Reading scientific papers

The ubiquitin multi-motif protein UHRF1 is a central player in targeting repressive chromatin marks. It contains a SRA domain, which binds to hemimethylated DNA, a Tudor domain binding to methylated H3 (H3K9me3) and a PHD finger interacting with an unmodified arginine residue within H3 (H3R2) [72–74]. Furthermore, UHRF1 interacts with DNMTs, G9a and HDAC1 and thereby unites various enzymes that can provide a repressive chromatin environment [75–77]. Interestingly, UHRF1 also recruits the H2AK5 actetyltransferase TiP60 thus integrating a multitude of different epigenetic signals [78]. A further example for the link between DNA methylation and histone modifications represent methyl C binding proteins such as MeCP2, which interact with co-repressor complexes including HDACs and HMTs [79,80]. Interestingly, a recent report shows that components of the piRNA pathway are required to target de novo DNA methylation to an imprinted region of the mouse genome implicating that selective methylation of imprinted regions can be regulated by non-coding piRNAs [81].

Imagine 21 pages of this. Okay, 9, if you discount the bibliography. That's the review article we're supposed to read (and understand) for the final part of the Epigenetic Control of Gene Expression MOOC at Melbourne University.

Well… I'm finding it a lot of work to read the text, parse the sentences, expand the abbreviations, and finally integrate it into a whole. I suppose it's mostly due to a lack of training − if I were to read that kind of paper on a daily basis, I guess I would acquire some paper-reading skills. That, or my brain would overheat and melt (and believe me, it's embarrassing to have to mop up your own liquefied brain matter that's dripped through your ear ducts to the carpet.)

I guess I'll have deserved that certificate.

Notes from the trenches: Epigenetics at Melbourne

[ed: it's been a while since I've posted, mostly because not much happened.]

While it's officially Week 5 in a 7-week course, I'm actually well into Week 6 (weekly content is released a week in advance), with only one quiz to take − I expect to do it later today − then it's down to the dreaded peer-reviewed essay.

All this to say that I feel capable of discussing the course.

(to digress, there's something I thought about yesterday: MOOCs are supposed to be “communities” for students, but they are transient − lasting only a handful of weeks. For the same reason it's hard to be able to discuss a course in general terms based on only a couple of weeks' content, it's difficult to get to know people − fellow students − in such a short time. I wonder if multiple-course sequences, like the ASTROx year-long series at ANU, will actually foster such a “community”.)

Anyway − epigenetics is the study of the process through which genes are stably turned on or off. The keyword here is “stably” − while a given cell will express genes differently at various times (for instance insulin secretion is turned up when glucose enters the pancreatic beta-cells), this is not an epigenetic change; rather, some genes are permanently turned off (or on) for the whole life of the cell, barring exceptional circumstances, and this state is preserved when the cell multiplies.

Something which the course makes very clear is that this is a field under active research. In other words, there is very little that we know for certain − some processes are well-understood, but most are not, and a large part of what is thought is “very controversial”, that is to say, researchers disagree strongly on how the mechanisms work, and even on whether they actually exist in the first place. So that's pretty exciting, if somewhat confusing: one doesn't quite expect to walk into a classroom and be told “okay, so we think there is some epigenetics here, but we're not sure, and we don't really know how it works anyway, so if you're thinking of doing some research of your own in the future, that's not a bad place to start.”

In keeping with this bleeding-edge focus, the course places a strong emphasis on reading scientific papers, over at PMC or PLOS − we're actually quizzed on the papers. In a way, the video lectures are only an introduction, the real meat of the course being the papers. That's the hardest part for me: reading and understanding jargon-laden, dry papers is a specific skill that I, erm, need to work on (I find my eyes glaze over pretty quickly). It's also not an activity that can easily fit into my normal MOOCing times (on the bus/train? No way, requires much more concentration − in the evenings? Nope, requires a freshness of mind that I just don't have after a full day's work − ideally I'd get up an hour earlier and read during breakfast, but er… I value sleep, too).

In terms of content: the first few weeks are about the well-understood mechanisms (DNA methylation, chromatin structure and histone modifications, X chromosome inactivation, epigenetic reprogramming), then we get on to more controversial topics (environmental disruption of epigenetic state, for instance the effects of tobacco smoke, or diet, at crucial periods of time). The last week of the course is about cancer, which is pretty interesting (epigenetic modifications are one of the hallmarks of cancer − it's actually one of the very few common points of all cancer types: not knowing anything about the subject, I'll refrain from qualifying cancer as an “epigenetic disease”, but it's certainly tempting.)

The lectures are basically slides with an embedded shot of the lecturer (Marnie Blewitt from Melbourne University) in the corner. While I usually dislike this format, here it works well, partly because Dr Blewitt is a great speaker with a clear voice, but mostly because the slides are only outlines / supporting material for the course itself. In fact I find I hardly read the slides − I just skim them and concentrate instead on the audio.

Every week there's a quiz, which is fairly difficult − you have three tries, but the questions change between each try. As I said, the quizzes are often about specific points raised not in the lecture, but in the required readings, forcing us to read the papers, not a bad thing. At the end of the course, there is a peer-reviewed essay; I'm not certain how it will play out, it's been ages since I wrote essays (and then again, never in a scientific subject). I'll keep you posted about how it turns out.

So, in general, I like this course quite a lot, and it's certainly given me a lot of things to think about.

Random musings: On Platforms

MOOCs are here to stay; that is a certainty[1]. But we're still in the early stages of the MOOC “revolution”: a new ecological niche has opened up, we are in the middle of a diversity explosion, but sooner or later the number of players will go down and each will differentiate.

(To continue with the bad analogy, the dinosaurs have died out and we're in the diversification phase: Coursera's the birds, edX's the mammals, OpenEdX's the metatherians − closely related to placental mammals but different − Udacity's the crocodylians, FutureLearn's the bony fish, etc.)

Sooner or later, the number of platforms will go down as “the industry” converges to a handful, sort of like happened in the social network space: gone are the Orkuts, LiveJournals, and MySpace of the past; only Facebook and Twitter (and Baidu in China) remain, and the two are sufficiently different to not be frontal competitors.

Listing the MOOC platforms out there is something many people have done, so I won't replicate for replication's sake. Safe to say there are many. They are subtly different, though in reality, the variability of individual MOOCs is often greater than the variability in the platforms hosting them.

If we take a cursory look at the megafauna in this landscape, we have two major beasts competing frontally, a third player which is different enough to carve its own niche, and a slew of minor players. A full review isn't what I intend to do here (I do mean to do a writeup of edX vs Coursera at some point, though), I'll just give some highlights on the platform types out there.

Coursera and edX are the big beasts, very similar in terms of structure. Between them, they must hold down a massive proportion of the MOOC market (70%, 80%? I have no idea.) Universities create courses with a mix of video lectures, text resources, homework and exams; students enroll to specific sessions of a course, on pre-set dates, and if they pass a certain threshold, they get a certificate. For certain courses, students may opt for a “verified” certificate (for which the platform takes minimal steps to ascertain the student's identity, for instance by comparing webcam images with a picture ID like a passport). There are even a handful of “sequences” of courses leading up to an overall certification in a specific field (they are called “Specializations” in Courserese and “XSeries” in EdXian). The similarity is sufficient for there being cross-talk: Caltech's Principles of Economics with Calculus course ran once on Coursera then migrated to edX.
There are differences between the two (Coursera's more mature overall, edX's more flexible) but switching from one to the other is mostly transparent for students − it's the actual course that's important.

Udacity has a different model. First off, it's a for-profit business (Coursera's one too, but less obviously so as they're at the gain-market-share-lose-money stage of startup development; edX's a non-profit); the focus is much more clearly on paying customers. In terms of content, it means the courses tend to be more oriented towards building specialized professional skills than towards acquiring general knowledge of a field − Udacity teams up with businesses to offer classes on specific products, for instance. (There are also more general, less immediately “useful” courses, but they do seem a bit lost in there.) The classes themselves are self-paced: students may take them whenever they want, over as much time as they need. To access the “full learning experience” (which I understand means access to TAs and certification) one can pay a monthly subscription fee.

Then there are national endeavours such as the UK's FutureLearn, France Université Numérique (actually built on top of edX software), Australia's Open2Study, etc. I am sort of confused why these are actually needed, and indeed some universities hedge their bets (it looks like most Australian universities have courses up on either edX or Coursera). I guess the point is to integrate into the national higher-education system, so that a − say − French student can mix and match courses for her local university and FUN[2] and still gain credit for both? We're not there yet; if that is indeed the point then that will be an exciting development, but in the meantime the national platforms look like something very unnecessary indeed.

Saylor Academy deserves a special mention. They don't actually create course materials, but rather use the wealth of material that's out there (from OpenCourseWare to YouTube videos via freely-accessible textbooks) to package coherent courses and full curricula, going a full step further than Coursera/edX's “sequences”: by mixing and matching existing resources, you can actually get the full equivalent (content-wise) to a traditional US college 16-course major. Saylor does the packaging, exams, and certification.

Possibly what is missing (or which I haven't found) is a “MOOC navigator” site, something that will suggest pathways through MOOCs (e.g. for a biology education, “take edX's 7.00x for introductory biology, then hop on to Coursera's Useful Genetics, pick up a couple of biochemistry courses here and there, a stats course here…”) − that is to say, go above the level of individual courses and establish curricula, taking into account things such as starting dates, etc. Actually, a “skill tree” as in many MMORPGs might do it. Kind of a metaplatform, really. Hmm. That might be an interesting side-project for when the days finally decide to last 36 hours to give me time to actually do stuff.

[1] What I am not so certain about − and that's the exciting part − is the impact the MOOC “movement”, if you want to call it that, will have on both the educational system as a whole, and the workplace. Speaking from a French, occasional recruiter point of view, one of the first things I look at when looking at a resume is the school the applicant graduated from. It's not the only thing and it's not discriminatory, but it's a better-than-null-hypothesis proxy for the overall “quality” of the applicant, especially if they graduated less than five or ten years ago.

That said, the French system is intensely competitive, with a lot of engineering schools (204 are officially allowed to deliver a diplôme d'ingénieur, not counting universities), often quite small (most have classes in the ballpark of 50-100 students a year), admission to which is based on a handful of nation-wide competitive examinations. Specialized (and not-so-specialized) media maintain a nationwide ranking of these schools, updated on a yearly basis. By this system, the “better” schools (i.e. better-ranked) naturally attract the “better” students (i.e. those that are better than the rest at taking gruelling exams they have spent the previous two or three years preparing for).

All in all the French system is exclusive − only the very best go to ENS or Polytechnique, the next best go to Mines or Centrale, etc. It makes a decent guarantee that a graduate from a toppish-tier school is a very clever person with a great capacity for hard work. This is fine and dandy, but what it doesn't do is guarantee that graduates from a lower-ranked school or a mainstream university are not good and indeed many very apt students are excluded (in technical terms, the sensitivity is decent but the specificity is awful; there are few − though not zero − false positives but a great many false negatives.)

MOOCs by nature are inclusive. I tend to believe that people who are willing to stick to the sometimes heavy schedule of a MOOC − and even better, many MOOCs − show themselves to be highly-motivated individuals which may be of great interest to companies. But that's a gut feeling; only time will tell, if and when MOOCs go mainstream (I've only once seen a resume mentioning a MOOC).

[2] I wonder whether the “France Université Numérique” name was picked specifically to bring a little FUN in the university system?

Notes from the trenches: Diabetes, a Global Challenges (Copenhagen University)

The Diabetes course from Copenhagen University is drawing to an end; only one week to go. I'll do a full postmortem when it's over (next week, basically), but in the meantime, some unordered remarks/comments:

Unlike most courses I take, I didn't register to this one to acquire useful skills or knowledge, but out of curiosity. I have, obviously, a specific interest in the subject matter (my son has Type 1 diabetes, as has my brother − sadly, the course is mostly silent about T1D and focuses almost exclusively on the much more widespread Type 2 diabetes), but I didn't expect to learn anything useful in daily life with a diabetic child. In that respect, I wasn't disappointed: I didn't.

I'm not certain, in truth, of what I expected. Some sort of generic high-level description of what diabetes is, its variants, its epidemiology, and a quick overview of the status of diabetes research, I guess. Instead, what I got is a series of seven very detailed, sometimes very technical, lectures about seven different aspects of diabetes research and/or treatment. While the first lecture, about the epidemiology of diabetes, was quite what I expected in terms of content (the production is consistently of a much higher quality than what I expected), the following ones have been both more challenging to follow / understand and much more interesting, in the sense of detailed, than what I expected.

Of course, since we have a different lecturer every week, the delivery is not always consistent (some people are basically better public speakers than others − week 4 in particular was very difficult to follow, with long lectures given in a monotonous voice; but maybe I was just especially tired that week). What is consistent though is, as I said above, the excellent production as shown in the smooth integration of graphics, videos, etc. in the lecture slides. Even the basic graphical elements are clear and crisp. Obviously Copenhagen University have invested some time and money to buy a professional-level production.

In the end, we have a smooth but surprisingly information-packed course. I am not certain what I'll remember of it in several months' time, though, since there is very little interactivity (just a few in-video quick questions and a quiz each week). Sadly, it is not possible to download lecture outlines for future reference, but we do get lecture transcripts.

As a last word, a quick reference to the “M” in MOOC. I guess I'm not the only one to have expected a more basic course (not that I'm complaining though); generally the level of the discussion forums is very low. The professors encourage directed discussion by asking several open questions every week; I am quite sure that were I brave enough to read every post in detail I'd find a fair number of intelligent, thoughtful insights. As it is, they're completely drowned in repetitive platitudes, so I find it better to just avoid the discussion forums altogether.

(It took less than three messages for a discussion about genetic screening for diabetes-associated risk alleles to reach the Godwin point…)

It's strange how some courses get intelligent, focused discussion forums and while some don't. So far the best I've seen were Caltech's Principles of Microeconomics and ANU's Astrophysics ones. I guess that's because both courses put a focus on collaborative problem-solving, while open-ended questions just reap a lot of clichés. In any case, that's a topic for another blog post, I guess.

Postmortem: Bioinformatics at Peking University

The Bioinformatics course at Beijing (or is that Peking? I never know) University is over. It's still being graded, but having had 10/10 for each homework and 97% on the final, it's not an overly wild guess to say it'll be my first Coursera certificate (or “Statement of Achievement” in Courserese).

So, what is Bioinformatics?

Bioinformatics is “the application of computer science to solve biological problems”. To say it's a growing field would be an understatement: the growth of biological data has been exponential, necessitating innovative data management and data analysis techniques; it's fair to say that nowadays, in a lot of fields of biological research, more work is being done with computers than with, say, Petri dishes or lab mice. Taken from the opposite angle, biological research labs are at the forefront of the “big data” revolution. Lists of innovative big data companies include institutions such as Mount Sinai's Icahn School of Medicine (they are also well-known as a big MongoDB customer, if I'm not mistaken).

Rigorously, “bioinformatics” comprises an understanding of the type of problems bioinformaticians face, and the algorithms they use to solve them. By extension, it also includes the major databases of publicly-available bioinformatics data on the Internet − and truth be told, I didn't think there were so many of them!

As is usual in fields associated with academic research, bioinformatics is a field in which open source is prevalent, both in terms of the software itself (indeed, algorithms and tools that are not properly described in a peer-reviewed paper have little chance of being widely used) and in terms of the actual data. To repeat myself: the amount of data in freely-available databases hosted by such organizations as the National Center for Biotechnology Information or the European Bioinformatics Institute is staggering. Theoretically, any private individual with an Internet connection, or indeed company, could do effective biological research in silico; I'm guessing we're just at the beginning of a wave of bioinformatics startups following the lead of such as 23andme. It'll be interesting to see.

What does the course cover?

The Peking course covers quite a lot, from a rather comprehensive (and welcome) review of the history of bioinformatics, the present situation of the field, and what we can expect in the near future, to alternating descriptions of the major algorithms and/or techniques in use in the field and exploration of the available resources. It ends with a couple of case studies highlighting how the various techniques and databases surveyed in the course were integrated by actual researchers to investigate real-world issues.

Who is the teaching team?

There are two main professors: Drs Liping Wei and Ge Gao. Dr Wei generally handles the high-level stuff (e.g. background information, overviews of databases, etc.) while Dr Gao dives into the details of algorithms. Both are evidently highly qualified; the course draws tightly on their own research and contributions, which is very welcome as it makes the course so much more concrete.

What about logistics?

The course lasts for 6 weeks; two topics are covered each week, with a series of video lectures and a quiz; except for the last week, when the lectures are about case studies and the quizzes are replaced by a 40-question final exam.

It is notable that the course is bilingual; that is to say, it is offered simultaneously in Chinese and English. The Chinese lectures have slides with classroom shots inserted (meaning you actually see the professor speaking); the English lectures are slides-only.

In addition to the lectures there are supplementary videos such as lab visits and student presentations. These tend to be Chinese-only, so I skipped over most. There are also a couple of interviews of famous bioinformaticians, but I didn't find these so interesting.

My impressions

It's undeniable that the team take pains to be welcoming. It's also undeniable that the content is actually of a pretty good level. But there are a couple of problematic aspects with this course.

The first is − and it's horrible to say this, not being a native English speaker myself − that the speakers' English is not so great. It's not so much that they make mistakes, but rather, their intonation and rhythm is… well, they're obviously reading from a transcript. Dr Gao's voice droning formulae (ecks-aye-jay-plus-ecks-jay-plus-one-aye-equals-ecks-aye-plus-one-jay) for long minutes was almost enough to take me out of the course altogether. Thankfully, the subject matter is interesting, so I could stick to it with a little effort. In the end, I viewed the lectures at 1.5x speed to compensate for the speaker's slow diction, and I referred back to the transcripts when I had doubts.

The second problem is perhaps due to Coursera's platform: the quizzes are, well, just quizzes. It feels strange, and generally wrong, to have an algorithmics course in which you do no coding at all. Generally speaking, you have three tries to answer questions which mostly have three or four options… At one point I was so immensely tired I almost dropped the course; instead I deprioritized it and spent a minimal amount of time on it. So, while I did learn a bunch of stuff about bioinformatics, I can hardly say that my final grade (which will be in the high nineties) reflects my mastery of the subject.

Strangely enough, the final exam was possibly the most interesting part of the course, as some questions required us to go search for information by ourselves (which means first identifying the right database to query, finding how to query it, etc.) Possibly, a vast improvement of the course would be to scrap about half the quizzes and replace them by practical case studies: give students a set of data (gene names, diseases, etc.) and send them off to search for information using the tools discussed in the lectures. As they stand, the quizzes help little in teaching.

Do not, however, let that discourage you. If you're interested in learning about bioinformatics, certainly there could be much worse options than this course. It may not make you a bioinformatics researcher, but it will at least give you a handle on where to get started.

Just starting: Epigenetic control of Gene Expression, at Melbourne University

So there's a new MOOC over at Coursera starting up today.

Why take this MOOC?

Because I really liked the genetics part of MIT's 7.00x Introduction to Biology, and wanted to learn more. Someone on the forums there pointed to this course, and here I am.
Plus, I'm kind of in an Australian mood, what with the Astrophysics course at ANU.

What's epigenetics anyway?

It's the study of how protein synthesis (from DNA by way of RNA) varies, between individuals, between cells in a specific individual, and at different points in time for a specific cell, without changes to the actual genome (the DNA sequence).
Basically that's why monozygotic twin cats may have completely different colours.

Sounds complicated.

That's right. Though you may spell it "fun".
I guess that'll be the real test, whether the instructor (Dr Marnie Blewitt) can make it easily understood. Based on the first 2/3rds of the first week's lecture, I'd say she does a good job of presenting the subject matter in an organized way that conveys the important information in a digestable package.

Right; let's go!