Videos and Slides: Next-Generation Sequencing Technologies - Elaine Mardis (2014)

Last time I mentioned Elaine Mardis videos giving a summary of next generation sequencing the video shot up and garnered more than 127,893 viewers. Coincidence ? I think not :-)

Here is the new survey on Next Generation Sequencing where she talks about the current PacBio and Nanopore technology add-ons to the lab. I note the biology people liking the term "massively parallel" sequencing. Anyway, those third generation technologies are very interesting because instead of cutting DNA strands in small pieces and trying to put them back together, they output very long reads (up 50Kbp from 200bp for earlier next generation sequencing technology) thereby reducing much of the guessing in the read alignments. The current downside of those technologies are that they have large error rates. PacBio, for instance, with its SMRT technology has about 15pct error rate for a single strand of DNA but that error goes away when combining several DNA strand reading together down to 0.01pct overall. Nanopore, according to Elaine, is in the 30pct realm but one would have to check people looking into it to be really more accurate on that figure. Irrespective, the longer reads with oversampling means that one can get much nicer view of the genome that was, for chemical reasons, not reachable otherwise. I also note that the PacBio approach uses fluorescence and hence uses camera technology, one of the steamrollers. Fluorescence is not unheard of in compressive sensing and maybe an improvement of the technique might provide enhanced accuracy. The question is how do algorithms featured here on Nuit Blanche can help in realizing A Second Inflection Point in Genome Sequencing. (For people confused Next Generation sequencing refers to 2nd generation sequencing while third generation sequencing refers to newer sensors). More on that later.

Without further ado, here is a summary of sequencing technology as July 30th, 2014.

The slides are in Color or Grayscale

This lecture was part of a larger series of talks in Current Topics in Genome Analysis 2014



Also of interest: PBSuite, Software for Long-Read Sequencing Data from PacBio

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Overview of the evolution of genome technology - Richard Wilson

This talk (Overview of the evolution of genome technology by Richard Wilson) is not very technical but it gives a good view of what it is like to grow up with both Moore's law and what I would call the Genomic Sequencer Law that we featured in the The Steamrollers last year. While the start of the video is a little slow (but worth it), you might want to skip to about 18 minutes into the talk. What are some of the take -home ideas there ? Many, here are a few I wrote down:

• In the case of Cancer, we are lucky to have a built in control (base reference) Genome as opposed to other diseases. My opinion is that it is very likely that rare diseases will be taken care off first as they are the first to have individuals with very different genomes from the larger population. 
• Right now, genome sequencing turn-around time is about a month (best we can do now).
• Richard relates a dramatic experience of one of their physician who was part of their group and who found a mutation that could be targeted with a kidney cancer drug. Remission took place in 12 days, and get this, he got tenure :-) Video is below. Enjoy!

Here is the dramatic story mentioned by Richard in his talk:



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From Correlation to Causation in Human Microbiome Studies - Rob Knight

We are about to reach the one year anniversary of Predicting the Future: The Steamrollers (Part 2 is here:Predicting the Future: Randomness and Parsimony). I note two elements: First, the viewership statistics of the presentation by Elaine Mardis (mentioned in The Steamrollers) dwarfs the statistics of all the other presentations at the same meeting, Second, the top image summarizes in one slide an argument developed in the The Steamrollers. The slide is extracted from this fascinating presentation by Rob Knight entitled From Correlation to Causation in Human Microbiome Studies. Go watch it, it is  absolutely fascinating.

The video is part of the meeting on Human Microbiome Science: Vision for the Future. View all videos in the video playlist on GenomeTV
Recent Related entries to sparsity and the microbiome:

• Quikr: a Method for Rapid Reconstruction of Bacterial Communities via Compressive Sensing. - implementation -
• AMP: Assembly Matching Pursuit, Metagenomic units (MGUs) discovery through sequence-based dictionary learning - implementation -
• Predicting the Future: Randomness and Parsimony
• Sparsity in Everything: The Gut Microbiome

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Engineering a Healthcare System to Deliver Genomic Medicine

Echoing some of the themes mentioned in

• Predicting the Future: The Steamrollers
• Predicting the Future: Randomness and Parsimony

May I present to you Dan Rosen's recent presentation on Engineering a Healthcare System to Deliver Genomic Medicine (video is below) where he presents ome of the issues and current state of the art of personalized medicine. And yes, you read right, BioVU has already more than 163,000 samples of DNA, which the genomes are just waiting to be decoded and other phenotype information. More after the video: 

 

I note several items of interest to some of the issues we have mentioned here. Namely, the fact that in medicine, even the 99.99% decoding capabilities of the whole genome by current techniques ( the nanopore approach is only at 96% or so) is not enough. There is also what I call the matching problem and finally the connection between ECG readings and genotype information, wow. Here some screenshots of the presentation (the slides are actually here)

Here is an earlier presentation: DNA Biobanks and Personalized Medicine: Translating genomics to the bedside Dan M. Roden

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A gut feeling review of 2012.

In 2012, we saw some trends:

• an acceleration of work around AMP solvers from sparse recovery into matrix factorization.
• the move towards more complex signal structures (we are moving beyond simply cardinality and power laws and it's a about time) 
• personally a somewhat better understanding of the analysis based approach
• a continuing slew of good solvers (BSBL, any of the AMP solvers)
• more implementation in C++/Python for reconstruction solvers
• The actual use of several places where people exchange views (LinkedIn also here, Google+ and Reddit).
• a few low level interesting hardware development, ghost imaging being one of them but also the potential replacement of the Anger logic, FREAK
• a few new results that render TV-minimization and NMF a more stable theoretical grounding
• a few promising approach such the QTT tensor factorization, manifold signal processing, Protein-DNA interaction.
• a surge in phase retrieval, blind deconvolution approaches
• a surge of code implementations made available by the researchers themselves (36 in the last three months), two or three are on Github. Our field is becoming a trailblazer when it comes to reproducible research.  
• the birth of the RandNLA movement
• some continuing thoughts on post publication peer review process
• a series of compelling videos from GenomeTV
• a series of themes: Predicting the future, accidental cameras, Sunday Morning Insights, some Monthly Reviews
• Curiosity landing on Mars

Finally, with Cable we had fun with the CAI series and expect to continue it into 2013. Like the pirates sailing from one island of knowledge to the other, forward we go. Happy New Year 2013 to y'all.



Image Credit: NASA/JPL/Space Science Institute
N00199979.jpg was taken on December 30, 2012 and received on Earth December 31, 2012. The camera was pointing toward SATURN-ERING at approximately 1,048,438 miles (1,687,298 kilometers) away, and the image was taken using the CL1 and CL2 filters.



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All you wanted to know about Human Genomics but were afraid to ask

It's one of the videos that popped up on the GenomeTV channel. The title is "The Human Genome and Individualized Medicine" by David Valle. The Q&A is also very informative, as this extract shows:

.
..First of all, acute lymphoblastic leukemia. When I was a house officer in the later '60s and early '70s, acute lymphoblastic leukemia was the most common form of childhood leukemia and had a 95 percent mortality rate - 95 percent mortality. Nowadays, acute lymphoblastic leukemia remains the most common chilhood leukemia. It has 95 percent survival rate, 95 percent survival/ So it went from 95 percent mortality to 95 percent survival. So what account for that change ? So actually if you look at it, the medicines that are currently being used are very similar, if not identical, to the medicines that we used all those years ago. So it's not the kinds of medicines that are being used. What it is, I would argue, is that oncologists have learned that this diagnosis is actually a heterogeneous group of disorders. And they've learned how to use gene expression profiling, age of onset, DNA sequence variation and other tools to subdivide the patients. In other words, move from one collective diagnosis to subcategories of diagnosis moving towards individualizing the diagnosis to individual patients and the manipulating their treatment according to which subdivision the patient falls. And that approach. a more informed approach in terms of differences between individual patient with the same diagnosis, has had a dramatic effect on the consequences of having ALL....

Enjoy.

As I suspected, the definition for gene is not as straightforward.

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"...The Window Doesn't Close..."

If you have been touched by Matt's account for the hunt of his son's killer, you might be interested in this fascinating talk by Hal Dietz on Rational therapeutics for genetic conditions. 

From the Q&A at the very end of the video:

Question: Are Adults with Marfan syndrome all treatable ?

Hal Dietz: Yeah, so that's a great question. The question is, are adults with Marfan all treatable or is the window of opportunity to make a difference over in childhood ? At least in our mice, we can allow them to become mature adults. They're sexually mature at about two months, by six months of age they are sort of mid-adult life and by a year of age they are old mice. And whether we start treatment right after birth, in the middle of that sequence, or at the end, we see the same kind of benefits. So we think that the window doesn't close, that there is an opportunity even later in life.

[Emphasis added]

Coming back to the engineering and mathematics of it,  whenever faced with this type of thorough presentation, one wonders whether the use of simple sparsity in metabolic network analysis is quite enough. In fact, we probably ought to connect our regularizers with the biochemistry and the sensors at hand..In particular, at every word, I always wonder if the sensors currently used in genomics are in fact answering the pertinent metabolic questions. You really need to also listen to the Q&A at the very end. There is the fascinating answer on the connection between the microscopic knowledge of a pathway and the attendant macroscopic result and the fact that like the plasticity observed for the brain, much of this therapy can take place at any time: "...the window doesn't close...".. I also like the imagery of the island on the first slide, it reminds me of other islands and the pirates that sail to them (What Island Is Next ?). Please note also the accidental discovery process.

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Next-Generation Sequencing Technologies Videos

It's Friday, here are some of the videos that might get you thinking following up on the videos presented a while back. It might perhaps give a new perspective to our current awful inabilities.

Next-Generation Sequencing Technologies by Elaine Mardis (2012). More videos and presentations from that meeting can be found here. The slides of the presentation are here.

by Elliott H. Margulies

 Two slides got my attention out of many other intriguing ones: From  Mardis' presentation, this one slide with what she says is nothing short of an extraordinary story (see the written text underneath the image below):

In short, chemotherapy brings a good news/bad news story for small cell cancer (a certain type of Lung cancer). The good news is that chemotherapy will fix it within two to three weeks but the bad news is that, in six months, it will kill you. Or at least this was the prognosis two years ago, chemo has certainly improved. I just find it amazing that we are able to find out exactly what clone survived and changed because of the chemo and how it eventually takes over. Talk about personalized medicine....

From Margulies' presentation, it looks like genomics is now going to have to work on entire populations (as opposed to just one person) and methods like those featured in Compressed Se(que)nsing or Compressed Sensing Approach for High Throughput Carrier Screen might help there as well:

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