sensible diet at the same time which, if I’m cynical, is more likely the reason for the weight loss.” And Natascha McElhone’s comments about tetanus after a visit to Angola: “It’s completely preventable if you’re inoculated against it.”
Negative:
Heather Mills “meat sits in your colon for 40 years and putrefies, and eventually gives you the illness you die of. And that is a fact.”
Roger Moore “eating foie gras can lead to Alzheimer’s, diabetes and rheumatoid arthritis. In short, eating foie gras is a tasty way of getting terminally ill.” I don’t eat foie gras on compassionate grounds but it is unlikely to be the cause of so many diseases, and I am not sure any of those Sir Patrick listed are actually terminal?
Alex Reid gave out a horrible message about unprotected sex saying “it’s actually very good for a man to have unprotected sex as long as he doesn’t ejaculate” and “semen has a lot of nutrition. A tablespoon of semen has your equivalent of steak eggs, lemons and oranges.” Irresponsible nutter if you ask me!
Julia Sawalha doesn’t get inoculated or take anti-malarials but uses “ homeopathic alternatives, called ‘nosodes’” and said “I’m the only one who never goes down with anything.”
Joanna Lumley, her AbFab co-star put the increase in cancer down to “the growth hormones in the food we eat, that try to make all the chickens, sheep and cows, more productive”.
Sarah Palin who’s autobiography “Going Rogue” says that she “didn’t believe in the theory that human beings — thinking, loving beings — originated from fish that sprouted legs and crawled out of the sea or from monkeys who eventually swung down from the trees.” Yikes how can such strong anti-evolution views be held by someone who (from a UK news coverage perspective) holds some power in the USA?
Michelle Bachman, member of the US House of Representatives and Republican Presidential Candidate, told journalists that a woman had told him her daughter suffered mental retardation after receiving the HPV vaccine, and that this vaccination program has dangerous consequences. What is the likelihood she is a right-wing, pro-christian, pro-guns, anti-abortion Republican?
These last two particularly disturb me. The first highlights how nuts some politicians are. The second because as the UK MMR scare showed, bad science can become mainstream fact and affect us all in a very negative way.
We shouldn’t believe everything we hear in the press, but politicians surely have an obligation to be careful about what they say.
Wednesday, 8 August 2012
What happened to Illumina’s single molecule sequencing or do you remember Solexa’s SMA-seq?
Eight
years is a long time in NGS. I recently re-read a 2004 article in Pharmacogenomics 2004,
and also found a EBI presentation from Clive Brown and Ewan Birney. Both of
these were from a small company based in Cambridgeshire called Solexa.
At the time of publication they had only just identified their first
alpha-test site and the presentation talked about a prototype instrument
ready for the end of 2004.
Trademarks mentioned in the paper such as SMA-seq and TotalGenotyping have not, I suspect, been heard of by most Illumina sequencing users (including myself).
The paper describes where Solexa came from (Shankar Balasubramanian and David Klenerman's patents of 1998 spun out of Cambridge University Department of Chemistry). It mentions Solexa's demonstration version of “a system that will allow rapid, base-by-base comparison of genomic DNA sequences” and that this will produce “four or five orders of magnitude improvement over conventional sequencing”. Read lengths of just 25-30bp are proposed, and a nice graph illustrates how just over 80% of the Human genome is uniquely mappable with these incredibly short reads.
Simon Bennett, business development director of Solexa at the time and author of the Pharmacogenomics paper suggests that Solexa will achieve the $1000 genome within the next ten years. That leaves us two more years to get to $1000 genomes. It does not seem unreasonable that we’ll get there although more discussion today is about the cost of bioinformatics analysis!
What happened to single molecule sequencing: There is an overview of the Solexa Single Molecule ArrayTM technology that the paper suggest can analyse a Human genome in a single experiment. As described there were just 100,000 DNA molecules per cm2 compared to 100M cm2 today. The basic chemistry description is unchanged from current SBS, although only 25 bases were being sequenced at the time of publication.
It is only towards the end of the paper that Solexa’s acquisition of Manteia’s solid surface bridge-amplification technology, this is the clustering we know and love today. Up until this point Illumina had been focusing on single molecule sequencing. Without the acquisition of Manteia perhaps Solexa would have continued to chase single molecule sequencing and ended up like Helicos or Pacific BioSciences. As it stands clustering and SBS chemistry have been the bedrock of next-gen sequencing for the past five years.
Personally I’d bet Illumina are still putting lots of effort into single molecule approaches, and not just by investing in companies like ONT. I’d like to know if it would be possible to sequence single molecules on a HiSeq with a more sensitive camera (massive oversimplification I know)? Imagine 1000M single molecule reads! This might not be what we ultimately use for single-molecule but I think we can be certain there is a lot more coming for next-gen in the next eight years.
PS: Would SOLiD have been the dominant technology if Agencourt had bought Manteia instead? Perhaps we should have a genomics version of Marvel’s “What if” comic books from the 80’s?
PPS: The Illumina history lesson also taught me that we share half our genes with bananas!
 |
| Prototpye GA1 |
The paper describes where Solexa came from (Shankar Balasubramanian and David Klenerman's patents of 1998 spun out of Cambridge University Department of Chemistry). It mentions Solexa's demonstration version of “a system that will allow rapid, base-by-base comparison of genomic DNA sequences” and that this will produce “four or five orders of magnitude improvement over conventional sequencing”. Read lengths of just 25-30bp are proposed, and a nice graph illustrates how just over 80% of the Human genome is uniquely mappable with these incredibly short reads.
Simon Bennett, business development director of Solexa at the time and author of the Pharmacogenomics paper suggests that Solexa will achieve the $1000 genome within the next ten years. That leaves us two more years to get to $1000 genomes. It does not seem unreasonable that we’ll get there although more discussion today is about the cost of bioinformatics analysis!
What happened to single molecule sequencing: There is an overview of the Solexa Single Molecule ArrayTM technology that the paper suggest can analyse a Human genome in a single experiment. As described there were just 100,000 DNA molecules per cm2 compared to 100M cm2 today. The basic chemistry description is unchanged from current SBS, although only 25 bases were being sequenced at the time of publication.
It is only towards the end of the paper that Solexa’s acquisition of Manteia’s solid surface bridge-amplification technology, this is the clustering we know and love today. Up until this point Illumina had been focusing on single molecule sequencing. Without the acquisition of Manteia perhaps Solexa would have continued to chase single molecule sequencing and ended up like Helicos or Pacific BioSciences. As it stands clustering and SBS chemistry have been the bedrock of next-gen sequencing for the past five years.
Personally I’d bet Illumina are still putting lots of effort into single molecule approaches, and not just by investing in companies like ONT. I’d like to know if it would be possible to sequence single molecules on a HiSeq with a more sensitive camera (massive oversimplification I know)? Imagine 1000M single molecule reads! This might not be what we ultimately use for single-molecule but I think we can be certain there is a lot more coming for next-gen in the next eight years.
PS: Would SOLiD have been the dominant technology if Agencourt had bought Manteia instead? Perhaps we should have a genomics version of Marvel’s “What if” comic books from the 80’s?
PPS: The Illumina history lesson also taught me that we share half our genes with bananas!
Friday, 3 August 2012
Is visual QC of NGS libraries needed anymore?
I have been using the Bioanalyser since its introduction in 1999. Originally intended for QC analysis of total RNA for microarray studies it quickly became a standard tool for many labs. Over the past few years we have run almost as many NGS libraries on DNA 1000 assays as we have RNA chips.
The Bioanalyser has been a great tool for quality control of NGS libraries. Users can clearly see if they have prepared a high-quality library, if there is lots of adapter-dimer present and if the insert size is what they expected. Unforunately running the Bioanalyser is a bit of a pain once you have more than 12 or 24 libraries.
In my lab we are now preparing 24, 48 and 96 libraries in each batch. QC of these has become too much work using current methods so we looked at alternatives. This included the Caliper LabChip GX, Shimazdu MultiNA, Agilent ScreenTape, Qiagen QIAxcel and Advanced Analytical’s Fragment Analyser (see the bottom of this post for a full list of features).
From our analysis of the system features we asked for demonstrations of the Caliper and Advanced Analytical instruments. These two both appeared to give us the throughput and sensitivity we need, both systems worked well and I know of several labs using these instruments very successfully. However we decided not to invest in a high-throughput Bioanalyser.
Why not and what do we want from library QC: most users want sequence results as soon as possible and are happy with some libraries failing so for some the QC is seen as a bar that gets in the way of their science. My lab wants to satisfy all users and return the highest percentage possible of high quality sequencing runs. Generating 40M reads of a poor library is no use to anyone.
With the introduction of 96 and 384 index kits from companies like Bioo Scientific and with Illumina finally catching up with the TruSeq HT kits I think we are ready to ditch gel-based analysis. Instead we will start using a QC pipeline that will use the data from a single lane analysis of up to 96 libraries. We can look at computed insert-size, verify quantification by checking pooling ratios, screen for adapter-dimer or contamination with other genomes and make sure duplication rates are not too high. Even with 96 samples we should get around 1-2M reads each, and some readers of this blog may remember when 1 M reads was considered enough for ChIP-seq analysis, let alone QC! There are also some hints that 1M reads might be acceptable for basic differential gene expression analysis of highly expressed transcripts.
We’ll be slowly retiring the Bioanalyser type analysis of libraries and using the qPCR quantification as a simple QC tool for pass/fail decisions. We might even get to a point that we only quantify the final pool after mixing equal volumes of all 96 libraries, such that cluster density is spot-on. Then we can use the sequence demultiplexing to indicate the actual balance of indexes to re-pool for the final high read number sequencing.
High Throughput Bioanalyser Platform Features
Caliper - Labchip GX
I think we are going to stop using it for all but a small proportion of libraries by next year.
In my lab we are now preparing 24, 48 and 96 libraries in each batch. QC of these has become too much work using current methods so we looked at alternatives. This included the Caliper LabChip GX, Shimazdu MultiNA, Agilent ScreenTape, Qiagen QIAxcel and Advanced Analytical’s Fragment Analyser (see the bottom of this post for a full list of features).
From our analysis of the system features we asked for demonstrations of the Caliper and Advanced Analytical instruments. These two both appeared to give us the throughput and sensitivity we need, both systems worked well and I know of several labs using these instruments very successfully. However we decided not to invest in a high-throughput Bioanalyser.
Why not and what do we want from library QC: most users want sequence results as soon as possible and are happy with some libraries failing so for some the QC is seen as a bar that gets in the way of their science. My lab wants to satisfy all users and return the highest percentage possible of high quality sequencing runs. Generating 40M reads of a poor library is no use to anyone.
With the introduction of 96 and 384 index kits from companies like Bioo Scientific and with Illumina finally catching up with the TruSeq HT kits I think we are ready to ditch gel-based analysis. Instead we will start using a QC pipeline that will use the data from a single lane analysis of up to 96 libraries. We can look at computed insert-size, verify quantification by checking pooling ratios, screen for adapter-dimer or contamination with other genomes and make sure duplication rates are not too high. Even with 96 samples we should get around 1-2M reads each, and some readers of this blog may remember when 1 M reads was considered enough for ChIP-seq analysis, let alone QC! There are also some hints that 1M reads might be acceptable for basic differential gene expression analysis of highly expressed transcripts.
We’ll be slowly retiring the Bioanalyser type analysis of libraries and using the qPCR quantification as a simple QC tool for pass/fail decisions. We might even get to a point that we only quantify the final pool after mixing equal volumes of all 96 libraries, such that cluster density is spot-on. Then we can use the sequence demultiplexing to indicate the actual balance of indexes to re-pool for the final high read number sequencing.
High Throughput Bioanalyser Platform Features
Caliper - Labchip GX
- High throughput bioanalyser with 96 and 384 well compatibility
- Asseses RNA quality and gives exact sizing and quantification of DNA fragments.
- Can analyse 96 samples in less than 1 hour
- RNA metrics are used to calculate the RGS value (RNA quality score) which has been validated to correlate with the agilent bioanalyser RIN score. This would be beneficial since users are already familiar with a RIN value for assessing RNA quality.
- Resolution down to 5bp and sensitivity of 0.1 ng/ul
- Can visualise the results on electropherogram or gel view similar to Agilent 2100.
- Data can be viewed in tabular form which can be easily exported/uploaded onto our LIMS system.
- High sensitivity kit also available
- There is a barcode reader for sample tracking which would be important when running large numbers of samples.
- This is a microchip electrophoresis system for DNA/RNA analysis.
- Reusable microchips are used which could reduce running and consumable costs.
- 120 samples can be run simultaneously across 4 separate microchips with 80 seconds per sample processing speed.
- It can also perform automatic or manual reanalysis of the samples as seen with the agilent bioanalyser and can export the results in a csv. format.
- The Lab901 ScreenTape system is a fully automated system for gel electrophoresis. The ScreenTape instrument loads, separates, images and analyses both DNA and RNA samples. It does this by loading each sample onto a screentape each of which contain 16 microgels which align to built in electrodes and imaging system.
- Only 1 ul of sample is required and analysis takes 1 minute per sample. It is fully automated with prepacked reagents so there is no gel preparation or chip priming.
- Different screentapes are available for DNA and RNA analysis.
- For RNA analysis, quality is displayed as the screentape degradation value (SDV)
- A microcapillary electrophoresis system, which is fully automated and can process up to 96 samples per run. Separation is performed in a capillary of precast gel cartridge which are reusable.
- Sensitivity of 0.1ng/ul Resolution down to 3-5 bp.
- Sample consumption is less than 0.1ul, although the minimum sample volume to load for analysis is 10ul.
- 96 samples can be processed in approximately 1 hour.
- The data can be viewed as electropherogram or gel images.
- is a fluorescence-based capillary electrophoresis instrument for both sizing and quantifying nucleic acids (DNA and RNA).
- Can run either 12 samples or 96 samples at a time
- The instrument provides space for up to six 96-well plates
- Can be used to quantify and qualify NGS fragments, RNA, genomic DNA and also for mutation detection, Microsatellite (SSR) analysis.
- Various capillary lengths can be used, depending on the application, required resolution and desired speed of analysis. Longer arrays provide resolution down to 2 bp for fragments under 300 bp in length. Shorter arrays still provide good resolution with run times as fast as 15 minutes
- PROSize™ software is used to analyse the data and this can be viewed as a gel view, electropherogram or a results table.
- The data is exportable and can be linked to the LIMS.
Tuesday, 31 July 2012
Sequencing acronyms updated
A year ago I wrote a post about the explosion of different NGS acronyms. When I wrote it I was surprised to see over 30 different acronyms and suggested that part of this was authors wanting to make their work stand out, hopefully coining the next “ChIP-seq”.
In the past year more and more NGS acronyms have been published. I am partly responsible for one of these TAm-seq and understand better the reasoning for using acronyms. Once I have spoken to someone about the work we did in the STM paper I can simply refer to TAm-seq in future conversations.
It might help if we as a community could agree on a naming convention to make searching for work using specific techniques easier. There are multiple techniques for analysis of RNAs and using the catch-all “RNA-seq” would allow much quicker PubMed searching. Of course we would need to add keywords around the particular technique being used, RNA-seq could encompass mRNA, ribosome removal, strand-specific, small, micro, pi, linc, etc, etc, etc.
Here is a list of acronyms that we in the community could use to simplify things today. It would obviously need tidying up every year or so as new acronyms get added.
Again, here is a link to the data.
In the past year more and more NGS acronyms have been published. I am partly responsible for one of these TAm-seq and understand better the reasoning for using acronyms. Once I have spoken to someone about the work we did in the STM paper I can simply refer to TAm-seq in future conversations.
It might help if we as a community could agree on a naming convention to make searching for work using specific techniques easier. There are multiple techniques for analysis of RNAs and using the catch-all “RNA-seq” would allow much quicker PubMed searching. Of course we would need to add keywords around the particular technique being used, RNA-seq could encompass mRNA, ribosome removal, strand-specific, small, micro, pi, linc, etc, etc, etc.
Here is a list of acronyms that we in the community could use to simplify things today. It would obviously need tidying up every year or so as new acronyms get added.
- DNA-seq: Unmodified genome sequencing.
- RNA-seq: All things RNA.
- SV-seq: Structural-variation sequencing.
- Capture-seq: Exomes and other target capture sequencing.
- Amplicon-seq: Amplicon sequencing.
- Methyl-seq: Methylation and other base modification sequencing.
- IP-seq: Immuno-Precipitation sequencing.
Again, here is a link to the data.
Monday, 23 July 2012
Visualisation masterclass
One of my favourite columns in any scientific journal is Nature Methods “Points of View” by Bang Wong. The column is focused on visualisation and presentation of scientific data and I’d highly recommend it if you haven’t already seen it.
Here is a link to Nature Methods and also a public Mendeley group (please feel free to join) so you can access the papers, Bang Wong's points of view. I'd be very interested in a hard-copy version, perhaps the articles expanded and collected into a book?
Data visualisation is improving all the time: In the March 2010 issue of Nature Methods the Creative Director of the Broad Institute, Bang Wong, was senior author on a paper highlighting some of the challenges we face in visualising complex data sets. The paper presents some of the developments over the past twenty years that today allow almost anyone to; create a phylogenetic tree, a complex pathway diagram or a transcriptome heat map. We are generating huge amounts of data and visual tools for interpretation are vital. Fortunately there are lots of people working on this.
Circos plots: I am always struck by how much data is conveyed in a circus plot, and these are becoming more complex as data sets grow. Can you imagine how many slides you would have needed to use just three or four years ago? The Circos tool was published in Genome Research in 2009. There is a Circos website and the New York Times had a great feature way back in 2007 highlighting what was possible with this new visualisation tool.
Points of view: The column covers many aspects of data visualisation and presentation. Some highlights for me are:
Colour: Spiralling through the colour wheel when choosing colours to use in figures can allow the same visual impact in both colour and black-and-white print. Adobe Illustrator and Photoshop allow you to simulate what Red:Green colour-blindness will do to your figures, and replacing red with magenta makes images accessible for all. Colour can be misleading and sometimes a simple black line will do.
Whitespace: Absence of colour is important. Many scientific presentations and posters covey too much information and don’t have enough empty page to allow readers to see how the text should flow.
Typeface: The reason we use serif typeface in text is because the ‘feet’ help us follow the line of the text. A generalisation is that serif fonts should be used for large blocks of text (posters and papers) and sans serif fonts for smaller strings of text (presentation slides). Spacing of words and paragraphs can have a dramatic impact on the readability of a document.
Simplification: If your data is easy to read then people will read it. Sounds simple, but I am sure many of us have prepared posters with far too much information, that need lots of explanation, yet we get less than one minute with people in the poster session. Identifying your most important idea and focusing on that can help.
I’d also recommend Bang’s website http://bang.clearscience.info which has links to lots of interesting visualisation and scientific art as well. Enjoy.
PS: If the posts on my blog are not taking all this into account, or if you see a poster or presentation of mine that could be improved then let me know. Remember that feedback has to be constructve!
Thursday, 19 July 2012
DNA multiplexing for NGS by weighted pooling has some practical limitations
The number of samples being run in sequencing projects seems is rapidly increasing. As groups move to running replicates (why on earth we didn't do this from day one is a little mind boggling). Most experiments today are using some form of multiplexing, commonly by sequencing single or dual-index barcodes as separate reads. However there are other ways to crack this particular nut.
DNA Sudoku was a paper I thought very interesting and uses a combinatorial pooling that upon deconvolution identifies the individual a specific variant comes from. We used similar strategies for cDNA library screening using 3-dimensioal pooling of cDNA clones in 384 well plates.
One of the challenges of next-gen is getting the barcodes onto the samples as early in the process as possible to reduce the number of libraries that ultimately get sequenced. If barcodes are added at ligation then every samples needs to be handled independently from DNA extraction, through QC, fragmentation and end repair. Ideally we would get barcodes on immediately after DNA extraction but how?
A paper in Bioinformatics addresses this problem very neatly, but in my view oversimplifies the technical challenges users might face in adopting their strategy. In this post I'll outline their approach and address the biggest challenge in pooling (pipetting) and highlight a very nice instrument from Labcyte that could help if you have deep pockets!
Varying the amount of DNA from each individual in your pool: In Weighted pooling—practical and cost-effective techniques for pooled high-throughput sequencing David Golan, Yaniv Erlich and Saharon Rosset describe the problems that multiplexing can address, namely the ease and costs of NGS. They present a method that relies on pooling DNA from individuals at different starting concentrations and using the number of reads in the final NGS data to deconvolute the samples without resorting to adding barcodes. They argue that their weighted design strategy could be used as a cornerstone of whole-exome sequencing projects. That's a pretty weighty statement!
The paper addresses some of the problems faced by pooling, in it they not e that the current modelling of NGS reads is not perfect, that a Poisson distribution is used where reads in actual NGS data sets are usually more dispersed but that this can be overcome by sequencing more deeply and that is pretty cheap to do.
There is a whole section in the paper (6.2) on "cost reduction due to pooling". The two major costs they consider are genome-capture ($3000/samples) and sequencing (PE100 $2,200/lane). Pooling reduces the number of captures required but increases sequencing depth per post-capture library. They use a simple example where 1Mb is targeted in 1000 individuals.
In a normal project the 1000 library prep and captures would be performed and 333 post-capture libraries sequenced in each lane to get 30x coverage. The cost is $306,600 (1000×$300+3×$2200).
In a weighted pooling design with 185 pools of DNAs (at different starting concentrations) now only 185 library prep and captures would be performed but only 10 post-capture libraries are sequenced in each lane to get the same 30x coverage of each sample. The cost of the project drops to $96,200 (185×$300+18.5×$2200).
There is a trade-off as you can lose the ability call variants of MAF >4% but this should be OK if you are looking for rare variants in the first place.
Multiplexing can go wrong in the lab: We have seen multiplexed pools that are very unbalanced. Rather than nice 1:1 equimolar pooling the samples have been mixed poorly and are skewed. The best might give 25M reads and the worst 2.5M reads, and if you need 10M reads per sample then this will result in a lot of wasted sequencing.
Golan et al's paper does not explicitly model pipetting error. This is a big hole in the paper from my perspective but one that should be easily filled. The major issues are pipetting error during quantification leading to poor estimation of pM concentration AND pipetting error during normalisation and/or pooling. This is where the Bioinformaticians need some help from us wet-lab folks as we have some idea as to how good or bad these processes are.
There are also some very nice robots that can simplify this process. One instrument in particular stands out for me and that is the Echo liquid handling platform, which uses acoustic energy to transfer 2.5nl droplets from source to destination plates. There are no tips, pin, or nozzles and zero contact with the samples. Even complex pools from un-normalised plates of 96 libraries could be quickly and robustly mixed in complex weighted designs. Unfortunately the instrument costs as much as a MiSeq, so expect to see one at Broad, Sanger, Wash U or BGI but not labs like mine.
DNA Sudoku was a paper I thought very interesting and uses a combinatorial pooling that upon deconvolution identifies the individual a specific variant comes from. We used similar strategies for cDNA library screening using 3-dimensioal pooling of cDNA clones in 384 well plates.
One of the challenges of next-gen is getting the barcodes onto the samples as early in the process as possible to reduce the number of libraries that ultimately get sequenced. If barcodes are added at ligation then every samples needs to be handled independently from DNA extraction, through QC, fragmentation and end repair. Ideally we would get barcodes on immediately after DNA extraction but how?
A paper in Bioinformatics addresses this problem very neatly, but in my view oversimplifies the technical challenges users might face in adopting their strategy. In this post I'll outline their approach and address the biggest challenge in pooling (pipetting) and highlight a very nice instrument from Labcyte that could help if you have deep pockets!
Varying the amount of DNA from each individual in your pool: In Weighted pooling—practical and cost-effective techniques for pooled high-throughput sequencing David Golan, Yaniv Erlich and Saharon Rosset describe the problems that multiplexing can address, namely the ease and costs of NGS. They present a method that relies on pooling DNA from individuals at different starting concentrations and using the number of reads in the final NGS data to deconvolute the samples without resorting to adding barcodes. They argue that their weighted design strategy could be used as a cornerstone of whole-exome sequencing projects. That's a pretty weighty statement!
The paper addresses some of the problems faced by pooling, in it they not e that the current modelling of NGS reads is not perfect, that a Poisson distribution is used where reads in actual NGS data sets are usually more dispersed but that this can be overcome by sequencing more deeply and that is pretty cheap to do.
There is a whole section in the paper (6.2) on "cost reduction due to pooling". The two major costs they consider are genome-capture ($3000/samples) and sequencing (PE100 $2,200/lane). Pooling reduces the number of captures required but increases sequencing depth per post-capture library. They use a simple example where 1Mb is targeted in 1000 individuals.
In a normal project the 1000 library prep and captures would be performed and 333 post-capture libraries sequenced in each lane to get 30x coverage. The cost is $306,600 (1000×$300+3×$2200).
In a weighted pooling design with 185 pools of DNAs (at different starting concentrations) now only 185 library prep and captures would be performed but only 10 post-capture libraries are sequenced in each lane to get the same 30x coverage of each sample. The cost of the project drops to $96,200 (185×$300+18.5×$2200).
There is a trade-off as you can lose the ability call variants of MAF >4% but this should be OK if you are looking for rare variants in the first place.
Multiplexing can go wrong in the lab: We have seen multiplexed pools that are very unbalanced. Rather than nice 1:1 equimolar pooling the samples have been mixed poorly and are skewed. The best might give 25M reads and the worst 2.5M reads, and if you need 10M reads per sample then this will result in a lot of wasted sequencing.
Golan et al's paper does not explicitly model pipetting error. This is a big hole in the paper from my perspective but one that should be easily filled. The major issues are pipetting error during quantification leading to poor estimation of pM concentration AND pipetting error during normalisation and/or pooling. This is where the Bioinformaticians need some help from us wet-lab folks as we have some idea as to how good or bad these processes are.
There are also some very nice robots that can simplify this process. One instrument in particular stands out for me and that is the Echo liquid handling platform, which uses acoustic energy to transfer 2.5nl droplets from source to destination plates. There are no tips, pin, or nozzles and zero contact with the samples. Even complex pools from un-normalised plates of 96 libraries could be quickly and robustly mixed in complex weighted designs. Unfortunately the instrument costs as much as a MiSeq, so expect to see one at Broad, Sanger, Wash U or BGI but not labs like mine.
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