Ion Torrent 316 First Impressions

Last week we had our Ion Torrent upgraded to support the 316 chips at the faster flow rate - many thanks to Life Tech for getting this update to us so quickly.

Although Life Tech supply an E. coli K-12 DH10B library for testing (yay, E. coli beats boring old PhiX! BTW is PhiX the most sequenced genome in the world now?) we have been testing our Ion Torrent on an O104:H4 isolate from the German outbreak supplied by the HPA (strain 280).

Our intention is to do a comparison of the benchtop sequencing platforms, of which more in a later post.

For those with short attention spans, I'll cut to the chase. Our first two runs of 316 chips yielded an impressive 251Mb and 209Mb respectively! Mean read length was about 110bp.

Run 1	Run 2

Pretty! Read distribution looks nice and tight - that's down to Chrystala's lovely clean library (we used Bioruptor and e-gel for this, previously we used Covaris).

So what is interesting here is that we've loaded the chips way higher than we are used to with the 314 - densities of 76-82% - which is why it's nice and red. Looks angry! The wells in the chip is arranged in a teardrop shape and the reagents flow diagonally. High loading density mean more beads (sorry, IonSpheres) in wells.

This reflects a change to the protocol - when we were running 314 chips we were told to load fewer beads to get better coverage - and from our trials when we loaded at 41, 43 and 46% density on the 314 chip the 41% run did do best. The 314 chip has about 1.2m wells, so we were filling about 550k wells. About two-thirds of those wells were live spheres (meaning they have DNA on them) and out of those about two-thirds pass the quality filter - about 200k reads in all (~20Mb data).

The 316 chip has 6.3m wells and we're filling about 5m of these. A little under half are passing the quality filters, meaning we're getting about 2.25m reads.

I am not sure if these protocol changes reflect changes to chemistry or software improvements but they are very welcome - although it does mean we are getting to the limit for the physical loading of this chip (there are 6.3m wells on the 316 and we have managed to fill 5m of them on our best run). But there's plenty of scope to get more reads which pass the quality filter. We lose almost a third to the poor signal filter alone.

The 318 chip has ~12m wells which means either quality improvements and/or read length improvements are needed to get to 1Gb.

Watching Chrystala getting to grips with this instrument in the lab, fair burning through 314 chips - occasionally messing up the loading, I did have a mini epiphany about Ion Torrent. It really is the first platform that permits smaller, cash-strapped centres to get to experiment with high-throughput sequencing. Messed up the loading? Doesn't matter, you only wasted a chip. Got a new library prep method to try? No problem. Want to experiment with different loading protocols? Again, you can do this without breaking the bank. 314 chips are coming down in price to $99 each. That's cheap enough to let undergrads have a fiddle!

It's not just the cost constraint either, being able to run the machine in a couple of hours (it's more like 3 than 2) means you can get that feedback, change things and re-run in the same day, making the whole process feel more reactive.

I realise these aren't original thoughts but it really hits home when you have a machine of your own - before we were terrified of even a single 454 Titanium failure, because of the costs (>$10k for a run). This perhaps isn't democratisation of sequencing but it certainly makes it feel much more hacker friendly.

And now the barrier to getting useful work done is lifted by the 316, the game feels a lot more real and I can see us using the Ion Torrent in anger for our stated aims - genomic epidemiology of bacterial pathogens. 200Mb is enough to get decent coverage of one or maybe two bacterial genomes (although MID kits are not yet available), or perhaps do a little bit of RNA-Seq (particularly with a normalised library). In contrast Life Tech did 10 x 314 chips to sequence a single German E. coli isolate and BGI did 7. And perhaps we shouldn't speak of the 1000 chips that were used to sequence Gordon Moore's genome!

There's a small fly in the ointment however. At a first glance, quality scores are distinctly lower than we are used to with the 314 (plots generated by the marvellous FastQC from the FASTQ files off the Ion Torrent server)

[caption id="attachment_723" align="aligncenter" width="800" caption="316 chip qualities by base (Torrent Suite 1.4.0)"][/caption]

[caption id="attachment_725" align="aligncenter" width="800" caption="316 chip mean qualities by read (Torrent Suite 1.4.0)"][/caption]

Here we're staring at a medium Q14 per sequence and a mean Q16 for the first 100 bases of the sequence (clearly the low quality end of the reads can be trimmed).

Compare this to one of our 314 runs for the same bug.

[caption id="attachment_726" align="aligncenter" width="800" caption="314 chip qualities by base (Torrent Suite 1.3.0)"][/caption]

Two things are striking. First both the mean qualities and the 5' qualities are much lower for the 316 run than we are used to with the 314 run.

Another thing that is clear is that the quality distribution has changed somewhat - it starts lower but doesn't fall quite so precipitously.

Is this the new pipeline doing this? I re-ran our 314 analysis to check.

So weirdly this does change the per-base picture - it dials down the 5' ends quality, but increases the 3' ends - which actually serves to increase the mean read quality. Notably also it means we end up with some long reads in our dataset, up to 200 bases (these are only a small fraction however of the total dataset).

So now I'm wondering whether the increased loading density has any effect on quality.

Well, yes and no - not obviously within a chip class, but perhaps the different protocol has made a difference. The higher yield 316 run (Run8) has a higher fraction of >=Q17 and >=Q20 bases than Run7. But clearly both are worse than the 314 runs.

OK, so not really sure what this means. The final question is - are these quality scores actually meaningful? The base caller works de novo and so it is theoretically possible that the base calls are actually much better than expected.

The final assessment is to look at alignment quality scores, i.e. alignment scores calibrated against a known reference (I am just using the inbuilt Ion Torrent pipeline for now, which uses Nils Homer's TMAP algorithm). Assuming the reference is similar enough this should be a better judge of quality values than de novo quality scores.

So far I've only had time to re-run the analysis for one of the 316 chips, as it takes some hours to run (longer than the sequencing takes, in fact):

A definite improvement on the de novo calls, but also a source of bias (because bad reads won't map to the reference).

So in summary what I think is happening is:

• It is possible that the 316 chip or new loading protocol results in a reduction in Q scores
• New Torrent Suite 1.4.0 signal processor generates different (not necessarily higher or lower) quality scores to v1.3.0

Looking around on the Ion Torrent community forum I did find a member that was pushing loading densities way high and getting 70Mb runs - but also getting Q scores more similar to mine, so I wonder whether it is the protocol rather than the chip. And I did see a 316 run with similar quality scores.

Anyway, I'm not too worried about this for my needs. How does it actually perform in real life?

A quick and dirty assembly using Newbler 2.6 (Hint: DO NOT load Ion Torrent FASTQ files into Newbler, only SFF files - I found this out through bitter experience) gives me a respectable assembly with 414 contigs >= 500bp, mean contig size 12.5kb, N50 37.5kb and largest contig of 118kb. 98.84% of the bases in the assembly hit consensus quality of Q40.

If you want the data files I can put them up for you.

Potential conflict of interest declaration: Mark Pallen (who I work for) won an Ion Torrent in their European PGM grant programme.

Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104 H4

Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104:H4

Holger Rohde, M.D., Junjie Qin, Ph.D., Yujun Cui, Ph.D., Dongfang Li, M.E.,
Nicholas J. Loman, M.B., B.S., Moritz Hentschke, M.D., Wentong Chen, B.S.,
Fei Pu, B.S., Yangqing Peng, B.S., Junhua Li, B.E., Feng Xi, B.E.,
Shenghui Li, B.S., Yin Li, B.S., Zhaoxi Zhang, B.S., Xianwei Yang, B.S.,
Meiru Zhao, M.S., Peng Wang, B.M., Yuanlin Guan, B.E., Zhong Cen, M.E.,
Xiangna Zhao, B.S., Martin Christner, M.D., Robin Kobbe, M.D.,
Sebastian Loos, M.D., Jun Oh, M.D., Liang Yang, Ph.D.,
Antoine Danchin, Ph.D., George F. Gao, Ph.D., Yajun Song, Ph.D.,
Yingrui Li, B.S., Huanming Yang, Ph.D., Jian Wang, Ph.D.,
Jianguo Xu, M.D., Ph.D., Mark J. Pallen, M.D., Ph.D, Jun Wang, Ph.D.,
Martin Aepfelbacher, M.D., Ruifu Yang, M.D., Ph.D.,
and the E. coli O104:H4 Genome Analysis Crowd-Sourcing Consortium*</p>

This article (10.1056/NEJMoa1107643) was
published on July 27, 2011, at NEJM.org.
N Engl J Med 2011.

Summary</p>

An outbreak caused by Shiga-toxin–producing Escherichia coli O104:H4 occurred in
Germany in May and June of 2011, with more than 3000 persons infected. Here, we
report a cluster of cases associated with a single family and describe an open-source
genomic analysis of an isolate from one member of the family. This analysis involved
the use of rapid, bench-top DNA sequencing technology, open-source data release, and
prompt crowd-sourced analyses. In less than a week, these studies revealed that the
outbreak strain belonged to an enteroaggregative E. coli lineage that had acquired genes
for Shiga toxin 2 and for antibiotic resistance.</p>

Birmingham Fellows: the University of Birmingham needs you!

The University of Birmingham has recently launched this Birmingham Fellows scheme:

http://www.birmingham.ac.uk/staff/excellence/fellows/index.aspx

We are looking for exceptional young post-doc researchers who will be future leaders in their field. I have been briefed by our PVC for Research, Adam Tickell, that we are looking for truly exceptional individuals not just "quite good", i.e. you must have an impressive publication track record (commensurate with age and experience). Also, you have to be within seven years of getting your PhD to qualify as a "bright young thing".

Sorry if these stringent criteria put you off!

But on the plus side, this scheme provides fellowships which lead to permanent lectureships at the end of five years. So, you could walk out of your post-doc and into a permanent job in a few months time. In fact, if you have research funds already, you could start sooner than that!

Applications have to be in by Sept 2nd. Priority areas are listed on the web site and include microbiology:

http://www.birmingham.ac.uk/staff/excellence/fellows/areas/microbiology.aspx

Follow this link to look at our recent publications (occasional false hit from PubMed, but provides good overview of what's going on here).

Contact me (m.pallen@bham.ac.uk) for informal enquiries about applications underthe microbiology theme.

Further reading for In Our Time Radio programme on Origins of Infection

A couple of days before my participation in the recent In Our Time programme I was given a list of questions that I was likely to be asked. On the day, the questioning deviated from the script and I didn't always get to say all I wanted to say on a given point. Here is a copy of the crib that I prepared before the programme, which I have marked up with hypertext links to relevant material  for anyone interested in learning more about the topics we discussed.

(to Mark Pallen)   Agents of infectious disease are known as pathogens – the best known are viruses and bacteria.  Would you explain what they are and how they differ?

Well, the party line is that they are very different, which is why microbiologists get cross when people talk of the E. coli virus or MRSA virus, when both are bacteria!

Bacteria like us are made of cells, membrane-bound bundles of metabolism, DNA, RNA, protein and the wherewithal to make these molecules. We are multicellular, made of many different cell types, whereas bacteria are unicellular-for bacteria, generally the organism is the cell and vice versa. Our cells are also much larger than bacterial cells. Another important difference between our cells and those of bacteria is that we wrap our DNA up in a microscopic bag called the nucleus whereas in bacteria it hangs free in the cell. In the jargon, cells with a nucleus are called eukaryotes, cells without a nucleus are called prokaryotes and include bacteria and a third domain of life called archaea, which are small and unicellular like bacteria but only distantly related to them.

Viruses are not cellular life forms. When we bacteriologists want to disparage viruses, we call them infectious chemicals. They form infectious but metabolically inert particles that can survive outside of cells but to reproduce, viruses have to hijack cells. Some viruses hijack eukaryotic cells, some hijack bacterial or even archaeal cells. They key point is that they don’t have the machinery for making their own proteins; they have to rely on the host cell for that. They are also much smaller than bacteria and can generally be seen only with an electron microscope, rather than a light microscope.

But what I have given you is the standard answer. Over the last ten years or so the distinction between bacteria and viruses has become blurred, particularly after the discovery of a very large virus called Mimivirus, which was discovered in a water tower in Bradford in Yorkshire of all places. It was first thought to be a bacterium and even called Bradfordococcus, but a group in France showed that it is in fact a very unusual virus, with very large virus particles and a very large genome, both similar in size to bacteria and it remarkably it encodes some of the proteins used to make other proteins. This has led to a reappraisal of and new respect for viruses as a fourth domain of life that perhaps even predates cellular life forms.

(to Mark Pallen)  Viruses and bacteria aren’t the only pathogens, though – what are the others?

Well at one extreme, smaller than viruses, we have prions, which really are infectious chemicals, in that they have no DNA or RNA genomes but instead appear to be misfolded versions of proteins normally present in the body. These misfolded proteins can catalyse misfolding of healthy proteins and eventually the accumulation of misfolded proteins can damage and even kill cells. There are very hard to destroy and can survive for long periods in the environment. The most famous example of a prion infection is BSE, or mad cow disease, as it is popularly known, and its human counterpart Creutzfeldt-Jakob disease or CJD.

Larger than bacteria, there are a number of groups of eukaryotic pathogens: fungi which includes infectious yeast-like fungi and moulds; unicellular eukaryotic pathogens, of which in global terms, the malarial parasite is the most important and then multicellular parasites, which include worms and so called ectoparasites, like lice, fleas and ticks.

Interestingly, there are two kinds of pathogens we do not see in humans : archaea never appear to cause invasive disease although they live in association with human tissues and might contribute with bacteria to disorders of the gut and periodontal disease. And we never see infectious cancers, although these have been described in some animals, particularly the Tasmanian devil, which is being decimated by Devil Facial Tumour Disease.

(to Mark Pallen)  How can genomics help establish where a disease came from?

Well, in some case we can obtain ancient DNA from archaeological or museum specimens. Examples here include TB and plague. Using modern genome sequences we can often reconstruct the evolution of a human pathogen by comparing its genome to those of close relatives. One early surprise in the field concerns tuberculosis. There are strains associated with human and cattle infections, and in the past people speculated that the human disease might have originated from cattle, crossing over to human during the process of domestication. In fact, genomics has shown the opposite: the cattle caught TB from us!

Another example comes from the elegant work of Mark Achtman and others on the bacterium Helicobacter pylori that lives in the human stomach and is passed on in families. By looking at patterns of variation in the genomes of these bacteria from around the world they have been able to provide evidence for the out-of-Africa hyothesis, i.e. that all non-African humans are descendents of a small band of humans that left Africa 60-70 thousand years ago.

For many important bacterial pathogens of humans if we look back over periods of thousands of years, we see a pattern of evolution in which a lineage of a very generalist species of bacterium, that can live in many environments or on many hosts, becomes specialized to live in a much more restricted niche.  During the process the organism starts to throw away parts of its genome that are no longer needed. This process of reductive evolution has happened for diseases like plague, anthrax, typhoid fever, whooping cough, where in each case the pathogen we see today is a cut-down version of its ancestor.

The most extreme example of this reductive evolution are the mitochondria, which live inside our cells and those of other eukaryotes functioning as energy-producing factories, but are in fact highly specialized bacteria, that entered such cells a billion years ago or so and have thrown away or outsourced 99% of their genomes.

(to Mark Pallen)  What other information is hidden in the DNA of bacteria and viruses?

On a more recent timescale we can look at very small-scale differences between genomes to reconstruct chains of transmission of infection.  For example, such an approach was used to find the source of the anthrax deliberately released into the US postal service. In fact we and others have used this kind of genomic epidemiology approach on a range of pathogens. Another interesting example come from studies on the genomes of leprosy bacilli from humans and armadillos in the southern United States suggest that humans gave armadillos the infection and then humans were catching the disease back from armadillos when skinning and eating them.

(to Mark Pallen)  Some pathogens are much more likely to cause death than others.  Would you explain the paradox of virulence?

Well, the paradox is why do pathogens damage and even kill their hosts when they depend on them for their own survival. Why would you burn your own house down?

There is no one-sits-fits-all answer to this question, but several answers that are not mutually exclusive but that all depend on Darwin’s theory of evolution. In some cases it is clear that causing disease provides an advantage to the pathogen in helping it get from one host to the next. For example, when a cold virus makes your nose stream and makes you sneeze, this is clearly helping spread the virus. For more severe infections, like cholera, one can see a trade-off, in that causing severe diarrhoea helps disperse the pathogen into the environment but at the risk of killing the host. But if the dispersal is effective, it may not matter some hosts die. But I have to say evidence for this hypothesis is still not conclusive.

In other cases virulence is harder to explain. Invading your blood to cause blood poisoning or your brain to cause meningitis doesn’t help spread the bacteria. But here, we can explain virulence as the result of short-sighted evolution that creates long term problems, because natural selection shows no foresight. So, for example, only a small fraction of the bacteria that can live in your throat can live in your blood or cerebrospinal fluid, but if they do spill over into these compartments natural selection will drive them to become better and better adapted to living, there. In addition, bacteria that get into deep tissues evoke host defences and inflammatory responses that generally help control infection but in some cases there is death or damage from friendly fire, as these responses damage the host.

Finally, some infections are best explained by Darwin’s principle of common descent, i.e. that all life is derived from a common ancestor and that organisms that superficially appear quite different in fact share common molecular toolkits in their cells and tissues. So for example, the bacterium that causes the kind of pneumonia called Legionnaire’s disease usually infects free living amoebae, but when aerosols from our hot water and air conditioning systems provide it with a route into our lungs, the bacterium simply treats our cells like unusual amoebae. And it can do this, because we and amoebae share a common ancestor.

(to Mark Pallen)   Recent research seems to indicate that humans and their diseases have mixed their genetic material – how is this possible?

Well, viruses quite commonly steal genes from their hosts, particularly those that cause cancer. As mentioned mitochondria represent highly specialized descendants of bacteria that entered eukaryotic cells and have now been deeply integrated into those cells and have even transferred many of the genes into the nucleus. Many viruses can integrate their DNA into our genome and in fact our genome is littered with the remains of viruses that have jumped into the genome, have lost the ability to jump out again and are slowly decaying away, but also providing fuel for rearrangements and other changes in the genome. A few years ago some scientists manage to reconstruct the ancestor of some of these so-called endogenous retroviruses and showed that when they rebuilt its genome it produced infectious virus particles.

(to anybody)  Can we ever hope for an end to infectious disease?

Well I am an optimist. It is interesting question that Steve and I could debate as to whether Darwin’s theory of evolution or the germ theory of infection developed in the decades following the publication of Darwin’s origin represents the greatest leap forward for humanity. I think this profoundly counter-intuitive theory, that infectious diseases are caused by microscopic organisms that we cannot see with the naked eye presents with such a profound step change that we cannot unthink it, we cannot imagine a world before this theory. And then there all the interventions that have flowed from it, including the development of safe water and sewerage systems, safe surgery, vaccines and antibiotics, safe sex etc,

And it is worth stressing that thanks to these interventions, we have already dramatically reduced the threat of infectious disease in Western societies and double life expectancies. OK, we still have a problem with hospital infection, but to put a positive spin on this, much of what we see here is the result of the heroic success of modern medicine in keeping vulnerable patients alive for long periods of time, when a generation or two they would have died before they could ever get infected. And the simple step of making the powers that be in the NHS more accountable for hospital infection has produced dramatic results.

On a global scale, It is important to recognize the dramatic successes in recent decades in disease control and for several infections, we are now contemplating eradication, for example polio, leprosy, river blindness or guinea worm and we have to thank bodies like the Gates Foundation, the Carter Foundation or the Rotary Club for this. These guys are not given the recognition they deserve as heroes in our fight against infection. And even with more intractable problems like TB and malaria there is new hope and some are even using the “E” word: eradication. We are winning and will win this fight.

All the tweets from ABPH11

The tweets from Applied Bioinformatics & Public Health 2011- preserved for posterity.

pathogenomenick: Started on my train journey through the bucolic scenery of middle England to Hinxton, nr. Cambs for #ABPH11 (invented hashtag)
attilacsordas: 'Applications of 3rd Gen Sequencing in Public Health Microbiology Andrew Kasarskis' @PacBio UK' #ABPH11 he was working 4 @Sagebio before
pathogenomenick: Checked-in at Hinxton Hall for #ABPH11 - view from my window. Weather is gorgeous!http://yfrog.com/h26h8apxj http://yfrog.com/h26h8apxj
pathogenomenick: OK time to kick off #ABPH11. Looking forward to talks from Roche, Illumina, Ion Torrent and PacBio shortly. Hoping for some juicy nuggets.
pjacock: @pathogenomenick What's the full name of #ABPH11 and is there a website for the meeting? Ta!
pathogenomenick: I'm not going tweet crazy this meeting to save your timelines. But I will tweet things I think are new / particularly interesting #ABPH11
jacarrico: @pathogenomenick So lets keeps #ABPH11 as oficial tag ?
jacarrico: "Mouth is a whole collection of microhabitats" -William Wade #ABPH11
pathogenomenick: William Wade is describing the process of trying to culture an "uncultivable" isolate of Synergistetes http://bit.ly/jwDC4Z #ABPH11 http://bit.ly/jwDC4Z
pathogenomenick: It took 4 months of laborious co-culturing and passage to cultivate P. micra #ABPH11
jacarrico: http://t.co/tK3GMqb - Human Oral Microbiome DB #ABPH11 http://t.co/tK3GMqb
pjacock: Thanks @pathogenomenick #ABPH11 is Applied Bioinformatics & Public Health Microbiology 2011, at Sanger Inst, Hinxton http://bit.ly/e6Dipx http://bit.ly/e6Dipx
pathogenomenick: William Wade has a very nice database www.homd.org - 619 bact "species" represented, 66% of those cultured. 113 un-named. #ABPH11
pathogenomenick: My 'p' key is broken. This is going to make life difficult. #ABpH11
pathogenomenick: You will struggle to identify many species by 16S - particularly Streptococcus - even with full-length sequences #ABpH11
pathogenomenick: Actinobacteria are underrepresented in 16S clone libraries: why - lysis? high GC? primers turned out to be the major problem #ABpH11
aunderwo: William Wade: Both bacterial culture and PCR amplification of 16S rRNA gene introduce their own biases when examining microbiomes #ABPH11
jacarrico: WilliamWade -"Mouth is sterile at birth and gets colonized in the first 18 h!" #ABPH11
aunderwo: William Wade: By 16S rDNA sequencing found 50% of oral microbiome is unculturable #ABPH11
pathogenomenick: OK time for some instrument talks - Jason Myers up first talking Ion Torrent - "democratising sequencing" #ABPH11
iddux: @pathogenomenick: My 'p' key is broken. This is going to make life difficult. #ABpH11 <- hard to 'p'
pathogenomenick: Not sure exactly the content of this talk but suspect it will have overlap with these slides from AACC http://bit.ly/ieQ81s #ABPH11 http://bit.ly/ieQ81s
avilella: Ion Torrent guy showing a slide that shows 1Gb and 200-400bp reads for 2012 #ABPH11
aunderwo: Ion Torrent is to sequencing technologies as PCs are to computing - everybody can potentially sequence a genome #ABPH11
aunderwo: Next year Ion Torrent should yield 400bp reads and 1Gb of sequence #ABPH11
fionabrinkman: At Applied Bioinformatics & Public Health Microbiology 2011 meeting and now have wifi. Agenda: http://goo.gl/hGVPM #ABPH11 http://goo.gl/hGVPM
jacarrico: Ion Torrent - Jasom Myers - PGM as a "disruptive technology " as PCs were #ABPH11
jennifergardy: Follow @pathogenomenick and I for scintillating back-and-forth genomics gossip and commentary live from #ABPH11 at Hinxton!
pathogenomenick: Ion Torrent guy is predicting linear read length improvement but his Y axis is all funny so not sure exactly when. #ABPH11
jacarrico: Linear read length predictions for PGM seem a little forced after those bumps on the line .. #ABPH11
pathogenomenick: I may give an impromptu talk tomorrow on Ion Torrent data analysis to make up for a missing speaker #ABPH11
aunderwo: Whole transcriptome RNA sequencing will be possible with Ion Torrent 'soon' according to their rep #ABPH11
pathogenomenick: Ion Torrent claim good consensus accuracy for 8/9-mer homopolymers, using a 36x coverage assembly example in H. pylori #ABPH11
aunderwo: H.pylori de novo assembly resulted in 150 contigs with Ion Torrent (data from Penn State U) #ABPH11
pathogenomenick: The Broad have done some early experiments with mate-pair sequencing on Ion Torrent using insert lengths of around 1.5kb #ABPH11
aunderwo: Mate paired libraries with 1.5kb inserts have been achieved with Ion Torrent PGM #ABPH11
fionabrinkman: Now doing Sequencing Tech talks (getting caught up...) #ABPH11
pathogenomenick: We asked each company to talk directly about public health applications. This is a little bit generic but early days I guess #ABPH11
peterdilaura: @pathogenomenick thanks in advance for the #ABPH11 tweets.
phylogenomics: RT @jennifergardy: Follow @pathogenomenick and I for scintillating back-and-forth genomics gossip and commentary live from #ABPH11 at Hinxton!
pathogenomenick: I asked why the 316 & 318 chips aren't out yet. The issue seems to relate to fluidics rather than the semiconductor design or mfctr #ABPH11
pathogenomenick: Geoff Smith talking about MiSeq - he's a very clever man in fact. Solexa started down the road from Hinxton in Gt. Chesterford #ABPH11
aunderwo: At volume it is now possible to sequence a human genome for $4k using Illumina HiSeq #ABPH11
pathogenomenick: HiSeq 2000 - 8 human genomes per Tb or 8000 bacterial genomes per run. I know what I'd prefer! #ABPH11
avilella: Illumina HiSeq now: 600Gb per run. Latest R&D number: more than 1Tb per run #ABPH11
pathogenomenick: MiSeq data more or less equivalent to HiSeq data, but less of it. More about this here: http://bit.ly/lB6Y9U #ABPH11 http://bit.ly/lB6Y9U
aunderwo: All methods on HiSeq are interchangeable with MiSeq - equivalent base accuracy etc also #ABPH11
pathogenomenick: MiSeq has a single well for sample loading. Does cluster gen on-board. So there are no "lane" equivalents as per HiSeq/GA2x #ABPH11
jacarrico: Geoff Smith giving a very convincing talk about MiSeq #ABPH11
pathogenomenick: HiSeq: 19m chemistry, 23m imaging per base. MiSeq: 4m chem, 1m imaging per cycle. I guessed this in Jan http://bit.ly/fkVMyL #ABPH11 http://bit.ly/fkVMyL
aunderwo: Improvements in chemistry and imaging enable the cycle time in the MiSeq to be 5 mins as opposed to 42 mins for HiSeq #ABPH11
pathogenomenick: Wireless network at Hinxton is super-fast compared to ASM. #ABPH11
assemblathon: For great coverage of the #ABPH11 conference, follow @pathogenomenick. He's dropping some tasty morsels of NGS information today.
jacarrico: 6 twitters so far ! Not bad! #ABPH11
pathogenomenick: Super deep sequencing of KRAS allows detection of 1.1% variant frequency using MiSeq. This is going to take over cancer screening. #ABPH11
5
fionabrinkman: #ABPH11 I'd love a MiSeq but my Dept says it needs higher capacity for Euk genomes :( Need to convince them otherwise...
fionabrinkman: Yes RT @assemblathon: For great coverage of #ABPH11 follow @pathogenomenick. He's dropping some tasty morsels of NGS information today.
pathogenomenick: Geoff claims Nextera library prep enables 'sample-to-answer' in a single working day with Miseq. That is a game-changer for ID/dx. #ABPH11
pathogenomenick: And that's an 8 hour working day like in UK not an American working day of 18 hours ;) #ABPH11
aunderwo: Nextera library prep (single tube) and sequencing 2x36bp on MiSeq enables sample to answer in 1 working day #ABPH11
pathogenomenick: Gets N50 of 148kb HiSeq 132kb MiSeq from E. coli assembly (not sure about other params) #ABPH11
aunderwo: Possible to find SNPs involved in drug resistance in TB strains using MiSeq sequencing #ABPH11
jennifergardy: I'm switching my Christmas wish from a pony to an Illumina MiSeq. If they could throw in a pony w/ the machine, that would be great #ABPH11
jacarrico: Very nice examples of use for microbiology using miseq: TB, pseudomonas, ecoli sequencing #ABPH11
pathogenomenick: Presenting CF sputum metagenomics using HiSeq., PA LESB58 came out at 636x depth, plus phage & 7 other genomes 50-86% covered #ABPH11
pathogenomenick: After depleting human DNA, CF sputum DNA is >70% bacterial #ABPH11
jacarrico: Hiseq for metagenomics / Miseq to characterize individual isolates #ABPH11
fionabrinkman: MiSeq can seq P. aeruginosa LES genome accurately vs ref. which is good since large, high G+C. Using HiSeq for metagenomics though #ABPH11
pathogenomenick: Good assembly results using Nextera sample prep on MRSA. That's good, was worried about biases forom this method. #ABPH11
GuillaumeMeric: RT @jennifergardy: Follow @pathogenomenick and I for scintillating back-and-forth genomics gossip and commentary live from #ABPH11 at Hinxton!
pathogenomenick: Geoff Smith thinks - like we do - that sequencing can replace the traditional clinical microbiology paradigm of culture & sens. #ABPH11
pathogenomenick: What we really want is HiSeq amounts of data in MiSeq timescales of course, for routine clinical metagenomics! #ABPH11
fionabrinkman: @jennifergardy #ABPH11 yes, even the MiSeq name is cute. Keeps reminding me of my Wii Mii!
aunderwo: Routine WGS for pathology: 3 colonies=> extraction=> library prep=> overnight MiSeq sequencing=> whole genome & analysis next am #ABPH11
jacarrico: Atb resistance prediction from genome sequence within a day with Miseq... Still lots to do but a goal #ABPH11
pathogenomenick: Geoff Smith seems to imply that Velvet would be supplied with the MiSeq for de novo assembly - seems odd but we'll see #ABPH11
jacarrico: PacBio sequencing now ! #ABPH11
jennifergardy: AAAAAAAHHHH IT'S PACBIO TIME AT #ABPH11 YAYAYAYAYAAAAY!!!!!
avilella: The MiSeq pipeline will run the latest version of Velvet assembler as you can find at dzerbino's website. Nothing closed and canned. #ABPH11
epiexperts: Interesting tweets from #abph11 Applied Bioinformatics & Public Health Microbiology 2011 http://search.twitter.com/search?q=%23ABPH11 #fb http://search.twitter.com/search?q=%23ABPH11
jennifergardy: I've heard the PacBio reads are indel-y & kind of crap, but the chiseled good looks of the PacBio rep make up for it #ABPH11
pathogenomenick: Pac Bio guy is rocking some really sci-fi slides. Well done. #ABPH11
pathogenomenick: Very cool real-time images of SMRT sequencing. Go to their site for more: http://bit.ly/l81uNH #ABPH11 http://bit.ly/l81uNH
HelicosUnveiled: @pathogenomenick Have there been any Helicos Biosciences sightings? #ABPH11
aunderwo: Trace coming off the PacBio machine looks noisy - I suspect quality of reads may take a while to get right #ABPH11
fionabrinkman: Like insights that could be gained about polymerase function/pausing from PacBio seq data. Realtime data = beautiful biology. #ABPH11
pathogenomenick: Raw error rate around 11%, mainly indels. But crucially they are constant over length of read which makes life much easier. #ABPH11
fionabrinkman: @pathogenomenick yes! Next Star Trek movie must show shots of PacBio sequencing #ABPH11
pathogenomenick: Hmm bit disappointed this is a pure sequencing talk so far and nothing about public health (yet, anyway) #ABPH11
pathogenomenick: OK, going to talk about Haiti cholera outbreak now. 12-fold genome coverage achieved in 90 minutes. Wow, those bugs are in log phase #ABPH11
jacarrico: PacBio can detect DNA methylation by the time that the polymerase pauses #ABPH11
kshameer: RT @jacarrico: PacBio can detect DNA methylation by the time that the polymerase pauses #ABPH11
mgollery: RT @avilella: The MiSeq pipeline will run the latest version of Velvet assembler as you can find at dzerbino's website. Nothing closed and canned. #ABPH11
pathogenomenick: PacBio gives more even coverage of genome compared to Illumina - this is due to amplification bias. Models Poisson very well. #ABPH11
fionabrinkman: RT @pathogenomenick: OK, going to talk about Haiti cholera outbreak now. 12-fold genome coverage achieved in 90 minutes. Wow, those bugs are in log phase #ABPH11
pathogenomenick: PacBio cholera sequencing is kind of cool but its not a killer app - not seeing anything we can't do with 454 just yet. #ABPH11
aunderwo: Long reads of PacBio may resolve genome gaps due to repeats #ABPH11
pathogenomenick: The 454 read length / throughput timeline rather compresses 2008 - 2011 :( #ABPH11 (because nothing happened)
aunderwo: So far the 454 presentation is a little underwhelming - needs to cut to the chase. We want something new and shiny! #ABPH11
pathogenomenick: follow #ABPH11 tweeps @jennifergardy @fionabrinkman @aunderwo @jacarrico @avilella
TParusheva: oh wow!!interesting..@kshameer @jacarrico RT PacBio can detect DNA methylation by the time that the polymerase pauses #ABPH11
jacarrico: @pathogenomenick: follow #ABPH11 tweeps @jennifergardy @fionabrinkman @aunderwo @jacarrico @avilella" @attilacsordas too ;-)
PacBio: RT @jennifergardy: AAAAAAAHHHH IT'S PACBIO TIME AT #ABPH11 YAYAYAYAYAAAAY!!!!!
pathogenomenick: 454 8kb PE data can produce single scaffolds for S. pneumoniae, E. coli, T. thermophilus, C. jejuni (it's true, we've done it too) #ABPH11
pathogenomenick: Oh that's a shame, 454 rep is presenting the FDA paper which is scheduled for a full talk tomorrow. #ABPH11
fionabrinkman: @pathogenomenick @jennifergardy NEJM in the lead with most citations at this #ABPH11 Seq Tech session
aunderwo: The scaffold size/number produced from large insert 454 library sequencing is still unparalleled. #ABPH11
pathogenomenick: Pac Bio guy said the machine weighs 7 of him, and let me tell you he is a big guy (ask @jennifergardy) #ABPH11
aunderwo: For most of my microbial projects I would love a 454 de novo assembly followed by cheap as chips Illumina fragment lib sequencing #ABPH11
jacarrico: 454 talk can have a slow pace but has very nice application examples published #ABPH11
pathogenomenick: >1,205 publications using 454 sequencing does show has been a very useful technology. #ABPH11
fionabrinkman: #ABPH11 Seq tech talks reveal benefit of really coming through with working tech. HiSeq, MiSeq, 454 looks good. PacBio still exciting.
PacBio: RT @jacarrico: PacBio can detect DNA methylation by the time that the polymerase pauses #ABPH11
PacBio: RT @fionabrinkman: Like insights that could be gained about polymerase function/pausing from PacBio seq data. Realtime data = beautiful biology. #ABPH11
PacBio: RT @pathogenomenick: Pac Bio guy is rocking some really sci-fi slides. Well done. #ABPH11
pathogenomenick: Let me tell you we won't be over-running in tomorrow's session that I am chairing with Muna! #ABPH11
aunderwo: 454 deep amplicon sequencing of HIV detects very minor variants in V3 loop which may alter viral tropism #ABPH11
PacBio: RT @pathogenomenick: PacBio gives more even coverage of genome compared to Illumina - this is due to amplification bias. Models Poisson very well. #ABPH11
pathogenomenick: XLR70 consensus accuracy is 99.997% versus 99.995% for FLX but this is not a meaningful measurement! #ABPH11
aunderwo: FLX+ has a modal read length of 700bp - approaching read lengths of Sanger sequencing. Base accuracy 99.99%+ with 15x coverage #ABPH11
avilella: 454 FLX Plus modal 700bp, 85% above 500bp, total 700MB per run, 23 hours, accuracy a couple of 10^-5 extra pc points: 99,997% #ABPH11
jacarrico: 454 GS Flex + has 80% of reads greater than 500 bp and up to 1kbp. #ABPH11
kshameer: RT @jacarrico: 454 GS Flex + has 80% of reads greater than 500 bp and up to 1kbp. #ABPH11
avilella: Roche+IBM so far only hopes, as much as the 100KB Oxford nanopore mountains of reads we heard before in the twittersphere #ABPH11
avilella: 454 guy, his 5 year old daughter thought he was behind learning the alphabet, stuck with only the A, T, C and G. #ABPH11
Jeylen: RT @avilella 454 FLX: modal 700bp, 85% >500bp, total 700MB per run, 23 hours, accuracy a couple of 10^-5 extra pc points: 99,997% #ABPH11
PacBio: RT @aunderwo: Long reads of PacBio may resolve genome gaps due to repeats #ABPH11
aunderwo: Julian Parkhill:WGS allows tracking of a single clone that spreads rapidly through a pop where MLST not sufficient to differentiate #ABPH11
fionabrinkman: Julian Parkhill starting talk about seq for tracking bacterial disease. Will mention recent data but my battery is now low darnit #ABPH11
pathogenomenick: I'm not going to tweet Julian Parkhill's talk as I did this at ASM but will jump in when/if there's new data. #ABPH11
aunderwo: Julian Parkhill - removing SNPs in regions with clusters of SNPs (probably due to recomb) results in a more robust SNP-based tree #ABPH11
fionabrinkman: Parkhill talking about rapid Strep pneumo evolution from this Science paper: http://goo.gl/hbREV #ABPH11 http://goo.gl/hbREV
jacarrico: In Spneumo Capsular switch is recombination of 20 kb or more , so it should be a rare event #ABPH11
pathogenomenick: Vibrio cholera outbreak - this is new. Sequenced 136 isolates from a global collection spanning 100 years. #ABPH11
aunderwo: Julian Parkhill:WGS of S.pneumo provides means of calculating frequency of base substitutions, recomb events and capsular switching #ABPH11
pathogenomenick: Little evidence of recombination in core genome of El Tor tree. Strains nicely cluster by date back to 1954. #ABPH11
CrowdedHead: RT @pathogenomenick: Vibrio cholera outbreak - this is new. Sequenced 136 isolates from a global collection spanning 100 years. #ABPH11
pathogenomenick: "Waves of transmission" - quite a complex phylogeographical pattern of multiple transmissions between continents. #ABPH11
pathogenomenick: Looks fairly compelling as a recent transmission from SE Asia to Haiti. Amazing data. #ABPH11
fionabrinkman: Parkhill showed nice new V. cholera genomic epi data. Says errs on reducing false +ve SNPs (~1/genome) vs missing 5-10% SNPs. Good. #ABPH11
lltripp: Reading #ABPH11 tweets and hoping to absorb some subject expertise by osmosis #imasciencegroupie
Chris_Evelo: Wow! Retweet @kshameer RT @jacarrico: 454 GS Flex + has 80% of reads greater than 500 bp and up to 1kbp. #ABPH11
lexnederbragt: RT @pathogenomenick: PacBio gives more even coverage of genome compared to Illumina - this is due to amplification bias. Models Poisson very well. #ABPH11
lexnederbragt: RT @pathogenomenick: PacBio cholera sequencing is kind of cool but its not a killer app - not seeing anything we can't do with 454 just yet. #ABPH11
lexnederbragt: RT @pathogenomenick: The 454 read length / throughput timeline rather compresses 2008 - 2011 :( #ABPH11 (because nothing happened)
lexnederbragt: We too! MT @pathogenomenick: 454 8kb PE data can produce single scaffolds for S. pneumoniae, E. coli, (it's true, we've done it too) #ABPH11
lexnederbragt: RT @pathogenomenick: >1,205 publications using 454 sequencing does show has been a very useful technology. #ABPH11
norseqcenter: RT @jacarrico: PacBio can detect DNA methylation by the time that the polymerase pauses #ABPH11
norseqcenter: RT @fionabrinkman: Like insights that could be gained about polymerase function/pausing from PacBio seq data. Realtime data = beautiful biology. #ABPH11
norseqcenter: RT @pathogenomenick: PacBio gives more even coverage of genome compared to Illumina - this is due to amplification bias. Models Poisson very well. #ABPH11
norseqcenter: RT @aunderwo: Long reads of PacBio may resolve genome gaps due to repeats #ABPH11
norseqcenter: RT @pathogenomenick: PacBio cholera sequencing is kind of cool but its not a killer app - not seeing anything we can't do with 454 just yet. #ABPH11
RebeccaGladston: Great first afternoon of talks, great to have so much relevant info #ABPH11
4a6a5a: RT @avilella: The MiSeq pipeline will run the latest version of Velvet assembler as you can find at dzerbino's website. Nothing closed and canned. #ABPH11
iddux: @fionabrinkman @pathogenomenick #ABPH11 So anything about the EHEC outbreak?
4
jennifergardy: Beautiful sunny day here at #ABPH11 - we should move the poster session outside. #genomicsismorefuninthesun
pathogenomenick: Oof, OK here goes for #ABPH11 day 2 - just ate a bacon sandwich in the conference room. I am chairing, tweeting and talking this session!
pathogenomenick: Victor Solovyev is presenting FgenesB & OligoZip assembler, one of the Softberry entries in the Assemblathon http://bit.ly/lenQ3G #ABPH11 http://bit.ly/lenQ3G
pathogenomenick: Victor is using a gene distance measurement to cluster "pathogenic" and "non-pathogenic" E. coli. But what is a pathogen? #ABPH11
jennifergardy: Exons at #ABPH11 !!! What are these things? What is this "intron" you speak of? #iprefermicrobialgenestructures
fionabrinkman: Too hungry to properly tweet #ABPH11. Fire alarm went just as I got to breakfast. Like some depend on coffee/tea, I need a big breaky!
pathogenomenick: Woops, next presenter started his presentation by turning off the laptop. #ABPH11
AntipodeanCharm: When computers are in presentation mode, eg in PowerPoint, the OS should shut up and stop sending alerts to the screen #ABPH11
pathogenomenick: RT @AntipodeanCharm: When computers are in presentation mode, eg in PowerPoint, the OS should shut up and stop sending alerts to the screen #ABPH11
GuillaumeMeric: RT @pathogenomenick: Victor is using a gene distance measurement to cluster "pathogenic" and "non-pathogenic" E. coli. But what is a pathogen? #ABPH11
GuillaumeMeric: Some pretty interesting talks today at the Applied Bioinformatics & Public Health Microbiology conference http://goo.gl/h5lI5 follow #ABPH11 http://goo.gl/h5lI5
aunderwo: Guillermo Lopez-Campos: Presents work that automatically extracts primers and probes from papers http://1.usa.gov/kFTCjl #ABPH11 http://1.usa.gov/kFTCjl
aunderwo: RT @pathogenomenick: Victor is using a gene distance measurement to cluster "pathogenic" and "non-pathogenic" E. coli. But what is a pathogen? #ABPH11
AntipodeanCharm: RT @assemblathon: For great coverage of the #ABPH11 conference, follow @pathogenomenick. He's dropping some tasty morsels of NGS information today.
jennifergardy: #ABPH11 speaker missed big issue in biomedical informatics:lab samples and epi data often belong to separate agencies who can't share easily
aunderwo: Call for a MIAMI style standard for clinical records to make clinical informatrics more interoperable #ABPH11
AntipodeanCharm: "Doctors should try to think in an organized way" G. Lopez-Campos #ABPH11
pathogenomenick: Now up - @jacarrico is talking about linked data for microbial typing. Proliferation of sequence-based typing methods & databases. #ABPH11
pathogenomenick: For Staph we have MLST.net, ccrbTyping.net, SpaServer, MLVAplus.net - want to connect these together #ABPH11
aunderwo: Joo Andr Carrio: Collating data from MLST, spa server, MLVA websites is for the moment manual - his dream to automate this #ABPH11
aunderwo: RT @jennifergardy: #ABPH11 speaker missed big issue in biomedical informatics:lab samples and epi data often belong to separate agencies who can't share easily
fionabrinkman: @jacarrico talking about molecular typing - many DBs not interconnected with little machine readable interfaces #ABPH11
pathogenomenick: Proposing semantic web principles for connecting molecular typing dbs. See linking open data cloud diag http://bit.ly/fTXGJP #ABPH11 http://bit.ly/fTXGJP
RebeccaGladston: #abph11 so true... A plethora of standards defeats the purpose of standardisation!
fionabrinkman: @jacarrico: 'I have a Dream' like MLK - since realized integrated molecular typing is a 'dream' about a social problem #ABPH11
AntipodeanCharm: @jacarrico presented an amazing overview of linked data on the web. I hope he tweets that slide, hint hint. #ABPH11
AntipodeanCharm: RT @pathogenomenick: Proposing semantic web principles for connecting molecular typing dbs. See linking open data cloud diag http://bit.ly/fTXGJP #ABPH11 http://bit.ly/fTXGJP
aunderwo: When linking data the problem (even within the same organisation - speaking from experience!) is the identifiers #ABPH11
AntipodeanCharm: Roll for surprise. @jacarrico just went from web standards for molecular typing to ontology. Good. #ABPH11
pathogenomenick: .@jacarrico got his students to define an ontology for sequence-based typing results 'TypOn': available here http://bit.ly/lsD2w0 #ABPH11 http://bit.ly/lsD2w0
jennifergardy: TyPon - an ontology for molecular epi typing methods - from @jacarrico at #ABPH11 phyloviz.net/typon Great idea and great talk.
avilella: Anybody hear any numbers about MiSeq price per run? Or per base? #ABPH11 tweeps @jennifergardy @fionabrinkman @aunderwo @jacarrico @avilella
pathogenomenick: You can link these data to RDF. Convert with: Triplify, Apache OODT, Virtuoso Sponger. This is cool, need to get into it more. #ABPH11
pathogenomenick: Webservices: use RDF repositories such as Triple stores: JENA, OpenLink Virtuoso, Mulgara, Bigdata, Sesame. Query with SPARQL. #ABPH11
Paper : http://bit.ly/jm7XDT Ontology: http://bit.ly/jzApXB #ABPH11 http://bit.ly/jm7XDThttp://bit.ly/jzApXB
aunderwo: Joo Andr Carrio: An ontology and REST API for microbial typing
Paper : http://bit.ly/jm7XDT Ontology: http://bit.ly/jzApXB #ABPH11 http://bit.ly/jm7XDThttp://bit.ly/jzApXB
pathogenomenick: RT @aunderwo: Joo Andr Carrio: An ontology and REST API for microbial typing
pathogenomenick: Has developed a RESTful MLST web interface. This is great. We just need it for next-gen now. #ABPH11
#ABPH11 http://rest.phyloviz.net/webui/
aunderwo: Joo Andr Carrio: RESTful MLST web interface http://rest.phyloviz.net/webui/
pathogenomenick: Developed data visualisation software called Phyloviz: http://bit.ly/isNJgj handles ST data, SNP data, looks pretty #ABPH11 http://bit.ly/isNJgj
pathogenomenick: .@jacarrico makes a compelling case for open data in molecular typing. What a shame it is not embraced by wider community #ABPH11
marina_manrique: Nick Loman @pathogenomenick starts the pipeline session. xBASE-NG A web interface for rapid analysis of bacterial genomes #ABPH11
aunderwo: Nick Loman: Web interface for WGS analysis http://ng.xbase.ac.uk/my/ #ABPH11 http://ng.xbase.ac.uk/my/
pjacock: RT @marina_manrique: Nick Loman @pathogenomenick starts the pipeline session. xBASE-NG A web interface for rapid analysis of bacterial genomes #ABPH11
aunderwo: Nick Loman: Use Illumina sequence to correct homopolymeric tracts in 454 scaffolds #ABPH11
jacarrico: Nick Loman - illumina corrected 133 putative erros in 454 assembly #ABPH11
marina_manrique: Once more: the importance of hybrid assemblies (in this case #454 & #illumina) for correcting seq errors @pathogenomenick #ABPH11 #ngs
fionabrinkman: Now @pathogenomenick talking about Pseudomonas genome sequencing and xBASE-NG #ABPH11
BigSporkDaggler: 'Xbase ng main selling point is not accuracy" #couldhavebeenphrasedbetter #ABPH11
aunderwo: Nick Loman: Best to aggressively 'trim' SNPs after mapping reads-better to have fewer false positives and more false negatives #ABPH11
jacarrico: Nick Loman Alignment software can introduce SNP errors since it is optimized for speed, not accuracy #ABPH11
fionabrinkman: .@pathogenomenick says their annotation pipeline not geared for accuracy but rather providing info - I like this when exploring data #ABPH11
jacarrico: Keith jolley -BIGsDB - http://t.co/3yqfn2o #ABPH11 http://t.co/3yqfn2o
fionabrinkman: .@pathogenomenick says Ion Torrent data released today re current E. coli outbreak #ABPH11 #ecoli
jacarrico: BIGsDB offers the possibility of linking a published dataset to the DB. #ABPH11
jacarrico: BIGsDB - can store to completely heterogeneous database and integrate it #ABPH11
jacarrico: BIGsDB can compare several geno es and create allele profiles like a super MLST schema #ABPH11
pathogenomenick: Keith Jolley talking BiGsDB: BLAST-based genome comparator for whole-genomes. Extends MLST idea to large numbers of genes. #ABPH11
pathogenomenick: 'rMLST' tree - universal typing from domain to strain. Gives good speciation within a genus. Subspeciation within S. pneumo #ABPH11
aunderwo: Keith Jolley: BIGSdb genome comprator allows comparison of genomes by MLST but with 1000s of loci not 7 #ABPH11
fionabrinkman: #ABPH11 @pathogenomenick discussed problem of repeats etc for SNP calling. He great to talk to re SNP analysis. See also http://XBASE.AC.UK http://XBASE.AC.UK
pathogenomenick: It's @marina_manrique talking about her NG7 genome annotation system #ABPH11
GigaScience: Nice linking of data http://ow.ly/58mpN RT @jacarrico BIGsDB offers the possibility of linking a published dataset to the DB. #ABPH11 http://ow.ly/58mpN
aunderwo: Marina Manrique: An annotation pipeline for NGS genome data http://www.era7bioinformatics.com/en/prokaryote_genome_annotation.html #ABPH11 http://www.era7bioinformatics.com/en/prokaryote_genome_annotation.html
jacarrico: www.ohnosequences.com - great name for a sequence assembler based on protein similarity #ABPH11
AntipodeanCharm: Do we need a meta-analyzer to pool and view data from the various auto annotators? We OCD types could never just stick to one. #ABPH11
pablopareja: RT @aunderwo: Marina Manrique: An annotation pipeline for NGS genome data http://www.era7bioinformatics.com/en/prokaryote_genome_annotation.html #ABPH11 http://www.era7bioinformatics.com/en/prokaryote_genome_annotation.html
aunderwo: Marina Manrique: Using cloud computing annotate 1 genome in 2 hours, annotate 100 genomes in 2 hours! #ABPH11
jacarrico: Cloud computing in Amazon Web services for assembly and pay-per-use approach of computing power. Very nice #ABPH11
pathogenomenick: Marina's annotation pipeline can run on EC2 http://bit.ly/kqhrZW #ABPH11 http://bit.ly/kqhrZW
aunderwo: www.ohnosequences.com nice looking website - very minimalist, low on clutter #ABPH11
jacarrico: They developed bio4j graph database http://t.co/mxV0ldu as well! That should allow sparql queries :) #ABPH11 http://t.co/mxV0ldu
pathogenomenick: RT @jacarrico: They developed bio4j graph database http://t.co/mxV0ldu as well! That should allow sparql queries :) #ABPH11 http://t.co/mxV0ldu
ldalcaraz: RT @fionabrinkman: .@pathogenomenick says Ion Torrent data released today re current Europe's E. coli outbreak #ABPH11 #ecoli
32nm: RT @jacarrico: They developed bio4j graph database http://t.co/mxV0ldu as well! That should allow sparql queries :) #ABPH11 http://t.co/mxV0ldu
jennifergardy: This AM's #ABPH11 talks from @jacarrico @pathogenomenick and @aunderwo suggest a positive correlation between tweeting & giving good talks.
pathogenomenick: .@aunderwo is telling us about his local pipeline for orthologue prediction and reference-guided annotation. Loads into SQLdb. #ABPH11
pathogenomenick: .@aunderwo wants to integrate his pipeline into Galaxy. We want to do that too #usegalaxy #ABPH11
jacarrico: galaxy.psu.edu - web Gui creation - sugestion from @aunderwo #ABPH11
pathogenomenick: Hmm, this next talk is comparing various pipelines including FgenesB, IMG/ER, xBASE, RAST, IGS. So bit nervous. #ABPH11
marina_manrique: Rebecca Jones comparing annotations pipelines: FgenesB IMG/ER xBASE RAST and IGS on Salmonella data #ABPH11 #ngs
jacarrico: Lots of diferent and interestign aproaches to genome assembly and annotation pipelines so far and all full of nice features #ABPH11
pathogenomenick: She's correctly stated our pipeline is the quickest and is easy to use, but probably won't do so well with quality #ABPH11
jacarrico: I've quit taking notes since the tag #ABPH11 is enough to get what I want :) #gobiotwitters
marina_manrique: Small CDS remains a challenge in bacterial genome annotation (Rebecca Jones) #ABPH11
aunderwo: RT @jacarrico: I've quit taking notes since the tag #ABPH11 is enough to get what I want :) #gobiotwitters
pathogenomenick: Rebecca has pointed out my pipeline can produce large overlapping genes. This is an effect of Glimmer, I should tidy it up . #ABPH11
1VQ9: RT @jacarrico: They developed bio4j graph database http://t.co/mxV0ldu as well! That should allow sparql queries :) #ABPH11 http://t.co/mxV0ldu
fionabrinkman: .@aunderwo mentioned how HiSeq produces so much data for microbial seq that can simply remove low quality reads & still have lots #ABPH11
fionabrinkman: Like the idea of having a pipeline roundtable discussion to get efficiently at issues re seq analysis pipelines #ABPH11
jacarrico: All anotation is a lie, or at least an approximation to the truth - Nick thanks for saying that out loud! #ABPH11
eduardopareja: Marina Manrique from Era7bioinformatics talking about NG7 http://bit.ly/j3wNAO in Cambridge #ABPH11 in a #bioinformatics meeting at Sanger http://bit.ly/j3wNAO
AntipodeanCharm: Automated annotation. Used think it was speed vs accuracy - pick one. Not so sure now. But people r reluctant 2 review gene by gene #ABPH11
AntipodeanCharm: RT @jacarrico: All anotation is a lie, or at least an approximation to the truth - Nick thanks for saying that out loud! #ABPH11
AntipodeanCharm: ditto. @jacarrico: I've quit taking notes since the tag #ABPH11 is enough to get what I want :) #gobiotwitters
aunderwo: Rebecca Jones: Comparison of annotation pipeline - FgenesB annotates addition features and comparable number of CDSs #ABPH11
aunderwo: RT @fionabrinkman: Like the idea of having a pipeline roundtable discussion to get efficiently at issues re seq analysis pipelines #ABPH11
jacarrico: Rohlf Kass on WGS 173(?) Ecoli strains - very actual given the #EHEC outbreak #ABPH11
jacarrico: Dynamic threshold for strain/clone differentiation - interesting approach that I support due to increasing sampling ! #ABPH11
jacarrico: Plot of gene diversity show that MLST genes in ecoli have low variabilty...as the great majority of genes #ABPH11
aunderwo: Rolf Kass: Described method to find gene families in E.coli and then find those gene families that show high/low conservation #ABPH11
3
marina_manrique: @lexnederbragt Have you come to the Applied Bioinformatics & Public Health conference at Cambridge? #ABPH11
jacarrico: Selection vs Drift : the eternal question, how to/ can we effectively measure it ? #ABPH11
jacarrico: Rebecca Gladstone - genetic diversity of S pneumo serotype 6C #ABPH11
jacarrico: Sero 6C - prevalent in carriage, can cause diseae, not in vacc, cross protection from 6A in 13V vaccine #ABPH11
jacarrico: WGS of 6C isolates ( diferent STs present) strains and analysis using split decomp show some recognizable clusters #ABPH11
aunderwo: Rebecca Gladstone: The number of genes NOT shared between strains correlates well with their clonal lineage as defined by MLST #ABPH11
GuillaumeMeric: RT @jacarrico: Plot of gene diversity show that MLST genes in ecoli have low variabilty...as the great majority of genes #ABPH11
jacarrico: 6C isolates were from 5 distinct lineage..clonal expansion is contributing for spread #ABPH11
GuillaumeMeric: RT @jacarrico: Selection vs Drift : the eternal question, how to/ can we effectively measure it ? #ABPH11
fionabrinkman: We need genomic epi on this new E. coli outbreak strain EAEC/EHEC asap! http://goo.gl/7L1gc http://goo.gl/a10TO #ecoli #ABPH11 http://goo.gl/7L1gchttp://goo.gl/a10TO
ldalcaraz: RT @fionabrinkman: We need genomic epi on this new E. coli outbreak strain EAEC/EHEC asap! http://goo.gl/7L1gc http://goo.gl/a10TO #ecoli #ABPH11 http://goo.gl/7L1gchttp://goo.gl/a10TO
BGI_Events: RT @fionabrinkman: .@pathogenomenick says Ion Torrent data released today re current E. coli outbreak #ABPH11 #ecoli
sehrrot: RT @fionabrinkman: .@aunderwo mentioned how HiSeq produces so much data for microbial seq that can simply remove low quality reads & still have lots #ABPH11
sehrrot: RT @fionabrinkman: We need genomic epi on this new E. coli outbreak strain EAEC/EHEC asap! http://goo.gl/7L1gc http://goo.gl/a10TO #ecoli #ABPH11 http://goo.gl/7L1gchttp://goo.gl/a10TO
jennifergardy: Fun lunch at #ABPH11 with @fionabrinkman and Martin Maiden discussing the lack of pathogenic archaea #sciencefunisdifferentfun
mattdotvaughn: RT @aunderwo: When linking data the problem (even within the same organisation - speaking from experience!) is the identifiers #ABPH11
pathogenomenick: Ulbrich Dobrindt is now going to talk about E. coli outbreaks - will he talk about German outbreak I wonder? #ABPH11
aunderwo: Ulbrich Dobrindt: Talking about ExPEC E.coli - Can be asymptomatic commensals but once leave gut cause disease #ABPH11
fionabrinkman: Ulbrich Dobrindt talking about E. coli genome plasticity - including impacts on diagnostics #ecoli #ABPH11
pathogenomenick: Ulrich trying to classify clinical manifestations of E. coli (ExPEC, EHEC etc.) based on clustering of COGs. Works well, #ABPH11
aunderwo: Ulrich Dobrindt: Single linkage clustering based on presence/absence of COGs groups pathotypes of E.coli #ABPH11
pathogenomenick: Ulbrich talks about German E. coli outbreak: >1400 suspected cases, 470 confirmed HUS, 15 deaths #ABPH11
jacarrico: Ulrich now talking about the #EHEC O104:H4 outbreak...really fresh data..the hybrid was confirmed some days ago! #ABPH11
aunderwo: Ulrich Dobrindt: ExPEC E.coli strains have clusters of virulence markers that may be markers for the ExPEC type #ABPH11
fionabrinkman: Dobrindt: Ureopathogenic E. coli can have genes from EHEC etc. So mixed virulence repertoires occur #ecoli #ABPH11
pathogenomenick: It's difficult to distinguish commensals and ExPEC by MLST alone. Can WGS help? #ABPH11
GuillaumeMeric: RT @fionabrinkman: Dobrindt: Ureopathogenic E. coli can have genes from EHEC etc. So mixed virulence repertoires occur #ecoli #ABPH11
GuillaumeMeric: RT @pathogenomenick: It's difficult to distinguish commensals and ExPEC by MLST alone. Can WGS help? #ABPH11
GuillaumeMeric: RT @jacarrico: Ulrich now talking about the #EHEC O104:H4 outbreak...really fresh data..the hybrid was confirmed some days ago! #ABPH11
jacarrico: Combination of MLST + MLVA to increase resolution of typing of e.coli #ABPH11
aunderwo: Ulrich Dobrindt: Clonal complexes containing ExPEC also have non-ExPEC strains.In contract IPEC complexes contain only IPEC isolates #ABPH11
GuillaumeMeric: Dobrindt papers are really really nice. It's nice to follow his talk via #ABPH11, thanks twitters
GuillaumeMeric: RT @jacarrico: Combination of MLST + MLVA to increase resolution of typing of e.coli #ABPH11
fionabrinkman: Dobrindt refers to E. coli outbreak strain as O104:H4 and confirms it contains shiga toxin and EAEC genes #ecoli #ABPH11
jacarrico: Nice trees very PHYLOViZ like! #blatantpublicitystunt #ABPH11
GuillaumeMeric: RT @fionabrinkman: Dobrindt refers to E. coli outbreak strain as O104:H4 and confirms it contains shiga toxin and EAEC genes #ecoli #ABPH11
RebeccaGladston: #abph11 a great example of artificial flora for protective purposes
pathogenomenick: Talking about long-term colonisation by asymptomatic E. coli being protective http://bit.ly/jiRPXS #ABPH11 http://bit.ly/jiRPXS
GuillaumeMeric: RT @pathogenomenick: Talking about long-term colonisation by asymptomatic E. coli being protective http://bit.ly/jiRPXS #ABPH11 http://bit.ly/jiRPXS
pathogenomenick: http://1.usa.gov/lU2Bv2 Host Imprints on Bacterial GenomesRapid, Divergent Evolution in Individual Patients #ABPH11 http://1.usa.gov/lU2Bv2
AntipodeanCharm: Some analogies to E coli Nissle being protective against IBD, I guess. #ABPH11
attilacsordas: RT @pathogenomenick: http://1.usa.gov/lU2Bv2 Host Imprints on Bacterial GenomesRapid, Divergent Evolution in Individual Patients #ABPH11 http://1.usa.gov/lU2Bv2
cgorman: RT @fionabrinkman: Dobrindt refers to E. coli outbreak strain as O104:H4 and confirms it contains shiga toxin and EAEC genes #ecoli #ABPH11
pathogenomenick: When tracking SNPs in vivo they can "come and go" - impact for tracking outbreaks. Homoplasy or mixed population? #ABPH11
jacarrico: SNPs can come and go over time..lots of implications for typing. are fast clock targets usefull besides outbreak detection at all @ #ABPH11
jacarrico: Ulrich @ the stand ! #IPadphototesting #ABPH11 http://t.co/2A6Yd6g http://t.co/2A6Yd6g
aunderwo: Interesting papers by Ubrindt re array based typing of Enterobacteria http://1.usa.gov/mGobnB http://1.usa.gov/l6nMUv #ABPH11 http://1.usa.gov/mGobnBhttp://1.usa.gov/l6nMUv
jacarrico: Actually it is amazing how fast we got data on the #EHEC outbreak from the first typing to sequences available. #RTgenomics #ftw #ABPH11
kshameer: RT @pathogenomenick: When tracking SNPs in vivo they can "come and go" - impact for tracking outbreaks. Homoplasy or mixed population? #ABPH11
jacarrico: Curious about next talk: whole genome sequences from clinical swabs Chlamydia trachomatis by Helena Seth-Smith #ABPH11
jacarrico: MOMP serotyping phylogeny reconstruction misleading due to recombination #ABPH11
pathogenomenick: Chlamydia doesn't have any significant repeats in it and so assembles very nicely #ABPH11 - but getting DNA is an issue
marina_manrique: Helena Seth-Smith on getting complete Chlamydia trachomatis genomes from clinical swabs at #ABPH11
jacarrico: Only 15+ MOMP serotypes ..10 already sequenced... #ABPH11
fionabrinkman: Helena Seth-Smith on Chlamydia trachomatis genomes from clinical swabs: really needed cause so hard to culture #ABPH11
GuillaumeMeric: Thanks for sharing tweets about #ABPH11 conference! @fionabrinkman @pathogenomenick @jacarrico
fionabrinkman: @pathogenomenick @jennifergardy @jacarrico Helena mentioned Chamydia culturing is stochastic :) #baysianstochasticism #ABPH11
pathogenomenick: Through immunomagnetic separation and MDA can sequence chlamydia direct from patient samples without culture #ABPH11
marina_manrique: They capture C. trachomatis with specific antibodies to enrich the sample in C. trachomatis DNA, I like it! :) #ABPH11
aunderwo: Helena Seth Smith: Purification of Chlamydia from clinical swabs and amplification by MDA allows genome assembly but uneven coverage #ABPH11
marina_manrique: RT @aunderwo: Helena Seth Smith: Purification of Chlamydia from clinical swabs and amplification by MDA allows genome assembly but uneven coverage #ABPH11
jacarrico: Only 20% success rate on the WGS from swabs: problems from transport to assembly (repeats) #ABPH11
jacarrico: Jacqueline Chan - Phylogenetic definition of species for Acinetobacter genus #ABPH11
pathogenomenick: Jackie Chan from our lab is talking about a phylogenomic definition of species - looking at Acinetobacter #ABPH11
jacarrico: Single gene trees (16s and rpoO) show different clusters #ABPH11
aunderwo: Jacqueline Chan: rpoB tree produces more robust tree than 16S for Acinetobacter. Matches the whole genome SNP tree fairly well #ABPH11
jacarrico: Genome fluidity z- Kislyuk et al, 2011 BMC genomics. Interesting concept. #ABPH11
aunderwo: Jacqueline Chan: Genomic fluidity gives a measure of gene diversity that can be used to draw a tree - promising http://bit.ly/mx7SJ0 #ABPH11 http://bit.ly/mx7SJ0
jacarrico: Cut-off value of 20% on single linkage (?) dendrogram of genome fluidity values seems to provide a good species definition #ABPH11
cgorman: To all #ABPH11 conference attendees: Have you heard if new #ecoli O104:H4 outbreak has any antibiotic resistance genes as well?
jacarrico: Little gene content difference between Acinetobacter species #ABPH11
fionabrinkman: Re Jackie Chan's talk: RT @jacarrico: Genome fluidity z- Kislyuk et al, 2011 BMC genomics. Interesting concept. #ABPH11
jacarrico: Angela McCann - presenting data on a Salmonella agona outbreak #ABPH11
marina_manrique: I've uploaded the slides of our talk at #ABPH11 on BG7 a bacterial genome annotation system designed for #ngs http://slidesha.re/iX1m7Y #in http://slidesha.re/iX1m7Y
pathogenomenick: Now Angela McCann from Cork is talking about a food-borne outbreak of Salmonella agona - Illumina sequencing #ABPH11
jacarrico: PFGE couldn't discriminate the strains - 38 strains were sequenced using Illumina paired end reads #ABPH11
pathogenomenick: They checked their SNP set was congruent between de novo assemblies (SOAPdenovo) and mapping alignments (SSAHA2) - good approach #ABPH11
marina_manrique: RT @pathogenomenick: They checked their SNP set was congruent between de novo assemblies (SOAPdenovo) and mapping alignments (SSAHA2) - good approach #ABPH11
jacarrico: ML phylogeny inferred from 3928 SNPs didn' match PFGE #ABPH11
aunderwo: Angela McCann: Differences in Salmonella PFGE profiles may be caused to some extent by phage acquisition/loss #ABPH11
GuillaumeMeric: RT @jacarrico: ML phylogeny inferred from 3928 SNPs didn' match PFGE #ABPH11
fionabrinkman: RT @pathogenomenick: They checked their SNP set was congruent between de novo assemblies (SOAPdenovo) and mapping alignments (SSAHA2) - good approach #ABPH11
jacarrico: Two different and distantly related clones (by SNPs) were responsible for the different outbreaks. PFGE misled by mobile elements ? #ABPH11
jennifergardy: Too tired to tweet from #ABPH11 right now. NEED CHOCOBISCUIT IF I AM GOING TO CARRY ON. #getmeabiscuitnownownow
aunderwo: RT @pathogenomenick: They checked their SNP set was congruent between de novo assemblies (SOAPdenovo) and mapping alignments (SSAHA2) - good approach #ABPH11
fionabrinkman: Angela McCann: PFGE potentially missled by mobile elements #ABPH11
fionabrinkman: Martin Maiden at #ABPH11: "the future is now" Lots of great data for bioinformaticists to get their hands on #wringinghandswithglee
marina_manrique: I wish there were more explanations about how the data was analysed in the talks (seq tech used, assembler, "annotators"...) #ABPH11
AntipodeanCharm: In 2008, there was an epidemic of testing for C. difficule. # #ABPH11
AntipodeanCharm: .@marina_manrique: Asking about individual pipelines would be a good survey to poll #ABPH11 attendees on.
pathogenomenick: Tim Peto from Oxford talking now .. he's got 30,000 stool samples to play with in his study. Smelly. #ABPH11
marina_manrique: @AntipodeanCharm maybe I could ask for that in the Q&A box. it'd be great if we could get some figures of used tools and pipelines #ABPH11
jennifergardy: Tim Peto is giving the best talk ever at #ABPH11 He is my new hero. And a poo hero. 30k stool samples.
AntipodeanCharm: How much poo can stay in a hospital ward? #ABPH11
pathogenomenick: "Genomics test is just a test" - requires usual challenges of any dx test: false pos / false neg rates. #ABPH11
pathogenomenick: Challenge is we don't have a gold standard for transmission. Need to use epi data and genomics data to create a gold standard. #ABPH11
AntipodeanCharm: What does the toothpaste genome look like? #ABPH11
aunderwo: Tim Peto: uses very high coverage (x85) and strict SNP filter to reduce false positives <1 error/100 strains #ABPH11
fionabrinkman: Tim Peto: MLST not descriminatory enough for C. difficile transmission study in hospital. Epi & genomes needed. #yesyetagain #ABPH11
jacarrico: I can see several twitters open from up here! You know who you are...#ABPH11
2
fionabrinkman: Tim Peto: C. difficile in their hospital: ~2.6 SNPs/yr in individuals over time #ABPH11
aunderwo: Tim Peto: analysis of c.difficile strains from a single patient estimates 2.6 SNPs/strain/year #ABPH11
pathogenomenick: Best slide title ever "12 picks from one nose" #ABPH11
pathogenomenick: RT @aunderwo: Tim Peto: analysis of c.difficile strains from a single patient estimates 2.6 SNPs/strain/year #ABPH11
fionabrinkman: Battery about to die again. #ABPH11
aunderwo: Tim Peto: taking multiple Staph aureus nasal samples gives a new meaning to nose picking! #ABPH11
marina_manrique: Tim Peto - Whole genome sequencing would be better than MLST but be careful with false positives #ABPH11
marina_manrique: Tim Peto - Whole genome sequencing would be better than MLST but be careful with false positives #ABPH11
jacarrico: Tim Peto deserves a picture. He gave an interesting and highly motivated talk! #ABPH11 http://t.co/5oLMJ3u http://t.co/5oLMJ3u
AntipodeanCharm: I am astonished that Tim Peto's bioinfs were cross when he tested their pipeline with dupes. I suggest that my colleagues do that! #ABPH11
jennifergardy: RT @jacarrico: Tim Peto deserves a picture. He gave an interesting and highly motivated talk! #ABPH11 http://t.co/5oLMJ3u http://t.co/5oLMJ3u
pathogenomenick: RT @jacarrico: Tim Peto deserves a picture. He gave an interesting and highly motivated talk! #ABPH11 http://t.co/5oLMJ3u http://t.co/5oLMJ3u
jennifergardy: Oooh, I just learned several interesting Luxembourg facts at #ABPH11 #tinybutinteresting
fionabrinkman: RT @jacarrico: Tim Peto deserves a picture. He gave an interesting and highly motivated talk! #ABPH11 http://t.co/5oLMJ3u http://t.co/5oLMJ3u
pathogenomenick: Joel Mossong & MRSA SNPs. He is very keen to mention Luxembourg is a real country (by Zappa test) - airline, beer, football team #ABPH11
pathogenomenick: Mossong: Was alarmed when he got 1Gb files per each MRSA strain sequenced, compared with 7 bytes for MLST! #ABPH11
marina_manrique: Great! some info about the kind of technology used in Jel Mossong talk at #ABPH11 Illumina 2x100bp 80x for MSRA genomes
jacarrico: Interesting spa type vs SNP typing max parsimony tree comparison #ABPH11
aunderwo: Joel Mossong: using 85x coverage illumina data could extract MLST profiles from 36/40 strains. Puzzled about missing 4? #ABPH11
aunderwo: RT @pathogenomenick: Mossong: Was alarmed when he got 1Gb files per each MRSA strain sequenced, compared with 7 bytes for MLST! #ABPH11
marina_manrique: Another idea I've liked at Jel Mossong talk: WGS data should not be limited to SNP analysis, Mobile elements also play a role! :) #ABPH11
marina_manrique: And once more, the need of platforms to share typing data #ABPH11
jacarrico: "Public health institutes should acquire bioinformatics expertise" Joel Mossong #ABPH11 Good point !#iwillneedabetterpayedjobsoon ;)
AntipodeanCharm: RT @jacarrico: "Public health institutes should acquire bioinformatics expertise" Joel Mossong #ABPH11 Good point !#iwillneedabetterpayedjobsoon ;)
aunderwo: RT @jacarrico: "Public health institutes should acquire bioinformatics expertise" Joel Mossong #ABPH11 Good point !#iwillneedabetterpayedjobsoon ;)
jacarrico: Robert Stones - Phylogenomic analysis pipelines #ABPH11
pathogenomenick: Robert Stones who was a co-author on cool FDA Salmonella outbreak paper recently in NEJM is doing a very brave live demo of his SW #ABPH11
jacarrico: Showing a software demo of GeneSpace... #ABPH11
fionabrinkman: Twitter and @AntipodeanCharm at #ABPH11 is my saviour (ty!). I now have iPhone juice!
aunderwo: Talks aren't getting less interesting. Just finding that I'm running out of tweeting juice #ABPH11
jacarrico: RT @aunderwo: Talks aren't getting less interesting. Just finding that I'm running out of tweeting juice #ABPH11
AntipodeanCharm: Back-to-back, fascinating talks. Intense and educational but my brain is full. My mind is ticking over mucho information & ideas. #ABPH11
fionabrinkman: At #ABPH11 I'm struck with how far we've come with genomic epi but how hard it will be to standardize
aunderwo: Marcus Claesson: comparing 454 and Illumina data for classifying bacteria using 16S. 454 outperforms Illumina #ABPH11
aunderwo: Marcus Claesson: Metagenomics - long reads of 454 give better classification , more data from Illumina => more OTUs detected #ABPH11
fionabrinkman: Claesson: 454 better vs Illumina for 16S seq (see http://goo.gl/u4CDD) but >60bp Illumina reads really helps & primer choice key #ABPH11 http://goo.gl/u4CDD)
torstenseemann: if we had concurrent sessions at #ABPH11 we'd be "stereo-typing"
aunderwo: Supang Martin: Single Genome Sequencing of full length HIV pol reveals covariance of resistance SNPs #ABPH11
lexnederbragt: RT @marina_manrique: Once more: the importance of hybrid assemblies (in this case #454 & #illumina) for correcting seq errors @pathogenomenick #ABPH11 #ngs
torstenseemann: the problem with conferences like #ABPH11 is that i get too many new ideas, but with no time to play with them
pathogenomenick: RT @torstenseemann: the problem with conferences like #ABPH11 is that i get too many new ideas, but with no time to play with them
aunderwo: RT @torstenseemann: the problem with conferences like #ABPH11 is that i get too many new ideas, but with no time to play with them
pathogenomenick: That's the end of the day 2 session. Great set of talks, I thought. Time for BEER. #ABPH11
jennifergardy: And another day of #ABPH11 talks ends. Now the real work begins. Beer drinking and hanging out.
jacarrico: @jennifergardy: And another day of #ABPH11 talks ends. Now the real work begins. Beer drinking and hanging out. #FTW
jennifergardy: @jacarrico @pathogenomenick @aunderwo I think we can also capture all #ABPH11 tagged tweets with Twapper Keeper. Mebbe Nick can blog them.
jennifergardy: Dinner at #ABPH11 - see if you can spot @pathogenomenick @fionabrinkman and @jacarrico at our table. http://t.co/N08OgOW http://t.co/N08OgOW
eduardopareja: RT @marina_manrique: I've uploaded the slides of our talk at #ABPH11 on BG7 a bacterial genome annotation system designed for #ngs http://slidesha.re/iX1m7Y #in http://slidesha.re/iX1m7Y
pjacock: RT @pathogenomenick: .@aunderwo wants to integrate his pipeline into Galaxy. We want to do that too #usegalaxy #ABPH11
rodyredo: RT @AntipodeanCharm: When computers are in presentation mode, eg in PowerPoint, the OS should shut up and stop sending alerts to the screen #ABPH11
pathogenomenick: Brendan Wren on Newsnight talking E. coli - big cheer went up in Red Lion when he came on the telly #ABPH11 http://yfrog.com/h4wrkfwj http://yfrog.com/h4wrkfwj
GuillaumeMeric: RT @pathogenomenick: Brendan Wren on Newsnight talking E. coli - big cheer went up in Red Lion when he came on the telly #ABPH11 http://yfrog.com/h4wrkfwj http://yfrog.com/h4wrkfwj
pathogenomenick: Here's an annotation of that EHEC assembly from @marina_manrique and team http://bit.ly/kXZrsu #ABPH11 http://bit.ly/kXZrsu
KamounLab: RT @pathogenomenick: Here's an annotation of that EHEC assembly from @marina_manrique and team http://bit.ly/kXZrsu #ABPH11 http://bit.ly/kXZrsu
AntipodeanCharm: RT @pathogenomenick: Here's an annotation of that EHEC assembly from @marina_manrique and team http://bit.ly/kXZrsu #ABPH11 http://bit.ly/kXZrsu
jacarrico: Last day of #ABPH11 . Will start with Consumption-Junction by @jennifergardy GO JENNiFER ! bring it on!
pathogenomenick: Final day of #ABPH11 - Jen Gardy is doing her excellent talk about TB genomic epidem. Won't tweet as did it at ASM http://bit.ly/mEJk1V http://bit.ly/mEJk1V
aunderwo: #ABPH11 tweeters - what questions should we try and get raised at the Q&A session later?
fionabrinkman: .@jennifergardy on integrating TB outbreak genomes with social network analysis (see our NEJM paper http://bit.ly/gxWcF0) Go Jenn! #ABPH11 http://bit.ly/gxWcF0)
aunderwo: #ABPH11 Qs: I was thinking of "How close are we to (what are the barriers to) near patient NGS analysis that impacts clinical management"
fionabrinkman: .@jennifergardy re genomic epidemiology: Its tricky. It's useful. Need good epi data & understand disease transmission #ABPH11
GuillaumeMeric: RT @pathogenomenick: Here's an annotation of that EHEC assembly from @marina_manrique and team http://bit.ly/kXZrsu #ABPH11 http://bit.ly/kXZrsu
jacarrico: Perfect world: just a few snps available to reconstruct a outbreak spanning a few weeks, but with TB they can be sick for years #ABPH11
jacarrico: Use of SNPs to subtype inside of MIRU type and then rely on epi data #ABPH11
jacarrico: Since they got 2 different clades they used it to prune the social network graph by removing impossible transmission links #ABPH11
jacarrico: Without the epi data the genomics couldn't tell us anything. Simple and Elegant demo by overlaying social graph with SNP tree #ABPH11
jacarrico: Jennifer Talking now of Kelowna TB Outbreak #ABPH11
fionabrinkman: .@jennifergardy mentions movie Outbreak influence #ABPH11 & did TV sci shows. She really would be a great movie consultant-hint @wbpictures!
aunderwo: Jennifer Gardy: For TB, genome data needs to be combined with comprehensive Epi data and social network analysis to get the true pic #ABPH11
aunderwo: In chronic infections an individual may have a diverse pop of bugs- this may confuse trying to infer transmission tree from genomics #ABPH11
jacarrico: Now Brendan Wren Function Junction - moving from trees to forests #ABPH11
aunderwo: Jennifer Grady:'Deep amplicon' sequencing of TB from a patient may resolve problems where phylo tree does not seem to match epi data #ABPH11
marina_manrique: Great! Less trees and more functions! :) Brendan Wen at #ABPH11
pathogenomenick: Brendan Wren is taking about "going beyond the trees" - "eco-evo" perspective on virulence. See this article http://bit.ly/mzbmDv #ABPH11 http://bit.ly/mzbmDv
fionabrinkman: @aunderwo It'd be great to frame Ques: What should we do collectively in the field to move genomic epi into clinic more in future #ABPH11
jacarrico: Humans became acidentally a part of Capylobacter life-cycle #ABPH11
fionabrinkman: Unfortunately I have to taxi pool to airport now. Will miss good talks darnit. Thank you #ABPH11 tweeters for great comments!
GuillaumeMeric: RT @jacarrico: Humans became acidentally a part of Capylobacter life-cycle #ABPH11
aunderwo: @fionabrinkman Agreed.Only together can we make this a reality- soooo much to investigate and test b4 we can have rigorous methods #ABPH11
pathogenomenick: Good intro slide to Oliver Pybus' talk on RNA viruses http://bit.ly/mvqjCV #ABPH11 http://bit.ly/mvqjCV
pathogenomenick: Virologists are ahead in epidemiology compared to bacteriologists; 10s of 1000s of sequenced strains #ABPH11
jacarrico: Oliver Pybus promised a cucumber free talk ! #ABPH11 http://t.co/RkOMXPk http://t.co/RkOMXPk
jacarrico: 1 year of HIV evolution is equivalent of millions of years of mammalian evolution #ABPH11
aunderwo: Oliver Pybus: Nice slides that help to clarify what coalescent theory is about - hope they get published somewhere #ABPH11
jacarrico: Tools in virus phylogenetics: molecular clock, coalescent theory, phylogeography and adaptation analysis #ABPH11
RebeccaGladston: #ABPH11 public health intervention responsible for highest HCV in the world in Eygpt, oops
jennifergardy: Ollie Pybus is giving the clearest talk on phylodynamics ever at #ABPH11 He is a supersmartie and I follow his papers like a giant dork.
attilacsordas: RT @pathogenomenick: Virologists are ahead in epidemiology compared to bacteriologists; 10s of 1000s of sequenced strains #ABPH11
aunderwo: Oliver Pybus: Series of seasonal fluA samples over 10+ years => each year's strain pop is derived from one strain from previous year #ABPH11
jacarrico: Nice google earth animation to illustrate the spread oh H1N1 #ABPH11
ALShahib: #ABPH11
attilacsordas: #ABPH11 'Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data' might check that one
aunderwo: Oliver Pybus:Adaptive substitutions in flu occur mainly in HA + NA (surface regions). Rate of change < 4 H1N1. < selective pressure? #ABPH11
RebeccaGladston: #abph11 Dig out your old isolates from the freezers to improve understanding of th epi of current disease cases
aunderwo: @ALShahib hey your tweets are now showing in the ABPH11 timeline!
pathogenomenick: Oliver Pybus gave an excellent overview of phylodynamics/phylogenetics. Must read his papers. http://bit.ly/jK1mAg #ABPH11 http://bit.ly/jK1mAg
jacarrico: Use of Effective population size or genetic diversity : presence of selection or not #ABPH11
RebeccaGladston: A really great set of talks @ #abph11 over all the days, looking forward to see what discussions they provoke in q&a! Welldone organisers!
jacarrico: Marijn van Ballegooijen presenting nice statistical evaluation of transmission network reconstruction #ABPH11
jacarrico: Outbreak correlates well wind direction! Nice example for heterogeneous data integratio ! #ABPH11